6. Brand Logos (Color Extraction from Images of the logos).ipynb

{
 "cells": [
  {
   "attachments": {
    "Screenshot%202021-05-04%20at%2017.22.56.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "mexican-paste",
   "metadata": {},
   "source": [
    "<h1>Brand Logos - Color Extraction from Images with Machine Learing application<span class=\"tocSkip\"></span></h1>\n",
    "\n",
    "\n",
    "In the last part of the project, I wanted to investigate brand logos, specifically the colors that are used by the companies from different categories. \n",
    "\n",
    "![Screenshot%202021-05-04%20at%2017.22.56.png](attachment:Screenshot%202021-05-04%20at%2017.22.56.png)\n",
    "\n",
    "\n",
    "<h2>How does it work?<span class=\"tocSkip\"></span></h2>\n",
    "\n",
    "\n",
    "\n",
    "The images are first imported into a notebook as a 3D Numpy array (the first 2 values in the shape relate to the pixels of the image and the 3rd value to the representation of each pixel as a combination of 3 colors (RGB - Red, Green, Blue)).\n",
    "\n",
    "In order to work with images, they need to be reshaped into a 2D array.\n",
    "\n",
    "In the next step, RBG is converted into hex so that the color labels could be used in charts representing the colors.\n",
    "\n",
    "After that, I built a K-means model with a selected number of clusters, which in this case are the top colors used in logos of brands from a specific category. \n",
    "\n",
    "With the use of Counter, I then got the count of all labels and transformed them back to the RGB and hex to use them in a plot.\n",
    "\n",
    "Here are some of the sources and inspirations I used in this part of the analysis:\n",
    "\n",
    "- https://opencv.org\n",
    "- https://towardsdatascience.com/color-identification-in-images-machine-learning-application-b26e770c4c71\n",
    "- https://towardsdatascience.com/image-color-identification-with-machine-learning-and-image-processing-using-python-f3dd0606bdca\n",
    "\n",
    "\n",
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Install-dependencies-and-import-the-libraries\" data-toc-modified-id=\"Install-dependencies-and-import-the-libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Install dependencies and import the libraries</a></span></li><li><span><a href=\"#Test:-Load-and-arrange-the-images\" data-toc-modified-id=\"Test:-Load-and-arrange-the-images-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Test: Load and arrange the images</a></span><ul class=\"toc-item\"><li><span><a href=\"#Converting-RGB-to-Hex\" data-toc-modified-id=\"Converting-RGB-to-Hex-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Converting RGB to Hex</a></span></li><li><span><a href=\"#Model-bulding:-K-Means\" data-toc-modified-id=\"Model-bulding:-K-Means-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Model bulding: K-Means</a></span></li><li><span><a href=\"#Visualisation:-Pie-chart\" data-toc-modified-id=\"Visualisation:-Pie-chart-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Visualisation: Pie chart</a></span></li></ul></li><li><span><a href=\"#Color-extraction-by-brand-categories\" data-toc-modified-id=\"Color-extraction-by-brand-categories-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Color extraction by brand categories</a></span><ul class=\"toc-item\"><li><span><a href=\"#Airlines-category\" data-toc-modified-id=\"Airlines-category-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Airlines category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.1.1\"><span class=\"toc-item-num\">3.1.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.1.2\"><span class=\"toc-item-num\">3.1.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Technology-and-Tech-Services-category\" data-toc-modified-id=\"Technology-and-Tech-Services-category-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Technology and Tech Services category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.2.1\"><span class=\"toc-item-num\">3.2.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.2.2\"><span class=\"toc-item-num\">3.2.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Automotive-category\" data-toc-modified-id=\"Automotive-category-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Automotive category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.3.1\"><span class=\"toc-item-num\">3.3.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.3.2\"><span class=\"toc-item-num\">3.3.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Apparel,-Accessories,-Footwear-category\" data-toc-modified-id=\"Apparel,-Accessories,-Footwear-category-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>Apparel, Accessories, Footwear category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.4.1\"><span class=\"toc-item-num\">3.4.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.4.2\"><span class=\"toc-item-num\">3.4.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Cosmetics-and-Self-Care-category\" data-toc-modified-id=\"Cosmetics-and-Self-Care-category-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>Cosmetics and Self-Care category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.5.1\"><span class=\"toc-item-num\">3.5.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.5.2\"><span class=\"toc-item-num\">3.5.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Consumer-Goods-category\" data-toc-modified-id=\"Consumer-Goods-category-3.6\"><span class=\"toc-item-num\">3.6&nbsp;&nbsp;</span>Consumer Goods category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.6.1\"><span class=\"toc-item-num\">3.6.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.6.2\"><span class=\"toc-item-num\">3.6.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Food-and-Beverage-Industry-category\" data-toc-modified-id=\"Food-and-Beverage-Industry-category-3.7\"><span class=\"toc-item-num\">3.7&nbsp;&nbsp;</span>Food and Beverage Industry category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.7.1\"><span class=\"toc-item-num\">3.7.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.7.2\"><span class=\"toc-item-num\">3.7.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Alcohol-category\" data-toc-modified-id=\"Alcohol-category-3.8\"><span class=\"toc-item-num\">3.8&nbsp;&nbsp;</span>Alcohol category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.8.1\"><span class=\"toc-item-num\">3.8.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.8.2\"><span class=\"toc-item-num\">3.8.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Restaurands-and-Fast-Food-category\" data-toc-modified-id=\"Restaurands-and-Fast-Food-category-3.9\"><span class=\"toc-item-num\">3.9&nbsp;&nbsp;</span>Restaurands and Fast Food category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.9.1\"><span class=\"toc-item-num\">3.9.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.9.2\"><span class=\"toc-item-num\">3.9.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Tobacco-category\" data-toc-modified-id=\"Tobacco-category-3.10\"><span class=\"toc-item-num\">3.10&nbsp;&nbsp;</span>Tobacco category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.10.1\"><span class=\"toc-item-num\">3.10.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.10.2\"><span class=\"toc-item-num\">3.10.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#e-commerce-category\" data-toc-modified-id=\"e-commerce-category-3.11\"><span class=\"toc-item-num\">3.11&nbsp;&nbsp;</span>e-commerce category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.11.1\"><span class=\"toc-item-num\">3.11.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.11.2\"><span class=\"toc-item-num\">3.11.