{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 5.2: Split-Apply-Combine of the frog data set\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continue working with the frog tongue adhesion data set.\n",
    "\n",
    "\n",
    "You'll now practice your split-apply-combine skills. First load in the data set. Then, \n",
    "\n",
    "**a)** Compute standard deviation of the impact forces for each frog.\n",
    "\n",
    "**b)** Compute the coefficient of variation of the impact forces *and* adhesive forces for each frog.\n",
    "\n",
    "**c)** Compute a data frame that has the mean, median, standard deviation, and coefficient of variation of the impact forces and adhesive forces for each frog.\n",
    "\n",
    "**d)** Now tidy this data frame. It might help to read [the documentation about melting](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, we start by loading in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/frog_tongue_adhesion.csv', comment='#')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**a)** To compute the standard deviation of impact forces for each frog, we first group by the frog ID and then apply the `std()` method to the `GroupBy` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "I      630.207952\n",
       "II     424.573256\n",
       "III    124.273849\n",
       "IV     234.864328\n",
       "Name: impact force (mN), dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('ID')\n",
    "\n",
    "grouped['impact force (mN)'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also do this by method chaining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "I      630.207952\n",
       "II     424.573256\n",
       "III    124.273849\n",
       "IV     234.864328\n",
       "Name: impact force (mN), dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ID')['impact force (mN)'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**b)** We first write a function to compute the coefficient of variation. We saw this in our [lesson on tidy data and split-apply-combine](l18_split_apply_combine.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coeff_of_var(data):\n",
    "    \"\"\"Coefficient of variation.\"\"\"\n",
    "    return np.std(data) / np.abs(np.mean(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we group the data frame with by the ID column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby('ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we select the columns of interest and apply the `coeff_of_var()` aggregating function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cv = grouped[['impact force (mN)', 'adhesive force (mN)']].agg(coeff_of_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we reset the index to get our resulting data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>impact force (mN)</th>\n",
       "      <th>adhesive force (mN)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>0.401419</td>\n",
       "      <td>0.247435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>II</td>\n",
       "      <td>0.585033</td>\n",
       "      <td>0.429701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>III</td>\n",
       "      <td>0.220191</td>\n",
       "      <td>0.415435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IV</td>\n",
       "      <td>0.546212</td>\n",
       "      <td>0.308042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  impact force (mN)  adhesive force (mN)\n",
       "0    I           0.401419             0.247435\n",
       "1   II           0.585033             0.429701\n",
       "2  III           0.220191             0.415435\n",
       "3   IV           0.546212             0.308042"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we could do this in one line by method chaining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>impact force (mN)</th>\n",
       "      <th>adhesive force (mN)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>0.401419</td>\n",
       "      <td>0.247435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>II</td>\n",
       "      <td>0.585033</td>\n",
       "      <td>0.429701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>III</td>\n",
       "      <td>0.220191</td>\n",
       "      <td>0.415435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IV</td>\n",
       "      <td>0.546212</td>\n",
       "      <td>0.308042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  impact force (mN)  adhesive force (mN)\n",
       "0    I           0.401419             0.247435\n",
       "1   II           0.585033             0.429701\n",
       "2  III           0.220191             0.415435\n",
       "3   IV           0.546212             0.308042"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"ID\")[[\"impact force (mN)\", \"adhesive force (mN)\"]].agg(\n",
    "    coeff_of_var\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**c)** Now we will apply all of the statistical functions to the impact force and adhesive force. This is as simple as using a list of aggregating functions in the `agg()` method of the `GroupBy` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th colspan=\"4\" halign=\"left\">impact force (mN)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">adhesive force (mN)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>coeff_of_var</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>coeff_of_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>1530.20</td>\n",
       "      <td>1550.5</td>\n",
       "      <td>630.207952</td>\n",
       "      <td>0.401419</td>\n",
       "      <td>-658.40</td>\n",
       "      <td>-664.5</td>\n",
       "      <td>167.143619</td>\n",
       "      <td>0.247435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>II</td>\n",
       "      <td>707.35</td>\n",
       "      <td>573.0</td>\n",
       "      <td>424.573256</td>\n",
       "      <td>0.585033</td>\n",
       "      <td>-462.30</td>\n",
       "      <td>-517.0</td>\n",
       "      <td>203.811600</td>\n",
       "      <td>0.429701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>III</td>\n",
       "      <td>550.10</td>\n",
       "      <td>544.0</td>\n",
       "      <td>124.273849</td>\n",
       "      <td>0.220191</td>\n",
       "      <td>-206.75</td>\n",
       "      <td>-201.5</td>\n",
       "      <td>88.122448</td>\n",
       "      <td>0.415435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IV</td>\n",
       "      <td>419.10</td>\n",
       "      <td>460.5</td>\n",
       "      <td>234.864328</td>\n",
       "      <td>0.546212</td>\n",
       "      <td>-263.60</td>\n",
       "      <td>-233.5</td>\n",
       "      <td>83.309442</td>\n",
       "      <td>0.308042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID impact force (mN)                                  adhesive force (mN)  \\\n",
       "                    mean  median         std coeff_of_var                mean   \n",
       "0    I           1530.20  1550.5  630.207952     0.401419             -658.40   \n",
       "1   II            707.35   573.0  424.573256     0.585033             -462.30   \n",
       "2  III            550.10   544.0  124.273849     0.220191             -206.75   \n",
