create_heatmaps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "d46b1638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11577, 148)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       'age_2', 'age_3', 'age_4', 'age_5', 'age_6', 'age_7', 'masculine_faces',\n",
      "       'feminine_faces', 'video_key_to_match', 'brand_category'],\n",
      "      dtype='object', length=148)\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "#reading and filtering out the data\n",
    "csv_file=\"raw_Mar31.csv\"\n",
    "raw_rp_data=pd.read_csv(csv_file,index_col=None)\n",
    "\n",
    "print(raw_rp_data.shape)\n",
    "print(raw_rp_data.columns)\n",
    "\n",
    "#filter only videos and valid RP values\n",
    "rp_data=raw_rp_data.loc[(raw_rp_data != -1).all(axis=1) & raw_rp_data[\"Filename\"].str.endswith(\".mp4\")].copy()\n",
    "rp_data.index = pd.RangeIndex(0, len(rp_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "10dc9bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract only the male data \n",
    "import ast \n",
    "male_cols=[col for col in rp_data.columns if 'male' in col and 'female' not in col]\n",
    "male_data=rp_data[male_cols]\n",
    "\n",
    "#create a dataframe with age bins as columns and index as skintone values \n",
    "\n",
    "age_bins=['[0, 15)','[15, 25)','[25, 35)','[35, 45)', '[45, 55)','[55, 65)', '[65, 75)']\n",
    "condensed_age_bins=['[0, 15)','[15, 25)','[25, 35)','[35, 45)','[45, 75)']\n",
    "skintone_strings=[\"[-inf, 1.1)\",\"[1.1, 2.1)\",\"[2.1, 3.1)\",\"[3.1, 4.1)\",\"[4.1, 5.1)\",\"[5.1, 6.1)\",\"[6.1, 7.1)\",\"[7.1, 8.1)\",\"[8.1, 9.1)\",\"[9.1, 10.1)\"]\n",
    "\n",
    "male_df=df = pd.DataFrame(0, index=skintone_strings, columns=age_bins)\n",
    "\n",
    "\n",
    "for age_bin in age_bins:\n",
    "    \n",
    "    col_sample=[col for col in male_data.columns if age_bin in col]\n",
    "    age_subsample_data=male_data[col_sample]\n",
    "    \n",
    "    for col_select in col_sample:\n",
    "        \n",
    "        tuple_ = tuple(x.strip() for x in col_select.strip('()').split(','))\n",
    "        skintone_bin=tuple_[3]+\", \"+tuple_[4]+\")\"\n",
    "        sum_faces=male_data[col_select].sum()\n",
    "        \n",
    "        male_df.loc[skintone_bin,age_bin]=sum_faces\n",
    "        #print(male_data[col_select])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "29fe7e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364390"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_df.sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "9bd80b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_df['[45, 75)']=male_df['[45, 55)']+male_df['[55, 65)']+male_df['[65, 75)']\n",
    "\n",
    "#drop columns from male_df \n",
    "male_df=male_df.drop(['[45, 55)', '[55, 65)', '[65, 75)'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "7c2165e5",
   "metadata": {},
   "outputs": [
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       "             [0, 15)  [15, 25)  [25, 35)  [35, 45)  [45, 75)\n",
       "[-inf, 1.1)     1113      5123     12846      2165       593\n",
       "[1.1, 2.1)      7307     57257    144218     30820     17326\n",
       "[2.1, 3.1)       279      4012      6523       643       256\n",
       "[3.1, 4.1)       257      4201     23682      1125       235\n",
       "[4.1, 5.1)       845      2041      4663        77        21\n",
       "[5.1, 6.1)        76       141       385         0         0\n",
       "[6.1, 7.1)       195       646      1771         5         1\n",
       "[7.1, 8.1)       673     12991     15782       538       348\n",
       "[8.1, 9.1)         4      1233      1908         5        35\n",
       "[9.1, 10.1)        0         9        16         0         0"
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     "execution_count": 213,
     "metadata": {},
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   ],
   "source": [
    "male_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "e5d13429",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transpose the dataframe\n",
    "male_df_transposed=male_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "63d702a3",
   "metadata": {},
   "outputs": [
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       "          [-inf, 1.1)  [1.1, 2.1)  [2.1, 3.1)  [3.1, 4.1)  [4.1, 5.1)  \\\n",
