Tidy_data_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy data in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handy hints "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In general, we are using plotting libraries that return objects encapsulating the plot. You can check the type of these returned objects with `type()`. Jupyter's tools for exploring objects and methods will also be useful: the `?` and `??` operators, and tab autocompletion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:24.957662Z",
     "start_time": "2019-01-22T04:59:23.553075Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:25.291273Z",
     "start_time": "2019-01-22T04:59:24.961940Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# This causes Jupyter to display any matplotlib plots directly in the notebook\n",
    "# It also works for seaborn, since seaborn uses matplotlib to render plots\n",
    "# There is an alterative `%matplotlib notebook` magic for interactivity, which we won't use today\n",
    "# - we will use other libraries for interactive plots\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:42.614788Z",
     "start_time": "2019-01-22T04:59:39.174287Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas warm-up "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:48.205979Z",
     "start_time": "2019-01-22T04:59:47.926114Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:49.344228Z",
     "start_time": "2019-01-22T04:59:49.336671Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:49.678076Z",
     "start_time": "2019-01-22T04:59:49.659785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:50.284114Z",
     "start_time": "2019-01-22T04:59:50.277608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:50.658461Z",
     "start_time": "2019-01-22T04:59:50.552293Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:50.939743Z",
     "start_time": "2019-01-22T04:59:50.924405Z"
    }
   },
   "outputs": [],
   "source": [
    "iris['sepal_shape'] = iris['sepal_length'] / iris['sepal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:51.504359Z",
     "start_time": "2019-01-22T04:59:51.467016Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.483871</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.388889</td>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  sepal_shape\n",
       "0           5.1          3.5           1.4          0.2  setosa     1.457143\n",
       "1           4.9          3.0           1.4          0.2  setosa     1.633333\n",
       "2           4.7          3.2           1.3          0.2  setosa     1.468750\n",
       "3           4.6          3.1           1.5          0.2  setosa     1.483871\n",
       "4           5.0          3.6           1.4          0.2  setosa     1.388889"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
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   "outputs": [
    {
     "data": {
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       "<matplotlib.collections.PathCollection at 0x1a18f5e908>"
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x=iris['sepal_width'],y=iris['sepal_length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidy data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can manipulate data using the column names and index, with `stack` and `unstack`.\n",
    "\n",
    "We can manipulate data using particular columns, with `melt` and `pivot` or `pivot_table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:56.137129Z",
     "start_time": "2019-01-22T04:59:56.129562Z"
    }
   },
   "outputs": [],
   "source": [
    "iris.index = ['Flower_{}'.format(n+1) for n in range(len(iris))]\n",
    "iris.index.name = 'Sample'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:56.189039Z",
     "start_time": "2019-01-22T04:59:56.162770Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_shape</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</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>Flower_1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.388889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sepal_length  sepal_width  petal_length  petal_width species  \\\n",
       "Sample                                                                   \n",
       "Flower_1           5.1          3.5           1.4          0.2  setosa   \n",
       "Flower_2           4.9          3.0           1.4          0.2  setosa   \n",
       "Flower_3           4.7          3.2           1.3          0.2  setosa   \n",
       "Flower_4           4.6          3.1           1.5          0.2  setosa   \n",
       "Flower_5           5.0          3.6           1.4          0.2  setosa   \n",
       "\n",
       "          sepal_shape  \n",
       "Sample                 \n",
       "Flower_1     1.457143  \n",
       "Flower_2     1.633333  \n",
       "Flower_3     1.468750  \n",
       "Flower_4     1.483871  \n",
       "Flower_5     1.388889  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:57.282702Z",
     "start_time": "2019-01-22T04:59:57.241950Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5</td>\n",
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       "      <th>5</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sepal_length</td>\n",
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       "      <th>8</th>\n",
       "      <td>sepal_length</td>\n",
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       "      <th>9</th>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sepal_length</td>\n",
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       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sepal_length</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.15625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.33333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.0303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.21429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.03333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.28571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.64286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.07895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.28571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.34615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.56667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>1.85294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.06452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.22581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.16129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.22581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.14815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.0303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.23333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>2.16667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>1.82353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>sepal_shape</td>\n",