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Electronics-category\" data-toc-modified-id=\"Electronics-category-3.12\"><span class=\"toc-item-num\">3.12&nbsp;&nbsp;</span>Electronics category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.12.1\"><span class=\"toc-item-num\">3.12.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.12.2\"><span class=\"toc-item-num\">3.12.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Energy/Gas/Petrol-category\" data-toc-modified-id=\"Energy/Gas/Petrol-category-3.13\"><span class=\"toc-item-num\">3.13&nbsp;&nbsp;</span>Energy/Gas/Petrol category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.13.1\"><span class=\"toc-item-num\">3.13.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.13.2\"><span class=\"toc-item-num\">3.13.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li><li><span><a href=\"#Finance-category\" data-toc-modified-id=\"Finance-category-3.14\"><span class=\"toc-item-num\">3.14&nbsp;&nbsp;</span>Finance category</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-3.14.1\"><span class=\"toc-item-num\">3.14.1&nbsp;&nbsp;</span>Pie chart</a></span></li><li><span><a href=\"#Bar-chart\" data-toc-modified-id=\"Bar-chart-3.14.2\"><span class=\"toc-item-num\">3.14.2&nbsp;&nbsp;</span>Bar chart</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pursuant-amber",
   "metadata": {},
   "source": [
    "# Install dependencies and import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "perfect-acrobat",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "maritime-fantasy",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "durable-element",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "from skimage import io\n",
    "import plotly.express as px\n",
    "\n",
    "#from google.colab.patches import cv2_imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "coated-carry",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting all images in png to jpg (changing the background to White):\n",
    "\n",
    "# #from glob import glob\n",
    "# import cv2\n",
    "# pngs = glob('./**/*.png', recursive=True)\n",
    "# for j in pngs:\n",
    "#     print(j)\n",
    "#     img = Image.open(j)\n",
    "#     print(img)\n",
    "#     image = Image.new(\"RGB\", img.size, \"WHITE\")\n",
    "#     try:\n",
    "#         image.paste(img, (0, 0), img)\n",
    "#         image.save(j[:-3] +  'jpg')\n",
    "#     except:\n",
    "#         img = Image.open(j).convert(\"RGBA\")\n",
    "#         image.paste(img, (0, 0), img)\n",
    "#         image.save(j[:-3] +  'jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relative-kentucky",
   "metadata": {},
   "source": [
    "# Test: Load and arrange the images\n",
    "\n",
    "First, I wanted to execute the code on a few images only. I used 3 logos for this purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dynamic-savings",
   "metadata": {},
   "outputs": [],
   "source": [
    "## image 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "greek-clearance",
   "metadata": {},
   "outputs": [],
   "source": [
    "url1 = '/Users/agieen/Downloads/AllWomen/_Final_Project/notebooks/Logos/Airlines/AeroflotRussianAirlines.jpg'\n",
    "img1 = io.imread(url1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "advised-clear",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the image into a workable format,such as a 2-dimensional shape\n",
    "img1 = img1.reshape((img1.shape[0] * img1.shape[1],img1.shape[2])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "expected-journey",
   "metadata": {},
   "outputs": [],
   "source": [
    "## image 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "respected-cancer",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "url2 = '/Users/agieen/Downloads/AllWomen/_Final_Project/notebooks/Logos/Airlines/AirAsia.jpg'\n",
    "img2 = io.imread(url2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "appreciated-geometry",
   "metadata": {},
   "outputs": [],
   "source": [
    "img2 = img2.reshape((img2.shape[0] * img2.shape[1],img2.shape[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "interesting-ceiling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# image 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "homeless-ceremony",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "url3 = '/Users/agieen/Downloads/AllWomen/_Final_Project/notebooks/Logos/Airlines/Airbus.jpg'\n",
    "img3 = io.imread(url3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "sporting-project",
   "metadata": {},
   "outputs": [],
   "source": [
    "img3 = img3.reshape((img3.shape[0] * img3.shape[1],img3.shape[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "closing-retro",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 1 (17500, 3)\n",
      "Image 2 (90000, 3)\n",
      "Image 3 (295582, 3)\n"
     ]
    }
   ],
   "source": [
    "# Check the dimensions of all images\n",
    "print('Image 1', img1.shape)\n",
    "print('Image 2', img2.shape)\n",
    "print('Image 3', img3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cutting-trail",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.concatenate((img1, img2, img3), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "pleasant-india",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(403082, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "passive-arrival",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backup of initial image\n",
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attended-benchmark",
   "metadata": {},
   "source": [
    "## Converting RGB to Hex\n",
    "\n",
    "First, we need to define a function that will convert RGB to hex so that we can use them as labels for our pie chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "incorporate-worthy",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RGB2HEX(color):\n",
    "    return \"#{:02x}{:02x}{:02x}\".format(int(color[0]), int(color[1]), int(color[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gorgeous-custody",
   "metadata": {},
   "source": [
    "## Model bulding: K-Means\n",
    "\n",
    "K = TOTAL number of major colors that we want to extract from the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "integrated-translator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bottom-endorsement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the labels (different cluster ids)\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "verified-thickness",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can also save it into a dataframe\n",
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "detailed-amateur",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    274705\n",
       "1     61509\n",
       "2     58142\n",
       "3      4006\n",
       "5      3052\n",
       "4      1668\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-poetry",
   "metadata": {},
   "source": [
    "## Visualisation: Pie chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "visible-fraction",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "southeast-compensation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 274705, 5: 3052, 3: 4006, 1: 61509, 4: 1668, 2: 58142})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "upper-speed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[253.83986603, 254.23247966, 253.95268935],\n",
       "       [  9.18186843,  28.62169807,  85.84046686],\n",
       "       [226.37537408,  37.63785216,  39.34847442],\n",
       "       [ 61.13048902,  95.38323353, 140.27919162],\n",
       "       [234.55395683, 145.87589928, 139.67446043],\n",
       "       [159.91644823, 174.39515072, 201.88499345]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_colors \n",
