replot_coverage_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5d09c4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import os \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d6c92a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file='/data/digbose92/MuSE_Loreal_study/mica-MUSE-LoREAL/RP_metrics_LOreal_coverage_updated.csv'\n",
    "coverage_data=pd.read_csv(csv_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6f30a18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22126/1013663382.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  coverage_data['ProductAdvertiser'].iloc[i]='MAYBELLINE'\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(coverage_data.shape[0]):\n",
    "    if(coverage_data['ProductAdvertiser'].iloc[i]=='MNY'):\n",
    "        coverage_data['ProductAdvertiser'].iloc[i]='MAYBELLINE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "60c1e2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7648, 183)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bf6919cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage_strings=[\"[-inf, 1.1)\",\"[1.1, 2.1)\",\n",
    "                  \"[2.1, 3.1)\",\"[3.1, 4.1)\",\"[4.1, 5.1)\",\n",
    "                  \"[5.1, 6.1)\",\"[6.1, 7.1)\",\"[7.1, 8.1)\",\n",
    "                  \"[8.1, 9.1)\",\"[9.1, 10.1)\"]\n",
    "\n",
    "coverage_subsample_data=coverage_data[coverage_strings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff4a093c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['[-inf, 1.1)', '[1.1, 2.1)', '[2.1, 3.1)', '[3.1, 4.1)', '[4.1, 5.1)',\n",
       "       '[5.1, 6.1)', '[6.1, 7.1)', '[7.1, 8.1)', '[8.1, 9.1)', '[9.1, 10.1)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage_subsample_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0bb45afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7648/7648 [00:00<00:00, 30457.89it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "coverage_value_skintone=[]\n",
    "for i in tqdm(np.arange(coverage_subsample_data.shape[0])):\n",
    "    sk_faces=coverage_subsample_data.loc[i]\n",
    "    sk_list=list(sk_faces)\n",
    "    non_zero_count = len([x for x in sk_list if x != 0])\n",
    "    coverage_sk=round(non_zero_count/len(sk_list),2)\n",
    "    coverage_value_skintone.append(coverage_sk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fae10bca",
   "metadata": {},
   "outputs": [
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       " 0.1,\n",
       " ...]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage_value_skintone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c6a9ba51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22126/3179755313.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  coverage_subsample_data['Coverage_skintone_10_bins']=coverage_value_skintone\n"
     ]
    }
   ],
   "source": [
    "coverage_subsample_data['Coverage_skintone_10_bins']=coverage_value_skintone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "baa4b288",
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage_data['Coverage_skintone_10_bins']=coverage_value_skintone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d3f09b10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.1: 2087, 0.2: 1934, 0.3: 1171, 0.4: 1002, 0.5: 643, 0.6: 367, 0.7: 294, 0.8: 139, 0.9: 11}\n",
      "['0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9']\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "import seaborn as sns \n",
    "sns.set_style('darkgrid')\n",
    "plt.rcParams[\"figure.figsize\"] = (15,10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "number_sk_coverage=dict(Counter(coverage_value_skintone).most_common(10))\n",
    "#rename the keys \n",
    "print(number_sk_coverage)\n",
    "\n",
    "keys_list=sorted(list(set(number_sk_coverage)))\n",
    "\n",
    "sk_coverage_list=[number_sk_coverage[k] for k in keys_list]\n",
    "keys_str_list=[str(k) for k in keys_list]\n",
    "print(keys_str_list)\n",
    " \n",
    "ax.bar(range(len(keys_list)),sk_coverage_list,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.15)\n",
    "\n",
    "for i, v in enumerate(sk_coverage_list):\n",
    "    ax.text(i, v+20, str(v), ha='center', fontsize=20)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Skintone bins', fontsize=20)\n",
