stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drug_freq(top=1):\n",
    "    drugs_freq = {}\n",
    "    for file in os.listdir('results-top-100/'):\n",
    "        data = pd.read_csv('results-top-100/' + file)\n",
    "        data = data.sort_values(by=['energy'])\n",
    "        top_data = data[:top]\n",
    "        for row in top_data.iterrows():\n",
    "            drug = row[1]['ligand']\n",
    "            if drug in drugs_freq:\n",
    "                drugs_freq[drug] += 1\n",
    "            else :\n",
    "                drugs_freq[drug] = 1\n",
    "    return drugs_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_freq_1 = drug_freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(drugs_freq_1)), list(drugs_freq_1.values()), align='center')\n",
    "plt.xticks(range(len(drugs_freq_1)), list(drugs_freq_1.keys()))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RZVPBGBYGMDSBG-GGAORHGYSA-N': 17,\n",
       " 'GZUITABIAKMVPG-UHFFFAOYSA-N': 38,\n",
       " 'QOYHHIBFXOOADH-UHFFFAOYSA-N': 54,\n",
       " 'LGAJOMLFGCSBFF-OLXDQKQCSA-N': 4,\n",
       " 'NETZHAKZCGBWSS-CEDHKZHLSA-N': 2,\n",
       " 'PWNMXPDKBYZCOO-NSHDSACASA-N': 10,\n",
       " 'FHXBMXJMKMWVRG-SLHNCBLASA-N': 7,\n",
       " 'OUDSBRTVNLOZBN-UHFFFAOYSA-N': 1,\n",
       " 'XDVCLKFLRAWGIT-ADOAZJKMSA-N': 5,\n",
       " 'UDMBCSSLTHHNCD-KQYNXXCUSA-N': 2,\n",
       " 'WOFMFGQZHJDGCX-ZULDAHANSA-N': 1,\n",
       " 'DKGZKTPJOSAWFA-UHFFFAOYSA-N': 3,\n",
       " 'JXZZEXZZKAWDSP-UHFFFAOYSA-N': 1,\n",
       " 'DYAIAHUQIPBDIP-AXAPSJFSSA-N': 1,\n",
       " 'HJBWBFZLDZWPHF-UHFFFAOYSA-N': 1,\n",
       " 'AIZFEOPQVZBNGH-UHFFFAOYSA-N': 1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs_freq_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indicators(data):\n",
    "    corr = data['best Energy'].corr(data['binding score'])\n",
    "    temp = data.sort_values(by=['binding score'], ascending=False)\n",
    "    set_1 = list(temp['ligand'][:10])\n",
    "    temp = data.sort_values(by=['best Energy'])\n",
    "    set_2 = list(temp['ligand'][:10])\n",
    "    \n",
    "    result = {'overlap_' + str(i) : len(set(set_1[:i]) & set(set_2[:i])) / len(set(set_1[:i]) | set(set_2[:i])) for i in [1, 5, 10]}\n",
    "    result['corr'] = corr\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = []\n",
    "ranks = []\n",
    "root = './Bs-Ie/'\n",
    "for file in os.listdir(root):\n",
    "    data = pd.read_csv(root + file)\n",
    "    data_sorted = data.sort_values(by=['binding score'], ascending=False)\n",
    "    min_energy = data['best Energy'].min()\n",
    "    for i, value in enumerate(data_sorted.iterrows()):\n",
    "        if value[1]['best Energy'] == min_energy:\n",
    "            r = i + 1\n",
    "            break\n",
    "    ranks.append(r)\n",
    "    results = indicators(data)\n",
    "    values.append((results['corr'], results['overlap_1'], results['overlap_5'], results['overlap_10']))\n",
    "indicator_val = pd.DataFrame(data=values, columns=['corr', 'overlap_1', 'overlap_5', 'overlap_10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>corr</th>\n",
       "      <th>overlap_1</th>\n",
       "      <th>overlap_5</th>\n",
       "      <th>overlap_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.145818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.286521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.039539</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.126329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>-0.065126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>-0.063819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0.216136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>-0.086804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-0.034335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         corr  overlap_1  overlap_5  overlap_10\n",
       "0    0.028920        0.0   0.250000    0.250000\n",
       "1   -0.145818        0.0   0.111111    0.333333\n",
       "2    0.286521        0.0   0.000000    0.250000\n",
       "3   -0.039539        1.0   0.111111    0.250000\n",
       "4   -0.126329        0.0   0.250000    0.250000\n",
       "..        ...        ...        ...         ...\n",
       "142 -0.065126        0.0   0.111111    0.250000\n",
       "143 -0.063819        0.0   0.111111    0.333333\n",
