{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 12: Data processing and analysis with `pandas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert Johansson\n",
    "\n",
    "Source code listings for [Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://www.apress.com/us/book/9781484242452) (ISBN 978-1-484242-45-2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pd.set_option('display.mpl_style', 'default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     909976\n",
       "1    8615246\n",
       "2    2872086\n",
       "3    2273305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 909976, 8615246, 2872086, 2273305])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s.name = \"Population\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305], \n",
    "              index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"], name=\"Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8615246"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"London\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909976"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2572695.5, 3667653.25, 3399048.5005155364)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.median(), s.mean(), s.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909976, 8615246)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.min(), s.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1932472.75, 2572695.5, 4307876.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.quantile(q=0.25), s.quantile(q=0.5), s.quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.000000e+00\n",
       "mean     3.667653e+06\n",
       "std      3.399049e+06\n",
       "min      9.099760e+05\n",
       "25%      1.932473e+06\n",
       "50%      2.572696e+06\n",
       "75%      4.307876e+06\n",
       "max      8.615246e+06\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x252 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(12, 3.5))\n",
    "\n",
    "s.plot(ax=axes[0], kind='line', title=\"line\")\n",
    "s.plot(ax=axes[1], kind='bar', title=\"bar\")\n",
    "s.plot(ax=axes[2], kind='box', title=\"box\")\n",
    "s.plot(ax=axes[3], kind='pie', title=\"pie\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-series-plot.pdf\")\n",
    "fig.savefig(\"ch12-series-plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, 8615246, 2872086, 2273305],\n",
    "                   [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": 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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>8615246</td>\n",
       "      <td>2872086</td>\n",
       "      <td>2273305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Italy</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0               1        2        3\n",
       "0  909976         8615246  2872086  2273305\n",
       "1  Sweden  United kingdom    Italy   France"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": 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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0               1\n",
       "0   909976          Sweden\n",
       "1  8615246  United kingdom\n",
       "2  2872086           Italy\n",
       "3  2273305          France"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = [\"Population\", \"State\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]],\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"],\n",
    "                  columns=[\"Population\", \"State\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Population\": [909976, 8615246, 2872086, 2273305],\n",
    "                   \"State\": [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]},\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stockholm', 'London', 'Rome', 'Paris'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Population', 'State'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[909976, 'Sweden'],\n",
       "       [8615246, 'United kingdom'],\n",
       "       [2872086, 'Italy'],\n",
       "       [2273305, 'France']], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909976"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    909976\n",
       "State         Sweden\n",
       "Name: Stockholm, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Stockholm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.loc[\"Stockholm\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": 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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population   State\n",
       "Paris     2273305  France\n",
       "Rome      2872086   Italy"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[\"Paris\", \"Rome\"], \"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2273305"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Paris\", \"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    3667653.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4 entries, Stockholm to Paris\n",
      "Data columns (total 2 columns):\n",
      "Population    4 non-null int64\n",
      "State         4 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population     int64\n",
       "State         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": 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>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank,City,State,Official population,Date of census/estimate\n",
      "1,London[2], United Kingdom,\"8,615,246\",1 June 2014\n",
      "2,Berlin, Germany,\"3,437,916\",31 May 2014\n",
      "3,Madrid, Spain,\"3,165,235\",1 January 2014\n",
      "4,Rome, Italy,\"2,872,086\",30 September 2014\n"
     ]
    }
   ],
   "source": [
    "!head -n5 /home/rob/datasets/european_cities.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Larger dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop = pd.read_csv(\"european_cities.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop = pd.read_csv(\"european_cities.csv\", delimiter=\",\", encoding=\"utf-8\", header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 105 entries, 0 to 104\n",
      "Data columns (total 5 columns):\n",
      "Rank                       105 non-null int64\n",
      "City                       105 non-null object\n",
      "State                      105 non-null object\n",
      "Population                 105 non-null object\n",
      "Date of census/estimate    105 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 4.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_pop.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
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       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop[\"NumericPopulation\"] = df_pop.Population.apply(lambda x: int(x.replace(\",\", \"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' United Kingdom', ' Germany', ' Spain'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop[\"State\"].values[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop[\"State\"] = df_pop[\"State\"].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Rome</td>\n",