    "ax.set_xlabel('Coverage values', fontsize=20)\n",
    "ax.set_ylabel('Counts', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "plt.xticks(range(len(keys_list)), keys_str_list)\n",
    "yticks = ax.get_yticks()\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "filename=\"Skintone_based_coverage_skintone_10.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b6fad302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Canada\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
      "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
      "       ...\n",
      "       '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)', '[65, 75)',\n",
      "       '[45, 75)', 'age_coverage', 'skintone_coverage',\n",
      "       'Coverage_skintone_10_bins'],\n",
      "      dtype='object', length=184)\n",
      "t-test between Brazil and UK: t=0.7793346285794049, p=0.43588698338829235\n",
      "t-test between UK and India: t=0.5555580915412793, p=0.5785835158110821\n",
      "t-test between India and Spain: t=0.0381389022345189, p=0.969604706789192\n",
      "t-test between Spain and USA: t=0.37390799210197667, p=0.708558363699474\n",
      "t-test between USA and Italy: t=0.14269180088764782, p=0.8865526926218232\n",
      "t-test between Italy and France: t=1.7400093761644173, p=0.08204715531323604\n",
      "t-test between France and Indonesia: t=0.17139697046426552, p=0.8639526719114201\n",
      "t-test between Indonesia and Mexico: t=1.1049403050169702, p=0.2727651516806897\n",
      "t-test between Mexico and Germany: t=0.3676534459371872, p=0.7131556271484921\n",
      "Brazil > UK = India = Spain = USA = Italy > France = Indonesia > Mexico = "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "country_list=list(coverage_data['Country'].unique())\n",
    "country_wise_skintone_coverage=[]\n",
    "country_filter_list=[]\n",
    "for country in country_list:\n",
    "    \n",
    "    rp_data_country=coverage_data[coverage_data['Country']==country]\n",
    "    print(rp_data_country.columns)\n",
    "    \n",
    "    if(len(rp_data_country)>=30):\n",
    "        sk_coverage_country=np.nanmean(list(rp_data_country['Coverage_skintone_10_bins']))\n",
    "        country_wise_skintone_coverage.append(sk_coverage_country)\n",
    "        country_filter_list.append(country)\n",
    "    else:\n",
    "        print(country)\n",
    "\n",
    "fig, ax = plt.subplots()   \n",
    "\n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "skintone_coverage_ids=np.argsort(-np.array(country_wise_skintone_coverage))\n",
    "sorted_skintone_coverage=[country_wise_skintone_coverage[i] for i in skintone_coverage_ids]\n",
    "country_filter_list_sorted=[country_filter_list[i] for i in skintone_coverage_ids]\n",
    "\n",
    "for i in range(len(country_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=coverage_data[coverage_data['Country']==country_filter_list_sorted[i]]\n",
    "        c_j=coverage_data[coverage_data['Country']==country_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['Coverage_skintone_10_bins']), list(c_j['Coverage_skintone_10_bins']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {country_filter_list_sorted[i]} and {country_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(country_filter_list_sorted) - 1) < confidence\n",
    "\n",
    "for j in range(len(country_filter_list_sorted)-1):\n",
    "    cat = country_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "\n",
    "ax.bar(country_filter_list_sorted,sorted_skintone_coverage,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_skintone_coverage):\n",
    "    ax.text(country_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Skintone vs Country', fontsize=20)\n",
    "ax.set_ylabel('Average coverage', fontsize=20)\n",
    "ax.set_xlabel('Country', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "filename=\"Country_based_average_skintone_coverage_10.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "22f84365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test between OAP and YSL: t=0.6863166833641882, p=0.49262014224007655\n",
      "t-test between YSL and NYX: t=0.08600240951698093, p=0.9314836943106859\n",
