analysis-classes.ipynb

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  {
   "source": [
    "# Analysis of dataset metadata_2013"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import community as cl\n",
    "import networkx as nx "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     i  class gender\n",
       "0  650  2BIO1      F\n",
       "1  498  2BIO1      F\n",
       "2  627  2BIO1      F\n",
       "3  857  2BIO1      F\n",
       "4  487  2BIO1      F"
      ],
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     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df = pd.read_table('data/metadata_2013.txt', names=['i', 'class', 'gender'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        i  gender\n",
       "class            \n",
       "2BIO1  36      36\n",
       "2BIO2  35      35\n",
       "2BIO3  40      40\n",
       "MP     33      33\n",
       "MP*1   29      29\n",
       "MP*2   38      38\n",
       "PC     44      44\n",
       "PC*    40      40\n",
       "PSI*   34      34"
      ],
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     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "classes = df.groupby('class')\n",
    "classes.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "class\n2BIO1    36\n2BIO2    35\n2BIO3    40\nMP       33\nMP*1     29\nMP*2     38\nPC       44\nPC*      40\nPSI*     34\nName: i, dtype: int64\n"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "labels = df['class'].unique()\n",
    "values = df.groupby('class')['i'].count()\n",
    "print(values)\n",
    "plt.pie(values, labels=labels, autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             i                                                             \n",
       "         count        mean         std   min     25%    50%     75%     max\n",
       "gender                                                                     \n",
       "F        146.0  482.760274  346.655455  39.0  210.25  442.0  716.50  1894.0\n",
       "M        176.0  611.068182  463.748227   1.0  244.75  493.0  884.75  1870.0\n",
       "Unknown    7.0  283.428571  337.008831  34.0   49.50  209.0  331.50   979.0"
      ],
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">i</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    </tr>\n    <tr>\n      <th>gender</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      <th>F</th>\n      <td>146.0</td>\n      <td>482.760274</td>\n      <td>346.655455</td>\n      <td>39.0</td>\n      <td>210.25</td>\n      <td>442.0</td>\n      <td>716.50</td>\n      <td>1894.0</td>\n    </tr>\n    <tr>\n      <th>M</th>\n      <td>176.0</td>\n      <td>611.068182</td>\n      <td>463.748227</td>\n      <td>1.0</td>\n      <td>244.75</td>\n      <td>493.0</td>\n      <td>884.75</td>\n      <td>1870.0</td>\n    </tr>\n    <tr>\n      <th>Unknown</th>\n      <td>7.0</td>\n      <td>283.428571</td>\n      <td>337.008831</td>\n      <td>34.0</td>\n      <td>49.50</td>\n      <td>209.0</td>\n      <td>331.50</td>\n      <td>979.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "genders = df.groupby('gender')\n",
    "genders.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x134193b8>,\n",
       "  <matplotlib.patches.Wedge at 0x13419730>,\n",
       "  <matplotlib.patches.Wedge at 0x13419a78>],\n",
       " [Text(0.19331124388955206, 1.0828807704386825, 'F'),\n",
       "  Text(-0.2652079066501681, -1.0675508260735111, 'M'),\n",
       "  Text(1.097543581258692, -0.0734716764327924, 'Unknown')],\n",
       " [Text(0.10544249666702839, 0.5906622384210994, '44.4%'),\n",
       "  Text(-0.14465885817281893, -0.5823004505855515, '53.5%'),\n",
       "  Text(0.5986601352320138, -0.04007545987243222, '2.1%')])"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "labels = df.gender.unique()\n",
    "values = df.groupby('gender')['i'].count()\n",
    "plt.pie(values, labels=labels, autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}