    "\n",
    "# rows: clusters; \n",
    "# columns: based on R, G and B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "gorgeous-germany",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "determined-remark",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#fdfefd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#9faec9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#3d5f8c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#091c55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#ea918b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#e22527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0  #fdfefd\n",
       "1  #9faec9\n",
       "2  #3d5f8c\n",
       "3  #091c55\n",
       "4  #ea918b\n",
       "5  #e22527"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "pd.DataFrame(hex_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "defensive-offense",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rgb_colors = [ordered_colors[i] for i in counts.keys()]\n",
    "#rgb_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "excess-species",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.pie(counts.values(),\n",
    "        #labels = hex_colors,\n",
    "        colors=hex_colors,\n",
    "        labels=hex_colors);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "confidential-occasion",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from pathlib import Path\n",
    "#for path in Path('./Logos/Airlines/').iterdir():\n",
    "    #print(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-consistency",
   "metadata": {},
   "source": [
    "# Color extraction by brand categories\n",
    "\n",
    "## Airlines category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "hawaiian-logan",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import cv2\n",
    "from numpy import asarray\n",
    "from PIL import Image\n",
    "\n",
    "# Load the folder with Airline logos:\n",
    "\n",
    "final_images = np.zeros(shape= (1,3))\n",
    "airlines_logos = glob('./Logos/Airlines/*.jpg', recursive=True)\n",
    "for j in airlines_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "average-mobility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.],\n",
       "       [255., 255., 255.],\n",
       "       [255., 255., 255.],\n",
       "       ...,\n",
       "       [199., 237., 238.],\n",
       "       [240., 255., 255.],\n",
       "       [248., 255., 255.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "professional-butler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18994307, 3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "constitutional-registrar",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-spell",
   "metadata": {},
   "source": [
    "**Note:** \n",
    "\n",
    "I decided to extract 5 dominant colors, however, in each category white \n",
    "seemed to be have the highest percentage. The reason for that is the white background \n",
    "of each logo. I wanted to remove white, that's why the number of clusters is 6, not 5.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "engaged-rescue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cosmetic-mistake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "indian-appendix",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "collectible-reset",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13983284\n",
       "1     3128506\n",
       "2     1088064\n",
       "5      539497\n",
       "4      160418\n",
       "3       94538\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "expensive-mercy",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "confident-deficit",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "olympic-harvest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#2d2a26', '#fefefe', '#ad8e9a', '#ed1528', '#153a95', '#012755']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "interstate-specialist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[539497, 13983284, 94538, 3128506, 160418, 1088064]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "previous-submission",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "inappropriate-grain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[539497, 94538, 3128506, 160418, 1088064]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "korean-variance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#2d2a26', '#ad8e9a', '#ed1528', '#153a95', '#012755']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fefefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wound-coast",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "measured-necessity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Airlines')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Airlines')\n",
    "#plt.savefig('Airlines_colors_pie.jpg');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharing-ecology",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "listed-ancient",
   "metadata": {
    "scrolled": true
   },
   "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>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>#2d2a26</th>\n",
       "      <td>539497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#ad8e9a</th>\n",
       "      <td>94538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#ed1528</th>\n",
       "      <td>3128506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#153a95</th>\n",
       "      <td>160418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#012755</th>\n",
       "      <td>1088064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts\n",
       "#2d2a26   539497\n",
       "#ad8e9a    94538\n",
       "#ed1528  3128506\n",
       "#153a95   160418\n",
       "#012755  1088064"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "comprehensive-matter",
   "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>counts</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>#ed1528</th>\n",
       "      <td>3128506</td>\n",
       "      <td>62.432481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#012755</th>\n",
       "      <td>1088064</td>\n",
       "      <td>21.713411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#2d2a26</th>\n",
       "      <td>539497</td>\n",
       "      <td>10.766205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#153a95</th>\n",
       "      <td>160418</td>\n",
       "      <td>3.201302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#ad8e9a</th>\n",
       "      <td>94538</td>\n",
       "      <td>1.886601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts  percentage\n",
       "#ed1528  3128506   62.432481\n",
       "#012755  1088064   21.713411\n",
       "#2d2a26   539497   10.766205\n",
       "#153a95   160418    3.201302\n",
       "#ad8e9a    94538    1.886601"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cardiovascular-insurance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "crude-index",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "colonial-isaac",
   "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>#ed1528</th>\n",
       "      <th>#012755</th>\n",
       "      <th>#2d2a26</th>\n",
       "      <th>#153a95</th>\n",
       "      <th>#ad8e9a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>3.128506e+06</td>\n",
       "      <td>1.088064e+06</td>\n",
       "      <td>539497.000000</td>\n",
       "      <td>160418.000000</td>\n",
       "      <td>94538.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage</th>\n",
       "      <td>6.243248e+01</td>\n",
       "      <td>2.171341e+01</td>\n",
       "      <td>10.766205</td>\n",
