Pandas Practice Assignments.ipynb

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    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns'); }\n",
       "    "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
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     "output_type": "display_data"
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   ],
   "source": [
    "df = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2ae8395",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
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       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
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       "      <td>3</td>\n",
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       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
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       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
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       "      <td>29.03</td>\n",
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       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2e14bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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       "<p>244 rows × 3 columns</p>\n",
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      "text/plain": [
       "       time  total_bill   tip\n",
       "0    Dinner       16.99  1.01\n",
       "1    Dinner       10.34  1.66\n",
       "2    Dinner       21.01  3.50\n",
       "3    Dinner       23.68  3.31\n",
       "4    Dinner       24.59  3.61\n",
       "..      ...         ...   ...\n",
       "239  Dinner       29.03  5.92\n",
       "240  Dinner       27.18  2.00\n",
       "241  Dinner       22.67  2.00\n",
       "242  Dinner       17.82  1.75\n",
       "243  Dinner       18.78  3.00\n",
       "\n",
       "[244 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df,columns=[\"time\",\"total_bill\",\"tip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f6eb2d0",
   "metadata": {},
   "outputs": [
    {
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       "<p>244 rows × 3 columns</p>\n",
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      "text/plain": [
       "       time  total_bill   tip\n",
       "0    Dinner       16.99  1.01\n",
       "1    Dinner       10.34  1.66\n",
       "2    Dinner       21.01  3.50\n",
       "3    Dinner       23.68  3.31\n",
       "4    Dinner       24.59  3.61\n",
       "..      ...         ...   ...\n",
       "239  Dinner       29.03  5.92\n",
       "240  Dinner       27.18  2.00\n",
       "241  Dinner       22.67  2.00\n",
       "242  Dinner       17.82  1.75\n",
       "243  Dinner       18.78  3.00\n",
       "\n",
       "[244 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"time\",\"total_bill\",\"tip\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd1be8cc",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     total_bill   tip\n",
       "0         16.99  1.01\n",
       "1         10.34  1.66\n",
       "2         21.01  3.50\n",
       "3         23.68  3.31\n",
       "4         24.59  3.61\n",
       "..          ...   ...\n",
       "239       29.03  5.92\n",
       "240       27.18  2.00\n",
       "241       22.67  2.00\n",
       "242       17.82  1.75\n",
       "243       18.78  3.00\n",
       "\n",
       "[244 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea84070d",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Dinner</td>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Dinner</td>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Dinner</td>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Dinner</td>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Dinner</td>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time  total_bill   tip\n",
       "0    Dinner       16.99  1.01\n",
       "1    Dinner       10.34  1.66\n",
       "2    Dinner       21.01  3.50\n",
       "3    Dinner       23.68  3.31\n",
       "4    Dinner       24.59  3.61\n",
       "..      ...         ...   ...\n",
       "239  Dinner       29.03  5.92\n",
       "240  Dinner       27.18  2.00\n",
       "241  Dinner       22.67  2.00\n",
       "242  Dinner       17.82  1.75\n",
       "243  Dinner       18.78  3.00\n",
       "\n",
       "[244 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,[\"time\",\"total_bill\",\"tip\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbd6ebcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5273bfff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711ef882",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e9775c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1f6200",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f99a20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64781e45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d9a638a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8a2745db",
   "metadata": {},
   "source": [
    "#### You're given a record of students with their registration dates and registration IDs. You're also given the marks scored by each student in Physics, Chemistry, and Mathematics. You need to find out the average marks by the students who registered on or after 01/05/2014 i.e. May 1st, 2014. Return a list with the corresponding register IDs of the students sorted by their average marks in all three subjects in increasing order.\n",
    "\n",
    "    Input Format\n",
    "    A dataframe with columns ['dates', 'RID', 'Phy', 'Chem', 'Math'] \n",
    "    Output Format\n",
    "    You are required to return a list consisting Reg Ids.\n",
    "\n",
    "    Sample Input\n",
    "\n",
    "    For input dataframes you can consider the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea31cbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'dates':[\"2015-12-06\", \"2011-12-27\", \"2015-09-07\", \"2012-12-21\", \"2020-02-13\"],\n",
    "                   'RID':[498, 721, 375, 464, 813], \n",
    "                   'Phy':[22, 45, 1, 65, 22], \n",
    "                   'Chem':[52, 56, 32, 50, 24],\n",
    "                   'Math':[63, 37, 68, 62, 43]}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a10eba26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>dates</th>\n",
       "      <th>RID</th>\n",
