3.3 - Regiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame with the following values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n",
    "        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n",
    "        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n",
    "        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called regiment.\n",
    "#### Don't forget to name each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>name</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Miller</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Milner</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacon</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ryaner</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Sone</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Sloan</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Piger</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Riani</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regiment company      name  preTestScore  postTestScore\n",
       "0   Nighthawks     1st    Miller             4             25\n",
       "1   Nighthawks     1st  Jacobson            24             94\n",
       "2   Nighthawks     2nd       Ali            31             57\n",
       "3   Nighthawks     2nd    Milner             2             62\n",
       "4     Dragoons     1st     Cooze             3             70\n",
       "5     Dragoons     1st     Jacon             4             25\n",
       "6     Dragoons     2nd    Ryaner            24             94\n",
       "7     Dragoons     2nd      Sone            31             57\n",
       "8       Scouts     1st     Sloan             2             62\n",
       "9       Scouts     1st     Piger             3             70\n",
       "10      Scouts     2nd     Riani             2             62\n",
       "11      Scouts     2nd       Ali             3             70"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment = pd.DataFrame(raw_data, columns = raw_data.keys())\n",
    "regiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the mean preTestScore from the regiment Nighthawks?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>15.25</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            preTestScore  postTestScore\n",
       "regiment                               \n",
       "Nighthawks         15.25           59.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment[regiment['regiment'] == 'Nighthawks'].groupby('regiment').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Present general statistics by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">preTestScore</th>\n",
       "      <th colspan=\"8\" halign=\"left\">postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1st</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>8.524475</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57.666667</td>\n",
       "      <td>27.485754</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.25</td>\n",
       "      <td>66.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2nd</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>14.652645</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>13.5</td>\n",
       "      <td>29.25</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>14.057027</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.25</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        preTestScore                                                      \\\n",
       "               count       mean        std  min   25%   50%    75%   max   \n",
       "company                                                                    \n",
       "1st              6.0   6.666667   8.524475  2.0  3.00   3.5   4.00  24.0   \n",
       "2nd              6.0  15.500000  14.652645  2.0  2.25  13.5  29.25  31.0   \n",
       "\n",
       "        postTestScore                                                       \n",
       "                count       mean        std   min    25%   50%   75%   max  \n",
       "company                                                                     \n",
       "1st               6.0  57.666667  27.485754  25.0  34.25  66.0  70.0  94.0  \n",
       "2nd               6.0  67.000000  14.057027  57.0  58.25  62.0  68.0  94.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby('company').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the mean each company's preTestScore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company\n",
       "1st     6.666667\n",
       "2nd    15.500000\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby('company').preTestScore.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Present the mean preTestScores grouped by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regiment    company\n",
       "Dragoons    1st         3.5\n",
       "            2nd        27.5\n",
       "Nighthawks  1st        14.0\n",
       "            2nd        16.5\n",
       "Scouts      1st         2.5\n",
       "            2nd         2.5\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).preTestScore.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>company</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Dragoons</td>\n",
       "      <td>3.5</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Scouts</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "company      1st   2nd\n",
       "regiment              \n",
       "Dragoons     3.5  27.5\n",
       "Nighthawks  14.0  16.5\n",
       "Scouts       2.5   2.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).preTestScore.mean().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Group the entire dataframe by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>3.5</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2nd</td>\n",
       "      <td>27.5</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>14.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2nd</td>\n",
       "      <td>16.5</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2nd</td>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    preTestScore  postTestScore\n",
       "regiment   company                             \n",
       "Dragoons   1st               3.5           47.5\n",
       "           2nd              27.5           75.5\n",
       "Nighthawks 1st              14.0           59.5\n",
       "           2nd              16.5           59.5\n",
       "Scouts     1st               2.5           66.0\n",
       "           2nd               2.5           66.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the number of observations in each regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company  regiment  \n",
       "1st      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "2nd      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['company', 'regiment']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Iterate over a group and print the name and the whole data from the regiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dragoons\n",
      "   regiment company    name  preTestScore  postTestScore\n",
      "4  Dragoons     1st   Cooze             3             70\n",
      "5  Dragoons     1st   Jacon             4             25\n",
      "6  Dragoons     2nd  Ryaner            24             94\n",
      "7  Dragoons     2nd    Sone            31             57\n",
      "Nighthawks\n",
      "     regiment company      name  preTestScore  postTestScore\n",
      "0  Nighthawks     1st    Miller             4             25\n",
      "1  Nighthawks     1st  Jacobson            24             94\n",
      "2  Nighthawks     2nd       Ali            31             57\n",
      "3  Nighthawks     2nd    Milner             2             62\n",
      "Scouts\n",
      "   regiment company   name  preTestScore  postTestScore\n",
      "8    Scouts     1st  Sloan             2             62\n",
      "9    Scouts     1st  Piger             3             70\n",
      "10   Scouts     2nd  Riani             2             62\n",
      "11   Scouts     2nd    Ali             3             70\n"
     ]
    }
   ],
   "source": [
    "for name, group in regiment.groupby('regiment'):\n",
    "    # print the name of the regiment\n",
    "    print(name)\n",
    "    # print the data of that regiment\n",
    "    print(group)"
   ]
  }
 ],
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