       "      <td>3.201302</td>\n",
       "      <td>1.886601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 #ed1528       #012755        #2d2a26        #153a95  \\\n",
       "counts      3.128506e+06  1.088064e+06  539497.000000  160418.000000   \n",
       "percentage  6.243248e+01  2.171341e+01      10.766205       3.201302   \n",
       "\n",
       "                 #ad8e9a  \n",
       "counts      94538.000000  \n",
       "percentage      1.886601  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "paperback-kingdom",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "considered-sudan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.71"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "medium-spelling",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_colors = new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "described-event",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['#ed1528', '#012755', '#2d2a26', '#153a95', '#ad8e9a'], dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "collaborative-shell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAAM9CAYAAAAhIwUbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAGkklEQVR4nO3dwUnEUBRAUSNTg9iFBdjF9OBmdlYibtzYg11YwViFiNiAfAuQCx8kThjOWf/F4/Ig8BPIMsa44LfLUw+wVcIEYYIwQZiwmz348vB8Fo+v/f3dMnPOxgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE5bZtwRXt09ncR/z/npwH/MXwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJ01+GX389rjnHPzpMnbIxQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcKE6f8SvH3frDnH5tiYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMGE3e/Dj87jmHJtjY4IwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTFjGGKeeYZNsTBAmCBOECcIEYcIPkEweJ9aJUUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_airlines.png')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "grave-coordination",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeating the steps for other categories:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-iceland",
   "metadata": {},
   "source": [
    "## Technology and Tech Services category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "searching-crack",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images_tech = np.zeros(shape= (1,3))\n",
    "tech_logos = glob('./Logos/Tech/*.jpg', recursive=True)\n",
    "\n",
    "for j in tech_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images_tech = np.concatenate((final_images_tech,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "soviet-cursor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16853673, 3)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images_tech.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "strange-california",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init_tech = final_images_tech.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "central-craps",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images_tech) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "super-parcel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "monthly-spring",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "valued-record",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12584553\n",
       "1     1400750\n",
       "2     1344828\n",
       "4      859415\n",
       "5      459657\n",
       "3      204470\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "appreciated-binding",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "alpha-governor",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "helpful-failing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#132050', '#fefefe', '#2198e5', '#70855f', '#fcbb07', '#d50b0c']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "floppy-drinking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1400750, 12584553, 459657, 859415, 204470, 1344828]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "noticed-museum",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "afraid-regular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1400750, 459657, 859415, 204470, 1344828]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "going-championship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#132050', '#2198e5', '#70855f', '#fcbb07', '#d50b0c']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fefefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordinary-queens",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "behind-flight",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Technology and Tech Services');\n",
    "#plt.savefig('Tech_colors_pie.jpg');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "musical-arizona",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "regulation-teach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_tech.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indie-truth",
   "metadata": {},
   "source": [
    "## Automotive category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bizarre-catch",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images_auto = np.zeros(shape= (1,3))\n",
    "auto_logos = glob('./Logos/Automotive/*.jpg', recursive=True)\n",
    "\n",
    "for j in auto_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images_auto = np.concatenate((final_images_auto,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "several-pregnancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9215048, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images_auto.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "moved-camping",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init_auto = final_images_auto.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "israeli-crack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images_auto) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "opposed-bennett",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 0, ..., 3, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "incident-newton",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "accomplished-animal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6792130\n",
       "5     939298\n",
       "1     445839\n",
       "3     402339\n",
       "2     376084\n",
       "4     259358\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "inappropriate-assumption",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "oriental-literature",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "continuing-vacuum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0b0f0a', '#fdfdfc', '#b4b0a9', '#1a6259', '#da0928', '#ef770a']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "light-anger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[939298, 6792130, 402339, 445839, 376084, 259358]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "romantic-railway",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "tutorial-plastic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[939298, 402339, 445839, 376084, 259358]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "equipped-sheep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0b0f0a', '#b4b0a9', '#1a6259', '#da0928', '#ef770a']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fdfdfc')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "personalized-creator",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "junior-projector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Automotive');\n",
    "#plt.savefig('Auto_colors_pie.jpg');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wireless-amount",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fewer-fields",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_automotive.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-drawing",
   "metadata": {},
   "source": [