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      ],
      "text/plain": [
       "   Rank       City           State Population Date of census/estimate  \\\n",
       "0     1  London[2]  United Kingdom  8,615,246             1 June 2014   \n",
       "1     2     Berlin         Germany  3,437,916             31 May 2014   \n",
       "2     3     Madrid           Spain  3,165,235          1 January 2014   \n",
       "3     4       Rome           Italy  2,872,086       30 September 2014   \n",
       "4     5      Paris          France  2,273,305          1 January 2013   \n",
       "\n",
       "   NumericPopulation  \n",
       "0            8615246  \n",
       "1            3437916  \n",
       "2            3165235  \n",
       "3            2872086  \n",
       "4            2273305  "
      ]
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     "execution_count": 62,
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  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                        int64\n",
       "City                       object\n",
       "State                      object\n",
       "Population                 object\n",
       "Date of census/estimate    object\n",
       "NumericPopulation           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop2 = df_pop.set_index(\"City\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop2 = df_pop2.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Aarhus</th>\n",
       "      <td>92</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>326,676</td>\n",
       "      <td>1 October 2014</td>\n",
       "      <td>326676</td>\n",
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       "      <th>Alicante</th>\n",
       "      <td>86</td>\n",
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       "      <td>1 January 2012</td>\n",
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       "      <th>Amsterdam</th>\n",
       "      <td>23</td>\n",
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       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>510,610</td>\n",
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       "      <th>Athens</th>\n",
       "      <td>34</td>\n",
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       "      <td>664046</td>\n",
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      ],
      "text/plain": [
       "           Rank        State Population Date of census/estimate  \\\n",
       "City                                                              \n",
       "Aarhus       92      Denmark    326,676          1 October 2014   \n",
       "Alicante     86        Spain    334,678          1 January 2012   \n",
       "Amsterdam    23  Netherlands    813,562             31 May 2014   \n",
       "Antwerp      59      Belgium    510,610          1 January 2014   \n",
       "Athens       34       Greece    664,046             24 May 2011   \n",
       "\n",
       "           NumericPopulation  \n",
       "City                          \n",
       "Aarhus                326676  \n",
       "Alicante              334678  \n",
       "Amsterdam             813562  \n",
       "Antwerp               510610  \n",
       "Athens                664046  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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      "text/plain": [
       "           Rank        State Population Date of census/estimate  \\\n",
       "City                                                              \n",
       "Aarhus       92      Denmark    326,676          1 October 2014   \n",
       "Alicante     86        Spain    334,678          1 January 2012   \n",
       "Amsterdam    23  Netherlands    813,562             31 May 2014   \n",
       "Antwerp      59      Belgium    510,610          1 January 2014   \n",
       "Athens       34       Greece    664,046             24 May 2011   \n",
       "\n",
       "           NumericPopulation  \n",
       "City                          \n",
       "Aarhus                326676  \n",
       "Alicante              334678  \n",
       "Amsterdam             813562  \n",
       "Antwerp               510610  \n",
       "Athens                664046  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_pop3 = df_pop.set_index([\"State\", \"City\"]).sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <th>Vienna</th>\n",
       "      <td>7</td>\n",
       "      <td>1,794,770</td>\n",
       "      <td>1 January 2015</td>\n",
       "      <td>1794770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Belgium</th>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>Brussels[17]</th>\n",
       "      <td>16</td>\n",
       "      <td>1,175,831</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>1175831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bulgaria</th>\n",
       "      <th>Plovdiv</th>\n",
       "      <td>84</td>\n",
       "      <td>341,041</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>341041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofia</th>\n",
       "      <td>14</td>\n",
       "      <td>1,291,895</td>\n",
       "      <td>14 December 2014</td>\n",
       "      <td>1291895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Varna</th>\n",
       "      <td>85</td>\n",
       "      <td>335,819</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>335819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <th>Zagreb</th>\n",
       "      <td>24</td>\n",
       "      <td>790,017</td>\n",
       "      <td>31 March 2011</td>\n",
       "      <td>790017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Rank Population Date of census/estimate  \\\n",
       "State    City                                                    \n",
       "Austria  Vienna           7  1,794,770          1 January 2015   \n",
       "Belgium  Antwerp         59    510,610          1 January 2014   \n",
       "         Brussels[17]    16  1,175,831          1 January 2014   \n",
       "Bulgaria Plovdiv         84    341,041        31 December 2013   \n",
       "         Sofia           14  1,291,895        14 December 2014   \n",
       "         Varna           85    335,819        31 December 2013   \n",
       "Croatia  Zagreb          24    790,017           31 March 2011   \n",
       "\n",
       "                       NumericPopulation  \n",
       "State    City                             \n",
       "Austria  Vienna                  1794770  \n",
       "Belgium  Antwerp                  510610  \n",
       "         Brussels[17]            1175831  \n",
