{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Matplotlib For Simple Plots (10/25/2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Visualization with Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandas package allows you to plot certain graphs with your data frames. This is one of the simplest techniques of visualizaing data. One huge benefit of using pandas is that it's super quick and requires less input from the user! First, let's plot some univariate data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv(\"Iris.csv\")\n",
    "#Check the head \n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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alcsrtMsJ8OaI+NeI+GJE/NQgw63yE8AzwF9Wu9s+FRGbW9YZhTmtkxNGY06PuQy4ZY3H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\nUiH+H2uDikaNubUzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's get a histogram of the Speal Length\n",
    "iris.SepalLengthCm.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's get the histogram of Petal Length cm\n",
    "iris.PetalLengthCm.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeat with Petal Width Cm:\n",
    "iris.PetalWidthCm.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ36H5L2nPQABX18AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create boxplot:\n",
    "iris.PetalWidthCm.plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KboyIuXbKHViZOd6yYvFGSScarG8YyuTXX2j5IgfZ3qzlUzAv1VZB258Mr/JJ8fWS/kHL\nnxZ/ulj321p+c0vST2n5/3KvSfqupONt19xAz1+RdEbS0eLnYNs1N9Dz70s6XvT7tKSfaLvmYfd8\n0bY9jfBVLiXn+HeKOf56Mcc/1nbNDfRsSfdL+oakeUkfrnN87hQFgCTW2ykXAEBFBDoAJEGgA0AS\nBDoAJEGgA0ASBDoAJEGgA0ASBDoAJPF/OuNz14TkUUoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get histogram of Iris-setosa by Petal Width \n",
    "#When filtering, decide what unique value you want to examine, and what particular continuous variable you choose to display.\n",
    "iris[iris.Species == 'Iris-setosa']['PetalWidthCm'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.iloc[:50, :].plot(x = 'Species', y=['SepalWidthCm', 'PetalWidthCm'], kind='hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the groupby function. This is another filtering application that allows users to group data by some category given in a column. For example, let's group the dataframe by species value and find their means:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check values of the species\n",
    "iris.Species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</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>Iris-setosa</th>\n",
       "      <td>25.5</td>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>75.5</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>125.5</td>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Species                                                              \n",
       "Iris-setosa       25.5          5.006         3.418          1.464   \n",
       "Iris-versicolor   75.5          5.936         2.770          4.260   \n",
       "Iris-virginica   125.5          6.588         2.974          5.552   \n",
       "\n",
       "                 PetalWidthCm  \n",
       "Species                        \n",
       "Iris-setosa             0.244  \n",
       "Iris-versicolor         1.326  \n",
       "Iris-virginica          2.026  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby('Species').mean()"
   ]
  },
  {
   "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",
       "        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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Species                                                                  \n",
       "Iris-setosa                5.0           3.4           1.50           0.2\n",
       "Iris-versicolor            5.9           2.8           4.35           1.3\n",
       "Iris-virginica             6.5           3.0           5.55           2.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Can also find the median:\n",
    "iris.groupby('Species').median().iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Species</th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>25.5</td>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>75.5</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>125.5</td>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Species     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "0      Iris-setosa   25.5          5.006         3.418          1.464   \n",
       "1  Iris-versicolor   75.5          5.936         2.770          4.260   \n",
       "2   Iris-virginica  125.5          6.588         2.974          5.552   \n",
       "\n",
       "   PetalWidthCm  \n",
       "0         0.244  \n",
       "1         1.326  \n",
       "2         2.026  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use df.plot func again to plot the multiple y column values:\n",
    "mean_groupby = (iris.groupby('Species').mean()).reset_index()\n",
    "mean_groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zfIvXY48D2eZVSyUicta+9a1vsXDhQnbs2AHAv//9b/75z38CEBoa6r9Rwq5du+jSpQsR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\niDhGwS0i4hgFt4iIYxTcIiKO+f9LDDTbY+wu/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(mean_groupby.iloc[0, :]).T.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fkLQc+AUwWtLPt9wpIqZERHlElPfu3buZyzQzsxqNBndEXBYRfSKiH/Bl4L8i4owWr8zM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\nQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_groupby.plot(x = 'Species', y =['SepalLengthCm', 'SepalWidthCm'], kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</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>Iris-setosa</th>\n",
       "      <td>25.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>75.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>125.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Species                                                              \n",