       "      <td>1.96667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable    value\n",
       "0    sepal_length      5.1\n",
       "1    sepal_length      4.9\n",
       "2    sepal_length      4.7\n",
       "3    sepal_length      4.6\n",
       "4    sepal_length        5\n",
       "5    sepal_length      5.4\n",
       "6    sepal_length      4.6\n",
       "7    sepal_length        5\n",
       "8    sepal_length      4.4\n",
       "9    sepal_length      4.9\n",
       "10   sepal_length      5.4\n",
       "11   sepal_length      4.8\n",
       "12   sepal_length      4.8\n",
       "13   sepal_length      4.3\n",
       "14   sepal_length      5.8\n",
       "15   sepal_length      5.7\n",
       "16   sepal_length      5.4\n",
       "17   sepal_length      5.1\n",
       "18   sepal_length      5.7\n",
       "19   sepal_length      5.1\n",
       "20   sepal_length      5.4\n",
       "21   sepal_length      5.1\n",
       "22   sepal_length      4.6\n",
       "23   sepal_length      5.1\n",
       "24   sepal_length      4.8\n",
       "25   sepal_length        5\n",
       "26   sepal_length        5\n",
       "27   sepal_length      5.2\n",
       "28   sepal_length      5.2\n",
       "29   sepal_length      4.7\n",
       "..            ...      ...\n",
       "870   sepal_shape  2.15625\n",
       "871   sepal_shape        2\n",
       "872   sepal_shape     2.75\n",
       "873   sepal_shape  2.33333\n",
       "874   sepal_shape   2.0303\n",
       "875   sepal_shape     2.25\n",
       "876   sepal_shape  2.21429\n",
       "877   sepal_shape  2.03333\n",
       "878   sepal_shape  2.28571\n",
       "879   sepal_shape      2.4\n",
       "880   sepal_shape  2.64286\n",
       "881   sepal_shape  2.07895\n",
       "882   sepal_shape  2.28571\n",
       "883   sepal_shape     2.25\n",
       "884   sepal_shape  2.34615\n",
       "885   sepal_shape  2.56667\n",
       "886   sepal_shape  1.85294\n",
       "887   sepal_shape  2.06452\n",
       "888   sepal_shape        2\n",
       "889   sepal_shape  2.22581\n",
       "890   sepal_shape  2.16129\n",
       "891   sepal_shape  2.22581\n",
       "892   sepal_shape  2.14815\n",
       "893   sepal_shape    2.125\n",
       "894   sepal_shape   2.0303\n",
       "895   sepal_shape  2.23333\n",
       "896   sepal_shape     2.52\n",
       "897   sepal_shape  2.16667\n",
       "898   sepal_shape  1.82353\n",
       "899   sepal_shape  1.96667\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:58.136445Z",
     "start_time": "2019-01-22T04:59:58.125674Z"
    }
   },
   "outputs": [],
   "source": [
    "iris.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T04:59:59.748200Z",
     "start_time": "2019-01-22T04:59:59.722631Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flower_1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Flower_2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flower_3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Flower_4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flower_5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.388889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample  sepal_length  sepal_width  petal_length  petal_width species  \\\n",
       "0  Flower_1           5.1          3.5           1.4          0.2  setosa   \n",
       "1  Flower_2           4.9          3.0           1.4          0.2  setosa   \n",
       "2  Flower_3           4.7          3.2           1.3          0.2  setosa   \n",
       "3  Flower_4           4.6          3.1           1.5          0.2  setosa   \n",
       "4  Flower_5           5.0          3.6           1.4          0.2  setosa   \n",
       "\n",
       "   sepal_shape  \n",
       "0     1.457143  \n",
       "1     1.633333  \n",
       "2     1.468750  \n",
       "3     1.483871  \n",
       "4     1.388889  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:00.435828Z",
     "start_time": "2019-01-22T05:00:00.411409Z"
    }
   },
   "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>Sample</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flower_1</td>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Flower_2</td>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flower_3</td>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Flower_4</td>\n",
       "      <td>sepal_length</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flower_5</td>\n",
       "      <td>sepal_length</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample      variable value\n",
       "0  Flower_1  sepal_length   5.1\n",
       "1  Flower_2  sepal_length   4.9\n",
       "2  Flower_3  sepal_length   4.7\n",
       "3  Flower_4  sepal_length   4.6\n",
       "4  Flower_5  sepal_length     5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.melt(id_vars=['Sample']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:01.002152Z",
     "start_time": "2019-01-22T05:00:00.925362Z"
    }
   },
   "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>variable</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_shape</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</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>Flower_1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.45714</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_10</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.58065</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_100</th>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.03571</td>\n",