       "Bulgaria Plovdiv                  341041  \n",
       "         Sofia                   1291895  \n",
       "         Varna                    335819  \n",
       "Croatia  Zagreb                   790017  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gothenburg</th>\n",
       "      <td>53</td>\n",
       "      <td>528,014</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>528014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malmö</th>\n",
       "      <td>102</td>\n",
       "      <td>309,105</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>309105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>20</td>\n",
       "      <td>909,976</td>\n",
       "      <td>31 January 2014</td>\n",
       "      <td>909976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank Population Date of census/estimate  NumericPopulation\n",
       "City                                                                  \n",
       "Gothenburg    53    528,014           31 March 2013             528014\n",
       "Malmö        102    309,105           31 March 2013             309105\n",
       "Stockholm     20    909,976         31 January 2014             909976"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.loc[\"Sweden\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                  53\n",
       "Population                       528,014\n",
       "Date of census/estimate    31 March 2013\n",
       "NumericPopulation                 528014\n",
       "Name: (Sweden, Gothenburg), dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop3.loc[(\"Sweden\", \"Gothenburg\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
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       "    <tr>\n",
       "      <th>City</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>Nottingham</th>\n",
       "      <td>103</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>308,735</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>308735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wirral</th>\n",
       "      <td>97</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>320,229</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>320229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coventry</th>\n",
       "      <td>94</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>323,132</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>323132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wakefield</th>\n",
       "      <td>91</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>327,627</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>327627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>87</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>331,606</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>331606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank           State Population Date of census/estimate  \\\n",
       "City                                                                  \n",
       "Nottingham   103  United Kingdom    308,735            30 June 2012   \n",
       "Wirral        97  United Kingdom    320,229            30 June 2012   \n",
       "Coventry      94  United Kingdom    323,132            30 June 2012   \n",
       "Wakefield     91  United Kingdom    327,627            30 June 2012   \n",
       "Leicester     87  United Kingdom    331,606            30 June 2012   \n",
       "\n",
       "            NumericPopulation  \n",
       "City                           \n",
       "Nottingham             308735  \n",
       "Wirral                 320229  \n",
       "Coventry               323132  \n",
       "Wakefield              327627  \n",
       "Leicester              331606  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.set_index(\"City\").sort_values([\"State\", \"NumericPopulation\"], ascending=[False, True]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "city_counts = df_pop.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "city_counts.name = \"# cities in top 105\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop3 = df_pop[[\"State\", \"City\", \"NumericPopulation\"]].set_index([\"State\", \"City\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop4 = df_pop3.sum(level=\"State\").sort_values(\"NumericPopulation\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>16011877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15119548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10041639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8764067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6267409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop5 = (df_pop.drop(\"Rank\", axis=1)\n",
    "                 .groupby(\"State\").sum()\n",
    "                 .sort_values(\"NumericPopulation\", ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>8764067</td>\n",
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       "      <td>6267409</td>\n",
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      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 79,
     "metadata": {},
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    }
   ],
   "source": [
    "df_pop5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "city_counts.plot(kind='barh', ax=ax1)\n",
    "ax1.set_xlabel(\"# cities in top 105\")\n",
    "df_pop5.NumericPopulation.plot(kind='barh', ax=ax2)\n",
    "ax2.set_xlabel(\"Total pop. in top 105 cities\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-state-city-counts-sum.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
       "               '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',\n",
       "               '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',\n",
       "               '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',\n",
       "               '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20',\n",
       "               '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24',\n",
       "               '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28',\n",
       "               '2015-01-29', '2015-01-30', '2015-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1\", periods=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
       "               '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',\n",
       "               '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',\n",
       "               '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',\n",
       "               '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20',\n",
       "               '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24',\n",
       "               '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28',\n",
