{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.rcParams['figure.figsize'] = (20, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题\n",
    "\n",
    "- 使用 Python 2 和 Python 3 的开发者的比例？\n",
    "- 做数据分析和机器学习的人中分别有多少人使用的是 Python 3？\n",
    "- 常用框架中使用 Python 2 和 Python 3 的比例？\n",
    "- 做数据分析和机器学习的人常用的框架？\n",
    "- 公司规模大小和是否使用 Python 3 的关系？\n",
    "- 开发者年龄和是否使用 Python 3 的关系？\n",
    "- 使用 Python 3 和 Python 2 的开发者的国别分布？\n",
    "- 开发者中使用 IDE 的情况？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is python the main language you use for your current projects?</th>\n",
       "      <th>none:what other language(s) do you use?</th>\n",
       "      <th>java:what other language(s) do you use?</th>\n",
       "      <th>javascript:what other language(s) do you use?</th>\n",
       "      <th>c/c++:what other language(s) do you use?</th>\n",
       "      <th>php:what other language(s) do you use?</th>\n",
       "      <th>c#:what other language(s) do you use?</th>\n",
       "      <th>ruby:what other language(s) do you use?</th>\n",
       "      <th>bash / shell:what other language(s) do you use?</th>\n",
       "      <th>objective-c:what other language(s) do you use?</th>\n",
       "      <th>...</th>\n",
       "      <th>technical support:which of the following best describes your job role(s)?</th>\n",
       "      <th>data analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>business analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>team lead:which of the following best describes your job role(s)?</th>\n",
       "      <th>product manager:which of the following best describes your job role(s)?</th>\n",
       "      <th>cio / ceo / cto:which of the following best describes your job role(s)?</th>\n",
       "      <th>systems analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>other - write in::which of the following best describes your job role(s)?</th>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>what country do you live in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PHP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60 or older</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Team lead</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40-49</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40-49</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No, I don’t use Python for my current projects</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17 or younger</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-20</td>\n",
       "      <td>Israel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  is python the main language you use for your current projects?  \\\n",
       "0                                                Yes               \n",
       "1                                                Yes               \n",
       "2                                                Yes               \n",
       "3     No, I don’t use Python for my current projects               \n",
       "4                                                Yes               \n",
       "\n",
       "  none:what other language(s) do you use?  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "\n",
       "  java:what other language(s) do you use?  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                    Java   \n",
       "\n",
       "  javascript:what other language(s) do you use?  \\\n",
       "0                                    JavaScript   \n",
       "1                                    JavaScript   \n",
       "2                                    JavaScript   \n",
       "3                                           NaN   \n",
       "4                                           NaN   \n",
       "\n",
       "  c/c++:what other language(s) do you use?  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      NaN   \n",
       "4                                      NaN   \n",
       "\n",
       "  php:what other language(s) do you use?  \\\n",
       "0                                    PHP   \n",
       "1                                    NaN   \n",
       "2                                    NaN   \n",
       "3                                    NaN   \n",
       "4                                    NaN   \n",
       "\n",
       "  c#:what other language(s) do you use?  \\\n",
       "0                                   NaN   \n",
       "1                                   NaN   \n",
       "2                                   NaN   \n",
       "3                                    C#   \n",
       "4                                   NaN   \n",
       "\n",
       "  ruby:what other language(s) do you use?  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "\n",
       "  bash / shell:what other language(s) do you use?  \\\n",
       "0                                    Bash / Shell   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                             NaN   \n",
       "4                                             NaN   \n",
       "\n",
       "  objective-c:what other language(s) do you use?             ...               \\\n",
       "0                                            NaN             ...                \n",
       "1                                            NaN             ...                \n",
       "2                                            NaN             ...                \n",
       "3                                            NaN             ...                \n",
       "4                                            NaN             ...                \n",
       "\n",
       "  technical support:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                          \n",
       "1                                                NaN                          \n",
       "2                                                NaN                          \n",
       "3                                                NaN                          \n",
       "4                                                NaN                          \n",
       "\n",
       "  data analyst:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                     \n",
       "1                                                NaN                     \n",
       "2                                                NaN                     \n",
       "3                                                NaN                     \n",
       "4                                                NaN                     \n",
       "\n",
       "  business analyst:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                         \n",
       "1                                                NaN                         \n",
       "2                                                NaN                         \n",
       "3                                                NaN                         \n",
       "4                                                NaN                         \n",
       "\n",
       "  team lead:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                  \n",
       "1                                          Team lead                  \n",
       "2                                                NaN                  \n",
       "3                                                NaN                  \n",
       "4                                                NaN                  \n",
       "\n",
       "  product manager:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                                NaN                        \n",
       "4                                                NaN                        \n",
       "\n",
       "  cio / ceo / cto:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                                NaN                        \n",
       "4                                                NaN                        \n",
       "\n",
       "  systems analyst:which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                                NaN                        \n",
       "4                                                NaN                        \n",
       "\n",
       "  other - write in::which of the following best describes your job role(s)?  \\\n",
       "0                                                NaN                          \n",
       "1                                                NaN                          \n",
       "2                                                NaN                          \n",
       "3                                                NaN                          \n",
       "4                                                NaN                          \n",
       "\n",
       "  could you tell us your age range? what country do you live in?  \n",
       "0                       60 or older                        Italy  \n",
       "1                             40-49               United Kingdom  \n",
       "2                             40-49                       France  \n",
       "3                     17 or younger                        Spain  \n",
       "4                             18-20                       Israel  \n",
       "\n",
       "[5 rows x 162 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df = pd.read_csv('pythondevsurvey2017_raw_data.csv')\n",
    "survey_df.columns = [c.lower() for c in survey_df.columns]\n",
    "survey_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9506, 162)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_cols(df, kws):\n",
    "    '''找到 df 中含有 kws 的列'''\n",
    "    return [item for item in df.columns if all ([w in item for w in kws])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['which version of python do you use the most?',\n",
       " 'installer from python.org:what do you typically use to upgrade your python version?',\n",
       " 'build from source:what do you typically use to upgrade your python version?',\n",
       " 'automatic upgrade via cloud provider:what do you typically use to upgrade your python version?',\n",
       " 'enthought:what do you typically use to upgrade your python version?',\n",
       " 'anaconda:what do you typically use to upgrade your python version?',\n",
       " 'activepython:what do you typically use to upgrade your python version?',\n",
       " 'intel distribution for python:what do you typically use to upgrade your python version?',\n",
       " 'os-provided python (via apt-get, yum, homebrew, etc.):what do you typically use to upgrade your python version?',\n",
       " 'pyenv:what do you typically use to upgrade your python version?',\n",
       " 'pythonz:what do you typically use to upgrade your python version?',\n",
       " 'other - write in::what do you typically use to upgrade your python version?']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_cols(df=survey_df, kws=['python', 'version'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 Python 2 和 Python 3 的开发者的比例？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         8112\n",
       "unique           2\n",
       "top       Python 3\n",
       "freq          6046\n",
       "Name: which version of python do you use the most?, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_version = survey_df['which version of python do you use the most?']\n",
    "python_version.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python 3    0.636019\n",
       "Python 2    0.217336\n",
       "NaN         0.146644\n",
       "Name: which version of python do you use the most?, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_version.value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python 3    6046\n",
       "Python 2    2066\n",
       "NaN         1394\n",
       "Name: which version of python do you use the most?, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_version.value_counts(normalize=False, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python 3    0.745316\n",
       "Python 2    0.254684\n",
       "Name: which version of python do you use the most?, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_version.value_counts(normalize=True, dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc3f6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "python_version.value_counts(normalize=True, dropna=True).plot(kind='pie', \n",
    "                                                              figsize=(5, 5), \n",
    "                                                              startangle=90, \n",
    "                                                              autopct='%.0f%%', \n",
    "                                                              fontsize=14,\n",
    "                                                              colors=sns.color_palette('rainbow')[:2])\n",
    "plt.title('Python 2 VS Python 3', fontsize=18)\n",
    "plt.ylabel('');\n",
    "plt.tight_layout()\n",
    "plt.savefig('python-version.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用 Python 的开发者中，大概有 75% 的人已经在使用 Python 3 了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 做数据分析和机器学习的人中分别有多少人使用的是 Python 3？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_da_ml = survey_df[['machine learning:\\xa0what do you use python for?', 'data analysis:\\xa0what do you use python for?', 'which version of python do you use the most?']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "machine learning: what do you use python for?    object\n",
       "data analysis: what do you use python for?       object\n",
       "which version of python do you use the most?     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_da_ml.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_da = pd.crosstab(python_da_ml['which version of python do you use the most?'], python_da_ml['data analysis:\\xa0what do you use python for?'], normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>data analysis: what do you use python for?</th>\n",
       "      <th>Data analysis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Python 2</th>\n",
       "      <td>0.233177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>0.766823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "data analysis: what do you use python for?    Data analysis\n",
       "which version of python do you use the most?               \n",
       "Python 2                                           0.233177\n",
       "Python 3                                           0.766823"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_ml = pd.crosstab(python_da_ml['which version of python do you use the most?'], python_da_ml['machine learning:\\xa0what do you use python for?'], normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data analysis</th>\n",
       "      <th>Machine learning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Python 2</th>\n",
       "      <td>0.233177</td>\n",
       "      <td>0.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>0.766823</td>\n",
       "      <td>0.806452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Data analysis  Machine learning\n",
       "which version of python do you use the most?                                 \n",
       "Python 2                                           0.233177          0.193548\n",
       "Python 3                                           0.766823          0.806452"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([python_da, python_ml], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc4396a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([python_da, python_ml], axis=1).T.plot(kind='bar', figsize=(10, 5), color=sns.color_palette('rainbow'))\n",
    "plt.xticks(rotation=0, fontsize=14)\n",
    "plt.title('Data Analysis and Machine Learning VS Python version', fontsize=18)\n",
    "plt.legend(title=None)\n",
    "plt.tight_layout()\n",
    "plt.savefig('data-analysis-machine-learning-vs-python-version.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['none:what framework(s) do you use in addition to python?',\n",
       " 'django:what framework(s) do you use in addition to python?',\n",
       " 'flask:what framework(s) do you use in addition to python?',\n",
       " 'tornado:what framework(s) do you use in addition to python?',\n",
       " 'bottle:what framework(s) do you use in addition to python?',\n",
       " 'web2py:what framework(s) do you use in addition to python?',\n",
       " 'numpy / pandas / matplotlib / scipy and similar:what framework(s) do you use in addition to python?',\n",
       " 'keras / theano / tensorflow / scikit-learn and similar:what framework(s) do you use in addition to python?',\n",
       " 'pillow:what framework(s) do you use in addition to python?',\n",
       " 'pyqt / pygtk / wxpython:what framework(s) do you use in addition to python?',\n",
       " 'tkinter:what framework(s) do you use in addition to python?',\n",
       " 'pygame:what framework(s) do you use in addition to python?',\n",
       " 'cherrypy:what framework(s) do you use in addition to python?',\n",
       " 'twisted:what framework(s) do you use in addition to python?',\n",
       " 'pyramid:what framework(s) do you use in addition to python?',\n",
       " 'requests:what framework(s) do you use in addition to python?',\n",
       " 'asyncio:what framework(s) do you use in addition to python?',\n",
       " 'kivy:what framework(s) do you use in addition to python?',\n",
       " 'six:what framework(s) do you use in addition to python?',\n",
       " 'aiohttp:what framework(s) do you use in addition to python?',\n",
       " 'other - write in::what framework(s) do you use in addition to python?',\n",
       " 'cloud platforms (google app engine, aws, rackspace, heroku and similar):what additional technology(s) do you use in addition to python?',\n",
       " 'jupyter notebook:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'komodo editor:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'komodo ide:what editor(s)/ide(s) have you considered for use in your python development?']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, 'what framework(s) do you use in addition to python?')\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>django:what framework(s) do you use in addition to python?</th>\n",
       "      <th>flask:what framework(s) do you use in addition to python?</th>\n",
       "      <th>tornado:what framework(s) do you use in addition to python?</th>\n",
       "      <th>bottle:what framework(s) do you use in addition to python?</th>\n",
       "      <th>web2py:what framework(s) do you use in addition to python?</th>\n",
       "      <th>numpy / pandas / matplotlib / scipy and similar:what framework(s) do you use in addition to python?</th>\n",
       "      <th>keras / theano / tensorflow / scikit-learn and similar:what framework(s) do you use in addition to python?</th>\n",
       "      <th>pillow:what framework(s) do you use in addition to python?</th>\n",
       "      <th>pyqt / pygtk / wxpython:what framework(s) do you use in addition to python?</th>\n",
       "      <th>tkinter:what framework(s) do you use in addition to python?</th>\n",
       "      <th>...</th>\n",
       "      <th>requests:what framework(s) do you use in addition to python?</th>\n",
       "      <th>asyncio:what framework(s) do you use in addition to python?</th>\n",
       "      <th>kivy:what framework(s) do you use in addition to python?</th>\n",
       "      <th>six:what framework(s) do you use in addition to python?</th>\n",
       "      <th>aiohttp:what framework(s) do you use in addition to python?</th>\n",
       "      <th>other - write in::what framework(s) do you use in addition to python?</th>\n",
       "      <th>cloud platforms (google app engine, aws, rackspace, heroku and similar):what additional technology(s) do you use in addition to python?</th>\n",
       "      <th>jupyter notebook:what editor(s)/ide(s) have you considered for use in your python development?</th>\n",
       "      <th>komodo editor:what editor(s)/ide(s) have you considered for use in your python development?</th>\n",
       "      <th>komodo ide:what editor(s)/ide(s) have you considered for use in your python development?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Django</td>\n",
       "      <td>Flask</td>\n",
       "      <td>Tornado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NumPy / pandas / Matplotlib / scipy and similar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pillow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Requests</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>six</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other - Write In:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Django</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Requests</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>six</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Komodo IDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NumPy / pandas / Matplotlib / scipy and similar</td>\n",
       "      <td>Keras / Theano / TensorFlow / scikit-learn and...</td>\n",
       "      <td>Pillow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  django:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN           \n",
       "1                                             Django           \n",
       "2                                             Django           \n",
       "3                                                NaN           \n",
       "4                                                NaN           \n",
       "\n",
       "  flask:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN          \n",
       "1                                              Flask          \n",
       "2                                                NaN          \n",
       "3                                                NaN          \n",
       "4                                                NaN          \n",
       "\n",
       "  tornado:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN            \n",
       "1                                            Tornado            \n",
       "2                                                NaN            \n",
       "3                                                NaN            \n",
       "4                                                NaN            \n",
       "\n",
       "  bottle:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN           \n",
       "1                                                NaN           \n",
       "2                                                NaN           \n",
       "3                                                NaN           \n",
       "4                                                NaN           \n",
       "\n",
       "  web2py:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN           \n",
       "1                                                NaN           \n",
       "2                                                NaN           \n",
       "3                                                NaN           \n",
       "4                                                NaN           \n",
       "\n",
       "  numpy / pandas / matplotlib / scipy and similar:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN                                                    \n",
       "1    NumPy / pandas / Matplotlib / scipy and similar                                                    \n",
       "2                                                NaN                                                    \n",
       "3                                                NaN                                                    \n",
       "4    NumPy / pandas / Matplotlib / scipy and similar                                                    \n",
       "\n",
       "  keras / theano / tensorflow / scikit-learn and similar:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN                                                           \n",
       "1                                                NaN                                                           \n",
       "2                                                NaN                                                           \n",
       "3                                                NaN                                                           \n",
       "4  Keras / Theano / TensorFlow / scikit-learn and...                                                           \n",
       "\n",
       "  pillow:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN           \n",
       "1                                             Pillow           \n",
       "2                                                NaN           \n",
       "3                                                NaN           \n",
       "4                                             Pillow           \n",
       "\n",
       "  pyqt / pygtk / wxpython:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN                            \n",
       "1                                                NaN                            \n",
       "2                                                NaN                            \n",
       "3                                                NaN                            \n",
       "4                                                NaN                            \n",
       "\n",
       "  tkinter:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN            \n",
       "1                                                NaN            \n",
       "2                                                NaN            \n",
       "3                                                NaN            \n",
       "4                                                NaN            \n",
       "\n",
       "                                            ...                                             \\\n",
       "0                                           ...                                              \n",
       "1                                           ...                                              \n",
       "2                                           ...                                              \n",
       "3                                           ...                                              \n",
       "4                                           ...                                              \n",
       "\n",
       "  requests:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN             \n",
       "1                                           Requests             \n",
       "2                                           Requests             \n",
       "3                                                NaN             \n",
       "4                                                NaN             \n",
       "\n",
       "  asyncio:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN            \n",
       "1                                                NaN            \n",
       "2                                                NaN            \n",
       "3                                                NaN            \n",
       "4                                                NaN            \n",
       "\n",
       "  kivy:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN         \n",
       "1                                                NaN         \n",
       "2                                                NaN         \n",
       "3                                                NaN         \n",
       "4                                                NaN         \n",
       "\n",
       "  six:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN        \n",
       "1                                                six        \n",
       "2                                                six        \n",
       "3                                                NaN        \n",
       "4                                                NaN        \n",
       "\n",
       "  aiohttp:what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN            \n",
       "1                                                NaN            \n",
       "2                                                NaN            \n",
       "3                                                NaN            \n",
       "4                                                NaN            \n",
       "\n",
       "  other - write in::what framework(s) do you use in addition to python?  \\\n",
       "0                                                NaN                      \n",
       "1                                  Other - Write In:                      \n",
       "2                                                NaN                      \n",
       "3                                                NaN                      \n",
       "4                                                NaN                      \n",
       "\n",
       "  cloud platforms (google app engine, aws, rackspace, heroku and similar):what additional technology(s) do you use in addition to python?  \\\n",
       "0                                                NaN                                                                                        \n",
       "1                                                NaN                                                                                        \n",
       "2                                                NaN                                                                                        \n",
       "3                                                NaN                                                                                        \n",
       "4                                                NaN                                                                                        \n",
       "\n",
       "  jupyter notebook:what editor(s)/ide(s) have you considered for use in your python development?  \\\n",
       "0                                                NaN                                               \n",
       "1                                                NaN                                               \n",
       "2                                                NaN                                               \n",
       "3                                                NaN                                               \n",
       "4                                                NaN                                               \n",
       "\n",
       "  komodo editor:what editor(s)/ide(s) have you considered for use in your python development?  \\\n",
       "0                                                NaN                                            \n",
       "1                                                NaN                                            \n",
       "2                                                NaN                                            \n",
       "3                                                NaN                                            \n",
       "4                                                NaN                                            \n",
       "\n",
       "  komodo ide:what editor(s)/ide(s) have you considered for use in your python development?  \n",
       "0                                                NaN                                        \n",
       "1                                                NaN                                        \n",
       "2                                         Komodo IDE                                        \n",
       "3                                                NaN                                        \n",
       "4                                                NaN                                        \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frameworks = survey_df[cols[1:]]\n",
    "frameworks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23]), <a list of 24 Text xticklabel objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc44d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_df = frameworks.count().sort_values(ascending=False)\n",
    "count_df.index = [item.split(':')[0] for item in count_df.index]\n",
    "\n",
    "count_df.plot(kind='bar', color=sns.color_palette('rainbow', frameworks.shape[1]))\n",
    "plt.xticks(fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc7090f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = frameworks.count().sort_values(ascending=False).values\n",
    "labels = [item.split(':')[0] for item in frameworks.count().sort_values(ascending=False).index]\n",
    "\n",
    "plt.figure(figsize=(20, 17))\n",
    "sns.barplot(x=values, y=labels, orient='h', palette=sns.color_palette(\"rainbow\", 24))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.savefig('frameworks.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 常用框架中使用 Python 2 和 Python 3 的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_ver = survey_df['which version of python do you use the most?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_col(col):\n",
    "    return pd.crosstab(index=python_ver, columns=col).iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process_col(frameworks['django:what framework(s) do you use in addition to python?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>django</th>\n",
       "      <th>flask</th>\n",
       "      <th>tornado</th>\n",
       "      <th>bottle</th>\n",
       "      <th>web2py</th>\n",
       "      <th>numpy / pandas / matplotlib / scipy and similar</th>\n",
       "      <th>keras / theano / tensorflow / scikit-learn and similar</th>\n",
       "      <th>pillow</th>\n",
       "      <th>pyqt / pygtk / wxpython</th>\n",
       "      <th>tkinter</th>\n",
       "      <th>...</th>\n",
       "      <th>requests</th>\n",
       "      <th>asyncio</th>\n",
       "      <th>kivy</th>\n",
       "      <th>six</th>\n",
       "      <th>aiohttp</th>\n",
       "      <th>other - write in</th>\n",
       "      <th>cloud platforms (google app engine, aws, rackspace, heroku and similar)</th>\n",
       "      <th>jupyter notebook</th>\n",
       "      <th>komodo editor</th>\n",
       "      <th>komodo ide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Python 2</th>\n",
       "      <td>841</td>\n",
       "      <td>678</td>\n",
       "      <td>144</td>\n",
       "      <td>83</td>\n",
       "      <td>97</td>\n",
       "      <td>727</td>\n",
       "      <td>264</td>\n",
       "      <td>333</td>\n",
       "      <td>299</td>\n",
       "      <td>175</td>\n",
       "      <td>...</td>\n",
       "      <td>763</td>\n",
       "      <td>95</td>\n",
       "      <td>70</td>\n",
       "      <td>237</td>\n",
       "      <td>44</td>\n",
       "      <td>223</td>\n",
       "      <td>551</td>\n",
       "      <td>346</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>2522</td>\n",
       "      <td>1929</td>\n",
       "      <td>366</td>\n",
       "      <td>199</td>\n",
       "      <td>235</td>\n",
       "      <td>2436</td>\n",
       "      <td>1096</td>\n",
       "      <td>924</td>\n",
       "      <td>830</td>\n",
       "      <td>763</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>664</td>\n",
       "      <td>319</td>\n",
       "      <td>389</td>\n",
       "      <td>395</td>\n",
       "      <td>426</td>\n",
       "      <td>1409</td>\n",
       "      <td>1394</td>\n",
       "      <td>121</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              django  flask  tornado  bottle  \\\n",
       "which version of python do you use the most?                                   \n",
       "Python 2                                         841    678      144      83   \n",
       "Python 3                                        2522   1929      366     199   \n",
       "\n",
       "                                              web2py  \\\n",
       "which version of python do you use the most?           \n",
       "Python 2                                          97   \n",
       "Python 3                                         235   \n",
       "\n",
       "                                              numpy / pandas / matplotlib / scipy and similar  \\\n",
       "which version of python do you use the most?                                                    \n",