       "[0, 15)          1113        7307         279         257         845   \n",
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       "\n",
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       "[0, 15)           76         195         673           4            0  \n",
       "[15, 25)         141         646       12991        1233            9  \n",
       "[25, 35)         385        1771       15782        1908           16  \n",
       "[35, 45)           0           5         538           5            0  \n",
       "[45, 75)           0           1         348          35            0  "
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     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_list=list(male_df_transposed.columns)\n",
    "male_df_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "9777afcf",
   "metadata": {},
   "outputs": [
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       "      <th>light</th>\n",
       "      <td>8699</td>\n",
       "      <td>66392</td>\n",
       "      <td>163587</td>\n",
       "      <td>33628</td>\n",
       "      <td>18175</td>\n",
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       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>1373</td>\n",
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       "      <td>30501</td>\n",
       "      <td>1207</td>\n",
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       "      <th>dark</th>\n",
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       "        [0, 15)  [15, 25)  [25, 35)  [35, 45)  [45, 75)\n",
       "light      8699     66392    163587     33628     18175\n",
       "medium     1373      7029     30501      1207       257\n",
       "dark        677     14233     17706       543       383"
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     "execution_count": 216,
     "metadata": {},
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   ],
   "source": [
    "male_df_transposed['light']=male_df_transposed['[-inf, 1.1)']+male_df_transposed['[1.1, 2.1)']+male_df_transposed['[2.1, 3.1)']\n",
    "male_df_transposed['medium']=male_df_transposed['[3.1, 4.1)']+male_df_transposed['[4.1, 5.1)']+male_df_transposed['[5.1, 6.1)']+male_df_transposed['[6.1, 7.1)']\n",
    "male_df_transposed['dark']=male_df_transposed['[7.1, 8.1)']+male_df_transposed['[8.1, 9.1)']+male_df_transposed['[9.1, 10.1)']\n",
    "\n",
    "#drop the intial list\n",
    "male_df_hm=male_df_transposed.drop(columns_list,axis=1)\n",
    "\n",
    "male_df_hm=male_df_hm.transpose()\n",
    "male_df_hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "e578c789",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_male_faces=male_df_hm.sum().sum()\n",
    "male_df_hm=(male_df_hm/total_male_faces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "a350dc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#heatmap plot \n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig=sns.heatmap(male_df_hm, cmap='Blues',  fmt=\".2%\", annot=True, linewidths=2, linecolor='black', vmin=0, vmax=0.60)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Skintone')\n",
    "cbar = fig.collections[0].colorbar\n",
    "cbar.set_ticks([0, .2, .3, .4, .5, .6])\n",
    "cbar.set_ticklabels(['low', '20%', '30%', '40%', '50%', '60%'])\n",
    "fig.figure.savefig('plots/male_gender_distribution_heatmap.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "96bef997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364390"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data['masculine_faces'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "66701be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.72"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.39+18.22+44.89+9.23+4.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "3adf6223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.08"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.93+8.37+0.33+0.07+0.38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "7d0c5319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.22"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.19+3.91+4.86+0.15+0.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b3adb4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.02"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "79.72+11.08+9.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "4a5d9fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_cols=[col for col in rp_data.columns if 'female' in col]\n",