       "               '2015-01-29', '2015-01-30', '2015-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(datetime.datetime(2015, 1, 1), periods=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
       "               '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
       "               '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
       "               '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
       "               '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
       "               '2015-01-01 10:00:00', '2015-01-01 11:00:00',\n",
       "               '2015-01-01 12:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1 00:00\", \"2015-1-1 12:00\", freq=\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts1 = pd.Series(np.arange(31), index=pd.date_range(\"2015-1-1\", periods=31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0\n",
       "2015-01-02    1\n",
       "2015-01-03    2\n",
       "2015-01-04    3\n",
       "2015-01-05    4\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1[\"2015-1-3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-01-03 00:00:00', freq='D')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2015, 1, 3)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].year, ts1.index[2].month, ts1.index[2].day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].nanosecond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2].to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts2 = pd.Series(np.random.rand(2), \n",
    "                index=[datetime.datetime(2015, 1, 1), datetime.datetime(2015, 2, 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0.431883\n",
       "2015-02-01    0.794106\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "periods = pd.PeriodIndex([pd.Period('2015-01'), pd.Period('2015-02'), pd.Period('2015-03')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts3 = pd.Series(np.random.rand(3), periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.102122\n",
       "2015-02    0.952019\n",
       "2015-03    0.874310\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.431883\n",
       "2015-02    0.794106\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03', '2015-04', '2015-05', '2015-06',\n",
       "             '2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1\", periods=12, freq=\"M\").to_period()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature time series example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1388530986\t4.380000\n",
      "1388531586\t4.250000\n",
      "1388532187\t4.190000\n",
      "1388532787\t4.060000\n",
      "1388533388\t4.060000\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 temperature_outdoor_2014.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('temperature_outdoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"outdoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('temperature_indoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"indoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": 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>time</th>\n",
       "      <th>outdoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  outdoor\n",
       "0  1388530986     4.38\n",
       "1  1388531586     4.25\n",
       "2  1388532187     4.19\n",
       "3  1388532787     4.06\n",
       "4  1388533388     4.06"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": 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>time</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>21.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  indoor\n",
       "0  1388530986   21.94\n",
       "1  1388531586   22.00\n",
       "2  1388532187   22.00\n",
       "3  1388532787   22.00\n",
       "4  1388533388   22.00"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.time = (pd.to_datetime(df1.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df1.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2.time = (pd.to_datetime(df2.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df2.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": 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>outdoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:03:06+01:00</th>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:13:06+01:00</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:23:07+01:00</th>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:33:07+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:43:08+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor\n",
       "time                              \n",
       "2014-01-01 00:03:06+01:00     4.38\n",
       "2014-01-01 00:13:06+01:00     4.25\n",
       "2014-01-01 00:23:07+01:00     4.19\n",
       "2014-01-01 00:33:07+01:00     4.06\n",
       "2014-01-01 00:43:08+01:00     4.06"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-01-01 00:03:06+0100', tz='Europe/Stockholm')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "df1.plot(ax=ax)\n",
    "df2.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-temperature-2014.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select january data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 49548 entries, 2014-01-01 00:03:06+01:00 to 2014-12-30 23:56:35+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    49548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 774.2 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_jan = df1[(df1.index > \"2014-1-1\") & (df1.index < \"2014-2-1\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index < \"2014-2-1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4452 entries, 2014-01-01 00:03:06+01:00 to 2014-01-31 23:56:58+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    4452 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 69.6 KB\n"
     ]
    }
   ],
   "source": [
    "df1_jan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_jan = df2[\"2014-1-1\":\"2014-1-31\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "\n",
    "df1_jan.plot(ax=ax)\n",
    "df2_jan.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-selected-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_month = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month[\"month\"] = df1_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": 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>time</th>\n",
       "      <th>outdoor</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:03:06+01:00</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:13:06+01:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:23:07+01:00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:33:07+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:43:08+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time  outdoor  month\n",
       "0 2014-01-01 00:03:06+01:00     4.38      1\n",
       "1 2014-01-01 00:13:06+01:00     4.25      1\n",