       "Python 2                                                                                  727   \n",
       "Python 3                                                                                 2436   \n",
       "\n",
       "                                              keras / theano / tensorflow / scikit-learn and similar  \\\n",
       "which version of python do you use the most?                                                           \n",
       "Python 2                                                                                    264        \n",
       "Python 3                                                                                   1096        \n",
       "\n",
       "                                              pillow  pyqt / pygtk / wxpython  \\\n",
       "which version of python do you use the most?                                    \n",
       "Python 2                                         333                      299   \n",
       "Python 3                                         924                      830   \n",
       "\n",
       "                                              tkinter     ...      requests  \\\n",
       "which version of python do you use the most?              ...                 \n",
       "Python 2                                          175     ...           763   \n",
       "Python 3                                          763     ...          2006   \n",
       "\n",
       "                                              asyncio  kivy  six  aiohttp  \\\n",
       "which version of python do you use the most?                                \n",
       "Python 2                                           95    70  237       44   \n",
       "Python 3                                          664   319  389      395   \n",
       "\n",
       "                                              other - write in  \\\n",
       "which version of python do you use the most?                     \n",
       "Python 2                                                   223   \n",
       "Python 3                                                   426   \n",
       "\n",
       "                                              cloud platforms (google app engine, aws, rackspace, heroku and similar)  \\\n",
       "which version of python do you use the most?                                                                            \n",
       "Python 2                                                                                    551                         \n",
       "Python 3                                                                                   1409                         \n",
       "\n",
       "                                              jupyter notebook  komodo editor  \\\n",
       "which version of python do you use the most?                                    \n",
       "Python 2                                                   346             43   \n",
       "Python 3                                                  1394            121   \n",
       "\n",
       "                                              komodo ide  \n",
       "which version of python do you use the most?              \n",
       "Python 2                                              59  \n",
       "Python 3                                             126  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frameworks_pyver = frameworks.apply(lambda col: pd.crosstab(index=python_ver, columns=col).iloc[:, 0])\n",
    "frameworks_pyver.columns = [item.split(':')[0] for item in frameworks.columns]\n",
    "frameworks_pyver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "frameworks_pyver_ratio = frameworks_pyver / frameworks_pyver.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23]), <a list of 24 Text xticklabel objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cea32358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frameworks_pyver_ratio.T.plot(kind='bar', color=sns.color_palette('rainbow'))\n",
    "plt.xticks(rotation=90, fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pyver</th>\n",
       "      <th>framework</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python 2</td>\n",
       "      <td>django</td>\n",
       "      <td>0.250074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python 2</td>\n",
       "      <td>flask</td>\n",
       "      <td>0.260069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Python 2</td>\n",
       "      <td>tornado</td>\n",
       "      <td>0.282353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python 2</td>\n",
       "      <td>bottle</td>\n",
       "      <td>0.294326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Python 2</td>\n",
       "      <td>web2py</td>\n",
       "      <td>0.292169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pyver framework     value\n",
       "0  Python 2    django  0.250074\n",
       "1  Python 2     flask  0.260069\n",
       "2  Python 2   tornado  0.282353\n",
       "3  Python 2    bottle  0.294326\n",
       "4  Python 2    web2py  0.292169"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = frameworks_pyver_ratio.stack().reset_index()\n",
    "df.columns=['pyver', 'framework', 'value']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23]), <a list of 24 Text yticklabel objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232ceafd4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 17))\n",
    "sns.barplot(x='value', y='framework', hue='pyver', data=df, orient='h', palette=sns.color_palette('rainbow'))\n",
    "plt.yticks(fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x232cf0333c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHMAAAFuCAYAAAACpVSVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XeYFPXhx/HP7Mzs3u0deFIsSFREUBRFA9hABUE6CFKkhyagRASMGvOYxMREYsOCIkpRkS4K0gMoKGKMSBQ0UjQqKiKCcOLd7u3Ozs7vj/tpitLvbra8X8/DE+MBfpTvzcx+5lsMz/M8AQAAAAAAIC0E/A4AAAAAAACAw0eZAwAAAAAAkEYocwAAAAAAANIIZQ4AAAAAAEAaocwBAAAAAABII5Q5AAAAAAAAacQ61t9g9+7vyiIH/kd+fkhFRTG/YwAVhjGPbMOYR7ZhzCMbMe6RbRjzZat69UoH/Bozc1KUZZl+RwAqFGMe2YYxj2zDmEc2Ytwj2zDmKw5lDgAAAAAAQBqhzAEAAAAAAEgjlDkAAAAAAABphDIHAAAAAAAgjVDmAAAAAAAApBHKHAAAAAAAgDRCmQMAAAAAAJBGKHMAAAAAAADSCGUOAAAAAABAGqHMAQAAAAAASCOUOQAAAAAAAGmEMgcAAAAAACCNWH4HAAAAwE9LJCTXlZLJ0h+e9++/TialUEjKy/M7JQAAqGiUOQAAACkgkZA2bw5o/XpT69ebevttU9u3H3wStWF4OuuspH7+c1cNG5b+79lnJ2WaFRQaAAD4gjIHAADAB9GotG7dv4ubDRtMRSKGJOmEE5Jq3NhVjx6OgkHJMKRAwFMgoP/6sW+foXfeMbVsma2ZM0t/bV6epwsucPXzn7tq0sTVlVe6lDsAAGQYyhwAAIAKtGOHoaeftjV9uq29ewMyTU/nnptUr16OGjd21aiRq5/9zJNhHP7v6XnSJ58Y2rDB1D/+Ufpj4sSgxo83dPrpSQ0dGlfPno7y88vv3wsAAFQcw/M871h+g927vyurLPgPBQVhFRZG/I4BVBjGPLINYz67eJ705pumJk+2tXSpJc+T2rRJqH9/Rxdf7JbLvjclJdJf/2pp4sSgNmwwVbmyp759HQ0ZElfNmsf0+HdUGPPIRox7ZBvGfNmqXr3SAb9GmZOi+CZAtmHMI9sw5rNDNCrNn29p0qSg/vlPUwUFnvr2jWvgQEc/+1nFFSpvvx3QU08FtWhR6aTs9u0TGjYsrsaNkxWWgTGPbMS4R7ZhzJetg5U5LLMCAAAoY4mENHmyrYcfDmrv3oDq1XM1blyJrr3WUThc8XkaNUqqUaMSffGFoSlTgnruOVsLF9q6+OKE7r8/prPPrrhSBwAAHLuDH5EAAACAI7J+fUBXXx3W736XowYNkpo/P6I1ayLq29efIuc/1azp6fe/j+ndd4s0dmyJPvoooJYtw3rooaAcx99sAADg8FHmAAAAlIG9e6UxY0Jq3z5P+/YZevrpqGbPjqpJE/eINjOuCPn50uDBjtaujaht24TGjg2pTZuw3n+fR0MAANIBd2wAAIBj4HnSrFmWmjTJ06xZtm68Ma7XXy9W+/aJlCtx/le1ap4mTSrR1KlRffWVoVatwrrvvqDicb+TAQCAg6HMAQAAOEqbNwfUqVOubr45V2ec4WnVqojuuiuWdkeAd+iQ0Nq1xercOaEHHgjp6qvD2riRx0QAAFIVd2kAAIAjlExKDz8cVIsWYX34YUAPPxzVokURnXtu+m4kXKWKNGFCiaZPj2jfPkNt2oT1l78E5bp+JwMAAP+LMgcAAOAIFBZK/fvn6p57QurQIaF16yLq3TuhQIY8VbVq5Wrt2mJ1757QuHEhDRyYo+Jiv1MBAID/lCGPHQAAAOXvvfcCatkyT6tXmxo7tkRPPlmiqlU9v2OVueOOkx59tET33FOiFSssdekS1q5dKb4BEAAAWYQyBwAA4DDMnGmpXbuwHEdasCCiwYOdlN/g+FgNGeLomWei2rYtoHbtwtq6lUdHAABSAXdkAACAgygpKT1yfNSoXF10kauXX46oceP03RvnSLVp4+qllyKKxaT27cNau9b0OxIAAFnPOpyf1LlzZ1WqVEmSVLNmTY0dO7ZcQwEAAKSC7dsNDR6cq02bTI0eHdNtt8VlZmGX0aBBUsuXR9S7d66uuy5X48aVqGfPhN+xAADIWocsc2KxmCTpueeeK/cwAAAAqeK110wNHpwrSZo+PaJWrbL7WKeaNT0tXhzRoEG5GjkyV59+GtPtt8czfqkZAACp6JDLrLZs2aJoNKpBgwapf//+evfddysiFwAAgG/mz7fUq1euatRIauXK4qwvcr5XubI0a1ZUffrENW5cSL/8ZY4STNABAKDCGZ7nHfQIhq1bt2rjxo3q3r27Pv30U11//fVavny5LKt0Uk80GpdlZeF843JmmgG5bvasxwcY88g2jPnU9dhjhsaMCejyyz298EJSBQV+J0o9nieNHWvorrsC6t8/qaee8g55NDtjHtmIcY9sw5gvW7Z94K7lkMusatWqpdNOO02GYahWrVoqKCjQ7t27dfLJJ0uSiopiZZcUPygoCKuwMOJ3DKDCMOaRbRjzqae0oAjq4YdDatfO0cSJJZKkwkKfg6WoG2+UiouDuv/+kCwrrrFjYwddcsWYRzZi3CPbMObLVvXqlQ74tUOWOfPmzdO2bdt01113adeuXSoqKlL16tXLNCAAAICfEgnp1ltDmjEjqH794rrvvlhWbnR8pH71q7iKiw1NmBBUfr6nO++M+x0JAICscMgyp1u3brrjjjvUq1cvGYahe+6554clVgAAAOkuEpGGD8/R8uW2xoxhU98jYRjS738fU3Gx9OijIeXnS6NGUegAAFDeDtnKBINBPfjggxWRBQAAoEIVFkp9++Zq/XpTY8eWaPBgx+9IaccwpHvvjSkSMXTPPSGFw56GDuW/IwAA5YkpNgAAICt99ZWhHj1y9fHHAU2aVKJOnTiW6WgFAtIjj5QoEpHuvDNHeXlSnz4UOgAAlJdDHk0OAACQab76ylDnzmF9/nlAs2ZFKXLKgGVJEyeW6KqrEhozJqT583lnCABAeeEuCwAAfjBtmu13hHK3f7/0xBNB7d9vaMiQuD75JKBPPuH9Vllp2TKhTz8N6IYbcvTmm47OPbf0iNpw2FAkkvnjqyz078+sJgDAwfHkAgAAssZ330kTJwb17belRU6tWp7fkTJOMCgNGhTXKad4eu45W599xm7SAACUNcocAACQFb4vcvbtMzR4MEVOecrJkQYPjqtSJU/PPBPU/v1+JwIAILNQ5gAAgIxXVFRa5Ozda2jwYEe1a1PklLf8fGngQEclJdLTTwflsHIIAIAyQ5kDAAAyWnGx9OSTpUXOoEGOzjwz6XekrFGjhqdevRx9/nlAM2YY8ujQAAAoE5Q5AAAgYxUXl87I2b27tMipU4cip6Kdd15SrVs7eustQ2vWmH7HAQAgI3CaFQAAyEiRSOmMHIoc/7Vs6Wr3bktLl1o68URP55zDnwUAAMeCmTkAACDjxGLSlClB7dplaOBAR3XrUh74yTCk/v091ajhacYMW7t2ccIVAADHgjIHAABklERCevbZ0iOx+/VzdNZZFDmpIBiUBg6My7alqVNtRSJ+JwIAIH1R5gAAgIyRTEqzZtnats1U9+4J1a9PkZNKCgqkAQPiKiw09NxztlzX70QAAKQnyhwAAJARPE968UVLGzea6tDB0UUX0RSkotNP99S1a0Iffmhq0SK2bwQA4GhwBwUAABnhr3+19Oablq66KqFmzShyUtlFF7naudPQ2rWWatVKqkEDZlABAHAkmJkDAADS3muvmVq1ytLFFyfUtm3C7zg4DB06JHTqqUk9/7ytb75hQ2QAAI4EZQ4AAEhrb78d0MKFts47z1XXrgkZ9AJpwTSlvn0dSdL06bYSdHAAABw2yhwAAJC2/vnPgObOtVWnjqs+fRwFeLJJK1WqeOrRw9Hnnwe0bBmr/wEAOFw88gAAgLT08celJyKdcoqnX/zCkUUXkJbOPz+pSy9N6NVXLX3wAY+mAAAcDu6YAAAg7Xz1laGnnw7q+OM9DRkSV06O34lwLDp1Sujkk5OaPdvWt9/6nQYAgNRHmQMAANLK/v3S5MlBWZZ0/fWO8vL8ToRjZdtSv36OHEeaMSOoJIdbAQBwUJQ5AAAgbZSUSFOmBBWJSIMHx1Wliud3JJSRE07w1LWro48/DmjlStbMAQBwMJQ5AAAgLbiu9NxztnbuNNSvn6OaNSlyMk2jRkk1bOhq1SpTH33EYyoAAAfCXRIAAKQ8z5PmzbO0dauprl0TqlePdTiZ6tprHVWr5mnmTFtFRX6nAQAgNVHmAACAlLdihaX16y1dfXVCF1/s+h0H5SgUKt0/JxKRZs+25TEBCwCAH6HMAQAAKe2tt0ytXGmpceOEWrVK+B0HFaBGDU8dOiS0ZYupN980/Y4DAEDKocwBAAApa8uWgObNs1S3rqtu3RIyDL8ToaI0aeKqbl1XixZZ2rOHP3gAAP4TZQ4AAEhJX3xhaNo0Wyed5Kl/f0cmEzSyimFIPXo4CgSkOXNsjisHAOA/UOYAAICUs3evoSlTggqHpSFD4srJ8TsR/FBQIHXp4uiTTwJ67TXaPAAAvkeZAwAAUkokIk2aZCuRkK6/Pq7Klf1OBD/9/OdJ1a/vatkySzt3stwKAACJMgcAAKQQx5GefjqovXsNDRgQ14kncpRRtjMMqVs3Rzk5padbJdgDGwAAyhwAAJAakklp1ixbn3wSUK9ejmrXpshBqfx8qXt3Rzt2BLRqleV3HAAAfEeZAwAAUsLixZY2bTLVsaOjCy5gt1v8t/r1k2rY0NUrr5j67DOWWwEAshtlDgAA8N1rr5l67TVLTZsmdMUVrt9xkKI6d3ZUqVLpcivH8TsNAAD+ocwBAAC+2rgxoEWLLJ13nqtOnRIymHSBA8jNla67ztHXXwe0dCnLrQAA2YsyBwAA+OaTTwzNmmXrtNM89e7tKMCTCQ6hbt2kLrssobVrLX30EQMGAJCduAMCAABffP21oalTgzr+eE8DB8Zl234nQrpo3z6hatWSmjPHVizmdxoAACoeZQ4AAKhw+/dLkyYFZZrSkCGO8vL8ToR0EgqVLrcqLJSWLWO5FQAg+1DmAACAClVSIk2ZElRRkTR4cFxVq3IEOY5crVqemjRxtW6dqU8/ZaMlAEB2ocwBAAAVxnWl556ztXOnof79Hf3sZxQ5OHpt2yZ03HHS3LmcbgUAyC6UOQAAoEJ4nvTCC5a2bjV17bUJ1auX9DsS0lwoJHXvXnq61csvs9wKAJA9KHMAAECFWLnS1FtvWWrZMqFLLnH9joMMcdZZSTVq5OqVV0x9+SXLrQAA2YEyBwAAlLu33jK1YoWthg1dtW6d8DsOMkynTo7CYWnOHFsuPSEAIAtQ5gAAgHK1dWtA8+ZZqlvXVffujgwmT6CMhcNSly6OduwI6LXXTL/jAABQ7ihzAABAudmxw9C0abZOOslT//6OLLY1QTk5//yk6td39de/Wtq9m8YQAJDZKHMAAEC52LvX0OTJQeXmlh5BnpPjdyJkMsOQrr3WkW1Lzz9vK8n+2gCADEaZAwAAylxxsTRpkq1EQhoyJK7jjvM7EbJB5cpSx44JffxxQG++yXIrAEDmoswBAABlKhaTJk8Oat8+QwMHxnXSSZ7fkZBFGjd2VaeOqyVLLBUW+p0GAIDyQZkDAADKjOtK06bZ+uILQ337OjrjDIocVCzDkLp1SyiZlObNs+UxBAEAGYgyBwAAlIlkUpo719bWraa6dk2ofn02LYE/qlb11LZtQlu2mNq0icddAEDm4e4GAADKxNKlljZsMNWmjaNLLnH9joMs17Spq5o1k1qwwFY06ncaAADK1mGVOd98842uvPJK/etf/yrvPAAAIA2tWWNqzRpLl12WUIsWFDnwXyAgdevmqKhIWrbM8jsOAABl6pBljuM4+t3vfqcczhMFAAA/YcOGgBYvtnX++a46d07IMPxOBJSqWdNT06au/vY3U9u3MzABAJnjkGXOvffeq549e+qEE06oiDwAACCNbN0a0Jw5tmrXdtW7t6MAC7iRYlq3Tqhy5dLNkF0mjQEAMsRBH7lefPFFValSRZdffnlF5QEAAGnis88MPfusrZNO8jRggCOLlSxIQTk5UpcujnbuDOi110y/4wAAUCYMzzvwgY19+vSRYRgyDEObN2/W6aefrieeeELVq1f/4edEo3FZFjfGsmaaAbkup4AgezDmkW1SdcxPnnx4S1F27JAeeshQOCzdcoun444r52BIe4Zh6CCPneXuyScNffCB9Lvfeapa1bcYh2XIEM5TzxSpeq0HygtjvmzZ9oG7loO+Q5sxY8YPf92vXz/ddddd/1XkSFJRUewY4+GnFBSEVVgY8TsGUGEY88g2qTrmIxH7kD9nzx5Djz8elG1L118fl217iqTevwpSTDgcVCQS9+2f37GjtGVLSDNmeBo82EnpvZ0KCx2/I6CMpOq1HigvjPmyVb16pQN+jZXtAADgsBUWSk8+GVQyKQ0dGlfVqswgQHooKCjdP2fLFlObNvEIDABIb4d9J3vuuedUu3bt8swCAABS2HfflRY50WjpjJwTT6TIQXpp0sTVKacktWCBrWjU7zQAABw9XksAAIBDikalSZOCKiw0NHhwXDVrUuQg/Zim1L27o6IiadkyduwGAKQvyhwAAHBQsZg0ZUpQu3YZGjDAUa1aFDlIXzVremra1NXf/mZq+/YU3jgHAICDoMwBAAAHlEhIzz5ra/t2Q336ODrrLE6oQPpr3TqhypWlefNsua7faQAAOHKUOQAA4Ce5rjR9uq1t20z16OHo/PMpcpAZcnKkLl0c7dwZ0GuvHfjYVwAAUhVlDgAA+BHXlWbNsvX++6Y6d3bUuDFFDjJL/fpJnXuuqxUrLO3dy3IrAEB6ocwBAAD/JZmUZs+29e67ptq3d9S0KetQkJm6dHFkGNKLL1ry2AoKAJBGKHMAAMAPvi9y3nnHVLt2jpo3p8hB5iookNq0SWjLFlObNvFYDABIH9y1AACApNKlVXPm2PrHP0y1bevoqqsocpD5mjRxdcopSS1YYCsa9TsNAACHhzIHAADIdaVRo3K0YYOpNm0ctWhBkYPsYJpS9+6OioqkZcssv+MAAHBYKHMAAMhyyaQ0ZkyO5syx1aqVo5YtKXKQXWrW9NS0qau//c3U9u1shgwASH2UOQAAZLFkUrrllpBmzbJ1yy0xtWpFkYPs1Lp1QpUrS/Pm2XL5NgAApDjKHAAAslQyKf3qVyHNmBHUmDEx3XZb3O9IgG9yckpPt9q5M6DXXjP9jgMAwEFR5gAAkIVcVxo9OkfTpwc1alRMt98el8HqEmS5+vWTOvdcVytWWNq7l28IAEDqoswBACDLxOPS8OE5PyytuuMOihzge126ODIM6cUXLXme32kAAPhplDkAAGSRkhJp0KBcvfSSrd//voQZOcD/KCiQ2rRJaMsWU5s28agMAEhN3KEAAMgSRUVSnz65WrnS1H33lWjECMfvSEBKatLE1SmnJLVgga1o1O80AAD8GGUOAABZ4NtvpR49wlq3ztT48SUaMIAiBzgQ05S6d3dUVCQtW2b5HQcAgB+hzAEAIMPt2WOoS5ewNm4MaPLkEvXokfA7EpDyatb01KSJq7/9zdT27axFBACkFsocAAAy2M6dhjp3ztW//hXQc89F1aEDRQ5wuNq0SahyZWnePFuu63caAAD+jTIHAIAM9emnhjp2DOvLLwOaPTuqq67i0yhwJHJypM6dHe3cGdDatabfcQAA+AFlDgAAGWjjxoDatQvru+8MvfBCRJdeSpEDHI369ZM65xxXf/2rpb17/U4DAEApyhwAADLMK6+YuuaasHJzpcWLI7rwwqTfkYC0ZRhSly6ODEOaP9+W5/mdCAAAyhwAADLKnDmW+vbNVa1aSS1ZElGdOhQ5wLE6/nipdeuENm829d57PD4DAPzH3QgAgAzgedIjjwR10025uvRSVwsXRnTSSUwhAMpK06auatRIasECWyUlfqcBAGQ7yhwAANKc60q//nVIf/5zSNde62jWrKgqVfI7FZBZTFPq1s3Rd99Jy5dbfscBAGQ5yhwAANJYNCoNHpyjp58O6sYb45owoUTBoN+pgMx06qmeLr3U1bp1pj77zPA7DgAgi/FaAQBQZqZNs/2OkDbCYUORyLH994pEpKlTg9q+3VCnTo7OOCOp6dP5MwDKU9u2Cb3/vqkXXrA1cmRcJieWAwB8wMwcAADS0NdfG3r00aA+/9xQnz6OrriCo8eBipCbK11zjaMdOwJat44mBwDgD8ocAADSzLZtAT36aFDRqKHhw+O64AJOrAIq0vnnJ1Wvnqvlyy3t3et3GgBANqLMAQAgjbzxhqnJk20VFHi6+ea4atXixCqgohmG1KWLI0l68UVbHt+GAIAKRpkDAEAacF1p/nxLL75o66yzkvrlL+OqUoVPkIBfqlSR2rRJaMsWU+++yyM1AKBicecBACDFRaPS1Km21q2zdMUVCQ0c6Cgnx+9UAJo2dVWzZlIvvWQrEvE7DQAgm1DmAACQwvbsMTR+fFAffhhQ9+6OOnVKKMDdG0gJgYDUo4ejSERavJhDYgEAFYfHQQAAUtRHH5VudFxUZGjoUEcXX8yJVUCqqVHD05VXunrrLUsffcSjNQCgYnDHAQAgxXietGaNqSeftJWf72nkyLjOPJMTq4BU1apVQlWrJjVvniXH8TsNACAbUOYAAJBCYjFpxgxbixfbql8/qZEj46pWjY2OgVRm21LXrgnt2RPQqlUstwIAlD/uNgAApIg9eww984ytXbsMtWvnqHlzV4bhdyoAh6Nu3aQaNXK1erWpBg1c1ahBCQsAKD/MzAEAIAV88EFADz8c1P79hq6/3tFVV1HkAOmmY0dHubnSvHm2kqyMBACUI8ocAAB8lExKK1aYevppW1Wreho1Kq66dfkUCKSjvDzpmmscffZZQG+8YfodBwCQwVhmBQCAT6JRafZsW//8p6mGDV116+bItv1OBeBYXHhhUhs2uFq2zFL9+q4KCvxOBADIRMzMAQDAB9u3Sw89FNTmzQF17uyoZ0+KHCATGEbpZsjJpPTCC7Y8ts4BAJQDyhwAACqQ50mvv27qgQcMJZOGRoyIq2lT9scBMkmVKp7atk1o82ZT77zD4zYAoOyxzAoAgAoSjUrPP29r0yZT9et76tEjpnDY71QAykPTpq42bjS1YIGtOnViqlTJ70QAgEzCqwIAACrAF18YevjhoN5/P6D27R0NH+5R5AAZLBCQevRwFItJ8+ezhhIAULYocwAAKEeeJ73xhqnx44NKJAzdcENczZu7CnAHBjLeiSd6atUqoU2bTG3axDc9AKDssMwKAIByUlIizZtn6913TZ11lqvevR3l5fmdCkBFatbM1aZNpubPt1W7doxrAACgTPCKAACAcrB9u6GHHgpq48aA2rZ1NHgwRQ6QjUxTuu46R8XF0sKFLLcCAJQNyhwAAMpQMim98oqpxx8PynUN3XhjXC1asKwKyGY1anhq0cLVhg2mPviAiwEA4NixzAoAgDLy7bfS7Nm2PvzQ1Pnnu+re3VFurt+pAKSCFi0Seu+9gF54wVatWjGuDQCAY8KrAQAAysAHHwQ0blxIn34aUPfujvr1o8gB8G+WVXq61f790uLFvE8FABwb7iQAABwDx5GWLLH0+uuWatRIqm9fRyec4PkdC0AKOvVUT1de6WrNGksNGiRVt27S70gAgDR1yJk5ruvqjjvuUM+ePdWnTx999tlnFZELAICU99VXhsaPD+r11y1dfnlCN90Up8gBcFCtWydUvXpSzz9vKRbzOw0AIF0dssxZvXq1JGn27NkaOXKkxo4dW+6hAABIZcmktHatqYcfDurbbw0NGhTXNdckZHNQDYBDsO3S5VaFhYaWLmWSPADg6BzyDtKyZUs1a9ZMkvTll1+qWrVq5Z0JAICU9e230pw5trZtM1WvnqsePRxVquR3KgDppFYtT02auHr9dUv16ydVpw7LrQAAR+awXgdYlqXbb79dK1eu1KOPPvpfX8vPD8myzHIJl81MM6CCgrDfMYAKw5jPDOGw4XeEcvWPf0gzZxpyHKlnz6Quv9yQYQSP6vcyDEPh8NH9WiAdMeb/W7du0rZtnubOtXXnnd5/bZheUMA0v0zB8w2yDWO+4hie5x324v7du3erR48eWrJkicLh8P//ve/KLVw2KygIq7Aw4ncMoMIw5jPDtGmZ+QGkpESaP9/Whg2mfvazpHr3dlS9+rHtjRMOBxWJxMsoIZD6GPM/tn27occeC6phw6R69nR++Pv9+zsH+VVIJzzfINsw5stW9eoHnv59yJk5CxYs0K5duzRs2DDl5ubKMAyZJjNxAADZ4eOPDc2aZauw0NDVVyfUsmVC3AYBlIXTTvPUooWrVass1a/vqn59llsBAA7PIcucVq1a6Y477lCfPn2USCT0m9/8RqFQqCKyAQDgm0RCWrHC0urVpqpU8TRiRFynn85JVQDKVsuWCW3eHNDzz9s67bQYe3ABAA7LIcuccDisRx55pCKyAACQEnbtMjRzpq0dOwK66KKEOnVKKCfH71QAMpFlST17Onr44aBeeMHWL37BEisAwKEd8mhyAACyhedJr79u6qGHgiosNDRgQFw9elDkAChfJ5/sqW3bhN5/39SGDTyeAwAOjbsFAACS9u+XJk+2tWCBrTPPTOqWW2LsXwGgwlxxhatatZJasMDWF19k9smAAIBjR5kDAMh6770X0AMPhPTxxwF16eJo8GBHlSv7nQpANgkESpdbJZPSzTfnKEmXDAA4CMocAEDWKimRZs+29OyzQVWp4mn06LiaNHFl8FIcgA+qVvXUqVNCa9damjrV9jsOACCFHXIDZAAAMtEnn5QeOb5vn6EWLRJq1YojxwH47+KLXe3bZ+iPfwypWbOEzjyTU/QAAD/GzBwAQFZxXWnZMksTJgQlSTfeGFfbthQ5AFKDYUgPPVSinBxpxIhcORxuBQD4CZQ5AICs8fU8DmYRAAAgAElEQVTXhsaPD+rlly01auRqzJi4atXirTeA1HLSSZ4efLBE77xj6t57g37HAQCkIJZZAQAynudJb7xhavFiS7Yt/eIXcZ13HruLAkhdHTsm1LdvXOPHB3XFFa6uuML1OxIAIIVQ5gAAMtr+/dLcuba2bDF11lmurruOk6oApIe7747p7383NWJEjlavjqhaNWYSAgBKscwKAJCx3n8/oAcfDOmjjwLq3NnRkCEUOQDSR16e9OSTJdq3z9DNN+fIo8sBAPw/yhwAQMaJxaTnn7f0zDNBFRSUHjnetClHjgNIP/XrJ/X738e0cqWlKVM4rhwAUIplVgCAjPLZZ4ZmzrT1zTeGmjdPqHXrhCzudgDS2JAhjtassfSHP4R0ySWu6tdnzy8AyHbMzAEAZATXlVatMvXYY0ElEoaGD4+rfXuKHADpzzCkRx4p0XHHeRo2LEeRiN+JAAB+o8wBAKS9b74x9MQTQS1fbqtBg6RuuSWm2rXZXAJA5qhWzdPjj5foo48C+u1vQ37HAQD4jPeVAIC05XnShg0BzZ9vyzCk3r3j+vnPWX4AIDNdeaWrESPieuyxkJo1c9WxY8LvSAAAn1DmAADSUiQivfCCrY0bTdWqlVSvXnFVqeJ3KgAoX7/+dVzr1lkaMyZHF15YrJo1mYUIANmIZVYAgLTz4YelR46/915A7do5uuEGihwA2SEYlCZOjCqRkIYPz5Hj+J0IAOAHyhwAQNpIJKRFiyw99ZStYNDTyJFxXXWVqwB3MwBZpFYtTw8+WKK33rJ0993snwMA2YhlVgCAtPDVV6VHjn/5ZUCXXppQx44JBYN+pwIAf1x7bULr18c1cWJQjRuzfw4AZBvKHABASvM8ad06U4sXWwqFpEGD4jrnHDY5BoA//CGmd981dfPNOapXr1hnnsn+OQCQLZiYDgBIWfv3S5Mn21qwwFadOkn96lcxihwA+H/BoDR5clTBoKdBg3JVXOx3IgBARaHMAQCkpK1bAxo3LqR//Suga691NGiQo0qV/E4FAKnllFM8TZxYoq1bA7r11hx5TM4BgKxAmQMASCnfb3I8aVJQ+fmeRo2K67LLXBmG38kAIDU1a+bqttvimjfP1jPP2H7HAQBUAPbMAQCkjD17DM2YYevzz0s3Oe7UKSGbzyUAcEijR8f19tum7rwzpAYNXP385yxJBYBMxswcAEBK+Mc/AnrooaD27DH0i1/E1bUrRQ4AHK5AQJowIaqTTvI0ZEiuvvmG6YwAkMkocwAAvorFpNmzbc2cGVSNGp7GjInpvPN4owwAR+r446UpU6L6+mtDN96YI9f1OxEAoLxQ5gAAfLNjh6GHHw5qw4aArr46oeHD4zr+eL9TAUD6uuCCpO65J6bVqy098EDQ7zgAgHLCnjkAgArnedLataaWLLGUny8NHx5X7docwQIAZaFfP0dvv23qwQdDOuecpDp2TPgdCQBQxihzAAAVqqhImjPH1ubNps4911WPHo7y8vxOBQCZwzCk++4r0YcfBnTTTTk6/fQIy1cBIMOwzAoAUGE+/DCgceNC2rYtoM6dHQ0YQJEDAOUhJ0d65pmojj/eU//+udq1iw2RASCTUOYAAMqd60rLlll66ilboZCnm2+Oq2lTVwafLQCg3Jx4oqdp06Lat8/QgAG5KinxOxEAoKxQ5gAAytXevYYmTAjq5ZctNW7satSouGrUYH8cAKgI552X1PjxJdqwwdQtt+TI4/ILABmBPXMAAOVm48aAnn/eliT16RPXhReyZwMAVLSOHRO6/faY7r03pLPPTuqmm+J+RwIAHCPKHABAmYvHpZdesvT3v1s69dSk+vRxVLUqr4MBwC9jxsS1dWtAf/pTUHXrumrd2vU7EgDgGFDmAADK1M6dhp57ztbXXwfUvHlCbdokZJp+pwKA7GYY0iOPlOjTT8MaPjxXS5dGVK8esyUBIF2xZw4AoEx4nrRunalHHgkqGjU0dGhc7dtT5ABAqsjNlZ59Nqr8fE/9+uVqzx52oQeAdEWZAwA4Zvv2SQMH5mj+fFu1ayc1ZkxMdevyxhcAUs3JJ5eecPX114YGDMhRNOp3IgDA0aDMAQAckzffNNW8eZ5WrrTUsaOjwYMdVarkdyoAwIFceGFSjz1WovXrTd1wQ45cts8BgLRDmQMAOCquK91/f1CdO+cqFJKWLInoyitdBbizAEDK69QpobvvjmnpUlu/+U2II8sBIM2wATIA4Ijt2GHoxhtz9Le/WerWzdF995UoP1/atIkNcgAgXQwd6mjnzoAefzyok0/2NGoUR5YDQLqgzAEAHJFlyyyNGpWjWEx67LGoevRI+B0JAHCUfvvbmL76ytA994R00klJ9ezJNR0A0gFlDgDgsJSUSHfdFdLUqUGdf76rp56K6owzmJcPAOksECg9snzPHkOjR+eoevWoWrRgEx0ASHXsbAAAOKRt2wJq3TqsqVODGj48riVLIhQ5AJAhgkHp6aejOuecpAYPztU77/ARAQBSHVdqAMABeZ40fbqtq68Oa/duQzNnRvTHP8YUCvmdDABQlvLzpZkzo6pWzVOfPrn6+GPD70gAgIOgzAEA/KRvv5WGDs3RmDE5atTI1erVEbVsydR7AMhUJ57oac6ciDxPuu66sL7+mkIHAFIVZQ4A4EfWrw+oRYs8LV5s6c47Y3r++ahOPJFlVQCQ6WrX9jR9elS7dxvq1StXhYV+JwIA/BTKHADAD5JJ6ZFHgurUKSxJWrQoopEj4wpwtwCArNGwYVJTp0a1ZUtAvXqF9d13ficCAPwvHs8BAJKkr74y1L17rv7855A6dEjo5ZeL1ahR0u9YAAAfXHWVq8mTS7RxY0C9e+equNjvRACA/0SZAwDQqlWmmjcP6+23TY0bV6KnnirRccf5nQoA4Ke2bRN64okSrV9vqn//XEWjficCAHzP8jsAAMA/sZj0pz+F9OSTQZ1zjqunnoqqbl1m4wCAn6ZNs/2O8F+uu87R7Nm2WrUKa+BARxafIA6pf3/H7wgAMhwzcwAgS330kaF27cJ68smgBg2Ka/nyCEUOAOBHGjZMqlu3hLZuNTVtmq1Ewu9EAICD9uqO4+g3v/mNduzYoXg8rhtuuEEtWrSoqGwAgHLgedKMGbbuvDOknBxP06ZF1KYNR44DAA7s4otdJRLS/Pm2Zs6U+vRxZJp+pwKA7HXQMmfhwoUqKCjQ/fffr3379qlLly6UOQCQxgoLpVtuydGiRbYuvzyhxx4r0cknc+Q4AODQmjQpLXQWLbJlmlKvXg6nHQKATw5a5rRp00atW7f+4f+b1O8AkLbefNPUDTfkaNcuQ3feGdMvf8mR4wCAI3PllaWFzrJlpYVOjx4UOgDgh4OWOXl5eZKkoqIijRw5UqNGjfrRz8nPD8myKHnKmmkGVFAQ9jsGUGEY8+UnkZD+/GdDY8caOuMM6bXXkmrUyFJ57IEfDhtl/ntmKsMwFA4H/Y4BVBjGfObo2FEKBJJassSU5wU0YIDHkqv/UVBQuok1zzfINoz5inPIJ/mdO3dqxIgR6t27tzp27PijrxcVxcolWLYrKAirsDDidwygwjDmy8dnnxkaPjxXb78d0HXXORo7tkT5+aXLrcpDJJJaJ7CksnA4qEgk7ncMoMIw5jNL8+aSZGrJElvRaFL9+jmyuQX8oLCw9DQrnm+QbRjzZat69UoH/NpBJ0Xu2bNHgwYN0q233qpu3bqVeTAAQPmZP99S8+Z52ro1oIkToxo/vrTIAQCgLDRv7qpLF0cffGBq6lRbMd7xAkCFOWiZM3HiRO3fv18TJkxQv3791K9fP5WUlFRUNgDAUSgqkkaOzNGwYbk666ykXnmlWNdeyzmyAICy16SJq+uui+ujjwKaNCmoaNTvRACQHQzP847pGJPdu78rqyz4D0xPQ7ZhzJeNd98NaNiwXG3fbmjUqLh+9au4rLLfGueApk1jjv3hYskJsg1jPrNt3BjQzJm2TjrJ09Chcf3/1ptZq39/llkhOzHmy9ZRL7MCAKSHZFIaPz6odu3CisWk+fOj+vWvK7bIAQBkrwYNkhowwNGuXYYmTAhq/36/EwFAZqPMAYA0t2uXoR49cnX33SG1aZPQmjXFuvRS1+9YAIAsU69eUkOGONq3z9Djjwe1d6/fiQAgc1HmAEAaW7LEUrNmYb39tqlx40o0ZUqJCgr8TgUAyFZnnpnUsGFxRSKGHnsspC+/NPyOBAAZiTIHANLQ/v3STTflaODAXJ1yiqeVKyPq29eRwTMzAMBnp53m6YYb4jIM6fHHg9q2jY8cAFDWuLICQJp54w1TzZvn6fnnLY0ZE9PSpRHVqZP0OxYAAD+oUcPTTTfFVKWKp8mTbb39Nh87AKAscVUFgDRRUiLddVdIXbrkyrKkxYsj+vWv4woG/U4GAMCPFRRIN94YV+3aSc2eHdSqVaaO7RxdAMD3KHMAIA28/35ArVuHNWFCUP37O3rllWI1asRsHABAasvNlQYPdtSwoavly23Nm2fJZY9+ADhmHFoLACnMdUv3G7j33qCOP97TrFkRtWjBUzAAIH1YltSzp6OCAk8vv2ypsNBQ//6OQiG/kwFA+mJmDgCkqE8/NXTNNbn6059Kjxx/9VWKHABAejIMqW3bhLp1c/ThhwFNmBDU/v1+pwKA9EWZAwApxvOk6dNtNW+ep82bTT3+eFSTJ5eoalU2GgAApLdLLnE1cKCj3bsNPfpoSF98wTGMAHA0KHMAIIV8/bWh/v1zNWZMji680NWrrxare/cER44DADJGvXpJjRgRl1S6lPjdd/lIAgBHiisnAKSIpUstNWsW1po1pu6+u0Tz5kVVsyazcQAAmeeUUzzdfHNMNWt6mj49qKVLLSXZ1x8ADhtlDgD47LvvpJtvztGAAbk6+WRPK1dGNGyYowBXaABABqtUSRo2LK5LLknolVcsPf20rWjU71QAkB74qAAAPlq71lTz5nmaM8fS6NExLVsW0dln82oSAJAdLEvq2jWhLl0cbd0a0PjxQe3ezdpiADgUyhwA8EFRkXTbbSF17RqWZUkLF0Z0xx1xBYN+JwMAoGIZhtSkiauhQx0VFRl69NGgtm7lYwoAHAxXSQCoYGvXmmrWLE/PPmtr2LC4XnmlWBddxGwcAEB2O/PMpEaNiqugwNPkybZWrzblsXUcAPwkyhwAqCD/OxvnpZeiuvvumMJhv5MBAJAaqlTx9MtfxnXeeUktWWLrmWfYRwcAfgplDgBUgLVrTV15ZelsnOHDS2fjXHKJ63csAABSTigk9evn6JprHG3eHNBDDwX1xRfsowMA/4kyBwDK0XffSbfeWjobx7alhQuj+uMfmY0DAMDBGIZ0+eWuRoyIK5k0NH58UG+8wbIrAPgeZQ4AlJNVq0xdfnmepk379944F1/MbBwAAA7Xaad5Gj06pjp1knrxRVszZ9qKxfxOBQD+o8wBgDL2zTeGbrghR717h1WpkqclSyLsjQMAwFHKy5MGDXLUpo2jd98N6JFHgtq5k2VXALIbZQ4AlBHPk1580VLTpmEtXGjpV7+KadWqiBo14qQqAACORSAgtWzpatgwR9Fo6fHl69ez7ApA9qLMAYAy8OWXhvr1y9Xw4bk69VRPK1dGdNttcYVCficDACBznHlmUqNHx3TqqUnNmWNrxgxOuwKQnShzAOAYJJPSs8/aato0T2vXmvrDH0q0dGlE55zDbBwAAMpD5crSsGGly642bQrowQdD+vhjll0ByC6UOQBwlLZuDahTp1zdemuOLrzQ1Zo1xbrhBkem6XcyAAAy2/fLrkaMiMs0PT3xRFDLl1tyOWcAQJagzAGAI1RSIv3lL0FddVVYH35o6pFHopo3L6patVi4DwBARSo97Squhg1drVplacKEoL75hlk6ADIfZQ4AHIF160w1b56nceNC6tQpoddfL1avXgkZPDcCAOCLnBypZ8+E+vaNa9cuQ+PGBbVhQ4DNkQFkNMvvAACQDvbulf7whxzNmmXrtNOSmjMnoubNmcsNAECquOCCpE49NaZZs4KaNSuozZtddeniKC/P72QAUPaYmQMAB+F50rx5lpo0ydPcuZZGjozp1VeLKXIAAEhBVapIN9wQ/2Fz5AceCOmDD/jIAyDzcGUDgAPYujWgrl1zdeONuTr9dE+rVkV0551xhcN+JwMAAAfy/ebIN98cV36+p6lTg5ozx+IIcwAZhWVWAPA/ioqkceOCmjgxqPx86b77StSvH6dUAQCQTk45xdPNN8e1cqWlV14x9eGHpnr0cFS3btLvaABwzChzAKSEyZMNRSK2rxk8T3rvvYAWLrRVWGioceOE2rdPKBCQZszwNxsAADhyliW1bZvQOee4mj3b1lNPBXXZZaX391DI73QAcPQocwBA0u7dhhYssLR1q6mTT06qT584R40DAJAhTjvN05gxcS1bZmntWlNbtwbUo4ej2rW51wNIT5Q5ALKa40ivvFI6/dqypGuucXTZZS5LqgAAyDC2LXXqlFD9+q7mzLH1xBMhXXZZQu3aJZST43c6ADgylDkAstYHHwS0YIGlvXsDuvBCVx07Oqpc2e9UAACgPJ1xRuksneXLLb3+uqkPPjDVtaujevXYSwdA+qDMAZB19u6VXnrJ1j//aeqEE5IaNiyuOnV4gAMAIFuEQtI11yR0wQWu5s61NWVKUA0buurUyVFent/pAODQKHMAZI1EQnr1VVOrVpVe+tq1c3TFFa4sroQAAGSl007zNHp0XKtWlS653rIlpC5dHDVokJRh+J0OAA6MjzAAssK2bQHNn29p9+6Azjuv9M3b8cf7nQoAAPjNsqQ2bRI6//zSWTrTpwf1zjuurr3W0XHH+Z0OAH4aZQ6AjLZvn7R4sa2NG01VrZrUkCFxnX02S6oAAMB/q1HD0003xbV2ranlyy3df39IbdsmdOmlrgIBv9MBwH+jzAGQkeJxac0aU6tXW/I8qVUrR82bu7Jtv5MBAIBUZZpSs2au6tdP6oUXLM2fb2vDBlPdujmqUYNjzAGkDsocABnF86SNGwNavNhWYaGhBg1ctW/vqEoVv5MBAIB0Ua2ap6FDHf3jH64WLrT18MNBXXGFq1atEgoG/U4HAJQ5ADLIjh2GFiyw9cknAdWokVSvXnHVrs1bNAAAcOQMQ2rYMKmzz45pyRJLa9ZY2rgxoK5dEyzZBuA7yhwAaa+oSFq2zNJbb5kKh6Vu3RxddBHr2wEAwLHLy5N69EioYUNXL7xga/LkoC64oPQwhcqV/U4HIFtR5gBIW4mE9MYbplassBSPS02blk5/zs31OxkAAMg0tWt7GjMmrtWrTa1aZWnLlpBat07osstcmabf6QBkG8ocAGlpy5aAXnqp9Kjxs85y1alTQieeyJIqAABQfixLuvpqVxdckNT8+ZZeesnWW2+ZuvZaR7Vq8RwCoOJQ5gBIK7t3G1q40NLmzaaqVUtq0KC46tVLyjD8TgYAALJF9eqerr/e0fvvu3rpJVuPPx5So0alhy5UquR3OgDZgDIHQFqIRqVVqyy9/ropy5I6dHDUtKkri6sYAADwgWFI552XVN26Mb38sqVXXzX1/vshtWmTUO/eDs8oAMoVlxgAKS2ZlNavN7VsmaXiYqlxY1dt2yZ46wUAAFJCKCS1a5dQo0auFiywtGCBrY8+CugvfylRq1Z+pwOQqShzAKSsbdsCWrTI0s6dAZ1+elJDhjiqWZP16AAAIPWccELp0qtNm1y9/LKlDh3y1L9/Urffbqh6dZ5fAJQtDu4FkHK++srQpEm2nnoqqFhM6ts3rhEj4hQ5AAAgpRmG1KBBUq+/Xqybbopp5kxDl12WpylTbLmu3+kAZJLDKnM2btyofv36lXcWAFnuu++kefMsPfhgUNu3B9Shg6PbbovrggvY4BgAAKSP/Hzpt7+Na8OGpM4/39Udd+SoVauw1q/nXTqAsnHIZVaTJk3SwoULlZubWxF5AGShSERatkxasSIkx5GaNHF19dUJ5eX5nQwAAODo1asnzZsX1cKFln7725Dat89T795x3XlnXNWqMeMYwNE7ZDV86qmnavz48RWRBUCWSSaluXMtXXZZnhYtCqhOnaRuvTWuzp0pcgAAQGYwDOmaaxJ6441ijRgR19y5ti69tHTpVSLhdzoA6eqQZU7r1q1lca4egDK2bp2pVq3C+uUvc3XCCZ5Gj05qwACHDQIBAEBGys+Xfv/7mFavjvyw9Oqqq8J69VXT72gA0tAxtzT5+SFZFhegsmaaARUUhP2OAZS5rVulO+4IaPFiQz/7madnnkmqZ0/p6acNeV7Q73hAhTEMQ+EwYx7ZgzGPbFJQYEv66Wf6iy+WVq2SFixw9etfB9S9e1gdOni6776kzjzTj7RA2eFzbMU55jKnqChWFjnwPwoKwiosjPgdAygze/YYeuCBoJ591lZurnTnnTFdf31cubnS/v2S5+UpEon7HROoMOFwkDGPrMKYRzYpLHQkHfyZvnlz6dVXpaeeCuqhh4Jq0CCgoUMdjRkTU6VKFZkWKDt8ji1b1asf+GLAduoAylU0Ko0fH9TFF+fp2Wdt9evn6O9/L9bIkaVFDgAAQLbKyZFGjozrzTeL1bVrQo8/XvrMNGMGR5kDOLjDKnNq1qypuXPnlncWABkkkZBmzrR06aV5uvvukC65xNWrr0Z0330x9sUBAAD4Dyee6OnRR0u0YkWxatXyNHp06VHmb77JdhYAfhozcwCUKc+Tli611KxZWKNG5eqkkzzNnx/RjBlR1a2b9DseAABAyrrggqQWL47oySej+uYbQ506hXX99Tn6/HPD72gAUgxlDoAy8+abptq3D2vAgFwlk9LUqVEtWxZRkybMEwYAADgchiF16VJ6lPmtt8a0YoWlJk3y9H/t3Xl4FHWex/FP9ZWkO0DA6KAIGS7lUFBu5JIbgcgDKEckjKLDDMp6PaMr3ju64kzWQcFFH4WFAUERBORSvHC5DOeijw6CC8oEUJcYrnQnfVXtHyUBRROUJJVO3q/nqaeqD+lv46+brk/9jqef9ikYdLo6AFUFYQ6A87Z7t0vjxqXo+uv9OnjQ0DPPFGv9+pCGDo3J4EISAADAL+b3S/fdF9HmzUENHhzT3/6WpK5dA1q82COTzs5AjUeYA+BXy8sz9C//kqxrr7XHdD/8cFi5uUFlZ0flOe+18gAAANCggaUXXyzWqlVB1a9v6Y47UjRkiF/bt3MqB9RkfAMA+MUKCqRHH7WvDi1f7tHtt0e1bVuh7rwzIr/f6eoAAACqn06dTL39dkjTpxcpL8/Q4MEB3Xprsvbvpxs0UBMR5gA4Z8GgNG2aTx07puqll7y64YaocnODeuyxsOrWdbo6AACA6s3lksaMiSk3155P5/33PerePaApU5KUn0+oA9QkhDkAyhSNSnPnetW5c0BTpyapW7eYPvwwpGefDatBA5YZBwAAqEypqfZ8Olu2BHXTTVHNnetVp04BPfusT6GQ09UBqAyEOQB+VjwuLVniUY8eAd1/f7IaNza1cmVI8+YVq0ULZt4DAABw0m9+YyknJ6z160Pq0SOmp55KUpcuAS1c6FGcxUSBao0wB8BZTFN6802PevXy6/bbU5ScbGn+/JBWrChS5878MgAAAKhKmjc39fe/F2vFipAaNLB0990p6t3br/fec8uiEzVQLRHmAChhWdLq1R717u3X73+fIsOQZs8u0gcfhDRwYJxlxgEAAKqwLl3iWrMmpNmzixQOG8rK8mvkyBTt2sVpH1Dd8KkGIMuS3n3Xrf79/brllhSFw4ZeeKFIH34YUmZmTC6+KQAAABKCYUiZmTFt2BDU1KnF2r3bpQEDAvrjH5N14ABX5oDqglM0oAazLGndOrcGD/brppv8OnbM0PTpRdq4MaiRI2Nyu52uEAAAAL+GzyfdemtUW7cGdc89Yb31lkfdugX0yCNJKihwujoA54swB6ihNm506/rrUzR6tF/ffGPomWeK9dFHQY0ZE5PH43R1AAAAKA+1aklTpkSUmxvUDTdE9fLLXnXqlKrp030KBp2uDsCvRZgD1DBbtrg1YkSKRozw68ABl55+uli5uUFlZ0fl9TpdHQAAACrCxRdbevbZsNatC6lz57iefDJJnToFNHu2V+Gw09UB+KUIc4AaYudOl0aPTlFmpl+ff+7SE08Ua8uWoCZMiCopyenqAAAAUBlatjS1YEGRVq4MqVkzU1OmJKtr14BefdWjWMzp6gCcK8IcoJrbscOlceNSNGhQQB9/7NKjjxZr27ag/vCHqFJSnK4OAAAATujcOa7ly4u0aFFI6emW7rorRT17+vXmmx6ZptPVASgLYQ5QTX30kVs33pii664LaNs2t6ZMCWv79qAmT44qEHC6OgAAADjNMKTeveNauzakOXOK5PFIv/99ivr29WvtWrcsy+kKAfwcwhygGjm1OtX116do2DC/PvvM7omzY0eh7rknotRUpysEAABAVWMY0pAhMa1bF9LMmUUKBg1lZ/vVvz+hDlBVEeYA1YBlSWvXunXddX6NHm1PbPzUU8XascPuiUOIAwAAgLK43dINN8S0aVNQzz1XpOPHCXWAqoowB0hgsZj0xhse9e7tV3a2X/n59hLjW7cGddttzIkDAACAX87rlcaOjWnzZkIdoKoizAESUDAozZrlVefOAU2alKJYTJoxo6hkiXFWpwIAAMD5Ki3UWbOGiZIBJxHmAAmkoEDKyfGpffuAHnwwWfXrW5o3L6T160MaPTomj8fpCgEAAFDd/FSoc/PNKerVy6/XX/coGnW6QqDmIcwBEkBenqGHHkpSu3apyslJUseOplauDGn16pAGDYrLxScZAAAAFexUqPPRR0G98EKRXC5p8uQUde0a0Jw5XhUXO10hUHNwCghUYdu2uXTbbcnq1Mn+BzIzM6b164OaP79InTvHnS4PAAAANZDHI40caa9+NV9LQq8AABQhSURBVG9eSBdeaOlf/zVZ7dsHNGOGTydPOl0hUP0R5gBVTCwmLV/u0XXX+TVkSEAffujRH/8Y1bZtQc2YUawWLRicDAAAAOe5XNKgQXGtWRPS0qUhtWpl6okn7N7k//7vPn37reF0iUC1xQwbQBVx/Lg0f75Xs2f7dOiQS40bm5o6tVijR7O0OAAAAKouw5C6d4+re/ci/c//uDR9uk/Tp/s0c6ZPI0bE9Ic/RHTFFVyQBMoTYQ7gsD17XJozx6vXXvMqFDLUrVtMU6cWa8AA5sIBAABAYrn6alNz5hTryy8NvfSST6++6tWiRV717BnTpEkR9ekTl0GHHeC8caoIOCAalVas8Gj48BT16BHQK694NXRoTO+/H9SyZUVMagwAAICE1rixpalTw9q1q1APPxzW3r0ujR3rV8+efr3yCpMlA+eL00WgEn39taG//tWndu0Cuu22FOXlufTww2Ht2hXU888X68or6X4KAACA6iMtTbrzzoi2bw/q+eeL5PFI996brKuuCujf/i1JBw7QTQf4NRhmBVQw05Q2bnRr7lyv3nrLI9OU+vaN629/K1afPnG53U5XCAAAAFQsn08aNSqmG2+MadMmt2bP9urFF72aOdOrfv3imjAhot696Z0OnCvCHKCCHDpk6LXXvHr1Va/++U+X6tUzNWlSROPHR/Xb31pOlwcAAABUutOTJcd1+LChefO8mj/fq7Fj/crIMHXzzRFlZUVVt67TlQJVm2FZ1nmdVR45crK8asEZ0tL8OnYs5HQZ+IUiEWntWo8WLPBq3Tq3LMtQjx4xZWVFNWRITMnJTldYdS1ZElAoFHG6DKDS+P0+2jxqFNo8apLx46OS+E1/riIRafVqj/7rv7zassWj5GRLmZkx3XRTVF27MmFyIqHNl68LL6z1s4/RMwcoB7t3u7RwoVdLlnj03XcuXXKJqXvuiWjs2KgyMuiFAwAAAPwcn08aPjym4cNj+vRTl+bO9WrpUq8WL/aqcWNTY8dGNXp0VBdfzO9q4BR65lRRJJpV36FDhpYu9eqNNzz6xz/c8notDRpkX0Ho1Yu5cH4peuagpqGXAmoa2jxqEnrmnL9QSFq1yqOFC73avNkjl8tSnz5xZWVFNWBATD6f0xXip9Dmyxc9c4Bycvy4tHKlHeBs3mwPo2rfPq6pU4s1bFhM6elcLQAAAADOl99vT5g8alRM+/cbWrTIq9de82rChBSlp5saPjymESOiatfOZBgWaiTCHKAMwaD0wQceLV3q0bvvehSJGGra1NT990c0YkRUjRsT4AAAAAAVpUkTS1OmRHT//RF9+KFbCxd6NW+eVy+/7FNGhqkRI6IaMSKmyy83nS4VqDSEOcBPOHlSeucdj1at8uiDDzwqKjJ00UWmbrklqpEjo2rblisAAAAAQGVyu6W+fePq2zeuEyekNWs8WrrUq+ee82natCS1bh3XiBExDR8e1aWXcsEV1RthDvC9ggJ7JapVq7z67/92KxIx9Jvf2BOuDR0aU5cucXn4xAAAAACOq11bGjMmpjFjYvq//zO0YoVHb7zh1RNPJOmJJ5LUvn1cgwfHNGRIVE2aEOyg+mEC5CqKiaMqx759ht591x4+tXmzW/G4oYYNTQ0ZEtPQoVF16GDK5XK6ypqBCZBR0zAZLGoa2jxqEiZAds5XXxlatsyr1as9+uQTe0WSFi3sYGfw4JiuvJIe9hWJNl++SpsAmTCniuJDUDHCYemjj9x67z07wPnySzupufzyuAYOjGno0BhDqBxCmIOahhNb1DS0edQkhDlVQ16eobfe8mjNGo9yc90yTfvC7XXXxTRgQEydO8eVlOR0ldULbb58sZoVaizLstP5DRs8eu89t9av9ygUMpSUZKl797gmToyoX7+YMjLoegkAAABUJw0bWpo4MaqJE6PKzzf0zjturVnj1d//7tVLL/nk99vnBL17x9S3b0y//S3nBEgchDmodr791tDGjW5t2ODWhg0e5eXZvW8aNDB1441R9e8fU/fucfn9DhcKAAAAoFKkp1vKyoopKyumwkJp82a3PvjAo/ff9+idd5IlSU2amOrTJ6Y+fez5MlNTHS4aKAVhDhLekSOGtm1zlwQ4e/bYY2Pr1LHUrVtMt98eUY8ecTVvzvApAAAAoKZLTZUGDIhrwIC4pLD27ze0bp0d7CxY4NWsWT55PJbatjV1zTUxdesWV6dOhDuoWghzkFBMU/riC5e2bnWXbKfmvUlJsdS5c1yjRoXVo4c9uZnb7XDBAAAAAKq0Jk0sNWkS1a23RlVcLOXmurV5s1ubNnn0wgs+zZhhyO22w52uXePq1i2mDh3iSktzunLUZIQ5qNK++87Qxx+79Mknbm3f7ta2bW4dPWp3r7ngAlMdO8aVnR1Rx46mrrqKCcwAAAAA/HrJydK118Z17bVxSREFg9L27afCHbdeesmr//xPnySpWbO42rc31a5dXB06xNWypSkPZ9ioJDQ1VBkFBdLHH7u/3+wA59R8N5LUvHlcgwdH1amT3c2xSROLYVMAAAAAKkwgIPXqFVevXnFJUihkhzs7dri1c6db77/v1qJFXkn2SIG2beNq185U27ZxtW5tqmlTRgugYhDmoNIVF9tDpXbvdunzz136/HO3du926dCh08FN48am2rePa8KEiNq2NXXllXHVqeNg0QAAAABqPL9f6tkzrp497XDHsqR//tPQzp12wLNjh1uzZnkVidi9d1JSLLVoYeqKK+xwx96YfwfnjzAHFeb4cWnfPpf273dp3z6X9u61A5z9+10yTbtLjddrqXlzU507x9W6dVRXXRVXmzYENwAAAACqPsOQMjIsZWTENHx4TJIUidgXrz/91KVPP3XrH/9wadUqr+bPPz2soGFDU82ambrssh/u09MZfYBzQ5iDX82y7JWk8vIMHTzo0pdfng5u9u839N13p3vauFyWMjIstWwZ1/XXx9SypakWLUw1aWLK63XwTQAAAABAOfL5VNILZ/RoO+CxLOnwYUOffWYHPHv3uvTFFy5t2eJVKHQ6valb11KzZvZ5UkbGmZuliy4i6MFphDn4WYWF0jffGPrmG5e++cbQoUMu5eUZystzlQQ4xcU//DapX98eFzp4cFxNmtjHTZpYysgwmZwYAAAAQI1kGFKDBpYaNDi1JLrNNKVDhwx98YWrZPvf/3Vpwwa3Xn/dI8s6fb6VkmKfVzVqZKlBA1OXXGLpkkvs/cUXm7r4YkspKU68OziBMKeGCQbtFaLy842SfX6+S999Z+jbb+3tVIBTWHh27JueburSSy21bGmqf/+4GjUy1bChfV+jRiZjPwEAAADgHLlcUsOGlho2jKtPn/gPHisulg4eNHTggEtffeXSgQMuHThg39661atjx84+X6tXzw51LrrI0oUXntrMM47trV49i5W3ElyZ//tM09Tjjz+uPXv2yOfz6cknn1RGRkZl1IYficXsMCYYNFRYaJQcn9ofP25vx44ZOn5cOnr09O1jxwwdPWr8oAvfmZKT7Q98/fqmWrUy1adPXPXrm6pf3/p+s78UAoFKftMAAAAAUAMlJ0vNmllq1iwuKX7W48GgPZLi8GGXDh829PXX9v7wYZeOHDG0d6+9j0R++hywdm1LdeuevaWlWapVy1JqqpSaan2/nX2ckiKGfTmozDDnvffeUyQS0aJFi7Rr1y49/fTTeuGFFyqjtkplWXbyGY/bx/G43eUtHjdkmirZ4vEzn2OU3GeadtgSDhuKRKRo9PRxOCxFIvaxvRkKh3/4nEhEKio6HcyEwy4dP+7/QVgTDp/bJyU52f4ApqVZqlPHUsOGpq64wh5/mZ5uKT3dnljrgguskn0gwAcRAAAAABJFICA1bWqpadOzg55TLEs6ccKe6/TIEdf3e0MFBfbF/lPbsWOGvvzSVdIx4MzhXT/H5To75ElLcykpKVnJyfbcQUlJlpKSfnxs78889vns81ifz77P45Hcbrvnktt9arN+dPvMx62S2/afUZ5/01VTmWHOjh071KNHD0nSVVddpU8//bTCi3LCf/yHTzk5lTupi9t9uuH6fJaSk6VAwA5W0tLsIU2BwKn7rDOO7Q/Mj++rVcsObxgnCQAAAAAwDKlOHalOnVM9fMoWj0uhkHTypD0ipLBQ3+9/eBwMnn1/UZH09dcuhcN2B4YzOzaEw+cWEp2vpCRLb78dUuvWZoW/lpPKDHMKCwuVesZEKG63W7FYTJ7vB9hdeGGtiquuEv31r/ZWuYyfOT6FZZ5Qc0yaJEnJTpcBVDLaPGoa2jxqitNtvbqcLwHnzu3w6xuSqv/8IK6ynpCamqpgMFhy2zTNkiAHAAAAAAAAlavMMKddu3Zav369JGnXrl267LLLKrwoAAAAAAAA/DTDsiyrtCecWs1q7969sixLTz31lJo2bVpZ9QEAAAAAAOAMZYY5qDhlLfv++uuv67XXXpPH49GkSZPUu3dvB6sFzl9ZbV6SCgoKNGbMGK1cuVJJSZU7KTlQEcpq93PnztXq1aslSb169dLkyZOdKhUoF2W1+QULFmjp0qUyDEN33HEHv2+Q8M7l941pmpo4caL69u2rsWPHOlQpUD7KavNPPvmkdu7cqUDAnrdm5syZqlWLuaPKG5PfOKi0Zd+PHDmi+fPn64033lA4HFZWVpa6desmX01YYw3VVmltXpI2bNigZ555Rvn5+Q5WCZSv0tp9Xl6eVqxYocWLF8swDGVlZalfv35q0aKFw1UDv15pbb6goEALFy7U8uXLFQ6HNWTIEF177bUyjIpf3QSoKGX9vpGkZ599VsePH3eoQqB8ldXmP/vsM82aNUv16tVzsMrqr8w5c1BxSlv2/ZNPPtHVV18tn8+nWrVqqVGjRvr888+dKhUoF6W1eUlyuVyaM2eO0tLSnCgPqBCltfv69etr1qxZcrvdcrlcisVi9EhDwiutzderV09vvvmmvF6v8vPzVbt2bYIcJLyyft+8/fbbMgxDPXv2dKI8oNyV1uZN09SBAwf06KOPasyYMVqyZIlTZVZ7hDkO+rll3089dmZXtEAgoMLCwkqvEShPpbV5SerWrZvq1q3rRGlAhSmt3Xu9XtWrV0+WZekvf/mLWrVqpcaNGztVKlAuyvqu93g8euWVVzR69GgNHDjQiRKBclVam9+7d69WrVqlu+66y6nygHJXWpsPhUIaN26ccnJyNGvWLC1cuJBOCRWEMMdBpS37/uPHgsEg4wyR8Epr80B1VVa7D4fD+tOf/qRgMKjHHnvMiRKBcnUu3/Xjxo3Thg0btG3bNuXm5lZ2iUC5Kq3NL1++XN9++61+97vfadmyZZo7d27JSsFAoiqtzaekpGj8+PFKSUlRamqqunTpQphTQQhzHFTasu9t2rTRjh07FA6HdfLkSe3bt49l4ZHwSmvzQHVVWru3LEu33367Lr/8cv35z3+W2+12qkyg3JTW5vfv36/JkyfLsix5vV75fD65XPwcRWIrrc3ff//9Wrx4sebPn6/hw4fr5ptvZrgVEl5pbf6rr75SVlaW4vG4otGodu7cqdatWztVarXGJXEH9e/fX5s2bdKYMWNKln2fM2eOGjVqpL59+yo7O1tZWVmyLEv33HMP8ygg4ZXV5oHqqLR2b5qmtm7dqkgkog0bNkiS7r33Xl199dUOVw38emV917do0UKjR4+WYRjq0aOHOnXq5HTJwHnh9w1qmrLafGZmpkaNGiWv16thw4apefPmTpdcLbE0OQAAAAAAQAKhXysAAAAAAEACIcwBAAAAAABIIIQ5AAAAAAAACYQwBwAAAAAAIIEQ5gAAAAAAACQQliYHAABV2pYtW3T33XerWbNmkqRwOKzMzExlZ2f/5PP37NmjEydOqGPHjurTp4/eeustJSUlnXcd0WhUDz74oA4dOqRIJKJJkyax7DAAAHAEYQ4AAKjyunTpomnTpkmSIpGIBg0apGHDhql27dpnPfedd95Renq6OnbsWK41rFixQmlpacrJydHRo0c1fPhwwhwAAOAIwhwAAJBQCgsL5XK5ZBiG+vXrp7Vr18rtdisnJ0fNmjXTsmXL5PV61bp1a0nS448/roMHD0qSnn/+efn9fj344IPKy8tTPB7XLbfcosGDBys7O1stWrTQF198ocLCQj333HNq0KBByesOGjRIAwcOLLntdrsr940DAAB8jzAHAABUebm5ucrOzpZhGPJ6vXrkkUdUq1YttW/fXhs3blT37t21fv163XXXXTp48KDS09PVpk0bSdLIkSPVoUMHPfDAA9q0aZMKCgpUt25d5eTkqLCwUCNGjFCXLl0kSW3atNFDDz2kadOmafXq1Zo4cWJJDYFAQJIdJt155526++67K/8vAgAAQIQ5AAAgAZw5zOpMN954o+bPny/TNHXNNdfI5/Od9ZwrrrhCkpSenq7i4mLt27dP11xzjSQpNTVVTZs2VV5eniSpVatWkqT69esrPz//rD/r66+/1h133KGsrCxlZmaW2/sDAAD4JVjNCgAAJKwOHTooLy9PS5Ys0Q033CBJMgxDpmmWPMcwjB/8N02bNtX27dsl2b1s9u7dq0svvbTM18rPz9eECRN03333lbwWAACAEwhzAABAQsvMzFR+fr6aN28uye6Js2DBAuXm5v7k80eNGqVjx45p7NixGj9+vCZPnqwLLrigzNd58cUXdeLECc2cOVPZ2dnKzs5WcXFxub4XAACAc2FYlmU5XQQAAMCv9fLLL6tu3br0lgEAADUGc+YAAICE9cADD+jo0aOaMWOG06UAAABUGnrmAAAAAAAAJBDmzAEAAAAAAEgghDkAAAAAAAAJhDAHAAAAAAAggRDmAAAAAAAAJBDCHAAAAAAAgARCmAMAAAAAAJBA/h/5mXu3WSayMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf042d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(frameworks_pyver_ratio.iloc[0, :], bins=5, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232cf090278>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf0a47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='framework', y='value', hue='pyver', data=df, size=5, palette=sns.color_palette('rainbow'))\n",
    "plt.xticks(rotation=90, fontsize=14);\n",