      "t-test between NYX and ARMANI: t=0.5229463645786956, p=0.6011981050178864\n",
      "t-test between ARMANI and LOP: t=0.5891535041791729, p=0.5560572634714384\n",
      "t-test between LOP and PRADA: t=0.26793828032507266, p=0.7890344571979592\n",
      "t-test between PRADA and GARNIER: t=0.38634623440303195, p=0.6992833638020735\n",
      "t-test between GARNIER and MAYBELLINE: t=0.9545689596576845, p=0.3398852516986941\n",
      "t-test between MAYBELLINE and KIEHLS: t=0.8430839401460054, p=0.3993522145987968\n",
      "t-test between KIEHLS and KERASTASE: t=2.177104834003453, p=0.02977896174880916\n",
      "t-test between KERASTASE and LANCOME: t=0.8199690472540441, p=0.4124671892535593\n",
      "t-test between LANCOME and REDKEN: t=0.44821314721822886, p=0.6541818544169488\n",
      "t-test between REDKEN and LRP: t=0.5905467571089111, p=0.5550266078445476\n",
      "t-test between LRP and CERAVE: t=1.1170109184028514, p=0.2642987492992825\n",
      "t-test between CERAVE and BIOTHERM: t=0.0258136997992449, p=0.9794221767850586\n",
      "t-test between BIOTHERM and VICHY: t=0.9009432786256345, p=0.36798670880679385\n",
      "['OAP', 'YSL', 'NYX', 'ARMANI', 'LOP', 'PRADA', 'GARNIER', 'MAYBELLINE', 'KIEHLS', 'KERASTASE', 'LANCOME', 'REDKEN', 'LRP', 'CERAVE', 'BIOTHERM', 'VICHY']\n",
      "OAP > YSL = NYX > ARMANI > LOP = PRADA > GARNIER > MAYBELLINE > KIEHLS > KERASTASE > LANCOME > REDKEN > LRP > CERAVE = BIOTHERM > "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compute brand coverage average versus product advertiser\n",
    "\n",
    "brand_wise_skintone_coverage=[]\n",
    "brand_filter_list=[]\n",
    "\n",
    "for brand in list(coverage_data['ProductAdvertiser'].unique()):\n",
    "    \n",
    "    rp_data_brand=coverage_data[coverage_data['ProductAdvertiser']==brand]\n",
    "    \n",
    "    if(len(rp_data_brand)>=30):\n",
    "        skintone_coverage_brand_mean=np.nanmean(list(rp_data_brand['Coverage_skintone_10_bins']))\n",
    "        brand_wise_skintone_coverage.append(skintone_coverage_brand_mean)\n",
    "        brand_filter_list.append(brand)\n",
    "    else:\n",
    "        print(rp_data_brand.shape)\n",
    "        \n",
    "        \n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "sorted_skintone_coverage_ids_brand=np.argsort(-np.array(brand_wise_skintone_coverage))\n",
    "sorted_skintone_coverage_brand=[brand_wise_skintone_coverage[i] for i in sorted_skintone_coverage_ids_brand]\n",
    "brand_filter_list_sorted=[brand_filter_list[i] for i in sorted_skintone_coverage_ids_brand]\n",
    "\n",
    "for i in range(len(brand_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=coverage_data[coverage_data['ProductAdvertiser']==brand_filter_list_sorted[i]]\n",
    "        c_j=coverage_data[coverage_data['ProductAdvertiser']==brand_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['Coverage_skintone_10_bins']), list(c_j['Coverage_skintone_10_bins']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {brand_filter_list_sorted[i]} and {brand_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(brand_filter_list_sorted) - 1) < confidence\n",
    "print(brand_filter_list_sorted)\n",
    "for j in range(len(brand_filter_list_sorted)-1):\n",
    "    cat = brand_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "    \n",
    "fig, ax = plt.subplots()    \n",
    "#plot the dictionary in terms of bar plot \n",
    "ax.bar(brand_filter_list_sorted,sorted_skintone_coverage_brand,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_skintone_coverage_brand):\n",
    "    ax.text(brand_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of skintone vs Brand', fontsize=20)\n",
    "ax.set_xlabel('Brand', fontsize=20)\n",
    "ax.set_ylabel('Average coverage', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=20, rotation=90)\n",
    "ax.tick_params(axis='x', labelsize=20, rotation=90)\n",
    "plt.tight_layout()\n",
    "filename=\"Brand_based_average_skintone_coverage_10.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)\n",
    "# filename=\"Brand_based_average_skintone_coverage_10.png\"\n",
    "# plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44c17cc9",
   "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
}