    "## Apparel, Accessories, Footwear category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "appreciated-closure",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images_apparel = np.zeros(shape= (1,3))\n",
    "apparel_logos = glob('./Logos/Apparel/*.jpg', recursive=True)\n",
    "\n",
    "for j in apparel_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images_apparel = np.concatenate((final_images_apparel,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "touched-marketing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2939762, 3)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images_apparel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "adaptive-retrieval",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init_apparel = final_images_apparel.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "floral-swedish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images_apparel) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "august-vietnam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "funny-helicopter",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "killing-cologne",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1780642\n",
       "3     456102\n",
       "0     386206\n",
       "2     170456\n",
       "5      76023\n",
       "4      70333\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "unknown-characteristic",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "sexual-alfred",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "fancy-fields",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0e0d0d', '#fbfbfb', '#cbae97', '#6a6c70', '#0e2e59', '#d61a2a']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "prescription-synthetic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[456102, 1780642, 70333, 76023, 386206, 170456]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "mexican-armor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "royal-dining",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[456102, 70333, 76023, 386206, 170456]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ranking-indonesian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0e0d0d', '#cbae97', '#6a6c70', '#0e2e59', '#d61a2a']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fbfbfb')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "built-hierarchy",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "continental-tsunami",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAFkCAYAAAB/6MMYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAvBklEQVR4nO3dd3xb1f3/8dex5W1fyc5OnD3F+Jo9M+gAWn7tty1taesCZVP2KFBGgZbS0lICFAKYtGxQoVACbYEySxJ2DEUQhQQSSEicQZbvje3ES/f3x70pIl/bseMrHd2rz/Px0CO2riW9pchvn3vv0b3Ktm2EEEL0X57uAEIIERRSqEII4REpVCGE8IgUqhBCeEQKVQghPCKFKoQQHpFCDTil1MtKqVN059BFKfVjpdRzunOI3CCF2g9uWW1WShXpzuIlpdS9SqkOpdQw3Vn6y7bth2zbPiKdj+G+Xm1KqaaUyw/6eZ85/YfQr6RQd5FSagwwDbCB/9WUIZSG+ywDvguYwLFe338mpeP16cH1tm2Xp1weyeBjp0WGX79AkELddccDbwD3Aj9JXeCOWOqUUs8rpbYopeYqpUanLLeVUucqpT5WSm1QSv1BKZXnLhuvlHpJKbXRXfaQUiqSctvlSqmfK6XeA5qVUiGl1EFKqdeUUo1KqbhS6rB+PK/vAo3ANV08ryql1D1KqdXuyPyJlGXfUkq9q5SylFLLlFJfc68PK6XuUkqtUUo1KKWuVUrlu8smuK+N6T7XR9zrlVLqJqXUZ+79va+U2iPl/u5XSq1XSq1QSv0i5bU7QSn1qnvbjcAv3eteSck5xf1/2aSUWqKUOiZl2VFKqUXu/1mDUuqifryO2+/zVKXUUvfx/q6UGp6y7BCl1AL3+S9QSh3iXv8bnD/Ws9zR7iyl1K+UUre6ywuUUs1KqT+435copbYpparc77t9PyilTlRKfeA+x4+VUqenLDtMKbXKfX+tBe7p7/PPObZty2UXLsBS4ExgX6AdGJKy7F5gCzAdKAL+CLySstwG/g1UAaOAD4FT3GUTgMPd2w0C5gE3p9x2OfAuMBIoAUYAG4GjcP5AHu5+P8j9+Ze333cvn9eLwPXAEKAD2Ddl2VPAI0AlUADMcK8/AGdEe7ibYQQwxV02B7gTKAMGA28Bp7vL/gJc4d6mGJjqXn8k8DYQARQQBYa5y+4HngQqgDHua3eyu+wEN/M5QMh9fU7Y/tq7GVYCJ7rL9wY2ALu5y9cA09yvK4F9evma3Qtc28X1X3bvfx/3//NWYJ67rArYDBznZvmR+/2Arv7f3Pt63/36EGAZ8GbKsrj79c7eD/8PGO++rjOAlu3PEzjMff1+7+Yt0f175reL9gB+vABTcUp0oPv9YuCClOX3Ag+nfF8OdAIj3e9t4Gspy88EXuzmsb4N/Cfl++XASSnf/xx4YIfbPAv8xP36C7+YO3leo4AksFfK/fzR/XqYu6yyi9vdCdzUxfVDgNbUX0y3OP7tfn0/MBuo3uF2X8YpyoOAvJTr84E23AJ0rzsdeNn9+gTg0x3u6wQ+L9QfAPO7yH61+/Wn7v0ZfXw/3AtswxnZNwIb3OvvwtkUkPo+aMf5Q3Ac8NYO9/M6cEJX/284fxy2AQOAS4HLgVXuff4KuKU374cusj8BnOd+fZj7+hbr/h3z60VW+XfNT4DnbNve4H4fY4fVY5yREAC2bTcBm4DhXS0HVmxfppQaopR62F3ltIAHgYHd3TcwGvi+u3rXqJRqxCn8XdmhdBzwgW3b77rfPwTUKqUKcEbEm2zb3tzF7UbijJh2NBpnJLsmJdudOCNVgEtwRkpvKaUSSqmTAGzbfgmYBdwGfKaUmq2UMnBehwKc12u7FTijsu1SX5uu8hy4w2v1Y2Cou/y7OCO7Fe6miIN7uK8d3WDbdsS9bP//Gp6a1X0fbHTzfmFZN8/lv2zb3grU44wqpwNzgdeAQ93r5qY8x27fD0qpryul3nA3QTS6zzf1/bXetu1tfXjeIoVsdO4jpVQJcAyQ725nAmf1KKKUqrFtO+5eNzLlNuU4q3irU+5qJJBwvx6Vsuy3OCPYPW3b3qSU+jZOuaRKPUTYSpwRyan9emKO44FRKc8rhDMiOgpnVb1KKRWxbbtxh9utxFmN3NFKnBHqQNu2O3ZcaNv2WuBUAKXUVOAFpdQ827aX2rZ9C3CLUmow8FfgYuCXOCO80cAi925GAQ2pd9vD81sJzLVt+/CuFtq2vQD4lvsH5Gz3cUd29bO9tNrNCvx3h98AN+8XlrlGAf/aHqeL+5uLM3rfG1jgfn8kziaXee7PdPt+UM5slL/h/D8/adt2u3K2g6uUH5PDz/WDjFD77ts4q++7AXu5lygwH+eNut1RSqmpSqlC4NfAG7Ztp46eLlZKVSqlRgLn4WybBGfbYBNgKqVG4BRJTx4EvqmUOlIpla+UKnZ3LlTv+INKqTHK2SE2potlB+OU4gEpz2sPnNH38bZtrwGeAW53cxcopaa7N78LOFEp9RWlVJ5SaoRSaop7m+eAmUopw102Xik1w33M76fk3Izzy5xUSu2vlDrQLbZmnFXdpG3bnTgl9xulVIVydvRd6L4GvfFPYJJS6jg3f4H7WFGlVKFy5qyGbdtuByycTRzbXx9b9X1n31/c12Uvt8x+i7PdcznwtJulVjk7Fn+A8576p3vbdcC4He5vLs57bJFt2224mwWAT2zbXu/+TE/vh0KcP/7rgQ6l1NeBtE4pyzm6tzn47YIzgpjZxfXHAGtxRnX3AnXA8zjlOA8Ym/KzNnAu8DHOKuBMIN9dtjvODpkmnJ1PPwNWpdx2OfDVHR77QJxftk04vyxPAaPcZS/z+Q6vae7tC7rIXwf8rYvrD8AZZVa5l/twftk3A4+n/Nx3gPdwdsYtBY50rw8Dd+Bs7zOB/wA/dJddjzNaa8LZZHCae/1X3Ptqwtmp8xBQ7i6rxCmN9Tijsatwt7OSsr00JdcXrgMmu6/Peve1fwnnj0eh+3+7GadMF/D5TrKR7nUDunlP3EsXO6XcZT91n9smnLKsTlk21f2/Nt1/p6YsOxhnO/JmPt8+un0b7NXu9wr4DLijD++Hs9z/v0bgAeDh7dlxtqGu6up5yKV3F+W+kMJDSql7cd6Yv+hmuQ1MtG17aYZz/QJnG9mdmXxcv1NKHQvsbtv2ZbqziOwm21BziG3b1+rO4Ee2bfd2k4LIcbINVQghPCKr/EII4REZoQohhEekUIUQwiNSqEII4REpVCGE8IgUqhBCeEQKVQghPCKFKoQQHpFCFUIIj0ihCiGER6RQhRDCI1KoQgjhESlUIYTwiBSqEEJ4RApVCCE8IoUqhBAekUIVQgiPSKEKIYRHpFCFEMIjUqhCCOERKVQhhPCIFKoQQnhEClUIITwihSqEEB6RQhVCCI9IoQohhEekUIUQwiNSqEII4REpVCGE8IgUqhBCeEQKVQghPCKFKoQQHpFCFUIIj0ihCiGER6RQhRDCIyHdAURuCRtGHjACGA1EAAOo6OKy/foyoBNoA1pT/m3d4bqtwHqgAVjtXtaYltWRmWcmBCjbtnVnEAETNoyBwNgdLuPcf0cBhRmKksQp2dV8XrSrgEVAHFhmWpb8AgjPSKGKfgkbRjWwP3CA++++OCNPP2gCFuKU63vb/zUta4vWVMK3pFBFr4UNo5Ivluf+wDCtobxnA8uB/wAvAy+alrVIZyDhH1KoolthwygFDgOOBI4ApmgNpM8a4MXtF9OyVmrOI7KUFKr4grBhTAD+FzgKmAoU6U2UlT7CKdcXgJdMy9qsOY/IElKoOc7d634Q8C2cIs3VUeiuascp10eBJ0zL2qQ5j9BICjVHuSPRE4DjgZF60wTG9nJ9GPibaVlNmvOIDJNCzSFhwygHjsEp0ml60wReC/Ak8ADwnGlZnZrziAyQQg24sGEoYAZOiX4PZ6K8yKx1wF3AbaZlrdYdRqSPFGpAhQ2jCjgDOAlnUr3Qrx1nW+tNpmXV6w4jvCeFGjDuRPufAacio9Fs9hpwM/C4bA4IDinUgAgbxhTgEuBYoEBzHNF7K4DbgD+ZltWoOYvoJylUnwsbxgHApTjTnuToYf7VDNwBXCdTr/xLCtWnwobxVeBy4Eu6swhPmcAfgJtNy2rWHUb0jRSqz4QNY3dgJs7HQUVwrQWuBWabltWuO4zoHSlUn3APiXcNcBqQrzmOyJxlwFXAX+RQg9lPCjXLhQ2jEDgX+AUQ1hxH6PMucLlpWc/oDiK6J4WaxcKGcTRwPTBedxaRNZ4CzpAjXmUnKdQsFDaMGuCPOJ9wEmJHTcAVwCzTspK6w4jPSaFmkbBhFOD8olyBnO9L7NybwKmmZb2vO4hwSKFmibBh7AncB+ytO4vwlQ6caVbXmJa1TXeYXCeFqlnYMPJxJuZfReZOXieC5yPgdNOy/q07SC6TQtUobBhRnFHp/rqziMCYDZxvWtZW3UFykRSqBu5R8n8G/Bo5xYjw3kLgGNOyPtAdJNdIoWZY2DBGAI8Ah+rOIgKtGWd61QO6g+QSKdQMChvGDOCvwGDdWUTOuBs4WzYBZIYcnShDwoZxPs5ZMqVMRSadBLzlHt5RpJmMUNPMPbf9bODHurOInNYM/NS0rAd1BwkyKdQ0ChvGOOBxoEZ3FiFcs4GzTMvq0B0kiKRQ0yRsGF8DYkCl7ixC7OA54HumZW3RHSRoZBtqGoQN41Kcg1hImYpsdAQwL2wYw3UHCRoZoXrIPWXzzTiH2xMi230KHGVaVkJ3kKCQQvVI2DBCOFNUjtOdRYg+aAS+Y1rWy5pzBIKs8nsgbBjFODufpEyF30SAZ8OGUas7SBBIofZT2DAM4Fngm7qzCLGLCoEHw4Zxme4gfier/P0QNozBwL+QQ+6J4Pi9aVmX6g7hVzJC3UVhwxgNvIKUqQiWn4cN4xrdIfxKRqi7IGwY44G5wAjdWYRIkytNy7pWdwi/kULtI3fu3ivAWN1ZhEizS0zL+oPuEH4ihdoHYcOoBOYBe+jOIkSGnG9a1h91h/AL2YbaS2HDKAOeRspU5Jabw4Zxpu4QfiGF2gthwyjEmWd6kO4sQmgwK2wYp+gO4QdSqDvhnq7kAZzPPwuRixRwZ9gwvqc7SLaTQt25O4BjdIcQQrM84P6wYeynO0g2k0LtQdgwfgucpjuHEFmiBHhSjlLVPdnL342wYZyIc7ATIcQX1QPT5TxV/5cUahfChnEQ8DJyimchuvMo8APTsqRAUsgq/w7c1ZnHkTIVoiffB36pO0S2kUJNUT+nrjAUyv8rMEx3FiF84KqwYfxQd4hsIoX6Rbc8dsuVFQMrjXW6gwjhE/eEDeMA3SGyhWxDddXPqTsWZ74pncnkuituvHvdgvc//B/NsYTwg1XAXqZlbdQdRDcpVKB+Tt3uwJtA2fbrbNtuf/Rf816/8+GnputLJoRv/N20rG/pDqFbzhdq/Zy6cmABMKWr5Us/Xf3KOdfM2q+tvaM4s8mE8J1zTMuapTuETrINFWbTTZkCTBg1fOrjs67+ZNigqlUZzCSEH90QNowa3SF0yukRav2cuu/hzKfbqWTS3njNbQ+smF+/cJ80xxLCzz4A9jEta5vuIDrkbKHWz6mrxPnPH9Lb29i23fnMvAWvzLz7sRnpSyaE791sWtYFukPokMur/DfShzIFUErlHzXjgBn3/f6S10uKCpvSlEsIvzsvbBhf1h1Ch5wcodbPqTsceK4/99Ha1r70jKv/GFqx+rMx3qQSIlBWAnualmXqDpJJOTdCrZ9TV4azI6pfigoLJtz1259Vfm3afm95EEuIoBkJzNQdItNyrlCB3wBjvLgjpVT4opO/v//VZx/7MpB7Q30henaSe6ChnJFTq/z1c+oOAl4lDX9I1m9qXHDKFTdNamrZGvb6voXwsXeA/U3LSuoOkgk5M0Ktn1NXCPyZND3nQVWR/R+79crGSWOrP0rH/QvhU/sAP9UdIlNyplCBy4Hd0/kABaHQ6NuvPmf4d4+Y+lo6H0cIn7k2bBiDdIfIhJxY5a+fU7cH8DZQmKnHfHvhh3MvveGuqUnbzs/UYwqRxe42Letk3SHSLVdGqLeRwTIF2HePSTMeveXK9yJG+YZMPq4QWerEXNhBFfgRav2cum8A/9D1+J2dnasvuv5Pje8t/ng3XRmEyBKB30EV6EKtn1OXB8SBPXTmsG279YEnXnjrvieen6YzhxBZ4CzTsm7XHSJdgr7KfzyayxRAKVV0/HcOnzbrqrPnh/Lz23TnEUKjX4YNo2znP+ZPgS3U+jl1xcA1unOkio4fNe1vs676cHBVZI3uLEJoMgg4S3eIdAlsoQJn4nz8LauUl5bs8dDMS0MH1UTjurMIoclFQR2lBnIbav2culJgOc5fw6xk23bHnOdfffW2h/4uhwIUuegS07L+oDuE14I6Qj2TLC5TAKVU6Ogjps74828ufLWwoGCr7jxCZNjFYcMo1R3Ca4ErVHd0erHuHL01tnrooY/fdvWnI4YMXKk7ixAZFMhtqYErVJzR6WDdIfqipKhw8r2/v7j8SwftVa87ixAZFLhRaqC2obqj00/wWaFuZ9t28oXX3pn/u9mPTAeU7jxCZECgtqUGbYR6Ij4tUwClVN7hh+4748EbLn2ztLhoi+48QmRAoEapQSvUQBwmbNigqoP+Nuvq9eNGDv1YdxYh0mwQzgdwAiEwq/z1c+oOwTl4dGDYtr3lpnsfX/TUy28eqDuLEGn0rmlZe+sO4YUgjVBP1x3Aa0qpigtOOPqAX59/wlylVGAPKCFy3l5BORJVIEao9XPqKoHVQLHuLOmysdGqP+WKG8dbTS2VurMIkQb3mZZ1gu4Q/RWUEepPCHCZAgyIGPs9+scrm6LjRy3RnUWINDgmbBi+HywEpVADt7rflVAof+StV5416gdHzQjUtmIhgBKcgZGv+X6Vv35O3XRgru4cmRZfvGzuxb//06GdyWRIdxYhPLLYtKyo7hD9EYQRaiCmSvVVzZTxMx679cpEVbhive4sQnhkStgwDtMdoj98Xaj1c+oGAt/VnUMXo7ys5uGbrujYZ7cJC3VnEcIjvh4g+bpQgRPI8Mn3sk1+ft6w6y85ddIp3//6PN1ZhPDAd8KGEdEdYlf5vVBP0x0gGyilCn/0jS9Nv+NX584vCOW36s4jRD8UAt/QHWJX+bZQ6+fU7Q1M1J0jm0waUz3t8VlXLxsysHK17ixC9MPRugPsKt8WKvBt3QGyUWlJ8W4P/uHnhYfsvdu7urMIsYuO9OsBU/xcqN/RHSBb5eXlDbzmvJ/sef4JR+fcdDIRCKXA13SH2BW+LNT6OXXjgD1158hmSqn8b37poBn3/O6i14oKC1p05xGij3w5YPJloeLTF1uHUcMGH/L4bVc3jBw2aIXuLEL0wTfChlGgO0Rf+bJQV21o2jdp2526c/hFcWHhxHuuuyhy+CH7LNCdRYheigBf1h2ir3z30dOzzrkgDGwAtoRLCz+YOCzMpOHh3YoK8iOao2U927btl9+Mz732jtgM5BQrIvvNNi3LV8fp8GOhHg38bYerOwtDeQtHDSpv3K26snpARfF4Hdn8Yt3GzW+d+oubJje3bAvrziJED9YBw03L8s2xgP1YqHeykwn9SrFysFHyyZTqSNnoQeV75OflFWUonm+0t3d8cs6vb+v8aEXDBN1ZhOhBjWlZ7+kO0Vt+LNTlwOg+3KS5vDiUGD803DZ5eHhiWXHBkDRF8x3btptmPfj395944dWDdWcRohtnm5Z1m+4QveWrQj3rnAsmA4v7cRd2KF8trq4qWzelunLw0EhJVCmV89sS33pv8dwrbrxnatK283VnEWIHD5uW9SPdIXrLb4V6LvBHD+/yswHlRR9OGhEpGD/E2L0glFfu4X37ymar6Z2TL5852tzSPEB3FiFSNJiWVa07RG/5rVAfI32H62stKcxfOGZwRXO0unJsuLRwZJoeJ2t1dHauuvC6O7ckPlru64P8isAZa1rWct0hesNvhboMGJeJx8rPU8uGRkpXRasjlSMGlO2ep1ROrA7btr3tnsefq3/o7y9O1Z1FCNfxpmU9oDtEb/imUM865wIDaETP/MnGcGnhoonDwkwcHo4WF+T7/mRiO7Pwo+XzLryu7uDOzqTvPq0iAsc381H9VKjTgGw4iHJnYSgvMWpQ+aboiMrqgUZxYKcdbWne+v4pV8wcvGGzJTMjhE4fmJa1m+4QveGnQj0HuEV3jh0pxapBRsnHU0ZEysYMDt6c185kct0VN969bsH7H/6P7iwiZ9nAINOyNuoOsjN+KtS7gJN059iJ5rLi0MLxQ4y2KSMik4Iy59W27fbHnp3/et1f/jlddxaRs75lWtbfdYfYGT8V6tvAPrpz9IEdyldLRlSVrYtWVw4KwpzXZZ+ufuXsa2bt19beUaw7i8g5V5iW9VvdIXbGF4V61jkXhIAmwM+r059VlRd9OHl4pGD8UP/Oed26rfWDU39xU8Wa9Zt8MzdQBMIDpmUdrzvEzvilUPcEfPN53l5oc+e8NkWrK8eESwtH6Q7UF8mkvenXtz+4fN6C9/20xiD8bYFpWQfoDrEzfinU44D7dedIlzylPh5aWbIyWl0Zqa4q2z0vT4V0Z9oZ27Y7/zW//pUb7np0hu4sIidYpmVl/dHR/FKoM4ELdefIENMoLVg0cVjYnjQ8kvVzXhvWbXj99Ctv3nNra5svN2EIXxlhWlZWn9HXL4X6Ij48ercHOgtDeYtGDSzfFK2uHJGtc15b29qXnfnLW/KWN6wbqzuLCLSvmJb1ku4QPfFLoW4Acv6gHSlzXkvHDC7fM5vmvNq2bc68+7Elz8xbkPXbuYRvnWVa1u26Q/Qk6wv1rHMuGAKs1Z0jCzWXFYUS44carVNGRCaWFRcM1R3Itm37lbcT834164Fptm378nxlIqvdalrWubpD9MQPhboP8LbuHFnODuWpJSMGZMec1w2bzfqTL79xYlPL1qzfiSB85XnTso7QHaInfijUbwD/0J3DZ9ZXlRctmTQ8HBo/1Ni9MJRfkekA7R0dK8699va2Dz9ZNTHTjy0Ca4VpWWN0h+iJHwr1VGC27hw+1lZckL9w7JCKpikjIqMjZUV9OX1Mv9i23VL3l3+++9iz8w/J1GOKQNtqWlap7hA98UOhXg38UneOoMhT6pOhlSWfRkdURqoHZGbO6zuJj+ZeesNdh3Ymk1k/v1ZkvXLTspp1h+iOHwq1DvDFsRB9aPuc1+SkYeFocWGoKm0PtKX53ZOvmDlis9k0KF2PIXLCGNOyVugO0R0/FOqTwP/qzpEDkoWhvMTIgeWbdquODB9olHi+7bOzM7nmoutnb35v8ce+OLalyEr7mZaVtTup/bAKJiOazMhr60juuWytxbK1ljvntfjjycMjpWMGV+wRys/r9xGm8vPzht146elVDz754vx75zw3zYvQIucM1B2gJ34YoX4ATNGdI8e1lBWFFo4farROHhGZWO7BnNfFH6+cf961tx/Y0dlZ6EVAkTOONS3rId0huuOHQl0LBOJAzUERylNLhleVrY1WRwYOqyzdbVfnvDa3bEuccsWNVZ9tahzmdUYRWOebluXlqeQ95YdC3Ya/j4MadP2a85pMJtdfefN9q9+If1CTroAiUK41LetK3SG6k9WFetY5F5QALbpziF5rKy7IT4wZXLElWt37Oa+2bXc8+eLrr936wBNyihWxM3WmZZ2hO0R3sn2nVER3ANEnhdvaO/de3NDI4oZGZ85rpOTTKdWR8MgB5Xt0N+dVKRX69lcPmb5XdPyrZ/7yln1a29pLMh1c+EbapvZ5IdtHqFFgke4cwhOmUeLOeR3e/ZzXba1tS0678ubShnUbRmY6oPCFJ03L+rbuEN3J9kLdDUjoziE89985r9HqyPBBO8x5tW278bd1f1n60hvv7qcroMha/zQt65u6Q3Qn21f523UHEGmx45zXhoEVxcumjIiUjBlcsWcoPy9y+U9/tM+BNVNevu7Oh2cAvj5brPBUvu4APcn2EepoYLnuHCKjWsqKQolxQ41tU0ZEJjRt2fLpqVfeFG3Z2mroDiaywnOmZR2pO0R3ZIQqsk1pc2vH/u+v2MT7KzbRmWTtUd/8wUsf/eMfg4e0t8r0uRzXgb1Gd4aeSKEKrdo6kqbZ0rGusbmjcXNzxzazuYOmbZ3F29qTkc4kQ4G9gb0nD9x76a8aPxquIKsP3ybSbrPuAD3J9kJt0x1A9E9re9K0tnasbWzuMLspzLB76dGSUOmE2aVDXzm9Ze3U9KcWWSypO0BPsr1QZYSa5Vrbk6bV0rF2c3NHY2Nzx7bGlg7V/HlhDqGXhdkb95QOmXpYa+Mrkzu3SanmLinUfpBC1ay1PdnoFqaZ7sLsjdMiE/d9duPCj4qx5dQqualDd4CeZPVefoCzzrkguwP6XGt7stFsSVklb+lQTds6i1vbk5VuYWbd3vUJHVs/fqDxw8EKynVnERkXizYkfqw7RHeyfYQKzii1QHcIv9rWntxsOYVpdVOYEXz2Ed+loZJxt5UOe/XsljWH6s4iMs7UHaAnUqg+5xbmus3NHY2NTR2tZkuHamrtLGn9fKdPpXsJlAdLBx/6pbbG+bt3bJUDVecWKdR+aiOHp8rkamH2xhnhCfs/u3HhkhLsybqziIyxdAfoiR8KNdA7pra1JTe58zDNzc1OYTZv6yxp7UhW5nph7kybyis+KTKpKNa4xFJZuK1XpIWMUPtpCz4+r1QvCrOKLD8kWTb7JFQ85say4a//rHn1wbqziIyQQu2nBmCc7hDd2drWudFq6Vy3ubnDanQLs2lbZ0lbR7JKCjMzHi0ZdPBXWxvn1XS0yAGqg08KtZ8adD54LwpzgHsRGp0VHn/Qs5sSi8rspJyiOtjW6w7QEz8U6qp03vnWts4NZkvnZ43NHebmpvY2c2unat7WWdranqxM2gxDCtMXOlRe4QmRSRV/3bzYVBn8oIHIuE91B+iJHwq1XyPUXhTmQLL8XN+id1bmF438XXn1m5c1rTpQdxaRFm3AWt0heuKHQu1xhNrS2rnBWSVvtxqbO9rMls68ZmdakRRmDnqyeMCBh7c2zt2vvWmG7izCcw3RhkRWf3Iy6wu1ubVzxZaWzkQXhVmVtBmKFKbYwXnGuEOe3bQwUW4nd9edRXgqq1f3wQeFOufNDSsB+cUQvdapVMHxkUmRxzYv3pwnc3iDJOsLNU93gJ0x47G1QLPuHMJfVucXjbi2fORSG7J6FVH0iRSqRz7RHUD4z9PFVfu/XlAxV3cO4ZkVugPsjF8KdZnuAMKfLjbGTrVU/nu6cwhPZP0p5f1SqB/oDiD8qVOp0HGRSYOSsFF3FtFvC3UH2Bm/FOp/dAcQ/rUuv3DY1RWjPpHtqb62ItqQyOojTYEUqsgRzxdV7jev0JDtqf71vu4AveGXQl2Kc9QpIXbZZRVjpjWq/Hd15xC7JOtX98EnhWrGYzYQ151D+FtSqfxjI5OHJ7P8ABuiSzJC9Zis9ot+25BfMPiyitGr7Cw/HbH4P6RQPfaO7gAiGOYWRfZ+sTA8T3cO0WtNwCLdIXrDT4UqI1ThmSsrRk/fpELyR9of3og2JDp1h+gNPxXqImCr7hAiGGyl8o6tnDSyE9bpziJ26hXdAXrLN4VqxmPtwGu6c4jg2JRXMOgSY+xaG3wx+slhUqhp8qLuACJYXi00ap4pqvTNL2wO6gDe0B2it6RQRc67pnzk9PV5oXrdOUSX/hNtSPjmaHN+K9S3yfKzHgofUkodF5k8rhPW6I4i/g9frT34qlDNeKwTkI8PCs815oWqLjTGbbCdVUyRPf6tO0Bf+KpQXbLaL9LizcKKPZ8sqnpVdw7xX9vw2e+7FKoQKX5XXj19bV7BW7pzCAD+HW1ItOgO0Re+K1QzHkuQ5aeSFT6mlDo+MmlSx07Otisy4p+6A/SV7wrV9aTuACK4rLxQ5FxjvGlDu+4sOU4KNUMe1R1ABNs7heW7P1Y8QD5Ios/70YZE1p+Ub0d+LdSXkUOwiTSbWV49oyGv0DeTygPGd6NT8GmhutOn5ujOIYLvJ5FJ0XYfnL44gHy5Wc+XheqS1X6Rdk15+eGzwhOabWjVnSWHLI02JN7UHWJX+LlQ/w1s0B1CBN97BWXRWMkgX/6C+9RDugPsKt8Wqqz2i0y6tWz49BX5RbKTKjMe1B1gV/m2UF2y2i8y5sTwxD3bUJ/ozhFwb0QbEkt1h9hVfi/UF5EdBiJDWvLyK34antBuOx+JFOnh29Ep+LxQzXgsCfxJdw6ROxYVlE66p2TwAt05AqodeER3iP7wdaG6/owcIUhk0OyyYdOW5RfJQVS893S0IeHrHc2+L1QzHluLT+esCf86OTJxr1bUMt05AuY23QH6y/eF6qrTHUDklm0qv+y0yATbBl8dDSmLfRBtSDyvO0R/BaVQXwQ+0h1C5JYlodIJs0uHyqmovTFLdwAvBKJQzXjMBmbrziFyzz2lQ6YuyS/21Wk6spAJ3K87hBcCUaiue5DpLEKD0yIT992GkjWkXXdPtCHRpDuEFwJTqGY8thG4W3cOkXtaVV7JyZGJIRsCUQoZliQgq/sQoEJ1/R45KLDQYFmoZOys0mFx3Tl86KloQyIwsyUCVahmPPYp8IDuHCI3PVQ6+NBEqHS+7hw+8xvdAbwUqEJ1XQd06g4hctMZ4fH7b0Ut0Z3DJ17w62H6uhO4QjXjsaX4/ONrwr/aVF7xiZFJxTZYurP4wK91B/Ba4ArV9RvA1h1C5KbloeLRN5YNT+jOkeX+HW1IzNMdwmuBLFQzHluEHCtVaPRoyaCD3w2VzdWdI4tdpTtAOgSyUF3X4EzJEEKLs8PjDm5WeYt058hCz0UbEoH8MERgC9WMx+LAfbpziNzVofIKT4hMqrCdTwIJhw1coTtEugS2UF2XI5OthUYr84tG/q68erHuHFnk/mhDol53iHQJdKG6h/b7ne4cIrc9WTzgwAUF5bI9FZqBy3SHSKdAF6prJnKaFKHZ+ca4Q5pU3kLdOTS7LtqQWKM7RDoFvlDNeGwb8HPdOURu61Sq4PjIpMokbNadRZMVOIObQAt8oQKY8djDwOu6c4jctjq/aMS15SOX2rk5R/qSaEMi8EeDy4lCdV1Abr6RRRZ5urhq/9cLKgI3oX0n5kcbEn/VHSITcqZQzXjsTeAO3TmEuNgYe6il8t/TnSNDWoHTdYfIlJwpVNfPgeW6Q4jc1qlU6LjIpEFJ8PUZPnvp19GGxAe6Q2RKThWqGY81AafqziHEuvzCYVdXjFoe8O2pcZxjFOeMnCpUADMeewG4S3cOIZ4vqtxvXqER1O2pHcBJ0YZEh+4gmZRzher6GdCgO4QQl1WMmdqo8t/VnSMNbog2JHLujLA5WahmPGaSQxvKRfZKKpV/bGTy8CSs153FQ0uAX+kOoUNOFiqAGY89hZwuRWSBDfkFgy+rGL3KDsbR0dqB43NhzmlXcrZQXecge/1FFphbFNn7hcJIELanXhZtSLylO4QuOV2o7qr/D5EzpYoscFXFqOkbVeht3Tn64WngRt0hdMrpQoX/Tvi/XHcOIWyl8o6rnDSqE9bqzrILGoCfRBsSQZ4GtlM5X6iumcBTukMIsSmvYNAlxth1tr/O3NsJ/DjakMiFDyr0SAoVMOMxGzgO2Z4qssCrhUbNM0WV83Xn6INrow0JOd4rUqj/ZcZjm4Hv4nz2WAitrikfOWN9XsgPR7b/FwE8HfSukkJNYcZj7wBn684hBEqp4yKTx3XCat1RerAY+GG0IeGnzRNpJYW6AzMe+zNwk+4cQjTmhaouNMZttJ2PcWabTcA3ow0JOQFhCinUrl0EzNEdQog3Cyv2fLKo6lXdOXbQAXwv2pBYqjtItlG2ndOzHLoVrqktAV4GDtAcRQie2LToraHJ9mx5L54RbUjU6Q6RjaRQexCuqR0MvAGM1Z1F5DYj2dH49KZEUwiqNUe5LdqQkP0M3ZBV/h6Y8dhnwP8DGjVHETnOygtFzjXGmza0aYzxOHCexsfPelKoO2HGYx8AR6P3jSwE7xSW7/5o8UBdJ5t8AaiVPfo9k1X+XgrX1P4AeAjI151F5LbHNn3wRnWy7aAMPuQbwFejDYnmDD6mL0mh9kG4pvZY4D5kZC80Kk92ms9sSjQWYI/OwMO9D8yINiQ2Z+CxfE+KoQ/MeOxB4CSCcdxK4VNNefnhM8Pjt9rp/1TfMuBIKdPek0LtIzMeuw/nRH8ytBfavF9QNuWhkkFvpvEhPgUOjzYk1qTxMQJHCnUXmPHY3TinUJFSFdrMKhs+fUV+0WtpuOulwLRoQ+KTNNx3oMk21H4I19SeAdyuO4fIXaXJzi3/2pTYUIjt1VzpBM4OKD8ek1U7GaH2gxmP3QH8FNmmKjRpycuv+Gl4QrsNWz24u3eAw6RMd50Uaj+Z8didOIf98+INLUSfLSoonXRPyeD+HurvNeDLcpDo/pFC9YAZjz0BfAXYqDmKyFGzy4ZNW5ZfvKsHUXkROEKOHNV/UqgeMeOx14FDkaP+C01OjkzYqxW1rI83+zPwdZm07w3ZKeWxcE3tUJyzP+6tO4vIPZM7Wpbe2/jRcAWlO/lRG7g02pC4PhO5coWMUD1mxmNrgRnAc7qziNyzJFQ6oa506Ds7+bEW4LtSpt6TQk0DMx7bgnOUqpw+R7nQ477SIVOX5Jd0d5K/1cD0aENCDqCeBrLKn2bhmtrvA3cD5bqziNxRZCe3Prtx4api7IkpV78NfDvakFilK1fQyQg1zcx47FGco/4v1p1F5I5WlVdycmRiyIYm96rZwKFSpuklI9QMCdfUluOMVL+vO4vIHT/a+tnL5zWvuSfakLhfd5ZcIIWaYeGa2guB3wMh3VlE4CWAY8x4bJHuILlCVvkzzIzHbgQOwzk0mhDp8idgfynTzJIRqibhmtoy4HrgDEBpjiOCYwNwprvtXmSYFKpm4Zrar+JsWx2pO4vwvUeAc8x4bL3uILlKCjULhGtqw8BNwIm6swhfWoszKpW5pZpJoWaRcE3tN3C2fQ3VnUX4xv3A+WY8JqcpyQJSqFkmXFMbAX4FnInMBBDdWwmcbsZjz+gOIj4nhZqlwjW1ewC3AF/SnUVklW3AzcBv3Y84iywihZrl3I+u3gCM0p1FaPcIcKkZjy3XHUR0TQrVB8I1taXApcDFQLHmOCLzFgAXmPHYrh5AWmSIFKqPhGtqx+BsX/0xkK83jciAVcBlwENmPCa/qD4ghepD4ZraScCVwI+QYg2idTiHfpxlxmMtusOI3pNC9bFwTe1kPi9W+Rix/63C+fTcn814TE766ENSqAHgFutVwA+RYvWjj4HfAfeZ8Vib7jBi10mhBohbrOcBxwNlmuOInfsAuA6ImfFYp+4wov+kUAMoXFNbCZwCnI1Mt8o2HcA/gDrgednZFCxSqAEWrqnNxzm31RnAEcjmAJ1W4nys+C4zHlutO4xIDynUHBGuqR0LnIqzA2uM3jQ5Iwn8C2c0+rSs1gefFGoOCtfUHoxTrMcAQzTHCaK3gMeAR8x47FPdYUTmSKHmMHeTwJdwyvVoIKI1kH/ZwJs4JfqYGY+t0JxHaCKFKgAI19QW4mxnPcr9d7zeRFkvCbzB5yW6UnMekQWkUEWXwjW143GK9UicUayhN1FWWAS86F7mmvFYo944IttIoYqdCtfUhoCDcQr2EGBfIKw1VGYsB17CKdCXzHhsrd44IttJoYo+C9fUKmAisH/KZW+gRGeufvoY+E/qxYzH1uiNJPxGClV4wh3F7g78D8721wnuZTwwUGO0VDbO+ZeWAUuB94B3gHfNeMzUGUwEgxSqSDv3JITbS3YsMAgYkHKpSvm3r0fPSgIWYLqX7V+vx5lMn3r5WA46ItJJClVkDXdTQhgoxflUV3cXgCac4mySj2+KbCGFKoQQHpHPdgshhEekUIUQwiNSqEII4REpVCGE8IgUqhBCeEQKVQghPCKFKoQQHpFCFUIIj0ihCiGER6RQhRDCI1KoQgjhESlUIYTwiBSqEEJ4RApVCCE8IoUqhBAekUIVQgiPSKEKIYRHpFCFEMIjUqhCCOERKVQhhPCIFKoQQnhEClUIITwihSqEEB6RQhVCCI9IoQohhEekUIUQwiNSqEII4REpVCGE8IgUqhBCeEQKVQghPCKFKoQQHpFCFUIIj0ihCiGER6RQhRDCI1KoQgjhESlUIYTwiBSqEEJ4RApVCCE88v8BQjaxzK+phbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors)\n",
    "plt.title('Apparel, Accessories, Footwear');\n",
    "#plt.savefig('Apparel_colors_pie.jpg');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integrated-employment",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "flexible-album",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAAM9CAYAAAAhIwUbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAGnUlEQVR4nO3du0lEURRAUZ9MPCZmViAGJhPYxnRhSWaWYAdWYCpiARYw08C1ANlwQebDsFZ8gsPmwIsedxljXPHX9akXOFfCBGGCMEGYsJod/Hh7uYjP12b7vMzMuZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZgw/Yfb6/v3Ifc4ms12bs7FBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYsMw+0vt193ARzyHe/3x6DvE/hAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIExYzQ4+3T4eco+j2U3OuZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYcIyxpgavFmv5wbP3G6/X2bmXEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYsIwxTr3DWXIxQZggTBAmCBOECb+9aCKY/BVOQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_apparel.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlike-member",
   "metadata": {},
   "source": [
    "## Cosmetics and Self-Care category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "sonic-shock",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images_cosm = np.zeros(shape= (1,3))\n",
    "cosm_logos = glob('./Logos/Cosm/*.jpg', recursive=True)\n",
    "\n",
    "for j in cosm_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images_cosm = np.concatenate((final_images_cosm,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bound-flexibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11474863, 3)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images_cosm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "spread-delivery",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init_cosm = final_images_cosm.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "breeding-warner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images_cosm) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "unlimited-jacket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "elder-savage",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "offensive-triangle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8592653\n",
       "2    1081241\n",
       "1     877528\n",
       "4     673316\n",
       "3     211489\n",
       "5      38636\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "developed-external",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "appointed-ultimate",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "improved-hopkins",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#010001', '#fefefe', '#e3d8cc', '#232f7c', '#116eb4', '#c52129']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "asian-chest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[877528, 8592653, 673316, 1081241, 211489, 38636]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "extensive-cameroon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "dense-worship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[877528, 673316, 1081241, 211489, 38636]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "plain-jurisdiction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#010001', '#e3d8cc', '#232f7c', '#116eb4', '#c52129']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fefefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aware-petite",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "appropriate-starter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors)\n",
    "plt.title('Cosmetics and Self-care');\n",
    "#plt.savefig('Cosm_colors3_pie.jpg');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "still-wales",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "prescription-annex",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_cosmetics.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tired-value",
   "metadata": {},
   "source": [
    "## Consumer Goods category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "inclusive-marijuana",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images_cg = np.zeros(shape= (1,3))\n",
    "cg_logos = glob('./Logos/ConsumerGoods/*.jpg', recursive=True)\n",
    "\n",