    "plt.legend(title=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232cf17c550>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAALqCAYAAABe0EfpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xl4TGf/x/H3ZLMTNATVED9iC1mk1tiXEhRFxVKKWlr7QwVRFKWKtoLWXiUtDdGUSlCtliKotbUmlCQtYokmQraZ3x8e80iTWBNj+byuq1dn5tznPt9zMpLzmfs+Zwwmk8mEiIiIiIiIWIyVpQsQERERERF53imYiYiIiIiIWJiCmYiIiIiIiIUpmImIiIiIiFiYgpmIiIiIiIiFKZiJiIiIiIhYmI2lCxAREXkWxcbGZ2t/+fPnIiEhKVv7zEmqN2ep3pzzNNUKqjenZXe9Dg4FslymETMREZGngI2NtaVLeCCqN2ep3pzzNNUKqjenPc56FcxEREREREQsTMFMRERERETEwhTMRERERERELEzBTERERERExMIUzERERERERCxMwUxERERERMTCFMxEREREREQsTF8wLSIiT6WPP/6Y/fv30759ez755BN++eUXS5ckIiL/ZTTBL79bERJuzaV/DLxQ0MSrNdNoW9f00H3u37+P994bQ5kyZTEYDCQlJdG8+St07Ngl0/aRkRHEx/+Dm5sHHTu2ITBwDbly5Xro7d+WmprKtGmT+Pvvv0lJSaZnzz7Uq9fgkftVMBMRkadaq1ataNiwoaXLEBGR/zKaYNo3Nhw8Y0VSigGAa9cNzPvewN4II/9pB1aGh+vb07MGkyZNAyA5OZmuXV+jRQsfChQokKHttm1bKVq0KG5uHg+9L5nZtGkjBQvaM378ZK5di+PNN7spmImIiOTOnZvcuXNbugx5DP5JhJQ0KJrx/EtEstE/aSYSTSYcbR7uqqdffrdKF8puS0oxsPckbP/digauxkeuMzExESsrKwwGA507v8rXXwdjbW3N/PlzKFvWmdDQDdjY2FKhQkUAZs2azl9/xQDwwQczyZs3L9OmTSImJoa0tDS6dOlGkybNGTSoH+XLu3D6dCRJSTeYOPEDHB1LmLfbqFFTGjVqYn5ubZ09kUrBTEREngoRERGMHz+eo0eP4uHhwUsvvQRAcHBwuqmMP/30E3PmzCEiIgI7Ozu8vb2ZMmUK+fPnJyAggNOnT1O4cGFCQkIoWLAgXbp0oX///gAYjUZmz55NUFAQAD179mTdunVMmTKFmjVrkpSUREBAAOvXr+fatWvUqlWL8ePHU6pUKcsclOfIsh+s+S7cmjSjgVouaYzqkIqtzmJEst38q8l8djWZFKBuHmvmFM9Nvgcc3goJt84Qym67mXxr+cMGs99+28egQf2wsrLCxsaG4cNHkT9/fqpVc2PPnl28/HJtwsN38tZbA/nrrxiKFi1K5cpVAfDxeZXq1d2YOnUie/eGExd3hUKFbo18JSZep3fv7nh6vgxApUpVGDr0PyxfvpAtWzbRo0cvcw158+YFIDHxOv7+o3nrrYEPtS//ppt/iIjIEy85OZl+/frx4osvEhwcTNOmTc3h6U5RUVEMHjyYLl26EBoayqeffsru3bv5+uuvzW22bNmCtbU1QUFBvP7668yePZuIiAgAFixYwLfffsvMmTNZtmwZ27ZtIyoqyrzuhAkT2Lx5Mx9++CGrV68mNTWVgQMHkpaWlvMH4Tl2LMrAul02pBlvnejtPmHNloM6hRHJbhHJRub8N5QB/HojjZXXUu66TmYu/XP3IHev5Xfj6VmDuXMXMmfO58yePZfatesB0KZNezZu3MDu3TupUeNlbG1tM6xbseKtkbMiRYqSlHSTP//8k+rVb01zzJs3H2XKlCUmJhqAChVcAHB0dCQ5OSlDXxcunGfw4AG0aNGK5s1feej9uZN+q4mIyBNv586dXL16lYkTJ1KuXDm6detG06ZNM7RLS0tj3LhxvP7667z44ovUq1ePOnXqmIMXQIECBfDz88PZ2ZkBAwZgb2/P77//DsBXX33F4MGD8fb2pnLlykyfPh2T6daF6teuXSMkJIRx48ZRq1YtXFxcmDlzJufOnWP79u2P50A8p6IvZzyJi8nkNRF5NGdSMo5inc7ktXt5oeDdb/Bxr+UPo3p1N2JiotmwIQQfn1cBsLKywmi8c1vpf2+UKVOGw4cPALdGvyIjIylZsuStloasf8dcuXKZESMGMXDgYFq3fjXb9kHBTEREnngRERGULl2afPnymV+rWrVqhnZlypShQYMGfPbZZ4wYMYI2bdoQGhqabkSrVKlSWFtbm5/ny5ePlJQUrly5wsWLF3F1dTUvc3Z2plChQgD8+eefGI1Gqlevbl5ub29P2bJliYyMzNb9lfTcyhqxtU5/IudV/tGvTxGR9LxyW5PvX3mkUd4HnzP8as00ctlmHr5y291anhOaN3+FK1cu4+xcDgAXl0oEB3/D/v37Mm3ftm0Hrl27xsCBfRg0qD+9e79F4cJF7rmdL79cRnx8PF98sZhBg/oxaFA/kpJuPnL9mp0tIiJPhdsjV7fZ2GT8E3b8+HF8fX1p1KgRnp6e9OrVi+XLl6drk9n0ljv7+/d2bj/P6hbLaWlpmsqYwxwKwYSuKXyz3YabKdDKMw035+z/xF3keWdvbWBxiTzMu5pMnNHEawVseSX/g8eF+lWN/HrUmOEGILlsTXhVMOBd9eE+WPHwqIGHR40sl6elpdGmTTvz8zp16lGnzq2pjmvWrDe/PnDgYPNjf/9JGfqZO3eh+fHrr3chLi4x3fJhw0YybNjIB9+Be1AwExGRJ1758uU5d+4c165dM49gHT16NEO7kJAQPDw8mD17tvm1s2fP4uTkdM9tFCxYkGLFivHHH39QpUoV4NY1a//88w8AL730EjY2Nhw6dIgGDW7dFvnq1aucPXsWZ2fnR95HubtqZUxUK/Pg17qIyINxz23N4hJ5HqkPKwOM6ZzK9ky+x6xN3Vz899dqtpo6dSLXrsUxdepH2d/5Y6JgJiIiT7w6depQsmRJxo4dy/Dhwzl48CCbNm3Czc0tXTt7e3tOnjzJoUOHKFSoEKtWreLIkSPmawbupUePHsydO5dSpUpRtGhRpkyZAty61iBv3rx06dKFqVOnkitXLgoXLszMmTMpXrw43t7e2b7PIiJPMysDNHA1Zrj7otXDfoHZPYwbNzFH+n2cFMxEROSJZ2try8KFC/H396dDhw5UrFiRrl27Zhg169GjB0ePHuXNN9/Ezs4OLy8vBg0axHfffXdf2+nduzcXL15k6NChWFtb07dvXw4cOGCe/jhq1ChMJhNDhw4lOTmZOnXqsHz58iynOYqIiNwvg+nfk+lFRESeIkFBQXz++eds3br1kfv65ZdfqFq1KkWK3Lr4+8qVK9SuXZutW7fy4osvPlBfsbHxj1zPnezt82a4zuFJpnpzlurNOU9TraB6c1p21+vgUCDLZboro4iIPLXOnDnDvn37KFasWLb0t3r1asaMGUNERASRkZFMnDgRV1fXBw5lIiIiD0rBTEREnlojR47k119/pX///tnS33vvvYe1tTVdunShc+fOGI1G5s2bly19i4iI3I2uMRMRkafW2rVrs7W/4sWLM3/+/GztU0TkeWQ0mfg+IZUvrqVwPtWIo40VvQrZ0q3Qw19FtX//Pt57bwxlypTFYDCQlJRE8+av0LFjl0zbR0ZGEB//D25uHnTs2IbAwDXZck1wWloaH344haios1hZWTN27ARKlXr0mRUKZiIiIiIikm2MJhODL9xk5400bvw3h11ONvLepSR+TL7GrCK2WBke7u6Mnp41mDRpGgDJycl07foaLVr4UKBAxmu3tm3bStGiRXFz83jofcnMr79uB+Czz5ayf/8+AgJmM3367HusdW8KZiIiIiIikm2+T0hNF8puu2GCn+OT+T6XgTb5bR95O4mJiVhZWWEwGOjc+VW+/joYa2tr5s+fQ9myzoSGbsDGxpYKFSoCMGvWdP76KwaADz6YSd68eZk2bRIxMTGkpaXRpUs3mjRpzqBB/Shf3oXTpyNJSrrBxIkf4OhYwrzd+vUbmr+4+sKF8xQuXPSR9wUUzEREREREJBt9cS0lQyi7LdEIX8SlPHQw++23fQwa1A8rKytsbGwYPnwU+fPnp1o1N/bs2cXLL9cmPHwnb701kL/+iqFo0aJUrlwVAB+fV6le3Y2pUyeyd284cXFXKFTInvHjJ5OYeJ3evbvj6fkyAJUqVWHo0P+wfPlCtmzZRI8evdLVYWNjw5QpE/jll21MmfLhQ+3LvymYiYiIiIhItjmfarz78rS7L7+bO6cy3qlNm/asWbMKo9FEjRovm79/8k4VK94aOStSpChJSTf5888/qVHjVhDLmzcfZcqUJSYmGoAKFVwAcHR0JDr670xr8fefxOXLl+jXrxcrVwaRJ0+eh94v0F0ZRUREREQkGzna3D1iOFpnfwSpXt2NmJhoNmwIwcfnVQCsrKwwGu8cukt/XVuZMmU4fPgAAImJ14mMjKRkyZK3Wt7lGriwsO9ZsWIZALlz58bKygorq0ffJwUzERERERHJNr0K2ZIni1yT1wp62T/69WWZad78Fa5cuYyzczkAXFwqERz8Dfv378u0fdu2Hbh27RoDB/Zh0KD+9O79FoULF7nndho0aMzJkyd45523GDFiMEOGjMiWuz0aTCbTw9+zUkRERDIVGxufrf3Z2+clLi4xW/vMSao3Z6nenPM01QpPZr2Z3ZURII8BGha0e6S7Mt5NYOByChWyp3XrV7Otz+w+vg4OGe8eeZtGzEREREREJNtYGQwEFM/N+w65qGJnRVFrqGJnxfsOuVjiXChHQtnUqRM5dOgALVq0yva+Hxfd/ENERERERLKVleHWLfH/fffFnAhlAOPGTcyRfh8njZiJiIiIiIhYmIKZiIiIiIiIhSmYiYiIiIiIWJiCmYiIiIiIiIUpmImIiIiIiFiYgpmIiIiIiIiFKZiJiMgz69ixYzRp0oTq1avj4uLC2bNnH6m/8PBwXFxcSE1NzaYKRUREblEwExGRZ1ZAQABlypTh+++/t3QpIiIid6UvmBYRkWdWfHw8Hh4eli5DckByKny725qT0QaqOJlo83IaNtaWrkrk6bP5eirr41MobmNFX3tbHG00bmMpOvIiIvJMaty4MXv27OHzzz/njTfeSLcsMjKSvn374u7ujqurK76+vpw6dcq8/NNPP8Xb2xtXV1def/11Dhw4kOk2Zs2aRd26dR95iqQ8uHnf27DyJxv2nLJm2Q82LNmiz5pFHtSmhFSGXLjJlsQ0Vv6Twht/3SDVZLJ0Wc8tBTMREXkmrVmzBnd3d3r27Mknn3xift1kMvH2229TsmRJQkJCWLVqFUajkRkzZgCwZcsWAgMDmTlzJhs3bqRy5coMGTIEo9GYrv/AwEBWrVrFkiVLcHJyeqz79rwzmmD77+lPYbYd0SmNyINan5CS7vm5VBMHbxqzaC05TR8viYjIM6lIkSLY2tqSJ08eihQpYn79xo0bdOzYka5du5IvXz4A2rdvz4IFCwCIiYnBxsaGkiVLUrp0af7zn//QvHnzdMFs8+bNzJo1i8WLF1OxYsXHu2OClQHs88Olf/73WpH8+pRf5EE52Bju6zV5PBTMRETkuZI3b166du1KSEgIv//+O6dPn+bo0aPY29sD0KZNG4KDg2nWrBmurq40btyYjh07YmPzvz+Zfn5+WFtbU6JECUvtxnOvT7NUZn9rQ0qagVy2Jno3S7N0SSJPnb6F7PglMY2Y1FsfbPQqZIuTrUafLUXBTEREnivXr1+nY8eOFCpUiKZNm9K6dWtOnz7NwoULAShatCjBwcHs2rWLn3/+mdWrVxMYGMjatWvNfUyfPp3AwECmTZvGnDlzLLUrz7W6lY1UdUrm9AUD5UuYyJ/H0hWJPH1K2VoRVjov+2+mUczairJ2CmWWpGAmIiLPlT179nD+/Hm+++47bG1tAdixYwem/17wvm3bNmJiYujWrRve3t6MHDmS2rVr89tvv1G0aFEAmjdvTtmyZenQoQPbt2/H29vbYvvzPCuUD9ydNYVR5FHYGgzUzKNI8CRQLBYRkeeKvb09N27cYMuWLURHRxMUFERgYCDJyckA5huBhIWFER0dzfr160lOTs5wLVmlSpXo1KkTkydPNq8rIiLysBTMRETkueLu7s6gQYOYPHkybdu2Ze3atUyYMIG4uDj++usvGjduzLBhw5gxYwavvPIKy5YtY9asWTg7O2foa9iwYcTFxZmnQYqIiDwsg8mkLysQERHJbrGx8dnan719XuLiErO1z5ykenOW6s05T1OtoHpzWnbX6+BQIMtlGjETERERERGxMAUzERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMRERERERELUzATERERERGxMAUzERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMRERERERELUzATERERERGxMAUzERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMREXniHDt2jH379lm6DAB8fX0JCAiwdBkiIvKMUzATEZEnzjvvvMOZM2csXYaIiMhjo2AmIiIiz7QLcXA0ykCa0dKViDxex5LSOJqUZuky5D4pmImIyBOlR48exMTE4O/vj5+fH5GRkfTp0wcPDw/q1atHQEAARuOtM+yAgAAGDBhAjx498PLy4pdffqFx48asXLmSLl26UL16dTp27Mjhw4fN/R84cICuXbtSvXp13Nzc6NOnDxcuXDAv37JlCy1atMDNzY0pU6ZgMpnS1RccHEyrVq2oVq0aHTp0IDw8/PEcGHkoK3+ypl+AHX5f2DFgnh0X4yxdkUjOSzGZeOvvG7SPuUGHmBv0/vsGyf/6XSZPHgUzERF5ogQEBODo6Iifnx+DBw+ma9euFCtWjKCgICZOnEhgYCBLly41t//pp59o0aIFK1aswMPDA4C5c+fSt29fVq9eTa5cuZg8eTIACQkJ9O/fnzp16rBhwwaWLFlCdHQ0n332GQAREREMGzYMX19f1q5dS3JyMgcOHDBvKzg4mPfff59+/foREhJC3bp16devH3/99ddjPEJyvy7EQdAOa0wY/vvcwJpfbSxclUjO23w9le03/jdStvNGGqEJqRasSO6HfjuJiMgTxd7eHmtra/Lnz8/WrVvJlSsX77//Pra2tpQrV47Y2Fg+/fRT+vbta27fvXv3dH20a9eOpk2bAtC7d28GDRoEwI0bN+jfvz+9e/fGYDBQunRpmjdvbg5fa9euxcPDg169egEwfvx4fvzxR3O/K1asoFu3brRr1w6A//znP+zZs4cVK1YwevToHD0u8uCuxBvMoey2S/EWKkbkMTqfmnF0LLPX5MmiYCYiIk+syMhIKleujK2trfk1d3d3rl69ypUrVwAoVapUhvVKly5tfpw/f36MRiNpaWk4ODjQvn17vvjiC44dO0ZERAQnTpygWrVq5u25uLiY17W1tU33PDIykoEDB6bblpubG6dPn86eHZZsVaGUCcfCJs5f/V84a1BFF5rJs69ZPhvmXE0m6b9ZzM5w6zV5suknJCIiT6xcuXJleO329WW3/59ZGzs7uwyvmUwmLly4wGuvvUalSpWoV68enTt3Ztu2bfz2229Z1nBnKMydO3eG5WlpaaSl6eL6J5G1FUztkcyaX2249A/Ur2KkgauCmTz7XrK14ssSefjyWgomoHshW5ztdAXTk07BTEREnljlypUjLCyMlJQUc0A6cOAA9vb2FClS5IH727JlC/ny5WPRokXm11asWGG+wUf58uXTfX9aWloaJ06coEqVKgA4Oztz6NAhmjdvbm5z6NAh3NzcHmr/JOc5FIKBrXRtjTx/que2ZlZua0uXIQ9A0VlERJ44+fLl4/Tp03h7e2M0GnnvvfeIjIxk69atBAQE0KVLF6ysHvxPmL29PRcvXuTXX38lKiqKhQsXsnnzZpKTkwHo1KkTR48eZe7cuZw+fZpp06Zx/vx58/pvvvkmX331Fd9++y1nzpxh1qxZHDt2jM6dO2fbvouIyPNJI2YiIvLE6datGx9++CHR0dEsXryYKVOm0K5dO4oUKcIbb7zBgAEDHqrfli1bsnfvXoYNGwaAq6srY8aM4eOPP+bmzZuUKVOGzz//nGnTprFw4UKaNWuGt7e3ef0WLVoQGxvLnDlziI2NpVKlSixbtozy5ctny36LiMjzy2D69xe0iIiIyCOLjc3e2//Z2+clLi4xW/vMSao3Z6nenPM01QqqN6dld70ODgWyXKapjCIiIiIiIhamYCYiIiIiImJhCmYiIiIiIiIWpmAmIiIiIiJiYQpmIiIiIiIiFqZgJiIiIiIiYmEKZiIiIiIiIhamYCYiIiIiImJhCmYiIiIiIiIWpmAmIiIiIiJiYQpmIiIiIiIiFqZgJiIiIiIiYmEKZiIiIiIiIhamYCYiIiIiImJhCmYiIiIiIiIWpmAmIiIiIiJiYQpmIiIiIiIiFqZgJiIiIiIiYmEKZiIi8kyIjo7GxcWFs2fPPvC6ycnJrFq1yvz8+vXrBAcHm583btyYoKCgbKlTREQkMwpmIiLy3Pv++++ZP3+++fmyZcsUxERE5LGysXQBIiIilmYyme76XJ4tCTdh5U82HI82ULm0ke6N0rC3dFEiD8lkMrH8WjIbElJxtDYwpIgd5e2sLV2WPASNmImIyDNl8+bNNGjQAA8PD6ZMmUJqaioABw4cwNfXFzc3Nxo3bkxgYCAA4eHhjBkzhgsXLuDi4kJwcDBz585l//79uLi4ZOjfZDIxf/58vL298fT0pE+fPvz555+PcxflEQWst2HjPmtOn7diw14b5n+vz6nl6bXi0k2mXU7mSJKRLYlp9P77Jsn6cOmppGAmIiLPlKCgIGbPns3nn3/ODz/8QEBAAJGRkfTs2RMvLy/WrVvH4MGD+eijjwgNDcXd3Z2xY8fi4ODAjh07eOWVV+jduzfVqlVjx44dGfpfuXIlISEhzJgxg2+++QYnJyd69erFjRs3LLC38jDCT6Q//dl9QqdD8vQKu5aU7nlsmokjSUYLVSOPQh8RiYjIM8XPzw9PT08Ahg4dyvTp07l58yYuLi6MGDECgLJlyxIZGcnixYtp2bIlBQoUwMrKCgcHBwDy5s2LjY2N+fmdFi9ejL+/P7Vr1wZg/Pjx/Pzzz2zatIl27do9pr2UR1GqqImoS4Z0z0WeVuVyWfPjHc9tgBdtDFk1lyeYgpmIiDxTXF1dzY8rV65MXFwckZGRVK9ePV07d3d383TG+3X9+nXOnz/PyJEjsbL63yhLUlKSpjM+RQa2SuXDtbZcu26gcH4TA1qmAraWLkvkoQxxzMfuf5I4kmQklwFGFLGjuI1GgZ9GCmYiIvJMuTMw3b6JR65cuTK0MxqNpKWlPVDft9vPnj2b//u//0u3rECBAg9aqlhIVScTS4cmc+GqAcfCJmx0nwR5ijnYWhFUKi9nU4wUtjJQ0FqjZU8rxWkREXmmnDx50vz48OHDODg4UK5cOQ4dOpSu3YEDByhbtiwABkP6E5l/P7+tYMGCFC1alNjYWJycnHBycuLFF19k9uzZnDhxIpv3RHKSrTW8+IJCmTw7nGytFMqecgpmIiLyTJkyZQoHDx5k586dzJkzh969e9O1a1dOnjzJ7NmzOXPmDN9++y1fffUV3bt3B25dUxYfH8+ZM2dITU0lb968xMbGEhUVlaH/Xr168emnn/LDDz9w9uxZJk2axM6dO3F2dn7cuyoiIs8QTWUUEZFnSvfu3XnnnXdITk6mU6dO9OrVCysrKxYsWMCMGTNYunQpJUuWZPTo0XTq1AmAWrVq4ezsTNu2bfn6669p3rw5q1atonXr1vz444/p+u/Tpw83btxg0qRJ/PPPP1SqVIklS5ZQvHhxS+yuiIg8IwwmfYumiIhItouNjc/W/uzt8xIXl5itfeYk1ZuzVG/OeZpqBdWb07K7XgeHrK9H1lRGERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMRERERERELUzATERERERGxMAUzERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMRERERERELUzATERERERGxMAUzERERERERC1MwExERERERsTAFMxEREREREQtTMBMREREREbEwBTMRERERERELUzATEZFnSnh4OC4uLqSmpmbZZuPGjbRu3Ro3Nzfatm3Ljz/++BgrFBERyUjBTEREniv79u3j3Xff5Y033iAkJISOHTsyePBgjh49aunSRETkOaZgJiIiz5V169bRvHlzOnfujJOTE2+88QY1a9Zk48aNli5NHpPzV+Gnw1ZEXzJYuhSRbHM1zcSGhBQO30yzdCnykBTMRETkidG2bVuWL19ufv7222/Ttm1b8/NNmzbRvHlz4uPjGT16NJ6entStW5fx48eTkJCQrq/AwEBq1apFzZo1+eyzz8yv9+jRg7fffjtdW4PBQFJSknn5nDlz6NatG9WqVcPX15eIiAgAFi5cSKtWrdKt+/XXX9OmTZvsOQCS43YctWLAPDs+DrHlnc9sCf1Np0Ly9DuSlEbTc9cZeTGJzn/dYOqlJEuXJA9Bv41EROSJUa9ePcLDwwEwmUz89ttvREREEB8fD8DOnTupX78+Y8eO5erVqwQGBrJgwQLOnDnDmDFj0vW1YcMGli5dygcffMDSpUsJCgoCoGLFivzf//2fud2pU6fYtWsXXl5e5tcWLVpEs2bNWLduHY6Ojrz11lskJSXh4+PD6dOnOXnypLltaGgorVu3zrFjItkr8CdrjKZbI2UmDKz8yQaTycJFiTyihVeTuX7H+zjwnxQupBotV5A8FAUzERF5Ynh7e7Nv3z6MRiMnTpygUKFClC5dmoMHDwK3glnZsmXZsmULM2bMoGLFilStWpUPP/yQzZs38/fff5v7mjJlCpUrV6ZJkyb07NmTr7/+OsP2Ll++zKBBg/D09KQa2vLtAAAgAElEQVRp06bm1+vVq0evXr0oV64ckydPJi4uju3bt1OqVCnc3d0JCwsD4NKlS+zbty/DKJo8ua4npZ++eDMZjApm8pSL/1cGMwLXlcueOgpmIiLyxPD09CQ5OZkTJ06wd+9ePD09cXNz47fffiMqKooLFy7g6OiIyWSiUaNGuLu74+7ubh6x+vPPPwGws7PDxcXF3G/lypU5c+ZMum2dP3+eHj16YGVlxZw5c7Cy+t+fRHd3d/Pj/PnzU7ZsWSIjIwFo3bo1oaGhAISFheHq6krp0qVz5HhI9nvFI/31N03djFjrbEiecp0L2qR77pXbCmc7vbGfNjb3biIiIvJ42NnZ8fLLLxMeHs7+/ftp0KABRqOR9evXU6JECby8vDCZTOTNm5dvv/02w/oODg4cOXIkXcgCMBqN2Nj8709eVFQUPXv2JE+ePHz55ZcULlw4Xfs72wKkpaVhMNwaaWnZsiUffPABp06dIiwsDB8fn+zafXkMfBukUbKoid/PWlG+pJGmbhpWkKdfq/y2FLIysPl6Ki/ZWvF6QVtLlyQPQVFaRESeKLevM/vtt9+oUaMGnp6eHD58mJ9++glvb2/Kli1LYmIiaWlpODk54eTkBMC0adPMNwC5efMm586dM/d55MgRypUrB0BcXBxvvvkmBQoUYMWKFbzwwgsZajh27Jj5cXx8POfOnTOPwBUpUoTatWsTEhLCwYMHadmyZY4dC8l+BgM0dDUyqHUqLTw0WibPjrp5bZjkkJs+9nbkt9IdR59G+nUkIiJPlHr16rF9+3YMBgNOTk44OzuTL18+tm3bhre3N+XKlcPb25t3332XQ4cOcfz4cUaPHs3ly5cpVqwYAFZWVvj5+XH06FHCwsL48ssvefPNNwH4+OOPuXr1KtOnTyctLY3Y2FhiY2PNNxiBWzf0CA4OJjIyknHjxlG8eHHq1KljXt66dWu+/PJLatSogYODw+M9QCIi8kxSMBMRkSeKs7MzxYoVw9PT0/yap6cnJUuWNI96zZgxAycnJ3r37k337t0pVqwY8+fPN7cvWLAgjRs3pmfPnrz//vsMHjyYFi1aALeuC0tISKBdu3bUq1fP/N+kSZPM67du3ZqgoCA6dOjA9evXWbJkCba2/5sa1KxZM0wmk6YxiohItjGYTLpJrIiIyG09evTAw8OD4cOHZ9kmJiaGli1bsmPHDgoWLJhpm9jY+Exff1j29nmJi0vM1j5zkurNWao35zxNtYLqzWnZXa+DQ4Esl+nmHyIiIvcpMTGRX375hbVr19K8efMsQ5mIiMiD0lRGERGR+2QwGBg/fjwXL15k1KhRli5HRESeIRoxExERucOKFSuyXJYnTx727t37GKsREZHnhUbMRERERERELEzBTERERERExMIUzERERERERCxMwUxERERERMTCFMxEREREREQsTMFMRERERETEwhTMRERERERELEzBTERERERExMIUzERERERERCxMwUxERERERMTCFMxEREREREQsTMFMRERERETEwhTMRERERERELEzBTERERERExMIUzERERERERCxMwUzkCTZ+/HjWrl1r6TKoX78+wcHBOdL3/v376d69e470fadjx46xb9+++2obEBCAr6/vfbVNTk5m1apV5uc9evTg448/BsDPz4+RI0c+cJ+3paSkULNmTW7evPlA62UlOjoaFxcXzp49my395ZSdO3fi4uLywOuFh4fj4uJCamrqA6/7qD8rERGRR2Vj6QJEJGu7du1i4MCBli4jR+3cuZNatWrl+HbeeecdBg4cSI0aNbK13++//5758+fTpUuXDMvGjRv3SH0fPHgQFxcXcufO/Uj93FaiRAl27NhBkSJFsqW/J427uzs7duzAxubB/7Q96s9KRETkUSmYiTyhoqKisLKyomTJkpYuJUft3r2b4cOHW7qMh2YymbJcVqBAgUfqe9euXdSuXfuR+riTtbU1Dg4O2dbfk8bOzu6h9+9Rf1byaH4/a2DZDzZcjjfQoGoabzROw1pzekSeWGvjU1gUl4zRBL3t7ehS0NbSJT0T9GtPnmm3p25t2rSJZs2a4enpyYABA7hy5QoAwcHB1K9fP906/56KNn36dIYPH0716tXx8fHh+PHjfPzxx9SoUYMGDRqwefPmdNv67rvvqF+/PjVq1OD9998nJSUFgJYtW7Jo0aJ02+rcuTPLli3LtPZdu3ZlOZLUo0cP5syZQ7du3ahWrRq+vr5ERESYl0dGRtK3b1/c3d1xdXXF19eXU6dOAbeme9WvX5/Vq1dTv359atasyahRo9JNl1u1ahUNGjTA09OTBQsWpNt2QkIC48aNo3bt2lStWpUWLVqwadMm8/KwsDBatWqFq6srzZs3v+tUzMTERE6dOkW1atUyLAsODsbX15fPP/8cLy8v6taty/r169m4cSMNGzbEy8uL2bNnm9tfvHiRIUOG4OXlRdWqVWnXrh179+41H6+YmBj8/f3x8/MjPDycunXrEhgYSM2aNalduzZz587Nss4DBw7g6+uLm5sbjRs3JjAw0Hwsx4wZw4ULF3BxcSE6OjrdendOjwNIS0vD39+f6tWr06RJE7Zt25blNuHWaGJWwSwwMJAmTZrg6upKmzZt+Omnn8zLoqKi6N+/P+7u7tSvX5/PP/8cyDiV0cXFhW+++YZmzZrh7u7OiBEjSEhIAKBPnz5MnDgx3TaHDx/OlClTMq1n7dq1tGzZkqpVq1KzZk0mTJhgnlLo5+fHlClTGDFiBG5ubrRo0SLd1NiEhARGjBiBu7s7LVq04I8//rjrcfn000/x9vbG1dWV119/nQMHDgDppzLe3tetW7fSuHFj3N3dmT59OidOnKBDhw64ubkxYMAAEhMTzTXe+bN6kH0bPXo07dq1o2bNmpw4ceKutUtGiUkwZbUtp/6y4kq8gXW7bPgu3NrSZYlIFo4kpTEuNok/U0ycSzUx8VISe2+kWbqsZ4KCmTwXFixYwMyZM/n88885fPgwS5Ysue91V65ciaenJyEhIRQoUIAePXpw9epVVq9eTd26dRk/fny6UZN58+Yxe/Zs5s2bxw8//MAnn3wCgI+PD6GhoeZ2MTExHDlyhJYtW2a63Z07d1KnTp0s61q0aBHNmjVj3bp1ODo68tZbb5GUlITJZOLtt9+mZMmShISEsGrVKoxGIzNmzDCve/nyZTZu3MjChQuZOnUqmzdvNp8ob9++nalTpzJ8+HBWrVrFwYMHuXDhgnndadOmERkZydKlS9mwYQNeXl6MHz+e5ORkLl++zMiRI+nVqxdhYWH0798ff39/IiMjM92HvXv34ubmhq1t5p+0HTlyhD///JM1a9bQsmVL3nvvPQIDA1mwYAEjRoxgwYIF5hPhd999l9TUVFatWsW3336Lo6MjEyZMAG5dM+To6Iifn595ylpcXBxr165l6dKlTJ48mWXLlvHVV19lqCEyMpKePXvi5eXFunXrGDx4MB999BGhoaG4u7szduxYHBwc2LFjByVKlMjy5wVw6NAhTCYTwcHBdO3albfffpvTp09n2jYhIYEzZ87g6uqaYdnRo0eZNm0aY8aMMQfhYcOG8c8//5CcnEyfPn2wsbFh9erVTJ06lcWLF/Pdd99lup05c+YwduxYvvzyS06dOoW/vz8Abdq0YcuWLaSl3fpje+PGDbZt24aPj0+GPvbt28ekSZMYPnw4mzZtYtKkSQQHB5s/tIBbYb9SpUoEBwdTr149Jk6cSFxcHAATJkzg9OnTrFy5En9/f7744ossj+GWLVsIDAxk5syZbNy4kcqVKzNkyBCMRmOm7RctWsT8+fOZOHEiy5YtY8iQIYwaNYpFixaxd+/ee17DeT/79t133/HOO++waNEiypcvf9f+JKOIvw0kJhnSvXbkT0MWrUXE0vZkEsJ23Xjwa3slIwUzeS4MGjSI6tWr4+XlRZs2bThy5Mh9r1uxYkW6d+9OmTJl8PHx4caNG4wbN45y5crRvXt34uLiuHr1qrn9yJEjqVGjBjVr1mTo0KGsWbMGo9FImzZt+OOPP4iKigIgNDSUGjVq4OjomGGbJpOJPXv2ULNmzSzrqlevHr169aJcuXJMnjyZuLg4tm/fzo0bN+jYsSOjR4/mpZdeokqVKrRv3z7diFpqaipjx46lYsWKNG3aFG9vb/MxCQoKwsfHh3bt2lG+fHmmTp2KnZ2deV1PT08mTZpEpUqVKFOmDL179+batWtcuHCBCxcukJKSQvHixSlVqhSvvfYaS5cu5YUXXsh0H3bv3n3XqXpGoxF/f3+cnJzo1KkTiYmJDB48GBcXF3x9fSlQoIA52DRq1Ijx48dTrlw5/u///o9u3boRGRmJyWTC3t4ea2tr8ufPb56ylpqaypQpU6hSpQpNmzalZ8+erF69OkMN33zzDS4uLowYMYKyZcvSvn17unfvzuLFi7Gzs6NAgQJYWVnh4OCAtfXdP+V3cHBgwoQJlCtXjj59+uDp6UlQUFCmbffu3YuHh0emfcbExABQqlQpSpUqRf/+/Zk3bx62trbs3LmTixcvMn36dCpUqIC3tzfvvfceefPmzXQ7ffv2pVGjRri6ujJu3Dg2bdpEXFwcTZs2JSEhwTzquG3bNgoXLoybm1uGPnLnzs3UqVNp3rw5pUqV4pVXXqFy5crp3nMVKlTgrbfewtnZmeHDh5OUlMSpU6eIj48nNDSUMWPGUKVKFby9ve96XWVMTAw2NjaULFmS0qVL85///IcZM2ZkGcwGDhxIxYoVefXVV7G3t8fHx4fatWvj5eXFyy+/nGUwfpB9q1SpEs2aNaNatWpYWenP6oNycjBhY51+SnC5EllPERYRy6qcK+Pvuaq5NMqdHXSNmTwXXnrpJfPj/PnzP9Bd20qXLm1+nDt3bl544QVy5coFYP5/cnKyuY27u7v5cdWqVYmLi+PSpUs4OTnh6upKaGgo/fr1Y+PGjXTu3DnTbR4/fpzixYtTuHDhLOu6czv58+enbNmyREZG0rRpU7p27UpISAi///47p0+f5ujRo9jb26dbP6tjEhkZSadOnczLihQpQqlSpczP27Vrxw8//EBQUBCnT582TzszGo1UqlSJ1q1b069fP1566SUaNWpEhw4dKFSoUKb7sHPnTj788MMs97Fw4cLkz58fwHwDjDtryZUrl/nY+/r6snHjRvbv38+ZM2f4/fffgVvTBzO7GUSuXLmoXLmy+XnVqlVZuHBhhnaRkZFUr1493Wvu7u7m6YwPwsXFJV3IrVKlSpajiXebxlivXj08PT1p164dFSpUoHHjxnTs2JE8efIQERHBSy+9lO6aqbZt2wJkmGp5e19uq1q1KkajkTNnzuDu7k7Dhg0JDQ2lVq1ahIaG4uPjg8GQcSSjatWq5M6dmzlz5hAREcGJEyc4e/Zsuqm4d/47uv0zTU1N5cyZM6SlpVGxYsV0/WWlTZs2BAcH06xZM1xdXc37ntUNP1588UXz41y5cqW7ZjN37tzp/u1m5n727c5tyIMrlA+Gtk1l8WYb4hOhpouR1+poWpTIk6p2Hhvetrdl2bUUjED3grY0zqdIkR300Z48F/49Ve721MPMTjL/Hdr+PWJxr0/E72x/+1P82+u0bt2aTZs2ERUVxcmTJ2nRokWmfdzPnQr/fSKalpaGwWDg+vXrdOzYke+++w5nZ2eGDBnCu+++m2H9rI7Jvx//u+27777L9OnTKVCgAL6+vumuQTMYDMyaNYvg4GDatm3L3r176dixIzt27Miw/StXrnDp0qW73hY9s9GizH5mRqOR3r17s2TJEkqUKEGfPn3STd28n76NRmOmP9vb4fvfbW9P8XsQ/+7faDRmOY3zbjf+yJMnD1988QWBgYHUr1+fsLAw2rVrx/Hjx7PsLyt3e7/ens6YkJDAzz//nOk0Rrg1/bV9+/bExsbi7e3NnDlz8PDwSNcms7qyes/d7a6KRYsWJTg4mEWLFlG9enVWr15N+/bt0023vdO/+3rQEa372bc7w7Y8nAZVjXwxLJnVo5MZ0ymV3DqkIk+0IUVyEV4mH3vL5GNU0Yx/J+XhKJjJc83W1pbr16+bTwpNJlOmowoP4vjx4+bHv//+Oy+88AJFixYFoFWrVhw7dow1a9ZQp06dLEfEdu3addfry+DW93LdFh8fz7lz53BxcWHPnj2cP3+eFStW0LdvX+rUqcNff/1117sH3ql8+fLppnomJCSYp18mJCSwYcMGZs2axdChQ2nWrBnXrl0Dbh27yMhIpk2bRpUqVRg8eDDr1q2jRo0abNmyJcN2du/eTc2aNTMNWg8qIiKCvXv3smTJEgYOHEjDhg25ePGiua7MJCYmcu7cOfPzI0eOZBoSy5Urx6FDh9K9duDAAcqWLQtkHhTvVued9Rw+fJhy5cplaHfp0iXi4uKyvF7pwIEDzJ8/nxo1ajBq1ChCQ0N54YUX+OWXXyhTpgxRUVHmm3jArevI/Pz8Mu3rzvfR77//jq2tLc7OzsCt769LSUlh0aJFlCpVKt2o1p2CgoJo3749kydPplOnTpQrVy7dsb0bZ2dnbG1t073n7qzp37Zt28bq1avx9vbG39+fsLAwrl+/zm+//XZf23tQj7Jv8mCsrSCXbuwm8tSwMxiwy4a/4fI/CmbyXHN1dSUhIYFFixYRFRXFjBkzzEHjYX3wwQccOXKEXbt2MWfOHLp27Wo+eS9WrBheXl4sW7Ysy9GH5ORkDh8+fM/v2woNDSU4OJjIyEjGjRtH8eLFqVOnDvb29ty4cYMtW7YQHR1NUFAQgYGB95yydVu3bt3YvHkzq1atIjIyEn9/f5KSkoBbo0d58uRh8+bNREdHs2PHDt5//31z3QULFmT16tUEBAQQFRXF7t27OXHiRKZT0+42Ve9BFSxYECsrKzZu3EhMTAxhYWEEBASY6wLIly8fp0+fNt9wAsDf35+TJ0+yadMmVqxYQbdu3TL03bVrV06ePMns2bM5c+YM3377LV999ZX5S7Hz5s1LfHw8Z86cuecU2fPnz/P+++8TGRnJvHnzOHr0KF27ds3Q7l4jprlz52b+/PmsWrWK6OhofvzxR/7++2+qVq1KvXr1cHR0NN905eeff2bFihUZ7j5629y5cwkPD+fQoUNMnTqVtm3bmqdB2tnZ0axZM5YtW0br1q2zrMfe3p4DBw5w/PhxTp06hZ+fH7Gxsff1nsufPz9t27Zl6tSpHDx4kN27dzN//vws29++kU1YWBjR0dGsX7+e5OTkLEPjo3qUfRMREXkQCmbyXHNycmL06NF88cUXvPrqq6SkpGQZmO6Xj48PAwYMYPjw4bz22msZbmRw+zqdJk2aZLr+oUOHqFChAnny5Lnrdlq3bk1QUBAdOnTg+vXrLFmyBFtbW9zd3Rk0aBCTJ0+mbdu2rF27lgkTJhAXF8dff/11z/q9vLyYNm0aixYtomPHjhQvXpwKFSoAt0YYP/roI3744QdatWrFBx98wIABAyhevDhHjx7FwcGBgIAAtm7dio+PD6NGjcLX15eOHTtm2M7dvg7gQTk6Oprvuufj48OCBQvw9/fH1tbWPPrSrVs3Vq1axfjx483rNWzYkG7dupnvQtmuXbtM+16wYAE7duygTZs2zJ8/n9GjR5uvw6tVqxbOzs60bds23WhpZho0aEB8fDzt27dn/fr1zJ8/P9Obv9zr+8sqVarEtGnTWL58OS1btmTatGmMHj2aOnXqYG1tzfz587l27Rrt27dn4sSJvPPOO7Rq1SrTvtq3b8+YMWPo3bs3NWrUMN/J8jYfHx+SkpLu+u9i0KBBFCtWjC5duvDmm29ia2tLt27dOHr06F2Px23vvfcenp6e9O7dm7Fjx5pDb2YaN27MsGHDmDFjBq+88grLli1j1qxZ5lG+7Pao+yYiInK/DKb7nd8kIncVHR1NkyZN2Lx5M05OTlm2mzt3LhEREebb6D+MHj164OHh8VR/MbMlhYeH88Ybb/DHH3/c9XqmZ52LiwvLli2767TZb7/9lsDAwCzvHilZi42Nz9b+7O3zEheXmK195iTVm7NUb855mmoF1ZvTsrteB4cCWS57fs9IRB6zEydOcOzYMVasWGH+AmuRJ1VUVBRHjhxh3rx59OvXz9LliIiIPPM0lVHkMTl69CgTJ06kTZs297yxh4ilRUdHM3bsWCpVqkSHDh0sXY6IiMgzT1MZRUREcoCmMqrenKR6c87TVCuo3pz2OKcyasRMRERERETEwhTMRERERERELEzBTERERERExMIUzERERERERCxMwUxERERERMTCFMxEREREREQsTMFMRERERETEwhTMRERERERELEzBTERERERExMIUzERERERERCxMwUxERERERMTCFMxEREREREQsTMFMRERERETEwhTMRERERERELEzBTB6b6OhoXFxcOHv2rKVLydT+/fvp3r17psvCwsKIjY0FICAgAF9f38dZ2iNJSUmhZs2a3Lx5M8OyY8eOsW/fPgtUdW/r1q2jZs2aeHp6snz5curXr/9Ytjt+/HjWrl2bbf316NGDjz/+GAA/Pz9GjhyZabs7l5lMJr7++muMRmOW/TZu3JigoKBsq/NJUr9+fYKDgx94vYc9JuHh4bi4uJCamvrE/54SEZFnl4KZyH/t3LmTWrVqZXg9JiaGoUOHkpiYaIGqHt3BgwdxcXEhd+7cGZa98847nDlzxgJV3dvUqVPp2rUrISEhFChQ4LFtd9euXdSuXTvb+gsICKBfv373bDdu3DgmTJgAwN69e5k4ceJdg5lktGbNGtq0afPA67m7u7Njxw5sbGxyoCoREZH7o2Am8l+7d+/O9ITcZDJZoJrsk91B43GJj4+nRo0avPjii49tm1FRUVhZWVGyZMls69Pe3p58+fLds12BAgXMAfRpf89ZSpEiRTL9AOJe7OzscHBwyIGKni8nYwys+dWaA6cNli5FRJ5wp5LTWByXzA/XUzHqb56ZgplYzOrVq3F3d+fgwYMAnD9/nrfffhs3NzcaNmzIzJkzSU5OBiA4OJjOnTszZMgQPD09CQoKIiEhgXHjxlG7dm2qVq1KixYt2LRpk7n/sLAwWrVqhaurK82bN7/r9LTExEROnTpFtWrVMixr0qQJAM2bNzdPr0pNTWXKlCl4enpSu3ZtFi9ebG5vMpmYP38+3t7eeHp60qdPH/7880/z8sjISPr27Yu7uzuurq74+vpy6tQp4NaUqvr167N69Wrq169PzZo1GTVqVLppiD/99BPt27enWrVqtGzZktDQ0Lse5507d2YazHr06EFMTAz+/v74+fkBcOrUKd544w2qVatGs2bNWLp0qTkkBAQEMHz4cN5//308PT1p1KgRCxYsMPd34sQJunXrhpubG3Xr1mX69OmkpqYCYDQaWbx4MU2bNqVatWp0796d48ePm9d1cXHhk08+oVatWvTq1QsXFxcAevfuba7tTpGRkfTp0wcPDw/q1atHQEAARqORo0ePUqlSJeLi4gC4du0aFStW5IsvvjCvO2DAAD777LNMj9WuXbsyHTW91/6lpaUxZ84cvL298fDwYODAgVy8eNF8nG9PZbzTtWvX8PHxYdiwYRiNRvNUxujoaN544w0AqlSpQnh4eKb13Ck73nO3f64BAQH4+fkxZcoURowYgZubGy1atLjr1MJHfU+vWrWKBg0a4Onpme49lZnw8HA6dOhAtWrVaNiwYbr2d05l7NGjBwsXLuTNN9+kWrVqdOrUiXPnzuHv74+7uzstWrQwT+O9cyrjw+zbncfuefXDQStGLrXjyx9tmBBox8qfrC1dkog8obZeS6J99A1mXklm0IWbjI1NsnRJTwwFM7GIrVu3Mm3aNObNm4ebmxsmk4l33nmHQoUKsXbtWmbOnMm2bduYPXu2eZ1Dhw7h5OREUFAQjRo1Ytq0aURGRrJ06VI2bNiAl5cX48ePJzk5mcuXLzNy5Eh69epFWFgY/fv3x9/fn8jIyEzr2bt3L25ubtja2mZYdvtEb/Xq1bRq1QqAw4cPA7eug+rfvz8fffQRJ06cAGDlypWEhIQwY8YMvvnmG5ycnOjVqxc3btzAZDLx9ttvU7JkSUJCQli1ahVGo5EZM2aYt3f58mU2btzIwoULmTp1Kps3bzafFO/atYvBgwfz6quvEhISwuuvv87IkSPN9fxbQkICZ86cwdXVNcOygIAAHB0d8fPzY9y4cdy8eZO+ffvi5ubGd999h7+/P8uXL2flypXmdbZs2YK1tTVBQUG8/vrrzJ49m4iICABGjRqFs7Mz69ev55NPPiEkJIQ1a9YAMG/ePJYuXcqYMWNYt24dL774In379iUhISHde+Krr75i3Lhx7NixA4BPPvmEcePGpav7ypUrdO3alWLFihEUFMTEiRMJDAxk6dKlVKpUiSJFiphPuPfu3Qvcun4QbgXqPXv2ZHm92s6dO6lTp06my+62fwEBAXzzzTdMmTKFoKAgkpKSGD16dKb9ACQlJTFw4EAcHR2ZMWMGVlb/+1VcokQJ8wn+L7/8gru7e5b93Pao77kLFy6QkJDAunXraN++PXArLFWqVIng4OD/Z+/O46Iq+/+Pv4ZhUQEFFbUQEUhxQ0FxDc2dn4KKay633mTpDYW3mZTmllvaomZumZrabZp7i+WKdVemqdya4gYKamCpuGDubPP7w69TEyBqwLi8n49Hj+acc51rPucw4Lznus4ZgoKCGDt2rDnw/tnffU3/8MMPvPnmmwwZMoTly5fz888/c+bMmVyPMysri3//+9+0aNGC9evXM2bMGGbPns0PP/yQa/sPPviA7t27s3btWi5dukTXrl2pUKECq1evxtPTk0mTJt3xvN7vuXscrfnRMoh9/pORjEx9Ci4iOX1w9hp//hjsiyuZnMnU1H1QMBMr2Lt3L9HR0bz99tvmN8E//fQTKSkpTJw4ER8fHwIDA4dZ+RkAACAASURBVBkzZgyffPKJxafY//rXv/D29qZs2bLUq1ePcePGUb16dSpXrkz//v25dOkSZ86c4cyZM2RkZFC+fHnc3d3p2rUrCxcupGzZsrnWlNc0Rrg1PQrA1dXVPE3Kzc2NESNGUKlSJcLDwylZsqQ5mC1YsIDo6GgaN26Mj48Po0ePxmg0smnTJq5fv063bt0YNmwYlSpVombNmnTu3NkcbuBWeBgxYgTVqlWjdevWNG3alLi4OACWLl1K69atCQ8Px8vLi/DwcNq2bWsxYvdnu3fvpm7duhiNOT+9dnFxwWg04uTkhLOzM+vWraNUqVK88sorVK5cmWeeeYaXX36Zjz/+2LyPs7Mzw4cPx9vbm4iICFxcXDhw4ABw61o8V1dXnnzySerXr8/8+fMJCgrCZDLxySefEBUVRatWrfDx8WHChAnY2tryxRdfmPt+9tln8fb2pkqVKuZpZaVKlcpxfdlXX32Fg4MD48ePx8fHh9atWzN48GAWLFiAwWCgSZMm5lGm3bt306xZM3Mw+/nnnylWrBg1atTIcT5MJhO7du2iYcOGuZ7LOx3fihUrGDx4MM888ww+Pj6MHTsWPz+/XK8RM5lMREdHk52dzaxZs7C3t7fYbjQaKVWqFABlypTJsT03f/c1B/DCCy9QqVIl89TRqlWrMmDAALy9vRkyZAg3b940jxT92d99Ta9atYqQkBDCwsKoUqUKb775Zp7HfPnyZdLS0ihTpgwVK1akZcuWLF68mGrVquXavlmzZrRv356nnnqKli1b4uTkRFRUFD4+PnTv3p2kpKQ7ntf7PXePo9zeUimWiUhusnP546C/F7foSmcpcqNHjyYrK8viOp7ExER+//13AgMDzetMJhMZGRn8+uuvwK0g4eTkZN4eFhZGTEwMq1atIikpiYMHDwK3ps1Vr16d0NBQBg4cSKVKlWjRogVdunQxv+H9q+3bt/P222/f9TG4u7tbjHI4Oztz8+ZNrl69yunTp4mOjrbYfvPmTU6cOEGJEiXMN7Q4cOAASUlJHDp0CBcXF4v+K1WqZH7s5ORkDqeJiYn06NHDom1AQAArV67M87ju9vqypKQkjh07ZjFCk52dTXp6unlKqbu7u0XIc3R0JCMjA4BXXnmFiRMnmqeshYSEUKtWLc6dO0daWhp16tQx72dnZ0etWrUsRjDd3d3vqs7ExERq1KhhMboZEBDAxYsXuXDhAkFBQSxcuBC4FcxeeeUVIiIi+OWXX9i+fTtNmzbFYMh5DcyRI0coX748rq6uuT5vXsd34cIFLly4QM2aNc1tK1WqxCuvvJJrP5s3byYjI4MWLVpQvHjxuzrmP/9M6tWrZxHEC+o199fz7+HhYX58+/cut6l+BfGa7t69u3lb6dKl83wtuLi4MHDgQMaNG8cHH3xAixYt6NixY57Xh/35GBwcHCz+5jg4OJhf13m533P3OOrcOIs5X//x+gutn4W9rS0P5y2TRKQwRZQvwc4rl8j6v+UQJ1sq2GqsCBTMxAoGDRrE0aNHGTduHCtXrsTGxobMzEw8PT1zvb6kQoUKwK03Un/22muvsWfPHjp16kSvXr1wc3Pj2WefBcBgMDB16lT69+/PN998wzfffMOyZcuYO3cuQUFBFv1cuHCBc+fOma9ruht/fgP8Z1lZt/7MTJs2jaeeespim7OzM1evXqVbt26UKlWK1q1bExoaSlJSEvPmzbNo+9cplbev8/rrOYBb4en28/7Vjh07cgS5vGRmZtKgQQPGjRuXY9vtu9XlNtXztj59+tCiRQu2bt3Kf//7X1588UUiIyMJDw/PtX1WVpZF3bkdW27yOge3/x8UFMTrr79OcnIyiYmJ1K9fHz8/P/bs2cP27dvz/EqEvO7Keb/Hl5dy5crx5ptv8sILL/Dtt9/SokWLfPf5/PPPzY//enOLgnrN/fW85vazzu2mJH/3NZ1bv3d6nQ0dOpTOnTuzdetWvv32W/r27cvEiRPp2rVrjrZ/HSnO6/c2L/d77h5H/69uNpXc0tl/3AafCibqV9W0JBHJXdtSDqx2L85/r2VR2c5AW0fFkdsUT6XIBQcHEx0dTWJionmkx8vLi9OnT+Pi4oKnpyeenp6kpqYyderUXN8MXrlyha+++oqpU6cyePBg2rRpw6VLl4Bbb/ISExOZPHkyNWvWZNCgQXz22WcEBgayZcuWHH399NNPNGzYMNdRFCDP9bkpWbIkZcqUITU11XwcFStWZNq0acTHx7Nr1y5Onz7NkiVLeOGFF2jSpAm//vrrXd+Fz8fHh3379lms27t3L15eXjna3h6pqlKlyl317eXlxYkTJ3B3dzfXfvjwYebPn5/vG9qbN28yceJEDAYDffv25aOPPiIqKor169fj7OyMm5ubRd0ZGRkcPHgw17rz4+Pjw6FDh8wjdXDrHLi4uFC6dGnKlCmDr68v8+bNo0aNGjg4OBAYGMi3337LgQMHePrpp3Ptd8eOHXleX5bf8ZUuXZpDhw6Z2584cYImTZrkek1WQEAAjRo1ok+fPkycODHX75f762vu9s/D09OT8uXLW2wr7Ndcfv5u/1WqVDFPa4Rbv9vJycm5tk1NTWXs2LG4u7szYMAAli1bRpcuXfK9Ac79Kuxz96ip4WGiZ7MshTIRyVd1ByORrva0c7LDeA/vsx51CmZiFeXLlycyMpL33nvPPP3Mw8OD6Ohojhw5wt69exk1ahQ2Nja5fhrt4OBA8eLF2bx5MykpKWzbto3x48cDkJ6eTsmSJVmxYgUzZ84kOTmZn376ifj4eGrVqpWjr/ym+5UoUQK4NdXt6tWr+R5beHg477//PjExMZw8eZJx48axfft2vL29cXFx4fr162zZsoWUlBRWrVrF0qVL851S9ee+t2zZwuLFizlx4gSLFy9my5Yt9OnTJ9fjutMIENyaipiUlERaWhodO3YkPT3dfJOUH3/8kfHjx+c5/fPPHBwc2LNnDxMmTCAxMZH4+Hi+//578/S+/v37M2vWLLZu3UpiYiJjxozh5s2bhIaG3tVx/1loaCjZ2dmMGTOGxMREtm7dysyZM+nZs6c5QAYFBZnDOEBgYCCbNm2iRo0auU5VTE9PZ//+/RZTae/l+Pr168fMmTP58ccfSUxMZPz48dSoUSPHlLc/i4qK4vr167mOEt9+zR06dIibN/O/W1Vhvuby83f779OnD5s3b2b58uUkJiYyatSoPI+5VKlSxMTE8Oabb3Ly5En2799PbGysxTTSglTY505EROTPFMzEasLDwylVqhTvvvsuRqOROXPmYDQa6dmzJxEREQQGBjJx4sRc97Wzs+Pdd98lJiaG9u3bM2nSJCIiIihfvjyHDh3Czc2NmTNnsnXrVkJCQnj11Vfp1asX3bp1y9HXnW6RDrdu+tGlSxeGDh1qvgvfnTz//PP07NmTcePG0bFjRxISEvjoo48oX748AQEBREVFMWHCBDp27MiaNWt44403SEtLM19Ldyd+fn5MmTKFFStWEBoaypo1a5g+fXquo0B38/1lffr0Yfny5YwePRonJycWLFjAqVOn6Ny5M8OGDaNz584MGTIk37oA3nvvPW7evEmPHj3o3bs3FStWZPTo0cCtn3XPnj1544036NKlC7/++itLlizJ82Ysd+Lo6MiCBQtITk4mLCyM8ePH069fPwYPHmxuExQUREZGhjlo1atXDxsbG5o2bZprn/v27aNq1ap3vObrTsc3YMAA2rdvz9ChQ+nRowfOzs75XrPo7OzMkCFDWLBggcWt7eHWjTeCgoLo3bs333//fb7npDBfc/n5u/3Xr1+fyZMnM3/+fLp160b58uWpWrVqrm3t7e354IMPOHr0KGFhYQwcOJCnn36al1566W8fR24K+9yJiIj8mcGkORkiIiIFLjX1coH25+JSgrS0h+d2Gqq3cKnewvMw1Qqqt7AVdL1ubs55btOImYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIPNLWrl1Ls2bNANi5cye+vr5kZmaSkpKCr68vJ0+etHKFIiIiYGvtAkRERApT+/btad68ubXLkEKWkQV2RmtXIfJwSTeZsDcYrF2G/B8FMxEReaQVK1aMYsWKWbsMKSTnf4dpn9sRd9IGj7LZDO6YSVV3k7XLEnmgXcgy8drZG2y7noWnrYEJbsVoUFyfbFibpjKKiMhD7/a0xC+//JJmzZoRGBjI+PHjycjIsJjKeCeXLl1i9OjRNGnShLp16zJ06FDS0tLIzs6mQYMGxMTEmNt26tSJyMhI8/LChQt57rnnCuXY5M7mb7Il7uSttzPJ52yY8pkdJuUykTt69/xNtl3PAuBkpomhZ2+QoV8cq1MwExGRR8bs2bOZNm0as2fPJiYmhunTp9/1vlFRURw+fJi5c+eyePFijh8/zmuvvYaNjQ2NGzdm586dwK0Ad/ToUfbu3Wved/v27XcV/qTgJfxq+Vbm9EUDl69bqRiRh8SBm9kWy6lZJk5nKphZm4KZiIg8MqKjowkMDKRhw4YMHjyY1atXk52dne9+R44cYdeuXbz99tvUrl2b2rVrM2XKFL777juOHj1KUFAQu3btAiA2NpZ69epx48YNkpKSSE9PJzY2lqZNmxb24Ukuanla/nwruWVTsoSVihF5SNT/y7RFd1sD7ra61szadI2ZiIg8MgICAsyPa9WqRVpaGufPn893v6SkJBwdHfHx8TGv8/b2plSpUiQmJtK0aVPGjBlDWloau3fvpmHDhphMJv73v/9x9uxZXF1deeqppwrlmOTOBgRnkpkFexNtqFzexIvtM61dksgD75XS9lzNNvHttUx87GwYXdYBG90ExOoUzERE5JFhNP7xKfDtkTLDXbzZsLe3z3V9VlYW2dnZVKhQAW9vb2JjY9m1axevvfYaGRkZ7Nmzh+TkZI2WWZFzcXitq8KYyL1wsjHwdjndFOlBo6mMIiLyyDhy5Ij58YEDByhbtixlypTJdz9vb2+uXr1KYmKied2xY8e4cuUKXl5eAAQFBbF161aOHTtGnTp1CAwMZM+ePWzbtk3Xl4mIyN+mYCYiIo+MSZMmERcXx44dO5gxYwa9e/e+qxEzb29vWrRowbBhw9i/fz/79+9n2LBh1KtXj+rVqwO3gtm6deuoXr06xYsXp27duqSkpJCQkECjRo0K+9BEROQRp6mMIiLyyAgJCSEiIoKsrCx69uxJZGQkn3/++V3t+9ZbbzFhwgTCw8MxGo20atWK119/3by9QYMGGI1G6tWrB4CjoyPVqlWjRIkSODk5FcrxiIjI48NgMulLC0RE5OGWkpJCq1at2Lx5M56entYuB4DU1MsF2p+LSwnS0q4VaJ+FSfUWLtVbeB6mWkH1FraCrtfNzTnPbZrKKCIiIiIiYmUKZiIiIiIiIlama8xEROShV7FiReLj461dhoiIyH3TiJmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiEgRS09PZ/ny5Xdsk5GRQcOGDblx40YRVQUmk4lPP/2U7OxsAIYPH050dPQ99REcHExycnJhlPe3HD58mNjYWAB27tyJr68vmZmZVq5KRETkDwpmIiJF7Ouvv2bOnDl3bPPzzz/j6+tLsWLFiqgq2L17N2PHjjUHs3v122+/kZmZiYeHRwFX9ve99NJLHD9+3NpliIiI5MnW2gWIiDxuTCZTvm127NhB48aNi6CaP9xNXXeyffv2Iq9ZBGD7YRv2n7DhqSeyaVE7G6M+dhbJ10/XM4m5moWnnYHuznYUszFYu6THnv50iYjkIiUlBV9fX7788kuaNWtGYGAg48ePJyMjA4B27doxf/58i3169OjBokWLANiyZQvBwcH4+/szYsQIoqOjee+999i5cyevv/46Z86cwdfXl5SUlFyfP6+Q83fr2rZtGx06dKB27dq88MILTJgwgeHDh5OSkkK/fv0AqFmzJjt37rTo49KlS4SEhPDyyy/nOaKWV82RkZFMmjTJvPzmm29Sv359cxA8cOAAAQEBHDlyhFq1arFq1Srg1nTOjh078vrrrwPQsmVLFi9eTKdOnfD39+eFF17gzJkzAIwZM4YBAwZYPO+UKVOIiIigb9++nDp1ilGjRjF8+HDz9pUrV9KsWTP8/f157bXXuHnzpnnbt99+S+fOnalduzbt2rVjw4YN5m19+/Zl9uzZPP/889SpU4cOHTrw3Xff5XpOpPCt+dHIW6vtWB9rZMY6Oz7cqM+cRfLz9ZUMwn+7wSe/Z/Dm+XQGnSm6afOSNwUzEZE7mD17NtOmTWP27NnExMQwffp0AEJCQizerJ86dYq4uDjatWtHfHw8gwcPpkePHqxZswaAjRs3AhAQEMCIESNwc3Nj27ZtPPHEEzme88qVKxw/fhw/P78CrSs5OZnIyEiCg4P5/PPP8fPzY+nSpQA88cQTzJw5E4Dvv/+egIAAcx83b94kMjKSChUq8M4772Bjk/s/HTt37qRRo0Y51gcFBVkEvd27d3P58mWOHj0K3Ap0DRs2pFq1akRERDBt2jR+//135s+fz8WLF83BDGDmzJk899xzrFy5kps3bzJo0CAAOnTowI4dO7h06ZK57caNGwkNDWXmzJlUqFCB4cOHM3LkSPP2DRs2MH/+fObMmcOWLVvMgXDHjh0MGjSITp068cUXX/Dss88SHR3N/v37zfvOmzePkJAQ1q5di5eXFyNHjiQrKyvPn5cUnvX/M1osx+y1IUOXD4rc0ae/Z1gs/3A9i5SM+5vGLgVHwUxE5A6io6MJDAykYcOGDB48mNWrV5OdnU2HDh04ePCg+UYXGzZsIDAwkAoVKrB27Vrq1q3L888/j4+PD2PHjsXV1RUAe3t7nJ2dsbGxwc3NDaPRmOM5d+/eTd26dXPd9nfqWrVqFTVr1iQqKgpvb28GDx6Mv78/AEajkVKlSgFQpkwZ7O3tgVvTG6Ojo8nOzmbWrFnm9X+VkJBA6dKlKVOmTI5tTZs2JT4+nrS0NH7//XdOnjxJ/fr12bNnD3ArmDVt2hSAgQMHUrp0aUaNGsUHH3zAhAkTKFmypLmvzp07ExYWRtWqVZk0aRL79u3j8OHDBAYGUrZsWWJiYgDYv38/586do2XLlri4uGA0GnFycsLZ2dnc1xtvvIGvry9NmjShSZMmHDlyBIClS5fSunVrwsPD8fLyIjw8nLZt27JgwQLzvs2aNaNLly74+Pjw4osvkpqaah69k6JVzM5yCq6DHeTx2YGI/J8SBstpizZAMc1ktDr96RIRuYM/jxzVqlWLtLQ0zp07h6enJ35+fubRqfXr1xMSEgJAUlIS1apVM+9nb29PzZo17/o57+ZarfupKz4+nlq1aln0U6dOnTs+z+bNm9myZQuurq4UL178vmquVKkSHh4exMbGEhsbi5+fH/Xr1+d///sfN27cYM+ePTRr1gy4da7GjRvHpk2baNmyJc2bN7foq27duubHHh4euLi4kJiYiMFgoH379ubj3rBhAy1btqREiRJ51lypUiXzY2dnZ/NUxsTExBznJSAggKSkJIvnvs3JyQlAd3m0kt7PZGFj+COcPdssS9eYieQjwtXeIoj1KWlHWVv94libfgIiInfw51Gr29dW3Z7KFxoayqZNm0hOTiYhIYHg4GCAXANMXiNNubmbG3/cT125jcDld8OPcuXKsXjxYn744Qe+/fbb+6759nTG3bt3ExgYSGBgIHv27CE2NpYnnnjCIujEx8djNBqJi4vj2rVreR43QFZWlvm4O3TowE8//cSlS5fYtGmTOZDm5a9TMm+fCwcHhxxts7OzLaYq2tnZ5Wjzd2+eIvfn6RrZfPBiOoM6ZPD+gHTCGmlKqUh+6hYzstmjBJPcHFj2ZHFGls35d0+KnoKZiMgd3J7eBrduUlG2bFnzdL327dtz+PBhVq9eTZMmTczTFX18fIiLizPvZzKZiI+PNy8bDHnPFzl37hxpaWlUqVKlwOuqUqUKBw4csOjn4MGDd6wrICCARo0a0adPHyZOnJjr96plZmby888/U79+/TzrvR3MYmNjCQwMxN/fn9OnT7N27VrzaBnAmTNnmDZtGm+++Sb29vbma+duO3z4sPnxyZMnuXz5Mr6+vgBUr16dSpUqsXDhQi5fvmyeHnmvfHx82Ldvn8W6vXv34uXldV/9SeF7ojS08c/Gq4LCscjdKmdrQxdnO+oWy3vavBQtBTMRkTuYNGkScXFx7NixgxkzZtC7d29zgClXrhz169dn0aJFFqMzvXv3JiEhgVmzZpGUlMQ777zDiRMnzNtLlCjB5cuXOX78eI7pb9u3b8/1BhoFUVePHj04cOAAc+fO5fjx43z44YfExsaa97s97e/QoUMWdygEiIqK4vr163z44Yc5atm3bx/e3t44OjrmWW+jRo04fvw4CQkJ+Pv7U6JECWrUqMH69estAtS4ceOoUaMGnTt3ZsyYMXzyyScWN9345JNPiImJ4ciRI4wYMYJGjRrh4+Nj3h4SEsKiRYto27atxSilo6MjSUlJpKWl5Xtuw8PD2bJlC4sXL+bEiRMsXryYLVu20KdPn3z3FRERuV8KZiIidxASEkJERARDhgyha9euREZG5thuMBho1aqVeZ2bmxsffPABGzduJCwsjLS0NItrwho1aoS3tzcdO3a0GPmCu//+svupy93dnRkzZvDZZ5/RoUMH9uzZQ+vWrc3T8qpWrUpQUBC9e/fm+++/t+jP2dmZIUOGsGDBAouQebc1Ozo64u/vT5UqVcwBMDAwEHt7exo0aADApk2b+P7773njjTcAaNKkCW3btmXUqFHmANu5c2fef/99evXqhZubG++//36O47558yahoaEW6/v06cPy5csZPXr0HesE8PPzY8qUKaxYsYLQ0FDWrFnD9OnTefrpp/PdV0RE5H4ZTJoULyKSQ0pKCq1atWLz5s14enrm2W7WrFkcO3Ysx5S7v+rbty9169ZlyJAhVqsrISGBzMxMatSoYV43cOBA/Pz8zLedf5C1bNmSyMhIunfvnmeb3bt3M2TIEL777rs73tWyKKSmXi7Q/lxcSpCWdi3/hg8I1Vu4VG/heZhqBdVb2Aq6Xjc35zy3acRMROQ+xMfH8/nnn7NkyRJ69Ohh7XLM7lTXL7/8Qnh4OD/++COnTp1i1apV7NixgzZt2lip2oJz7tw5Nm7cyNtvv03Xrl2tHspERETula21CxAReRgdOnSIcePG0a1bN5o0aWLtcszuVFfr1q05evQoI0eO5Pz583h5efHee+9Z3Nr/YXXlyhVef/11atWqxYABA6xdjoiIyD3TVEYREZFCoKmMqrcwqd7C8zDVCqq3sGkqo4iIiIiIyGNEwUxERERERMTKFMxERERERESsTMFMRERERETEyhTMRERERERErEzBTERERERExMoUzERERERERKxMwUxERERERMTKFMxERERERESsTMFMRERERETEyhTMRERERERErEzBTERERERExMoUzERERERERKxMwUxERERERMTKFMxEROShdPjwYWJjY9m5cye+vr5kZmbmaHOnbbnZuHEjqampBV2qiIhIvhTMRETkofTSSy9x/PjxO7YJCAhg27Zt2Nra5tvfqVOnGDx4MNeuXSuoEkVERO6agpmIiDyy7O3tcXNzu6u2JpOpkKt59FxPh7Np1q5CRB5257OyuZClv8EKZiIi8tDp27cvp06dYtSoUbz++usW26ZOncrTTz/NyZMnLaYypqSk4Ovry6ZNm2jTpg316tUjIiKCCxcuANCqVSsA2rZty9q1awGIiYkhJCSEOnXq0LlzZ77//nuLGsaPH0+bNm1o2rSpuZ/HxaY9Nvxzmj0vzHQgeqEdaVetXZGIPGyyTCZGnL1B05PXCDp5ldGpN8h+jD8kUzATEZGHzsyZM6lQoQLDhw9nxIgR5vVLly5l+fLlfPTRR3h6eua674cffsiUKVOYO3cu+/fv56OPPgJg1apVAKxYsYL27dtz5MgRXn31VQYMGMC6devo0aMHUVFRHD582NzX2rVrmTx5MnPmzKF06dKFeMQPlktX4cONttzIMACQcMqGFT/kP11UROTPYq5msfZKJtlANrDqcib/vZZl7bKsRn9FRUTkoePi4oLRaMTJyQlnZ2cANm/ezNSpU1mwYAHVqlXLc9+oqCjq1KkDQIcOHYiLiwMwBytXV1eKFSvGRx99RNeuXQkLCwOgUqVK7N+/nyVLljBp0iQAmjVrRmBgYKEd54PqdJqBzCyDxbqUc4Y8WouI5C4xIzvXdS2tUMuDQMFMREQeCcOHD8doNPLEE0/csV2lSpXMj52cnPK8Y2NiYiIJCQmsWbPGvC4jI4PatWubl93d3f9m1Q8n7womyjibOH/5jzAWWCXnGywRkTt5poSRWRdvjZYBGIFmJYzWLMmqFMxEROSR8NZbb7F06VImT57MjBkz8mxnZ2dnsZzXTT+ysrJ4/vnn6dKli8V6e3v7XB8/TuyMMLZ3Bku+NXI2zUBQzWw6NHh8px+JyP2p6WBkevliLEpLxwA872KPr72CmYiIyEOtbdu2eHl50aVLF3744QeaNm16T/sbDJZT8by8vEhOTra4Vm3GjBm4uLjQ6S3UEAAAIABJREFUr1+/Aqn5YeZZzsSoZ+/u++FERPLS1tGWto6KJKCbf4iIyEPK0dGRpKQkLl26ZF5XvXp1unfvzoQJE0hPT7+n/kqUKAHAkSNHuHr1KuHh4WzcuJHFixdz8uRJPv30U+bOnWsxFVJERKSgKJiJiMhDqU+fPixfvpzRo0dbrH/55ZdJS0tj3rx599Sfq6srXbp0YejQoaxevRp/f3+mTJnCypUrCQkJYfHixUyaNInmzZsX4FGIiIjcYjDpGzVFREQKXGrq5QLtz8WlBGlp1wq0z8KkeguX6i08D1OtoHoLW0HX6+bmnOc2jZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIldlauwARERF5cGRmwbpdRg7+YsDX3URYoyzs9G5BRKzsqysZbLqSibudDS+UsqOs7aM3vvToHZGIiDyUUlJS8PX15csvv6RZs2YEBgYyfvx4MjIyaNeuHfPnz7do36NHDxYtWgTAtm3b6NChA7Vr1+aFF15gwoQJDB8+3Nx23rx5tGrVilq1ahEUFMT7779v3ta3b1/mzZvHc889R+3atenevTu//PILo0aNIiAggODgYGJjY83tjx49Sr9+/ahduzZt2rRh4cKFmEymQj47ReejLbYsirFlV4KRJd/aMme9UpmIWNdnlzOIPnuTLdeyWHwpg+dP33ik/u7epmAmIiIPlNmzZzNt2jRmz55NTEwM06dPJyQkhA0bNpjbnDp1iri4ONq1a0dycjKRkZEEBwfz+eef4+fnx9KlS81tv/jiCxYuXMjEiRPZuHEjL730EnPmzGH//v3mNh988AHdu3dn7dq1XLp0ia5du1KhQgVWr16Np6cnkyZNAuDGjRu88MIL+Pv78+WXXzJq1Cg+/vhjPvnkk6I7QYXsv/st3xp8F2fDI/j+R0QeIl9eybRYjk/PJj4920rVFB4FMxEReaBER0cTGBhIw4YNGTx4MKtXryYkJISDBw+SnJwMwIYNGwgMDKRChQqsWrWKmjVrEhUVhbe3N4MHD8bf39/cX/ny5Zk8eTKNGzemYsWK9OrVCzc3N44ePWpu06xZM9q3b89TTz1Fy5YtcXJyIioqCh8fH7p3705SUhIA69ato1SpUrzyyitUrlyZZ555hpdffpmPP/64aE9SIXJ1skxhLk5gMFipGBERwM1o+UfICJQ2Pnp/mDQ/QUREHigBAQHmx7Vq1SItLQ1HR0f8/PzYsGEDAwcOZP369fTo0QOA+Ph4atWqZdFHnTp1uHTpEgCNGjVi3759TJ06lcTERA4fPkxqairZ2X982urh4WF+7ODgwJNPPmmxnJ6eDkBSUhLHjh2zqDE7O5v09HTS09Oxt7cvwDNhHf3bZPHWagPpmQbsjCaeb5OZ/04iIoUowsWen65ncTbLhAH4l4sd5R7Ba8wUzERE5IFiNBrNj2+HJxsbG0JDQ1m3bh3t2rUjISGB4ODgHO1v+/O1B6tWrWLSpEl069aNtm3bMmzYMPr165fnc95+vtxkZmbSoEEDxo0bl2Obre2j8U9qYJVsFr2czrFfDXhXMFHK0doVicjjztvehi0eJdhzMwt3Wxsq2T16oQw0lVFERB4wR44cMT8+cOAAZcuWpUyZMrRv357Dhw+zevVqmjRpgqurKwBVqlThwIEDFn0cPHjQ/PjTTz8lIiKCkSNHEhYWhqurK+fPn7+vC8e9vLw4ceIE7u7ueHp64unpyeHDh5k/f36eYe5h5FwcAnwUykTkweFgY6BxcdtHNpSBgpmIiDxgJk2aRFxcHDt27GDGjBn07t0bg8FAuXLlqF+/PosWLSIkJMTcvkePHhw4cIC5c+dy/PhxPvzwQ2JjYzH834VRrq6u7Nixg6SkJA4cOMCQIUPIyMgwT0+8Fx07diQ9PZ1Ro0aRmJjIjz/+yPjx4ylVqlSBHb+IiDyeFMxEROSBEhISQkREBEOGDKFr165ERkZabDMYDLRq1cq8zt3dnRkzZvDZZ5/RoUMH9uzZQ+vWrbGzswNgxIgR3Lhxg86dOxMVFUXVqlUJDg7m0KFD91ybk5MTCxYs4NSpU3Tu3Jlhw4bRuXNnhgwZ8vcPXEREHmsG06P4JQAiIvLQSUlJoVWrVmzevBlPT89c28yaNYtjx44xffp087qEhAQyMzOpUaOGed3AgQPx8/Nj0KBBhV53XlJTLxdofy4uJUhLu1agfRYm1Vu4VG/heZhqBdVb2Aq6Xjc35zy3acRMREQeePHx8Xz++ecsWbLEfDfG23755RfCw8P58ccfOXXqFKtWrWLHjh20adPGStWKiIjcu0fjFlIiIvJIO3ToEOPGjaNbt240adLEYlvr1q05evQoI0eO5Pz583h5efHee+9RrVo1K1UrIiJy7zSVUUREpBBoKqPqLUyqt/A8TLWC6i1smsooIiIiIiLyGFEwExERERERsTIFMxEREREREStTMBMREREREbEyBTMRERERERErUzATERERERGxMgUzERERERERK1MwExERERERsTIFMxEREREREStTMBMREREREbEyBTMRERERERErUzATERERERGxMgUzERERERERK8s3mP3000/mxzdu3GDMmDGFWpCIiIiIiMjjJt9g9v777xMXF8fPP/9Mt27dqFixYlHUJSIij6mUlBR8fX05efKktUsREREpMrb5NZg9ezYvvvgi6enpvP/++/j4+BRFXSIiIiIiIo+NPIPZ1KlTMRgMAHh5efHDDz/wxRdfAPDKK68UTXUiIiIiVnTpKiSfM/DUEyaK2Vu7GnncZJtMxN3MppTRQGU73RriUZdnMPP29gYgOzsbLy8vGjRoUGRFiYjI4yE5OZmJEyeya9cunJ2d6d27N6GhoQB88803LFu2jDNnztC4cWPeeustXF1dAYiNjeWtt94iISEBDw8PBgwYQFhYGADDhw/HZDIRHx/Pb7/9xn/+8x86duxIZGQky5cvp1q1apw5c4YuXbowYMAAcy09evSgXbt2lCpViuXLl9O4cWOWLFmCs7MzkZGR9OzZkzNnztC8eXNWrFhB7dq1Abhy5QqNGzdm+fLl1KxZs4jPoBSmb/fbMPMrWzKzDDgVMzGmVwbVKpqsXZY8Ji5kmQj/7ToJ6dkA9Cppyxtli1m5KilMeQazzp07A9C/f38WLlxYZAWJiMjjIT09neeff54qVaqwYsUKzpw5w5AhQzCZbr3xXbt2LVOnTsVkMhEVFcW8efMYNmwYqampDBw4kMGDB9O8eXMOHjzImDFjKFmyJC1btgTgyy+/ZMaMGZQvX54qVaoAsHXrVpYtW0ZWVhabNm1iw4YN5mB26tQp4uLimDFjBtu3b+fQoUM4OzuzfPly4uLieOONN3jiiSd45plnqF+/Phs2bDAHs5iYGJ588kmFskdMZhYs2HwrlAFcuWFgcYwtb4VnWLkyeVwsuZRuDmUAn/6eSXfnLGo4GK1YlRSmfK8xc3Z2ZuvWrVSuXBkbm1tDqF5eXoVemIiIPNq2b9/O2bNnWbNmDc7OzlStWpUxY8Zw4cIFAKKjo83hp127dhw5cgSApUuX0rBhQ/75z38C4OnpSVJSEh9//LE5mFWvXp02bdpYPN+zzz5rng1ib2/PzJkzSU5OxsPDgw0bNhAYGEiFChXM7d9++23Kli1L1apV2bVrFytWrOCZZ56hQ4cOfPDBBwwbNgyA9evXm0f55NFxMwMuXzdYrDt32ZBHa5GC91tmztHZ05kmajhYoRgpEvkGswsXLrB48WLzssFg4D//+U9h1iQiIo+BY8eOUalSJZydnc3rOnbsSEpKCpMnT8bDw8O83tnZmZs3bwKQlJTEDz/8QEBAgHl7ZmYmpUuXNi/ndgdhd3d382NPT0/8/PzYsGEDAwcOZP369fTo0cO83cPDg7Jly5qXa9WqxSeffAJAcHAw48aNY9++fVSuXJnt27ebQ5o8OhyLQeBTWcQe+2N0olnNLCtWJI+bECdbPr+SaV4uYzTQqLhGyx5l+QazJUuWcPHiRZKTk6lYsaLFP3wiIiL3y87O7o7bjUbLNyC3pzhmZmYSEhLCiy++aLH99qwOuDUi9lcODpYfM4eGhrJu3TratWtHQkICwcHB5m22tpb/PGZlZZn7L1myJM2aNWPjxo089dRTVKlSRXcsfkRFd8lk1TYTx8/Y4O+dTYcGCmZSdJqWsGV2+WKsvZyBi9HA86XsKWGjUdtHWb7BbMOGDUyfPh0fHx+OHj1KVFQUnTp1KoraRETkEVa5cmWSk5O5cuUKTk5OAMyYMYNff/31jvt5eXkRGxuLp6ened3SpUs5e/YsQ4YMuevnb9++Pe+88w6rV6+mSZMm5huLADnqOnDgAL6+vubtoaGhzJgxg1OnThESEnLXzykPlxIO8M9WWYACmVhHK0dbWjnm+3ZdHhH53ndz8eLFrF27ljlz5vDZZ59pGqOIiBSIoKAgKlSowKhRo0hMTOS7775jyZIl5uvA8tK7d28OHz7M1KlTOXHiBBs3buTdd9+lfPny9/T85cqVo379+ixatChHuLp+/TpjxowhMTGRlStXsnHjRvr06WPe3rJlS86cOcN3331H+/bt7+l5RUREcpNvMDMYDDg6OgLg5OSUYyqIiIjI/TAajcyZM4dLly7RuXNnxo4dy0svvZRv0HF3d+fDDz9k+/bthIaG8vbbbzNo0CB69+59zzWEhIRgMBho1aqVxfpy5crh7u5Ot27dWLBgAe+88w7169c3by9WrBgtW7akZs2aPPnkk/f8vCIiIn+V79hopUqVeOuttwgMDCQ2NpZKlSoVRV0iIvIY8PLyYtGiRTnWx8fHWywPGjSIQYMGmZcbN27MmjVrcu3zrbfeyre/286ePUuLFi3MUxZvMxgMDB06lKFDh+ZZe2pqqu7GKCIiBSbfEbNJkybh4eHB9u3b8fDwYMKECUVRl4iISKGJj4/n888/Z8mSJRZ3Y7wbO3fuZN68eRw8eFDBTERECky+I2b/+te/aNu2LS+99BJlypQpippEREQK1aFDhxg3bhzdunWjSZMm97TvunXr2LRpE2+88QYlS5YspApFRORxYzDdvv9wHs6cOcPWrVv5/vvvSU9Pp3nz5vTr16+o6hMREXkopaZeLtD+XFxKkJZ2rUD7LEyqt3Cp3sLzMNUKqrewFXS9bm7OeW7LdypjuXLl8PPzw9/fn99//53169cXWGEiIiIiIiJyF1MZGzVqxBNPPMGAAQNYtGgRzs55pzwRERERERG5d/kGs7lz57Jt2zbWrFnDpk2baNKkCT179iyK2kRERERERB4L+U5lDAgIoEePHgQHB3Px4kU+++yzoqhLRERERETksZHviFlYWBiurq60bt2aKVOmUL58+aKoS0RERERE5LGRbzBbvHgxFy9e5JdffsFkMmEymTAYDEVRm4iIiIiIyGMh32D21VdfsWXLFi5dukRYWBi//PILY8aMKYraREREREREHgv5XmP29ddfs3jxYpydnQkPD2ffvn1FUZeIiIiIiMhjI99gdvv7p29PX7S3ty/cikRERERERB4z+U5lDA0NpU+fPvz6668MGDCA1q1bF0VdIiIiIiIij418g9k//vEPGjduTEJCAt7e