    "female_data=rp_data[female_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b2c5b43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "b1c0c484",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df=df = pd.DataFrame(0, index=skintone_strings, columns=age_bins)\n",
    "\n",
    "\n",
    "for age_bin in age_bins:\n",
    "    \n",
    "    col_sample=[col for col in female_data.columns if age_bin in col]\n",
    "    age_subsample_data=female_data[col_sample]\n",
    "    \n",
    "    for col_select in col_sample:\n",
    "        \n",
    "        tuple_ = tuple(x.strip() for x in col_select.strip('()').split(','))\n",
    "        skintone_bin=tuple_[3]+\", \"+tuple_[4]+\")\"\n",
    "        sum_faces=female_data[col_select].sum()\n",
    "        \n",
    "        female_df.loc[skintone_bin,age_bin]=sum_faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "b7c38b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>[0, 15)</th>\n",
       "      <th>[15, 25)</th>\n",
       "      <th>[25, 35)</th>\n",
       "      <th>[35, 45)</th>\n",
       "      <th>[45, 55)</th>\n",
       "      <th>[55, 65)</th>\n",
       "      <th>[65, 75)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[-inf, 1.1)</th>\n",
       "      <td>4263</td>\n",
       "      <td>40503</td>\n",
       "      <td>53936</td>\n",
       "      <td>2387</td>\n",
       "      <td>686</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1.1, 2.1)</th>\n",
       "      <td>23636</td>\n",
       "      <td>305761</td>\n",
       "      <td>455215</td>\n",
       "      <td>41539</td>\n",
       "      <td>40260</td>\n",
       "      <td>7957</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2.1, 3.1)</th>\n",
       "      <td>766</td>\n",
       "      <td>20670</td>\n",
       "      <td>30283</td>\n",
       "      <td>1216</td>\n",
       "      <td>467</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[3.1, 4.1)</th>\n",
       "      <td>1004</td>\n",
       "      <td>34154</td>\n",
       "      <td>36252</td>\n",
       "      <td>6050</td>\n",
       "      <td>3640</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[4.1, 5.1)</th>\n",
       "      <td>1154</td>\n",
       "      <td>20683</td>\n",
       "      <td>19528</td>\n",
       "      <td>2771</td>\n",
       "      <td>1721</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[5.1, 6.1)</th>\n",
       "      <td>213</td>\n",
       "      <td>4164</td>\n",
       "      <td>3422</td>\n",
       "      <td>50</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[6.1, 7.1)</th>\n",
       "      <td>647</td>\n",
       "      <td>13454</td>\n",
       "      <td>7772</td>\n",
       "      <td>243</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[7.1, 8.1)</th>\n",
       "      <td>1322</td>\n",
       "      <td>32160</td>\n",
       "      <td>14394</td>\n",
       "      <td>245</td>\n",
       "      <td>107</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[8.1, 9.1)</th>\n",
       "      <td>53</td>\n",
       "      <td>618</td>\n",
       "      <td>412</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>[9.1, 10.1)</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "             [0, 15)  [15, 25)  [25, 35)  [35, 45)  [45, 55)  [55, 65)  \\\n",
       "[-inf, 1.1)     4263     40503     53936      2387       686       136   \n",
       "[1.1, 2.1)     23636    305761    455215     41539     40260      7957   \n",
       "[2.1, 3.1)       766     20670     30283      1216       467       123   \n",
       "[3.1, 4.1)      1004     34154     36252      6050      3640       227   \n",
       "[4.1, 5.1)      1154     20683     19528      2771      1721        36   \n",
       "[5.1, 6.1)       213      4164      3422        50        14         0   \n",
       "[6.1, 7.1)       647     13454      7772       243       135         1   \n",
       "[7.1, 8.1)      1322     32160     14394       245       107        22   \n",
       "[8.1, 9.1)        53       618       412         1         0         0   \n",
       "[9.1, 10.1)        0        10         4         0         0         0   \n",
       "\n",
       "             [65, 75)  \n",