       "2 2014-01-01 00:23:07+01:00     4.19      1\n",
       "3 2014-01-01 00:33:07+01:00     4.06      1\n",
       "4 2014-01-01 00:43:08+01:00     4.06      1"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month = df1_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month[\"month\"] = df2_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_month = df1_month.join(df2_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": 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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        outdoor     indoor\n",
       "month                     \n",
       "1     -1.776646  19.862590\n",
       "2      2.231613  20.231507\n",
       "3      4.615437  19.597748"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n",
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "df_month = pd.concat([df.to_period(\"M\").groupby(level=0).mean() for df in [df1, df2]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": 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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          outdoor     indoor\n",
       "time                        \n",
       "2014-01 -1.776646  19.862590\n",
       "2014-02  2.231613  20.231507\n",
       "2014-03  4.615437  19.597748"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "df_month.plot(kind='bar', ax=axes[0])\n",
    "df_month.plot(kind='box', ax=axes[1])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-grouped-by-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": 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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04</th>\n",
       "      <td>8.105193</td>\n",
       "      <td>22.149754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05</th>\n",
       "      <td>12.261396</td>\n",
       "      <td>26.332160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06</th>\n",
       "      <td>15.586955</td>\n",
       "      <td>28.687491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07</th>\n",
       "      <td>20.780314</td>\n",
       "      <td>30.605333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08</th>\n",
       "      <td>16.494823</td>\n",
       "      <td>28.099068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09</th>\n",
       "      <td>12.823905</td>\n",
       "      <td>26.950366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10</th>\n",
       "      <td>9.352000</td>\n",
       "      <td>23.379460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11</th>\n",
       "      <td>4.992142</td>\n",
       "      <td>20.610365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12</th>\n",
       "      <td>-0.058940</td>\n",
       "      <td>16.465674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           outdoor     indoor\n",
       "time                         \n",
       "2014-01  -1.776646  19.862590\n",
       "2014-02   2.231613  20.231507\n",
       "2014-03   4.615437  19.597748\n",
       "2014-04   8.105193  22.149754\n",
       "2014-05  12.261396  26.332160\n",
       "2014-06  15.586955  28.687491\n",
       "2014-07  20.780314  30.605333\n",
       "2014-08  16.494823  28.099068\n",
       "2014-09  12.823905  26.950366\n",
       "2014-10   9.352000  23.379460\n",
       "2014-11   4.992142  20.610365\n",
       "2014-12  -0.058940  16.465674"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_hour = df1.resample(\"H\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_hour.columns = [\"outdoor (hourly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_day = df1.resample(\"D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_day.columns = [\"outdoor (daily avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_week = df1.resample(\"7D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_week.columns = [\"outdoor (weekly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month = df1.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month.columns = [\"outdoor (monthly avg.)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_diff = (df1.resample(\"D\").mean().outdoor - df2.resample(\"D\").mean().indoor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6))\n",
    "\n",
    "df1_hour.plot(ax=ax1, alpha=0.25)\n",
    "df1_day.plot(ax=ax1)\n",
    "df1_week.plot(ax=ax1)\n",
    "df1_month.plot(ax=ax1)\n",
    "\n",
    "df_diff.plot(ax=ax2)\n",
    "ax2.set_title(\"temperature difference between outdoor and indoor\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-timeseries-resampled.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": 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>None</th>\n",
       "      <th>ffill</th>\n",
       "      <th>bfill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:05:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:15:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           None  ffill  bfill\n",
       "time                                         \n",
       "2014-01-01 00:00:00+01:00  4.38    NaN   4.38\n",
       "2014-01-01 00:05:00+01:00   NaN   4.38   4.25\n",
       "2014-01-01 00:10:00+01:00  4.25   4.38   4.25\n",
       "2014-01-01 00:15:00+01:00   NaN   4.25   4.19\n",
       "2014-01-01 00:20:00+01:00  4.19   4.25   4.19"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1.resample(\"5min\").mean().rename(columns={\"outdoor\": 'None'}),\n",
    "           df1.resample(\"5min\").ffill().rename(columns={\"outdoor\": 'ffill'}),\n",
    "           df1.resample(\"5min\").bfill().rename(columns={\"outdoor\": 'bfill'})], axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_dec25 = df1[(df1.index < \"2014-9-1\") & (df1.index >= \"2014-8-1\")].resample(\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_dec25 = df1.loc[\"2014-12-25\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:01:45+01:00</th>\n",
       "      <td>-4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:11:45+01:00</th>\n",
       "      <td>-4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:21:46+01:00</th>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:31:47+01:00</th>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:41:47+01:00</th>\n",
       "      <td>-5.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor\n",
       "time                              \n",
       "2014-12-25 00:01:45+01:00    -4.88\n",
       "2014-12-25 00:11:45+01:00    -4.94\n",
       "2014-12-25 00:21:46+01:00    -5.06\n",
       "2014-12-25 00:31:47+01:00    -5.06\n",
       "2014-12-25 00:41:47+01:00    -5.12"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_dec25.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_dec25 = df2.loc[\"2014-12-25\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:01:45+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:11:45+01:00</th>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:21:46+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:31:47+01:00</th>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-25 00:41:47+01:00</th>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           indoor\n",