3vj6+hZFXSIiIiIiIo+NfIPZ/v37+frrr7l58yY7d+4EYOzYsYVdl4iIiIiIyGMj32A2bNgwBgwYQMmSJYuiHhERERERkcdOvsHM09OTLl26FEUtIiIiIiIij6V8g1lwcDBDhgzBx8fHvC4qKqpQixIREREREXmc5Hu7/GXLllG9enXKli1r/k9ERKQoHT58mNjY2Ltun5KSgq+vLydPnsy3bXJyMv/973/vu7ZevXoxc+bM+95fREQE7mLErFSpUgwcOLAoahEREcnVSy+9RGRkJIGBgXfV/oknnmDbtm2ULl0637YjRoygbt26NG/e/G9WKSIicv/yDWaurq6MGTOGGjVqmL9k+tlnny30wkRERO6X0WjEzc3N2mWIFKhrN2Hpf40c+sWGOj7ZdG8CjsWsXdWjw2QysfhSBptP36C8Af7tao+3fb6Ty0QKTL6vNk9PT8qVK8e5c+dITU0lNTW1KOoSEREBoG/fvpw6dYpRo0bRoEEDYmJizNs6depEZGSkeXnhwoU899xzOaYybty4kfbt2+Pn50fbtm1Zs2YNAMOHD2fXrl3MnTuXvn37AnD69GlefPFF/P39ad68OVOmTCE9Pd38HFu2bCE4OBh/f38mTpyIyWQqitMgwpyvbVm3y5bE0zas/dHEzHX5fr4u92Dp7xm8fSGdvdcy2Xg1kxdOXydTv99ShPL9jY6KiuLs2bNkZmZiMpk4e/ZsUdQlIiICwMyZM+nUqRPh4eHExsayc+dOWrduzaVLlzh69Chnzpwxt92+fTvNmjWz2P/8+fNER0czZswYnn76aX766SdGjRqFv78/I0eO5MSJE9SuXZsXX3wRk8nESy+9RNWqVVmzZg0XL15k7NixZGZmMnz4cI4dO8bLL7/Mq6++StOmTfn444/Zu3cvTz/9dFGfFnkM/RRv+Xn6zgSN5hSkrdeyLJZ/zTRx6GY2tYsZrVSRPG7yDWYjRozg559/5vr169y4cQMPDw9WrlxZFLWJiIjg4uKC0WjEycmJ5s2b88knnwAQGxtLvXr1iIuLIykpiYoVKxIbG8vw4cMt9j9z5gwZGRmUL18ed3d3unbtypNPPknZsmVxdnbGzs6O4sWL4+Liwo4dO0hJSWHlypUYjbfejI0ZM4b+/fsTHR3NmjVrqFu3LuHh4QCMHj2ab775pkjPhzy+nixt4sRZg8WyFJzKdgZ2XP9j2Q5wtzPk2V6koOX7UUtSUhJff/01QUFBfP311zg4OBRFXSIiIjk0bdqUhIQE0tLS2L17Nw0bNqRWrVr873//Y8+ePbi6uvLUU09Z7FO9enVCQ0MZOHAgbdq0YdKkSbi6ulKqVKkc/ScmJvL7778TGBhIQEAAAQEBDBw4kIyMDH799VcSExPx9fU1t7ezs7NYFilMEe0zcXG8FcZcnCCyfaaVK3q0RLrYU/3/rilzMMCwMvaUMWpUUopOviNmjo6OGAwGrl27RunSpcnIyCiKukRERHKoUKEC3t7exMbGsmvXLl577TUyMjLYs2cPycnJNG3aNMc+BoOBqVOn0r9/f7755hu++eYbli1bxty5cwkKCrJom5mZiaenJx9++GGuz50bOzu7gjk4kXzU8DDx0eB0frtgoLpXMa5e0YhZQSpna8NnFUtwvpg9dldvUtKo0TIpWvl+DFCzZk0++ugjypUrx5AhQ8jKyspvFxERkUITFBTE1q1bOXbsGHXq1CEwMJA9e/awbdu2HNeXwa1RsMmTJ1OzZk0GDRrEZ599RmBgIFu2bMnR1svLi9OnT+Pi4oKnpyeenp6kpqYydepUTCYTVapUIS4uztw+KyuL+Pj4Qj1ekT+zM0IlNxN2tgoNhcWnmK1CmVhFviNmYWFhlCtXjmLFivH9999Tu3btoqhLRETEzNHRkaSkJNLS0ggKCiIyMpKaNWtSvHhx6tatS0pKCgaDgUaNGuXYt2TJkqxYsQInJyfCwsI4deoU8fHxhISEmPv+5ZdfOH/+PEFBQXh4eBAdHc3QoUO5fv06o0aNolq1ajg4ONC9e3f+85//MGvWLNq3b8+yZcs4ffp0UZ8OERF5BOU7YjZy5EicnJywtbWlZcuWlC1btijqEhERMevTpw/Lly9n9OjRNGjQAKPRSL169YBbwapatWoEBATg5OSUY183NzdmzpzJ1q1bCQkJ4dVXX6VXr15069YNuPXdnD/++CMDBgzAaDQyZ84cjEYjPXv2JCIigsDAQCZOnAhA5cqVmTt3Lhs3biQsLIyLFy/mOn1SRETkXhlMeXwBy+XLl3F2dub555/Hx8cHLy8vbGxu5Th9wbSIiMidpaZeLtD+XFxKkJZ2rUD7LEyqt3Cp3sLzMNUKqrewFXS9bm7OeW7Lc8QsIiICAHd3d0qWLMn58+f1BdMiIiIiIiKFIM9rzIoVK0bXrl05efIkPj4+5vUGg4GoqKgiKU5ERERERORxkGcwmz9/PmfPnmXMmDG88cYbRVmTiIiIiIjIYyXPYGZjY0OFChWYN29eUdYjIiIiIiLy2NHXmYuIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiIiIiIiVKZiJiIiIiIhYmYKZiIiIiIiIlSmYiYiIiIiIWJmCmYiIiIiIiJUpmImIiIiIiFiZgpmIiMgdzJw5k169euW5vVevXsycObMIKxIRkUeRrbULEBEReZD179+fvn37WrsMERF5xCmYiYiI3IGjo6O1S5ACduCkgUtXDQT4ZFPCwdrViBSMLJOJ7dezyDBBUAkj9gaDtUuSe6SpjCIi8kBISUnB19eXL7/8kmbNmhEYGMj48ePJyMigXbt2zJ8/36J9jx49WLRoEWvXrqVHjx78+9//pl69eqxatYorV64wcuRIGjduTK1atQgODmbTpk3mfX19ffn6669p164dderUYejQoSQnJ9O3b1/q1KnDP/7xD86ePQvknMq4ZcsWgoOD8ff3Z+LEiZhMpqI5QVIg3l5ty4j/2PP2GjsiZtvz2wVrVyTy96WbTPzj1+sMOH2DF8/coFPKNS5l6W/Tw0bBTEREHiizZ89m2rRpzJ49m5iYGKZPn05ISAgbNmwwtzl16hRxcXG0a9cOgH379uHp6cmqVato0aIFkydPJjExkYULF/LVV19Rv359Ro8eTXp6urmPGTNmMHnyZObOncvGjRvp1asX//jHP1i2bBmnTp1i4cKFOWo7duwYL7/8Mr169WLNmjWkp6ezd+/ewj8pUiASfzPw42GjeTntqoEvd2nykDz8vrmaxd6b2ebl4xkm1l7OsGJFcj/010hERB4o0dHRBAYGAjB48GDeeecdli9fzsyZM0lOTsbDw4MNGzYQGBhIhQoVzPv961//wsnJCYB69erRr18/fH19gVvXia1atYozZ87g4eEBQL9+/fD39wdujaBVqVKF4OBgAFq1akVSUlKO2tasWUPdunUJDw8HYPTo0XzzzTeFcyKkwF25kXPd1VzWiTxsrmTnHB27nMs6ebBpxExERB4oAQEB5se1atUiLS0NR0dH/Pz8zKNm69evJyQkxNzOxcXFHMoAwsLCOHnyJBMnTqR///7mqYjZ2X98onw7oAE4ODjw5JNPmpeLFStmMbp2W2JiojnsAdjZ2Vksy4OtlqeJimX+eA3YGEy08c+yYkUiBaOVoy2lbf64pqy4ATo42VmxIrkfGjETEZEHitH4x1Sz20HKxsaG0NBQ1q1bR7t27UhISDCPbsGtYPVnr732Gnv27KFTp0706tULNzc3nn32WYs2traW/wTa2NzfZ5V2dnrz87Aw2sDkf2awPtZI2lUDzf2yqO6hUQV5+LkaDax0L86K3zO4aYLuJW3xstf4y8NGwUxERB4oR44coXHjxgAcOHCAsmXLUqZMGdq3b88777zD6tWradKkCa6urrnuf+XKFb766is+/fRT8+jbd999B/C3b9RRpUoVYmNjzctZWVnEx8dTs2bNv9WvFJ1SjtDrGY2SyaOnop0NQ8voNqMPM0VpERF5oEyaNIm4uDh27NjBjBkz6N27NwaDgXLlylG/fn0WLVpkMY3xrxwcHChevDibN28mJSWFbdu2MX78eIBcpyfei+7du3Po0CFmzZpFUlISkydP5vTp03+rTxEREVAwExGRB0xISAgREREMGTKErl27EhkZabHNYDDQqlWrPPe3s7Pj3XffJSYmhvbt2zNp0iQiIiIoX748hw4d+lu1Va5c2XwXx7CwMC5evEjTpk3/Vp8iIiIABpO+gEVERB4AKSkptGrVis3/n717j++xjv8//tjRbMaI0DAsRhl2QDRKwncbMnzlEDmVDkTlkFOxzMpZcyaHRtgih2LOJSWHLIcfhg1tK4ewmNMOn/3+8N3FhzllXIvn/Xbrls/nOr2u6/p8rs/1vN7v69qaNXh4eOQ4zsSJEzl8+DDjx49/yNXdu1Onzufq/NzcnElJuZir83yQVO+DpXofnP9SraB6H7TcrrdYMddbDtM9ZiIikufFxcWxf/9+IiMjGTdunNnliIiI5Dp1ZRQRkTxv3759DB06lKZNm1KnTh2zyxEREcl1ajETEZE8oVSpUsTFxeU4LCQkhJCQkIdckYiIyMOjFjMRERERERGTKZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiIiIiImIyBTMRERERERGTKZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiInIbWVlZLFiwAIvFYnYpIiLyCFMwExERuY3t27czdOhQBTMREXmg7M0uQEREJC/LysoyuwS5RweSbJi11p6T/9hQ95lMXm+Qib2d2VWJmCf6XDpf/pMGwBtujrR0dTC5IsmJWsxERMQUSUlJeHl5MWnSJGrUqMGAAQNYt24dwcHBVKtWjZCQEDZt2mSMn5aWxvDhw/H39+eFF15g1apVeHl5cezYMQC8vLz45ZdfjPGXLFlCvXr1jNeHDh2iY8eOVK1alYYNGzJr1iw6UoUCAAAgAElEQVQjdKWmptK7d29q1qyJr68vPXr04NSpUyQlJdGxY0cAnn32WbZu3cpff/1Ft27d8PX1pWbNmgwYMIALFy48jE0md+FKOny60IEDSbacOW/Dsq32LNmiVCaPr52XMxny9xWOpmdxND2LQaeusPtyptllSQ4UzERExFQ7duxg8eLFvP766/Tt25c33niDFStW0Lp1a3r06MH+/fsBGD9+POvWrWPy5MmMHTuWiIiIu17G5cuX6datG9WrV2f58uUMHjyYuXPnMm/ePGPeycnJREZGEhUVxenTpwkPD6dkyZLGcjZt2oSPjw+hoaHY29uzePFiZs2aRWxsLFOnTs39DSP/ypETNpy/ZGP13u4jOt2Rx9evl24OYVtyeE/Mp66MIiJiqo4dO1KmTBn69u1Ly5Ytad68OQBlypRh9+7dREZGMnz4cBYvXky/fv2oWbMmAH379uWtt966q2WsWLGCQoUK8cEHHwBQtmxZevfuzaRJk+jQoQPJyck4OztTqlQpXFxcGDlyJOfOncPOzo5ChQoB8MQTT2Bvb09ycjJeXl64u7vj6OjIxIkTsbGxud3i5SEqVTQLR/ss0jKu7RPPkro/UB5fz+S7+cJETu+J+RTMRETEVO7u7gDEx8dz8OBBFi9ebAxLT0+natWqnD17lpSUFCpXrmwM8/HxuetlJCQkcPjwYatpLBYLaWlppKWl8eabb/Lmm29Su3ZtatWqRcOGDY2AeKP33nuP999/n/Xr1xMQEECjRo0ICgq619WWB6SAE/R+JYPpMfb8cwH8K1hoHaDWAXl8vehsT7dCDkSeS8cG6FjIgbrOigB5kfaKiIiYKl++fABkZmbStWtXWrRoYTXc0dERJycnwPpBHI6Ojredb2bmtZPxjIwMatasybBhw24az97eHh8fHzZu3MiGDRv48ccf+eyzz1ixYgWRkZE3jf/yyy/z448/sm7dOjZt2sSAAQPYvHkzn3322d2vtDxQAc9YqF0pjfQMcLr9x0TksdDniXy8V+Tql8FRLfx5ltoxRUQkTyhXrhyJiYl4eHgY/y1btoy1a9fi4uJCyZIl2bNnjzH+vn37rKZ3cHAgNTXVeJ2YmGg176NHj+Lu7m7Me//+/cyYMQNbW1vmzJnDrl27aNasGWPGjGH69Ols27aNv//++6ZuiuPGjeP48eO0bt2aiRMnMnz4cFauXPmAtor8W3a2CmUi13O0sVEoy+MUzEREJE/o1KkTMTExzJkzh2PHjrFgwQKmTp1KmTJlAOjatSsTJ05k8+bN7N27l+HDh1tN7+3tzezZszl69CgbN25kyZIlxrBmzZqRlpbG4MGDiY+P5+effyY0NNS4f+z48eN8+umn7Ny5k8TERFasWMFTTz1F4cKFcXZ2Bq4GwStXrpCQkEBoaCj79u0jISGBNWvW8Oyzzz6krSQiIo8qBTMREckTqlevzujRo4mKiiI4OJg5c+YwYsQIXnzxRQDat29Py5Yt+fDDD3nzzTcJDg62mn7IkCFcuXKFJk2aMG3aNHr16mUMK1CgADNnziQ5OZmQkBD69+9PSEgI77//PgC9evXC39+fd999l+DgYBISEpg6dSp2dnZUrFiRgIAA2rVrx6ZNmxg6dCjFixenU6dOtGjRgszMTMaMGfPQtpOIiDyabLL0lzNFROQ/KCkpiQYNGrBmzRo8PDzMLucmp06dz9X5ubk5k5JyMVfn+SCp3gdL9T44/6VaQfU+aLldb7FirrccphYzERERERERkymYiYiIiIiImEyPyxcRkf+kUqVKERcXZ3YZIiIiuUItZiIiIiIiIiZTMBMRERERETGZgpmIiIiIiIjJFMxERERERERMpmAmIiIiIiJiMgUzERERERERkymYiYiIiIiImEzBTERERERExGQKZiIiIiIiIiZTMBMRERERETGZgpmIiIiIiIjJFMxERERERERMpmAmIiIiIiJiMgUzERERERERkymYiYiI3MZHH31Enz59zC5DREQecfZmFyAiIpKXDRo0yOwSRETkMaBgJiIichuurq5ml3DXsrJgxyFb/vjbBj9PC2WLZ5ldkohInvF3hoVVFzJwtrUh0MUeZ1sbs0uyoq6MIiKS58TGxtKuXTuqVatG9erV6dq1KydOnCA9PZ1PPvmE2rVrU61aNTp37kxCQgIAgYGBzJgxw2o+rVu3Zvbs2SxZsoS2bdsyceJEnnvuOQICAggLC8NisRjjRkZG0qBBA3x8fOjYsSPx8fHAzV0ZN27cSEhICFWrViUwMJBVq1Y9hC1yd6astOfTRQ7MXW9P7xkObI3Tz7yICEBSuoWmSRcJO53GoFNXaPvnJdKy8tbFKx2xRUQkT0lNTaV79+7UqVOH7777ji+//JKkpCSmTJnC/Pnz+fnnn5k2bRrLly/HxcWFAQMGABAcHGwVkpKTk9mzZw+BgYEA7Nmzh/j4eObPn0/v3r2ZN28eP/30EwDR0dGMGzeO999/n6VLl1KiRAneeecdsm740d6yZQs9e/bklVdeYdmyZbz66qv06dOH3bt3P6Stc2vnLsKa2Gs/65YsG5ZssTOxIhGRvOOb8+mcvXYtjrg0Cz9ezDSvoByoK6OIiOQply5donv37nTp0gUbGxtKly5No0aNiI2Nxd7eHicnJ0qVKkWRIkUYOnQoR48eBaBp06ZERESQmJhI6dKlWbVqFf7+/pQoUQKAjIwMQkNDcXV1xdPTk/nz57Nnzx5eeOEFFi5cSIcOHWjSpAkAQ4YMYfLkyaSmplrVNn/+fF5++WU6deoEQLly5di1axczZ87kiy++eGjbKCc5XfjNYxeDRURMY8npvTx2jFSLmYiI5CnFihUjJCSEOXPm0K9fP1q0aMGsWbPIzMzktddeIzU1lbp169KhQwe+++47vLy8APDw8MDb29toNVu5ciXBwcHGfAsXLmx1v1iBAgXIyMgAID4+nipVqhjDXF1d6d+//033l8XHx1OtWjWr93x8fIzulGYq5AL1q1479bAhi1dq5a2rwSIiZmnl6kDB65KPp4MNLzrnrV4FajETEZE85cSJE7Rs2ZLKlSsTEBBA69at+eGHH/jtt98oW7Ysq1evZtOmTfzwww9MnTqVqKgolixZgpOTE02aNGHFihUEBgZy8OBBGjdubMzXwcHhpmVld1XMaVhO8uXLd9N7FouFzMy8EYB6NMnA/2kLiX/b4Pe0hQpP5bHLwSIiJinjYMvyUs58n3r14R9NC9iTTw//EBERubW1a9fi4uLCjBkzeP311/H39ycxMZGsrCyWLl3KunXraNiwIWFhYSxdupT4+Hji4uIACAoKYv/+/XzzzTfUqVOHwoUL39UyPTw82Ldvn/H64sWLPP/88xw8eNBqPE9PT3bt2mX1XmxsLOXKlbvPtc4ddrbw/DMW2tTLVCgTEblBCXtburo50ragAwXyWCgDBTMREclj3NzcOHnyJD///DOJiYlMnz6dNWvWkJaWxvnz5wkLC2Pz5s0kJSWxePFinJ2dKVu2LABPPvkkNWrUYPbs2VbdGO+kY8eOREZGsnr1ao4ePconn3yCm5sbTz/9tNV4nTp1Yu3atcyZM4ejR48yZ84c1q5dS/v27XNzE4iIyGNIXRlFRCRPCQwMZPv27fTu3RsAb29vBgwYwLhx42jZsiUnTpxgwIABpKSkUKFCBaZNm0ahQoWM6YODg/n9999p0KDBXS+zWbNmnDhxgrCwMM6fP4+vry9TpkzB1tb6+qW3tzejR4/miy++YPTo0ZQrV47x48fz/PPP587Ki4jIY8sm68ZnAYuIiPyHTZw4kcOHDzN+/HhT6zh16nyuzs/NzZmUlIu5Os8HSfU+WKr3wfkv1Qqq90HL7XqLFXO95TC1mImIyCMhLi6O/fv3ExkZybhx48wuR0RE5J7oHjMREXkk7Nu3j6FDh9K0aVPq1KljdjkiIiL3RC1mIiLySAgJCSEkJMTsMkRERP4VtZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiIiIiImIyBTMRERERERGTKZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiIiIiImIyBTMREXmsJCUl4eXlxbFjx+7qfRERkYfB3uwCRERE8oKSJUuyefNmihQpYnYpeV5GJtjbmV2FiDyKMrOysAVsbGzMLuWhUzATEREB7OzsKFasmNll5Gn7/rAhYoU9yWds8Slv4f3m6bi5mF2ViDwKMrOyGHE6jejz6TjbQK8i+Whb0MHssh4qdWUUEZHH2qJFi/Dx8eH33383ujKOHj2atm3bWo03ffp0WrZsyfTp0wkKCrIatmDBApo2bfowy37oMi0wcokDyWeunjrEJtgye52u74pI7liamsH8c+mkZUGKBYb9fYXDaRazy3qoFMxEROSxtX79esLDw5k0aRJFixY13g8ODiY2NpaTJ08a78XExBAcHExwcDAJCQkcPHjQGLZq1SqaNGnyUGt/2E6fgzPnrbsWHUp+/LoaiciDsedy5s3vXbn5vUeZgpmIiDyWYmNj6dOnD59//jl16tSxGla5cmXKly/P2rVrAUhMTGT//v0EBgbi7u6Oj48PMTExAPz999/s2LHjpla0R03RQlDcLcvqvSoeWbcYW0Tk3vjnt75x1Rbwc3q8bmZVMBMRkcfSkCFDuHLlCk899VSOw4OCgoxgFhMTg4+PDyVLlgSgSZMmrFq1yhjm7e1N6dKlH07hJrG1gQH/m06lUhZc8mXxQpVMXm+QYXZZIvKIaFLAgR6FHSlqZ4OHgw2jnsxHGYfHK6qoc7iIiDyWevbsyaFDhxg2bBhRUVE3DQ8ODmbKlCmcPXuWmJgYWrRoYQwLDAxkxIgRHDp0yOji+DgoXyKLkZ3TzS5DRB5RPQo70qOwo9llmObxiqEiIiL/p3HjxvTp04f4+Pgcg1m5cuWoWLEi0dHRHDhwgMaNGxvDihQpQu3atVm2bBm///47gYGBD7N0ERF5BCmYiYjIY6t48eK8/fbbjBs3jrNnz940PLvVrGbNmlYPB4Gr3Rm/+uor/P399Zh9ERG5bwpmIiLyWOvUqROFChVi1KhRNw0LDg7m0qVLOT7Yo2HDhmRlZT023RhFROTB0j1mIiLyWClVqhRxcXHGa0dHR9asWZPjuCVLluTAgQM5DktJScHGxsaqi6OIiMi/pWAmIiJyDy5evMimTZtYvHgxjRo1omDBgmaXJCIijwB1ZRQREbkHNjY2DBkyhJMnT9K3b1+zyxERkUeEWsxERETuQf78+dm+fbvZZYiIyCNGLWYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIiIiIiJhMwUxERERERMRkCmYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIiNxGUlISXl5eHDt2zOxSRETkEWZvdgEiIiJ5WcmSJdm8eTNFihQxuxQREXmEKZiJiIjchp2dHcWKFTO7DHnEZVpg425b4o/bUq2chee8LGaXJLew70omK1IzKGJnw/+6OuBmZ2N2SfKIUFdGERGR/zN//nwaNGiAt7c3TZs2ZePGjVZdGbds2YKXlxdbtmwB4Ny5cwQEBDBx4kSTK5f/uikr7flihQPfb7djRJQDy361M7skycGuy5m8mnyJ2f+kM+ZMGu3/vEhGVpbZZckjQsFMREQE2LdvH+Hh4QwYMICYmBiCgoLo3bs358+fN8apXbs2LVq0YPjw4WRkZPD555/zxBNP0L17dxMrl/+6y2mwfpf1Kdn3OxTM8qLo8+mkX/c6Pj2LXy9lmlaPPFrUlVFERARITk4GwN3dHXd3d7p37463tzcODg5W4/Xv35+goCD69+/P6tWriY6OvmkckXthZwsOdle7M2ZzclArTF6U3+bmbovOturKKLlDLWYiIiJAQEAAfn5+NG/enKZNmzJhwgQ8PDxwcnKyGs/NzY3+/fvz3Xff0bFjRypXrmxSxfKocLCH1nWvtbrY2mTR9gW1wuRFHQo5UOS6IFY3vx2+TmrdlNyhFjMREREgf/78zJkzh99++42NGzcSExPDvHnzmD9//k3jHjhwADs7O7Zv347FYsHWVtc55f60ej4Tn/IW4o/bULWshRKFza5IclLGwZaY0s78cDGDwnY2PJ9foUxyj35JREREgNjYWCZPnoy/vz99+/Zl1apVFC1alE2bNlmNt2/fPiIjI5k4cSJ//PEHkZGRJlUsjxrPklk08lEoy+sK2tnQzNWBus722ObQtVHk31IwExERAZycnJg8eTILFy4kKSmJDRs28Ndff1G48LWz5MzMTAYPHkzTpk156aWX6NOnD+PHj+fPP/80sXIREXkUKJiJiIgAlStXJjw8nLlz5xIYGEh4eDj9+/endu3axjhz584lMTGRvn37AtCqVSsqVqzI0KFDTapaREQeFTZZWfrjCyIiIrnt1Knzdx7pHri5OZOScjFX5/kgqd4HS/U+OP+lWkH1Pmi5XW+xYq63HKYWMxEREREREZMpmImIiIiIiJhMwUxERERERMRkCmYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIiIiIiJhMwUxERERERMRkCmYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIyH/akiVLqFev3l2NGxERQdu2bW85/MKFCyxZssR4ffr0aVauXHnfNYqIiNyJgpmIiPynBQUFsXTp0lyZ1+zZs4mOjjZejx49mg0bNuTKvEVERG7H3uwCRERE7oeTkxNOTk65Mq+srKzbvjZLeib8eToLJ1uwtTG7GhGRx8OJDAt2mZaHtjy1mImIyH9CbGws7dq1o1q1alSvXp2uXbty4sSJm7oyxsfH07VrV3x9fQkICCAiIgKL5doPa0ZGBsOHD8fPz4/atWszc+ZM4GqXyIkTJ7Jz5068vLyIiIjg22+/ZcWKFbz00ksAeHl5ERUVRcOGDfHx8eGDDz4gNTX1wa53gg1dJjjSZoSFtyc78MdJJTMRkQcp1ZJFl78u8cIfF/He/TczUtIeynIVzEREJM9LTU2le/fu1KlTh++++44vv/ySpKQkpkyZYjXemTNnaNeuHU8++STR0dEMHTqU+fPnM2vWLGOc3bt3A/Dtt9/SvXt3Ro0aRVxcHEFBQXTp0oWqVauyefNmunTpQmBgII0bN+abb74xpv/iiy8YOHAgX331FYcOHWLw4MEPbL0tWRCxwoF/LlwNY3+dsWXGGnV2ERF5kL76J51fLmUCcDkLxp5J42j6g285UzATEZE879KlS3Tv3p13332X0qVL4+fnR6NGjTh8+LDVeN999x358uUjNDQUT09PXn75ZXr16mW0igEUK1aMgQMHUqZMGTp16kTBggWJi4vDyckJZ2dn7O3tKVasGC4uLjg5OeHo6EiRIkWM6bt160b9+vXx9vZm0KBBrF69mpSUlAey3pfT4O9z1i1kiafUYiYi8iDFp1mHsCwgIe3BBzNddhMRkTyvWLFihISEMGfOHPbv38/hw4eJi4ujatWqVuPFx8fzzDPP4ODgYLzn4+PD2bNnOXPmDADu7u7Y2l67Lunq6sqVK1fuuhYfHx/j31WqVMFisXDkyBGr93OLcz54poyFfX9cq9e/wsO730FE5HH0oosd31/IMF4XsAF/J7sHvlwFMxERyfNOnDhBy5YtqVy5MgEBAbRu3ZoffviB3377zWq8fPny3TRt9v1l2f+/PpT9G3Z2136cc2uet9OvRTqz19lz7JQd3h4ZdKif+cCWJSIi0LSAA2czs/j2fAbF89nzlqsdBe0efG8FBTMREcnz1q5di4uLCzNmzDDei4yMvOmpiZ6ensTExJCenm60msXGxuLm5mbVHfFWbGxsbvsaYP/+/VSpUgWAvXv34uDgQPny5e95ne5WEVf4MCQDNzdHUlLuvmVPRET+vY6FHOlYyBE3N2dSUi4+lGXqHjMREcnz3NzcOHnyJD///DOJiYlMnz6dNWvWkJZm/aSsJk2aYLFY+Pjjj4mPj2f9+vVERETQpk2bu2rVcnZ25tSpUyQmJhqv//zzT06cOGGMM3HiRLZu3cquXbsICwujWbNmuLq65u4Ki4jIY0fBTERE8rzAwECaNWtG7969adGiBb/++isDBgzgyJEjXL582RjPxcWFmTNnkpiYSPPmzQkNDaVjx4706tXrrpbTqFEjbG1tadKkCadPn+aVV17hjz/+oFmzZkbrXEhICAMGDKBLly74+/vzySefPJB1FhGRx4tNVl7565kiIiJ5nJeXF7Nnz6ZOnTp3HPfUqfO5uuyH2Z0mN6jeB0v1Pjj/pVpB9T5ouV1vsWK37mGhFjMRERERERGTKZiJiIiIiIiYTE9lFBERuUtxcXFmlyAiIo8otZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiIiIiImIyBTMRERERERGTKZiJiIiIiIiYTMFMRERERETEZApmIiIiIiIiJlMwExERERERMZmCmYiIiIiIiMkUzEREREREREymYCYiIiIiImIyBTMRETHExMRw6tQpACIiImjbtq3JFf07SUlJeHl5cezYMQASExP54Ycf/tW8/svbQURE/jsUzEREBIDk5GR69erFxYsXzS7lvpUsWZLNmzdTqlQpAAYOHEhsbOy/mleXLl2YMmVKbpYnIiJyE3uzCxARkbwhKyvL7BJyjZ2dHcWKFcuVebm4uOTKfOTRl3jKhmVb7biSDv/jl8mzZR6d75TkjqR0C7P+SSclM4sQV3vqOutUXK5Ri5mIyGPk+PHj9OrVi5o1a1KrVi1CQ0O5cuUKAA0aNACgUaNGLFmyBICMjAyGDx+On58ftWvXZubMmca8srKymDx5MnXr1sXPz4+uXbty9OhRY7iXlxfjx4/nueeeo1OnTvdc6/Dhw3n33XeN13PmzKFSpUqkpKQAcObMGSpXrsyJEyfo0KEDoaGhNGzYkLp167Jnzx6jK+NHH33Etm3bmDp1Kh06dDC2wzvvvEP16tV58cUXGT16NGlpaTnWcX1XxiVLltC2bVsmTpzIc889R0BAAGFhYVgslnteP3m0nLsI/ec4sCbWjh/32jE40oH4v2zMLkvykMuWLNr/eYmvz6Wz8kIGbx6/zC8XM8wuS/IQBTMRkcdEWloar7/+OhcvXuSrr75iwoQJbNq0ic8++wyA6OhoABYtWkRQUBAAu3fvBuDbb7+le/fujBo1iri4OADmzZvHsmXLGDlyJFFRUXh4eNCpUycuXbpkLHP9+vV8/fXXDBo06J7rrVu3Ltu3bzdCz/bt2wGMLom//vorTz/9NMWLFweuhqbw8HAmT55M4cKFjfkMGjQIHx8fXn/9dSIiIsjKyuLdd9+lUKFCLF68mNGjR/PDDz8wduzYu6prz549xMfHM3/+fHr37s28efP46aef7nn95NGy/ZAtqZevBbFMiw2b/p9Os+SaXy9nciLzWitqFrA8VcFMrtERQ0TkMfHTTz9x/PhxRo0aRaVKlXjuuef4+OOPWbRoEefPn6dIkSIAFC5cGCcnJwCKFSvGwIEDKVOmDJ06daJgwYJGMJs5cyZ9+vShdu3aeHp6MmTIEOzs7Fi9erWxzFdffZXy5ctToUKFe663Zs2aXLp0ibi4OLKystixYwf16tVj586dAPz888/UrVvXGL9evXr4+/vj7e1tNR9XV1ccHBzInz8/bm5u/PrrryQlJTF8+HA8PT3x9/fn448/Zt68eWRk3PkkKSMjg9DQUDw9PWnVqhWVKlViz54997x+8mhxc7m526KbesHKdYra3dyCWsxerapyjTq2iog8JuLj4ylTpgxubm7Ge76+vmRmZnL06FGrVqZs7u7u2Npeu4bn6urKlStXuHDhAsePH6dPnz5Ww69cuWLVndHd3f2W9QQHB/Pnn38C8NRTT/H9999bDc+fPz9+fn5s3boVW1tbChQoQOPGjVm8eDEAv/zyC+Hh4Xe1rBu3w7lz5/D39zfey8rKIj09nT///JMyZcrcdvrChQvj6upqvC5QoMBdBTp5tPl4ZlGrYiZbD9oBUL6EhUY+mSZXJXlJlXx2tChgz5L/ayXzcLChY0EHk6uSvETBTETkMZEvX76b3svMvHrieKt7pK4PXTlNN3bsWJ5++mmrYdeHlpyWmW369OlGoLG3z/nnKCAggG3btmFvb4+/vz/+/v4MGzaMuLg4UlJS8PX1NcZ1dHS85bKul5GRgYeHB9OmTbtpWIkSJe44vYPDzSdSj9KDU+TfsbWBQa9mEP9XJlfSoVLpLGzVGCI3GPGkE53cMjmbmYWfkx32NvqQyDXqyigi8pjw9PTkjz/+MB6eAfD7779jZ2dHmTJlsLmHE4SCBQvyxBNPcOrUKTw8PPDw8KBUqVKMHTvW6Op4J+7u7sa0t2rtCggIYPv27Wzbtg1/f388PDwoWLAg06ZNo3bt2ncdxq5Xrlw5jh8/jpubm7H8U6dOMWbMGAUsuW+eJbN4poxCmdxaRUc7auW3VyiTmyiYiYg8JurUqUPZsmXp168fBw4cYOvWrQwfPpygoCAKFy6Ms7MzAAcOHODChQt3nF+nTp2YMGEC69at49ixYwwbNoxffvmF8uXL51rNlSpVIl++fGzYsAE/Pz8A/P39WblypdX9ZXfi4uLCH3/8wenTpwkICKB06UZS2WsAACAASURBVNL06dOHAwcOEBsby+DBg7G1tb1tC5+IiMiDpGAmIvKYsLW1ZdKkSdjY2PDqq6/Su3dv6tevT1hYGHD13qkWLVrw4Ycf8s0339xxfl27dqVNmzYMGzaMZs2acfDgQb788kvjKYm5JSAggIIFCxqBr0aNGmRlZd1TMHv11Vf5+eefeeONN7Czs2Py5MnY2dnRpk0b3nrrLfz9/Rk+fHiu1i0iInIvbLLUb0NERCTXnTp1Plfn5+bmTErKxVyd54Okeh8s1fvg/JdqBdX7oOV2vcWKud5ymFrMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIiIiIiJhMwUxERERERMRkCmYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETKZgJiIiIiIiYjIFMxEREREREZMpmImIiIiIiJhMwUxERERERMRkCmYiIiIiIiImUzATERERERExmYKZiIiIiIiIyRTMRERERERETPZQg1lSUhJeXl4cO3bsgcy/Xr16LFmy5J6nu9e6fv31Vw4ePGi8/vbbb6lVqxZ+fn6cP3/+npf/oE2YMIH58+ebXQYfffQRffr0MbsMUy1ZsoR69eqZXcZ/kpeXF7/88ovZZeSah7k+D/rYez+io6N56aWX7nm6+/kudejQgXHjxgH3d1xKS0ujefPmnD59+l9NLyIicj21mP0Lr7/+On///bfxOiwsjHbt2rFs2TJcXV1NrOxmx44dY+XKlbRu3drsUgQICgpi6dKlZpch8p93P9+liIgI3nzzzfuuwdHRkddee41Ro0bd97xEREQUzHLB+fPn8ff3p1SpUmaXcpOZM2fyyiuv4ODgYHYpAjg5OVGkSBGzyxD5z7uf75KbmxsuLi65UkezZs3YsGEDycnJuTI/yX3pmbA/0YbTea9Di8gDd8GSxY5LmfyTmWV2KXIXHkgwS0xMpHv37vj4+FCvXj2mTp2a43j//PMPQ4YMoU6dOvj6+vLhhx+SkpICwNatW/Hy8iIjI8MY/8YuJwsXLuSFF17Az8+PadOm3baml156iTlz5vDKK69QvXp1unXrxokTJ3IcNz4+nm7duuHj44O3tzdt27bl0KFDxnwAOnfuzEcffYSXlxcAXbp04aOPPgIgNjaWtm3bUr16dV566SWrboQfffQR/fv3p3nz5tSqVYu4uDi8vLz4/vvvCQwMpFq1anz44YckJibSoUMHqlWrxmuvvcbJkycBSE1NpXfv3tSsWRNfX1969OjBqVOnclyP1NRUVqxYwcsvv2y8d/nyZQYNGoSfnx9169YlOjqaZ555hqSkpDvuk+xt07VrV3x9fQkICCAiIgKLxWIMX758OS+//LKxHh988AERERE51rdu3TqCg4OpVq0aISEhbNq06Zb77+TJk7z33nvUqFGDKlWq0Lx5c7Zv3w5c66a1fPly6tWrh7+/P6GhoaSnpwNXuzy1bt2acePG4evrywsvvMDChQtvuaysrCwmT55M3bp18fPzo2vXrhw9etQY7uXlxdKlS2natCk+Pj506NCBP/74wxi+d+9eWrduTdWqVWnTpg0TJkygQ4cORi3Z3a+2bt1KvXr1WLRoEfXq1aNWrVr07duXy5cv/6ttdKNbfY4tFgs1a9Zk3bp1xrivvPIKb7/9tvF61qxZdO7cGYD58+fToEEDvL29adq0KRs3bryr5Wdv9/feew8/Pz+io6NJTU1l0KBB1K5dmypVqtC4cWNWr15tTHP27Fk+/PBD/Pz8qF27NmFhYVbHgGw//vgj3t7exjrExMQQFBSEt7c3jRo1YvHixca4Xl5eREVF0bBhQ3x8fPjggw9ITU01hm/cuJGQkBC8vb3x8/Ojd+/eVsO///57Yx+0atWK2NhYY9j97B+AnTt30qxZM7y9vWnXrh2JiYnGsEOHDtGxY0eqVq1Kw4YNmTVrFllZV39YIyIieOutt+jQoQM1atRg06ZNXLlyhdGjR/PCCy9QvXp13nrrrVuGhUWLFuHj48Pvv/+eYzfHiIgI2rZte8u6p0+fToMGDahSpQoBAQFMmDDBGNahQwcmTZpE165dqVatGk2bNuXHH380hp84cYJu3bpRvXp1WrRoYRx7cpKens4nn3xC7dq1qVatGp07dyYhIQHI+bu0ePFinn/+eWrUqMGsWbPYunUr//M//4OPjw8DBgwwjlXXd2W813ULDQ2lYcOG1K1blzNnzuDo6EidOnVYtGjRLddDzJP0tw3dJzrSf44jXSc48u0WO7NLEnlotl7K4MU/LvDaX5d44Y8LrE69+fdU8pZcD2ZpaWl07doVe3t7Fi1aRFhYGDNnzmT58uU3jdujRw/279/P1KlTmTNnDkeOHKFfv353tZyffvqJsLAw3n//fRYuXMjvv/9+y6CVLSIigs6dOxMVFcWVK1fo2bPnTeNkZWXxzjvv8NRTT7Fs2TIWLlyIxWJh5MiRAHzzzTcAjB8/nkGDBrF582ar1/Hx8bz++uvUqFGDb7/9lp49ezJq1ChWrVplLGP58uW8++67zJgxgwoVKgDwxRdfEB4eztSpU4mJiaFt27a89tprfP311yQnJzNr1ixjOcnJyURGRhIVFcXp06cJDw/PcX23b99OgQIFqFixovHe8OHD+e2335g5cybjxo1j5syZZGZmGsNvt0/OnDlDu3btePLJJ4mOjmbo0KHMnz/fqG3Hjh0MHDiQLl26sGTJEvLnz8/KlStzrO3AgQP07duXN954gxUrVtC6dWtj2Tnp168fGRkZLFy4kKVLl1KiRAk++eQTq3EmTZrE2LFjmTRpEuvWrWP8+PHGsH379rF3714WLlzIe++9x/Dhw61OFq83b948li1bxsiRI4mKisLDw4NOnTpx6dIlY5yJEycycOBAvvrqK/7++2/Gjh0LXG097datG5UrV+bbb7+lSZMmTJ8+PcflAJw+fZqVK1cyffp0wsLCWLNmjXGf5L1uo+vd7nNsa2tL7dq12bp1K3A1jB86dMgqcPzyyy/Uq1ePffv2ER4ezoABA4zw07t3b86dO3fHGgB27dqFh4cH0dHR1K9fn/DwcOLj45k1axbfffcdNWrUYMiQIaSlpQFXP3/JycnMnTvX2I8zZsywmufu3bvp3bs3oaGhvPzyy5w+fZo+ffrQqVMnYmJi6N69O4MHDyY+Pt6Y5osvvjD216FDhxg8eDBw9SJSz549adOmDatWrWLChAn8+uuvLFiwAIAtW7bQr18/2rVrx/Lly6lVqxbdu3cnNTX1vvZPtqioKAYMGMA333zD+fPnjS5xly9fNsLL8uXLGTx4MHPnzmXevHnGtBs3bqRx48ZERkbi6+vLJ598wpo1a/j8889ZtGgRGRkZvP3221bfb4D169cTHh7OpEmTqF69+l3Xmm3ZsmXMmjWL4cOHExMTw7vvvsvkyZPZvXu3Mc706dMJDg5myZIllCtXjkGDBhl19OrVC4vFQnR0NN26deOrr7665bLmz5/Pzz//zLRp01i+fDkuLi4MGDAgx3FPnz7N6tWr+eqrr3jjjTcYPXo0n3/+OZ9//jkjR45k+fLl/PDDD/e9bkuWLCE8PJzJkycbLXbPP//8PYdyeTgWbLLj73M2AFiybIjcaMe5iyYXJfKQfH46jfP/d+38chaEnb5iXOCTvMk+t2f4yy+/cPLkSRYvXoyrqysVK1bk448/xtnZ2Wq8AwcOsG3bNlauXImnpycAo0ePJjAw0Gidup3o6GiCg4Np3rw5cPU+rxdeeOG204SEhBjjjxgxgpdffpn9+/db3Rd26dIlWrVqRbt27YyuLiEhIUaLXPYPcaFChXB1dTWmzX49ceJEvLy8+OCDDwAoV64c8fHxzJw5k8DAQAAqV65Mw4YNrWrr2LGjcZLk5eVFhQoVaNy4MQANGjQwrhInJyfj7OxMqVKlcHFxYeTIkbc8Sd67dy/ly5c3Xl+4cIGlS5cydepUfHx8ABg8eDDdunUD7rxPtmzZQr58+QgNDcXBwQFPT09OnTrFhAkT6NatGwsWLKBx48a0a9cOgKFDhxrB9UZffvklLVu2NPZHmTJl2L17N5GRkYwYMeKm8evXr0+jRo0oWbIkAO3bt6dbt25WB5g+ffrg7+8PXD35GzlyJB9++KEx/PPPP6do0aJUrFiRbdu2sWjRohw/MzNnzmTw4MHUrl0bgCFDhvDjjz+yevVqo97XX3/dGN62bVvmzp0LwMqVK3FycmLIkCHY29vj6enJzp07b9mqmZGRwcCBA/Hy8qJSpUrUrVuXPXv2/KttdL07fY4DAgKMk/wdO3bg5+fHnj17SEhIoFSpUuzYsYOPPvqII0eOAODu7o67uzvdu3fH29v7nrrGdu/enQIFCgDg5+dHx44drVqao6OjOXHiBJcvX2bHjh2sWbMGDw8P4Opn6Pptd/ToUSIiIujduzchISHA1RaY9PR0ihcvjru7Oy1btuSpp56iaNGixnTdunWjfv36AAwaNIjOnTuTkpJCZmYmgwYN4tVXXwWgVKlS1KlTh8OHDwOwYMECAgMDad++PQAffvghWVlZnDt37r72z/XbJvtz1KpVK6N1fcWKFRQqVMg4jpQtW5bevXszadIko/XVzc2N1157DbgarpctW8bUqVN57rnngKvf3RdffJGffvqJp59+Grjamj9s2DA+//xz6tSpc1c13qh48eKEh4dbff4nTZrEoUOHqFq1KnD1QUwtWrQA4J133uGVV17hxIkTXLhwgdjYWNavX0+pUqWoUKECe/bssWo1vV5SUhJOTk6UKlWKIkWKMHToUKvW6+tlZGTQr18/PD09KV68OGPGjKF9+/ZUq1YNAE9PTxISEm77oJG7Xbfs40w2T09P4uLiSEtLw9HR8S63pDwM2aEsW0amDSkXbCjorJNTefT9lWGxev13ZhbpgI5SeVeuB7PDhw9TpkwZq7DTrFkzAKsuKwkJCbi4uBgBAKB8+fIUKlSI+Ph4ChcufNvlxMfH87//+7/G6yJFiuDu7n7baXx9fY1/ly5dGjc3N+Lj462uGjs7OxsP8ti7dy8JCQns27cPNze3O6z5tbqyTwSy+fj4WHVnzOletNKlSxv/zpcvH0899ZTx2snJyWhRePPNN3nzzTepXbs2tWrVomHDhsaJ4Y1Onz5ttR0TEhJIT0/H29vbqrbrh99un8THx/PMM89YnZT7+Phw9uxZzpw5Q1xcHK1atTKG2dvbU6VKlRxri4+P5+DBg1ZdztLT042Tnxu1bduWlStXsnPnTo4cOcLevXsBrFoDrl+XKlWqkJKSYjykpXTp0lYn6lWqVLFqfch24cIFjh8/Tp8+fbC1vdagfOXKFasTwjJlyhj/LlCggNHdLi4ujsqVK2Nvf+2rVb16ddauXZvjet1uXve6ja53p89x3bp1+fjjj0lJSWH79u3UqlWLrKwsfvvtN06ePEnhwoV5+umncXd3x8/Pj+bNm1OxYkVeeuklWrVqRf78+e9YA1wND9mhDKB58+asW7eO6OhoEhIS+H//7/8BYLFYOHz4MAUKFDBCGXBTcA4PDyc9Pd3q+1G5cmWaNGnCm2++SZkyZahfvz4tWrSgUKFCxjg3fjYsFgtHjhzBx8cHR0dHpkyZwqFDhzh06BCHDx8mODjY2AfXH2dsbW2NFuT72T/Zrt/3rq6uXLlyBbj6XTx8+LBV3RaLhbS0NONYcP3x7ujRo1gsFqtjj5ubm3FhKDuYDRkyhMzMTKvtd6+ee+45du3axZgxY4iPj2f//v2cOnXKqkvz9cez7P2fkZFh7OPrj4FVqlS5ZTB77bXXWLduHXXr1sXX15cGDRrQsmXLW9aWvVwnJyeAWx5H72fdcvqdcXNzw2KxcPbsWYoXL37bZcjDVe/ZTPYnXjuWlytuoXRRhTJ5PAQXcGDeuXTjdUMXOxxtbG4zhZgt14PZ3V5Jv9VVxczMTCwWCzY5fHBuvNfkxubYOy3bzs66b3lmZqbVyTdcPTFv1aoVhQoV4uWXX6ZJkyYkJCTctjva9fLly3fTexaLxSpA5LTu15/IAzfVlc3Hx4eNGzeyYcMGfvzxRz777DNWrFhBZGTkTePa2NhYbaPsZVz/3vX/vtM+udW6Zf/fzs7upn1yqybzzMxMunbtalxVv10NFouFLl268M8//xAUFMRLL71Eeno6PXr0sBrv+v2bXVf2drxx++a077PfBxg7dqxxMpvt+osNN37WstfzXrbBneZ1L9voRnf6HJcoUYLy5cuzY8cOtm3bRr9+/UhPT2fnzp0kJiZSt25dAPLnz8+cOXP47bff2LhxIzExMcybN4/58+dTqVKlO9Zx42emX79+7Ny5k1deeYW2bdtSrFgxo7Xqbo4dLVu2xM3NjREjRhAQEED+/PmxsbFhzJgxdOnShQ0bNrBhwwa+/vprpk6dSkBAAHDrz8aBAwdo27Yt9evXx8/Pj06dOhmtn3eq6X72T7Ybj0nZ+z4jI4OaNWsybNiwm6bJ/ixfv21z+m5m13j9sadnz54cOnSIYcOGERUVha2t7V0da68XHR3NiBEjaNWqFY0aNaJ///507NjRapyctlv2ut34fbjxu3m9smXLsnr1ajZt2sQPP/zA1KlTiYqKuuWfRblxe97qOHord7NutzpG5bR8MV9wDQsO9un8st+OkkWy+N/nM9B5qTwu+j/hSDE7G7ZdzqRKPlvedFNbWV6X6/eYlS1blsTERKub57/44gvjwRjZypcvz4ULF6zuAzl8+DCpqamUK1fO+GG/fj7Xt7hld4HJlpqaanXjfE6uv/fj2LFjnD9/3uhSlW3btm0cP36cyMhIunXrRp06dfjzzz/vuk+up6cnu3btsnovNjaWcuXK3dX0dzJnzhx27dpFs2bNGDNmDNOnT2fbtm1Wj+/PVrRoUc6ePWu8LlOmDA4ODkYrBWC0PMGd94mnpyf79u0zHqqRvW5ubm4UKVKEp59+2mp+mZmZt7zfply5ciQmJuLh4WH8t2zZshxblg4fPsz27dv58ssvefvtt3nxxReNh6Fcv18OHDhgtV5FixbliSeeALjpM7l3796b9j1AwYIFeeKJJzh16pRRV6lSpRg7dixxcXE5rsv1KlSoQFxcnNXJ8PXb+17cyza60d18jgMCAli/fj2HDx+mWrVq+Pv7s3PnTjZv3mw8VCE2NpbJkyfj7+9P3759WbVqFUWLFv1X99Okpqby3XffMWbMGHr16kXDhg35559/gKv7sWzZsqSmplo9SCU6OtrqxLhRo0Z0794di8XC5MmTgastV+Hh4Tz77LP07NmTb7/9Fn9/f6vtdP3ncO/evTg4OFC+fHmWLVuGr68vY8eOpX379lStWpVjx44Z28nDw4N9+/YZ02ZlZREUFMSmTZvua//cSbly5Th69Cju7u7GvPfv38+MGTNyDBtlypTB3t7e6thz9uxZjh07ZtWduXHjxvTp04f4+HiioqIA7nisvdGCBQt46623GDRoEM2bN6dw4cKcPn36ro6RFStW5MKFC0bXbMBq+95o6dKlrFu3joYNGxIWFsbSpUuJj4+/q+/iv/Fv1y0lJQVbW9s79vQQczTysTC0XTrd/yeDInnrL9qIPFAONjZ0L+zIlyXz836RfLjY6qpEXpfrwSwgIIASJUoYN9//+OOPREZG3vSHQMuXL0/9+vXp378/u3fvZvfu3fTv3x8/Pz8qV65MhQoVcHJyYuLEiSQmJjJ79myrH/D27duzZs0aFi5cSHx8PIMHDza6Ad3KvHnzWLduHQcOHGDgwIE899xzVt324GqXlEuXLrF27VqSkpKIjo5m/vz5Vl1gnJ2dOXToUI5/TLpdu3YcPHiQsWPHcuTIEZYuXcrXX39t3Atyv44fP86nn35qtGysWLGCp556KscTgmeffdbqD2G7uLjQokULwsPD+f333/n9998JCwsDrrau3WmfNGnSBIvFwscff0x8fDzr168nIiKCNm3aYGtry2uvvcbq1auJioriyJEjhIeHk5ycnOMV+ewHNcyZM4djx46xYMECpk6datW1K1vBggWxtbVl5cqVJCcnExMTYzzp8fr9MmLECPbs2cOWLVv44osvaNeunbHsS5cuGXVHRUURExNj3DeUU20TJkxg3bp1HDt2jGHDhvHLL79YneDeSnBwMBcvXmTEiBEkJCQQHR19yweg3MmdttHly5dvee/a3XyOAwICWLFiBZUrVyZ//vz4+vqSlJTEwYMHjfuUnJycmDx5MgsXLiQpKYkNGzbw119/GV1UU1JS7vqPqufLl4/8+fOzZs0akpKS2Lx5M6GhocDV/fj0009Tp04dBg0axIEDB9ixYwdTpkwxWr2y5c+fn379+jF79mzi4+MpWLAgixYtIiIigsTERH799Vfi4uKsutFOnDiRrVu3smvXLsLCwmjWrBmurq64ublx8OBBdu3axdGjR/nss8/Ys2ePcfGhY8eOrFy5kujoaI4dO8bo0aP5559/8PHxua/9cyfNmjUjLS3NOI7+/PPPhIaGWnXPvJ6zszNt2rQhLCzMWP9+/fpRvHhxo/UzW/HixXn77bcZN24cZ86coWjRopQsWZJp06aRmJjI0qVLb/uQjMKFC7NlyxYSEhLYu3cv77//Punp6XfsJghXL1w999xzDBw4kAMHDrBu3TrjQSs5OX/+PGFhYWzevJmkpCQWL16Ms7MzZcuWveOy/o1/u24HDhygcuXKajETEZH7kuvBzM7OjsmTJ/PPP/8QEhLC0KFDeffddwkKCrpp3M8++8x44l3Xrl2pUKECU6ZMAa7el/Dpp5+yfv16mjRpwt69e62unNeoUYPw8HBmzJhBq1atKF68uNXTB3MSEhLChAkTjC5U1z8GOZuPjw89evTg008/pVmzZixevJhPPvmElJQU/vzzT+DqCfOYMWOYOHHiTdOXKFGCadOmsXnzZpo2bcrkyZPp37+/1X0q96NXr174+/vz7rvvEhwcTEJCAlOnTs3xhKBWrVpcunTJ6mEq/fv3p1KlSnTu3JmePXvStGlT4NpV89vtExcXF2bOnEliYiLNmzcnNDSUjh070qtXL2PbffLJJ0yePJnmzZtz7tw5fH19c+zWVL16dUaPHk1UVBTBwcHMmTOHESNG8OKLL940bokSJRg6dCizZ88mODiYadOmMXjwYBwcHKxaQoKDg3nrrbd4//33admypdXj35988knc3d1p1aoVM2fOZOTIkdSoUSPHbdy1a1fatGnDsGHDaNasGQcPHuTLL7+8q3tHXFxcmDp1Kr/99hvNmjVjyZIlNG3a9F89EOBO22jlypU3hZZsd/M5rlmzJnZ2dvj5+Rm1V6pUCR8fH+O+oMqVKxMeHs7cuXMJDAwkPDyc/v37Gw+O6NmzpxHu78TBwYFRo0axbt06goKCGDFiBG+99RbFixc3LrqMHDkSNzc32rRpQ69evQgODqZr1643zSv7EfWhoaEUK1aMiIgI1q9fT3BwMH379qVt27ZW9zuGhIQwYMAAunTpgr+/v/FEzw4dOuDr60vnzp1p06YNycnJVk9W9PPz49NPP2XatGk0bdqU3377jWnTpuHq6npf++dOChQowMyZM0lOTiYkJIT+/fsTEhLC+++/f8tp+vbtS0BAAL169aJNmzY4Ojoyd+7cHLs5durUiUKFCjFq1ChsbW0JCwvj4MGDBAUF8d133/HOO+/ccjkDBw7k8uXLhISE0KNHDypWrEjjxo1v2/J1vfHjx1O0aFHatGnDuHHjjIeZ5KR9+/a0aNGCAQMGEBgYyPr165k2bdotA+r9+rfr9v/bu//4GuvGj+Ovs5/YVrPaTYhmMUnZlIxNGOVXikq2MUNu8S3fud0iv1kov3MPN7WQGW00drsTom4/vmVF8+POrxzb0O1XbN0Ym+1c3z88nDqNiTbX2Pv5ePSoc67r+lzv65y1nfe5Puc6O3bsuObvLhERkZthMcrJdTPDwsIYMGBAiRWkO8XIkSPx9fVl0KBBwJXvXWratKn9Sn27d+8mMjKS9PT0P/wl1Lt378bT09PhzNLVF9a//RxOSTp27BitW7d2uJrfr6WkpPDee+/dlstZHz16lJMnTzpctW38+PFcvHiRd999t8T316dPH/vXFZjh6NGjfPDBB/YzX2VRQEAACxcuvOWrEP4RZj8/UvouXLhAixYtSE1NLXJhkNOnS/Ybjb29K5GTc+dc6115S5fylp47KSsob2kr6by+vtefU10qXzAtZcerr77KP/7xD/tUnNmzZzNx4kSysrLYu3cvU6dOJSws7A+XMrjyeaR+/frZp1nOmzeP48ePF5lKdTc7f/68fYrbjz/+yPr160lNTaVdu3Ylvq/t27dTtWrVEh/3Znz44Yelcmx3g7Lw/EjpS01NJSws7IZXBRYREbmREr8qo5QttWvXplOnTnz88cf07NmTadOm8fbbb9O5c2fc3NwICwtjxIgRJbKv7t27c+zYMQYOHMi5c+d45JFH+OCDD/D19S2R8e8EjzzyCGPHjmXGjBkcP36catWqMXz48FKZ5hQYGGifhmiWkSNHlkipvxuVhedHSld+fj7Lli3TWVERESkR5WYqo4iIyO2kqYzKW5qUt/TcSVlBeUubpjKKiIiIiIiUIypmIiIiIiIiJlMxExERERERMZmKmYiIiIiIiMlUzEREREREREymYiYiIiIiImIyFTMRERERERGTqZiJiIiIiIiYTMVMRERERETEZCpmIiIiIiIiJlMxExERERERMZmKmYiIiIiIiMlUzEREREREREymYiYiIiIiImIyFTMRERERERGTqZiJiNxhjh07RkBAAFlZWaW2D8MwWLZsGTabrdT2cTPOnDnDmjVrfte6aWlpBAQEUFBQUCpZAgIC+Oqrr0plbBERKb9UzERE7jAPPPAAW7dupUaNGqW2j2+//ZZx48aVmWI2xWiuUwAAIABJREFUbdo0vvjiC7NjiIiIlBoXswOIiMjNcXZ2xtfXt1T3YRhGqY5/s8paHrm2gkJYvtWZ7Yec8K9m45UQuP8es1OJSFmQbxjMzc7n6xOXqOMMMZXd8HXROaJf06MhInKH+fVUxt9Oq0tJSeHpp58GrkzpCwkJITExkSZNmtC0aVNmz54NQH5+Pk8++aTD9ECbzUbz5s1ZuHAhPXv2BODRRx8lLS0NgKSkJFq3bk1QUBARERHs3r3bvm1YWBhTpkwhNDSUDh06FJlGmJKSQkREBLNnzyY4OJjQ0FAmTpzocEYuJSWFDh068Pjjj/Piiy/a9xsXF8fKlStZvXo1YWFhAJw7d45hw4bxxBNPEBISwujRozl//rzDPhMTEwkODqZJkyb8/e9/d1j25Zdf0qVLFx5//HHat2/PZ5995vA4xMfH06ZNGx5//HF69OjB/v37r/lcbNq0iccee4wNGzZc9/kqTz7e7MyyzS788B8n1m43mJjkanYkESkjpp/NZ17OZXblFrDiXAGDTl0yO1KZo2ImInIXy8nJ4ZNPPmHBggW8/fbbLFy4kKVLl+Lm5sYzzzzD2rVr7evu2LGD3NxcunXrRlxcHACbN28mKCiIL774glmzZjF8+HBWrlzJ008/TXR0NKdOnbJv/49//IP4+HimT5+Oi0vRCRl79uzBarWSmJjIoEGDWLJkCVu2bAGulLLY2Fj69etHamoqISEh9OvXj//85z/06dOH9u3b07ZtW1asWAHAiBEjyM7OJjExkfnz55ORkcHw4cMd9vfPf/6TBQsWMGnSJBYsWMDy5csB+Prrrxk4cCAvvPACqampdOvWjSFDhtiL5pw5c1iwYIH9WGvUqEHfvn2LFL/du3czaNAgYmNjadOmzR99qu4K2w44vqywnnDi9M8mhRGRMmXjBcc37HZcspFdqNkQv6ZiJiJyFysoKGDChAk8+uijtGnThujoaJKSkgDo1KkTmzZtIjc3F4A1a9bwzDPPUKlSJe69914A7rvvPtzc3IiPj6dfv360adOGhx56iAEDBtCgQQN72bk6Xr169XjkkUeumyU2NhZ/f39efvll6tWrx549ewBISEige/fudO7cGT8/P/76179Sr149EhIS8PDwoEKFCri5ueHj48ORI0f4/PPPmTJlCvXq1aNBgwZMnjyZ9evXc/z4cfv+JkyYQP369WndujXR0dEsW7YMuHImrU2bNvTq1Qs/Pz969erFs88+S3x8PIZhsGTJEt544w1at26Nv78/b7/9Ni4uLqSmptrHzszM5LXXXmPQoEF06dKlBJ+xO1s1H8cXWR4VDO6pZFIYESlTark61o77nC14qok40MMhInIXc3d3p379+vbbDRo0wGq1AhAcHIyXlxf/+te/KCwsZP369XTs2PGa41itVmbMmEFQUJD9n++++47MzEz7OtWrVy82S+XKlfHy8rLf9vT0tE95tFqtNGzY0GH9wMBADh8+fM0shmHQqlUre5bnnnsOwJ7Hzc2NgIAA+zb169cnIyPjuvsKCgri8OHDnDlzhpycHIflrq6uDo8bwDvvvEN2djbVqlUr9pjLm55hhVStfKWcVXSH/u0LcNdsRhEBht3nRjUXCwCeFhh7vzuuFovJqcoWXfxDROQuUlhY6HDb2dnZ4bbNZsPJ6cp7ck5OTrRv3561a9fi4+ODzWajadOm1x132LBhhIaGOtxfqdIvp0Pc3d2LzebqWvQV+tWLelSoUOGa+/zt8Vy9v1KlSqxatarIMl9fX/bs2WM/xqtsNpt9euW1ctpsNgoLC697DL/N8tJLL+Ht7c2kSZMIDQ2lYsWK19yuvKlxv8G81/M59pOFhx+sQP6lsnFVTxExX103Zz5/sBKnK7hzz8U8KjmplP2WzpiJiNzBXF1dHT77dPToUYflubm5HDlyxH57z549DmeSnnvuObZu3cqGDRto166dvbxYfvMupp+fHydOnKBWrVr2fxYsWMA333xTIsdRu3Ztdu3a5XDfrl278PPzK5LHz8+P3NxcCgsL7Vngylmsq4/FpUuXihy3v78/AP7+/kX2lZ6ejp+fH15eXvj6+josv3z5Mt9//709C8Czzz7La6+9hs1mY+7cuSXxENw1nCxQ09egUgW96BIRR84WC/UquqiUXYeKmYjIHeyxxx5j4cKFZGZm8uWXX5KSklJknVGjRnHw4EHWrVtn/yzXVQ0bNuS+++4jOTnZYRrj1TNhe/fuJS8vj969e5OQkMDKlSs5cuQIs2fP5pNPPqF27dolchy9e/dm6dKlrFq1ioyMDKZPn86+fft45ZVX7Hn+85//cPLkSfz9/WnevDlDhw5l165d7N+/n2HDhnHmzBn+9Kc/AVfOBr711lvs3buXtWvXsnjxYnr37g1Ar169+Pzzz1m0aBGZmZksWrSIzz//3P649OnTh9mzZ7Nx40asVitjxowhLy/PPl3yqooVKzJ06FAWLlzoMM1RRETkVmgqo4jIHWz06NGMGjWK5557jgYNGhATE2O/ouJVLVu2pHv37lSsWJG//OUvdO7c2WF5hw4dSE1N5YknnrDfV7duXUJDQ4mMjGTmzJl06NCBM2fOMHv2bE6dOkXt2rWZM2fOdS/0cbPatm3L6dOn+dvf/sbp06d55JFHWLhwIXXq1AHghRdeYN26dTz//PNs27aNKVOmMHHiRPr06YPFYqFZs2aMHj3aPt4999xDWFgY0dHRuLq6MnDgQNq2bQtcKbPTpk3jb3/7G9OmTcPPz4/33nuPkJAQ4EpxO3/+PGPHjuXcuXMEBgaSkJDA/fffXyR3x44dWbp0KbGxsXz00Ucl8liIiEj5ZDH0rZ0iIneUrKwsnn32Wb788stiLz6RlpZGz549+f777695+fqrhg8fjo+PD2+++WZpxC23Tp8+V6LjeXtXIicnt0THLE3KW7qUt/TcSVlBeUtbSef19fW67jKdMRMRuYOcPHmSLVu24Orqio+Pzx8aa/fu3Xz//fesXbuWTz75pIQSioiIyK1QMRMRuYMsWrSI5ORkXnvttWteyfBmbNmyhfj4eF5//fUS+6yYiIiI3BpNZRQRESkFmsqovKVJeUvPnZQVlLe03c6pjLoqo4iIiIiIiMlUzEREREREREymYiYiIiIiImIyFTMRERERERGTqZiJiIiIiIiYTMVMRERERETEZCpmIiIiIiIiJlMxExERERERMZmKmYiIiIiIiMlUzEREREREREymYiYiIiIiImIyFTMRERERERGTqZiJiIiIiIiYTMVMRERERETEZCpmIiLlyLFjxwgICCArK8vsKEXMnDmTqKio27Kv5cuXExYWBkBaWhoBAQEUFBQAsG/fPrZv335bcoiIiFylYiYiIuVaUFAQW7duxcXFBYDXX3+djIwMk1OJiEh5o2ImIiLlmpubG76+vmbHkFt05LSFTf924sw5s5OI3HlybQbrLxTwzcVCDMMwO065p2ImIlKOJSUlERQUxM6dOwE4ceIEMTExPPXUUzRp0oTY2Fjy8vIASElJISIignnz5tG4cWNCQkJYvXo1a9asoWXLljRu3JgZM2bYx87Ly2PatGm0aNGCwMBA+vfvz48//mhffujQISIiImjYsCG9e/cmJyfHIVt6ejoREREEBgYSFhZGYmLidY/DMAzmzp1L8+bNeeKJJ3j11VfJzMy0Lz958iR9+/YlMDCQF198kWPHjtmX/XoqY1RUFD/++COjRo3irbfeAsBqtfLqq6/SqFEjQkNDiYuLw2azARAXF0f//v2JioqicePGbN68+RafCbkVq7Y588Y8N6avdOXPcW7sOGQxO5LIHeN4gY32R3P535OX6Hn8Iq+fvGR2pHJPxUxEpJzauHEj77zzDnPmzCEwMJD8/Hyio6PJzc1l8eLFzJo1i82bN/Puu+/at9mzZw+ZmZmsWLGC9u3bM2bMGBITE5k/fz6DBw9m/vz5HDhwAICxY8eyfv16Jk+eTFJSEgUFBQwYMIDCwkLy8/Pp168fNWrUICUlhTZt2rB8+XL7fqxWK9HR0TRu3JiVK1cycOBApk6dymeffXbNY1myZAmpqalMmTKF5ORkatWqRa9evbh48SIAMTEx2Gw2li9fTt++fVm8ePE1x4mLi6Nq1aq89dZbjBw5krNnzxIZGcmf/vQnli9fzrhx40hMTGTBggX2bb788kvatm1LQkICjRo1+sPPi/w++QWwbJOz/XZBoYWlm1xMTCRyZ1ny82VOFv5yluyL3EK+u1RoYiLRbzARkXIoPT2d8ePHM3nyZJo1awbAli1bOHHiBElJSXh7ewMwZswY+vfvz+DBgwGw2WyMGjUKT09PunbtSkJCAgMHDiQgIICAgACmT5/O4cOHqVq1KqmpqcybN4/g4GAApk2bRsuWLdmyZQsA2dnZjBs3Dg8PD/z9/UlLSyM7OxuA5ORkAgIC7Pv18/PDarUSHx9P+/btixxPfHw8o0aNomnTpgCMHj2aTZs2sW7dOh599FHS09PZuHEjNWrUoE6dOuzZs4d169YVGcfb2xtnZ2c8PT3x8vJi8eLFuLu7Exsbi6urK/7+/pw+fZpZs2bRt29f+zY9evQosedGfp+CQsi77HjfBb3hL/K7/ddWdOrite6T20dnzEREyqHRo0eTl5dHtWrV7PdZrVZq1qxpL2UAjRo1orCw0D4tsHLlynh6egJQoUIFAKpXr25f393dnfz8fDIzM7HZbDRs2NC+zNvb216wDh06xIMPPoiHh4d9eYMGDRyy/HpbuHKRjsOHDxc5lgsXLnDixAmGDBlCUFAQQUFBNGrUiOPHj5OZmcmhQ4fw9PSkRo0a19xXcaxWK/Xr18fV1dUhR3Z2NmfPni1y/HL7VHKH5o/aHO5r28h2nbVF5Lde9HJ1OEPzoIuFZhWdr7u+lD6dMRMRKYcGDhzIDz/8wPjx40lOTsbJyQl3d/ci6xUWXpnWcvUzVc7ORf9oWyxFP9dzrbGujldYWIiTk1ORD5pfvSri9ba32Wz2PNfKOGPGDB5++GGHZV5eXqSlpRW7r+JcL8ev/329Y5XS97/PFxBQ3UbGSScCa9uKFDURub6gCs4kVqtI6vkCvJ0g4h5X3K7x+1xuH50xExEph9q2bcuQIUOwWq0kJycD4O/vz5EjRxwuwrFz506cnZ2pWbPmTY1fs2ZNXFxc2LVrl/2+7OxssrKyqF27NnXq1OHIkSP8/PPP9uV79+61/7e/v7/DtnBl+qWfn1+Rfd1zzz3cd999nD59mlq1alGrVi1q1KjBjBkzOHDgAHXr1uXChQsOZ9t+va/i+Pv7s3fvXi5f/mXOXHp6Ot7e3vj4+PyuMaT0uDrDc0/ZGNipQKVM5BY0rODMmPvd+V8fd3xdVAvMpmdARKScqlKlCgMGDGDmzJmcPXuWZs2a8dBDDzF06FD2799PWloaEyZMoEOHDlSuXPmmxq5UqRLh4eFMnDiRbdu2ceDAAYYOHUqVKlVo3rw5zZo1o1q1aowYMYJDhw6xYsUKh898RUZGcvDgQWbMmEFGRgarVq1i6dKl1/0sV69evZg1axYbNmwgKyuL8ePH89VXX1G7dm38/f0JDg5mxIgR7N+/nw0bNrBs2bLrZvfw8ODw4cPk5OTw3HPPYbPZGDNmDFarlY0bNxIXF0d4eDhOTvoTKiIiJUd/VUREyrFevXpx7733MnXqVJycnJgzZw4Wi4Vu3boxaNAgWrVqxcSJE29p7DfffJPQ0FBiYmIIDw/Hzc2Njz76CHd3d1xdXXn//fc5f/48L774IsnJyURGRtq3rVq1KvPnz2fr1q106tSJuXPnMmzYMLp27XrNfb366quEh4czfvx4nn/+eQ4ePMiHH35IlSpVAHjvvfe4//77CQ8PZ+bMmURFRV03d/fu3fn4448ZPXo0Hh4exMfHc/ToUTp37kxsbCw9e/YkJibmlh4TERGR67EY+jY5ERGREnf6dMl+47G3dyVycnJLdMzSpLylS3lLz52UFZS3tJV0Xl9fr+su0xkzERERERERk6mYiYiIiIiImEzFTERERERExGQqZiIiIiIiIiZTMRMRERERETGZipmIiIiIiIjJVMxERERERERMpmImIiIiIiJiMhUzERERERERk6mYiYiIiIiImEzFTERERERExGQqZiIiIiIiIiZTMRMRERERETGZipmIiIiIiIjJVMxERERERERMpmImIiIiIiJiMhUzERERERERk6mYiYiIiIiImEzFTEREfrdjx44REBBAVlaW2VGKmDlzJlFRUbe0bVRUFDNnzrzmsoKCAgICAkhLS/sj8URERIrlYnYAERERs8XFxeHq6mp2DBERKcdUzEREpNzz9vY2O4KUUQd+tLDgcxdO5VgIrV9IdOtCXJzNTiXlwfL/XubDn/NxAv7s7UYXL715dLfTVEYREbllSUlJBAUFsXPnTgBOnDhBTEwMTz31FE2aNCE2Npa8vDwAUlJSiIiIYN68eTRu3JiQkBBWr17NmjVraNmyJY0bN2bGjBn2sfPy8pg2bRotWrQgMDCQ/v378+OPP9qXHzp0iIiICBo2bEjv3r3JyclxyJaenk5ERASBgYGEhYWRmJh43eP47VTG2bNn07RpU4KDg1m5cqXDuvn5+UycOJHg4GCaNGlCTEwMP/30060/iFJm5V2Gtz92Zd9RJ86cs5Ca5kLKV2plUvp2XCpk9E95ZF42OHzZYPjpPPbkFZodS0qZipmIiNySjRs38s477zBnzhwCAwPJz88nOjqa3NxcFi9ezKxZs9i8eTPvvvuufZs9e/aQmZnJihUraN++PWPGjCExMZH58+czePBg5s+fz4EDBwAYO3Ys69evZ/LkySQlJVFQUMCAAQMoLCwkPz+ffv36UaNGDVJSUmjTpg3Lly+378dqtRIdHU3jxo1ZuXIlAwcOZOrUqXz22Wc3PK6kpCQWL17MpEmTWLhwIZ988onD8hkzZrBz507mz59PQkIChmHw2muvYRhGCT2yUlZknrLw31yLw327MvXSSUrf1xeLlrBt17hP7i6ayigiIjctPT2d8ePHM3nyZJo1awbAli1bOHHiBElJSfapgWPGjKF///4MHjwYAJvNxqhRo/D09KRr164kJCQwcOBAAgICCAgIYPr06Rw+fJiqVauSmprKvHnzCA4OBmDatGm0bNmSLVu2AJCdnc24cePw8PDA39+ftLQ0srOzAUhOTiYgIMC+Xz8/P6xWK/Hx8bRv377YY0tOTiYqKopWrVoBEBsbS6dOnQC4ePEiS5YsITk5mfr16wMwZcoUmjRpwo4dO3jyySdL7DEW81W/z8Dd1SDv8i/lrHZVm4mJpLyo71b0DYBHrnGf3F30DIuIyE0bPXo0eXl5VKtWzX6f1WqlZs2aDp/XatSoEYWFhWRmZgJQuXJlPD09AahQoQIA1atXt6/v7u5Ofn4+mZmZ2Gw2GjZsaF/m7e1tL1iHDh3iwQcfxMPDw768QYMGDll+vS1AUFAQhw8fvuGxWa1W6tWrZ79dt25d3N3dATh69CiXL1+me/fuBAUFERQURNOmTcnLyyMjI+OGY8udxbMCDHq+AG8PAwsGTz5cSLfmOmshpa9VJWf63OuKmwUqWOA1b1dCK+l8yt1Oz7CIiNy0gQMH8sMPPzB+/HiSk5NxcnKyl5dfKyy88iLWZrtylsHZuejncywWS5H7rjXW1fEKCwtxcnIqMnXQxeWXP2nX2t5ms9nz3Mhvx76a++r2CQkJeHl5Oazj4+Pzu8aWO0tIfRvB9fLJL4CKbmankfLCYrEw9D53Yiq7YbGA2zV+T8rdR2fMRETkprVt25YhQ4ZgtVpJTk4GwN/fnyNHjjhchGPnzp04OztTs2bNmxq/Zs2auLi4sGvXLvt92dnZZGVlUbt2berUqcORI0f4+eef7cv37t1r/29/f3+HbeHK9Es/P78b7rtOnTrs2bPHfjsrK4vc3FwAHnzwQZydncnOzqZWrVrUqlULHx8f3nnnHYcLk8jdxdlJpUzM4e5kUSkrR1TMRETkllSpUoUBAwYwc+ZMzp49S7NmzXjooYcYOnQo+/fvJy0tjQkTJtChQwcqV658U2NXqlSJ8PBwJk6cyLZt2zhw4ABDhw6lSpUqNG/enGbNmlGtWjVGjBjBoUOHWLFiBevWrbNvHxkZycGDB5kxYwYZGRmsWrWKpUuX0qNHjxvuu3v37ixZsoTPPvuMgwcPMmrUKJycrvy5vPrZuLfffpuvv/4aq9XKsGHDOHjwIA899NBNHaOIiMivqZiJiMgt69WrF/feey9Tp07FycmJOXPmYLFY6NatG4MGDaJVq1ZMnDjxlsZ+8803CQ0NJSYmhvDwcNzc3Pjoo49wd3fH1dWV999/n/Pnz/Piiy+SnJxMZGSkfduqVasyf/58tm7dSqdOnZg7dy7Dhg2ja9euN9xv586diYmJYeLEiXTv3p2WLVs6fJZt+PDhhIaG8pe//IWXX36ZvLw8PvzwQ/tn5kRERG6FxdD1fUVERErc6dPnSnQ8b+9K5OTkluiYpUl5S5fylp47KSsob2kr6by+vl7XXaYzZiIiIiIiIiZTMRMRERERETGZipmIiIiIiIjJVMxERERERERMpmImIiIiIiJiMhUzERERERERk6mYiYiIiIiImEzfYyYiIiIiImIynTETERERERExmYqZiIiIiIiIyVTMRERERERETOZidgARERH5hc1mY9y4cRw4cAA3NzcmTJhArVq1HNY5e/Ys4eHhrF69Gnd3d5OSXnGjvIsWLeLTTz8FoEWLFrzxxhtmRb1h1sTERFJSUrBYLLz++uu0atXKtKzw+34WbDYb/fr1o3Xr1kRERJiU9JcsxeWdMGEC3333HR4eHgDMnTsXLy8vs+LeMO+mTZuYM2cOAPXr12fs2LFYLBaz4habd9++fUyaNMm+7s6dO5kzZw5PP/20WXFv+Ph++OGHfPrpp1gsFvr3788zzzxTZrO+//77fPrpp3h6etK3b9/S+91giIiISJmxbt06Y9iwYYZhGEZ6errRv39/h+WbN282XnjhBSMoKMi4dOmSGREdFJf3yJEjRpcuXYyCggKjsLDQ6Natm7Fv3z6zohab9cyZM0aHDh2M/Px849y5c8bTTz9t2Gw2s6IahnHjnwXDMIzp06cbL7/8srF06dLbHa+IG+UNDw83zpw5Y0a0ayou77lz54yOHTva877//vumZ/89Pw+GYRhr1qwxBg8efDujXVNxeX/++WejRYsWRl5enpGTk2O0bNnSrJiGYRSfdf/+/UanTp2MS5cuGZcuXTI6d+5s5ObmlkoOTWUUEREpQ3bs2EHz5s0BCAwM5N///rfDcicnJxYuXIi3t7cZ8YooLm/VqlWJj4/H2dkZJycnCgoKTD3DV1xWHx8fUlNTcXV15aeffuKee+4x9ewI3PhnYe3atVgsFlPPivxacXltNhtZWVmMGTOG8PBwVqxYYVZMu+LypqenU7duXSZPnkxkZCT3338/Pj4+ZkUFbvzzAJCbm0tcXBwjR4683fGKKC5vxYoVqVatGhcvXuTixYtl+v81q9XKU089hbu7O+7u7tSqVYsDBw6USg4VMxERkTLk/PnzeHp62m87OztTUFBgvx0SEkLlypXNiHZNxeV1dXXFx8cHwzCYPHky9evXx8/Pz6yoN3xsXVxcWLJkCd26daNt27ZmRHRQXN6DBw/yz3/+k5iYGLPiFVFc3tzcXHr06MHUqVOJj49n6dKl7N+/36yoQPF5s7OzSUtLY8iQIXzwwQd89NFHZGRkmBUVuPHPL8CKFSto166d6SUSbpz3gQceoGPHjnTp0oWePXuaEdGuuKwBAQFs376d8+fPk52dTXp6OhcvXiyVHCpmIiIiZYinpycXLlyw37bZbLi4lN2PhN8ob15eHkOGDOHChQuMHTvWjIh2v+ex7dGjB1u2bOHbb79l27Zttzuig+Lyrlq1ipMnTxIdHc3KlStZtGgRmzdvNisqUHzeihUr0rNnTypWrIinpyfBwcGmF7Pi8np7e/PYY4/h6+uLh4cHTz75JPv27TMrKvD7fn5Xr15N165db3e0ayou7+bNmzl16hQbN27kX//6Fxs2bGD37t1mRS02q7+/P927d+fPf/4zkydPpmHDhqX25piKmYiISBnSqFEj+wvsnTt3UrduXZMTFa+4vIZh8D//8z8EBAQQGxuLs7OzWTGB4rMePnyYN954A8MwcHV1xc3NDScnc18mFZd36NChLF++nISEBLp06UKvXr1Mn9JYXN7MzEwiIyMpLCzk8uXLfPfddzz66KNmRQWKz9ugQQMOHjzI2bNnKSgoYNeuXTz88MNmRQVu/Lvh3Llz5Ofn88ADD5gRr4ji8t57771UqFABNzc33N3d8fLy4r///a9ZUYvNevbsWbKzs1m2bBkjR47k+PHj1KlTp1RylN234ERERMqhZ555hv/7v/8jPDwcwzCYNGkSCxcupGbNmrRu3drseEUUl9dms/HNN9+Qn5/Pli1bABg8eDBBQUFlLmvr1q2pV68e3bp1w2Kx0Lx5c5566ilTcv7evGXNjfJ26tSJV155BVdXV1544YVSe3FbUnn/+te/0rdvXwDatWtn+pskN8qbkZFB9erVTc34azfK+9VXX/HKK6/g5OREo0aNCAkJKZNZw8LCOHbsGC+99BKurq4MHTq01N5kshiGYZTKyCIiIiIiIvK7aCqjiIiIiIiIyVTMRERERERETKZiJiIiIiIiYjIVMxEREREREZOpmImIiIiIiJhMxUxERERESkVUVBRWq9XsGCJ3BBUzERERERERk6mYiYiIiMhNeeONN/jmm28A2L17N1FRUcTExNCnTx+6dOnC0qVLHdaPi4tj2bJlAFitVqKiogD45ptviIiIoEePHgwfPpzLly/f3gMRKUNUzERERETkpnTt2pW/zwTPAAABeElEQVSVK1cCsHLlSpo0aULHjh1ZsGAB8+bNY9GiRTccwzAMRo8ezezZs1myZAlVqlSxjylSHrmYHUBERERE7izNmzdn6tSp5OTksH37duLj45k+fTrr16/H09OTgoKCG45x9uxZTp06xaBBgwC4dOkSISEhpR1dpMxSMRMRERGRm+Lk5ES7du0YN24cbdq0YcGCBQQGBhIZGcm2bdvYtGmTw/ru7u6cPn0agO+//x6AypUrU7VqVebOnYuXlxcbN26kUqVKt/1YRMoKFTMRERERuWkvvfQSbdq0Yd26dRw7doxx48axevVqvL29cXZ2Jj8/375u+/btGTRoEN9++y0NGjQArpS7kSNH0q9fPwzDwMPDgylTpph1OCKmsxiGYZgdQkREREREpDzTxT9ERERERERMpmImIiIiIiJiMhUzERERERERk6mYiYiIiIiImEzFTERERERExGQqZiIiIiIiIiZTMRMRERERETGZipmIiIiIiIjJ/h80peH7bHYZMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf17c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 13))\n",