       "Iris-setosa       25.5            5.0           3.4           1.50   \n",
       "Iris-versicolor   75.5            5.9           2.8           4.35   \n",
       "Iris-virginica   125.5            6.5           3.0           5.55   \n",
       "\n",
       "                 PetalWidthCm  \n",
       "Species                        \n",
       "Iris-setosa               0.2  \n",
       "Iris-versicolor           1.3  \n",
       "Iris-virginica            2.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_groupby = iris.groupby('Species').median()\n",
    "median_groupby\n",
    "#notice we can igonore the ID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KNTMzk8zMTFJSUnjggQfo168f2dnZla6/b7tVM2PGjBmMHDmSZcuWUVRURGZmZqBjuzun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\nHUsjlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_groupby.plot(x = 'Species', y = ['PetalLengthCm', 'PetalWidthCm', 'SepalLengthCm', 'SepalWidthCm'], kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AHy1Vr69M+hERMxrLJACn5jH1Q07S8cxdMG6+T5xRaGfB5kmCarPNdjStXVZI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\nZlaZk4mZmVXmZGJmZpX9f5n6ofSKZk4nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using the salaries dataset, let's make a bar chart showing salaries by position\n",
    "#Again same concept for multiple columns in Y; however putting level doesn't make the graph clear since the Y axis limit is too large to show.\n",
    "salaries = pd.read_csv(\"Position_Salaries.csv\")\n",
    "salaries.plot(x = 'Position', y=['Salary'], kind = 'Bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic graphs with single/multiple plots & Using the Seaborn Package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anatomy of a figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image: https://matplotlib.org/3.1.1/gallery/showcase/anatomy.html\n",
    "\n",
    "Figure: the outermost container of the graphic. One figure can hold multiple graphs. These graphs are known as Axes objects. A figure can be thought of as a basket of graphs(Axes objects). Axes: the individual plot (Not the same as axis !!). Title: Sort of obvious. Axis: X and Y axis X/Y label: labels for both x & y. Ticks are the increments. Spine: the line noting the data area boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first create a simple plot. Here we will tell python what to directly plot given 2 arrays that hold the X and Y values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+XzNjTG1jzGpjzAb3XM+WsU0tY8y77vVaZYyJ9JO57nQ/7ffket3j7blO2Xd1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\nSJBQ0EVEgoSCLiISJP4fFUCyo4+N5hIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5], [1,5,10,18, 22])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that Matplotlib automatically calibarates the points in both the X and Y axes to what it sees best. You may also customize the frequency of ticks, labels, and colors of the plot. A great article from Medium.com may explain this process of controlling the aesthetics of your figure: https://towardsdatascience.com/customizing-plots-with-python-matplotlib-bcf02691931f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F4AvAv7Vtn++PbSDa/+cBJ7Xb27dj+TbgA23Zy6lgb2Pg34Eft+WLAR+kCmMx\n+Czn8T48Edii3X4NVSu8RTsffxfYpD32w3a+3R44nmp93qjv7+BR7N+Rg3NuZ9krqFanH7ff4cS2\nfHHqGnsq8FLgPcCVwG5978e8+Ju/Sxl6VHKk+XOXiNgiIh6fmX+g8unel5k/Bw4HXkQ1760bEUtl\n1dpsS9U6HpGZe+dIaXbw2kM5yUurRTmYOgFeR/WevwD4NnViPoZKY/gBVdN/N3Ui/zawPFXrtHBm\nngts115zMPTZhGFOeejULixFXeRfRDUPvoOqET0/M38P/CEivkEF5TsD/0vVJJ9NXfA2hepYk5kf\nnpvbnCPpEytHde4ZeA1wXGb+iurQdDFVYPw0sEZEfCUiTqKCmojKhf5pZv5tbm7vMMqRWtbtIuLU\niHhZaxpeisqNHtQIfhF4NlXrdhjwhIg4h+qE/dXM/M7gNYe5pqpzXntLRLyvc9w8jjreAc6iWrme\n1855x1AF0l9QQdHHM/P2zLy9vdYc+113a9cz83+Bp0bEZdR5ZwuqRWdNajSim6hC5r9RgfohwOGt\nxvEiqrbxj5n51/n92I7ph0/9KbBLRPyBOo/dSZ2fntYe/xmVkrZNZn4V+H1r6fsZlZd8VJab5+L2\nbhgRH2qtjoPUz2OoCoQdIuJUatSpY4D9qe/4N7RWOyqd4xpqeNjfAltn5q/n1vbOCVFjsm9GFeS7\nx/pZVC3+YZl5ZGbeC5DVWrk3lTb0cqrz5k5ZLa7zv75LAv7N2z8qyJwwatkUKoA5ldbBrS2fTJ0Q\nlmn3N6U6v10MrNqWTRz1WkNbAz7Y//Z/Eap2+/NtP8+gaiFWHOwXVQO1c7t/aHt8KSr9ZB9G1f6P\ntz+q5mUP6iKwEhWAHAgs2h5/Lq31o92/iBojHipQWGJubtuo+6tSndOupGow39uWv43qVAewMFWY\nuIBqvZhIdSz9z8E+LUh/Y3yGi1KtVoMOq8dShZdlqJq2rTvrXkfVvg6OhdX73p9H2NcYfe6hclJ/\nTY2MsT01CsPyVBrdQcDabb19gEuBLdv9pYDV5uG2r0ZVCnyCGt1n7c5jvwVe224/iUqdOajdXwHY\naQE9tpemrlXfbufln3QeGwSyy7f7L6MKl5u1+2syD1sNqBbXzwJ7tPtPA/Zrt7ejUkt2avd3oWqL\nj6RSxKLvz3oW93k14EeMXE+j89h/AGeP+nxeCyzb7i/S9/bP67+hrc3QnNVyQQfjQD8Q00/48Hcq\nIPtP6uQ+NSImZ+YlVNPovgCZ+UvgfdSF++a27N72+g8bZWNYdGuxsv3SqYvthdSMoGtR+34zVdM9\nse3XqxnJv1uJqkXbhBrv9JgH5BcrAAAgAElEQVQcVfs/XrQa+8OoFI6zqQB2PSoFZw1GamKuovL5\n3hgRXwH+Gziq1Uj9KGvIyjm9baM7qQ1qUp4JXJKZ61NNtf8RETtThYN1ImKjrLGTF6Em5nhlZt6b\nmadm5hezet4PdUvFnDLGZ/i0iJiQmfdQebMvp1pCNqfSL+6lOli/vtWSv54q8KwDPNDOG9e31xqq\nzpmjzmsPRvX1GHRe+wNVEPs4sDWVtrE3ta9LAe+NmhRlfWp/n9SOj//NzBvaS8+xa+Tozy4iJrba\n0P2AuzPzHdSxvUNntYOozvAAf6by9h/fWi1vy8zT2vc6XxrrNxsRW1M14fcCe2Xmi6lRT9Zpq1xO\ndWbcv92/lBruD4DM/FPOpVaD9lvbMzozzmZ1rryKmoRnBSq9c/eofgpvAV6Wmae1dU+hUsMGlQfj\ntQ/LfVRhaDDLazcv/nxg2dZSdRy1vwHc2X7L9837ze2XgfgCoHOhGswu+Gng5xGxf0Qslpk/pQKw\n/6YC7yOoHFuoJv03di7uV1LNZJO67zGMAfhA92QW1VFmX2ropD9T+7sFlVd4PZU/PJj+/EPAmyPi\neqo2bc/MvKAT4Ax1UDfGhf/x7Vh4gCp8HJjVJPvfwO6Z+UdqP9eKGvHkn1SgNgm4LDMPysyvzc3v\nOkfSJ3aIiJOpfHWoJs2zIuJMqqBwPtX0eQ2Vt/yxiLiybeu1VOFisN+DTsgPjOML20yNKmw+GBGL\nRE3LfjVVI/yJ9tjvqd/2Jq1Q899U+tkxwDeBPanWh32oAHXZ7meWQzLkY+c7HZzXlouIT1KB6tER\n8cJWUFyU6th4OVWIfgNV8fAJKtXqK1SH7OuAJ4/a15wTx/oYBaOVI2KRVthflaqpHZxvD6cKmYMh\nFr9BpQbt0rbt+5n51qxh3+Zrg3NV+37Xj4hB6tTVVLrgFZk5mNn1ZCpdZ3C+Pw74z4hYtAXeH8lK\nsZxb2zromPh16tz6jIhYorPKT6g86M2pWvynAwdn5pTMPLsF8Ou3dY8DNsjMw+bW9s4Dt1CFn62i\npqt/oJ2XVqT6nnyHar38YWY+IzO/PPgt97nRvRldRe7f+P6jSpYLUzMIvrYtW6j9fzvV5PVBahzg\nU4FD2mPvAN7Rbr+IGhN8nXb/FVQaw1LUheJMhjgtg1Ed+xhJWZjQ7j+Nyv1enzqhH0vluq8LnEjV\nUnwaeAoV2C3fea2hbyrk4U30C1NNoKcx0lS4ATCN6pn/ZCo1Z6P2mXwG+CtVQ/74uXysDr6TQcrQ\nuu0YO4VqkfgtrcMO1Sn4hHb7GW0bX9rurwo8td0+Fdih7+9hXn3Xo49JKv3iQuqCvi7VjP8j4DXt\n8S8z0kz/hfZbH3wPi1KpWt9o54qJ82pfHuX+jtWx/FXtePl4u/964Lft9jbAKe32RKpweXC7v3A7\n/qdQgdKL5+B2PpsqRC7PyPn3NdSwr6cwkl7yLKo2vPvcH1O15O+hOiNuQeu4Nz//tWP149S1a4m2\nbEsq9eTnVLA9SBX8LPClznNXoPLD96VS7Z5MDfX30LllLm/7dB0TO7+nhTvL3k6l/y3Z9uVzVCfS\n97f9e1Hf38Ec/kxWaeeaU6kWzV2ployjGOL4oY8/a8TnM1m/gMWo2tyjosafHYzLOYHK+/1e1ljA\nn6Q6ugQ1MsDyEbE7dRE5kzYGeGZ+I6tEezfVs3/HHOK0jBzp2LdWRLwgK2VhLyoPDarG9MfA6zNz\nGlVbNoVqyj6O2v//A67LzGsz82+DJtL2+Q61HKlV3i0iTgN2pGo+lwQ2bKklV1D5swdkTe38M2oo\ntCupfPiXZeZ/5FyqeYvq6Jo5UsM6OBet3bb3tMz8OjUU5jZRE0bdBWwaNbHQttT3Rkuz+gfw4oj4\nPVXAuGhubPcwiBpucAl46FjPqDGx94qI5bKa3X9JpR7dnJl/pwLufdpL3EsN8/gdqhVov87Lb0QV\nhE7LzAOypZ71KToT6eRIrfKWUcO4QXX+WobKryYzv0ClbzyHGl3k3qjhLb9EFbrPbevdTwVGewDv\nyszvzoFt3TIizqNGI3oe9Vm+ICLWoio4Xkn1w3hrROzYzsNXRc1WO3AAlRa0MfC3rDSwi2d324ZV\n1BCE51BB213UqBnvbilGa1F9QDZrq78vakbXzwCbRMSqAJl5G5V2tA41Ac41WamVg2vi3Nz+h3VM\nHBynmXl/RCwaEZOoGWmXpvpa7UcNl/lh6nf6msz83tzcznktM2+kKrWuoObceA3w/qyhbYc2fuhF\n3yUB/+bsHxXQTKACmNOomt1Bx5DlqI5sz2WkxH4mFaQuQXXO+ik1Nvjo1x3ammAe3intSVSHvt8C\n/9mW7Qz8bLA+FWz/kCq1b0iV2rfse19mYd8f1vm2Lf8E1fz3PEaGiHo31TN/0Cnm7dSIDFAdnN4H\nLD43trH9341WE9juL0l1UrucyoXdti0/nJGWmoWolIlXtuP3Y1T6wRF0hhyjaje3YD7vuNZ+u/9H\nXdQWovouHEvlxB5J1RSuTQUwFzDSIXEhKlf2he3+m4F9+96fR7G/X6AKieu1+8+jaq/PpIKxt7Xl\nB7Xje9CJ/B3Ahe32ZKqAvVXndQe11EvO4e2dAvyp3Z5IBSCfaJ//4lQQfkk795zQ1tuGKjB1X2eo\nWiLm8ne8BPA7qvAPNfPlicCz2v1tqUqDI9u5Ys+2/IvAF4Zg+6frmNiWRft7LRVwf6zdP4i6Jg86\nks7X56vO5zFHf2fz25814vOZrNrQ5RkZBeMbwK4RsWXWEFw/pIKawayHxwAfzMx/ZOZHM/M5mXk2\nPGx4raGrCR4j93KQF7wZ8K/MXC8zv9jW+SawSETs3NafQI2s8abMvJTKk72o+9rjIAe82/l2sYh4\nQYx0VFuMujhcQ3VkehIVuDyFkRrmidQELVtk5m8y88M5Fybh6Rw7B1MdAgfDyB1ABShbUzXah7TP\n/DxglYh4ejueL6KaNZMaKeE5mfnmzLw5ykKZeX9WzeF813EtRqZgh2qluoWRCUgWA87KzMlUIXoq\nlZJ2NdXCM7XlyT5IpUS8EiAzj8jMz7XXj2E61ge/67bfg2vU4JhZjipA7Eid517Vasb/i+pw/FSA\nzPwEsFnUhDyXZOZemXnB4PXb50HWsIFz0jnAxIhYOas1YQI1z8CDVF7wFu27eiHVgvOirCFQb4qI\nZw5eJIegJWJuGX2sZeXyf5JKKSJr1uJ1qBx+qLS6L2TmAVR+9YHt2vQF4OYhOHan65jYye9PqgZ8\n+6wJxZLq2/I94PZ2/p7vzldjmQu/s/mKgfg41K6bMxy9IGsq8knUBB0/o8bAPiIitqFK4xtRFwWy\nmmMHJ5DBBXBwIhmKjlkzkiMpGLtGxBnUsIJQNYbrR8QXI+LQiDgiqkf9B6nZQo+gaoM/QxVUyBpD\neLrXHubCB9SJPiKWiIg3UEHYIVRHtUWoC9be1H5+gDr5r0kVzqa09f9GTQzxo7mwnUtExH9ExJPb\n/cWpQPCHjKQIHU3V5g7GqF+MSp84r23b1Lbel6kasjtbwP2PQUGpFUSGtqPw7Igab/gbwBdbagNU\np+J7qVSMjTLzOuC8qNE3XknVvq4SEc+gagxfSXXEhsqh3rvz+oOCbA7DsR4RG0XEL2lTzmeNnrA4\nVVu6ckSskzW6xFIR8T9U2sm3gFdl5u+osbS3ipFOfatm5h8HgVrnvDY3OxsnVTN/UPvOtqI6F0Ll\n3y8VEYtR59/bqZYogA1zHMxCPDsGBb4ZHGvfotKJ3hg1xvZ1VOFkOaows0zUlO9LU8f/k1sB6wND\ncOyO1TExo8bovxW4tnPs/Tirw/9Q/OY0HAzEx6FOLejiETElIpYePNYJ1L4HfL5dsBamxgn+CNUh\n5oe0/O/2ele3E+SglmjoAvBu4aNzYX1WRHyUyin+LLBHROyTmWdQAclpVH7aP6kRQr5HNQ3eD3wo\nM4/NIZ8YYaBzEXuws2x5amizfahm9y2oWtKXttq/p7Wa45dSTflrteVvoVIWvjAXayrWpoLs97Tv\n7T6qIDAIpDbPaqF5LXBtZm7Z1n9tqyW6ElgsauKof2Tmd7oXrmEtKM1hi1KFyk2ppm2ofOgzqeb8\njaJGIdiAaup+OVW4fC41Cs6vqE5hg9zpajMf3qFGF6FquT8QETu2ZT+iUpgmUiMaQY0LfVBm7gf8\nC3h2RDyfkXS02wAy8/boTP4yD89rX6PGSj6WmsHzS235DVSazSVUP4z9M/Mjbdvm92P5oQJfREyK\niA+3VrrBY3dQKWhHUZUEu+bIREpnUelFp1EpKhtntfgMhfbdfY06Rk+IiGdGxK7U73Rzalzsobum\nangs/MirqG/twpmdC+njqB71u1JNspdRQ3J1L64LUzUx78rMyzuv83QqDWO6i/AwXwg6tSgPtNqk\nRake8mtSzZZHZOYPIuIdVCefkzPz2+25y1M5h4OOO7+kOrKNfu2hEzXm7HKZ+YfOd/8sahSbszLz\ngoj4OVUQWYMKuE4Bto2IXwC3RMRLqFElVqYucuQ8GPosMy+NiN9Qo5m8nrqIXkrVAP6obdNPqAvs\nZ9rTVqTGBN8S+FpWZ7oFxuhjMTNvi4hvAX8Bto6IPan0nKup3/y+1AhAv6GC0W2oYPWXwCVRQ+Qd\nMfp9hjAAH/gDlQe8G1W7uAQ1tv/pVKrdelEd9ZYFXhE1m+IzqUBtkVaonq5g3ce+ZnXQeyewVGZ+\nJ1rnvcy8JiLeQ02YNteG0hsW0em02O4vTw09+iJq1KqbRx3zJ1PXtB+0iqbHZea/2rn90sy8dZ7v\nxKOUmTdGxFuoscvfCTyeKiye3++WaTywRnwcyJGREVaJyoN+MjUm7lqZ+d7M/Otg3RjJk74PuC8z\nL28Xr0Fe6HFZI4r0nVc3Q63yd7+I+ERErNT2ffGI+DAVgBwZEe/Kmvzgx1Sz5YTMPAu4B3hJ1JjZ\n76FqoO6ixgvuvsdDzfLzdOcehagRME6i8gl3HaQlRI2scCRwE3BwRLyfarY/i8qzhqqZWYkacWEl\nqgPjN7Ny/2+ct3vC2xjpiPUlKoC8mgrIn9Jqc8+j0oWupYbR2yQzLxoE4THEU6jPad1jsfP7vIoq\ndP6MKmjtzMikNH+gvt9/UB2u96dSVt6QmV/PNjHGsH2GMYO0ulYreik1ksvFVJD9EapS4VQqTWVr\nqm/BndRv+oeZeWBmntd5/WE4tx1NzUEwGLcfeGgs+/k+CIfp+u6s2RYtRnW8XK61xv1rVMHzJupc\n9rm26J7OY0MbhA9k5p2ZeQiwd9bIYgbhelSG6gStEiO52oMUjI0j4ptU3u/TqBy55YGTIuKgiPhU\nROw2eHr7/z/UsF2LZafjTyfFYegCUIBW63cF1aT3O2D19tCmVG3qs6khn94UEetSAcokRnItv0IF\ngA9QNWn/ltVR5u/d9xnWWsGoofmOoYbg24QKvAcB9FOo2v9PURPtPItqtr+SmoRnzbafv6Z68t+U\nmftl5tfm8W4MXEClQv2C+k6/0Lb3l1SAeWBmfobKbX9JZh4yusl5WL+nOaEbkEblvC8XNdkUtN9x\nVt+F66hJpgatOXdQrQfnUR2Tn5E11OPUzHxLZt41+J231xiKzzBG9T3pBsyd27+jOhivAnyeSj15\nembeQNWOb9fWOyAzN87ML7fnT9d3Yi7vyiPKGp7tK1Tt73wvapbQ/SJiSmfZ9hHxQ+DLEfElqsB4\nKjAt2uQ1YxSaTqRmWBy3s0qmHRP1GMU4PdbnS9Hpzd9dRgXgZ2XmsZ3lL6TSDf5FXbT2okaT+Ed7\nfPGcCyNgzE0RsRSV6/25lkLSfez9VA3/Ye3+oVRh5O1UYD4tMz/bHpuamad3njsYuWDoD/bWpL1K\nZr551PJlqKD8JOAnmfmviPg21cT7LeAw4H8y86ioMbqHIq0jIl5LBYpviRqj/m+ZeWZUTu/61GgI\n0wVm4+F7mh1j/c7b8hWoFKoNM/OOwXqtwLk3NRnM16jCzdUtBeLZWR2yH3ptOmlswygiXkSNoHM6\n8N2WxjShpSMENaLIAdSETstQQ/n9MSI2oEZDmtZ5renSH4bJjL7n+cmgUqe1xK7UbXWLiP+izQcQ\nEV+hCpAfpfq1XJaZJ/Sy0dKQsUZ8iOTIKCB7RsTbWq3BqowMQfeqiNg7Ijanxsj9KpV7ew81Yct9\nndf6Z3utGY6uMoRWpmp5r4AavqxT03U7NZviwElU7fg/qBrDhSJiSYBuEN7uD+XU5qNqBAf7eR+V\nBzzdKDaZeSdVI7gFlXIC1Xy/GDXCyPHU9MoMSxDefBV4TWuZOSkzz4QaoiwzPz+q2X6oA8jZNbqG\nOiImR8TpEbF5VPrYwtQsgit016NaRy4DdqDGTr9q8B13g/DBc4bhM2wV8hNGLds8ahSYZ1EB2T3U\niD4MgvC27b+hOjYekJnXZ+Yf2zpXdIPwwfPmwe7Mkvk5CG/fb7cj7P3A/0aNVPXKGBke9YL2lDdR\n+eH3UN/v02NkJCBpgWYg3pMZXKhWiYivU50Lb6GGbVuZqjlajLpAT6Fm5ZoSES+mRgl4PjX72MPG\nnh3WC1WMnbe6JlXDuxw8NHzZIKj4AjApIt7Q0jd2oWpbEjg2M48aL02CY6UHdS7aDwL/jIgntNrQ\nhRj5DE6mTWrTmnr/Hfh+C75+Mox5lO0C/XlqpBZghikJ86WIWDLa6BCD7zsiNoiIL1PDM14OvJwK\nVO6ihha9rvsa7TO8gurcOtRi+iERByM7DdLGrqVyvK/PGtf/q8B9EfHq9vjgnHAb8H3aaC8aLoNz\nVzs/rRIRO0TEEllpcVdRLRo3UhOrLQuVP02lHT2PSlWDkbkspAWao6b0oNNk+UBELEqlXDxInbQu\nA04A9qRqw5fO6vTx3+25F1Kzxg1qQ7fJGkt4XBh1Ep8APDEr/xNqdIgnUTnfg2UT2mc0kRoHfAuq\nQ8804P3w0IQQQz0CSlcnIJtEDXP2xc53eBU1McmmVO1otqbfZ1LDu72XqllampqM6P/m7dbPkg+N\nrvke6/Z8akNg3Yg4nypETaKGm7wuM98PEBGrU8ON/ogaM3kwOc9DcowRQYZJRDyNyof+KjX+88JU\nR8vtqNF7LqAmbTmR6ucCVdlwJtUB9es5Mi34AxFxxgJwbIwbnTSpQSrKKtQY6e+lZo7cK6qf0neo\nUZDWoo6FD0TEp6nj+h7gp5l5S0S8Y35uMZAeC2vE55GIeH5EDCbRebDVlH2CCrzfEzWCxBpUTe/Z\nVOesdTPz/JaisVFEnEDVil5L5dpel5nXRXX0GtrvMh4+Cc3EiNiBqv3euvPYldTECNu3C/ugNvDp\n1Ji752Tm+6iZynbJh3fsG8oL9xgtH8tHdUr9FDVCxM2dz+gC6sK2e0Rs1T6vrahx0Z+emXdn5smZ\nefQ4CcLH7Jw3vxo02XcW3UPlO19EDct3AfB7qhl/6RbYXE+laLyOahX6OzMwbJ9h59i+lppA6La2\n/0+lOlpvRHWeXoma6Obj1KhGy7UWr4uAJ0YNvfiQTmF1qPZ3QROjxpwfnL+pidB2ouYveBHVkveW\nzPwTVYjcGziCasU5mCqEfbcF4WEQLo0Y2uBtfhI1O9hSVIBFROxHdby7G3gp1dnyfYwMtffGzPxg\nZt4d1cFtvbbuucBmmXnwqBrGB4fxxBYjsx92J6GZSKUq7Ad8IDNPHKzbVvkCNSrMNyPi5VHD+H2Z\nqmVbqNXM3DrshY+ufHTDeA1qm+6nAvRfAvtFxI+BD1HpN8f3sf1zyrAWlOaEbrpR+y6Xb8fnX6nh\nJs8E3pk1QclPqRrCNTo5tqcDB1KTTa0weM3R7zMsn2E8fPbde6kC8+epdLq1qIqEB6iUhOOooPw2\nqjVvMAvujcBeWdO8P8yw7O+CJCJWjzZJXI70Z9ghIr4eEYPZbo+hWuUGPk0VNAHOoFr0Ns3Mz1HD\n+W2RbWIjv1NpeuMikBmPurWg7eJ7HrBzyxe9jjpR/SxraubDgBdTtQqnUpPSfDQizqYC1qUy8+pW\nE3pPREwY1pqiiFg0IjaF6cY/XzdqTPDtqGEFv0WNLz3ojPjQ6AJZHdEOppo8N6GG5ntm1iyYD3bW\nG9bCxywP49UJyh7IzE9Q05O/ITM3z8xvzPu90SOJ6XOiswXgn6Ryuj9KpZu9jpoh8yXtaWdQLV4b\ntrSrwW/gTip1Y7qc8mHUKVxOjoi3UgXLq6i8302p3PfrI2KjrL4rC1GtAxOoGSfXbcf8fZl5VS87\noelExHMj4lSqT9L6MTKT8bupFLpTqI62R1M14hOpfjuR1Wn4zxHx9sy8gip4/Q0gM/8y7/dGGj8M\nxOeSzoXqBRGxBVUj/nwqp/u7wJ+BJaKGmvsz1WT93sz8PDUN+11ULeimmfnj0a89jBfpVjjYhlaj\n15btSgWdD1Cd007JzO9T+/+Etv8PjnoNMvO0rIk6PjbshQ+YrvbyQeDbmXl25+HXAkdnTeO+KNVU\n+3MqIN8Exg66MvP+bLOiajiNOnZfT9V8/5HK874FeHdLPfkb1bJFZt5NdbLekhrvfZCuNoWacXS6\noTv71k236fzfOGpW13cBb6BSFG6jtn1TqqDxI+DTrRVse+occE9mXpCZOw/jOWxB1dLfDqP63zy3\nBdYPRg2buiY1PO4kahbfG7PGSf8J8DKqUgVq6NlVADLzhByiaeilYWYgPoeMDhIjYrOIuIS6UC2V\nNaLFz6hpmpejpqbfmZoKF+oktlfU+N+XZuZhOTJN+1APQdhpps7M/B5wWURs2x5+BvC+FlS/Edik\n1ZifSU3H/aTua42+OHfSW4a28BEO47XAiogVI+JdEbERlTq2OjXG91+oWsOlIuK5VGfMFSLiKxHx\nWWpG2M9lG5qvuRN4xTDVEA+O7W5LVHvoVdSoRa+gArgV2n5+k/oM1mAkxep7VHD+wdZq8NCwnPN2\nbzQTawFXtgD6nxGxSPve76RSiq6iOhu/JFsnYyo9ZXtgHYDMPCMz3zrWi0uaMQPx2TDIC4XpOhet\n0x5+OfD5zJySNfU6VDA2kWrG/TpVG7Z5u9hdBmzcToLT1T7lkA9B2N2+Vvv1SmC39tk8g5HCBtTJ\ne28qEF8b2GhmNd05JOMij2VUTrDDeM2nur/zzrJN2827qI6Jz8vMa6hawk3aY3+lahj3ycz/ofo6\n/B/wncz8ZdZIKN1WoJ8Plg2LHOlY/tGIOK6lWS1M5bIPAulvUPu6VSuAXE2l2i2fme8EXpaZL8+a\nJbTb8W8oz2sLqBuB1SLivRFxDHBoRBwWNVrT54CfZ+ZBWYMDbBQRb2ktPe+iWnckzSID8dkwyAsd\n3I+IvYF3tFrQP9GGboqIwyPiZGp4vkuomuBFqVzpx9PGiW4nuciH1z4NjW5AkiMded4QEb+MiNdR\nzZRnUWkXm1OB99s7L3EXcEVrnj8e+NGwBtpj6RSSHhrGKyL+HTgfeCNwUkQsTg3jtQrTD+M1OSKe\nxcgwXlcC72hBmoZQp0a4+zt/OnB4RGyalf/8Ayrn+SnAx4B/j4jHtcfOB1aPiKdl5q8zc++s4Ugf\nMgzHf0v9OiAiTo2IbaJ11ouIJ1IdMG+nChIHAa+h0m7ui4iVW8EzgK2iRjs6harxvwNqyu9ueouG\n0jnt7wVU5+IHqE63B1MtPatExBERcQY1mdog//vMHCejN0nDyinuH6XWjLovsCs1m+Vpmfm7qMko\nVs7MwyNibWo0gHMz81sRsQtVU/QAcCjVbPt9KmA7dpiaoGdkEHDO4LF/p2p2b6E6Zz0HuDcz3xER\nBwBrZuabI+JMatzvx1HB+f6ZecFYrzmsYozpqlvt/4XUhetNmXlT1MyBl2XmoRHxHmB5atz3N1Az\nIy4NnJiZX5rZZ6v+RPXpuLy1XhA1Y+u7gf+lgpJfUWPYL5OZb42Ix1PpGZdn5ucj4iJqoqWPtdrj\nxVuwOnj9oZr6PCq3/SCqle5vwDOBX2Tmp1ra1JuAr1F5whtTfR6WAHanCpXHUYXtAL6R1QdG41RL\nS7kvItYADqeG37wX+Ddq3oev9LqB0nzGGopHoV2obqCaoA+hgqn3dVZ5YUtJ+APVUWvjiFgzM0/J\nzIuoIbv+Sl2orwbeNgjCx2r2HhbRyX1u9yMiFosaVnAlKqf1ZcD3MvO/qBqzjaNGhjmf6oz6fCon\n+gfUWMPP6wbhw7rv4DBeC5Kovghvj4hfU0Hl8yJi2YhYmarhXZoa3/urVEfMC6gRI9bNzLuAfwG7\ntBrkg4F/wkMdbv8+VkvSELkXIDPflpkfoUZ4WrpVPqwEbEYd1+dl5kaZ+Rsq9eoT1FCcn6LSF+5v\n689o5lwNuXbOv6/d3ZmqWLkhM2/JzPMMwqU5z5Plo3MvFTft3/K9/0g1vUJdkG8FBtM0n0/V+q4X\nEctFxOlUCsaFmXkhPJR3Od2wZ/NwXx6LsyPiYIDWBJ3UiCivAqZkdcycRuspT+WGXkyNC3wFVfjY\nMquj5ZmZ+dHMvL17kR7GfQ+H8VoQ7U/N2vrqzJxK/V7voJrnF8vMN7XC1MlU7v+tVPrZ+1pL2HJU\nruxymXlhZn6m++LDeJwPZOaXgfujRs6Aarm6JyuH+yZqv45phW0i4p1UPvg11PCqr6Rmy1yPGrZx\nGAsbegStsLhuRBwcEZdSo3wd3vNmSfM9A/FHoV2oHoiI10XER6icyc0j4pDMvJlqrt6prftrKh1h\nK6qp9sDMfHZmHj3qNYf2QtWpvfs4sGdErAt8NSJWyuqg8yPgGa3J/miq6Xow1fx3gZdGDXt1ZNaY\n4NO99pDvu8N4LWAiYgUqbei9mfn7doze3R5+AnBljIxscw41lfufqHzwB6mC9tmZuU9W3v/gdYe2\ntWcMRwGfiIg3ULN87hARL2/B9heAXSPiqy1Aey5VAIe6hnyWKpjulpm/GOO1NQ60wuJfqI6br8vM\nqZk5VENpSvMjc8QfpYjYhwrA96dqRIMafu4lmXlxRJxHzX65LDVM4ZeyMzRZREzIcThKQGuqP50K\nOP6RmZ+OGhnmbcBZmfWk3PAAAANuSURBVHlaRPwWeHNm/iAilgCemp0OiOMpF7qlIW2Uma9v9xcZ\nNNVGDUe5GhWYHJ+Z17Xlq1O5/3tk5q/62XLNqohYhCpM7Z7V7yOoc+ODEfFkKj/8p61ATkRc0Na9\nISKWzMz/7bzWUOV/PxYRcQvV0vc6qpb7ZVRQ9hbq978l8OfBb3uwr1EdU//Vz1ZL0vhmIP4YRMRt\n1BCDgwDsOOC+zNwnIlalJrO5LGsownGtc5F9KdU8uQfVaWf3rFEQvkodP6+JiAOBJXJkfNlxKyJe\nDOxDTUO+OpUX/CBV+NoQmJqZO7V1NwKen9VRd0fggnQEgXEnIpYCPkz14fhSW/ZQQB3VIftlVEF7\nM6rT9cE5/bCd47Kg3RURhwLrZObO7f7K1CyYn8vMczrrBbDQeN9fSRoGBuKPQfx/e3fMGlUQRXH8\nXLWwsFCstDFYRXs/Q6wES7GOCGJjrUQQESWdWGipvWBlITZaWIkfwE7QgCDYWAjKsbizOgZBosve\n7Lz/D1LsYwKz8Ja5M2/emYhbklZtn42IFeUR1o/8Kyd81u7nitriezl/EfFW0l3l1oz3tjcj4oFy\nC86NESYeM5FJKBeVL5i+VO6XPaJ80nFJ0mNlkX68/d2x/bCmt5iXiLisPJjkuvOEyNn1NeWLid8l\nnZb0eqT7vRcR+5XJMCfdMr/7J0Lt89I83QKAZUAhvgNtoPqi3D98WNILSRu2v3ZthhmoulXxC8qX\nFM8rM2QPKqPO7tn+tL19TW/nL4jxmoy2+rsh6YAybtLKiMIVSeu2t7q2Q020exGxKem57afdtaF+\n1wCwm1CI71BEXFMO0rf7laLRRcSWpDVlBOHeligx1MSjt21rwhVJp2yf+8u/YYlFxCHlkx8pj/V+\novydf+7aDHm/AwBqUIj/hxZrt2uPYJ+H2d7XiLgq6Zjt9XZ9j1r6Ym0P56+teJ5QxjSekfRO0k0S\nBKYhIo5K+mj7W/s8qRXhqX1fAKi0r7oDy6grQod/WakV4aHMD3/VXR92oLbtiOhjvN5U9wmLY/uD\n9NtEe9h7/U+m9n0BoBKF+D+Y4EC1qswLflbdkUVxnpZ4v7ofqDOFiTYAoBZbUwAAAIACnKwJAAAA\nFKAQBwAAAApQiAMAAAAFKMQBAACAAhTiAAAAQAEKcQAAAKAAhTgAAABQ4Aflwd8Bb9AEUQAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure1, axes = plt.subplots(nrows=1,ncols=1, figsize = (12,5))\n",
    "ax1 = plt.bar(np.arange(10), salaries.Salary)\n",
    "plt.xticks(np.arange(10), salaries.Position, rotation = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we'll do the same with the Seaborn package!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DysvL0549ezRo0CDZbDYVFBS4cz4AAACgRnJ5ecZNmzYpOjra+QuECHUAAACg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\nIBGoC4IgCIIgCMIQ9P+xmrU6XrXBxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "figure1, axes = plt.subplots(nrows=1,ncols=1, figsize = (12,8))\n",
    "axes = sns.barplot(x = salaries.Position, y = salaries.Salary, color = 'red')\n",
    "axes.set_xticklabels(axes.get_xmajorticklabels(), fontsize=8, rotation = 10) #rotate the xticks in counterclockwise direction\n",