       "time                             \n",
       "2014-12-25 00:01:45+01:00   16.31\n",
       "2014-12-25 00:11:45+01:00   16.25\n",
       "2014-12-25 00:21:46+01:00   16.31\n",
       "2014-12-25 00:31:47+01:00   16.31\n",
       "2014-12-25 00:41:47+01:00   16.25"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_dec25.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>outdoor</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-7.788819</td>\n",
       "      <td>1.456085</td>\n",
       "      <td>-10.06</td>\n",
       "      <td>-9.075</td>\n",
       "      <td>-7.75</td>\n",
       "      <td>-6.8625</td>\n",
       "      <td>-4.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count      mean       std    min    25%   50%     75%   max\n",
       "outdoor  144.0 -7.788819  1.456085 -10.06 -9.075 -7.75 -6.8625 -4.88"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_dec25.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "\n",
    "df1_dec25.plot(ax=ax)\n",
    "\n",
    "fig.savefig(\"ch12-timeseries-selected-month.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01 00:03:06+01:00', '2014-01-01 00:13:06+01:00',\n",
       "               '2014-01-01 00:23:07+01:00', '2014-01-01 00:33:07+01:00',\n",
       "               '2014-01-01 00:43:08+01:00', '2014-01-01 00:53:08+01:00',\n",
       "               '2014-01-01 01:03:09+01:00', '2014-01-01 01:13:09+01:00',\n",
       "               '2014-01-01 01:23:10+01:00', '2014-01-01 01:33:26+01:00',\n",
       "               ...\n",
       "               '2014-12-30 22:26:30+01:00', '2014-12-30 22:36:31+01:00',\n",
       "               '2014-12-30 22:46:31+01:00', '2014-12-30 22:56:32+01:00',\n",
       "               '2014-12-30 23:06:32+01:00', '2014-12-30 23:16:33+01:00',\n",
       "               '2014-12-30 23:26:33+01:00', '2014-12-30 23:36:34+01:00',\n",
       "               '2014-12-30 23:46:35+01:00', '2014-12-30 23:56:35+01:00'],\n",
       "              dtype='datetime64[ns, Europe/Stockholm]', name='time', length=49548, freq=None)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn statistical visualization library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('temperature_outdoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"outdoor\"])\n",
    "df1.time = pd.to_datetime(df1.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "\n",
    "df1 = df1.set_index(\"time\").resample(\"10min\").mean()\n",
    "df2 = pd.read_csv('temperature_indoor_2014.tsv', delimiter=\"\\t\", names=[\"time\", \"indoor\"])\n",
    "df2.time = pd.to_datetime(df2.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "df2 = df2.set_index(\"time\").resample(\"10min\").mean()\n",
    "df_temp = pd.concat([df1, df2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "df_temp.resample(\"D\").mean().plot(y=[\"outdoor\", \"indoor\"], ax=ax)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-plot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#sns.kdeplot(df_temp[\"outdoor\"].dropna().values, shade=True, cumulative=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n",
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_temp.to_period(\"M\")[\"outdoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "sns.distplot(df_temp.to_period(\"M\")[\"indoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-distplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    sns.jointplot(df_temp.resample(\"H\").mean()[\"outdoor\"].values,\n",
    "                  df_temp.resample(\"H\").mean()[\"indoor\"].values, kind=\"hex\");\n",
    "    \n",
    "plt.savefig(\"ch12-seaborn-jointplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_temp.resample(\"H\").mean()[\"outdoor\"].dropna().values,\n",
    "            df_temp.resample(\"H\").mean()[\"indoor\"].dropna().values, shade=False);\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-kdeplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "sns.boxplot(df_temp.dropna(), ax=ax1, palette=\"pastel\")\n",
    "sns.violinplot(df_temp.dropna(), ax=ax2, palette=\"pastel\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-boxplot-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=df_temp.dropna().index.month, y=df_temp.dropna().outdoor, color=\"skyblue\");\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_temp[\"month\"] = df_temp.index.month\n",
    "df_temp[\"hour\"] = df_temp.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": 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>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>21.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:40:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor  indoor  month  hour\n",
       "time                                                   \n",
       "2014-01-01 00:00:00+01:00     4.38   21.94      1     0\n",
       "2014-01-01 00:10:00+01:00     4.25   22.00      1     0\n",
       "2014-01-01 00:20:00+01:00     4.19   22.00      1     0\n",
       "2014-01-01 00:30:00+01:00     4.06   22.00      1     0\n",
       "2014-01-01 00:40:00+01:00     4.06   22.00      1     0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table = pd.pivot_table(df_temp, values='outdoor', index=['month'], columns=['hour'], aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>1</th>\n",
       "      <td>-1.692312</td>\n",
       "      <td>-1.750162</td>\n",
       "      <td>-1.826649</td>\n",
       "      <td>-1.879086</td>\n",
       "      <td>-1.922527</td>\n",
       "      <td>-1.968065</td>\n",
       "      <td>-2.020914</td>\n",
       "      <td>-2.035806</td>\n",
       "      <td>-2.101774</td>\n",
       "      <td>-2.001022</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.457849</td>\n",
       "      <td>-1.696935</td>\n",
       "      <td>-1.814194</td>\n",
       "      <td>-1.812258</td>\n",
       "      <td>-1.853297</td>\n",
       "      <td>-1.898432</td>\n",
       "      <td>-1.839730</td>\n",
       "      <td>-1.806486</td>\n",
       "      <td>-1.854462</td>\n",
       "      <td>-1.890811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.613690</td>\n",
       "      <td>1.521190</td>\n",
       "      <td>1.479405</td>\n",
       "      <td>1.464371</td>\n",
       "      <td>1.506407</td>\n",
       "      <td>1.485595</td>\n",
       "      <td>1.499167</td>\n",
       "      <td>1.516946</td>\n",
       "      <td>1.669226</td>\n",
       "      <td>2.067725</td>\n",
       "      <td>...</td>\n",
       "      <td>3.573593</td>\n",
       "      <td>3.360741</td>\n",
       "      <td>2.939390</td>\n",