    "sns.stripplot(x='value', y='framework', hue='pyver', data=df, orient='h', size=5, palette=sns.color_palette('rainbow'))\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(title=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf56f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 13))\n",
    "sns.stripplot(x='value', y='framework', hue='pyver', \n",
    "              data=df, \n",
    "              order=frameworks_pyver_ratio.T['Python 3'].sort_values(ascending=False).index, \n",
    "              orient='h', \n",
    "              size=7, \n",
    "              palette=sns.color_palette('rainbow'))\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.legend(title=None, loc='upper center')\n",
    "plt.tight_layout()\n",
    "plt.savefig('frameworks-python-version.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 做数据分析和机器学习的人常用的框架？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['what do you use python for the most?',\n",
       " 'which version of python do you use the most?']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, ['use', 'python', 'most'])\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    DevOps / System administration / Writing autom...\n",
       "1                                      Web development\n",
       "2                                      Web development\n",
       "3                                                  NaN\n",
       "4                                  Desktop development\n",
       "Name: what do you use python for the most?, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uses = survey_df['what do you use python for the most?']\n",
    "uses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>django</th>\n",
       "      <th>flask</th>\n",
       "      <th>tornado</th>\n",
       "      <th>bottle</th>\n",
       "      <th>web2py</th>\n",
       "      <th>numpy / pandas / matplotlib / scipy and similar</th>\n",
       "      <th>keras / theano / tensorflow / scikit-learn and similar</th>\n",
       "      <th>pillow</th>\n",
       "      <th>pyqt / pygtk / wxpython</th>\n",
       "      <th>tkinter</th>\n",
       "      <th>...</th>\n",
       "      <th>requests</th>\n",
       "      <th>asyncio</th>\n",
       "      <th>kivy</th>\n",
       "      <th>six</th>\n",
       "      <th>aiohttp</th>\n",
       "      <th>other - write in</th>\n",
       "      <th>cloud platforms (google app engine, aws, rackspace, heroku and similar)</th>\n",
       "      <th>jupyter notebook</th>\n",
       "      <th>komodo editor</th>\n",
       "      <th>komodo ide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Computer graphics</th>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data analysis</th>\n",
       "      <td>424</td>\n",
       "      <td>395</td>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>926</td>\n",
       "      <td>397.0</td>\n",
       "      <td>159</td>\n",
       "      <td>200</td>\n",
       "      <td>154</td>\n",
       "      <td>...</td>\n",
       "      <td>376</td>\n",
       "      <td>85.0</td>\n",
       "      <td>44</td>\n",
       "      <td>94.0</td>\n",
       "      <td>46</td>\n",
       "      <td>81</td>\n",
       "      <td>279</td>\n",
       "      <td>594</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Desktop development</th>\n",
       "      <td>151</td>\n",
       "      <td>114</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>156</td>\n",
       "      <td>28.0</td>\n",
       "      <td>79</td>\n",
       "      <td>193</td>\n",
       "      <td>139</td>\n",
       "      <td>...</td>\n",
       "      <td>119</td>\n",
       "      <td>20.0</td>\n",
       "      <td>51</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>61</td>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DevOps / System administration / Writing automation scripts</th>\n",
       "      <td>271</td>\n",
       "      <td>289</td>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>230</td>\n",
       "      <td>58.0</td>\n",
       "      <td>83</td>\n",
       "      <td>106</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>343</td>\n",
       "      <td>97.0</td>\n",
       "      <td>20</td>\n",
       "      <td>64.0</td>\n",
       "      <td>53</td>\n",
       "      <td>68</td>\n",
       "      <td>227</td>\n",
       "      <td>113</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Educational purposes</th>\n",
       "      <td>160</td>\n",
       "      <td>91</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>186</td>\n",
       "      <td>53.0</td>\n",
       "      <td>55</td>\n",
       "      <td>68</td>\n",
       "      <td>115</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>17.0</td>\n",
       "      <td>35</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    django  flask  tornado  \\\n",
       "Computer graphics                                       20     15        7   \n",
       "Data analysis                                          424    395       73   \n",
       "Desktop development                                    151    114       17   \n",
       "DevOps / System administration / Writing automa...     271    289       41   \n",
       "Educational purposes                                   160     91       13   \n",
       "\n",
       "                                                    bottle  web2py  \\\n",
       "Computer graphics                                        3       3   \n",
       "Data analysis                                           38      49   \n",
       "Desktop development                                     13      21   \n",
       "DevOps / System administration / Writing automa...      33      28   \n",
       "Educational purposes                                    16      21   \n",
       "\n",
       "                                                    numpy / pandas / matplotlib / scipy and similar  \\\n",
       "Computer graphics                                                                                47   \n",
       "Data analysis                                                                                   926   \n",
       "Desktop development                                                                             156   \n",
       "DevOps / System administration / Writing automa...                                              230   \n",
       "Educational purposes                                                                            186   \n",
       "\n",
       "                                                    keras / theano / tensorflow / scikit-learn and similar  \\\n",
       "Computer graphics                                                                                11.0        \n",
       "Data analysis                                                                                   397.0        \n",
       "Desktop development                                                                              28.0        \n",
       "DevOps / System administration / Writing automa...                                               58.0        \n",
       "Educational purposes                                                                             53.0        \n",
       "\n",
       "                                                    pillow  \\\n",
       "Computer graphics                                       23   \n",
       "Data analysis                                          159   \n",
       "Desktop development                                     79   \n",
       "DevOps / System administration / Writing automa...      83   \n",
       "Educational purposes                                    55   \n",
       "\n",
       "                                                    pyqt / pygtk / wxpython  \\\n",
       "Computer graphics                                                        34   \n",
       "Data analysis                                                           200   \n",
       "Desktop development                                                     193   \n",
       "DevOps / System administration / Writing automa...                      106   \n",
       "Educational purposes                                                     68   \n",
       "\n",
       "                                                    tkinter     ...      \\\n",
       "Computer graphics                                        16     ...       \n",
       "Data analysis                                           154     ...       \n",
       "Desktop development                                     139     ...       \n",
       "DevOps / System administration / Writing automa...       80     ...       \n",
       "Educational purposes                                    115     ...       \n",
       "\n",
       "                                                    requests  asyncio  kivy  \\\n",
       "Computer graphics                                         16      3.0     9   \n",
       "Data analysis                                            376     85.0    44   \n",
       "Desktop development                                      119     20.0    51   \n",
       "DevOps / System administration / Writing automa...       343     97.0    20   \n",
       "Educational purposes                                      68     17.0    35   \n",
       "\n",
       "                                                     six  aiohttp  \\\n",
       "Computer graphics                                    3.0        1   \n",
       "Data analysis                                       94.0       46   \n",
       "Desktop development                                 26.0        8   \n",
       "DevOps / System administration / Writing automa...  64.0       53   \n",
       "Educational purposes                                 6.0        9   \n",
       "\n",
       "                                                    other - write in  \\\n",
       "Computer graphics                                                  8   \n",
       "Data analysis                                                     81   \n",
       "Desktop development                                               41   \n",
       "DevOps / System administration / Writing automa...                68   \n",
       "Educational purposes                                              22   \n",
       "\n",
       "                                                    cloud platforms (google app engine, aws, rackspace, heroku and similar)  \\\n",
       "Computer graphics                                                                                  16                         \n",
       "Data analysis                                                                                     279                         \n",
       "Desktop development                                                                                61                         \n",
       "DevOps / System administration / Writing automa...                                                227                         \n",
       "Educational purposes                                                                               80                         \n",
       "\n",
       "                                                    jupyter notebook  \\\n",
       "Computer graphics                                                 10   \n",
       "Data analysis                                                    594   \n",
       "Desktop development                                               71   \n",
       "DevOps / System administration / Writing automa...               113   \n",
       "Educational purposes                                              96   \n",
       "\n",
       "                                                    komodo editor  komodo ide  \n",
       "Computer graphics                                               5           3  \n",
       "Data analysis                                                  27          25  \n",
       "Desktop development                                            13          15  \n",
       "DevOps / System administration / Writing automa...             24          23  \n",
       "Educational purposes                                           16          20  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frameworks_uses = frameworks.apply(lambda col: pd.crosstab(index=uses, columns=col).iloc[:, 0])\n",
    "frameworks_uses.columns = [item.split(':')[0] for item in frameworks_uses.columns]\n",
    "frameworks_uses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>django</th>\n",
       "      <th>flask</th>\n",
       "      <th>tornado</th>\n",
       "      <th>bottle</th>\n",
       "      <th>web2py</th>\n",
       "      <th>numpy / pandas / matplotlib / scipy and similar</th>\n",
       "      <th>keras / theano / tensorflow / scikit-learn and similar</th>\n",
       "      <th>pillow</th>\n",
       "      <th>pyqt / pygtk / wxpython</th>\n",
       "      <th>tkinter</th>\n",
       "      <th>...</th>\n",
       "      <th>requests</th>\n",
       "      <th>asyncio</th>\n",
       "      <th>kivy</th>\n",
       "      <th>six</th>\n",
       "      <th>aiohttp</th>\n",
       "      <th>other - write in</th>\n",
       "      <th>cloud platforms (google app engine, aws, rackspace, heroku and similar)</th>\n",
       "      <th>jupyter notebook</th>\n",
       "      <th>komodo editor</th>\n",
       "      <th>komodo ide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data analysis</th>\n",
       "      <td>424</td>\n",
       "      <td>395</td>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>926</td>\n",
       "      <td>397.0</td>\n",
       "      <td>159</td>\n",
       "      <td>200</td>\n",
       "      <td>154</td>\n",
       "      <td>...</td>\n",
       "      <td>376</td>\n",
       "      <td>85.0</td>\n",
       "      <td>44</td>\n",
       "      <td>94.0</td>\n",
       "      <td>46</td>\n",
       "      <td>81</td>\n",
       "      <td>279</td>\n",
       "      <td>594</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine learning</th>\n",
       "      <td>239</td>\n",
       "      <td>186</td>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>462</td>\n",
       "      <td>416.0</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25</td>\n",
       "      <td>37.0</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>139</td>\n",
       "      <td>297</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  django  flask  tornado  bottle  web2py  \\\n",
       "Data analysis        424    395       73      38      49   \n",
       "Machine learning     239    186       48      19      31   \n",
       "\n",
       "                  numpy / pandas / matplotlib / scipy and similar  \\\n",
       "Data analysis                                                 926   \n",
       "Machine learning                                              462   \n",
       "\n",
       "                  keras / theano / tensorflow / scikit-learn and similar  \\\n",
       "Data analysis                                                 397.0        \n",
       "Machine learning                                              416.0        \n",
       "\n",
       "                  pillow  pyqt / pygtk / wxpython  tkinter     ...      \\\n",
       "Data analysis        159                      200      154     ...       \n",
       "Machine learning      90                       88       76     ...       \n",
       "\n",
       "                  requests  asyncio  kivy   six  aiohttp  other - write in  \\\n",
       "Data analysis          376     85.0    44  94.0       46                81   \n",
       "Machine learning       163     40.0    25  37.0       22                33   \n",
       "\n",
       "                  cloud platforms (google app engine, aws, rackspace, heroku and similar)  \\\n",
       "Data analysis                                                   279                         \n",
       "Machine learning                                                139                         \n",
       "\n",
       "                  jupyter notebook  komodo editor  komodo ide  \n",
       "Data analysis                  594             27          25  \n",
       "Machine learning               297             10           7  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_ml_frameworks_uses = frameworks_uses.loc[['Data analysis', 'Machine learning']]\n",
    "da_ml_frameworks_uses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc66ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da_ml_frameworks_uses.T.sort_values(by='Data analysis').plot.area(stacked=False, alpha=0.5, figsize=(20, 15), \n",
    "                                                                  color=sns.color_palette('rainbow')[:2])\n",
    "plt.xticks(range(da_ml_frameworks_uses.shape[1]), \n",
    "           da_ml_frameworks_uses.T.sort_values(by='Data analysis').index, \n",
    "           rotation=90, fontsize=18);\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig('frameworks-data-analysis-machine-learning.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23]), <a list of 24 Text yticklabel objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cc66b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 15))\n",
    "df = da_ml_frameworks_uses.stack().reset_index()\n",
    "df.columns = ['use', 'framework', 'value']\n",
    "sns.barplot(x='value', y='framework', hue='use', \n",
    "            data=df, \n",
    "            orient='h', \n",
    "            order=da_ml_frameworks_uses.T.sort_values(by='Data analysis', ascending=False).index,\n",
    "            palette=sns.color_palette('rainbow'))\n",
    "plt.yticks(fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到数据分析和机器学习从业者使用的框架大致差不多，只是在 keras、theano、tensorflow 和 scikit-learn 等机器学习库上差别较大，当然这也理所当然。\n",
    "\n",
    "只是让我想不到的是 web 框架 Django 和 Flask 能够排的这么前。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模大小和是否使用 Python 3 的关系？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['how many people are in your project team?']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, ['how', 'many', 'people', 'project'])\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2-7 people\n",
       "1    2-7 people\n",
       "2           NaN\n",
       "3           NaN\n",
       "4    2-7 people\n",
       "Name: how many people are in your project team?, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_scale = survey_df[cols[0]]\n",
    "team_scale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           3635\n",
       "unique             5\n",
       "top       2-7 people\n",
       "freq            2620\n",
       "Name: how many people are in your project team?, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_scale.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5871"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_scale.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>how many people are in your project team?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2-7 people</th>\n",
       "      <td>769</td>\n",
       "      <td>1553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-12 people</th>\n",
       "      <td>183</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13-20 people</th>\n",
       "      <td>65</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-40 people</th>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>More than 40 people</th>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "how many people are in your project team?                       \n",
       "2-7 people                                         769      1553\n",
       "8-12 people                                        183       327\n",
       "13-20 people                                        65       105\n",
       "21-40 people                                        25        42\n",
       "More than 40 people                                 28        40"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_pyver = pd.crosstab(team_scale, python_ver)\n",
    "team_pyver = team_pyver.reindex(['2-7 people', '8-12 people', '13-20 people', '21-40 people', 'More than 40 people'])\n",
    "team_pyver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>how many people are in your project team?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2-7 people</th>\n",
       "      <td>0.331180</td>\n",
       "      <td>0.668820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-12 people</th>\n",
       "      <td>0.358824</td>\n",
       "      <td>0.641176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-40 people</th>\n",
       "      <td>0.373134</td>\n",
       "      <td>0.626866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13-20 people</th>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.617647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>More than 40 people</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "how many people are in your project team?                       \n",
       "2-7 people                                    0.331180  0.668820\n",
       "8-12 people                                   0.358824  0.641176\n",
       "21-40 people                                  0.373134  0.626866\n",
       "13-20 people                                  0.382353  0.617647\n",
       "More than 40 people                           0.411765  0.588235"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_pyver_sorted = team_pyver.div(team_pyver.sum(axis=1), axis=0).sort_values(by='Python 3', ascending=False)\n",
    "team_pyver_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf9c04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team_pyver_sorted['Python 3'].plot(label='Python 3', marker='o', markersize=10, color='b', linewidth=3)\n",
    "plt.xticks(range(5), team_pyver_sorted.index, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('team scale', fontsize=16)\n",
    "plt.ylabel('use ratio of python 3', fontsize=16)\n",
    "plt.legend(fontsize=14)\n",
    "plt.title('Team scale VS Use ratio of Python 3', fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.savefig('team-scale-python-3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到团队越小，使用 Python 3 的比例越高，可能这也是因为重构代价吧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开发者年龄和是否使用 Python 3 的关系？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['could you tell us your age range?']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, ['age', 'range'])\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      60 or older\n",
       "1            40-49\n",
       "2            40-49\n",
       "3    17 or younger\n",
       "4            18-20\n",
       "Name: could you tell us your age range?, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = survey_df[cols[0]]\n",
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9502\n",
       "unique        7\n",
       "top       21-29\n",
       "freq       3717\n",
       "Name: could you tell us your age range?, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is python the main language you use for your current projects?</th>\n",
       "      <th>none:what other language(s) do you use?</th>\n",
       "      <th>java:what other language(s) do you use?</th>\n",
       "      <th>javascript:what other language(s) do you use?</th>\n",
       "      <th>c/c++:what other language(s) do you use?</th>\n",
       "      <th>php:what other language(s) do you use?</th>\n",
       "      <th>c#:what other language(s) do you use?</th>\n",
       "      <th>ruby:what other language(s) do you use?</th>\n",
       "      <th>bash / shell:what other language(s) do you use?</th>\n",
       "      <th>objective-c:what other language(s) do you use?</th>\n",
       "      <th>...</th>\n",
       "      <th>technical support:which of the following best describes your job role(s)?</th>\n",
       "      <th>data analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>business analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>team lead:which of the following best describes your job role(s)?</th>\n",
       "      <th>product manager:which of the following best describes your job role(s)?</th>\n",
       "      <th>cio / ceo / cto:which of the following best describes your job role(s)?</th>\n",
       "      <th>systems analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>other - write in::which of the following best describes your job role(s)?</th>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>what country do you live in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5375</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Java</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ghana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9478</th>\n",
       "      <td>No, I use Python as a secondary language</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PHP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>Objective-C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9489</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/C++</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/C++</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     is python the main language you use for your current projects?  \\\n",
       "5375                                                Yes               \n",
       "9478           No, I use Python as a secondary language               \n",
       "9489                                                Yes               \n",
       "9499                                                Yes               \n",
       "\n",
       "     none:what other language(s) do you use?  \\\n",
       "5375                                     NaN   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     java:what other language(s) do you use?  \\\n",
       "5375                                    Java   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     javascript:what other language(s) do you use?  \\\n",
       "5375                                    JavaScript   \n",
       "9478                                    JavaScript   \n",
       "9489                                           NaN   \n",
       "9499                                           NaN   \n",
       "\n",
       "     c/c++:what other language(s) do you use?  \\\n",
       "5375                                      NaN   \n",
       "9478                                      NaN   \n",
       "9489                                    C/C++   \n",
       "9499                                    C/C++   \n",
       "\n",
       "     php:what other language(s) do you use?  \\\n",
       "5375                                    NaN   \n",
       "9478                                    PHP   \n",
       "9489                                    NaN   \n",
       "9499                                    NaN   \n",
       "\n",
       "     c#:what other language(s) do you use?  \\\n",
       "5375                                   NaN   \n",
       "9478                                   NaN   \n",
       "9489                                   NaN   \n",
       "9499                                   NaN   \n",
       "\n",
       "     ruby:what other language(s) do you use?  \\\n",
       "5375                                     NaN   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     bash / shell:what other language(s) do you use?  \\\n",
       "5375                                             NaN   \n",
       "9478                                    Bash / Shell   \n",
       "9489                                             NaN   \n",
       "9499                                    Bash / Shell   \n",
       "\n",
       "     objective-c:what other language(s) do you use?  \\\n",
       "5375                                            NaN   \n",
       "9478                                    Objective-C   \n",
       "9489                                            NaN   \n",
       "9499                                            NaN   \n",
       "\n",
       "                 ...               \\\n",
       "5375             ...                \n",
       "9478             ...                \n",
       "9489             ...                \n",
       "9499             ...                \n",
       "\n",
       "     technical support:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                          \n",
       "9478                                                NaN                          \n",
       "9489                                                NaN                          \n",
       "9499                                                NaN                          \n",
       "\n",
       "     data analyst:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                     \n",
       "9478                                                NaN                     \n",
       "9489                                                NaN                     \n",
       "9499                                                NaN                     \n",
       "\n",
       "     business analyst:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                         \n",
       "9478                                                NaN                         \n",
       "9489                                                NaN                         \n",
       "9499                                                NaN                         \n",
       "\n",
       "     team lead:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                  \n",
       "9478                                                NaN                  \n",
       "9489                                                NaN                  \n",
       "9499                                                NaN                  \n",
       "\n",
       "     product manager:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     cio / ceo / cto:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     systems analyst:which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     other - write in::which of the following best describes your job role(s)?  \\\n",
       "5375                                                NaN                          \n",
       "9478                                                NaN                          \n",
       "9489                                                NaN                          \n",
       "9499                                                NaN                          \n",
       "\n",
       "     could you tell us your age range? what country do you live in?  \n",
       "5375                               NaN                        Ghana  \n",
       "9478                               NaN                          NaN  \n",
       "9489                               NaN                          NaN  \n",
       "9499                               NaN                          NaN  \n",
       "\n",
       "[4 rows x 162 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df.loc[age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['60 or older', '40-49', '17 or younger', '18-20', '21-29', '30-39',\n",
       "       '50-59', nan], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17 or younger</th>\n",
       "      <td>98</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-20</th>\n",
       "      <td>124</td>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-29</th>\n",
       "      <td>835</td>\n",
       "      <td>2378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>632</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>249</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>101</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60 or older</th>\n",
       "      <td>25</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "could you tell us your age range?                               \n",
       "17 or younger                                       98       564\n",
       "18-20                                              124       647\n",
       "21-29                                              835      2378\n",
       "30-39                                              632      1568\n",
       "40-49                                              249       611\n",
       "50-59                                              101       190\n",
       "60 or older                                         25        86"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_pyver = pd.crosstab(index=age, columns=python_ver)\n",
    "age_pyver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d15bb048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_pyver.div(age_pyver.sum(axis=1), axis=0)['Python 3'].plot(label='Python 3',\n",
    "                                                              marker='o', \n",
    "                                                              markersize=10, \n",
    "                                                              color='b', \n",
    "                                                              linewidth=3)\n",
    "plt.xticks(range(age_pyver.shape[0]), age_pyver.index, fontsize=14);\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('age', fontsize=16)\n",
    "plt.ylabel('use ratio of python 3', fontsize=16)\n",
    "plt.legend(fontsize=14)\n",
    "plt.title(\"The developers' age VS The use ratio of Python 3\", fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.savefig('age-python-3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，随着年龄段的增长，Python 3 的使用比例在降低，但是到了 60 岁以上的时候反而提高了。。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is python the main language you use for your current projects?</th>\n",
       "      <th>none:what other language(s) do you use?</th>\n",
       "      <th>java:what other language(s) do you use?</th>\n",
       "      <th>javascript:what other language(s) do you use?</th>\n",
       "      <th>c/c++:what other language(s) do you use?</th>\n",
       "      <th>php:what other language(s) do you use?</th>\n",
       "      <th>c#:what other language(s) do you use?</th>\n",
       "      <th>ruby:what other language(s) do you use?</th>\n",
       "      <th>bash / shell:what other language(s) do you use?</th>\n",
       "      <th>objective-c:what other language(s) do you use?</th>\n",
       "      <th>...</th>\n",
       "      <th>technical support:which of the following best describes your job role(s)?</th>\n",
       "      <th>data analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>business analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>team lead:which of the following best describes your job role(s)?</th>\n",
       "      <th>product manager:which of the following best describes your job role(s)?</th>\n",