       "      <th>Phy</th>\n",
       "      <th>Chem</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>721</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-07</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-21</td>\n",
       "      <td>464</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>813</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dates  RID  Phy  Chem  Math\n",
       "0  2015-12-06  498   22    52    63\n",
       "1  2011-12-27  721   45    56    37\n",
       "2  2015-09-07  375    1    32    68\n",
       "3  2012-12-21  464   65    50    62\n",
       "4  2020-02-13  813   22    24    43"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6903e095",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"dates\"] = pd.to_datetime(df[\"dates\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2e5d35b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dates</th>\n",
       "      <th>RID</th>\n",
       "      <th>Phy</th>\n",
       "      <th>Chem</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>721</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-07</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-21</td>\n",
       "      <td>464</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>813</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates  RID  Phy  Chem  Math\n",
       "0 2015-12-06  498   22    52    63\n",
       "1 2011-12-27  721   45    56    37\n",
       "2 2015-09-07  375    1    32    68\n",
       "3 2012-12-21  464   65    50    62\n",
       "4 2020-02-13  813   22    24    43"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b90a23f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01/05/2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bfd4f6c",
   "metadata": {},
   "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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       "        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>dates</th>\n",
       "      <th>RID</th>\n",
       "      <th>Phy</th>\n",
       "      <th>Chem</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-07</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>813</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates  RID  Phy  Chem  Math\n",
       "0 2015-12-06  498   22    52    63\n",
       "2 2015-09-07  375    1    32    68\n",
       "4 2020-02-13  813   22    24    43"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"dates\"]>\"2014-05-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96098100",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"avg_marks\"] = df[df[\"dates\"]>\"2014-05-01\"][[\"Phy\",\"Chem\",\"Math\"]].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9329997c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dates</th>\n",
       "      <th>RID</th>\n",
       "      <th>Phy</th>\n",
       "      <th>Chem</th>\n",
       "      <th>Math</th>\n",
       "      <th>avg_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>813</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-07</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates  RID  Phy  Chem  Math  avg_marks\n",
       "4 2020-02-13  813   22    24    43       89.0\n",
       "2 2015-09-07  375    1    32    68      101.0\n",
       "0 2015-12-06  498   22    52    63      137.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"dates\"]>\"2014-05-01\"].sort_values(by=\"avg_marks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68e1f66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[813, 375, 498]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df[df[\"dates\"]>\"2014-05-01\"].sort_values(by=\"avg_marks\")[\"RID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f316ef6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "416c12ad",
   "metadata": {},
   "source": [
    "### Which of the following discipline(s) observed more female participants as compared to male participants in Olympics-2021? Use the 'EntriesGender' file\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "58414a3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('EntriesGender.xlsx', engine='openpyxl')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b4201f79",
   "metadata": {},
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3x3 Basketball</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Archery</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Artistic Gymnastics</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>969</td>\n",
       "      <td>1072</td>\n",
       "      <td>2041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discipline  Female  Male  Total\n",
       "0       3x3 Basketball      32    32     64\n",
       "1              Archery      64    64    128\n",
       "2  Artistic Gymnastics      98    98    196\n",
       "3    Artistic Swimming     105     0    105\n",
       "4            Athletics     969  1072   2041"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "97bba80c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cycling BMX Freestyle</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Diving</td>\n",
       "      <td>72</td>\n",
       "      <td>71</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rhythmic Gymnastics</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Discipline  Female  Male  Total\n",
       "3       Artistic Swimming     105     0    105\n",
       "12  Cycling BMX Freestyle      10     9     19\n",
       "17                 Diving      72    71    143\n",
       "28    Rhythmic Gymnastics      96     0     96"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Female\"])>(df[\"Male\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcef4549",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b36b1a6c",
   "metadata": {},
   "source": [
    "### Who of the following has served as the coach of his/her national men and women team (here teams should necessarily be different for men and women, not duet) in Olympics 2021?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "871a7df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "coaches = pd.read_excel('Coaches.xlsx', engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b32fe119",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2c3884c1",
   "metadata": {},
   "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>Name</th>\n",