       "      <td>2.501607</td>\n",
       "      <td>2.357425</td>\n",
       "      <td>2.236190</td>\n",
       "      <td>2.204458</td>\n",
       "      <td>2.137619</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>1.896190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.192366</td>\n",
       "      <td>2.866774</td>\n",
       "      <td>2.628000</td>\n",
       "      <td>2.524140</td>\n",
       "      <td>2.384140</td>\n",
       "      <td>2.235538</td>\n",
       "      <td>2.243387</td>\n",
       "      <td>2.622258</td>\n",
       "      <td>3.419301</td>\n",
       "      <td>4.466290</td>\n",
       "      <td>...</td>\n",
       "      <td>7.790323</td>\n",
       "      <td>7.930914</td>\n",
       "      <td>7.595892</td>\n",
       "      <td>6.770914</td>\n",
       "      <td>5.731508</td>\n",
       "      <td>4.983784</td>\n",
       "      <td>4.437419</td>\n",
       "      <td>4.022312</td>\n",
       "      <td>3.657903</td>\n",
       "      <td>3.407258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.832738</td>\n",
       "      <td>5.336012</td>\n",
       "      <td>4.926667</td>\n",
       "      <td>4.597059</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>4.109769</td>\n",
       "      <td>4.123699</td>\n",
       "      <td>4.741437</td>\n",
       "      <td>5.878035</td>\n",
       "      <td>7.272299</td>\n",
       "      <td>...</td>\n",
       "      <td>12.175556</td>\n",
       "      <td>12.500059</td>\n",
       "      <td>12.494483</td>\n",
       "      <td>12.361156</td>\n",
       "      <td>11.989240</td>\n",
       "      <td>10.454881</td>\n",
       "      <td>8.857619</td>\n",
       "      <td>7.712619</td>\n",
       "      <td>6.974762</td>\n",
       "      <td>6.293512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.792204</td>\n",
       "      <td>9.369351</td>\n",
       "      <td>9.009839</td>\n",
       "      <td>8.670914</td>\n",
       "      <td>8.463387</td>\n",
       "      <td>8.446919</td>\n",
       "      <td>8.772324</td>\n",
       "      <td>9.562742</td>\n",
       "      <td>10.723622</td>\n",
       "      <td>12.047717</td>\n",
       "      <td>...</td>\n",
       "      <td>15.542581</td>\n",
       "      <td>15.744624</td>\n",
       "      <td>15.784839</td>\n",
       "      <td>15.799570</td>\n",
       "      <td>17.009892</td>\n",
       "      <td>15.685161</td>\n",
       "      <td>13.632796</td>\n",
       "      <td>12.216290</td>\n",
       "      <td>11.291237</td>\n",
       "      <td>10.622849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.209556</td>\n",
       "      <td>12.792889</td>\n",
       "      <td>12.382889</td>\n",
       "      <td>11.967889</td>\n",
       "      <td>11.735778</td>\n",
       "      <td>11.886667</td>\n",
       "      <td>12.503778</td>\n",
       "      <td>13.338167</td>\n",
       "      <td>14.343444</td>\n",
       "      <td>15.665475</td>\n",
       "      <td>...</td>\n",
       "      <td>18.630556</td>\n",
       "      <td>18.866292</td>\n",
       "      <td>18.680611</td>\n",
       "      <td>18.529832</td>\n",
       "      <td>20.057877</td>\n",
       "      <td>18.853389</td>\n",
       "      <td>16.969777</td>\n",
       "      <td>15.675111</td>\n",
       "      <td>14.658778</td>\n",
       "      <td>13.898167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.956344</td>\n",
       "      <td>17.348641</td>\n",
       "      <td>16.793152</td>\n",
       "      <td>16.309892</td>\n",
       "      <td>16.001559</td>\n",
       "      <td>15.986774</td>\n",
       "      <td>16.506613</td>\n",
       "      <td>17.478226</td>\n",
       "      <td>18.850054</td>\n",
       "      <td>20.533763</td>\n",
       "      <td>...</td>\n",
       "      <td>24.598441</td>\n",
       "      <td>25.030000</td>\n",
       "      <td>24.869194</td>\n",
       "      <td>24.764409</td>\n",
       "      <td>26.155161</td>\n",
       "      <td>24.896505</td>\n",
       "      <td>22.550269</td>\n",
       "      <td>20.882649</td>\n",
       "      <td>19.699022</td>\n",
       "      <td>18.822634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.498205</td>\n",
       "      <td>13.960128</td>\n",
       "      <td>13.555128</td>\n",
       "      <td>12.995641</td>\n",
       "      <td>12.651410</td>\n",
       "      <td>12.485974</td>\n",
       "      <td>12.680130</td>\n",
       "      <td>13.403506</td>\n",
       "      <td>14.578780</td>\n",
       "      <td>16.170833</td>\n",
       "      <td>...</td>\n",
       "      <td>20.473810</td>\n",
       "      <td>20.292381</td>\n",
       "      <td>20.328795</td>\n",
       "      <td>19.642436</td>\n",
       "      <td>19.373846</td>\n",
       "      <td>18.713462</td>\n",
       "      <td>17.034872</td>\n",
       "      <td>15.843590</td>\n",
       "      <td>15.146154</td>\n",
       "      <td>14.596667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.133000</td>\n",
       "      <td>10.725667</td>\n",
       "      <td>10.362444</td>\n",
       "      <td>9.976833</td>\n",
       "      <td>9.729333</td>\n",
       "      <td>9.503944</td>\n",
       "      <td>9.357500</td>\n",
       "      <td>9.689778</td>\n",
       "      <td>10.600778</td>\n",
       "      <td>11.829106</td>\n",
       "      <td>...</td>\n",
       "      <td>16.336983</td>\n",
       "      <td>16.828268</td>\n",
       "      <td>17.031056</td>\n",
       "      <td>16.786983</td>\n",
       "      <td>15.853556</td>\n",
       "      <td>14.534637</td>\n",
       "      <td>13.350444</td>\n",
       "      <td>12.545278</td>\n",
       "      <td>11.954190</td>\n",
       "      <td>11.399056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.602011</td>\n",
       "      <td>8.490598</td>\n",
       "      <td>8.382486</td>\n",
       "      <td>8.257097</td>\n",
       "      <td>8.166774</td>\n",
       "      <td>8.140054</td>\n",
       "      <td>8.140161</td>\n",
       "      <td>8.148333</td>\n",
       "      <td>8.410914</td>\n",
       "      <td>9.054946</td>\n",
       "      <td>...</td>\n",
       "      <td>11.330323</td>\n",
       "      <td>11.189194</td>\n",
       "      <td>10.836865</td>\n",
       "      <td>10.361568</td>\n",
       "      <td>9.781022</td>\n",
       "      <td>9.373441</td>\n",
       "      <td>9.134570</td>\n",
       "      <td>8.956505</td>\n",
       "      <td>8.820270</td>\n",
       "      <td>8.623297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.847111</td>\n",
       "      <td>4.765922</td>\n",
       "      <td>4.815642</td>\n",
       "      <td>4.773240</td>\n",
       "      <td>4.809611</td>\n",
       "      <td>4.785833</td>\n",
       "      <td>4.741222</td>\n",
       "      <td>4.739778</td>\n",
       "      <td>4.794500</td>\n",