       "      <td>2.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_101</th>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.90909</td>\n",
       "      <td>3.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_102</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.14815</td>\n",
       "      <td>2.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_103</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.36667</td>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_104</th>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.17241</td>\n",
       "      <td>2.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_105</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.16667</td>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_106</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2.53333</td>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_107</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.96</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_108</th>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.51724</td>\n",
       "      <td>2.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_109</th>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_11</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.45946</td>\n",
       "      <td>3.7</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_110</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_111</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.03125</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_112</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.37037</td>\n",
       "      <td>2.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_113</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.26667</td>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_114</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_115</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.07143</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_116</th>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_117</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.16667</td>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_118</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.02632</td>\n",
       "      <td>3.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_119</th>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.96154</td>\n",
       "      <td>2.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_12</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.41176</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_120</th>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2.72727</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_121</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.15625</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_122</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_123</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_124</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_125</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0303</td>\n",
       "      <td>3.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_72</th>\n",
       "      <td>4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.17857</td>\n",
       "      <td>2.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_73</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_74</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.17857</td>\n",
       "      <td>2.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_75</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.2069</td>\n",
       "      <td>2.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_76</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_77</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.42857</td>\n",
       "      <td>2.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_78</th>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.23333</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_79</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2.06897</td>\n",
       "      <td>2.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_8</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5</td>\n",
       "      <td>1.47059</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_80</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.19231</td>\n",
       "      <td>2.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_81</th>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.29167</td>\n",
       "      <td>2.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_82</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.29167</td>\n",
       "      <td>2.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_83</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.14815</td>\n",
       "      <td>2.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_84</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.22222</td>\n",
       "      <td>2.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_85</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_86</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.76471</td>\n",
       "      <td>3.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_87</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.16129</td>\n",
       "      <td>3.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_88</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.73913</td>\n",
       "      <td>2.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_89</th>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.86667</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_9</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.51724</td>\n",
       "      <td>2.9</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_90</th>\n",
       "      <td>4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_91</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.11538</td>\n",
       "      <td>2.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_92</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.03333</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_93</th>\n",
       "      <td>4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.23077</td>\n",
       "      <td>2.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_94</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.17391</td>\n",