       "144  0.216136        0.0   0.111111    0.250000\n",
       "145 -0.086804        0.0   0.111111    0.250000\n",
       "146 -0.034335        0.0   0.111111    0.250000\n",
       "\n",
       "[147 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicator_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor_serie = pd.Series(corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    147.000000\n",
       "mean      -0.067254\n",
       "std        0.135064\n",
       "min       -0.350689\n",
       "25%       -0.160484\n",
       "50%       -0.073704\n",
       "75%        0.027408\n",
       "max        0.286521\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_serie.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overlapping score\n",
    "overlap_score = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'overlap_1': 0.0, 'overlap_5': 0.1111111111111111, 'overlap_10': 0.25}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>ligand</th>\n",
       "      <th>E1</th>\n",
       "      <th>E2</th>\n",
       "      <th>E3</th>\n",
       "      <th>binding score</th>\n",
       "      <th>best Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COLNVLDHVKWLRT-QMMMGPOBSA-N</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>70105.41</td>\n",
       "      <td>-6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MFBCDACCJCDGBA-UHFFFAOYSA-N</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-9.3</td>\n",
       "      <td>126306.37</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AYFVYJQAPQTCCC-GBXIJSLDSA-N</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>74156.13</td>\n",
       "      <td>-5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KZTZJUQNSSLNAG-UHFFFAOYSA-N</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>117532.82</td>\n",
       "      <td>-4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UDMBCSSLTHHNCD-KQYNXXCUSA-N</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>289460.05</td>\n",
       "      <td>-8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JEIPFZHSYJVQDO-UHFFFAOYSA-N</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>141180.54</td>\n",
       "      <td>-4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KRMDCWKBEZIMAB-UHFFFAOYSA-N</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>188626.39</td>\n",
       "      <td>-8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WDSCBUNMANHPFH-UHFFFAOYSA-N</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>-4.7</td>\n",
       "      <td>155050.15</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IKWTVSLWAPBBKU-UHFFFAOYSA-N</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>171412.94</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DHMQDGOQFOQNFH-UHFFFAOYSA-N</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>79567.34</td>\n",
       "      <td>-4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JURKNVYFZMSNLP-UHFFFAOYSA-N</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>23456.49</td>\n",
       "      <td>-8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>JMOVFFLYGIQXMM-UHFFFAOYSA-N</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>8557.49</td>\n",
       "      <td>-6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ZCXUVYAZINUVJD-AHXZWLDOSA-N</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>124239.53</td>\n",
       "      <td>-6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UVKZSORBKUEBAZ-UHFFFAOYSA-N</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>115786.29</td>\n",
       "      <td>-7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FDMBBCOBEAVDAO-AWEZNQCLSA-N</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>46842.54</td>\n",
       "      <td>-6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ZQPPMHVWECSIRJ-KTKRTIGZSA-N</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>108643.47</td>\n",
       "      <td>-5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OCAXFDULERPAJM-BQJUDKOJSA-N</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>110709.72</td>\n",
       "      <td>-8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LGAJOMLFGCSBFF-OLXDQKQCSA-N</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>-9.8</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>330142.22</td>\n",