       "      <th>team_count</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REQUENA PERICAS Judit</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>REQUENA PERICAS Judit</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FUJIKI Mayuko</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FUJIKI Mayuko</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TARRES CAMPA Ana</td>\n",
       "      <td>2</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TARRES CAMPA Ana</td>\n",
       "      <td>2</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GUERRERO Rolando</td>\n",
       "      <td>2</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Baseball/Softball</td>\n",
       "      <td>Softball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GUERRERO Rolando</td>\n",
       "      <td>2</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Baseball/Softball</td>\n",
       "      <td>Softball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RETEGUI Carlos Jose</td>\n",
       "      <td>2</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Men</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RETEGUI Carlos Jose</td>\n",
       "      <td>2</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MONTICO Loredana</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MONTICO Loredana</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ELAFANDI Nour</td>\n",
       "      <td>2</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ELAFANDI Nour</td>\n",
       "      <td>2</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GIALLOMBARDO Patrizia</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GIALLOMBARDO Patrizia</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SZAUDER Gabor</td>\n",
       "      <td>2</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SZAUDER Gabor</td>\n",
       "      <td>2</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CHERNETSKA Natalia</td>\n",
       "      <td>2</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CHERNETSKA Natalia</td>\n",
       "      <td>2</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CHEPAK Anastasiya</td>\n",
       "      <td>2</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CHEPAK Anastasiya</td>\n",
       "      <td>2</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FARINELLI Roberta</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FARINELLI Roberta</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>WANG Jie</td>\n",
       "      <td>2</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Duet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>WANG Jie</td>\n",
       "      <td>2</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>Artistic Swimming</td>\n",
       "      <td>Team</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  team_count                         NOC         Discipline     Event\n",
       "0   REQUENA PERICAS Judit           2                       Spain  Artistic Swimming      Duet\n",
       "1   REQUENA PERICAS Judit           2                       Spain  Artistic Swimming      Team\n",
       "2           FUJIKI Mayuko           2                       Spain  Artistic Swimming      Duet\n",
       "3           FUJIKI Mayuko           2                       Spain  Artistic Swimming      Team\n",
       "4        TARRES CAMPA Ana           2                      Greece  Artistic Swimming      Duet\n",
       "5        TARRES CAMPA Ana           2                      Greece  Artistic Swimming      Team\n",
       "6        GUERRERO Rolando           2                      Mexico  Baseball/Softball  Softball\n",
       "7        GUERRERO Rolando           2                      Mexico  Baseball/Softball  Softball\n",
       "8     RETEGUI Carlos Jose           2                   Argentina             Hockey       Men\n",
       "9     RETEGUI Carlos Jose           2                   Argentina             Hockey     Women\n",
       "10       MONTICO Loredana           2                   Australia  Artistic Swimming      Duet\n",
       "11       MONTICO Loredana           2                   Australia  Artistic Swimming      Team\n",
       "12          ELAFANDI Nour           2                       Egypt  Artistic Swimming      Duet\n",
       "13          ELAFANDI Nour           2                       Egypt  Artistic Swimming      Team\n",
       "14  GIALLOMBARDO Patrizia           2                       Italy  Artistic Swimming      Duet\n",
       "15  GIALLOMBARDO Patrizia           2                       Italy  Artistic Swimming      Team\n",
       "16          SZAUDER Gabor           2                      Canada  Artistic Swimming      Duet\n",
       "17          SZAUDER Gabor           2                      Canada  Artistic Swimming      Team\n",
       "18     CHERNETSKA Natalia           2                      Greece  Artistic Swimming      Duet\n",
       "19     CHERNETSKA Natalia           2                      Greece  Artistic Swimming      Team\n",
       "20      CHEPAK Anastasiya           2                       Egypt  Artistic Swimming      Duet\n",
       "21      CHEPAK Anastasiya           2                       Egypt  Artistic Swimming      Team\n",
       "22      FARINELLI Roberta           2                       Italy  Artistic Swimming      Duet\n",
       "23      FARINELLI Roberta           2                       Italy  Artistic Swimming      Team\n",
       "24               WANG Jie           2  People's Republic of China  Artistic Swimming      Duet\n",
       "25               WANG Jie           2  People's Republic of China  Artistic Swimming      Team"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coaches_sample = coaches.Name.value_counts().to_frame()\n",
    "coaches_sample = coaches_sample.reset_index()\n",
    "coaches_sample = coaches_sample.rename(columns={\"index\": \"Name\", \"Name\": \"team_count\"})\n",
    "coaches_sample = coaches_sample.merge(coaches, how=\"left\", on=\"Name\")\n",
    "ans=coaches_sample[coaches_sample[\"team_count\"] > 1]\n",
    "(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5573c6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "id": "7f7de9f8",
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   "id": "a65e7498",
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