       "      <td>2.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_95</th>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.07407</td>\n",
       "      <td>2.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_96</th>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_97</th>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.96552</td>\n",
       "      <td>2.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_98</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.13793</td>\n",
       "      <td>2.9</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flower_99</th>\n",
       "      <td>3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable   petal_length petal_width sepal_length sepal_shape sepal_width  \\\n",
       "Sample                                                                     \n",
       "Flower_1            1.4         0.2          5.1     1.45714         3.5   \n",
       "Flower_10           1.5         0.1          4.9     1.58065         3.1   \n",
       "Flower_100          4.1         1.3          5.7     2.03571         2.8   \n",
       "Flower_101            6         2.5          6.3     1.90909         3.3   \n",
       "Flower_102          5.1         1.9          5.8     2.14815         2.7   \n",
       "Flower_103          5.9         2.1          7.1     2.36667           3   \n",
       "Flower_104          5.6         1.8          6.3     2.17241         2.9   \n",
       "Flower_105          5.8         2.2          6.5     2.16667           3   \n",
       "Flower_106          6.6         2.1          7.6     2.53333           3   \n",
       "Flower_107          4.5         1.7          4.9        1.96         2.5   \n",
       "Flower_108          6.3         1.8          7.3     2.51724         2.9   \n",
       "Flower_109          5.8         1.8          6.7        2.68         2.5   \n",
       "Flower_11           1.5         0.2          5.4     1.45946         3.7   \n",
       "Flower_110          6.1         2.5          7.2           2         3.6   \n",
       "Flower_111          5.1           2          6.5     2.03125         3.2   \n",
       "Flower_112          5.3         1.9          6.4     2.37037         2.7   \n",
       "Flower_113          5.5         2.1          6.8     2.26667           3   \n",
       "Flower_114            5           2          5.7        2.28         2.5   \n",
       "Flower_115          5.1         2.4          5.8     2.07143         2.8   \n",
       "Flower_116          5.3         2.3          6.4           2         3.2   \n",
       "Flower_117          5.5         1.8          6.5     2.16667           3   \n",
       "Flower_118          6.7         2.2          7.7     2.02632         3.8   \n",
       "Flower_119          6.9         2.3          7.7     2.96154         2.6   \n",
       "Flower_12           1.6         0.2          4.8     1.41176         3.4   \n",
       "Flower_120            5         1.5            6     2.72727         2.2   \n",
       "Flower_121          5.7         2.3          6.9     2.15625         3.2   \n",
       "Flower_122          4.9           2          5.6           2         2.8   \n",
       "Flower_123          6.7           2          7.7        2.75         2.8   \n",
       "Flower_124          4.9         1.8          6.3     2.33333         2.7   \n",
       "Flower_125          5.7         2.1          6.7      2.0303         3.3   \n",
       "...                 ...         ...          ...         ...         ...   \n",
       "Flower_72             4         1.3          6.1     2.17857         2.8   \n",
       "Flower_73           4.9         1.5          6.3        2.52         2.5   \n",
       "Flower_74           4.7         1.2          6.1     2.17857         2.8   \n",
       "Flower_75           4.3         1.3          6.4      2.2069         2.9   \n",
       "Flower_76           4.4         1.4          6.6         2.2           3   \n",
       "Flower_77           4.8         1.4          6.8     2.42857         2.8   \n",
       "Flower_78             5         1.7          6.7     2.23333           3   \n",
       "Flower_79           4.5         1.5            6     2.06897         2.9   \n",
       "Flower_8            1.5         0.2            5     1.47059         3.4   \n",
       "Flower_80           3.5           1          5.7     2.19231         2.6   \n",
       "Flower_81           3.8         1.1          5.5     2.29167         2.4   \n",
       "Flower_82           3.7           1          5.5     2.29167         2.4   \n",
       "Flower_83           3.9         1.2          5.8     2.14815         2.7   \n",
       "Flower_84           5.1         1.6            6     2.22222         2.7   \n",
       "Flower_85           4.5         1.5          5.4         1.8           3   \n",
       "Flower_86           4.5         1.6            6     1.76471         3.4   \n",
       "Flower_87           4.7         1.5          6.7     2.16129         3.1   \n",
       "Flower_88           4.4         1.3          6.3     2.73913         2.3   \n",
       "Flower_89           4.1         1.3          5.6     1.86667           3   \n",
       "Flower_9            1.4         0.2          4.4     1.51724         2.9   \n",
       "Flower_90             4         1.3          5.5         2.2         2.5   \n",
       "Flower_91           4.4         1.2          5.5     2.11538         2.6   \n",
       "Flower_92           4.6         1.4          6.1     2.03333           3   \n",
       "Flower_93             4         1.2          5.8     2.23077         2.6   \n",
       "Flower_94           3.3           1            5     2.17391         2.3   \n",
       "Flower_95           4.2         1.3          5.6     2.07407         2.7   \n",
       "Flower_96           4.2         1.2          5.7         1.9           3   \n",
       "Flower_97           4.2         1.3          5.7     1.96552         2.9   \n",
       "Flower_98           4.3         1.3          6.2     2.13793         2.9   \n",
       "Flower_99             3         1.1          5.1        2.04         2.5   \n",
       "\n",
       "variable       species  \n",