    "axes.set_ylabel(\"Corresponding Salary\")\n",
    "figure1.suptitle(\"Salaries by Position\", fontsize = 18) #The title of the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, this seems a bit more intuitive than the previous example.  Note the changeable characteristics: size of xticks, x/y labels, figure title, color. Next, let's make a 2 subplots that each contain a scatterplot. We will be using the iris dataset for this problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nxhqFx1JxrVYHjwZBro+dMeEWNKUEfSBftWqV8Dg3Nxdz5swRroG3atUKJSUl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\nQlix/wPMnMZsDEUpogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = pd.read_csv(\"Iris.csv\")\n",
    "figure2, axes = plt.subplots(nrows=1, ncols = 2, figsize = (8,5))\n",
    "scatterplot1 = sns.scatterplot(iris.SepalLengthCm, iris.SepalWidthCm, ax= axes[0])\n",
    "scatterplot2 = sns.scatterplot(iris.PetalLengthCm, iris.PetalWidthCm, ax = axes[1])\n",
    "scatterplot1.set_title(\"Sepal\") #Assigns an individial title for each subplot\n",
    "scatterplot2.set_title(\"Petal\")\n",
    "figure2.subplots_adjust(wspace=0.3) #Adjusts the width of each subplots\n",
    "figure2.suptitle(\"Comparing Widths/Lengths for Sepal/Petal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now make some boxplots. Our x-axis here will be categorical. The 'Species' column will serve as the categories. We can then compare the boxplot for each category with a given continious variable (such as sepal width/length or petal width/length). Let's make one graph that compares the petal length for each category. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoA\nAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACL\nIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKk\nAQCwKB9XLXjBggXavHmzCgsL1a9fP8XGxrqqKQAAKiWXhPSXX36pr7/+WitXrtTp06e1ePFiVzQD\nAECl5pKQ/sc//qEbbrhBjz/+uHJzczV27FhXNAMAQKXmkpDOzMzU0aNHNX/+fB05ckSPPvqoPv74\nY9lstlKn9/a2KTDQ3xWlAADcxMfn7DAnfp+XH5eEdGBgoMLCwuTn56ewsDBVq1ZNGRkZqlOnTqnT\nFxcbZWXluaIUAICbFBXZJYnf504IDq7p1HQuGd1922236fPPP5cxRsePH9fp06cVGBjoiqYAAKi0\nXNKTjoqK0s6dO9WnTx8ZY5ScnCxvb29XNAUAQKVlM8YYTxdRWFjM4REAlc7ixQuVnn7A02W4TVra\n2XUNDQ3zcCXu0ahRmIYMGXZZ8zp7uNtl10kDQFWXnn5A3+7bLVWVs33/f8D02593e7YOd8hyTzOE\nNAC4UqBkj7R7ugqUM68t7rlhJ7cFBQDAoghpAAAsipAGAMCiCGkAACyKkAYAwKIIaQAALIqQBgDA\noghpAAAsipAGAMCiCGkAACyKkAYAwKK4dzcAuEhWVqaU5b77PMONsqQsv0yXN8OeAwCARdGTBgAX\nCQwM0pGCwzwFqxLy2uKlwMAg17fj8hYAAMBlIaQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIa\nAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsCgfTxcA\nVHVbtmzS5s0b3d5uVlamJLnlwfX/q0OHToqM7Oj2dj0iS/LaUkX6Q2f+/9/qHq3CPbIk1XN9M4Q0\nUEVlZnoupKuKRo3CPF2CW6WlHZAkhdarAutdzz3b12aMMa5YcK9evVSzZk1JUv369TV9+vQLTltY\nWKysrDxXlAHgApKT4yVJkyfP8HAlqCzYp5wXHFzTqelc0pPOz8+XJC1btswViwcAoEpwyYmSvXv3\n6vTp0xoyZIgGDRqkf/3rX65oBgCASs0lPenq1atr6NChio2NVXp6uh5++GF9/PHH8vEpvTlvb5sC\nA/1dUQqAC/DxOfs3Oj97KC/sU+XPJSEdGhqqhg0bymazKTQ0VIGBgTpx4oSuvvrqUqcvLjackwbc\nrKjILkn87KHcsE85z9lz0i453L169WrNmHF24MDx48eVm5ur4OBgVzQFAECl5ZKedJ8+fTR+/Hj1\n69dPNptNzzzzzAUPdQMAgNK5JDn9/Pz0/PPPu2LRAABUGVXkNjgAAFQ8hDQAABZFSAMAYFGENAAA\nFkVIAwBgUYQ0AAAWRUgDAGBRTl0nnZOTox07djiebiVJ3bp1c1lRAADAyZAeMmSIGjdurFq1akmS\nbDYbIQ0AgIs5FdI1a9Z03IsbAAC4h1Mhfeedd2rlypVq0qSJ473bb7/dZUUBAAAnQ3rXrl0qKCjQ\nzp07JZ093E1IozJavHih0tMPeLoMt0hLO7ueycnxHq7EfRo1CtOQIcM8XQbgNKdCOi8vT2+88YaL\nSwE8Lz39gNK+/0YNAoo9XYrLXSmbJKn4x396uBL3OJTr7ekSgEvmVEhff/31+uCDD9S8eXPZbGd/\nsENDQ11aGOApDQKKldg619NloJxN3RXg6RKAS+ZUSO/du1d79+51vLbZbFq6dKnLigIAAE6EdHFx\nsZYtWyZJys3NVfXq1eXj45LHUAMAysGWLZu0efNGt7fryXEOHTp0UmRkR7e362pl3nFs37596tKl\ni3799VdJ0vbt29WlSxf98MMPbikOAFBxBAUFKSgoyNNlVCpldomnTZum2bNn68orr5QkRUdHq3bt\n2po6dSoDyQDAoiIjO1bKXmVVVGZP2m6365ZbbinxXqtWrVRYWOjSogAAgBMhXZqioiKXFAMAAP6r\nzJCOiIjQzJkzlZOTI0k6deqUZs6cqTZt2rilOAAAqrIyz0kPGzZMixYtUkxMjM6cOaMrr7xSPXv2\n1EMPPeSu+gC3ysrKVEaON9fUVkIHc7xVOyvT02UAl6TMkLbZbBo2bJiGDeM2egAAuJtTFzy/8847\nevPNN3X69GkZY2Sz2bRp0yZX1wa4XWBgkGqeSuOOY5XQ1F0B8g7k8iBULE6F9MqVK7VgwQIFBwe7\nuh4AAPD/nArpoKAgXXvtta6uBQAA/EaZIT179mxJUkFBgYYOHaobb7zR8YCNMWPGuL46AACqsDJD\n+tyTrv73iVfnghoAALhOmddJx8TEKCYmRqmpqY7/x8TEaOvWre6qDwCAKqvMnvSKFSs0b948ZWVl\nacOGDZIkY4yaNGniluIAAKjKygzpAQMGaMCAAZo/f76GDx/urpoAAICcHN1dVFSkl19+2fHa19dX\nV111lbp16yZfX1+XFQcAQFVW5jnpc77//nulp6erbt26+vHHH7Vt2zb94x//UEJCgqvrAwCgynIq\npLOzszVr1iz17dtX06dPl5eXl5577jkdOXLE1fUBAFBlORXSOTk5ysjIkCRlZmYqJydHhYWFOnPm\nzAXnOXnypNq3b6/9+/eXT6UAAFQxTp2THjFihO677z4FBAQoLy9PiYmJWrJkifr06VPq9IWFhUpO\nTlb16tXLtVgAAKoSp0I6KipK7du3V0ZGhurUqSObzaaIiIgLTj9z5kz17dtXCxcuLLdCAXc5lFs1\nHlX5a8HZmxJd6Wc8XIl7HMr1VujFJwMsxamQ/uKLL/TGG28oPz/f8d7SpUtLnXbt2rWqXbu2wsPD\nnQ5pb2+bAgP9nZoWcKWmTW+Qj49TZ4EqvOwffpAk1WtYNe57cL2kxo2b8LsGFYrNGHPRP6O7d++u\nhIQEXXXVVY73wsLCSp12wIABstlsstls2rNnjxo1aqR58+aV+QStwsJiZWXlXUb5AC5XcnK8JGny\n5BkergSoeoKDazo1nVM96auvvlrt2rVzaoErVqxw/D8uLk4TJ07kEZcAAFwGp0K6Tp06Sk5OLvEU\nrPvvv9+lhQEAUNU5FdL169eXJP3yyy+XtPBly5ZdekUAAECSkyH9xBNPaOvWrTpy5IhatGhx3qMr\nAQBA+XMqpGfPnq2ffvpJ+/fvl6+vrxYuXKjZs2e7ujYAAKo0p641+eqrr/Tss8/K399fMTEx3A4U\nAAA3cCqki4uLlZ+fL5vNpuLiYnl5VY3rSAEA8CSnDncPHjxYvXv3VkZGhmJjYzV48GAXlwUAAJwK\n6S5duqht27Y6ePCg6tevrxo1ari6LgAAqjynj1tfeeWVatGihWrXrq24uDhX1gQAAHQJIf1bTtxJ\nFAAA/E6XFdLn7joGAABcp8xz0s8///x5gWyM0fHjx11aFAAAuEhIX+hJV2PGjHFJMQAA4L/KDOmW\nLVu6qw4AAPA/ygzp5ORk2Wy28waK2Ww2LV261KWFAQBQ1ZUZ0hd6ilVBQYFLigEAAP/l1M1MUlJS\ntGTJEhUVFckYI19fX/3tb39zdW0AAFRpTl2CtWrVKi1btkwRERGaPn26Gjdu7Oq6AACo8pwK6aCg\nINWrV0+nTp3SHXfcoV9//dXVdQEAUOU5FdI1a9bUJ598IpvNppSUFGVkZLi6LgAAqjynQnrq1Km6\n5ppr9NRTTyk9PV2TJk1ydV0AAFR5ToX07NmzdeONN6pevXqKj4/X6tWrXV0XAABVXpmju1esWKF5\n8+YpKytLGzZscLzPwDEAAFyvzJAeMGCABgwYoPnz52v48OHuqgkAAMjJ66QHDhyoOXPm6Oeff1Zk\nZKSaNm2qhg0buro2AACqNKdCOiEhQREREdqxY4fq1q2rCRMmaPny5a6uDagStmzZpM2bN7q93bS0\nA5Kk5OR4t7fdoUMnRUZ2dHu7QEXj1MCxrKws9enTRz4+PmrVqtV59/IGUPEEBQUpKCjI02UAKINT\nPWlJ2r9/vyTpp59+kpeXU9kOwAmRkR3pVQIolc1cpFucm5uro0ePKikpSfv371dYWJj+8pe/6Kab\nbiq3IgoLi5WVlVduywMAwMqCg2s6NV2ZPenly5dr8eLF8vHxUWJioiIiIsqlOAAAcHFlHrdev369\nPv74Y6WkpPD8aAAA3KzMkPbz85Ofn59q166twsJCd9UEAADk5OhuSYzoBgDAzcocONauXTu1bdtW\nxhht375dbdu2dXz2/PPPl1sRDBwDAFQlzg4cKzOkd+zYccEZ//jHP156VRdASAMAqpJyCenLVVxc\nrMTERKWlpcnb21vTp09XgwYNLjg9IQ24X2ZmhmbPnqkxY8YpKKi2p8sBqhRnQ9oldyX59NNPJUkp\nKSkaOXKkpk+f7opmAPwO77yzUnv2fKd33knxdCkALsAlIR0dHa0pU6ZIko4ePaq6deu6ohkAlykz\nM0OffvqJjDH69NONyszM8HRJAErhsvt7+vj4aNy4cZoyZYo6d+7sqmYAXIZ33lkpu90uSbLb7fSm\nAYtyyTnp3zpx4oTuu+8+ffDBB/L39y91GrvdruJiLvEC3KVHj7uVl/ffcSD+/v76618/8GBFQNXi\n6+vt1HROP2DjUqxbt07Hjx/XI488oho1ashms8nb+8IFFRcbBo4BbhQeHqlNmzaoqKhIPj4+Cg+P\n4mcQcCOPDhy766679O9//1sDBgzQ0KFDlZCQoGrVqrmiKQCXITa2n+Npdl5eXoqN7evhigCUxiU9\naX9/f82dO9cViwZQDoKCaisqKlobNnykqKhOXIIFWJRLQhqA9cXG9tPhw4foRQMW5vKBY87gZiYA\ngKrEo+ekAQDA70dIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgD\nAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBg\nUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGE\nNAAAFkVIAwBgUT7lvcDCwkIlJCToxx9/VEFBgR599FF17NixvJsBAKDSK/eQfv/99xUYGKjnnntO\nmZmZiomJIaQBALgM5R7SXbp0UefOnR2vvb29y7sJAACqhHIP6SuuuEKSlJubq5EjR2rUqFHl3QQA\nAFVCuYe0JB07dkyPP/64+vfvrx49elx0em9vmwID/V1RSqXxww8/aMyYUXrhhblq3Lixp8sBALiB\nzRhjynOBv/zyi+Li4pScnKy2bds6NU9hYbGysvLKs4xKZ9SoR3X48CFdd11DzZnzqqfLAQD8DsHB\nNZ2artwvwZo/f76ys7P16quvKi4uTnFxcTpz5kx5N1OlpKUd0OHDhyRJhw8fVHr6AQ9XBABwh3Lv\nSV8OetJlO9eLPofeNABUbB7rSaP8/Tagz74+6KFKAADuREhXANdd1+B/Xjf0UCUAAHcipCuAJ598\nusTrUaP+7KFKAADuREhXAKGhYY7e9HXXNVSjRmEerggA4A6EdAXx5JNPy9/fn140AFQhjO4GAMDN\nGN0NAEAF55LbglZmW7Zs0ubNG93eblZWpiQpMDDI7W136NBJkZE8yQwA3I2QriAyMz0X0gAAz+Cc\ndAWRnBwvSZo8eYaHKwEA/F6ckwYAoIIjpAEAsKgKfbh78eKFVeaJUGlpZ9czNLTq3MikUaMwDRky\nzNNlAEC5c/Zwd4UeOJaefkDf7v1edv/ani7F5Wz2s5tq96ETHq7EPbzyMjxdAgB4XIUOaUmy+9fW\nmRu7e7oMlLPq/17v6RIAwOM4Jw0AgEVV6J50VlamvPJO0uuqhLzyTiorq0LvngDwu9GTBgDAoip0\nVyUwMEiHsos4J10JVf/3eu6uBqDKoycNAIBFVeietHT2Up2qcE7aVnhakmR8a3i4Evc4ewlWsKfL\nAACPqtAh3ahR1bmxh+NmJg2qSnAFV6ntCwClqdB3HKtKeMAGAFQePGADAIAKjpAGAMCiCGkAACyK\nkAYAwKIIaQAALIrR3Zdoy5ZN2rx5o9vb9eTzpDt06KTIyI5ubxcAKitGd1cytWrVUlFRoQoLCz1d\nCgDATehJVxALF76iDRs+0l1YP0r4AAAMm0lEQVR3ddOwYY95uhwAwO9AT7oSyczM0KeffiJjjD79\ndKMyMzM8XRIAwA0I6QrgnXdWym63S5LsdrveeSfFwxUBANyBkK4APvtsi4qKiiRJRUVF+uyzTz1c\nEQDAHVwW0t98843i4uJctfgqJSIiUjabTZJks9kUERHl4YoAAO7gkpBetGiREhMTlZ+f74rFVzmd\nOnXVufF9xhjddVcXD1cEAHAHl4R0gwYN9NJLL7li0VXSxo0flehJb9jwsYcrAgC4g0ueJ925c2cd\nOXLE6em9vW0KDPR3RSmVwuefbynRk/788081duyfPVwVAMDVXBLSl6q42HCddBnCwyO1adMGFRUV\nycfHR+HhUXxfAFCBcZ10JRIb209eXmc3lZeXl2Jj+3q4IgCAOxDSFUBQUG1FRUXLZrMpKqqTgoJq\ne7okAIAbcFvQCiIzM0OzZ8/UmDHjCGkAqOCcPdxNSAMA4GackwYAoIIjpAEAsChCGgAAiyKkAQCw\nKEIaAACLIqQBALAoQhoAAIuyxHXSAADgfPSkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAA\niyKkXeDIkSO67777Srz32Wef6e233y73tjZu3Kjjx4+X+3Lhfu7cby5m4cKF2r179yXNExcXp/37\n97uoIvxerti/LmU/uVhba9eu1aZNmy67lsrKx9MFVBUREREuWe7SpUs1ceJEhYSEuGT58CxX7TcX\nM2zYMI+0C/f6vfvXpewnF2urd+/ev6uWyoqQdqG4uDgFBQUpOztbd999tw4ePKgRI0boySefVG5u\nrs6cOaOnn35ad9xxR4n5NmzYoEWLFsnHx0fXXnutnn32WZ06dUoTJkxQZmamJCkxMVHHjh3Tnj17\nNG7cOL311ltavny5PvjgA/n4+Kh169Z6+umn9dVXX2nmzJny8fFRrVq1NGvWLEnShAkTlJOTo8zM\nTMXGxqp///5u/35QusvZbwoLC9WtWze999578vf312uvvSYfHx917txZSUlJys/PV7Vq1TRlyhQV\nFxfr0UcfVWBgoCIiIuTv769169bJy8tLrVq10rhx4xQfH69u3brpj3/8o8aPH6+jR4+qsLBQSUlJ\nuvnmm5WQkKDDhw+ruLhYDz74oLp16+aoJTs7W08//bRyc3NVXFysJ598Um3btlX37t3VqFEj+fn5\nafbs2Z74aqHy3b/27t2rbt266ZdfftGaNWtkt9s1cuRIHTlyRCtWrNCVV14pX19fx/5x4MAB9e3b\nV0899ZSuuuoqHT58WLfccosmTZqkl156SXXr1tX999+vqVOnavfu3SosLNSIESMUFRWl5ORk/fTT\nT8rMzFRERIRGjRrlqa/QrQhpF+vRo4c6deqktWvXSpIOHTqkX375RW+88YZOnjyp9PT08+ZZv369\nBg8erLvvvlvr1q1Tbm6uFixYoDZt2qh///5KT0/X+PHjtXLlSjVv3lwTJ05UWlqaPvroI6WkpMjH\nx0cjRozQp59+qh07dqhTp04aOnSoNm/erOzsbGVmZuruu+/WXXfdpePHjysuLo6QtphL3W98fX11\n1113acOGDerVq5c+/PBDvf7665o0aZLi4uLUvn17bdu2TbNmzdLo0aN14sQJrVmzRn5+frr33nuV\nlJSkli1b6q233lJRUZFjuSkpKbr22mv1wgsvaN++fdq6dau+++47BQUF6bnnnlNubq569+6tNm3a\nOOaZN2+e2rVrpwceeEDHjx9Xv3799MknnygvL0+PPfaYbrzxRrd8h7iw8tq/9u7d65imVq1amjdv\nnjIyMjRx4kStW7dOfn5+GjRo0Hntp6en6/XXX1eNGjUUHR2tEydOOD7btGmTMjMztXr1ap04cULL\nly9Xs2bN1LJlS8XGxio/P5+QRvkJDQ0t8fr666/XgAEDNGbMGBUVFSkuLk67du3S3LlzJUlDhw7V\n+PHjtWDBAq1cuVJhYWGKjo7Wvn37tH37dn300UeSzvZWfuvAgQO69dZb5evrK0lq3bq1/vOf/2j4\n8OGaP3++HnjgAYWEhKhFixaqW7eu3nzzTW3YsEEBAQElfinDGi5nv4mNjdXEiRMVFhamRo0aKSgo\nSPv27dOCBQv02muvyRjj2D/q168vPz8/SdL06dO1ePFizZo1Sy1bttRv7xR84MABx2HKG264QTfc\ncIMmTZqkdu3aSZICAgLUuHFjHT582DHP/v371aNHD0lSSEiIAgIClJGRUep6wTPKa/8qbZmHDh1S\n48aNVaNGDUnSH/7wh/Pab9CggQICAiRJwcHBys/Pd3yWlpamli1bOj4bPXq0cnNzlZqaqu3btysg\nIEAFBQXl9E1YHyHtYjabrcTr77//XqdOndLChQv1888/q2/fvtq8ebOWLVvmmGbOnDkaMWKE6tSp\no+TkZG3cuFFhYWG655571KNHD508eVLvvPOOY/nGGIWFhWnJkiUqKiqSt7e3du7cqV69eumvf/2r\nYmJiNG7cOC1YsECrVq1STk6OWrZsqf79+2v79u36+9//7tbvBBd3OfuNJBlj9Nprr6lfv36SpLCw\nMA0ZMkStWrXS/v37tXPnTkmSl9d/x4yuWrVKkyZNUrVq1TR06FB9/fXXjs8aN26s1NRURUdH6/Dh\nw5ozZ47+8Ic/aNeuXerUqZNyc3O1b98+1a9fv8Q8u3bt0o033qjjx48rOztbgYGB57ULzymv/eu3\nzm3bBg0a6MCBAzpz5oz8/Py0e/duhYWFldn+b4WFhenjjz+WJOXk5GjUqFFq3769atasqcmTJ+vg\nwYNatWqVjDFlLqeyIKTdrFGjRnrllVe0bt06+fr6auTIkedN06JFCz344IMKDAzUFVdcocjISEVG\nRmrChAlatWqVcnNz9cQTT0g6+1fq2LFjtXjxYnXt2lX9+vWT3W7XbbfdpujoaO3evVvx8fHy9/eX\nr6+vJk+erB9//FETJ07UX//6VwUGBsrb21sFBQWOnhWsx5n9RpL69OmjuXPnOg4/jxs3ThMnTlR+\nfr7OnDmjCRMmnDdP06ZN1adPHwUFBSkkJES33nqr4zBo3759lZCQoIEDB6q4uFgJCQlq2rSpkpKS\n1K9fP+Xn5+uJJ55QnTp1HMt75JFHlJCQoL/97W86c+aMJk+eLB8fftVY2eXuX6WpXbu2Hn74YfXv\n31+BgYHKz8+Xj4+P00fsOnbsqG3btqlfv34qLi7W448/rmuuuUZjxozRV199pRo1aqhhw4b6+eef\nq8SAWZ6CBQAoN0VFRVq0aJEeffRRSdKAAQM0atQo3X777R6urGLiz1sAQLnx8fHR6dOnFRMTI19f\nX7Vo0UKtW7f2dFkVFj1pAAAsilEcAABYFCENAIBFEdIAAFgUA8eACmzhwoXaunWrvLy8ZLPZNHr0\naN18882/a5nTpk3Tgw8+qGuuuaacqgRwuRg4BlRQP/zwgxITE7Vy5UrZbDbHfdzff/99T5cGoJwQ\n0kAFlZGRoV69emnEiBGKiIhQSEiICgoKNHToUIWGhiotLU3GGL3wwgsKDg7W888/r507d8oYo8GD\nB6tr16765ptvNG3aNBljFBISolmzZunhhx/WxIkTVa9evfMe6tK0aVPFx8fr0KFDys/P19ChQ0s8\nXANA+eJwN1BB1a5dW/PmzdPy5cv1yiuvqHr16ho9erQkqVWrVpo8ebJWrFihBQsWKDw8XEeOHFFK\nSory8/N133336U9/+pOSkpL0wgsvqHHjxlqxYkWJ50HPnz//vIe6LFq0SF9++aXWrFkjSfriiy88\nsu5AVUFIAxXUwYMHFRAQoOnTp0uSUlNTNWzYMNWtW9dx28ZWrVpp8+bNCgkJ0Xfffae4uDhJZ+8K\ndfToUZ08eVKNGzeWdPbOUL9V2kNdAgIClJSUpKSkJOXm5uqee+5x1+oCVRIhDVRQ33//vVauXKn5\n8+erWrVqCg0NVc2aNeXt7a1vv/1WV111lf75z3+qSZMmCgsL0x133KEpU6bIbrfr1VdfVf369VWv\nXj2lp6erUaNGWrhwYYmnI5X2UJeff/5Z3333nV555RXl5+erffv26tmzJ/fmBlyEnyyggrrrrru0\nf/9+xcbGyt/fX8YYjR07Vm+++abeffddvfHGG6pRo4aeffZZBQYGaseOHerfv7/y8vIUHR2tgIAA\nTZo0SQkJCfLy8lJwcLAGDx6spUuXSpKGDx9+3kNdgoODdeLECfXq1Uv+/v4aMmQIAQ24EAPHgEom\nLi5OEydOdBzGBlBxcTMTAAAsip40AAAWRU8aAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCw\nqP8D4qujNujDe18AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure3, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (8,5))\n",