       "[-inf, 1.1)         0  \n",
       "[1.1, 2.1)         57  \n",
       "[2.1, 3.1)          0  \n",
       "[3.1, 4.1)          0  \n",
       "[4.1, 5.1)          0  \n",
       "[5.1, 6.1)          0  \n",
       "[6.1, 7.1)          0  \n",
       "[7.1, 8.1)          0  \n",
       "[8.1, 9.1)          0  \n",
       "[9.1, 10.1)         0  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "bea041a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df['[45, 75)']=female_df['[45, 55)']+female_df['[55, 65)']+female_df['[65, 75)']\n",
    "\n",
    "#drop columns from male_df \n",
    "female_df=female_df.drop(['[45, 55)', '[55, 65)', '[65, 75)'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "c9498572",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "             [0, 15)  [15, 25)  [25, 35)  [35, 45)  [45, 75)\n",
       "[-inf, 1.1)     4263     40503     53936      2387       822\n",
       "[1.1, 2.1)     23636    305761    455215     41539     48274\n",
       "[2.1, 3.1)       766     20670     30283      1216       590\n",
       "[3.1, 4.1)      1004     34154     36252      6050      3867\n",
       "[4.1, 5.1)      1154     20683     19528      2771      1757\n",
       "[5.1, 6.1)       213      4164      3422        50        14\n",
       "[6.1, 7.1)       647     13454      7772       243       136\n",
       "[7.1, 8.1)      1322     32160     14394       245       129\n",
       "[8.1, 9.1)        53       618       412         1         0\n",
       "[9.1, 10.1)        0        10         4         0         0"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "74bda9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>light</th>\n",
       "      <td>28665</td>\n",
       "      <td>366934</td>\n",
       "      <td>539434</td>\n",
       "      <td>45142</td>\n",
       "      <td>49686</td>\n",
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       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>2566</td>\n",
       "      <td>59647</td>\n",
       "      <td>60973</td>\n",
       "      <td>8876</td>\n",
       "      <td>5639</td>\n",
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       "    <tr>\n",
       "      <th>dark</th>\n",
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      "text/plain": [
       "        [0, 15)  [15, 25)  [25, 35)  [35, 45)  [45, 75)\n",
       "light     28665    366934    539434     45142     49686\n",
       "medium     2566     59647     60973      8876      5639\n",
       "dark       1375     32788     14810       246       129"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df_transposed=female_df.transpose()\n",
    "female_df_transposed['light']=female_df_transposed['[-inf, 1.1)']+female_df_transposed['[1.1, 2.1)']+female_df_transposed['[2.1, 3.1)']\n",
    "female_df_transposed['medium']=female_df_transposed['[3.1, 4.1)']+female_df_transposed['[4.1, 5.1)']+female_df_transposed['[5.1, 6.1)']+male_df_transposed['[6.1, 7.1)']\n",
    "female_df_transposed['dark']=female_df_transposed['[7.1, 8.1)']+female_df_transposed['[8.1, 9.1)']+female_df_transposed['[9.1, 10.1)']\n",
    "\n",
    "#drop the intial list\n",
    "female_df_hm=female_df_transposed.drop(columns_list,axis=1)\n",
    "\n",
    "female_df_hm=female_df_hm.transpose()\n",
    "female_df_hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "25d1980d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1236544"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df.sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "e753b7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_female_faces=rp_data['feminine_faces'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "a190069a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_df_hm=(female_df_hm/total_female_faces)\n",
    "fig=sns.heatmap(female_df_hm, cmap='Blues',  fmt=\".2%\", annot=True, linewidths=2, linecolor='black', vmin=0, vmax=0.70)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Skintone')\n",
    "cbar = fig.collections[0].colorbar\n",
    "cbar.set_ticks([0, .2, .3, .4, .5, .6, .7])\n",
    "cbar.set_ticklabels(['low', '20%', '30%', '40%', '50%', '60%', '70%'])\n",
    "fig.figure.savefig('plots/female_gender_distribution_heatmap.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb646cfd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}