entries-gender-analysis.ipynb

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  {
   "cell_type": "code",
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   "id": "d6ebc1cf",
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   "source": [
    "# import required libraries\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "150106fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_excel_file = '/home/arrow/2021-Olympics-Data-Analysis/EntriesGender.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5be7d320",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(path_to_excel_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "025bc44d",
   "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",
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       "    }\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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28842637",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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