    "boxplot = sns.boxplot(x = \"Species\", y = \"PetalLengthCm\", data = iris, ax= axes)\n",
    "boxplot.set_title(\"Boxplots for petal lengths by iris species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create four plots where each subplots compare boxplots by certain measure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OaejQobrmmmtksVT8lThmzJiaNI0AYbcX6ujxYFaYqIGC48GKtheaHQN+5K9//auWLl2q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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure4, axes = plt.subplots(nrows = 2, ncols = 2, figsize = (12,8))\n",
    "boxplot1 = sns.boxplot(x = \"Species\", y = \"PetalLengthCm\", data = iris, ax= axes[0,0])\n",
    "boxplot2 = sns.boxplot(x = \"Species\", y = \"PetalWidthCm\", data = iris, ax= axes[0,1])\n",
    "boxplot3 = sns.boxplot(x = \"Species\", y = \"SepalLengthCm\", data = iris, ax= axes[1,0])\n",
    "boxplot4 = sns.boxplot(x = \"Species\", y = \"SepalWidthCm\", data = iris, ax= axes[1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the pattern of creating these subplots and their particular syntax when choosing a certain plot. You may observe a reccuring pattern (\"sns.{name of plot}\"(Parameters...)). Keep in mind that this isn't the pattern for a lot of illustrations, these are just the basics. Let's make another similar figure but with violin plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brian\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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uuUWWMRRty/r1H7B//17mJFTSI7Dh662iwP19Kgn1tvLsM8spLi5yU8qOKSgo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\nTERE/khrzO2W/zFKRByOwWDgt99+Y8yYMbi4uNCnT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      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure5, axes = plt.subplots(nrows = 2, ncols = 2, figsize = (12,8))\n",
    "violinplot1 = sns.violinplot(x = \"Species\", y = \"PetalLengthCm\", data = iris, ax= axes[0,0])\n",
    "violinplot2 = sns.violinplot(x = \"Species\", y = \"PetalWidthCm\", data = iris, ax= axes[0,1])\n",
    "violinplot3 = sns.violinplot(x = \"Species\", y = \"SepalLengthCm\", data = iris, ax= axes[1,0])\n",
    "violinplot4 = sns.violinplot(x = \"Species\", y = \"SepalWidthCm\", data = iris, ax= axes[1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Other Seaborn Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brian\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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h08GXx6aoXYoQly0/PZrJ2XGsfPck//76QZ6fN4axMoMrYPR/cFz0i8en8Ofi\nc0zJjCMlKgy7W36FFcHn2pwEfnZHGE+9c5wHNxzi0S8O54ZRSX5tw2oyYJFfZC+bhPoge/9UIw0O\nN4/PSlW7FCH6rdPj40yjk0UFQ3jzcC3/87eTvH+ikRkj4v223IVMm+wfWSZgkK0/UE1adBjXZcep\nXYoQV8xqNrB44hAmpkezt8LOxoM1dMhvn6qSUB9EJXXtFFW3saggTW6QCs0w6HV8aXQSN41O4qy9\nk9V7z1LT6lK7rJAloT6I1h+oxmoyyA1SoUn56dEs+UI6OmDt/ioOVrWiyJ6ng05CfZA0Od28W9rA\nrXnJ2CxyK0NoU2p0GPdOyiAjLpytx+t551id7KI0yCTUB8nmohq6vQoLC9LULkWIAWU1G1hYkMbU\n7DiO1LSzZn8V9o5utcsKGRLqg8Dp9rCxqIbrc+JlY2kREvQ6HdNz4lmQn0ZrZzev7T3LqQZn7yeK\nKyahPgjeOFRLm8vDVydnqF2KEINqeGIE907KIDrcxKaiGnaeasIn4+wDSkJ9gLm6vaz9uIpJw2LI\nk42lRQiKtZpYcnU649Oi2F3ezMaDNbJuzACSUB9gfyquo7mjm69Okqt0EbpMBj03j0nixtFJVDR3\nsGZ/JXbZJm9ASKgPILfHx5r9lUxIi2JierTa5QihKp1OR0F6NHdOHIKzy8vv9lVR1dKpdlmaI6E+\ngDYfquFcexcPXDsMnZ8enRYi2GXEWbnnmqGEmfT84eNqimvb1C5JUyTUB4ijy8PqvZVckxHDJFkz\nXYgLxEeYuefqoaRFh/Hn4jp2lTXJg0p+IqE+QNZ+XEVLZzffmp6ldilCBCSr2cCdVw1hXGokH5xu\n5i/H6vH5JNivlDzaOAAanW7+UFjFDbmJjE6WGS9CfB6DXscteclEhhnZU27H4fZw2/hUzAa53uwv\n+cwNgJd2ldPtVXjouky1SxEi4Ol0Oq4fnsCXRiVS1tjBusJqWenxCkio+9mh6lbePlrHV76QTkZs\nuNrlCBE0Jg6N4fYJqdS1d7FmfyW1stJjv0io+5HHp7DyvVMkR1q4X54eFeKyjUyycefEIXS4vTy6\n+TCldQ61Swo6Eup+tKmohpMNTh6bmUO4SXZsEaI/hsaGs+TqdAx6HV/feIi9FXa1SwoqEup+Umnv\n5Fe7ypmSGcvM4fFqlyNEUEuwWfj5gvGkRFl45I1ith6vV7ukoCGh7gden8LTW0sxGfT815xcedBI\nCD9ItFn4f4vyGZcWxQ/+r4S1H1epXVJQkFD3g7UfV3G4po3vzsohOdKidjlCaEZkmJEX5o/jhtwE\nfvH+aZb/5bis8tgLCfUrdKi85tyTAAAOCklEQVSqhVW7zzArN4EbRyWpXY4QmmMx6nnu1tEsKkhj\n9Z4KfvBOCW6P7Kb0eeThoyvQ5HTzzXUHSbKZ+f4NI2TYRYgBotfpWDozh4xEG8+/e4LmDjfPz8uT\nrSEvQq7U+8nj9fHE28dp7ezmf+blERNuUrskITRNp9Px4LRsnrlpJAer23hwwyEaHF1qlxVwJNT7\nQVEUVrx3ioNVrfxo3lhGJtnULkmIkHHzmGR+/m95VLV0cv+6Itkm719IqPfDi7vOsOXIOb46aSjz\nJshG0kIMtsmZcfx60QS6vQpf/cNB3i2RKY//JKF+mX63r5Lf769k/oRUHpqaqXY5QoSs0cmRrLm7\ngJFJNv7rnRJ+uqNMbqAiod5niqLw8gflvLirnDkjE/nuF4fLjVEhVJZgs/DywvEsKkhj3YFq7l9X\nREVzh9plqUpCvQ88PoXn/naSV/dWMm9cCs/cPAqDXgJdiEBgMuj5zheH85N5Y6htc7Fk7QE2HqwO\n2fnsEuq9aHB08fCmw2w5co77JmfwX7NHYJRAFyLgXD88gdfvuYoJQ6J5fnsZ39hwiPKm0Ltql1C/\nhN3lzXzl9wc4fq6dZ24ayUNTM2XIRYgAlhxp4Ze3j+WpG3M51djBnb8v5Gd/L6Pd5VG7tEEjM/cv\notHp5mc7yni3tIGseCur5o4nOz5C7bKEEH2g0+m4NS+FqVlxvLz7DOsKq3nnH3scLCxII8Ks7djT\ndu8uU7vLw7oDVfyhsBq318eDU4ZxzzVDsRjlFxohgk2s1cyTs3OZPyGNVbvP8KsPzvD6x1XcPiGV\n+RPSNLtOk4Q6cKa5g7cOn2NLcS2OLi8zRyTw8HWZZMZZ1S5NCHGFRibZ+Nm/jeVobRur91by2t5K\nfr+vkmk58dw0JpnrsuIwa+jCLWRDvaqlk/dPNbHjZCOHatow6HXMGB7PVydlyBOiQmhQXmoUP7nt\n/JOom4tq+cvxOv5+qokIs4EpmbFMzY7j6ozYoL+C7zXUfT4fTz/9NKWlpZjNZn70ox8xbNiwnuMb\nN25k/fr1GI1GHnroIWbOnDmgBV8uRVGod7g509zBmaYOjte1c7CqlZq282tGjEiM4OHrMpk7NoWE\nCLPK1QohBlp6TDiPzMjmW9Oz+PisnW0nGtl9upltJxoBSI2yMDY1iux4K9kJEWTHW0mPCQ+aWW+9\nhvq2bdtwu91s2LCBoqIiVqxYwcsvvwxAQ0MDa9as4Y9//CNdXV3cddddTJ06FbN5YMKx0dGF0+3F\n7fXh9vjo8vro8pz/s6vbh72z+/xHhxt7Rzd17V1UNHfS0f3JzuRxVhMF6dHcdVU6U7PjSI+RzaGF\nCEVGvY7JmXFMzoxDURRONDg5UNVKUVUrR