       "3   IV            419.10   460.5  234.864328     0.546212             -263.60   \n",
       "\n",
       "                                   \n",
       "  median         std coeff_of_var  \n",
       "0 -664.5  167.143619     0.247435  \n",
       "1 -517.0  203.811600     0.429701  \n",
       "2 -201.5   88.122448     0.415435  \n",
       "3 -233.5   83.309442     0.308042  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = (\n",
    "    df.groupby(\"ID\")[[\"impact force (mN)\", \"adhesive force (mN)\"]]\n",
    "    .agg([np.mean, np.median, np.std, coeff_of_var])\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# Take a look\n",
    "df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**d)** We can index these things using the `MultiIndex` of the columns, but we much prefer tidy data frames, which we can generate again use `pd.melt()`. The `var_name` kwarg specifies what we name the levels of the `MultiIndex`, and the `id_vars` kwarg tells us which column to keep as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>quantity</th>\n",
       "      <th>statistic</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>1530.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>II</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>707.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>III</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>550.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IV</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>419.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>1550.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>II</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>573.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>III</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>544.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IV</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>460.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>630.207952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>II</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>424.573256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>III</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>124.273849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IV</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>234.864328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.401419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>II</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.585033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>III</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.220191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IV</td>\n",
       "      <td>impact force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.546212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>I</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>-658.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>-462.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>III</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>-206.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IV</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>mean</td>\n",
       "      <td>-263.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>I</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>-664.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>-517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>-201.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>IV</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>median</td>\n",
       "      <td>-233.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>I</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>167.143619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>II</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>203.811600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>88.122448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>IV</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>std</td>\n",
       "      <td>83.309442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>I</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.247435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>II</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.429701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>III</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.415435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>IV</td>\n",
       "      <td>adhesive force (mN)</td>\n",
       "      <td>coeff_of_var</td>\n",
       "      <td>0.308042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID             quantity     statistic        value\n",
       "0     I    impact force (mN)          mean  1530.200000\n",
       "1    II    impact force (mN)          mean   707.350000\n",
       "2   III    impact force (mN)          mean   550.100000\n",
       "3    IV    impact force (mN)          mean   419.100000\n",
       "4     I    impact force (mN)        median  1550.500000\n",
       "5    II    impact force (mN)        median   573.000000\n",
       "6   III    impact force (mN)        median   544.000000\n",
       "7    IV    impact force (mN)        median   460.500000\n",
       "8     I    impact force (mN)           std   630.207952\n",
       "9    II    impact force (mN)           std   424.573256\n",
       "10  III    impact force (mN)           std   124.273849\n",
       "11   IV    impact force (mN)           std   234.864328\n",
       "12    I    impact force (mN)  coeff_of_var     0.401419\n",
       "13   II    impact force (mN)  coeff_of_var     0.585033\n",
       "14  III    impact force (mN)  coeff_of_var     0.220191\n",
       "15   IV    impact force (mN)  coeff_of_var     0.546212\n",
       "16    I  adhesive force (mN)          mean  -658.400000\n",
       "17   II  adhesive force (mN)          mean  -462.300000\n",
       "18  III  adhesive force (mN)          mean  -206.750000\n",
       "19   IV  adhesive force (mN)          mean  -263.600000\n",
       "20    I  adhesive force (mN)        median  -664.500000\n",
       "21   II  adhesive force (mN)        median  -517.000000\n",
       "22  III  adhesive force (mN)        median  -201.500000\n",
       "23   IV  adhesive force (mN)        median  -233.500000\n",
       "24    I  adhesive force (mN)           std   167.143619\n",
       "25   II  adhesive force (mN)           std   203.811600\n",
       "26  III  adhesive force (mN)           std    88.122448\n",
       "27   IV  adhesive force (mN)           std    83.309442\n",
       "28    I  adhesive force (mN)  coeff_of_var     0.247435\n",
       "29   II  adhesive force (mN)  coeff_of_var     0.429701\n",
       "30  III  adhesive force (mN)  coeff_of_var     0.415435\n",
       "31   IV  adhesive force (mN)  coeff_of_var     0.308042"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt the DataFrame to make it tidy\n",
    "pd.melt(df_result, var_name=['quantity', 'statistic'], id_vars='ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.7.7\n",
      "IPython 7.13.0\n",
      "\n",
      "numpy 1.18.1\n",
      "pandas 0.24.2\n",
      "jupyterlab 1.2.6\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,jupyterlab"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}