       "      <th>cio / ceo / cto:which of the following best describes your job role(s)?</th>\n",
       "      <th>systems analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>other - write in::which of the following best describes your job role(s)?</th>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>what country do you live in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30-39</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-29</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Yes</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-29</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>No, I use Python as a secondary language</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/C++</td>\n",
       "      <td>PHP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Technical support</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Team lead</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30-39</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>No, I use Python as a secondary language</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-29</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    is python the main language you use for your current projects?  \\\n",
       "414                                                Yes               \n",
       "481                                                Yes               \n",
       "483                                                Yes               \n",
       "490           No, I use Python as a secondary language               \n",
       "507           No, I use Python as a secondary language               \n",
       "\n",
       "    none:what other language(s) do you use?  \\\n",
       "414                                     NaN   \n",
       "481                                     NaN   \n",
       "483                                    None   \n",
       "490                                     NaN   \n",
       "507                                     NaN   \n",
       "\n",
       "    java:what other language(s) do you use?  \\\n",
       "414                                     NaN   \n",
       "481                                     NaN   \n",
       "483                                     NaN   \n",
       "490                                     NaN   \n",
       "507                                     NaN   \n",
       "\n",
       "    javascript:what other language(s) do you use?  \\\n",
       "414                                    JavaScript   \n",
       "481                                    JavaScript   \n",
       "483                                           NaN   \n",
       "490                                           NaN   \n",
       "507                                    JavaScript   \n",
       "\n",
       "    c/c++:what other language(s) do you use?  \\\n",
       "414                                      NaN   \n",
       "481                                      NaN   \n",
       "483                                      NaN   \n",
       "490                                    C/C++   \n",
       "507                                      NaN   \n",
       "\n",
       "    php:what other language(s) do you use?  \\\n",
       "414                                    NaN   \n",
       "481                                    NaN   \n",
       "483                                    NaN   \n",
       "490                                    PHP   \n",
       "507                                    NaN   \n",
       "\n",
       "    c#:what other language(s) do you use?  \\\n",
       "414                                   NaN   \n",
       "481                                   NaN   \n",
       "483                                   NaN   \n",
       "490                                   NaN   \n",
       "507                                   NaN   \n",
       "\n",
       "    ruby:what other language(s) do you use?  \\\n",
       "414                                     NaN   \n",
       "481                                     NaN   \n",
       "483                                     NaN   \n",
       "490                                     NaN   \n",
       "507                                     NaN   \n",
       "\n",
       "    bash / shell:what other language(s) do you use?  \\\n",
       "414                                    Bash / Shell   \n",
       "481                                    Bash / Shell   \n",
       "483                                             NaN   \n",
       "490                                    Bash / Shell   \n",
       "507                                             NaN   \n",
       "\n",
       "    objective-c:what other language(s) do you use?  \\\n",
       "414                                            NaN   \n",
       "481                                            NaN   \n",
       "483                                            NaN   \n",
       "490                                            NaN   \n",
       "507                                            NaN   \n",
       "\n",
       "                ...               \\\n",
       "414             ...                \n",
       "481             ...                \n",
       "483             ...                \n",
       "490             ...                \n",
       "507             ...                \n",
       "\n",
       "    technical support:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                          \n",
       "481                                                NaN                          \n",
       "483                                                NaN                          \n",
       "490                                  Technical support                          \n",
       "507                                                NaN                          \n",
       "\n",
       "    data analyst:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                     \n",
       "481                                                NaN                     \n",
       "483                                                NaN                     \n",
       "490                                                NaN                     \n",
       "507                                                NaN                     \n",
       "\n",
       "    business analyst:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                         \n",
       "481                                                NaN                         \n",
       "483                                                NaN                         \n",
       "490                                                NaN                         \n",
       "507                                                NaN                         \n",
       "\n",
       "    team lead:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                  \n",
       "481                                                NaN                  \n",
       "483                                                NaN                  \n",
       "490                                          Team lead                  \n",
       "507                                                NaN                  \n",
       "\n",
       "    product manager:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                        \n",
       "481                                                NaN                        \n",
       "483                                                NaN                        \n",
       "490                                                NaN                        \n",
       "507                                                NaN                        \n",
       "\n",
       "    cio / ceo / cto:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                        \n",
       "481                                                NaN                        \n",
       "483                                                NaN                        \n",
       "490                                                NaN                        \n",
       "507                                                NaN                        \n",
       "\n",
       "    systems analyst:which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                        \n",
       "481                                                NaN                        \n",
       "483                                                NaN                        \n",
       "490                                                NaN                        \n",
       "507                                                NaN                        \n",
       "\n",
       "    other - write in::which of the following best describes your job role(s)?  \\\n",
       "414                                                NaN                          \n",
       "481                                                NaN                          \n",
       "483                                                NaN                          \n",
       "490                                                NaN                          \n",
       "507                                                NaN                          \n",
       "\n",
       "    could you tell us your age range? what country do you live in?  \n",
       "414                             30-39                        China  \n",
       "481                             21-29                        China  \n",
       "483                             21-29                        China  \n",
       "490                             30-39                        China  \n",
       "507                             21-29                        China  \n",
       "\n",
       "[5 rows x 162 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china = survey_df.loc[survey_df['what country do you live in?'] == 'China']\n",
    "usa = survey_df.loc[survey_df['what country do you live in?'] == 'United States']\n",
    "china.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>17 or younger</th>\n",
       "      <th>18-20</th>\n",
       "      <th>21-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60 or older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>pyver</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Afghanistan</th>\n",
       "      <th>Python 2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Albania</th>\n",
       "      <th>Python 2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <th>Python 2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "could you tell us your age range?  17 or younger  18-20  21-29  30-39  40-49  \\\n",
       "country     pyver                                                              \n",
       "Afghanistan Python 2                           2      0      1      1      0   \n",
       "            Python 3                           1      1      0      0      0   \n",
       "Albania     Python 2                           0      1      1      0      0   \n",
       "            Python 3                           0      3      7      4      1   \n",
       "Algeria     Python 2                           0      0      5      2      0   \n",
       "\n",
       "could you tell us your age range?  50-59  60 or older  \n",
       "country     pyver                                      \n",
       "Afghanistan Python 2                   0            0  \n",
       "            Python 3                   0            0  \n",
       "Albania     Python 2                   0            0  \n",
       "            Python 3                   1            0  \n",
       "Algeria     Python 2                   0            0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_age = pd.crosstab([survey_df['what country do you live in?'], survey_df['which version of python do you use the most?']], survey_df['could you tell us your age range?'])\n",
    "country_age.index.names = ['country', 'pyver']\n",
    "country_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>17 or younger</th>\n",
       "      <th>18-20</th>\n",
       "      <th>21-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60 or older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "could you tell us your age range?  17 or younger  18-20  21-29  30-39  40-49  \\\n",
       "country                                                                        \n",
       "Afghanistan                                    3      1      1      1      0   \n",
       "Albania                                        0      4      8      4      1   \n",
       "Algeria                                        1      2     15      4      1   \n",
       "Andorra                                        0      0      1      0      0   \n",
       "Antigua and Barbuda                            2      0      0      0      0   \n",
       "\n",
       "could you tell us your age range?  50-59  60 or older  \n",
       "country                                                \n",
       "Afghanistan                            0            0  \n",
       "Albania                                1            0  \n",
       "Algeria                                0            0  \n",
       "Andorra                                0            0  \n",
       "Antigua and Barbuda                    0            0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_age_total = country_age.sum(level=0)\n",
    "country_age_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_age_total['60 or older'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAAGoCAYAAADLmIB6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd8jef/x/F3EokYrVWxNydCjKBGiFlVq1StxqbUHjVCv0UHFasUbWmNhiBWq75o7VFqU6NC7U2IFURCcv/+8Mj5OjLcDonj19fz8fB45Fznuu/7c9/nykn7Pte5bifDMAwBAAAAAAAAAPCMnF92AQAAAAAAAACAVxMBMwAAAAAAAADALgTMAAAAAAAAAAC7EDADAAAAAAAAAOxCwAwAAAAAAAAAsAsBMwAAAAAAAADALgTMAAC8ogYPHixPT8+n/hs8ePBLq3HgwIHq1KlTgs+dPn1a3bp105tvvqkKFSpoyJAhunnzZgpX+OKFhYXp/v37T+3Xr18/lShRIgUqenabNm1SnTp1VKJECbVv3z7JvocPH5anp6fOnTsnSbp69ao8PT21a9euFK0lJiZGFy5csD6eP3++PD099ddff72QOp7Fyzy2I9XgKDZv3ixPT0+tWLHiZZcCO5w5c0a9evWy/q3o1q2bTp8+Ha/fwYMH1bZtW5UpU0aVK1fWqFGjTL0XAwCA55fqZRcAAADs06JFC1WqVMn6eM+ePVqwYIFatGihsmXLWtvz5s37MsrT3LlztWzZMlWpUiXec9euXVPbtm3l7OysLl26KCoqSjNmzNCxY8cUEhKiVKlezf9EWbt2rQYNGqRVq1bJ3d09yb6tWrVSrVq1Uqgy8x48eKABAwYoTZo0GjJkiHLmzJlk/8OHD+u1115T7ty5rY+dnJzk5eWVYrXcvHlTHTp0UN26ddWlS5fnPi4Ax3Du3Dm1aNFCbm5u6tq1q2JiYjRz5ky1bt1ay5YtU+bMmSVJx48fV7t27ZQjRw717t1bYWFhmj17ts6fP69vv/32JZ8FAAD//72a//cGAADk4+MjHx8f6+OYmBgtWLBApUuXVqNGjV5aXQ8fPtTkyZM1derURPtMnz5d169f14oVK5QvXz5JUvHixdW1a1ctX75cjRs3TqlyX6i//vpLd+/eNdW3XLlyyVyNfS5fvqzbt2/L399f/v7+T+0fGhoqLy8vOTk5SXoUMOfLl0/p06dPsVrCw8N1+PBh1a1b97mPCcBxjBkzRtHR0Vq0aJHy5MkjSapYsaKaNWumhQsXqmvXrpKkb775Rm5ubpo7d64yZswoScqZM6e+/PJL7d6922HfbwEA+P+CJTIAAMALc/fuXTVq1EhTp05Vs2bNlClTpgT7rVixQpUrV7aGy5JUo0YN5cqVSytXrkypcpGA6OhoSVK6dOlM9T98+LCKFi1qfRwaGqpixYq9lFoA/P9x8+ZNrV+/Xi1atLCGy5JUsmRJ9e7dW4ULF5Yk3b9/X+vXr1e9evWs4bIkNWvWTG5ubokujdKvXz+98847yXsSAAD8SxAwAwDwL7Jt2za1adNGpUuXlo+Pjzp27Ki9e/fa9KlcubK++OILzZ8/XzVq1JCPj49atWql3bt3P3X/9+7dU3R0tL799luNGDFCLi4u8fpcvXpVYWFhKl68eLznihUrpkOHDj31OLt371anTp1UtmxZVahQQV27dtXx48ftOteE1oh+sr1y5coaOXKkFi9erLp166pEiRKqU6eOFi5caO3Tr18//fjjj5KkKlWqWLdv3ry5unfvrtGjR6t06dKqXLmyTp48meAazOfPn1f//v1VoUIFlSxZUk2aNIkXuMfGxuqbb76xrktcpUoVDRkyRFeuXHnqdQsPD9fQoUNVpUoVeXt7q27dupo5c6ZiY2MlSePGjVO9evUkSePHj090Dd9x48ZZ1/jeu3evZs+ebX28atUqrVy5Up6enrp69Wqy17J58+Z4/R4/blhYmPr166eyZcuqbNmy6t27d7xrFRkZqXHjxqlmzZry9vZW7dq19e233+rhw4dPvaZhYWEaOHCgKlSooHLlyunzzz9PcLunHeP8+fMqWrSoxowZE2/bcePGycvLS2FhYc9V7927dzV69GjVqFFD3t7eqlWrliZMmKCoqChrn7i1m0NDQ9WlSxeVLl1aVatWtc4kfdbrFre/tWvXqnr16ipVqpSmTZsm6dEHTU2aNJGPj4/KlSunTp06af/+/YnW/9dff8nT01Pz58+3aa9fv76KFSumO3fuxOu7bt06a1tERISGDRumChUqWN8TTpw4YbOvBw8eaNq0aXr77bfl7e0tPz8/ffHFF7p165ZNvz///FMffPCBypYtKx8fH/n7+2vz5s3xal64cKEaNmyoEiVKyNfXV//5z38UHh6e6DnGuX37tsaMGWP9Pffx8VHLli21adMmm36GYWj69OmqXbu2SpYsqaZNm2r37t2qWrWqhg0bZtN37dq1atasmUqVKqXy5curT58+Onv27FNriXtPb9iwoUqVKqVSpUqpcePGWrp0qbVPVFSUPD09NWXKFH344Yfy9vZWo0aNrL/P9hx7//79evjwoSpXrizp0XtfZGSkJKlHjx566623JD36UOvhw4fx/qakTp1ahQsXNvU3BQAAPB+WyAAA4F/it99+U79+/VSwYEH17NnTuqRG27Zt9d1336lq1arWvhs2bNCSJUvUtm1bZcyYUXPnzlWHDh00e/Zsm2U5npQ5c2b9/vvvCQbLcS5fvixJypYtW7znsmbNqvDwcEVHR8vNzS3B7bdt26bOnTsrR44c+uijj+Tq6qqffvpJbdq00S+//KLs2bM/07matXbtWi1btkxt2rRRpkyZFBISoqFDhypPnjyqVKmSWrVqpcjISG3YsEHDhg1TwYIFbWo+ceKEAgICdOHCBRUoUCDe/i9evKjmzZvL1dVV7du3V/r06bV69Wr169dP4eHhatOmjSRp0qRJ+vHHH9W6dWsVLlxY586dU1BQkA4fPqylS5dal6p40vXr19W8eXOFhYXJ399fefPm1ebNmzV69GgdOXJEY8aMUf369ZUxY0aNHTtW9erVU/Xq1RNcw7t+/foqUqSILl26pAkTJiggIEBZsmTRnTt39MUXX6hr164qWLCgXn/99WSvpWjRoho4cKBNv8ePO2jQIFWsWFGDBg3SkSNHFBISosuXL1s/HHj48KE+/PBDHTp0SC1btlT+/Pm1f/9+TZ48WUePHtWkSZMSHRP37t1Tq1atFBYWpnbt2ilz5sxavHix/vvf/9r0M3OM3Llzy8fHR7///rsGDRpks/1vv/2mihUrysPDw+56o6Ki1K5dO/39999q2rSpihYtqn379mnq1Knat2+fZs6cabP2effu3ZUjRw71799fhw4d0owZM3Tq1Cl9//33dl23gIAAtW/fXqlTp1aFChW0detWDRgwQLVq1VKLFi10584dBQcHq127dvrtt9+UI0eOeOdQsmRJZcqUSdu3b9cHH3wg6dEHFXEfLu3bt09+fn6SpC1btsjNzU2VKlWyfjgWGBgoLy8v9e3bVxcvXtRPP/2kTp06afXq1XJzc5NhGOrVq5c2bNigunXrql27dtZ14Xfs2KEFCxYoffr0+ueff9StWzeVLFlS/fv3t76/dO3aVSEhISpZsqSkRx94/PDDD6pfv778/f114cIFzZ07Vzt37tTixYuVIUOGBF+r2NhYderUSSdPnlTr1q2VO3duXbx4UfPnz1ePHj20du1aZc+eXZI0cuRIzZkzR3Xq1FH79u21c+fOBD80CwkJ0fDhw+Xn56dBgwbpxo0bmjdvnpo1a6bFixfbzBB+0oABA7R+/Xr5+/urbdu2Cg8P18KFCxUQEKACBQqoVKlS1r4zZsxQ2bJl9emnn+rBgwdydna2+9hnzpyRJKVJk0YBAQFauXKloqOjVbx4cX322WfW6xz3gVFif1MOHz6c6LkBAIAXxAAAAP8vLFmyxLBYLMaSJUviPRcVFWX4+voab731lnH37l1re3h4uFGpUiWjZs2aRkxMjGEYhuHr62tYLBZj06ZN1n5XrlwxSpcubbRu3fqZavL19TU6duxo07Zt2zbDYrEYS5cujdc/MDDQsFgsxo0bNxLdZ8OGDY1q1aoZt27dsrYdPXrUsFgsxsSJE5/5XJ+sL6F2X19fo2jRosbx48etbefPnzcsFosxZMgQa9vYsWMNi8VihIWFWduaNWtmWCwWY9++fTbH6Nu3r+Ht7W3zuFKlSkZ4eLi1LTY21ujZs6dRqlQp6zWpWbOm0atXL5t9BQUFGY0aNTIuXryY6HUbMWJEvNfVMAxjyJAhhsViMbZt22YYhmEcP37csFgsxrRp0xLdV5wVK1YYxYsXNx48eGAYhmHs3Lkz3vmnRC0J9Zs3b55hsViMPn362PT9+OOPDYvFYly+fNmm3/bt2236BQUFGRaLxdi8eXOix50+fXq884iIiDBq165t85qbPcbcuXMNi8Vi7N+/39pn//79Nr/XZvcV1y+uhlmzZhkWi8WYN2+ezXZTpkwxLBaLsXjxYpvtPvjgA+vrahj/+93csWOHXXUMHTrUpt/gwYONChUq2LQdOHDAqFOnjrFu3boErvYjH3/8sVGxYkUjNjbWMIxHY9Db29soX7688fXXX1v7tWjRwvo7vGnTJsNisRgtWrQwHj58aO0zfvx4w2KxGHv27DEMwzDWrFljWCwWY+zYsTbH/OWXXwyLxWJ88803hmEYxuTJkw2LxWJERERY+1y5csV4++23jfnz5xuG8WhMenp6GpMnT7bZ16FDhwwvLy9j3LhxiZ7jjh07DIvFYvz888827XH1BQcHG4ZhGCdOnDCKFi1qfPLJJzb9hg0bZnPNb9y4YZQqVcoYPHiwTb9Lly4ZPj4+Rr9+/RKtJe597snzCA0NNSwWizF69GjDMAzj/v37hsViMSpUqGBzXZ7n2HHXuXbt2karVq2M//73v8b8+fMNPz8/o3Tp0sbJkycNwzCMxYsXGxaLxdi1a1e8ffTq1cvw8fExDMMwHj58aISHh1v/9ejRw6hdu7ZNW1RUVKL1AACAxLFEBgAA/wL79+/XtWvX1KZNG6VNm9banjlzZn3wwQc6f/68jhw5Ym338vKymeXr4eGhBg0aaPfu3YqIiHiuWgzDkKREZ9pKkrNzwv+JcunSJR09elSNGjWymaVqsVi0ZMkStWvX7pnP1SxPT08VKlTI+jhXrlx6/fXXde3atadu+9prr6l06dKJPv/w4UNt2LBBFSpUkPRohu/169d148YNvf3224qMjNSOHTskSdmzZ9eWLVs0d+5cXb9+XZLUtm1bLV26NMFZn3HWr1+vYsWKxZu93b17d0mPZmg/q6NHj6pIkSLWma9HjhzRG2+8oaxZsya5XXLUkpgGDRrYPI5bliTudVu9erWyZ8+uIkWKWK/79evXVaNGDTk5OWnjxo2J7nvz5s3KmTOnzXmkT59e77//vk0/s8eoW7euXF1d9dtvv1m3XbFihVKnTq233377uepdv369MmXKpObNm9u0d+rUSalTp453zTt27Ggzo7l9+/bW/dhTR9zM4jjZs2fXjRs3FBgYqFOnTkl69Nr8/vvvqlmzZoLnIElVq1bV9evXdfToUUnSjh07rMvgxM1UvnXrlg4cOKDq1avbbPvOO+/YfLviybGwbt06OTk5qUuXLjbbNWrUSLly5bJeo7jZw59//rlCQ0MlPXqPXLVqlVq2bGm9PoZhqEaNGjbXJ0eOHCpSpEiS46p8+fLauXOnzdiNiYmxvnfeu3fPWm9sbKw6dOhgs/2T9W/evFmRkZGqVauWTS1ubm4qX768Nm7caN33k3LlyqU9e/boww8/tLYZhmFdBiWuljg+Pj42N/h8nmM/ePBAkuTu7q6goCA1aNBALVu21KxZsxQVFaXvvvtOkqzLcCT2NyXu78np06dVqVIl6781a9bozJkz8doAAMCzY4kMAAD+Bc6fPy9JCS7NEBeaXrx40XpztseD1Dj58uVTbGysLl68KE9PT7tribth2+Prvsa5f/++TZ8nXbhwQZKUP3/+eM95e3tLevZzNStz5szx2tzc3BQTE2PXto8LCwtTZGSkVq5cmehNDi9duiRJGjJkiLp166YvvvhCI0aMsK6l26xZM2XJkiXBbQ3D0MWLF22+yh4nd+7ccnd318WLF596HnEiIyMVGRmpQ4cOKX/+/Nag++DBgypYsKD1caZMmeKFPi+6lqd58tqnTp1a0v/Cq7Nnz+ry5cuqVKlSgtsnVcuFCxcS/Hr/48ujPMsxMmXKpMqVK2vVqlUKCAiQYRj6/fffVaNGDWtoZ2+958+fV758+eItX+Pu7q5cuXLF2y7uBmpxsmXLpjRp0lh/B5+1jidfh/bt22vbtm2aNWuWZs2apbx586pGjRpq2rSpLBZLgvuUHgXVzs7O2r59u4oWLaodO3aoXr16Sp06taZMmaLo6Gj9+eefiomJUbVq1Wy2ffL348mxcP78eb3xxhvxlnZxcnJSwYIFrWuAv/vuu1q3bp2WLVumZcuWKVu2bKpevbree+896xJCcesLN2nSJMHzeDyETYiLi4vmzp2rXbt26ezZszpz5oz1PTMukD1z5oycnJziLR2TK1cuubu7Wx/H1dKjR49Ej3f79u1El+xwc3PTr7/+qq1bt+r06dM6c+aMNViOC3fjPHmNn+fYadKkkfToGj4+bgsVKqQSJUpo586dkp7+NyXu+Zw5c2rWrFnW577//nudO3dOX331lbUtqbEHAAASR8AMAMC/QGIzxKT/BQSurq7WtoTWP47rl9T6ymbEzbKNu2HZ48LCwvTGG28keoy4MDep2c/Peq5JHedxSR3zaRKbkf3k8Ro0aBBv9mucfPnySXoUpK9bt06bNm3Shg0b9Mcff2jChAmaNWuWFi1alOA6xUldE8MwZBjGU6/J47799lvrDQ0lxQvF40LHLVu2xJvN/KJreZqnXfvY2FgVKVJEn3zySYLPZ8qUKdFtnZyc4t34Lm6f9h6jYcOG6t+/vw4cOKDo6GhdvnxZDRs2fCH1JiY2NjbeNU/oNYiNjbVez2et48nf6QwZMigkJER79uzR2rVrtWXLFgUFBWnOnDmaOHGi6tSpk+B+M2fOrOLFi2v79u2qV6+eTp06pTfffFPu7u6Kjo7WgQMHtGXLFhUoUCDe78LTxkJSHh+Xbm5u+v777xUaGqrVq1frjz/+0MKFC7VgwQINGTJE7du3t46BH3/80WYmuJlarl+/rpYtW+rKlSvy9fVVrVq15OXlpSxZsqhVq1bWfg8fPpSTk1OCr9Xj7+FxtQQGBia4TrH0vzD3SXFrdx88eFAVK1a03sC0ZMmS1ln1SZ3X8xw7rn9CH9BlzpzZeoPGnDlzSkr8b0rcftKkSSNfX1/rc4sWLdLVq1dt2gAAgH0ImAEA+BfIlSuXJOnkyZPxvqoe9/X0uK99S/+bdfa406dPy9XV1fo/8/bKmjVrojdeCg0NtX5tPSFxx06ovlGjRilbtmwqXry4JHPn6uzsHC8gjI6Ofu5lQJ6Vh4eHXF1dFRsbGy/sOHfunI4ePao0adLo4cOHOnLkiDJkyKDatWurdu3akqSlS5cqICBAS5YsUb9+/eLt39nZWTly5NDJkyfjPXfhwgVFRUUlubzGk5o1ayaLxaKBAwdq2LBhKlCggB48eKCPPvpIvXv3ti4HktCsxBddy/PKlSuX9avzj3+IEBUVpXXr1iU53vPkyaMjR47YBK/So9fM3mPUqlVLadOm1dq1a3Xv3j1lyJDBZgkOe+vNlSuXjh07ppiYGJuw9/79+7p06VK8mZtnz561vm9Ij2bQR0VFWb898DzXTXr0+3nv3j2VLVtWZcuWVUBAgI4cOaLWrVtr1qxZiQbMklStWjUFBQVp+/btcnV1lY+Pj1xcXJQ2bVrt2rVLW7du1TvvvJPk8ROSK1cu7dq1S7dv37aZxWwYhk6dOmUdl+fPn1dYWJjKlCkjLy8v9enTRxcuXFCbNm00Y8YMtW/f3nrtcuXKFe8bIevXr0/yWw0//fSTzpw5o/nz56tMmTLW9m3bttn0y507t2JjY3X27FnrB1DSo4D69u3bNuclPZpd/OT7y7Zt2+Tk5JToTVWXLl2qffv2ady4cTYfdDw5xhPzPMeOey+Pu4nj486fP299PYoUKSJXV1cdPnxYjRs3tvaJiorSiRMn4i0LAwAAXjzWYAYA4F/Ax8dHmTJlUnBwsM2ambdu3dKCBQuUK1cum4Bpz549NgHwlStXtHLlSlWpUsVmXWN71alTR5s3b7YJijds2KALFy6oXr16iW6XJ08eFSxYUP/973919+5da/vJkycVHBysGzduPNO5Zs2aVSdOnLAJmVetWmVdX/RZxQV3T85gfZrUqVOrSpUqWrNmjXVWnvQo2Bo5cqR69uypiIgIPXjwQK1atdLYsWNtti9ZsqSkpGdF1qhRQ6Ghodq8ebNN+w8//CBJ8darTUq+fPmUPn16ubi4qEmTJvL19VXmzJllGIYaN24sX19f+fr6JhocvchaJPuvuyTVrFlTV69e1eLFi23a58yZo379+mnXrl2Jblu7dm2Fh4dr6dKl1rbo6GgtWrTI7mOkSZNGb731ljZs2KB169apTp06NtfR3npr1KihGzduaOHChTbts2fPVlRUVLxrHhwcbPN4xowZ1nN+njriDB8+XD179lRkZKS1rXDhwtZxlZSqVasqIiJCs2fPVokSJeTu7i5XV1eVLl1aS5Ys0aVLl+Itj2FGjRo1ZBiGdRzGWblypS5cuGC9RlOmTFHHjh1t1l/PlSuXPDw8rLXHrSM9bdo0m30dOHBA3bt319y5cxOt4+bNm3JycrIJpmNjY63bxL0/xb0WT+5r3rx5No/9/Pzk6uqq6dOn27y3nT9/Xt26ddOkSZOSrEWKv2RKUFCQpIS/7fGijm2xWOTp6anFixdbl92RpO3bt+uff/6xzqBOly6dqlSpouXLl1vrlR7NUI6Ojk7ybwoAAHgxmMEMAMC/gJubm/7zn/9o4MCBatq0qd5//33FxMRo4cKFunHjhr777jubWYhubm7q0KGD2rVrJ1dXVwUHBytVqlQaOHDgC6mnS5cuWr58udq2bav27dvr7t27mjFjhnx8fFS3bt0kt41bg7hZs2Zq2rSpYmJiFBwcrCxZsqhDhw7PdK7169fXmDFj1KVLF+vX7RcvXmz3DNq4ZQF+/PFHValS5ZmC0oEDB2r37t1q2bKlWrVqpWzZsmndunX6448/1LZtW+sMRX9/f82cOVO9e/eWr6+v7t27p5CQEKVLly7R9V6lRzfQW7dunXr06KFWrVopT5482rJli9avX68GDRqoYsWKz3SuBw8eVKFChaxfb//777+VOXNmUzPcX3Qtcdd99erVypIly1PH0OP8/f21bNkyDRs2TAcOHJC3t7cOHz6sRYsWqVSpUnr33XcT3bZp06ZatGiRhg4dqiNHjih37txaunSpbt269VzHaNiwoZYtW2b9+UXUG7fdF198ocOHD8vLy0v79+/X0qVLVa5cOZuZn5L0xx9/qGvXrvLz89PevXu1fPlytWjRwrrW+fNcN0nq0KGDunfvrtatW6tRo0ZKlSqVVq9erUuXLikgICDJbUuUKKFMmTLp4MGD6tq1q7W9fPny+vPPP5UuXTqVK1cuyX0kpHbt2vLz89OPP/6oCxcu6M0339SJEycUEhKiAgUKqGPHjpKk1q1ba8WKFWrVqpWaN2+u9OnTa+vWrdq3b5/1PdLb21stWrTQggULFB4erpo1a+r69esKDg5WhgwZ1LNnz0TrqFq1qhYsWKDOnTvrvffeU1RUlJYvX269sWHch2uenp5q1qyZgoKCFBYWpvLly2v//v36/fffJf1vWR8PDw/16tVLX3/9tfz9/VW/fn1FRUVp7ty5io2NTfJ9vXLlyvrmm2/08ccfy9/fX87Ozlq7dq119vjjH/Ql5HmOLT36IKJDhw5q2bKl/P39FRERYV2z+/GbG/bt21fNmzdX69at5e/vrwsXLigoKEh16tRR2bJlE9z3hAkTkjw2AAAwj4AZAIB/iYYNGypDhgyaOnWqJk+erFSpUsnHx0djx4613pgqzptvvqnq1atr2rRpunfvnsqXL68BAwYkePM/e2TLlk3BwcEKDAzUxIkTlS5dOtWtW1cDBgx46vq7VatW1cyZMzVlyhRNmjRJadKkUYUKFTRgwADr187Nnmu7du0UERGhn3/+WV9++aWKFSum77//XlOmTLHrvOJu/hUSEqLdu3c/U8BcqFAhLVy4UN98843mz5+v+/fvK2/evBo6dKj8/f2t/fr3768sWbLol19+0ebNm+Xq6qqyZctq0qRJCd5wLk6WLFm0YMECTZw4UcuWLVNERITy5cunIUOGqG3bts98rocOHbKGjdKjgPnxx0l50bVkyJBBvXr1UlBQkEaOHBlvtmVS3N3dNWfOHE2ePFlr1qzRL7/8Ig8PD7Vp00bdu3e33gguIa6urgoKCtK4ceO0fPlyRUZGqmbNmmrXrp0GDRpk9zF8fX2VJUsWubq66s0333wh9cZtN2nSJK1evVq//PKLcubMqR49eqhr167xZg2PGTNGISEhGj16tLJly6ZBgwZZA9bnvW7Soxm+U6ZM0fTp0zV58mQ9ePBAnp6emjRpUpLLY0iPZupXrlxZy5cvtwmS465V5cqV7VrH29nZWd99952mTZumX3/9VWvWrNEbb7whf39/9erVy3pjPm9vb82cOdO6Fvm9e/dUoEABff7552rZsqV1f59//rkKFy6sRYsWKTAwUK+//roqVKigPn362Cxp8aS33npLn332mYKCgjRq1ChlzJhR3t7eGjFihPr376/t27db+w4fPlxZs2bVzz//rLVr16p48eKaOnWq2rdvbzPz/aOPPlKOHDkUFBSk8ePHK02aNCpRooR69eqV4A0343h7e2vChAn67rvvNG7cOKVPn14Wi0VBQUGaPn26du3a9dRZzPYeW5LKli2r4OBgTZgwQRMnTlTq1KlVq1YtBQQE2NwosWjRopo5c6bGjRuIMlddAAAgAElEQVSnwMBAZcqUSW3atFGfPn2S3D8AAHgxnIyk7rQCAAD+dSpXrqyiRYtavxIP4N9j/vz5+uyzz7RgwQLrWtpwTHfu3JGzs3O8ZYuuXLmiqlWrql+/fjYzvAEAAJILazADAAAAwCtmz549KlOmjNasWWPTvmLFCkn/W5sdAAAgubFEBgAAAAC8YsqXL6/s2bNr6NChOnr0qLJly6bQ0FAtXLhQlStXVqVKlV52iQAA4F+CgBkAAAAAXjFp0qTR3LlzNWXKFC1atEjXr19X9uzZ1alTJ3Xv3t3mxq0AAADJiTWYAQAAAAAAAAB2cfgZzFevRrzsEhxO+vSpdedO1MsuA68AxgrMYJzALMYKzGCcwCzGCsxirMAMxgnMYqzADMZJwrJmfS3Bdm7y9wpKlcrlZZeAVwRjBWYwTmAWYwVmME5gFmMFZjFWYAbjBGYxVmAG4+TZEDADAAAAAAAAAOxCwAwAAAAAAAAAsAsBMwAAAAAAAADALgTMAAAAAAAAAAC7EDADAAAAAAAAAOxCwAwAAAAAAAAAsAsBMwAAAAAAAADALgTMAAAAAAAAAAC7EDADAAAAAAAAAOxCwAwAAAAAAAAAsAsBMwAAAAAAAADALgTMAAAAAAAAAAC7EDADAAAAAAAAAOxCwAwAAAAAAAAAsAsBMwAAAAAAAADALgTMAAAAAAAAAAC7pHrZBbwqurZJ+7JLeIJj1DN1zr2XXQIAAAAAAACAl4QZzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALALATMAAAAAAAAAwC4EzAAAAAAAAAAAuxAwAwAAAAAAAADsQsAMAAAAAAAAALBLqpQ8WOPGjfXaa69JknLnzq0WLVpo5MiRcnFxUZUqVdSzZ8+ULAcAAAAAAAAA8BxSLGCOioqSJM2ZM8fa1qhRI02ePFl58uRRly5d9Pfff6t48eIpVRIAAAAAAAAA4DmkWMB85MgRRUZGqmPHjnr48KF69eql6Oho5c2bV5JUpUoVbdu2LV7AnD59aqVK5ZJSZeIZZcyY9mWXgCS4uDjzGuGpGCcwi7ECMxgnMIuxArMYKzCDcQKzGCswg3HybFIsYHZ3d1enTp3UrFkznT59Wp07d9brr79ufT5dunQ6d+5cvO3u3IlKqRKfgkGVkJs3773sEpCEjBnT8hrhqRgnMIuxAjMYJzCLsQKzGCswg3ECsxgrMINxkrCsWV9LsD3FAuYCBQooX758cnJyUoECBfTaa6/p5s2b1ufv3r1rEzgDAAAAAAAAABybc0odaPHixQoMDJQkXblyRZGRkUqbNq3Onj0rwzC0ZcsWlStXLqXKAQAAAAAAAAA8pxSbwdy0aVMNGTJEH3zwgZycnPTVV1/J2dlZAwYMUExMjKpUqaJSpUqlVDkAAAAAAAAAgOeUYgGzm5ubxo8fH6994cKFKVUCAAAAAAAAAOAFSrElMgAAAAAAAAAA/78QMAMAAAAAAAAA7ELADAAAAAAAAACwCwEzAAAAAAAAAMAuBMwAAAAAAAAAALsQMAMAAAAAAAAA7ELADAAAAAAAAACwyzMFzGXKlNG5c+eSqxYAAAAAAAAAwCvkmQJmwzCSqw4AAAAAAAAAwCsm1dM67Nq1y/pzbGysDhw4oMuXL0uS3nzzzeSrDAAAAAAAAADg0J4aMAcEBFh/jo6O1pgxY+Ti4iInJyetW7cuWYsDAAAAAAAAADiupwbM69evt/7s4+Oj4OBg5cmTJ1mLAgAAAAAAAAA4vmdagxkAAAAAAAAAgDjPFDC/++67SpcuXXLVAgAAAAAAAAB4hTx1iYzHff7558lVBwAAAAAAAADgFZNkwPzXX39p3rx5+uuvv3T58mU9ePBA7u7uypo1q0qVKqU2bdqoZMmSKVUrAAAAAAAAAMCBJBowL126VMOGDdO7776rbt26KUuWLHJzc1N0dLSuXbumPXv2qE2bNho5cqQaNGiQkjUDAAAAAAAAABxAogHz5MmTNXz4cL3//vsJPt+kSROVKVNGEydOJGAGAAAAAAAAgH+hRG/yd/PmTZUqVSrJjb29vXXt2rUXXhQAAAAAAAAAwPElGjBXrlxZI0aM0Llz5xJ8/uLFixoxYoR8fX2TrTgAAAAAAAAAgONKdImML7/8UgEBAapdu7Y8PDzk4eFhswbzlStXVKVKFY0YMSIl6wUAAAAAAAAAOIhEA+YMGTJo6tSpOnv2rA4cOKCrV68qMjJSqVOnVrZs2VSqVCnlyZMnJWsFAAAAAAAAADiQRAPmOHnz5lXevHmtj69du6aLFy8qbdq0yVoYAAAAAAAAAMCxJboGc0REhAYOHKh3331XknTnzh317NlTfn5+at68ufz8/NSnTx/dvXs3xYoFAAAAAAAAADiORAPm4cOH68SJExoyZIgkadSoUbpw4YIWLVqkPXv2aP78+Tp37hxrMAMAAAAAAADAv1SiAfPmzZs1atQoVapUSZL0xx9/6D//+Y+8vb2VLl06lSpVSl9++aXWrFmTYsUCAAAAAAAAABxHogFz2rRpde3aNevjrFmz6sGDBzZ9IiMjlSrVU5dxBgAAAAAAAAD8P5RowOzv76+PP/5Yixcv1u3bt9W/f38FBgZqx44dCgsL08aNGzVkyBA1adIkJesFAAAAAAAAADiIRKcfd+3aVe7u7po4caKGDRum9OnTKyoqSu3bt5ckubm5qUWLFurfv39K1QoAAAAAAAAAcCBJrm/Rvn17tWvXTsePH9fZs2d19+5dpUqVSh4eHvLy8lK6dOlSqk4AAAAAAAAAgIN56gLKTk5OKlKkiIoUKZIS9QAAAAAAAAAAXhGJrsEMAAAAAAAAAEBSCJgBAAAAAAAAAHYhYAYAAAAAAAAA2MV0wHzt2jV9//33CggIUHh4uFauXKkjR44kZ20AAAAAAAAAAAdmKmA+ePCg6tSpo23btmnFihW6d++edu7cqebNm2vLli3JXSMAAAAAAAAAwAGZCpgDAwPVpUsXzZ49W66urpKkzz77TF26dNH48eOTtUAAAAAAAAAAgGMyFTAfPnxYdevWjdfeqFEjnTx58oUXBQAAAAAAAABwfKYC5ixZsujEiRPx2vfs2SMPD48XXhQAAAAAAAAAwPGlMtOpc+fOGjp0qDp37izDMLR161ZdunRJs2fP1oABA5K7RgAAAAAAAACAAzIVMLdo0UJZs2bVjBkz5O7urvHjx6tAgQIaOXKk6tWrl9w1AgAAAAAAAAAckKmAecqUKWrSpInmzp2b3PUAAAAAAAAAAF4RptZg/umnnxQTE5PctQAAAAAAAAAAXiGmAuZGjRrp22+/1YkTJxQZGanY2FibfwAAAAAAAACAfx9TS2SsXbtWV65c0a+//prg86GhoS+0KAAAAAAAAACA4zMVMI8dOza56wAAAAAAAAAAvGJMBczly5dP9LnLly+/sGIAAAAAAAAAAK8OUwHziRMnNGbMGB07dsy65rJhGIqOjtbNmzdZIgMAAAAAAAAA/oVM3eRv6NChunXrlj766CPduHFDnTt3Vv369RUZGalRo0Yld40AAAAAAAAAAAdkagbzoUOHtGDBAnl5eWnp0qUqWLCgWrVqpQIFCmjhwoVq3LhxctcJAAAAAAAAAHAwpmYwp0qVSq+99pokqWDBgtYlMXx9ffXPP/8kX3UAAAAAAAAAAIdlKmAuW7asZsyYocjISHl7e2vt2rWKjY3V/v37lTp16uSuEQAAAAAAAADggEwFzIMHD9aff/6pefPmqVGjRrpx44bKlSunAQMGyN/fP7lrBAAAAAAAAAA4IFNrMBcqVEirVq1SZGSk0qRJoyVLlmjnzp3KmDGjSpcundw1AgAAAAAAAAAckKmAOU6aNGkkSWnTplX16tWTox4AAAAAAAAAwCvC1BIZL0p4eLiqVaumEydO6MyZM/rggw/k7++v4cOHKzY2NiVLAQAAAAAAAAA8pxQLmB88eKBhw4bJ3d1dkjRq1Cj17dtX8+bNk2EYWrduXUqVAgAAAAAAAAB4AVIsYB49erRatmwpDw8PSdLff/+t8uXLS5KqVq2qP//8M6VKAQAAAAAAAAC8AKbXYL527ZoWLVqk06dPa9CgQdqxY4cKFiyookWLPnXbn3/+WZkzZ5afn59++OEHSZJhGHJycpIkpUuXThEREQlumz59aqVK5WK2TKSwjBnTvuwSkAQXF2deIzwV4wRmMVZgBuMEZjFWYBZjBWYwTmAWYwVmME6ejamA+eDBg2rfvr2KFy+uvXv3qmfPntq5c6cGDx6s7777TlWqVEly+yVLlsjJyUnbtm1TaGioAgICdP36devzd+/e1euvv57gtnfuRD3D6SQnBlVCbt6897JLQBIyZkzLa4SnYpzALMYKzGCcwCzGCsxirMAMxgnMYqzADMZJwrJmfS3BdlNLZAQGBqpLly6aPXu2XF1dJUmfffaZunTpovHjxz91+7lz5yo4OFhz5syRl5eXRo8erapVq2rHjh2SpM2bN6tcuXJmzwUAAAAAAAAA4ABMBcyHDx9W3bp147U3atRIJ0+etOvAAQEBmjx5slq0aKEHDx6oTp06du0HAAAAAAAAAPBymFoiI0uWLDpx4oTy5s1r075nzx7rTfvMmjNnjvXn4ODgZ9oWAAAAAAAAAOA4TAXMnTt31tChQ9W5c2cZhqGtW7fq0qVLmj17tgYMGJDcNQIAAAAAAAAAHJCpgLlFixbKmjWrZsyYIXd3d40fP14FChTQyJEjVa9eveSuEQAAAAAAAADggEwFzJJUs2ZN1axZMzlrAQAAAAAAAAC8QkwFzFOmTEmw3cnJSa6urvLw8JCfn5+yZMnyQosDAAAAAAAAADguUwHzqVOntHLlSmXPnl3e3t4yDEOhoaG6ePGiypQpo1u3bmnEiBGaPn26Spcundw1AwAAAAAAAAAcgOklMpo2barPPvtMLi4ukqTY2Fh99dVXunPnjubOnaupU6cqMDBQISEhyVYsAAAAAAAAAMBxOJvptH79enXs2NEaLkuSs7OzWrdurVWrVkmS6tevryNHjiRPlQAAAAAAAAAAh2MqYH7jjTe0c+fOeO27du1SxowZJUnXrl1T+vTpX2x1AAAAAAAAAACHZWqJjF69eumTTz7Rrl27VKJECRmGob///lu///67hg8frlOnTmnQoEGqX79+ctcLAAAAAAAAAHAQpgLmd999Vzlz5tT8+fO1ePFiubi4qHDhwpozZ45Kly6tAwcOqE2bNmrVqlVy1wsAAAAAAAAAcBCmb/JXrlw5lStXLsHnSpYsqZIlS76wogAAAAAAAAAAjs9UwHz37l2FhITo+PHjiomJkSQZhqHo6GgdOXLEeqM/AAAAAAAAAMC/h6mb/H366aeaPn26oqOjtWLFCsXGxur06dNatWqVGjZsmNw1AgAAAAAAAAAckKkZzH/88YcmTZokX19fHTt2TO3bt5e3t7cCAwP1zz//JHeNAAAAAAAAAAAHZGoGc3R0tPLnzy9JKlKkiA4ePChJatmypXbv3p1sxQEAAAAAAAAAHJepgLlw4cLaunWrpEcBc1yofPv2bUVHRydfdQAAAAAAAAAAh2VqiYxevXqpd+/eio2NVaNGjVSvXj19+OGHOnbsmPz8/JK7RgAAAAAAAACAAzIVMNeoUUO//fabYmJilCNHDs2fP1+//vqrypcvrzZt2iR3jQAAAAAAAAAAB2QqYJak3LlzW38uWrSoihYtmiwFAQAAAAAAAABeDabWYAYAAAAAAAAA4EkEzAAAAAAAAAAAuxAwAwAAAAAAAADs8kwB8549e7RkyRLduXNH//zzj6Kjo5OrLgAAAAAAAACAgzN1k7/w8HB17dpVx44dU3R0tMqXL68JEybon3/+0cyZM5UvX77krhMAAAAAAAAA4GBMzWD+8ssvlTNnTm3fvl2pU6eWJI0dO1aenp4aMWJEshYIAAAAAAAAAHBMpgLmbdu2qVevXnJ3d7e2pU+fXv3799fevXuTrTgAAAAAAAAAgOMyFTA7OzsrMjIyXvvVq1etM5oBAAAAAAAAAP8upgLmBg0aaMSIETpy5IicnJx0584dbd26VcOHD1e9evWSu0YAAAAAAAAAgAMydZO/gQMH6uuvv1azZs304MEDvffee3JxcVGzZs00cODA5K4RAAAAAAAAAOCATAXMbm5uGjx4sPr27atz584pJiZGefLkUbp06ZK7PgAAAAAAAACAgzK1RIYkbd26VXfv3lWRIkUUGhqqfv36adKkSXrw4EFy1gcAAAAAAAAAcFCmAuYffvhBPXr00Llz57Rnzx59+umn8vDw0MqVKzV69OjkrhEAAAAAAAAA4IBMBcwhISGaOHGiSpcurV9//VU+Pj4aMWKExowZo5UrVyZ3jQAAAAAAAAAAB2QqYA4PD5enp6ckaePGjapRo4YkKWPGjLp//37yVQcAAAAAAAAAcFimbvJXqFAh/fzzz8qSJYvCwsJUq1YtRUdHa/r06fLy8kruGgEAAAAAAAAADshUwBwQEKDevXvr1q1b+uijj5Q/f359/vnnWr9+vaZNm5bcNQIAAAAAAAAAHJCpgLlChQratm2bIiIilCFDBklSjx499Mknn8jV1TVZCwQAAAAAAAAAOCZTAbMkhYWFac6cOTpx4oRiY2NVoEABNW/eXIUKFUrO+gAAAAAAAAAADsrUTf527typd955R3v27FH+/PmVL18+7du3T++995727NmT3DUCAAAAAAAAAByQqRnMo0ePVtu2bfXxxx/btI8fP15jx45VSEhIshQHAAAAAAAAAHBcpmYwHz9+XO+//3689vfff1+hoaEvvCgAAAAAAAAAgOMzFTDnzp1b+/fvj9f+119/KUuWLC+8KAAAAAAAAACA4zO1REanTp00fPhwHT9+XCVLlpQk7d+/X3PnzlX//v2TtUAAAAAAAAAAgGMyFTA3adJEkhQcHKygoCC5u7urQIECCgwM1Ntvv52sBQIAAAAAAAAAHJOpgFl6FDLHBc0AAAAAAAAAAJgOmDdt2qQjR44oKipKhmHYPNenT58XXhgAAAAAAAAAwLGZCphHjx6tn376SUWLFlX69OltnnNyckqWwgAAAAAAAAAAjs1UwLx48WKNHz9e9erVS+56AAAAAAAAAACvCGcznVKlSqVixYoldy0AAAAAAAAAgFeIqYC5TZs2mjRpku7du5fc9QAAAAAAAAAAXhGJLpFRrVo16/rKhmHoypUrWr16tTJlyiQXFxebvhs3bkzWIgEAAAAAAAAAjifRgLlv374pWQcAAAAAAAAA4BWTaMD83nvvWX+eNm2aqlatKi8vrxQpCgAAAAAAAADg+BINmB+3b98+/fDDD0qTJo38/Pzk5+enypUrK0OGDMldHwAAAAAAAADAQZkKmKdOnaqYmBjt27dP27Zt0+zZszV48GAVK1ZMfn5+6tGjR3LXCQAAAAAAAABwMKYCZklycXFRuXLllC1bNuXJk0cbN27UqlWrdOjQIVMBc0xMjD799FOdOnVKLi4uGjVqlAzD0ODBg+Xk5KQiRYpo+PDhcnZ2fq4TAgAAAAAAAACkDFMBc1BQkPbu3au9e/fq1q1bKlGihMqWLatp06apTJkypg60YcMGSVJISIh27NhhDZj79u2rChUqaNiwYVq3bp1q165t/9kAAAAAAAAAAFKMqYA5MDBQklStWjW1bdtW5cqVk5ub2zMd6K233lL16tUlSRcvXtQbb7yhjRs3qnz58pKkqlWrauvWrQTMAAAAAAAAAPCKMBUw79ixQ7t27dKuXbs0btw4nTp1SsWKFVO5cuVUtmxZVa1a1dzBUqVSQECA1qxZo0mTJmnDhg1ycnKSJKVLl04RERHxtkmfPrVSpXJ5hlNCSsqYMe3LLgFJcHFx5jXCUzFOYBZjBWYwTmAWYwVmMVZgBuMEZjFWYAbj5Nk4GYZhPOtG58+f1w8//KBffvlFDx8+VGho6DNtf/XqVTVv3lx37tzRrl27JElr167Vn3/+qWHDhj3RN37o/DJ0bcOgSsjUOfdedglIQsaMaXXzJq8RksY4gVmMFZjBOIFZjBWYxViBGYwTmMVYgRmMk4Rlzfpagu2mZjDfvn1bu3bt0s6dO7Vz504dO3ZMFotFHTp0UJUqVUwVsHTpUl25ckUfffSR0qRJIycnJ3l7e2vHjh2qUKGCNm/erIoVK5o/IwAAAAAAAADAS2UqYK5YsaIyZ84sX19fa6icOXPmZzrQ22+/rSFDhqhVq1Z6+PChPvnkExUqVEhDhw7V119/rYIFC6pOnTp2nQQAAAAAAAAAIOWZCpiXLFkiLy+v5zpQ2rRp9c0338RrDw4Ofq79AgAAAAAAAABeDmcznZ43XAYAAAAAAAAA/P9jKmAGAAAAAAAAAOBJBMwAAAAAAAAAALvYFTBHR0frwIEDioiIeNH1AAAAAAAAAABeEaYC5lOnTqlly5bau3ev7ty5oyZNmqh58+aqXr26du/endw1AgAAAAAAAAAckKmA+csvv5SHh4fy58+vJUuW6Pr169q0aZM6duyowMDA5K4RAAAAAAAAAOCATAXMe/fu1cCBA5U5c2atW7dOtWrVUrZs2dS4cWMdO3YsuWsEAAAAAAAAADggUwFz2rRpdfv2bV2/fl179+5VtWrVJEmnT59WxowZk7VAAAAAAAAAAIBjSmWmU+3atdWvXz+lTp1aHh4eqlq1qlauXKmRI0fq/fffT+4aAQAAAAAAAAAOyFTAPHToUAUHB+vChQtq3bq13NzcFB0drW7duqlVq1bJXSMAAAAAAADwf+3dd1iV9f/H8RdDNBVXX7Vy4EghV6KikuIAlRIHmJswV6lFhnuhOHBkbsw0Z6mFA/fe4kqtTDPFAQ7Ur2apKThA4PeHP85XBPR45Mihno/r4ro497nH+3A+3ON9fz7vG4AFMirB/PHHH2vIkCEqXbq0YZq3t7fZggIAAAAAAAAAWD6jajCfPHlStrZG5aIBAAAAAAAAAP8SRmWN27Ztq549e6pNmzYqUqSI7OzsUrzv6upqluAAAAAAAAAAAJbLqATz119/LUkaOXJkqvesrKx08uTJjI0KAAAAAAAAAGDxjEowR0REmDsOAAAAAAAAAEAWY1QNZkl68OCB1qxZo5CQEN26dUs//vijrl+/bs7YAAAAAAAAAAAWzKgezBcuXFDHjh1lY2Ojq1evytvbW6GhoTpw4IDmzp2rChUqmDtOAAAAAAAAAICFMaoHc3BwsDw8PLR161Zly5ZNkjRp0iR5enpqzJgxZg0QAAAAAAAAAGCZjEowHzlyRL6+vrKysvrfgtbW6tq1Kw/4AwAAAAAAAIB/KaMSzDlz5kyz3vLp06eVJ0+eDA8KAAAAAAAAAGD5jEowt23bVsOGDdO2bdskSZGRkVq6dKmGDRumli1bmjVAAAAAAAAAAIBlMuohf5988ons7e0VHByse/fuqXv37nr11VfVqVMndenSxdwxAgAAAAAAAAAskFEJZkny8/OTn5+f7t69q4SEBNnb25szLgAAAAAAAACAhTM6wXzmzBktX75ckZGRsra2lpOTk1q1aqVixYqZMz4AAAAAAAAAgIUyqgbztm3b5O3trRMnTqhMmTIqUaKEfvrpJzVp0kSHDh0yd4wAAAAAAAAAAAtkVA/mCRMmqFevXuratWuK6TNnztSYMWO0atUqswQHAAAAAAAAALBcRvVgvnr1qtzd3VNN9/T01Pnz5zM6JgAAAAAAAABAFmBUgtnLy0uzZ89WfHx8iulLliyRl5eXWQIDAAAAAAAAAFg2o0pk3LlzRzt27NCePXtUrlw52djY6PTp07py5YoqVKggX19fw7yLFy82W7AAAAAAAAAAAMthVIK5bNmyKlu2bIpp5cuXN0tAAAAAAAAAAICswagEs7+/v+H3W7duKTExUQUKFDBbUAAAAAAAAAAAy2dUglmS5s2bpzlz5ujmzZuSpLx586p9+/bq2bOn2YIDAAAAAAAAAFguoxLM06dP1+LFi/X555/L2dlZiYmJ+uWXXzR9+nRlz55d3bp1M3ecAAAAAAAAAAALY1SCeenSpRo9erTc3d0N09566y299tprCg4OJsEMAAAAAAAAAP9C1sbMFBsbqxIlSqSaXqJECd24cSOjYwIAAAAAAAAAZAFGJZidnZ01b948JSQkGKYlJCRo3rx5qlSpktmCAwAAAAAAAABYLqNKZAwaNEi+vr7au3evypUrJ0k6ceKEEhISNGfOHLMGCAAAAAAAAACwTEYlmEuXLq2NGzdq7dq1ioqKUvbs2VWvXj01adJEOXPmNHeMAAAAAAAAAAALZFSCWZLy58+vDh06mDMWAAAAAAAAAEAWkm6CuW7durKysjJqJbt27cqoeAAAAAAAAAAAWUS6CeaAgICXGQcAAAAAAAAAIItJN8Hs4+OTalpiYqIuX76s119/XYmJibKzszNrcAAAAAAAAAAAy2VtzEwPHz7UF198obfffluenp7673//q379+qlPnz66e/euuWMEALULoiQAACAASURBVAAAAAAAAFggoxLMU6dO1d69ezVv3jxlz55dkuTn56cTJ05o3LhxZg0QAAAAAAAAAGCZjEowr1+/XsOHD5eLi4thWrVq1TRmzBht3brVbMEBAAAAAAAAACyXUQnmmzdv6tVXX001/ZVXXtH9+/czPCgAAAAAAAAAgOUzKsHs6uqq2bNnKykpyTDtzp07mjRpkmrWrGm24AAAAAAAAAAAlsuoBHNQUJBOnTolV1dXPXjwQN26dVOdOnV09epVBQYGmjtGAAAAAAAAAIAFsjVmpsKFC2v58uU6cOCAoqKi9PDhQ5UsWVK1a9eWtbVROWoAAAAAAAAAwD+MUQnmgIAANWnSRHXq1JGrq6u5YwIAAAAAAAAAZAFGJZjz5s2r4cOH6/79+/Lw8JCXl5dq1aolGxsbc8cHAAAAAAAAALBQRtW3GDFihPbs2aPp06crZ86cGjx4sGrVqqVhw4bp4MGD5o4RAAAAAAAAAGCBjC6gbGVlpZo1ayooKEg7d+5Uly5dtG7dOnXs2NGM4QEAAAAAAAAALJVRJTIk6eHDh9q/f7+2bt2q7du3y9raWt7e3vLy8jJnfAAAAAAAAAAAC2VUgrlfv37avXu3kpKS1KBBA3355ZdydXWVtbXRHaABAAAAAAAAAP8wRiWYExISNGbMGNWpU0d2dnbmjgkAAAAAAAAAkAUYlWCeNGmSJOnnn3/W+fPn5enpqStXrqhEiRJGJZzj4+M1ePBgXb58WXFxcerRo4fefPNNDRw4UFZWVipTpoyCgoLoEQ0AAAAAAAAAWYhRCeYbN26oW7duOnPmjOLi4lS9enVNnjxZp0+f1rx58+Tg4PDU5desWaN8+fLpyy+/1M2bN+Xj4yMnJycFBASoRo0aGjZsmLZv366GDRtmyIcCAAAAAAAAAJifVVJSUtKzZgoICFBSUpK++OILubq6as2aNcqfP7/69++v+Ph4zZ49+6nLx8bGKikpSblz59bNmzfVsmVLxcXFKTw8XFZWVtq2bZv27dunoKCgVMveuxcnW1sb0z9hBmnbNLMjsEyhazM7AjyNjY21EhISMzsMWDjaCYxFW4ExaCcwFm0FxqKtwBi0ExiLtgJj0E7Sli1b2jlao3owHzhwQIsXL1aOHDkM03Lnzq0+ffqodevWz1w+V65ckqSYmBj17NlTAQEB+uKLL2RlZWV4/86dO2kuGxPzwJgQX4KcmR2ARbp1625mh4CnyJcvJ98Rnol2AmPRVmAM2gmMRVuBsWgrMAbtBMaircAYtJO0FSxon+Z0o4oeW1tb6969e6mmX79+XdmzZzcqgP/+97/q0KGDmjdvrqZNm6aotxwbG6s8efIYtR4AAAAAAAAAgGUwKsHcpEkTBQcHKyIiQlZWVoqJiTGUtGjcuPEzl//zzz/VuXNn9evXTy1btpQklStXTgcPHpQkhYeHq1q1ai/wMQAAAAAAAAAAL5tRJTL69eunSZMmqVWrVoqPj5ePj49sbGzUqlUr9evX75nLz5w5U7dv39aMGTM0Y8YMSdKQIUMUHBysSZMmqVSpUvL09HyxTwIAAAAAAAAAeKmMeshfsvv37ys6OloJCQkqVqyYobayOV2/nnZt5petux81mNMycyH1aCwZNYNgDNoJjEVbgTFoJzAWbQXGoq3AGLQTGIu2AmPQTtKWXg3mdHswHz58+KkrPHHihOF3FxcXE8MCAAAAAAAAAGRV6SaY/fz8DL9bWVlJkpKSkpQ9e3bZ2Njo7t27srGxUa5cuXTo0CHzRwoAAAAAAAAAsCjpJph///13w+8rVqxQWFiYRo0apTJlykiSLly4oKFDh6p+/frmjxIAAAAAAAAAYHGs03vDxsbG8DN58mQNHz7ckFyWJAcHBwUGBmrmzJkvJVAAAAAAAAAAgGVJN8H8pGvXrqWaFhUVJTs7uwwNCAAAAAAAAACQNaRbIuNx7du3V//+/dWhQwc5OjpKkn777TctWrRIPXv2NGuAAAAAAAAAAADLZFSC2d/fXwULFtSyZcs0Z84cSVKZMmU0bNgwNW/e3KwBAgAAAAAAAAAsk1EJZklq06aN2rRpY85YAAAAAAAAAABZiNE1mAEAAAAAAAAAeBwJZgAAAAAAAACASUgwAwAAAAAAAABM8twJ5ocPH5ojDgAAAAAAAABAFmN0gnnx4sVq2LChKleurOjoaA0dOlSTJk1SYmKiOeMDAAAAAAAAAFgooxLM8+fP15w5c/Tpp5/K1tZWklSrVi2FhYVpypQpZg0QAAAAAAAAAGCZjEowh4aGauTIkfL29paVlZUk6d1339X48eO1atUqswYIAAAAAAAAALBMRiWYr169qhIlSqSa/tprr+n27dsZHRMAAAAAAAAAIAswKsFcuXJlhYWFpZiWmJioOXPmqFKlSmYJDAAAAAAAAABg2WyNmSkwMFAfffSRdu3apbi4OAUFBen8+fOKi4vTnDlzzB0jAAAAAAAAAMACGZVgLlOmjDZv3qw1a9YoKipKCQkJatiwoZo1a6ZcuXKZO0YAAAAAAAAAgAUyKsEsSdmzZ1erVq3MGQsAAAAAAAAAIAsxKsF8+fJljR8/XhEREYqLi1NSUlKK93ft2mWO2AAAAAAAAAAAFsyoBHP//v11584d+fr6yt7e3twxAQAAAAAAAACyAKMSzL/99pvCwsJUpkwZc8cDAAAAAAAAAMgirI2ZqWTJkrp586a5YwEAAAAAAAAAZCHp9mA+cOCA4fdGjRqpf//+6t69u4oVKyZr65R5aVdXV/NFCAAAAAAAAACwSOkmmDt16pRq2vDhw1NNs7Ky0smTJzM0KAAAAAAAAACA5Us3wRwREfEy4wAAAAAAAAAAZDFG1WD28PDQrVu3Uk2/du0a5TEAAAAAAAAA4F8q3R7MGzZs0K5duyRJly9fVlBQkLJnz55initXrsjWNt1VAAAAAAAAAAD+wdLtwVyzZk3Z2NjIxsbm0YzW1obXyT9OTk6aMWPGSwsWAAAAAAAAAGA50u1+XKBAAY0dO1aSVKRIEXXu3Fk5c+Z8aYEBAAAAAAAAACybUfUt/P39zR0HAAAAAAAAACCLMeohfwAAAAAAAAAAPIkEMwAAAAAAAADAJOkmmAcMGKCbN29Kkg4fPqyHDx++tKAAAAAAAAAAAJYv3QTzxo0bde3aNUlShw4ddPv27ZcWFAAAAAAAAADA8qX7kL86deqoVatWKlCggJKSkuTt7S1r67Tz0bt27TJXfAAAAAAAAAAAC5Vugnnq1KnauXOn7ty5o0GDBqlr166yt7d/mbEBAAAAAAAAACxYuglmGxsbNWjQQJJ0+fJltWrVSq+88spLCwwAAAAAAAAAYNnSTTA/zt/fX7/99pvmzp2ryMhIJSYmqmTJkvL19ZWrq6u5YwQAAAAAAAAAWKB0H/L3uE2bNqldu3aytrZW69at1apVK9na2uqjjz7Stm3bzB0jAAAAAAAAAMACGdWDOSQkRH379lXHjh1TTF+wYIFCQkIMpTQAAAAAAAAAAP8eRvVgvnTpkurXr59qev369XXu3LkMDwoAAAAAAAAAYPmMSjCXLl1a4eHhqabv2rVLRYsWzfCgAAAAAAAAAACWz6gSGZ999pk+++wzHTlyRJUqVZIkHTt2TFu2bNHEiRPNGiAAAAAAAAAAwDIZ1YO5fv36mj17tuLj47V06VKtXr1aVlZWCg0Nlaenp7ljBAAAAAAAAABYIKN6MEuSq6urXF1dzRkLAAAAAAAAACALMaoHMwAAAAAAAAAATyLBDAAAAAAAAAAwCQlmAAAAAAAAAIBJjEowDxo0SDExMYbXw4cP140bN8wWFAAAAAAAAADA8qWbYK5Ro4a6dOmiiRMnatWqVTp79qwSExMlSWvWrFFsbOxLCxIAAAAAAAAAYHls03tj7dq1OnHihI4fP66kpCT5+/vrzp07KlWqlOLi4rR69Wq5ubnprbfekp2d3cuMGQAAAAAAAABgAdJNMBcqVEiFChVSvXr1NH36dK1atUo5cuRQRESEunTpotOnT2v79u06f/68jhw58jJjBgAAAAAAAABYgHQTzF26dFGFChVUrlw5WVlZydraWrlz51a1atVkbW2tfv36qVixYoqLi3uZ8QIAAAAAAAAALES6NZh9fX2VLVs2rVy5UklJSWrQoIHatm2roUOH6uHDh4qIiND9+/efqzzG0aNH5efnJ0m6cOGC2rVrp/bt2ysoKMhQ3xkAAAAAAAAAkDWkm2B2d3eXv7+/Zs6cKUlatmyZ+vbtq/Lly0uSpk6dqpo1a6p58+ZGbWj27NkKDAzUgwcPJEljx45VQECAvv/+eyUlJWn79u0v+lkAAAAAAAAAAC9Rugnmx73xxhsqUKCAqlWrprZt28rBwUGzZ8/W4cOHFRwcbNSGihcvrpCQEMPr33//XdWrV5ck1alTR/v37zchfAAAAAAAAABAZkm3BvPjduzYkeL1unXrDL9XrFjRqA15enrq0qVLhtdJSUmysrKSJOXKlUt37txJc7ncubPL1tbGqG3g5cuXL2dmh4CnsLGx5jvCM9FOYCzaCoxBO4GxaCswFm0FxqCdwFi0FRiDdvJ8jEowm4O19f86T8fGxipPnjxpzhcT8+BlhfQMNKq03Lp1N7NDwFPky5eT7wjPRDuBsWgrMAbtBMaircBYtBUYg3YCY9FWYAzaSdoKFrRPc7pRJTLMoVy5cjp48KAkKTw8XNWqVcusUAAAAAAAAAAAJsi0BPOAAQMUEhKiNm3aKD4+Xp6enpkVCgAAAAAAAADABC+1REbRokW1dOlSSVLJkiW1aNGil7l5AAAAAAAAAEAGyrQezAAAAAAAAACArI0EMwAAAAAAAADAJCSYAQAAAAAAAAAmIcEMAAAAAAAAADAJCWYAAAAAAAAAgElIMAMAAAAAAAAATEKCGQAAAAAAAABgEhLMAAAAAAAAAACTkGAGAAAAAAAAAJiEBDMAAAAAAAAAwCQkmAEAAAAAAAAAJiHBDAAAAAAAAAAwCQlmAAAAAAAAAIBJSDADAAAAAAAAAExim9kBAP8078y3yewQHvNAUubHs79TQmaHAAAAAAAAADOgBzMAAAAAAAAAwCQkmAEAAAAAAAAAJiHBDAAAAAAAAAAwCQlmAAAAAAAAAIBJSDADAAAAAAAAAExim9kBAMC/VYkTdzM7hP9nKXFI58vlzOwQLI534qXMDuF/bmR2AI+ssi6a2SEAAAAAAP4fPZgBAAAAAAAAACYhwQwAAAAAAAAAMAkJZgAAAAAAAACASUgwAwAAAAAAAABMQoIZAAAAAAAAAGASEswAAAAAAAAAAJOQYAYAAAAAAAAAmIQEMwAAAAAAAADAJCSYAQAAAAAAAAAmIcEMAAAAAAAAADAJCWYAAAAAAAAAgElIMAMAAAAAAAAATEKCGQAAAAAAAABgEhLMAAAAAAAAAACTkGAGAAAAAAAAAJiEBDMAAAAAAAAAwCQkmAEAAAAAAAAAJiHBDAAAAAAAAAAwCQlmAAAAAAAAAIBJSDADAAAAAAAAAExCghkAAAAAAAAAYBISzAAAAAAAAAAAk5BgBgAAAAAAAACYhAQzAAAAAAAAAMAkJJgBAAAAAAAAACaxzewAAAAA8OKCChzJ7BD+p0BmB/DIiBvOmR2CxVleYH1mh5CShbSVlje8MjsEi3M6cWZmh/A/NzI7gEfKWnfP7BAsUs4TAzI7BElSnKScmR3E/7tb7ovMDsHiFJzfKrNDSKFgZgcg6XqnZZkdgkUq6Fcls0MwsIR2IknXF/6S2SE8Ez2YAQAAAAAAAAAmIcEMAAAAAAAAADAJCWYAAAAAAAAAgElIMAMAAAAAAAAATEKCGQAAAAAAAABgEhLMAAAAAAAAAACTkGAGAAAAAAAAAJiEBDMAAAAAAAAAwCQkmAEAAAAAAAAAJrHNzI0nJiZq+PDhOnXqlOzs7BQcHCwHB4fMDAkAAAAAAAAAYKRM7cG8bds2xcXFacmSJerTp4/GjRuXmeEAAAAAAAAAAJ6DVVJSUlJmbXzs2LGqVKmSvLy8JElubm7as2dPZoUDAAAAAAAAAHgOmdqDOSYmRrlz5za8trGx0cOHDzMxIgAAAAAAAACAsTI1wZw7d27FxsYaXicmJsrWNlPLQgMAAAAAAAAAjJSpCeYqVaooPDxckvTrr7+qbNmymRkOAAAAAAAAAOA5ZGoN5sTERA0fPlynT59WUlKSxowZo9KlS2dWOAAAAAAAAACA55CpPZitra01cuRIhYaGasmSJRaRXHZ3d9eyZctSTd+/f78cHR2NWsfBgwfl6OhoqCd98uRJ/fTTTybHVKdOHa1YsSLd9zdu3Kj3339flSpVkouLi7p3764TJ04Y3k9KStIPP/ygxMREo7b3119/acOGDSbH+0+R1dqCn5+fJk+enGLan3/+qQYNGqhNmza6e/euBg4cqL59+5q8/ad5nr8LTHf79m198cUX8vDw0Ntvvy1PT0998803io+Pf+mxXLhwQY6Ojrp06dJL3zZSunfvnqZPny4vLy+9/fbbql69urp166ajR49mdmjIAtzd3eXo6Gj4KV++vDw8PPTNN98Ytbyjo6P2799vltjatWunkJAQs6wbz/Z4u3B0dFSNGjU0ePBgxcTEPNc6jG0f5mxLsCyPn7du2rRJ169ff+7lkDU8foxxcnKSs7Oz2rZtqz179mR2aEYx5/UTnm7dunVydHTUvHnzMmyd0dHR2rVrl8nLP74Pom1YluT9THR0dKr3fvjhBzk6OmbI8YPjUPoyNcH8T+Xs7Ky9e/ca6kl/+umnOnfunFm2tWvXLg0ZMkQdOnTQunXrtHDhQuXPn19+fn66cuWKJOnw4cMaPny40QnmCRMmaMeOHWaJ99/mZbaFJ8XExKhr167KlSuXZs+erZw5c2rIkCEKCgp6KdtHxrt165ZatWqlo0ePKjg4WOvWrVPv3r21cOFCDRo0KLPDQya5d++efH19tXLlSn3++edav369vvvuOzk4OOiDDz7QsWPHMjtEZAEDBw7U3r17tXfvXm3btk3+/v6aMmWKVq1aldmhIZNNmTJFe/fuVXh4uGbNmqXjx49r3LhxZtnW3r17Va1aNbOsG5bp8uXL+vzzz3X37t3MDgVmlHyM2b17t5YsWaIqVaqoW7du3FDCU61bt04ODg5auXJlhq1z8ODBOnLkSIasi2try5MtW7Y0c1nbtm2TlZVVhmwjJCREH3/8cYas65+GBLMZ2NnZqWDBgi9lW2FhYfL29lbz5s1VvHhxOTk5afTo0cqbN6/WrVsn6VEP5ueRiVVT/nFeZlt43IMHD9SjRw/du3dP8+bNU548eSRJ9vb2sre3f+nxIGNMmDBB2bJl0/z58+Xq6qpixYrJ09NTEydO1Nq1a+mt+i/19ddf6+rVqwoLC1OjRo1UtGhROTk5afDgwXrvvff09ddfZ3aIyAJy586tggULqmDBgnr99dfl4+MjV1dXbdmyJbNDQybLmzevChYsqMKFC6ty5crq0KGD2Ua6FSxYUHZ2dmZZNywT1x3/DsnHmMKFC6ts2bLq37+/vLy8NHbs2MwODRbq77//1t69e+Xv76/Tp0+nGKFtKbi2tjzVqlVLlWCOiYnRkSNHVK5cuQzZRr58+ZQrV64MWdc/DQlmEzk6OmrVqlVq2rSpnJ2d5efnp4sXL0pKWRbBz89Ply9fVmBgoAYOHChJOnPmjDp06KBKlSqpYcOGmjdvXoqTq9DQUNWtW1dVq1bVrFmznhqHlZWVjh49mmKoorW1tb777ju1bt1aly5dUocOHSRJ5cuX18GDBxUfH68vvvhCderUUfny5VW/fn19//33kh7djVm5cqXWrl0rd3d3SdKdO3c0YMAAVa1aVbVq1dLQoUNTbG/q1Klyc3NTxYoV1aZNmwy7I5hVWEpbSJaQkKDevXvr0qVLWrBggV599VXDe48P4wkJCVGvXr00cuRIVa1aVfXr10+xjcTERE2YMEE1atRQjRo1NGPGDDVs2FAHDx6U9GhH3bt3bzk7O8vT01O///57ijiuXr2qzz//XNWrV1eNGjU0cuRIPXjwQJK0YsUKtWvXTjNnzpSLi4tq1aqltWvXasOGDapXr55cXFw0adKk5/0q/tHi4uK0fv16+fr6Knv27Cneq169ur799luVLVtWkZGR6tq1q5ydnVWxYkW1a9dOZ86ckfSoPdapU0dLlixRnTp1VKNGDfXr10/37983rOubb76Rh4eHKlSooNq1a2vq1KmG9+Lj4zVy5EhVq1ZNdevWTTW08WnbhnkkJiYqLCxMHTt2VL58+VK9P3DgQH355ZeSnr6/CQkJUffu3eXn5ycXFxeFh4fL3d1dS5YsMZRg6tKliy5fvix/f3+9/fbb8vb2VmRkpGFbYWFheu+991ShQgXVqFFDQUFBhvJAAwcOVHBwsHr37q3KlSvL09PTUO5n/fr1cnFxUVxcnGFd4eHhqlGjRqaUfsH/2Nrays7OTomJiZozZ44aNGigSpUq6YMPPlBERESay/zxxx/q2bOnXFxcVKFCBXl7e+vw4cOSpEuXLsnR0VGbN29Ww4YNVbVqVXXv3l03btwwLL9161Z5enqqcuXKCg4OJvlkgQoUKJDidVJSkmbMmCE3NzdVrVpVXbp00fnz59Nc9v79+xoyZIiqVq0qNzc3LVu2TOXKlTOUWnq8RMaTpcoeP6dKbkvbt2+Xu7u7nJ2dNW7cOJ06dUotWrRQ5cqV1b17d3rFZgEeHh6SpEaNGhmOC087F0l27do1vfXWWylG6cTExKhixYqpzklhmdq0aaPTp0/rwoULT73efNb5a0hIiPr06aPg4GA5OzvL3d1dBw4c0MKFC/XOO+/I1dVVixcvNmzXmHPl5OujJ0s0/f333/Ly8lJAQIDRo4Nhms2bN8vOzk6NGzdWiRIlUpSJfLJEQfIx4cKFC5Ield1p3LixKlasqEaNGiksLEzSo/PRQ4cOaebMmfLz8zMs99VXX8nFxcUwItSYfVDy+h4vkWHscjAfDw8P/fzzz7pz545h2u7du1WtWrVUSeFt27YZygv6+PgoPDxcknTjxg3VqFFDU6ZMMcz70UcfqUOHDkpKSkrV/hYuXCgPDw85OzurQ4cOKa6PVqxYocaNG6tSpUpq0aKFIZfyT0WC+QVMnz5dgwcP1nfffac///wzzYRYSEiIXnvtNQ0cOFBDhgzR/fv31bVrV1WuXFlr1qxRYGCgvv32Wy1atEiStGfPHo0ePVq9evVSaGiofv31V127di3dGNq3b6+IiAi5ubmpZ8+e+v7773X58mUVLVpU+fLl0+uvv244MIaHh8vZ2VmzZ8/Wjh07NG3aNG3atEk+Pj4KDg7WtWvX1LlzZ7333nvy9PTU8uXLJT0aRnLz5k0tXrxYs2bN0rlz5ww7361bt2rx4sWaMGGCNmzYoHLlyqlnz57/ugOuJbSFZMOGDdO2bds0fPhwvf7660+dd+vWrbKxsdGyZcvUpk0bTZo0SWfPnpUkzZo1S6tWrdKECRM0f/587dq1K0U9o6CgIEVFRWnRokUKDAzUggULDO/FxcXpww8/1N27d/Xdd99p6tSpCg8PTzGk9rffftP58+e1fPlyvffeexo2bJihjfXu3VuzZs3SqVOnnvl5/y0uXryou3fvqmLFimm+X7NmTeXIkUOffPKJ3njjDa1evVqhoaFKTEzU+PHjDfMl11j/5ptvNHr0aG3ZssVwwrZ69WrNmzdPwcHB2rRpkz799FPNmDHDcPEWEhKiXbt26euvv9aUKVO0cOFCw3qTkpKeuW1kvOjoaP3555+qXr16mu8XKFBAuXPnfub+RpJ27twpT09PLVy4UFWqVJEkTZs2Tb169dLixYt1/Phx+fj4GJJC1tbWhhOvn376SSNGjFCvXr20efNmjRgxQitWrEjR+zU0NFRvvfWWVqxYodq1a2v48OG6deuW3N3d9fDhQ+3bt88w78aNG+Xp6als2bKZ48+GZ4iPj9eWLVu0b98+ubu766uvvtK8efM0aNAgrVy5UkWLFlXXrl3TrMPbv39/PXz4UKGhoVq1apVee+21VMNHZ82apQkTJmjmzJk6duyY5s6dK0k6e/asAgIC1K5dO4WFhSkuLu5fd9Pa0t24cUMLFy5Us2bNDNMWLVqk1atXa/z48Vq6dKkcHBzUsWNH3bt3L9XywcHB+vnnnzVnzhxNnjxZc+bMUUJCgsnxzJ49WzNmzNDw4cM1f/589ezZU/369dPs2bN1+PBhQ1IBliv5JsKSJUvUuHHjZ56LJCtcuLBcXFy0ceNGw7Rt27bpjTfeUPny5V/qZ4Bpkp+/dPbs2adeb0pPP3+VHiUjc+bMqdWrV6tChQrq2bOnIcncpk0bjR07Vjdv3jTqfPXatWuKiYnRypUr5ePjY5iePEL0tdde0/jx42VtTSrFnNauXas6derI1tZWHh4eWrdunVEdD/766y/17dtXHTt21KZNm9StWzcFBgYqMjJSQ4YMkbOzsz788MMUNw9++uknhYWF6eOPPzZ6H/QkU5dDxipdurSKFCliSBZL0vbt29WgQYMU80VERKhfv3766KOPtHbtWrVu3Vr+/v46efKkChQooIEDB2ru3LmKjo7W6tWr9dNPP2n06NGpymwsW7ZMkydPVq9evQznvZ988omSkpK0YsUKjRw50tCuatWqpY8//thQyvafiL3iC/jwww/l6upquPP522+/pZonX758srGxUe7cuWVvb6+1a9cqb9686t27t0qUKKG6desqICBA3377raRHDdTLy0ve3t4qU6aMRo8e/dShgjVr1tQPP/wgNzc37dmzRyNGjJCHh4f69OmjBw8eyMbGRnnz5pUkvfrqq7Kzs1PZsmU1evRoVa5cWcWKFVP37t2VkJCgc+fOKVeuXMqRI4fs7OxUoEABXbx4UVu3btX4yUp24QAAFY5JREFU8ePl5OSkChUq6IsvvtCWLVv03//+V5cvX5atra3eeOMNFStWTH369NH48eP/dQlmS2gL0qMehNu2bVPZsmU1Y8aMZ1602dvba+DAgSpVqpS6d++ufPny6fjx45Kk77//Xp999pnc3NxUrlw5jRs3ztCT7M6dO9q4caMGDRqk8uXLy83NTT169DCsd8+ePbp69aq+/PJLOTk5qWbNmho2bJiWLFliuJuYmJiowMBAOTg4qFWrVrp7964+++wzOTo6ql27drK3t1dUVJTxX8I/3O3btyXpqcOw7t27p5YtW2rAgAEqXry4ypcvLx8fH8NNA0l6+PChBg8eLCcnJzVo0EBubm6G9lq4cGGNHTtWrq6uKlq0qNq1a6eCBQvqzJkzSkpK0rJly+Tv7y8XFxc5OzsbeuIbu21kvOSen4/3Xj527JicnZ1T/Dxrf5O8jg8++EBOTk7KnTu3JKl58+aqXbu2KlasqOrVq6ts2bJq06aNypYtq2bNmhn+R3PkyKHRo0erUaNGKlKkiN59912VK1cuxfdftmxZffTRRypVqpR69eqlBw8e6MyZM3rllVfk4eGhTZs2SXp0gyq5RwFenpEjRxraS6VKlTRgwAB9+OGHatq0qRYtWiR/f395eHiodOnSGjVqlGxtbbV69epU66lfv76GDh2q0qVL680335Svr68iIyNT9ERO7gXv4uKipk2bGvZBYWFhqlKlijp27KjSpUtr6NChmVJmCil1795dzs7Oqly5slxdXXXixAn5+fkZ3p8zZ4769u0rV1dXw/dmY2OjzZs3p1hPbGysVq1apcDAQDk7O6tatWoKDAx8odh69OghJycnNW/eXPny5ZOXl5dcXV3l4uKi6tWrcx6RBST3iM+fP79y5Mjx1HORJzVt2jRFO9uwYYOaNGny0mLHi0k+pz19+vRTrzelp5+/So9K+fTq1UvFixeXt7e3bt++rUGDBql06dLq1KmT4uPjFR0dbfT5ateuXVW8eHEVLVpU0qOOFH379lViYqKmT59OKR8zu3btmn766SdDUrBRo0a6efOmdu/ebdSy8fHxKly4sIoUKaL3339f8+bN03/+8x/Z29srW7ZseuWVV1KcO3fo0EHFixdXyZIln2sf9DhTl0PGc3d3N5TJiI+P1969ew2j85PNnTtX77//vry9vVW8eHG1a9dOXl5ehg5UPj4+qlatmoYPH66xY8eqb9++KlasWKpthYaGys/PT02aNJGDg4OGDh0qd3d3xcTEaOHChfL19ZW3t7dKliypPn36yMnJKUUnrX8a28wOwNLY2tqmmRxNTEw0PKgtWfHixQ2/586d2zAU+GmioqJ09uxZOTs7p1h3XFyc4uLiFBkZqVatWhneK1CggIoUKfLUdVaqVEnTpk1TXFycfvnlF61fv17Lly/Xq6++qsGDB6eav0GDBtq3b5/GjRunqKgoQz2jtD538kVh/fr1U713/vx5NW3aVCtWrFDDhg1VsWJFubu7q2XLlqn+VllRVmwLd+/e1YIFC2RnZ6f3339f8+fPV9euXdOdv0iRIrKxsTG8zpUrl+Lj43Xjxg398ccfKXrLlipVynCz4ty5c0pISJCTk5Ph/QoVKhh+j4yMVPHixVMcuKtUqaKEhATDsNn8+fMbklg5cuQwxJMse/bsKYbM/9vlz59f0qOheenJmTOn2rdvr9WrV+v48eOG/+8nSyek115r1qypo0ePauLEiYqMjNTJkyd1/fp1JSYm6ubNm7px40a637mx20bGSq6vnnwDQpKcnJwMD2Y7cuSIBgwY8Mz9jaQ09y+Pn0hlz55db7zxRorXyctWqFBBOXLk0LRp03T27FmdOnVKFy5cUM2aNdNcV/L/fnLba9q0qfr06aO4uDjt27dPOXLkkIuLi4l/FZjC399f7777rqRH323BggVlY2OjP//8U7du3dLbb79tmDdbtmyqUKFCiiGAydq1a6cNGzbol19+0blz5ww3LR+/4ZnePigyMlKOjo4ptvP4a2SO5JsP0qNj0Nq1a9WmTRstW7ZMhQoV0tWrV9W3b98UvfkePHiQqkxGVFSU4uPjU5xbPL5PMkVy8kdKvY/KkSMH5xFZ0NPORZ7k6empESNG6OjRoypRooT279+vAQMGZELUMEXyKBhHR8enXm8m71uedr1VpEgRQ8/CJ68rkl/HxcUZfb765DnRli1bFB8fr/r16+uVV155oc+NZ9uwYYNsbGxUt25dSdLbb7+tQoUKadWqVal6oj7prbfeUpMmTfTxxx+rePHiql+/vlq0aGG4jk3L49/38+yDHmfqcsh4Hh4e+uSTT/Tw4UP9+OOPevPNN/Wf//wnxTyRkZE6ffp0ipFO8fHxqlSpkuH1yJEj9d5776lcuXJq3759mtuKjIxU9+7dDa/t7e0Nx6HIyMgUnfAkqXLlyv/om99ZPwuYwezt7dMc8nn79u1UPQefHLprTJ3Ahw8fqnr16hoxYkSq95KTlk+uJ70hwrGxsZo0aZI6deqkokWLys7OTjVr1lTNmjWVO3fuVPVRk02ePNlQV7N58+YKCgpKdUcnWUJCgnLmzJnmU+QLFiyonDlzasWKFTpw4IDhqcCLFy9WWFiYChcu/NS/haXLSm0hWfv27Q07xS5dumjatGmqX7++YQjak9JbX3rbf9rrx5PuT9YIlv6XXEg+yD6e2E6WUU92/SdKTtj/9ttvKQ58yQICAlS/fn3NnDlTefPmVYMGDdSkSRNFRUXpm2++STFveu112bJlGjNmjFq2bKlGjRppwIABhhruT84rpfzOY2Nj1bJly2duGxnLwcFB+fLl05EjRwztws7OTg4ODpKky5cvSzJuf5PW/+2TN9PSGw66Z88effLJJ/L29pabm5s+/fTTVNtKa3+T3J5q1aolW1tb7d+/31A3j6GnL1eBAgUM7eZxabUL6dE+/clRMomJiercubP+/vtvNW7cWO7u7oqPj5e/v3+K+Z7nmEmZlMxXqFChFG2jUqVKCg8P19KlSw0XTpMmTdKbb76ZYrknz5XSOrd4nhrbaY3KMnYfhazDmHORZHny5FGdOnW0adMmvfnmmypTpky657ywPMml8C5evPjU683knspPO3akdV2R1v7A2PPVJ499hQoV0ujRo9W1a1ft3LkzzWQ4Mk5yOYwaNWoYpiUmJmrXrl0pntuQ7PHjg5WVlSZOnKjOnTtrx44d2rFjh77//nvNnDlTtWvXTnN7j3/fz7MPepypyyHjValSRTY2Nvr555+1fft2NWzYMNU8CQkJ6tKli1q0aJFi+uOjEyIjI5WYmKioqCj98ccfaea3nnaemnxz68ntvkhpMEvHWdgTHB0d06z3l1FPnSxZsqTOnz+vIkWKyMHBQQ4ODjp58qRmz54ta2trlSlTJsVwn5iYmBR1bx+XI0cOrVmzJs0nedvb2xt6PD6ZtAsNDVVgYKD69esnLy8vQ4285IP04/OXLFlSd+/eVUJCgiFeSRo7dqxiYmK0a9cuLVmyRG5ubgoMDNSmTZsUGxurn3/++QX+SpYhK7WFZI+fXH366ad6/fXXNWjQoOfeieXJk0eFChVK8ZCU6OhoQy/JUqVKKVu2bCniO3nypOH30qVL6+LFi7p165Zh2q+//iobG5sUvQ9gPBsbG3l5eWnRokWpemT9+OOP2rhxo6Kjo3X16lUtXLhQXbt21TvvvKMrV64YfQH/ww8/qHv37hoyZIi8vb2VP39+/fXXX0pKSlL+/Pn1n//8J93v/NChQy+0bZjG1tZW77//vr799tsUD7NIlly3/Vn7mxe1bNky+fj4aNSoUWrVqpVhH/A8n8PT01Pbt2/Xnj17KI9hQezt7VWwYEEdPXrUMC0+Pl6///67SpYsmWLes2fP6vDhw5o7d6569OihevXq6Y8//pBkXCLxyeNeQkICtfgtWEJCgvLkyaNXX31V169fN+xbihYtqkmTJqX67ooXL65s2bKlOLdI7uGelmzZsqW40f+scyBkTU9epzztXCQtTZo00c6dO7V7926OHVlMWFiYodTe0643M5Kp56vOzs6qWbOmfH19FRwcnOIB2chY58+f1/HjxzVo0CCtWrXK8DNr1izFx8dr/fr1srOzU2xsrGGZx48PkZGRGjt2rMqXL6/PPvtMK1euVLVq1bR161ajtv+8+6AXXQ4Zz9raWvXq1dOOHTu0c+fONHu9lyxZUtHR0YZ9joODg1avXm1oJ7GxsRoxYoR69uyp8uXLa9SoUWluy8HBwVARQHo0orxWrVo6ffq0SpUqleL8WZKOHj2a6vz5n4QE8xN8fX21a9cuhYSE6Ny5czpz5ozmz5+vZcuWqVOnTiatM1euXIqKitKtW7fUrFkzxcXFGQrN79u3TyNHjjQM2fD19dWWLVsUGhqqyMhIBQYG6sGDB2mu18bGRj169NC0adP01VdfKTIyUpGRkVq6dKnmzZunLl26SHo0dF2STpw4oQcPHihfvnzauXOnoqOj9fPPP6t///6SZEha5cyZU1euXNG1a9dUunRpubm5qX///jp69KgiIiI0YMAA/fXXXypUqJDhoQibNm3SpUuXtHbtWsXFxaUYRp9VZaW2kJbs2bNr1KhROnbsmObNm/fcsfr5+Wn69Onat2+fIiIiDA/asLKyUu7cudWsWTONHj1av/76q3788UfNmDHDsOw777yjEiVKqH///oqIiNDBgwcVHBysxo0bG2584Pn5+/vrwYMH6tSpk3788UddvHhRK1euVEBAgFq0aKFatWrp3r172rp1qy5duqRly5Zp8eLFRg8Rzp8/vw4cOKCoqCgdP35cvXr1Unx8vOLi4mRlZaX27dsb2sSxY8dSPLQxX758L7RtmK5nz54qXLiwWrdurfXr1ys6OlonTpzQuHHjNHToUFWtWvWZ+5sXldyLOiIiQmfOnNHAgQN1/fr15/r+mzZtqtWrVytXrlxp9tJH5uncubOmT5+u7du3KzIyUsOGDdODBw9S1TrNkyePrK2ttWHDBl2+fFmbNm0yPETHmLbQqlUrnThxQtOnT1dUVJTGjh2rq1evmuUzwXh///23rl+/ruvXrys6OlpTpkzRhQsXDCVVOnbsqKlTp2rbtm26cOGCRowYof3796tUqVIp1pMrVy61aNFCY8eO1a+//qpff/1Vo0ePlpT2CKaKFSsqNDRUp0+f1qFDhzR//nzzf1i8dMnXKREREYqNjX3quUha3N3dde3aNe3evVuNGzd+maHjOcTExOj69ev6448/dOrUKY0ePVobNmzQwIEDn3m9mZFe9HzV399f9+7d06xZszI0LvzPunXrlCdPHrVr105ly5Y1/NStW1fOzs5auXKlKlSooLVr1+rYsWM6fvy4QkJCDMeRPHnyaMmSJQoJCVF0dLR+/PFHnTp1ylDaL1euXLp48aL++uuvNLf/vPugF10O5uHh4aFly5YpX758adZOTn4I5IIFC3ThwgX98MMPmjlzpqEz3JQpU2RnZ6fOnTtr2LBh2rVrV4qHlyfr0KGDFi5cqM2bN+v8+fMKCgpSvnz59Oabb6pTp076/vvvtWrVKp07d04TJ07UyZMn1bp1a7N//sxCgvkJ5cuX15w5c3To0CG1bNlSrVu31saNGzV58mS5ubmZtE5fX1+FhoZq6NChyp07t+bMmaPLly/Lx8dHAwYMkI+Pj3r16iVJcnFx0dixYzV79my1bNlShQsXVtmyZdNdd+fOnTVq1Cjt3r1bLVu2VIsWLRQWFqbx48erXr16kh49WKl27dpq3769wsPDNWbMGJ0+fVpeXl4aMGCA3n33XVWuXNlw56V58+a6ePGimjVrpqSkJI0fP14ODg7q3LmzPvjgAxUqVMiQTHR3d1dAQIDGjx+vd999V/Pnz9fEiRNTXVRkRVmtLaSlevXqatWqlaZNm5Zmrcyn6dy5sxo1aqTPP/9cH374oerWrStbW1vDMJBhw4apatWq6ty5swYPHqwPPvjAsKy1tbW++uorWVlZqU2bNobyDckXkjBNgQIF9MMPP6h06dIaMGCAmjRpotmzZ+vjjz821Mj09/fXqFGj1KxZM4WFhSkoKEi3bt0y6mm1gwcP1v379+Xj4yN/f3+VLVtWnp6ehn1Djx495OPjo969e6t79+5q06aNYdkX3TZMlyNHDn333Xdq3bq1Zs+erWbNmsnPz08nTpxQcHCwFi1a9Mz9zYvy9/dXoUKF1LZtW3Xq1EnZsmWTr69vijv6z1K1alXlz5+fHmgWqGPHjmrbtq2CgoLUokULXblyRQsXLkxVz+61117T8OHDNX/+fHl5eWnWrFkKDAxUtmzZUox4SE+JEiU0c+ZMbdq0Sd7e3rp586bJx1tknICAANWuXVu1a9eWl5eX9u/fr5CQEFWpUkXSo5Jcbdu21YgRI9SsWTOdPn1ac+fOTXMo6YABA+Tk5KROnTrps88+U9OmTSWlPcQ0ICBAhQoVUosWLTRq1CgFBASY94MiU+TPn18tWrRQnz59tHz58meeizwpR44ccnd3V/ny5VPU4IZlGTdunGrXri03Nzd16tRJ586d04IFC1S9enVJeur1ZkZ60fNVe3t79erVS3PmzElVZx4ZY/369WrSpEmaJbratWun33//XbVr11aVKlX0wQcfqFevXurWrZthRF7BggUVEhKi7du3y8vLS/369VO7du3UsmVLSVKbNm20b98+ffTRR2lu/3n3QS+6HMyjdu3aSkxMTLdmd+XKlTVhwgQtXbpUXl5eWrBggcaMGaN69erp2LFjWrx4sYYNGyY7OzuVLl1aH374oUaOHJlqxGizZs3UrVs3jR49Wj4+Prpx44a+/vprWVtby9PTU3369NG0adPUrFkzHTx4UPPnz1eZMmVexp8gU1gl0WcfQBrCw8NVoUIFw9O9b9y4IVdXV23fvj3FQ3UAICPcu3dP77zzjpYvX04NTeAfatu2bXJ1dVWuXLkkSceOHVP79u115MgR6m3DZB06dNC7776b7kOYAACA+fGQPwBpSn5gY79+/WRlZaWpU6eqYsWKJJcBZLhNmzZpx44dcnJyIrkM/INNnz5dO3bsULdu3RQbG6svv/xS7u7uJJdhkoMHD+ro0aP6/fffNX369MwOBwCAfzV6MANI07Vr1zRixAgdOnRISUlJcnV11dChQ9Mc8goAL8LT01MPHz7UjBkz5OjomNnhADCTs2fPGp4PYWdnJ3d3dw0ePFj29vaZHRqyoMDAQG3evFlDhw5Vs2bNMjscAAD+1UgwAwAAAAAAAABMwkP+AAAAAAAAAAAmIcEMAAAAAAAAADAJCWYAAAAAAAAAgElIMAMAAAAAAAAATEKCGQAAAAAAAABgkv8DkYsC4OB6ICoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232cf96a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_age_total['60 or older'].sort_values(ascending=False)[:10].plot(kind='bar', color=sns.color_palette('rainbow', 10))\n",
    "plt.xticks(fontsize=14, rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('# of the developers whose age are 60+', fontsize=14)\n",
    "plt.title('Top 10 countries of # of the developers whose age are 60+', fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.savefig('top10-60-or-older.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>17 or younger</th>\n",
       "      <th>18-20</th>\n",
       "      <th>21-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60 or older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>121</td>\n",
       "      <td>102</td>\n",
       "      <td>425</td>\n",
       "      <td>416</td>\n",
       "      <td>227</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>46</td>\n",
       "      <td>234</td>\n",
       "      <td>585</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>375</td>\n",
       "      <td>124</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "could you tell us your age range?  17 or younger  18-20  21-29  30-39  40-49  \\\n",
       "country                                                                        \n",
       "United States                                121    102    425    416    227   \n",
       "India                                         46    234    585    144     32   \n",
       "China                                         17     45    375    124     20   \n",
       "\n",
       "could you tell us your age range?  50-59  60 or older  \n",
       "country                                                \n",
       "United States                        100           56  \n",
       "India                                  7            2  \n",
       "China                                  0            0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_countries = country_age_total.loc[['United States', 'India', 'China']]\n",
    "three_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>17 or younger</th>\n",
       "      <th>18-20</th>\n",
       "      <th>21-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60 or older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>0.083621</td>\n",
       "      <td>0.070491</td>\n",
       "      <td>0.293711</td>\n",
       "      <td>0.287491</td>\n",
       "      <td>0.156876</td>\n",
       "      <td>0.069109</td>\n",
       "      <td>0.038701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.043810</td>\n",
       "      <td>0.222857</td>\n",
       "      <td>0.557143</td>\n",
       "      <td>0.137143</td>\n",
       "      <td>0.030476</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.001905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.029260</td>\n",
       "      <td>0.077453</td>\n",
       "      <td>0.645439</td>\n",
       "      <td>0.213425</td>\n",
       "      <td>0.034423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "could you tell us your age range?  17 or younger     18-20     21-29  \\\n",
       "country                                                                \n",
       "United States                           0.083621  0.070491  0.293711   \n",
       "India                                   0.043810  0.222857  0.557143   \n",
       "China                                   0.029260  0.077453  0.645439   \n",
       "\n",
       "could you tell us your age range?     30-39     40-49     50-59  60 or older  \n",
       "country                                                                       \n",
       "United States                      0.287491  0.156876  0.069109     0.038701  \n",
       "India                              0.137143  0.030476  0.006667     0.001905  \n",
       "China                              0.213425  0.034423  0.000000     0.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_countries_ratio = three_countries.div(three_countries.sum(axis=1), axis=0)\n",
    "three_countries_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d18bda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "three_countries_ratio.plot(kind='bar', figsize=(12, 6), color=sns.color_palette('rainbow', 7))\n",
    "plt.xticks(range(3), ['United States', 'India', 'China'], rotation=0, fontsize=14);\n",
    "plt.xlabel('')\n",
    "plt.legend(title=None)\n",
    "plt.title(\"Age distribution of the developers who're from USA, India and China\", fontsize=18);\n",
    "plt.tight_layout()\n",
    "plt.savefig('age-distribution.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>17 or younger</th>\n",
       "      <th>18-20</th>\n",
       "      <th>21-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50-59</th>\n",
       "      <th>60 or older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pyver</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Python 2</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>110</td>\n",
       "      <td>129</td>\n",
       "      <td>66</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python 3</th>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "      <td>315</td>\n",
       "      <td>287</td>\n",
       "      <td>161</td>\n",
       "      <td>61</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "could you tell us your age range?  17 or younger  18-20  21-29  30-39  40-49  \\\n",
       "pyver                                                                          \n",
       "Python 2                                      23     13    110    129     66   \n",
       "Python 3                                      98     89    315    287    161   \n",
       "\n",
       "could you tell us your age range?  50-59  60 or older  \n",
       "pyver                                                  \n",
       "Python 2                              39           15  \n",
       "Python 3                              61           41  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_age.loc['United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7321428571428571"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "41 / 56"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 Python 3 和 Python 2 的开发者的国别分布？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['what country do you live in?']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, ['country', 'live'])\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Italy\n",
       "1    United Kingdom\n",
       "2            France\n",
       "3             Spain\n",
       "4            Israel\n",
       "Name: what country do you live in?, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = survey_df[cols[0]]\n",
    "countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              9502\n",
       "unique              154\n",
       "top       United States\n",
       "freq               1638\n",
       "Name: what country do you live in?, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is python the main language you use for your current projects?</th>\n",
       "      <th>none:what other language(s) do you use?</th>\n",
       "      <th>java:what other language(s) do you use?</th>\n",
       "      <th>javascript:what other language(s) do you use?</th>\n",
       "      <th>c/c++:what other language(s) do you use?</th>\n",
       "      <th>php:what other language(s) do you use?</th>\n",
       "      <th>c#:what other language(s) do you use?</th>\n",
       "      <th>ruby:what other language(s) do you use?</th>\n",
       "      <th>bash / shell:what other language(s) do you use?</th>\n",
       "      <th>objective-c:what other language(s) do you use?</th>\n",
       "      <th>...</th>\n",
       "      <th>technical support:which of the following best describes your job role(s)?</th>\n",
       "      <th>data analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>business analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>team lead:which of the following best describes your job role(s)?</th>\n",
       "      <th>product manager:which of the following best describes your job role(s)?</th>\n",
       "      <th>cio / ceo / cto:which of the following best describes your job role(s)?</th>\n",
       "      <th>systems analyst:which of the following best describes your job role(s)?</th>\n",
       "      <th>other - write in::which of the following best describes your job role(s)?</th>\n",
       "      <th>could you tell us your age range?</th>\n",
       "      <th>what country do you live in?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7898</th>\n",
       "      <td>Yes</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17 or younger</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9478</th>\n",
       "      <td>No, I use Python as a secondary language</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PHP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>Objective-C</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9489</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/C++</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/C++</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bash / Shell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     is python the main language you use for your current projects?  \\\n",
       "7898                                                Yes               \n",
       "9478           No, I use Python as a secondary language               \n",
       "9489                                                Yes               \n",
       "9499                                                Yes               \n",
       "\n",
       "     none:what other language(s) do you use?  \\\n",
       "7898                                    None   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     java:what other language(s) do you use?  \\\n",
       "7898                                     NaN   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     javascript:what other language(s) do you use?  \\\n",
       "7898                                           NaN   \n",
       "9478                                    JavaScript   \n",
       "9489                                           NaN   \n",
       "9499                                           NaN   \n",
       "\n",
       "     c/c++:what other language(s) do you use?  \\\n",
       "7898                                      NaN   \n",
       "9478                                      NaN   \n",
       "9489                                    C/C++   \n",
       "9499                                    C/C++   \n",
       "\n",
       "     php:what other language(s) do you use?  \\\n",
       "7898                                    NaN   \n",
       "9478                                    PHP   \n",
       "9489                                    NaN   \n",
       "9499                                    NaN   \n",
       "\n",
       "     c#:what other language(s) do you use?  \\\n",
       "7898                                   NaN   \n",
       "9478                                   NaN   \n",
       "9489                                   NaN   \n",
       "9499                                   NaN   \n",
       "\n",
       "     ruby:what other language(s) do you use?  \\\n",
       "7898                                     NaN   \n",
       "9478                                     NaN   \n",
       "9489                                     NaN   \n",
       "9499                                     NaN   \n",
       "\n",
       "     bash / shell:what other language(s) do you use?  \\\n",
       "7898                                             NaN   \n",
       "9478                                    Bash / Shell   \n",
       "9489                                             NaN   \n",
       "9499                                    Bash / Shell   \n",
       "\n",
       "     objective-c:what other language(s) do you use?  \\\n",
       "7898                                            NaN   \n",
       "9478                                    Objective-C   \n",
       "9489                                            NaN   \n",
       "9499                                            NaN   \n",
       "\n",
       "                 ...               \\\n",
       "7898             ...                \n",
       "9478             ...                \n",
       "9489             ...                \n",
       "9499             ...                \n",
       "\n",
       "     technical support:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                          \n",
       "9478                                                NaN                          \n",
       "9489                                                NaN                          \n",
       "9499                                                NaN                          \n",
       "\n",
       "     data analyst:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                     \n",
       "9478                                                NaN                     \n",
       "9489                                                NaN                     \n",
       "9499                                                NaN                     \n",
       "\n",
       "     business analyst:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                         \n",
       "9478                                                NaN                         \n",
       "9489                                                NaN                         \n",
       "9499                                                NaN                         \n",
       "\n",
       "     team lead:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                  \n",
       "9478                                                NaN                  \n",
       "9489                                                NaN                  \n",
       "9499                                                NaN                  \n",
       "\n",
       "     product manager:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     cio / ceo / cto:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     systems analyst:which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                        \n",
       "9478                                                NaN                        \n",
       "9489                                                NaN                        \n",
       "9499                                                NaN                        \n",
       "\n",
       "     other - write in::which of the following best describes your job role(s)?  \\\n",
       "7898                                                NaN                          \n",
       "9478                                                NaN                          \n",
       "9489                                                NaN                          \n",
       "9499                                                NaN                          \n",
       "\n",
       "     could you tell us your age range? what country do you live in?  \n",
       "7898                     17 or younger                          NaN  \n",
       "9478                               NaN                          NaN  \n",
       "9489                               NaN                          NaN  \n",
       "9499                               NaN                          NaN  \n",
       "\n",
       "[4 rows x 162 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df.loc[countries.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d18e0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(countries, order=countries.value_counts().index, palette=sns.color_palette('rainbow', 16))\n",
    "plt.xlim(xmax=15.5);\n",
    "# plt.xlabel('国家', fontsize=18, fontproperties=chinese);\n",
    "# plt.ylabel('频数', fontsize=18, fontproperties=chinese);\n",
    "plt.xticks(fontsize=14, rotation=15);\n",
    "plt.xlabel('')\n",
    "plt.ylabel('# of developers', fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig('country-counts.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>what country do you live in?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "what country do you live in?                                    \n",
       "Afghanistan                                          4         2\n",
       "Albania                                              2        16\n",
       "Algeria                                              7        16\n",
       "Andorra                                              0         1\n",
       "Antigua and Barbuda                                  0         2"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_pyver = pd.crosstab(index=countries, columns=python_ver)\n",
    "countries_pyver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>0.272979</td>\n",
       "      <td>0.727021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>0.303810</td>\n",
       "      <td>0.696190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.254733</td>\n",
       "      <td>0.745267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.220126</td>\n",
       "      <td>0.779874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.219638</td>\n",
       "      <td>0.780362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.208571</td>\n",
       "      <td>0.791429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.161572</td>\n",
       "      <td>0.838428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.278970</td>\n",
       "      <td>0.721030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>0.193069</td>\n",
       "      <td>0.806931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.196891</td>\n",
       "      <td>0.803109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "United States                                 0.272979  0.727021\n",
       "India                                         0.303810  0.696190\n",
       "China                                         0.254733  0.745267\n",
       "United Kingdom                                0.220126  0.779874\n",
       "Germany                                       0.219638  0.780362\n",
       "Brazil                                        0.208571  0.791429\n",
       "Russia                                        0.161572  0.838428\n",
       "France                                        0.278970  0.721030\n",
       "Poland                                        0.193069  0.806931\n",
       "Canada                                        0.196891  0.803109"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_countries = countries_pyver.loc[countries.value_counts()[:10].index]\n",
    "top10_countries = top10_countries.div(top10_countries.sum(axis=1), axis=0)\n",
    "top10_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>pyver</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>Python 2</td>\n",
       "      <td>0.272979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>Python 3</td>\n",
       "      <td>0.727021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Python 2</td>\n",
       "      <td>0.303810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Python 3</td>\n",
       "      <td>0.696190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>Python 2</td>\n",
       "      <td>0.254733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country     pyver     value\n",
       "0  United States  Python 2  0.272979\n",
       "1  United States  Python 3  0.727021\n",
       "2          India  Python 2  0.303810\n",
       "3          India  Python 3  0.696190\n",
       "4          China  Python 2  0.254733"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = top10_countries.stack().reset_index()\n",
    "df.columns = ['country', 'pyver', 'value']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d160cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='country', y='value', hue='pyver', data=df, palette=sns.color_palette('rainbow'))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('use ratio', fontsize=14)\n",
    "plt.legend(title=None, fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('country-pyver.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>which version of python do you use the most?</th>\n",
       "      <th>Python 2</th>\n",
       "      <th>Python 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>what country do you live in?</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "which version of python do you use the most?  Python 2  Python 3\n",
       "what country do you live in?                                    \n",
       "Afghanistan                                   0.666667  0.333333\n",
       "Albania                                       0.111111  0.888889\n",
       "Algeria                                       0.304348  0.695652\n",
       "Andorra                                       0.000000  1.000000\n",
       "Antigua and Barbuda                           0.000000  1.000000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_pyver_ratio = countries_pyver.div(countries_pyver.sum(axis=1), axis=0)\n",
    "countries_pyver_ratio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d165a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.distplot(countries_pyver_ratio['Python 3'], rug=True, color='b')\n",
    "plt.xlabel('use ratio', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.title('Use ratio of Python 3 in the world', fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.savefig('use-ratio-of-python3-world.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x232d190bfd0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d190b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "sns.stripplot(x='Python 3', data=countries_pyver_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232d1977400>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d1ae09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.transforms as transforms\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(countries_pyver_ratio['Python 3'], c='b')\n",
    "ax.axhline(countries_pyver_ratio['Python 3'].mean(), c='k', label='Mean')\n",
    "plt.xticks([]);\n",
    "xmin, _ = plt.xlim()\n",
    "ax.text(0, countries_pyver_ratio['Python 3'].mean(), round(countries_pyver_ratio['Python 3'].mean(), 2), \n",
    "        transform=transforms.blended_transform_factory(ax.get_yticklabels()[0].get_transform(), ax.transData), \n",
    "        rotation='horizontal', \n",
    "        horizontalalignment='right', \n",
    "        verticalalignment='center');\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x232d18f2da0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232d19342e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.violinplot(countries_pyver_ratio['Python 3'], palette=sns.color_palette('rainbow'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    148.000000\n",
       "mean       0.753568\n",
       "std        0.224079\n",
       "min        0.000000\n",
       "25%        0.666667\n",
       "50%        0.774194\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Python 3, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_pyver_ratio['Python 3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    148.000000\n",
       "mean       0.246432\n",
       "std        0.224079\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.225806\n",
       "75%        0.333333\n",
       "max        1.000000\n",
       "Name: Python 2, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_pyver_ratio['Python 2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "autocolorscale": false,
         "colorbar": {
          "autotick": false,
          "ticksuffix": "%",
          "title": "Percent"
         },
         "colorscale": "Set3",
         "locationmode": "country names",
         "locations": [
          "Afghanistan",
          "Albania",
          "Algeria",
          "Andorra",
          "Antigua and Barbuda",
          "Argentina",
          "Armenia",
          "Australia",
          "Austria",
          "Azerbaijan",
          "Bahamas, The",
          "Bahrain",
          "Bangladesh",
          "Barbados",
          "Belarus",
          "Belgium",
          "Belize",
          "Benin",
          "Bolivia",
          "Bosnia and Herzegovina",
          "Brazil",
          "Bulgaria",
          "Cambodia",
          "Cameroon",
          "Canada",
          "Cape Verde",
          "Chad",
          "Chile",
          "China",
          "Colombia",
          "Congo, Democratic Republic of the",
          "Costa Rica",
          "Cote d'Ivoire",
          "Croatia",
          "Cuba",
          "Cyprus",
          "Czech Republic",
          "Denmark",
          "Dominica",
          "Dominican Republic",
          "Ecuador",
          "Egypt",
          "El Salvador",
          "Estonia",
          "Ethiopia",
          "Finland",
          "France",
          "Gabon",
          "Gambia, The",
          "Georgia",
          "Germany",
          "Ghana",
          "Greece",
          "Guatemala",
          "Haiti",
          "Honduras",
          "Hong Kong",
          "Hungary",
          "Iceland",
          "India",
          "Indonesia",
          "Iran",
          "Iraq",
          "Ireland",
          "Israel",
          "Italy",
          "Jamaica",
          "Japan",
          "Jordan",
          "Kazakhstan",
          "Kenya",
          "Kuwait",
          "Kyrgyzstan",
          "Latvia",
          "Lebanon",
          "Libya",
          "Lithuania",
          "Luxembourg",
          "Macedonia",
          "Madagascar",
          "Malaysia",
          "Maldives",
          "Malta",
          "Mauritania",
          "Mauritius",
          "Mexico",
          "Moldova",
          "Monaco",
          "Mongolia",
          "Montenegro",
          "Morocco",
          "Myanmar",
          "Namibia",
          "Nepal",
          "Netherlands",
          "New Zealand",
          "Nicaragua",
          "Nigeria",
          "North Korea",
          "Norway",
          "Oman",
          "Pakistan",
          "Panama",
          "Paraguay",
          "Peru",
          "Philippines",
          "Poland",
          "Portugal",
          "Qatar",
          "Romania",
          "Russia",
          "Rwanda",
          "San Marino",
          "Saudi Arabia",
          "Senegal",
          "Serbia",
          "Singapore",
          "Slovakia",
          "Slovenia",
          "South Africa",
          "South Korea",
          "Spain",
          "Sri Lanka",
          "Sudan",
          "Sweden",
          "Switzerland",
          "Syria",
          "Taiwan",
          "Tajikistan",
          "Tanzania",
          "Thailand",
          "Timor-Leste",
          "Togo",
          "Tunisia",
          "Turkey",
          "Uganda",
          "Ukraine",
          "United Arab Emirates",
          "United Kingdom",
          "United States",
          "Uruguay",
          "Uzbekistan",
          "Vanuatu",
          "Venezuela",
          "Vietnam",
          "Yemen",
          "Zambia",
          "Zimbabwe"
         ],
         "marker": {
          "line": {
           "color": "rgb(180,180,180)",
           "width": 0.5
          }
         },
         "reversescale": true,
         "text": [
          "Afghanistan",
          "Albania",
          "Algeria",
          "Andorra",
          "Antigua and Barbuda",
          "Argentina",
          "Armenia",
          "Australia",
          "Austria",
          "Azerbaijan",
          "Bahamas, The",
          "Bahrain",
          "Bangladesh",
          "Barbados",
          "Belarus",
          "Belgium",
          "Belize",
          "Benin",
          "Bolivia",
          "Bosnia and Herzegovina",
          "Brazil",
          "Bulgaria",
          "Cambodia",
          "Cameroon",
          "Canada",
          "Cape Verde",
          "Chad",
          "Chile",
          "China",
          "Colombia",
          "Congo, Democratic Republic of the",
          "Costa Rica",
          "Cote d'Ivoire",
          "Croatia",
          "Cuba",
          "Cyprus",
          "Czech Republic",
          "Denmark",
          "Dominica",
          "Dominican Republic",
          "Ecuador",
          "Egypt",
          "El Salvador",
          "Estonia",
          "Ethiopia",
          "Finland",
          "France",
          "Gabon",
          "Gambia, The",
          "Georgia",
          "Germany",
          "Ghana",
          "Greece",
          "Guatemala",
          "Haiti",
          "Honduras",
          "Hong Kong",
          "Hungary",
          "Iceland",
          "India",
          "Indonesia",
          "Iran",
          "Iraq",
          "Ireland",
          "Israel",
          "Italy",
          "Jamaica",
          "Japan",
          "Jordan",
          "Kazakhstan",
          "Kenya",
          "Kuwait",
          "Kyrgyzstan",
          "Latvia",
          "Lebanon",
          "Libya",
          "Lithuania",
          "Luxembourg",
          "Macedonia",
          "Madagascar",
          "Malaysia",
          "Maldives",
          "Malta",
          "Mauritania",
          "Mauritius",
          "Mexico",
          "Moldova",
          "Monaco",
          "Mongolia",
          "Montenegro",
          "Morocco",
          "Myanmar",
          "Namibia",
          "Nepal",
          "Netherlands",
          "New Zealand",
          "Nicaragua",
          "Nigeria",
          "North Korea",
          "Norway",
          "Oman",
          "Pakistan",
          "Panama",
          "Paraguay",
          "Peru",
          "Philippines",
          "Poland",
          "Portugal",
          "Qatar",
          "Romania",
          "Russia",
          "Rwanda",
          "San Marino",
          "Saudi Arabia",
          "Senegal",
          "Serbia",
          "Singapore",
          "Slovakia",
          "Slovenia",
          "South Africa",
          "South Korea",
          "Spain",
          "Sri Lanka",
          "Sudan",
          "Sweden",
          "Switzerland",
          "Syria",
          "Taiwan",
          "Tajikistan",
          "Tanzania",
          "Thailand",
          "Timor-Leste",
          "Togo",
          "Tunisia",
          "Turkey",
          "Uganda",
          "Ukraine",
          "United Arab Emirates",
          "United Kingdom",
          "United States",
          "Uruguay",
          "Uzbekistan",
          "Vanuatu",
          "Venezuela",
          "Vietnam",
          "Yemen",
          "Zambia",
          "Zimbabwe"
         ],
         "type": "choropleth",
         "uid": "631396",
         "z": [
          33.33333333333333,
          88.88888888888889,
          69.56521739130434,
          100,
          100,
          72.72727272727273,
          100,
          75.18248175182481,
          72.72727272727273,
          80,
          50,
          75,
          78.94736842105263,
          66.66666666666666,
          75.86206896551724,
          74.6268656716418,
          100,
          100,
          33.33333333333333,
          100,
          79.14285714285715,
          69.56521739130434,
          100,
          66.66666666666666,
          80.31088082901555,
          100,
          100,
          91.30434782608695,
          74.52667814113597,
          66.66666666666666,
          100,
          60,
          50,
          65.625,
          55.55555555555556,
          50,
          81.35593220338984,
          80.3921568627451,
          100,
          87.5,
          66.66666666666666,
          71.42857142857143,
          75,
          73.33333333333333,
          16.666666666666664,
          76,
          72.1030042918455,
          100,
          100,
          86.48648648648648,
          78.0361757105943,
          76.92307692307693,
          79.24528301886792,
          66.66666666666666,
          100,
          100,
          100,
          79.06976744186046,
          0,
          69.61904761904762,
          65.3061224489796,
          75,
          50,
          78.37837837837837,
          52.22222222222223,
          72.88135593220339,
          100,
          91.37931034482759,
          75,
          83.33333333333334,
          77.77777777777779,
          83.33333333333334,
          50,
          100,
          100,
          0,
          53.84615384615385,
          100,
          75,
          100,
          76,
          100,
          50,
          100,
          50,
          64.55696202531645,
          50,
          100,
          100,
          100,
          52.63157894736842,
          33.33333333333333,
          100,
          77.41935483870968,
          79.06976744186046,
          64.51612903225806,
          66.66666666666666,
          78.33333333333333,
          50,
          83.87096774193549,
          100,
          82.45614035087719,
          100,
          66.66666666666666,
          71.42857142857143,
          77.41935483870968,
          80.6930693069307,
          54.54545454545454,
          100,
          66.19718309859155,
          83.84279475982532,
          75,
          100,
          71.42857142857143,
          0,
          65.51724137931035,
          71.42857142857143,
          80,
          81.25,
          88.63636363636364,
          88.37209302325581,
          69.91869918699187,
          71.42857142857143,
          50,
          81.66666666666667,
          63.829787234042556,
          100,
          68.96551724137932,
          0,
          62.5,
          80,
          100,
          100,
          66.66666666666666,
          74.4186046511628,
          100,
          82.4742268041237,
          82.6086956521739,
          77.9874213836478,
          72.70214236351072,
          54.54545454545454,
          100,
          0,
          68.18181818181817,
          78.57142857142857,
          100,
          100,
          88.88888888888889
         ],
         "zauto": true,
         "zmax": 100,
         "zmin": 0
        }
       ],
       "layout": {
        "autosize": true,
        "geo": {
         "projection": {
          "type": "Mercator"
         },
         "showcoastlines": false,
         "showframe": false
        },
        "title": "Python 3 in the world"
       }
      },
      "text/html": [
       "<div id=\"4f003623-aa62-454a-954c-132438e04cdd\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4f003623-aa62-454a-954c-132438e04cdd\", [{\"type\": \"choropleth\", \"locations\": [\"Afghanistan\", \"Albania\", \"Algeria\", \"Andorra\", \"Antigua and Barbuda\", \"Argentina\", \"Armenia\", \"Australia\", \"Austria\", \"Azerbaijan\", \"Bahamas, The\", \"Bahrain\", \"Bangladesh\", \"Barbados\", \"Belarus\", \"Belgium\", \"Belize\", \"Benin\", \"Bolivia\", \"Bosnia and Herzegovina\", \"Brazil\", \"Bulgaria\", \"Cambodia\", \"Cameroon\", \"Canada\", \"Cape Verde\", \"Chad\", \"Chile\", \"China\", \"Colombia\", \"Congo, Democratic Republic of the\", \"Costa Rica\", \"Cote d'Ivoire\", \"Croatia\", \"Cuba\", \"Cyprus\", \"Czech Republic\", \"Denmark\", \"Dominica\", \"Dominican Republic\", \"Ecuador\", \"Egypt\", \"El Salvador\", \"Estonia\", \"Ethiopia\", \"Finland\", \"France\", \"Gabon\", \"Gambia, The\", \"Georgia\", \"Germany\", \"Ghana\", \"Greece\", \"Guatemala\", \"Haiti\", \"Honduras\", \"Hong Kong\", \"Hungary\", \"Iceland\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Ireland\", \"Israel\", \"Italy\", \"Jamaica\", \"Japan\", \"Jordan\", \"Kazakhstan\", \"Kenya\", \"Kuwait\", \"Kyrgyzstan\", \"Latvia\", \"Lebanon\", \"Libya\", \"Lithuania\", \"Luxembourg\", \"Macedonia\", \"Madagascar\", \"Malaysia\", \"Maldives\", \"Malta\", \"Mauritania\", \"Mauritius\", \"Mexico\", \"Moldova\", \"Monaco\", \"Mongolia\", \"Montenegro\", \"Morocco\", \"Myanmar\", \"Namibia\", \"Nepal\", \"Netherlands\", \"New Zealand\", \"Nicaragua\", \"Nigeria\", \"North Korea\", \"Norway\", \"Oman\", \"Pakistan\", \"Panama\", \"Paraguay\", \"Peru\", \"Philippines\", \"Poland\", \"Portugal\", \"Qatar\", \"Romania\", \"Russia\", \"Rwanda\", \"San Marino\", \"Saudi Arabia\", \"Senegal\", \"Serbia\", \"Singapore\", \"Slovakia\", \"Slovenia\", \"South Africa\", \"South Korea\", \"Spain\", \"Sri Lanka\", \"Sudan\", \"Sweden\", \"Switzerland\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Tanzania\", \"Thailand\", \"Timor-Leste\", \"Togo\", \"Tunisia\", \"Turkey\", \"Uganda\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Uruguay\", \"Uzbekistan\", \"Vanuatu\", \"Venezuela\", \"Vietnam\", \"Yemen\", \"Zambia\", \"Zimbabwe\"], \"locationmode\": \"country names\", \"z\": [33.33333333333333, 88.88888888888889, 69.56521739130434, 100.0, 100.0, 72.72727272727273, 100.0, 75.18248175182481, 72.72727272727273, 80.0, 50.0, 75.0, 78.94736842105263, 66.66666666666666, 75.86206896551724, 74.6268656716418, 100.0, 100.0, 33.33333333333333, 100.0, 79.14285714285715, 69.56521739130434, 100.0, 66.66666666666666, 80.31088082901555, 100.0, 100.0, 91.30434782608695, 74.52667814113597, 66.66666666666666, 100.0, 60.0, 50.0, 65.625, 55.55555555555556, 50.0, 81.35593220338984, 80.3921568627451, 100.0, 87.5, 66.66666666666666, 71.42857142857143, 75.0, 73.33333333333333, 16.666666666666664, 76.0, 72.1030042918455, 100.0, 100.0, 86.48648648648648, 78.0361757105943, 76.92307692307693, 79.24528301886792, 66.66666666666666, 100.0, 100.0, 100.0, 79.06976744186046, 0.0, 69.61904761904762, 65.3061224489796, 75.0, 50.0, 78.37837837837837, 52.22222222222223, 72.88135593220339, 100.0, 91.37931034482759, 75.0, 83.33333333333334, 77.77777777777779, 83.33333333333334, 50.0, 100.0, 100.0, 0.0, 53.84615384615385, 100.0, 75.0, 100.0, 76.0, 100.0, 50.0, 100.0, 50.0, 64.55696202531645, 50.0, 100.0, 100.0, 100.0, 52.63157894736842, 33.33333333333333, 100.0, 77.41935483870968, 79.06976744186046, 64.51612903225806, 66.66666666666666, 78.33333333333333, 50.0, 83.87096774193549, 100.0, 82.45614035087719, 100.0, 66.66666666666666, 71.42857142857143, 77.41935483870968, 80.6930693069307, 54.54545454545454, 100.0, 66.19718309859155, 83.84279475982532, 75.0, 100.0, 71.42857142857143, 0.0, 65.51724137931035, 71.42857142857143, 80.0, 81.25, 88.63636363636364, 88.37209302325581, 69.91869918699187, 71.42857142857143, 50.0, 81.66666666666667, 63.829787234042556, 100.0, 68.96551724137932, 0.0, 62.5, 80.0, 100.0, 100.0, 66.66666666666666, 74.4186046511628, 100.0, 82.4742268041237, 82.6086956521739, 77.9874213836478, 72.70214236351072, 54.54545454545454, 100.0, 0.0, 68.18181818181817, 78.57142857142857, 100.0, 100.0, 88.88888888888889], \"text\": [\"Afghanistan\", \"Albania\", \"Algeria\", \"Andorra\", \"Antigua and Barbuda\", \"Argentina\", \"Armenia\", \"Australia\", \"Austria\", \"Azerbaijan\", \"Bahamas, The\", \"Bahrain\", \"Bangladesh\", \"Barbados\", \"Belarus\", \"Belgium\", \"Belize\", \"Benin\", \"Bolivia\", \"Bosnia and Herzegovina\", \"Brazil\", \"Bulgaria\", \"Cambodia\", \"Cameroon\", \"Canada\", \"Cape Verde\", \"Chad\", \"Chile\", \"China\", \"Colombia\", \"Congo, Democratic Republic of the\", \"Costa Rica\", \"Cote d'Ivoire\", \"Croatia\", \"Cuba\", \"Cyprus\", \"Czech Republic\", \"Denmark\", \"Dominica\", \"Dominican Republic\", \"Ecuador\", \"Egypt\", \"El Salvador\", \"Estonia\", \"Ethiopia\", \"Finland\", \"France\", \"Gabon\", \"Gambia, The\", \"Georgia\", \"Germany\", \"Ghana\", \"Greece\", \"Guatemala\", \"Haiti\", \"Honduras\", \"Hong Kong\", \"Hungary\", \"Iceland\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Ireland\", \"Israel\", \"Italy\", \"Jamaica\", \"Japan\", \"Jordan\", \"Kazakhstan\", \"Kenya\", \"Kuwait\", \"Kyrgyzstan\", \"Latvia\", \"Lebanon\", \"Libya\", \"Lithuania\", \"Luxembourg\", \"Macedonia\", \"Madagascar\", \"Malaysia\", \"Maldives\", \"Malta\", \"Mauritania\", \"Mauritius\", \"Mexico\", \"Moldova\", \"Monaco\", \"Mongolia\", \"Montenegro\", \"Morocco\", \"Myanmar\", \"Namibia\", \"Nepal\", \"Netherlands\", \"New Zealand\", \"Nicaragua\", \"Nigeria\", \"North Korea\", \"Norway\", \"Oman\", \"Pakistan\", \"Panama\", \"Paraguay\", \"Peru\", \"Philippines\", \"Poland\", \"Portugal\", \"Qatar\", \"Romania\", \"Russia\", \"Rwanda\", \"San Marino\", \"Saudi Arabia\", \"Senegal\", \"Serbia\", \"Singapore\", \"Slovakia\", \"Slovenia\", \"South Africa\", \"South Korea\", \"Spain\", \"Sri Lanka\", \"Sudan\", \"Sweden\", \"Switzerland\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Tanzania\", \"Thailand\", \"Timor-Leste\", \"Togo\", \"Tunisia\", \"Turkey\", \"Uganda\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Uruguay\", \"Uzbekistan\", \"Vanuatu\", \"Venezuela\", \"Vietnam\", \"Yemen\", \"Zambia\", \"Zimbabwe\"], \"colorscale\": \"Set3\", \"autocolorscale\": false, \"reversescale\": true, \"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"colorbar\": {\"autotick\": false, \"ticksuffix\": \"%\", \"title\": \"Percent\"}}], {\"title\": \"Python 3 in the world\", \"geo\": {\"showframe\": false, \"showcoastlines\": false, \"projection\": {\"type\": \"Mercator\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"4f003623-aa62-454a-954c-132438e04cdd\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4f003623-aa62-454a-954c-132438e04cdd\", [{\"type\": \"choropleth\", \"locations\": [\"Afghanistan\", \"Albania\", \"Algeria\", \"Andorra\", \"Antigua and Barbuda\", \"Argentina\", \"Armenia\", \"Australia\", \"Austria\", \"Azerbaijan\", \"Bahamas, The\", \"Bahrain\", \"Bangladesh\", \"Barbados\", \"Belarus\", \"Belgium\", \"Belize\", \"Benin\", \"Bolivia\", \"Bosnia and Herzegovina\", \"Brazil\", \"Bulgaria\", \"Cambodia\", \"Cameroon\", \"Canada\", \"Cape Verde\", \"Chad\", \"Chile\", \"China\", \"Colombia\", \"Congo, Democratic Republic of the\", \"Costa Rica\", \"Cote d'Ivoire\", \"Croatia\", \"Cuba\", \"Cyprus\", \"Czech Republic\", \"Denmark\", \"Dominica\", \"Dominican Republic\", \"Ecuador\", \"Egypt\", \"El Salvador\", \"Estonia\", \"Ethiopia\", \"Finland\", \"France\", \"Gabon\", \"Gambia, The\", \"Georgia\", \"Germany\", \"Ghana\", \"Greece\", \"Guatemala\", \"Haiti\", \"Honduras\", \"Hong Kong\", \"Hungary\", \"Iceland\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Ireland\", \"Israel\", \"Italy\", \"Jamaica\", \"Japan\", \"Jordan\", \"Kazakhstan\", \"Kenya\", \"Kuwait\", \"Kyrgyzstan\", \"Latvia\", \"Lebanon\", \"Libya\", \"Lithuania\", \"Luxembourg\", \"Macedonia\", \"Madagascar\", \"Malaysia\", \"Maldives\", \"Malta\", \"Mauritania\", \"Mauritius\", \"Mexico\", \"Moldova\", \"Monaco\", \"Mongolia\", \"Montenegro\", \"Morocco\", \"Myanmar\", \"Namibia\", \"Nepal\", \"Netherlands\", \"New Zealand\", \"Nicaragua\", \"Nigeria\", \"North Korea\", \"Norway\", \"Oman\", \"Pakistan\", \"Panama\", \"Paraguay\", \"Peru\", \"Philippines\", \"Poland\", \"Portugal\", \"Qatar\", \"Romania\", \"Russia\", \"Rwanda\", \"San Marino\", \"Saudi Arabia\", \"Senegal\", \"Serbia\", \"Singapore\", \"Slovakia\", \"Slovenia\", \"South Africa\", \"South Korea\", \"Spain\", \"Sri Lanka\", \"Sudan\", \"Sweden\", \"Switzerland\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Tanzania\", \"Thailand\", \"Timor-Leste\", \"Togo\", \"Tunisia\", \"Turkey\", \"Uganda\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Uruguay\", \"Uzbekistan\", \"Vanuatu\", \"Venezuela\", \"Vietnam\", \"Yemen\", \"Zambia\", \"Zimbabwe\"], \"locationmode\": \"country names\", \"z\": [33.33333333333333, 88.88888888888889, 69.56521739130434, 100.0, 100.0, 72.72727272727273, 100.0, 75.18248175182481, 72.72727272727273, 80.0, 50.0, 75.0, 78.94736842105263, 66.66666666666666, 75.86206896551724, 74.6268656716418, 100.0, 100.0, 33.33333333333333, 100.0, 79.14285714285715, 69.56521739130434, 100.0, 66.66666666666666, 80.31088082901555, 100.0, 100.0, 91.30434782608695, 74.52667814113597, 66.66666666666666, 100.0, 60.0, 50.0, 65.625, 55.55555555555556, 50.0, 81.35593220338984, 80.3921568627451, 100.0, 87.5, 66.66666666666666, 71.42857142857143, 75.0, 73.33333333333333, 16.666666666666664, 76.0, 72.1030042918455, 100.0, 100.0, 86.48648648648648, 78.0361757105943, 76.92307692307693, 79.24528301886792, 66.66666666666666, 100.0, 100.0, 100.0, 79.06976744186046, 0.0, 69.61904761904762, 65.3061224489796, 75.0, 50.0, 78.37837837837837, 52.22222222222223, 72.88135593220339, 100.0, 91.37931034482759, 75.0, 83.33333333333334, 77.77777777777779, 83.33333333333334, 50.0, 100.0, 100.0, 0.0, 53.84615384615385, 100.0, 75.0, 100.0, 76.0, 100.0, 50.0, 100.0, 50.0, 64.55696202531645, 50.0, 100.0, 100.0, 100.0, 52.63157894736842, 33.33333333333333, 100.0, 77.41935483870968, 79.06976744186046, 64.51612903225806, 66.66666666666666, 78.33333333333333, 50.0, 83.87096774193549, 100.0, 82.45614035087719, 100.0, 66.66666666666666, 71.42857142857143, 77.41935483870968, 80.6930693069307, 54.54545454545454, 100.0, 66.19718309859155, 83.84279475982532, 75.0, 100.0, 71.42857142857143, 0.0, 65.51724137931035, 71.42857142857143, 80.0, 81.25, 88.63636363636364, 88.37209302325581, 69.91869918699187, 71.42857142857143, 50.0, 81.66666666666667, 63.829787234042556, 100.0, 68.96551724137932, 0.0, 62.5, 80.0, 100.0, 100.0, 66.66666666666666, 74.4186046511628, 100.0, 82.4742268041237, 82.6086956521739, 77.9874213836478, 72.70214236351072, 54.54545454545454, 100.0, 0.0, 68.18181818181817, 78.57142857142857, 100.0, 100.0, 88.88888888888889], \"text\": [\"Afghanistan\", \"Albania\", \"Algeria\", \"Andorra\", \"Antigua and Barbuda\", \"Argentina\", \"Armenia\", \"Australia\", \"Austria\", \"Azerbaijan\", \"Bahamas, The\", \"Bahrain\", \"Bangladesh\", \"Barbados\", \"Belarus\", \"Belgium\", \"Belize\", \"Benin\", \"Bolivia\", \"Bosnia and Herzegovina\", \"Brazil\", \"Bulgaria\", \"Cambodia\", \"Cameroon\", \"Canada\", \"Cape Verde\", \"Chad\", \"Chile\", \"China\", \"Colombia\", \"Congo, Democratic Republic of the\", \"Costa Rica\", \"Cote d'Ivoire\", \"Croatia\", \"Cuba\", \"Cyprus\", \"Czech Republic\", \"Denmark\", \"Dominica\", \"Dominican Republic\", \"Ecuador\", \"Egypt\", \"El Salvador\", \"Estonia\", \"Ethiopia\", \"Finland\", \"France\", \"Gabon\", \"Gambia, The\", \"Georgia\", \"Germany\", \"Ghana\", \"Greece\", \"Guatemala\", \"Haiti\", \"Honduras\", \"Hong Kong\", \"Hungary\", \"Iceland\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Ireland\", \"Israel\", \"Italy\", \"Jamaica\", \"Japan\", \"Jordan\", \"Kazakhstan\", \"Kenya\", \"Kuwait\", \"Kyrgyzstan\", \"Latvia\", \"Lebanon\", \"Libya\", \"Lithuania\", \"Luxembourg\", \"Macedonia\", \"Madagascar\", \"Malaysia\", \"Maldives\", \"Malta\", \"Mauritania\", \"Mauritius\", \"Mexico\", \"Moldova\", \"Monaco\", \"Mongolia\", \"Montenegro\", \"Morocco\", \"Myanmar\", \"Namibia\", \"Nepal\", \"Netherlands\", \"New Zealand\", \"Nicaragua\", \"Nigeria\", \"North Korea\", \"Norway\", \"Oman\", \"Pakistan\", \"Panama\", \"Paraguay\", \"Peru\", \"Philippines\", \"Poland\", \"Portugal\", \"Qatar\", \"Romania\", \"Russia\", \"Rwanda\", \"San Marino\", \"Saudi Arabia\", \"Senegal\", \"Serbia\", \"Singapore\", \"Slovakia\", \"Slovenia\", \"South Africa\", \"South Korea\", \"Spain\", \"Sri Lanka\", \"Sudan\", \"Sweden\", \"Switzerland\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Tanzania\", \"Thailand\", \"Timor-Leste\", \"Togo\", \"Tunisia\", \"Turkey\", \"Uganda\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Uruguay\", \"Uzbekistan\", \"Vanuatu\", \"Venezuela\", \"Vietnam\", \"Yemen\", \"Zambia\", \"Zimbabwe\"], \"colorscale\": \"Set3\", \"autocolorscale\": false, \"reversescale\": true, \"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"colorbar\": {\"autotick\": false, \"ticksuffix\": \"%\", \"title\": \"Percent\"}}], {\"title\": \"Python 3 in the world\", \"geo\": {\"showframe\": false, \"showcoastlines\": false, \"projection\": {\"type\": \"Mercator\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [ dict(\n",
    "        type = 'choropleth',\n",
    "        locations = countries_pyver_ratio.index,\n",
    "        locationmode = 'country names', \n",
    "        z = countries_pyver_ratio['Python 3'] * 100,\n",
    "        text = countries_pyver_ratio.index,\n",
    "        colorscale = 'Set3',\n",
    "        autocolorscale = False,\n",
    "        reversescale = True,\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(180,180,180)',\n",
    "                width = 0.5\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            autotick = False,\n",
    "            ticksuffix = '%',\n",
    "            title = 'Percent'),\n",
    "      ) ]\n",
    "\n",
    "layout = dict(\n",
    "    title = 'Python 3 in the world',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = False,\n",
    "        projection = dict(\n",
    "            type = 'Mercator'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict( data=data, layout=layout )\n",
    "iplot(fig, validate=False, filename='Python 3 in the world', image_height=1080, image_width=1920, show_link=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开发者中使用 IDE 的情况？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pycharm professional edition:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'pycharm community edition:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'sublime text:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'vim:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'atom:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'vs code:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'eclipse + pydev:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'aptana:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'jupyter notebook:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'intellij idea:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'notepad++:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'idle:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'emacs:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'python tools for visual studio (ptvs):what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'netbeans:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'spyder:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'rodeo:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'gedit:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'ninja-ide:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'komodo editor:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'komodo ide:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'wing ide:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'textmate:what editor(s)/ide(s) have you considered for use in your python development?',\n",
       " 'other - write in::what editor(s)/ide(s) have you considered for use in your python development?']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = find_cols(survey_df, ['what', 'editor(s)/ide(s)'])\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pycharm professional edition</th>\n",
       "      <th>pycharm community edition</th>\n",
       "      <th>sublime text</th>\n",
       "      <th>vim</th>\n",
       "      <th>atom</th>\n",
       "      <th>vs code</th>\n",
       "      <th>eclipse + pydev</th>\n",
       "      <th>aptana</th>\n",
       "      <th>jupyter notebook</th>\n",
       "      <th>intellij idea</th>\n",
       "      <th>...</th>\n",
       "      <th>netbeans</th>\n",
       "      <th>spyder</th>\n",
       "      <th>rodeo</th>\n",
       "      <th>gedit</th>\n",
       "      <th>ninja-ide</th>\n",
       "      <th>komodo editor</th>\n",
       "      <th>komodo ide</th>\n",
       "      <th>wing ide</th>\n",
       "      <th>textmate</th>\n",
       "      <th>other - write in</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other - Write In:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wing IDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PyCharm Professional Edition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sublime Text</td>\n",
       "      <td>Vim</td>\n",
       "      <td>Atom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eclipse + Pydev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Komodo IDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PyCharm Professional Edition</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pycharm professional edition pycharm community edition  sublime text  vim  \\\n",
       "0                           NaN                       NaN           NaN  Vim   \n",
       "1                           NaN                       NaN           NaN  NaN   \n",
       "2  PyCharm Professional Edition                       NaN  Sublime Text  Vim   \n",
       "3                           NaN                       NaN           NaN  NaN   \n",
       "4  PyCharm Professional Edition                       NaN           NaN  NaN   \n",
       "\n",
       "   atom vs code  eclipse + pydev aptana jupyter notebook intellij idea  \\\n",
       "0   NaN     NaN              NaN    NaN              NaN           NaN   \n",
       "1  Atom     NaN              NaN    NaN              NaN           NaN   \n",
       "2  Atom     NaN  Eclipse + Pydev    NaN              NaN           NaN   \n",
       "3   NaN     NaN              NaN    NaN              NaN           NaN   \n",
       "4   NaN     NaN              NaN    NaN              NaN           NaN   \n",
       "\n",
       "         ...         netbeans spyder rodeo gedit ninja-ide komodo editor  \\\n",
       "0        ...              NaN    NaN   NaN   NaN       NaN           NaN   \n",
       "1        ...              NaN    NaN   NaN   NaN       NaN           NaN   \n",
       "2        ...              NaN    NaN   NaN   NaN       NaN           NaN   \n",
       "3        ...              NaN    NaN   NaN   NaN       NaN           NaN   \n",
       "4        ...              NaN    NaN   NaN   NaN       NaN           NaN   \n",
       "\n",
       "   komodo ide  wing ide textmate   other - write in  \n",
       "0         NaN       NaN      NaN  Other - Write In:  \n",
       "1         NaN  Wing IDE      NaN                NaN  \n",
       "2  Komodo IDE       NaN      NaN                NaN  \n",
       "3         NaN       NaN      NaN                NaN  \n",
       "4         NaN       NaN      NaN                NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "editors = survey_df[cols]\n",
    "editors.columns = [item.split(':')[0] for item in editors.columns]\n",
    "editors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'What editor(s)/IDE(s) have you considered for use in your Python development?')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1d448c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = editors.count().sort_values(ascending=False).values\n",
    "labels = editors.columns\n",
    "\n",
    "plt.figure(figsize=(20, 17))\n",
    "sns.barplot(x=values, y=labels, orient='h', palette=sns.color_palette(\"rainbow\", len(labels)))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.title('What editor(s)/IDE(s) have you considered for use in your Python development?', fontsize=20)\n",
    "# plt.savefig('frameworks.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['what is the main editor you use for your current python development?']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = find_cols(survey_df, ['what', 'main', 'editor'])\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                             Vim\n",
       "1                        Wing IDE\n",
       "2                    Sublime Text\n",
       "3                             NaN\n",
       "4    PyCharm Professional Edition\n",
       "Name: what is the main editor you use for your current python development?, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_editor = survey_df[col[0]]\n",
    "main_editor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1d4f78c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_editor_sorted = main_editor.value_counts(ascending=False)\n",
    "values = main_editor_sorted.values\n",
    "labels = main_editor_sorted.index\n",
    "\n",
    "plt.figure(figsize=(20, 17))\n",
    "sns.barplot(x=values, y=labels, orient='h', palette=sns.color_palette(\"rainbow\", len(labels)))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.title('What is the main editor you use for your current python development?', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}