2vb+FtpQ89rTQYdKZEWEiLMxH/qI9JixGo2EGE2YDUb\nsJoMGA16DHodRp0Og/6TD4tRPygL//Ua6oWFhUybNg2A/Px8iouLe44dPnyYgoICzGYzZrOZjIwM\nSkpKGD9+vN8L3VXWxGNvHe31dXodxISbiLWaiLeauTUvmcx4K1lxVjLjrcRbTTItUQhxAZ1Ox8gk\nW8/G1wAdbi9nmjs43eTkdGMH59q7aHK6OdHgpOmMHafb28u7/ksbwMsLx3PV0JgB6MEneg11h8OB\nzfbJGLPBYMDj8WA0GnE4HERGRvYci4iIwOG49O7fJpOBxMTIS77mYm5PjOT2yZmXfd5g6E9/es4F\ncocM7BcZYHxGrLQRAO8/WG2MGRI94G0MRj8u5kp+3i7XsCExXD9orfmnb73e8rXZbDidnyxt6fP5\nMBqNFz3mdDovCHkhhBCDq9dQnzhxIjt37gSgqKiI3NzcnmPjx4+nsLCQrq4u2tvbKSsru+C4EEKI\nwaVTlEuvevPP2S8nTpxAURSWL1/Ozp07ycjIYNasWWzcuJENGzagKApf//rX+dKXvjRYtQshhPgX\nvYa6EEKI4KGdx6iEEEJIqAshhJaE7DIB/tDU1MTtt9/Oq6++Sk5Ojtrl+NVtt93WM5MpPT2dH//4\nxypX5D+//vWv2b59O93d3dx5550sWLBA7ZL84o033uDNN98EoKuri+PHj7N7926ioqJUruzKdXd3\n8/3vf5/q6mr0ej0//OEPNfMz53a7eeKJJ6isrMRms7Fs2TIyMzP7/X4S6v3U3d3NsmXLCAsLU7sU\nv+vqOr+Ewpo1a1SuxP/27t3LwYMHWbduHZ2dnbz66qtql+Q3t99+O7fffjsAzzzzDPPnz9dEoAO8\n//77eDwe1q9fz+7du/n5z3/OCy+8oHZZfrFx40asVisbN27k9OnT/PCHP+S3v/1tv99Phl/6aeXK\nlSxevJikpCS1S/G7kpISOjs7ue+++7jnnnsoKipSuyS/+eCDD8jNzeWb3/wm3/jGN5gxY4baJfnd\nkSNHOHXqFIsWLVK7FL/JysrC6/Xi8/lwOBw9z8powalTp5g+fToA2dnZlJWVXdH7aeczM4jeeOMN\n4uLimDZtGq+88ora5fhdWFgY999/PwsWLODMmTM88MADbN26VRM/SHa7nZqaGlatWkVVVRUPPfQQ\nW7du1dTSEb/+9a/55je/qXYZfmW1Wqmuruamm27CbrezatUqtUvym9GjR7Njxw5uuOEGDh06RF1d\nHV6vF4PB0K/3kyv1fvjjH//Inj17WLJkCcePH+fxxx+noaGh9xODRFZWFl/+8pfR6XRkZWURExOj\nmf7FxMRw3XXXYTabyc7OxmKx0NzcrHZZftPW1sbp06eZPHmy2qX41WuvvcZ1113HX//6V7Zs2cL3\nv//9nmHCYDd//nxsNhv33HMPO3bsIC8vr9+BDhLq/fL666+zdu1a1qxZw+jRo1m5ciWJiYlql+U3\nmzdvZsWKFQDU1dXhcDg007+rrrqKXbt2oSgKdXV1dHZ2EhMz8GvvDJb9+/dz7bXXql2G30VFRfXc\nuI+Ojsbj8eD1Xt6CWoHqyJEjXHXVVaxZs4YbbriBoUOHXtH7Bf/v08Lv7rjjDp544gnuvPNOdDod\ny5cv18TQC8DMmTPZv38/d9xxB4qisGzZsiu6Kgo05eXlpKenq12G39177708+eST3HXXXXR3d/Po\no49itWpjZ7Jhw4bxi1/8gldffZXIyEiee+65K3o/eaJUCCE0RIZfhBBCQyTUhRBCQyTUhRBCQyTU\nhRBCQyTUhRBCQyTUhepeeeUV7r33Xu677z7uv//+CzY376+qqioWLlwIwJIlS6740euLqampYfv2\n7Zdso7a2lm9/+9ssWbKEBQsW8PTTT+N2u/1eixD/JKEuVHXq1Cm2b9/O6tWrefXVV/nOd77Dk08+\nqXZZffLRRx9x4MCBzz3u9Xp5+OGHue+++1izZg2bNm3CaDTyy1/+chCrFKFGG0+UiKAVFxdHTU0N\nmzdvZvr06YwePZrNmzdTWlrKj370I+D8o/3Lly/n2LFjrFq1Cr1eT0NDA4sWLeIrX/kK+/bt48UX\nXwTA5XKxcuVKTCbTJdvt7u7mqaeeoqKiAp/PxyOPPMKkSZOYO3cu11xzDaWlpeh0On71q19hs9l4\n5plnKC4uJiEhgerqal566SVeeeUVXC4XBQUFALz00ks0NjbS2dnJT3/6U2pra0lJSWHChAk97X73\nu9/F5/NRVVXFo48+SmpqKlVVVdxyyy2cPHmSY8eOMWPGDB577LEB+owLrZNQF6qKi4vj5ZdfZu3a\ntbz00kuEhYXx6KOP8tvf/pbly5czfPhwNm3axG9+8xuuvfZa6urqeOutt/D5fMydO5cbb7yRkydP\n8vzzz5OcnMyqVavYunUrc+fOvWS7mzZtIjY2luXLl2O327n77rt55513cDqd3HLLLfzgBz9g6dKl\n7Ny5E4vFQktLC5s3b6a5uZk5c+ag1+t58MEHOX36NLNmzeK1117j+uuvZ968ebzwwgts3bqV1NTU\nzzzybbFYev5cWVnJq6++isvlYtasWezcuZPw8HBmzpwpoS76TUJdqKqiogKbzdazCceRI0d48MEH\ncblcPPPMM8D5q+qsrCwACgoKMJvNAIwYMYKzZ8+SnJzMc889h9Vqpa6ujokTJ/ba7okTJygsLOTw\n4cMAeDwe7HY7AGPGjAEgNTWVrq4uqquryc/PB87/J5SdnX3R9xw7diwACQkJNDY2kpaWxrvvvnvB\na+x2O0VFRYwYMYKhQ4cSGRmJ2WwmISGhZw0aLa0YKQafhLpQVWlpKevWrWPVqlVYLBaysrKIjIwk\nOTmZlStXkpaWRmFhYc8qkcePH8fr9eJ2uzl16hTDhg3joYceYtu2bdhsNh5//HH6svJFdnY2KSkp\nfOMb38DlcvHyyy8THR0NfDZUR4wYwZYtWwBobW3lzJkzAOj1enw+3+e2kZ+fT1VVFYcPH2b8+PEo\nisKLL76IxWJhxIgREt5iQEioC1XNmTOHsrIyFixYgNVqRVEUvve975GSksLjjz/esxLfc889R319\nPR6PhwceeICWlhYeeugh4uLimDdvHgsXLiQqKoqEhATq6+s/0863v/3tniv8SZMm8eijj/Lf//3f\n3H333TgcDu666y70+ovPG5gxYwY7d+5k8eLFJCQkEBYWhslkIjc3l5dffpm8vLyLnqfX6/nFL37B\ns88+S2dnJx0dHeTn5/PII49ctEYh/EEW9BJBY+/evaxfv56f/exng9puWVkZJSUl3HLLLdjtdm69\n9VZ27NjR85+EEIFErtSF6EVqaio/+clP+N3vfofX6+U73/mOBLoIWHKlLoQQGiIPHwkhhIZIqAsh\nhIZIqAshhIZIqAshhIZIqAshhIZIqAshhIb8f8635q2mej76AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(iris['SepalLengthCm'], kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that for this figure we didn't type nor assigned a 'figure,ax' variable. Creating thesecan give you more customization - compare the creation of these variables and how we assigned for the scatterplots, boxplots, and violin plots.\n",
    "\n",
    "The following graphs are also created without the two assignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'Species', y='SepalLengthCm', data = mean_groupby)\n",
    "plt.show()\n",
    "\n",
    "#Similar to mean_groupby.plot(x = 'Species', y =['SepalLengthCm'], kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brian\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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jM7aJ4q/AFBXYaQUY+NoiEX/RxD4chnBxc9aY78WJImtdliclF7quri7e//73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      "text/plain": [
       "<Figure size 455.875x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Examples of some multivariate graphs:\n",
    "sns.lmplot(x = 'PetalLengthCm', y = 'PetalWidthCm', data = iris, hue = 'Species', markers =['x', 'o', '*'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brian\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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L0zSoqtoiqBRFgd/vj3hhRJS4AqqG6gY/TyKms9Jp1+knP/kJ5s+fj3379sHtduPAgQO4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\nIjKU/wcXffVzRcU0TQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 442.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = 'PetalLengthCm', y = 'PetalWidthCm', data = iris.iloc[:50, :], hue = 'Species')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IvWVXtA4hXzgR0UTrEPKNO5c2ax1CvtGsdi+tQyhQcnNygZ+fH7GxsbRq1Yr9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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a heatmap:\n",
    "sns.heatmap(iris.iloc[:50, 1:5].corr(), annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are tons of examples to follow from in the Seaborn tutorial webpage: https://seaborn.pydata.org/examples/index.html\n",
    "Furthermore, understanding the basics of matplotlib are so important! A more extensive tutorial in understanding the figure and plotting the area of the figure can be found here: https://matplotlib.org/tutorials/introductory/pyplot.html. "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}