       "      <td>-10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>YFDSDPIBEUFTMI-UHFFFAOYSA-N</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>246260.20</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>XRVCFZPJAHWYTB-UHFFFAOYSA-N</td>\n",
       "      <td>-4.7</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>228158.84</td>\n",
       "      <td>-5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RVMGXWBCQGAWBR-UHFFFAOYSA-N</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-6.9</td>\n",
       "      <td>153557.34</td>\n",
       "      <td>-7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PMTSPAGBAFCORP-HBUONDEYSA-N</td>\n",
       "      <td>-10.6</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>-11.1</td>\n",
       "      <td>5382.79</td>\n",
       "      <td>-11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PHOQVHQSTUBQQK-SQOUGZDYSA-N</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>352574.43</td>\n",
       "      <td>-6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ZPMKQFOGINQDAM-UHFFFAOYSA-N</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>94084.78</td>\n",
       "      <td>-8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RQNADZJRELIICT-UHFFFAOYSA-N</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>100256.38</td>\n",
       "      <td>-7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>RTRQQBHATOEIAF-UUOKFMHZSA-N</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>32032.03</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ligand    E1   E2    E3  binding score  best Energy\n",
       "0   COLNVLDHVKWLRT-QMMMGPOBSA-N  -6.2 -6.1  -5.7       70105.41         -6.2\n",
       "1   MFBCDACCJCDGBA-UHFFFAOYSA-N -10.0 -9.2  -9.3      126306.37        -10.0\n",
       "2   AYFVYJQAPQTCCC-GBXIJSLDSA-N  -5.7 -5.7  -4.4       74156.13         -5.7\n",
       "3   KZTZJUQNSSLNAG-UHFFFAOYSA-N  -4.6 -4.4  -4.2      117532.82         -4.6\n",
       "4   UDMBCSSLTHHNCD-KQYNXXCUSA-N  -8.2 -8.8  -7.8      289460.05         -8.8\n",
       "5   JEIPFZHSYJVQDO-UHFFFAOYSA-N  -4.3 -4.0  -3.9      141180.54         -4.3\n",
       "6   KRMDCWKBEZIMAB-UHFFFAOYSA-N  -8.1 -8.0  -7.8      188626.39         -8.1\n",
       "7   WDSCBUNMANHPFH-UHFFFAOYSA-N  -6.0 -5.6  -4.7      155050.15         -6.0\n",
       "8   IKWTVSLWAPBBKU-UHFFFAOYSA-N  -1.5 -1.3  -1.0      171412.94         -1.5\n",
       "9   DHMQDGOQFOQNFH-UHFFFAOYSA-N  -4.1 -4.1  -3.3       79567.34         -4.1\n",
       "10  JURKNVYFZMSNLP-UHFFFAOYSA-N  -8.2 -7.8  -7.7       23456.49         -8.2\n",
       "11  JMOVFFLYGIQXMM-UHFFFAOYSA-N  -6.6 -6.6  -6.1        8557.49         -6.6\n",
       "12  ZCXUVYAZINUVJD-AHXZWLDOSA-N  -6.7 -6.7  -6.0      124239.53         -6.7\n",
       "13  UVKZSORBKUEBAZ-UHFFFAOYSA-N  -7.4 -7.7  -7.4      115786.29         -7.7\n",
       "14  FDMBBCOBEAVDAO-AWEZNQCLSA-N  -6.1 -6.2  -5.7       46842.54         -6.2\n",
       "15  ZQPPMHVWECSIRJ-KTKRTIGZSA-N  -5.4 -5.0  -5.5      108643.47         -5.5\n",
       "16  OCAXFDULERPAJM-BQJUDKOJSA-N  -8.7 -8.7  -8.7      110709.72         -8.7\n",
       "17  LGAJOMLFGCSBFF-OLXDQKQCSA-N -10.7 -9.8  -9.9      330142.22        -10.7\n",
       "18  YFDSDPIBEUFTMI-UHFFFAOYSA-N  -3.7 -4.0  -3.2      246260.20         -4.0\n",
       "19  XRVCFZPJAHWYTB-UHFFFAOYSA-N  -4.7 -5.1  -4.0      228158.84         -5.1\n",
       "20  RVMGXWBCQGAWBR-UHFFFAOYSA-N  -7.8 -7.5  -6.9      153557.34         -7.8\n",
       "21  PMTSPAGBAFCORP-HBUONDEYSA-N -10.6 -9.4 -11.1        5382.79        -11.1\n",
       "22  PHOQVHQSTUBQQK-SQOUGZDYSA-N  -6.7 -6.8  -6.0      352574.43         -6.8\n",
       "23  ZPMKQFOGINQDAM-UHFFFAOYSA-N  -8.1 -8.3  -8.2       94084.78         -8.3\n",
       "24  RQNADZJRELIICT-UHFFFAOYSA-N  -7.4 -7.7  -7.2      100256.38         -7.7\n",
       "25  RTRQQBHATOEIAF-UUOKFMHZSA-N  -7.4 -8.0  -7.6       32032.03         -8.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([19.,  6.,  5.,  5., 11.,  6.,  3.,  3.,  0., 89.]),\n",
       " array([ 1. ,  3.5,  6. ,  8.5, 11. , 13.5, 16. , 18.5, 21. , 23.5, 26. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"rank distribution\")\n",
    "plt.xlabel(\"rank\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.hist(ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.median(ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}