       "      <td>4.965389</td>\n",
       "      <td>...</td>\n",
       "      <td>5.526034</td>\n",
       "      <td>5.342753</td>\n",
       "      <td>5.081250</td>\n",
       "      <td>5.056629</td>\n",
       "      <td>4.959106</td>\n",
       "      <td>4.868111</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>4.774389</td>\n",
       "      <td>4.720722</td>\n",
       "      <td>4.699722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.366369</td>\n",
       "      <td>-0.390556</td>\n",
       "      <td>-0.447374</td>\n",
       "      <td>-0.370111</td>\n",
       "      <td>-0.353128</td>\n",
       "      <td>-0.319832</td>\n",
       "      <td>-0.358667</td>\n",
       "      <td>-0.410278</td>\n",
       "      <td>-0.483167</td>\n",
       "      <td>-0.344667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.738944</td>\n",
       "      <td>0.367056</td>\n",
       "      <td>0.152167</td>\n",
       "      <td>-0.106111</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>-0.244167</td>\n",
       "      <td>-0.290000</td>\n",
       "      <td>-0.305333</td>\n",
       "      <td>-0.302778</td>\n",
       "      <td>-0.325642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour          0          1          2          3          4          5   \\\n",
       "month                                                                     \n",
       "1      -1.692312  -1.750162  -1.826649  -1.879086  -1.922527  -1.968065   \n",
       "2       1.613690   1.521190   1.479405   1.464371   1.506407   1.485595   \n",
       "3       3.192366   2.866774   2.628000   2.524140   2.384140   2.235538   \n",
       "4       5.832738   5.336012   4.926667   4.597059   4.380000   4.109769   \n",
       "5       9.792204   9.369351   9.009839   8.670914   8.463387   8.446919   \n",
       "6      13.209556  12.792889  12.382889  11.967889  11.735778  11.886667   \n",
       "7      17.956344  17.348641  16.793152  16.309892  16.001559  15.986774   \n",
       "8      14.498205  13.960128  13.555128  12.995641  12.651410  12.485974   \n",
       "9      11.133000  10.725667  10.362444   9.976833   9.729333   9.503944   \n",
       "10      8.602011   8.490598   8.382486   8.257097   8.166774   8.140054   \n",
       "11      4.847111   4.765922   4.815642   4.773240   4.809611   4.785833   \n",
       "12     -0.366369  -0.390556  -0.447374  -0.370111  -0.353128  -0.319832   \n",
       "\n",
       "hour          6          7          8          9   ...         14         15  \\\n",
       "month                                              ...                         \n",
       "1      -2.020914  -2.035806  -2.101774  -2.001022  ...  -1.457849  -1.696935   \n",
       "2       1.499167   1.516946   1.669226   2.067725  ...   3.573593   3.360741   \n",
       "3       2.243387   2.622258   3.419301   4.466290  ...   7.790323   7.930914   \n",
       "4       4.123699   4.741437   5.878035   7.272299  ...  12.175556  12.500059   \n",
       "5       8.772324   9.562742  10.723622  12.047717  ...  15.542581  15.744624   \n",
       "6      12.503778  13.338167  14.343444  15.665475  ...  18.630556  18.866292   \n",
       "7      16.506613  17.478226  18.850054  20.533763  ...  24.598441  25.030000   \n",
       "8      12.680130  13.403506  14.578780  16.170833  ...  20.473810  20.292381   \n",
       "9       9.357500   9.689778  10.600778  11.829106  ...  16.336983  16.828268   \n",
       "10      8.140161   8.148333   8.410914   9.054946  ...  11.330323  11.189194   \n",
       "11      4.741222   4.739778   4.794500   4.965389  ...   5.526034   5.342753   \n",
       "12     -0.358667  -0.410278  -0.483167  -0.344667  ...   0.738944   0.367056   \n",
       "\n",
       "hour          16         17         18         19         20         21  \\\n",
       "month                                                                     \n",
       "1      -1.814194  -1.812258  -1.853297  -1.898432  -1.839730  -1.806486   \n",
       "2       2.939390   2.501607   2.357425   2.236190   2.204458   2.137619   \n",
       "3       7.595892   6.770914   5.731508   4.983784   4.437419   4.022312   \n",
       "4      12.494483  12.361156  11.989240  10.454881   8.857619   7.712619   \n",
       "5      15.784839  15.799570  17.009892  15.685161  13.632796  12.216290   \n",
       "6      18.680611  18.529832  20.057877  18.853389  16.969777  15.675111   \n",
       "7      24.869194  24.764409  26.155161  24.896505  22.550269  20.882649   \n",
       "8      20.328795  19.642436  19.373846  18.713462  17.034872  15.843590   \n",
       "9      17.031056  16.786983  15.853556  14.534637  13.350444  12.545278   \n",
       "10     10.836865  10.361568   9.781022   9.373441   9.134570   8.956505   \n",
       "11      5.081250   5.056629   4.959106   4.868111   4.833333   4.774389   \n",
       "12      0.152167  -0.106111  -0.182500  -0.244167  -0.290000  -0.305333   \n",
       "\n",
       "hour          22         23  \n",
       "month                        \n",
       "1      -1.854462  -1.890811  \n",
       "2       2.024671   1.896190  \n",
       "3       3.657903   3.407258  \n",
       "4       6.974762   6.293512  \n",
       "5      11.291237  10.622849  \n",
       "6      14.658778  13.898167  \n",
       "7      19.699022  18.822634  \n",
       "8      15.146154  14.596667  \n",
       "9      11.954190  11.399056  \n",
       "10      8.820270   8.623297  \n",
       "11      4.720722   4.699722  \n",
       "12     -0.302778  -0.325642  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "sns.heatmap(table, ax=ax);\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch12-seaborn-heatmap.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>IPython</td><td>7.5.0</td></tr><tr><td>OS</td><td>Darwin 18.2.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.16.3</td></tr><tr><td>matplotlib</td><td>3.0.3</td></tr><tr><td>pandas</td><td>0.24.2</td></tr><tr><td>seaborn</td><td>0.9.0</td></tr><tr><td colspan='2'>Mon May 06 15:39:35 2019 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n",
       "IPython & 7.5.0 \\\\ \\hline\n",
       "OS & Darwin 18.2.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.16.3 \\\\ \\hline\n",
       "matplotlib & 3.0.3 \\\\ \\hline\n",
       "pandas & 0.24.2 \\\\ \\hline\n",
       "seaborn & 0.9.0 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Mon May 06 15:39:35 2019 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
       "IPython 7.5.0\n",
       "OS Darwin 18.2.0 x86_64 i386 64bit\n",
       "numpy 1.16.3\n",
       "matplotlib 3.0.3\n",
       "pandas 0.24.2\n",
       "seaborn 0.9.0\n",
       "Mon May 06 15:39:35 2019 JST"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib, pandas, seaborn"
   ]
  }
 ],
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