       "Sample                  \n",
       "Flower_1        setosa  \n",
       "Flower_10       setosa  \n",
       "Flower_100  versicolor  \n",
       "Flower_101   virginica  \n",
       "Flower_102   virginica  \n",
       "Flower_103   virginica  \n",
       "Flower_104   virginica  \n",
       "Flower_105   virginica  \n",
       "Flower_106   virginica  \n",
       "Flower_107   virginica  \n",
       "Flower_108   virginica  \n",
       "Flower_109   virginica  \n",
       "Flower_11       setosa  \n",
       "Flower_110   virginica  \n",
       "Flower_111   virginica  \n",
       "Flower_112   virginica  \n",
       "Flower_113   virginica  \n",
       "Flower_114   virginica  \n",
       "Flower_115   virginica  \n",
       "Flower_116   virginica  \n",
       "Flower_117   virginica  \n",
       "Flower_118   virginica  \n",
       "Flower_119   virginica  \n",
       "Flower_12       setosa  \n",
       "Flower_120   virginica  \n",
       "Flower_121   virginica  \n",
       "Flower_122   virginica  \n",
       "Flower_123   virginica  \n",
       "Flower_124   virginica  \n",
       "Flower_125   virginica  \n",
       "...                ...  \n",
       "Flower_72   versicolor  \n",
       "Flower_73   versicolor  \n",
       "Flower_74   versicolor  \n",
       "Flower_75   versicolor  \n",
       "Flower_76   versicolor  \n",
       "Flower_77   versicolor  \n",
       "Flower_78   versicolor  \n",
       "Flower_79   versicolor  \n",
       "Flower_8        setosa  \n",
       "Flower_80   versicolor  \n",
       "Flower_81   versicolor  \n",
       "Flower_82   versicolor  \n",
       "Flower_83   versicolor  \n",
       "Flower_84   versicolor  \n",
       "Flower_85   versicolor  \n",
       "Flower_86   versicolor  \n",
       "Flower_87   versicolor  \n",
       "Flower_88   versicolor  \n",
       "Flower_89   versicolor  \n",
       "Flower_9        setosa  \n",
       "Flower_90   versicolor  \n",
       "Flower_91   versicolor  \n",
       "Flower_92   versicolor  \n",
       "Flower_93   versicolor  \n",
       "Flower_94   versicolor  \n",
       "Flower_95   versicolor  \n",
       "Flower_96   versicolor  \n",
       "Flower_97   versicolor  \n",
       "Flower_98   versicolor  \n",
       "Flower_99   versicolor  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.melt(id_vars=['Sample']).pivot(index='Sample',columns='variable',values='value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidy data exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Link to \"untidy data\" discussion spreadsheet](https://docs.google.com/spreadsheets/d/1tnLVYxd6k5oGWVHsiF6D78ds1nOztU3Ynri_Bx0oOjE/edit?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are two tiny \"wide\" datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:06.921077Z",
     "start_time": "2019-01-22T05:00:06.888055Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price1</th>\n",
       "      <th>price2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  bedrooms    price1    price2\n",
       "0   5631500400         2  180000.0       NaN\n",
       "1   6021501535         3  430000.0  700000.0\n",
       "2   7129300520         3  221900.0       NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_wide1 = pd.read_csv('housing-data-wide1.csv')\n",
    "sales_wide1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:07.661282Z",
     "start_time": "2019-01-22T05:00:07.619024Z"
    }
   },
   "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>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>date1</th>\n",
       "      <th>price1</th>\n",
       "      <th>date2</th>\n",
       "      <th>price2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-07-25</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>2014-12-23</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  bedrooms      date1    price1      date2    price2\n",
       "0   5631500400         2 2015-02-25  180000.0        NaT       NaN\n",
       "1   6021501535         3 2014-07-25  430000.0 2014-12-23  700000.0\n",
       "2   7129300520         3 2014-10-13  221900.0        NaT       NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_wide2 = pd.read_csv('housing-data-wide2.csv', parse_dates=['date1','date2'])\n",
    "sales_wide2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And two slightly larger datasets of the same form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:08.424458Z",
     "start_time": "2019-01-22T05:00:08.412468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4)\n",
      "Index(['property_id', 'bedrooms', 'price1', 'price2'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "sales_wide1_100 = pd.read_csv('housing-data-wide1-100.csv')\n",
    "print(sales_wide1_100.shape)\n",
    "print(sales_wide1_100.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:08.944041Z",
     "start_time": "2019-01-22T05:00:08.911251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 6)\n",
      "Index(['property_id', 'bedrooms', 'date1', 'price1', 'date2', 'price2'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "sales_wide2_100 = pd.read_csv('housing-data-wide2-100.csv', parse_dates=['date1','date2'])\n",
    "print(sales_wide2_100.shape)\n",
    "print(sales_wide2_100.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercises**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Use Pandas to get `sales_wide1` into tidy form. Once you've reshaped the data, you may also want to remove null rows using either `.dropna()` or `.loc[]` and `.isnull()`.\n",
    "* Apply the same tidying to `sales_wide1_100`. Once this data is tidy, plot a histogram using `plt.hist()` (or better, `ax.hist()` !) and passing it the column of house prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:11.294785Z",
     "start_time": "2019-01-22T05:00:11.276782Z"
    }
   },
   "outputs": [],
   "source": [
    "tidied = sales_wide1.melt(id_vars=['property_id','bedrooms'], value_name='price')\n",
    "tidied = tidied.loc[~tidied['price'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:11.739563Z",
     "start_time": "2019-01-22T05:00:11.723560Z"
    }
   },
   "outputs": [],
   "source": [
    "tidied = sales_wide1_100.melt(id_vars=['property_id','bedrooms'], value_name='price')\n",
    "tidied = tidied.loc[~tidied['price'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:12.870881Z",
     "start_time": "2019-01-22T05:00:12.504427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([63., 52., 29., 12., 12.,  3.,  0.,  4.,  2.,  3.]),\n",
       " array([  82000.,  267800.,  453600.,  639400.,  825200., 1011000.,\n",
       "        1196800., 1382600., 1568400., 1754200., 1940000.]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.hist(tidied['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:00:13.427198Z",
     "start_time": "2019-01-22T05:00:13.091331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3.,  8., 19., 40., 27., 35., 21., 16.,  5.,  6.]),\n",
       " array([11.31447453, 11.63084693, 11.94721933, 12.26359173, 12.57996413,\n",
       "        12.89633653, 13.21270893, 13.52908133, 13.84545373, 14.16182613,\n",
       "        14.47819853]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try the log\n",
    "fig,ax = plt.subplots()\n",
    "ax.hist(np.log(tidied['price']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Use Pandas to get `sales_wide2` into tidy form. Note that this is much harder than the first exercise!\n",
    "* If you've tidied this data, apply the same tidying to `sales_wide2_100` and use matplotlib's `plt.plot_date()` or `ax.plot_date()` to create a scatter plot of sale price against sale date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:01:12.100458Z",
     "start_time": "2019-01-22T05:01:12.062465Z"
    }
   },
   "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>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>salenum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>date</td>\n",
       "      <td>2015-02-25 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>date</td>\n",
       "      <td>2014-07-25 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>date</td>\n",
       "      <td>2014-10-13 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>price</td>\n",
       "      <td>180000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>price</td>\n",
       "      <td>430000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>price</td>\n",
       "      <td>221900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>date</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>date</td>\n",
       "      <td>2014-12-23 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>date</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>price</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  bedrooms variable                value salenum\n",
       "0   5631500400         2     date  2015-02-25 00:00:00       1\n",
       "1   6021501535         3     date  2014-07-25 00:00:00       1\n",
       "2   7129300520         3     date  2014-10-13 00:00:00       1\n",
       "3   5631500400         2    price               180000       1\n",
       "4   6021501535         3    price               430000       1\n",
       "5   7129300520         3    price               221900       1\n",
       "6   5631500400         2     date                  NaT       2\n",
       "7   6021501535         3     date  2014-12-23 00:00:00       2\n",
       "8   7129300520         3     date                  NaT       2\n",
       "9   5631500400         2    price                  NaN       2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There's more than one reasonable solution to this problem\n",
    "\n",
    "# One approach: Melt down the variables we don't want to keep. \n",
    "# Then split the variable column to get useful groupings\n",
    "melted = sales_wide2.melt(id_vars=['property_id','bedrooms'])\n",
    "melted['salenum'] = melted['variable'].str[-1]\n",
    "melted['variable'] = melted['variable'].str[:-1]\n",
    "melted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:01:18.186496Z",
     "start_time": "2019-01-22T05:01:18.058358Z"
    }
   },
   "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>variable</th>\n",
       "      <th>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>salenum</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-25 00:00:00</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-25 00:00:00</td>\n",
       "      <td>430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-12-23 00:00:00</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-13 00:00:00</td>\n",
       "      <td>221900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable  property_id  bedrooms salenum                 date   price\n",
       "0          5631500400         2       1  2015-02-25 00:00:00  180000\n",
       "1          6021501535         3       1  2014-07-25 00:00:00  430000\n",
       "2          6021501535         3       2  2014-12-23 00:00:00  700000\n",
       "3          7129300520         3       1  2014-10-13 00:00:00  221900"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now pivot back to get date and price as columns again\n",
    "melted.pivot_table(\n",
    "    index=['property_id','bedrooms','salenum'],\n",
    "    columns='variable',\n",
    "    values='value',\n",
    "    aggfunc='first').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:01:53.601280Z",
     "start_time": "2019-01-22T05:01:53.567824Z"
    }
   },
   "outputs": [],
   "source": [
    "# Apply to larger dataset\n",
    "melted = sales_wide2_100.melt(id_vars=['property_id','bedrooms'])\n",
    "melted['salenum'] = melted['variable'].str[-1]\n",
    "melted['variable'] = melted['variable'].str[:-1]\n",
    "tidied = melted.pivot_table(\n",
    "    index=['property_id','bedrooms','salenum'],\n",
    "    columns='variable',\n",
    "    values='value',\n",
    "    aggfunc='first').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-22T05:01:57.156480Z",
     "start_time": "2019-01-22T05:01:56.793576Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a19269eb8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.plot_date(tidied['date'],tidied['price'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "nbconvert_exporter": "python",
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  "toc": {
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
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   "types_to_exclude": [
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