    "for j in cg_logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images_cg = np.concatenate((final_images_cg,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "complimentary-sheep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4145752, 3)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images_cg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "considered-preference",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init_cg = final_images_cg.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "elect-lancaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images_cg) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "military-forty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "departmental-motorcycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "antique-database",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2936524\n",
       "2     369266\n",
       "1     274533\n",
       "3     263053\n",
       "4     152377\n",
       "5     149999\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "transsexual-recycling",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "liquid-stevens",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "municipal-simulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#07080b', '#fdfdfd', '#69afac', '#155b77', '#ead93f', '#d51d1b']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "offensive-nelson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[274533, 2936524, 369266, 149999, 152377, 263053]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "neural-coach",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "imposed-processor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[274533, 369266, 149999, 152377, 263053]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "cutting-christopher",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#07080b', '#69afac', '#155b77', '#ead93f', '#d51d1b']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fdfdfd')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annual-health",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "geological-enough",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Consumer Goods');\n",
    "#plt.savefig('ConsumerGoods_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detected-infrastructure",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "pretty-yesterday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_consumergoods.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-nylon",
   "metadata": {},
   "source": [
    "## Food and Beverage Industry category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "latter-bibliography",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Food/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "annoying-infrastructure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17241807, 3)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "favorite-george",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "friendly-rebecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "married-filter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 1, 1, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "wired-daughter",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "meaningful-windsor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11940501\n",
       "2     1842909\n",
       "0     1421147\n",
       "4      915455\n",
       "3      620985\n",
       "5      500810\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "catholic-worse",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ecological-elder",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "treated-atlas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#271b19', '#fdfdfc', '#f88e0e', '#cf2129', '#179241', '#1b4c87']"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "international-authorization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[500810, 11940501, 1421147, 620985, 915455, 1842909]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "quiet-spice",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "fatal-oakland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[500810, 1421147, 620985, 915455, 1842909]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "confidential-things",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#271b19', '#f88e0e', '#cf2129', '#179241', '#1b4c87']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fdfdfc')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "balanced-landscape",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "binding-johnson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Food and Beverage Industry');\n",
    "#plt.savefig('Food2_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-career",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "statutory-greensboro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_food.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "available-minority",
   "metadata": {},
   "source": [
    "## Alcohol category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "periodic-insert",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Alcohol/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "christian-istanbul",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23237245, 3)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "recorded-ideal",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "graduate-frame",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "confidential-maria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "identical-kruger",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "funny-hampton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17644957\n",
       "1     2525140\n",
       "3     1010127\n",
       "4      883708\n",
       "2      697142\n",
       "5      476171\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "shared-trinity",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "twelve-differential",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "demographic-mailing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#201112', '#fefefe', '#b9b1a5', '#0e6543', '#e0a236', '#d70f1a']"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "variable-disabled",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2525140, 17644957, 476171, 883708, 697142, 1010127]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "seeing-escape",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "annual-watson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2525140, 476171, 883708, 697142, 1010127]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "national-lindsay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#201112', '#b9b1a5', '#0e6543', '#e0a236', '#d70f1a']"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fefefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alien-reserve",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "accredited-vocabulary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Alcohol');\n",
    "#plt.savefig('Alcohol_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rising-uncle",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "likely-vinyl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_alcohol.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-enforcement",
   "metadata": {},
   "source": [
    "## Restaurands and Fast Food category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "incorporate-demand",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Rest/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "signal-gibson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8885559, 3)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "senior-democrat",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "coated-environment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "sporting-detective",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "preliminary-valuation",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "modern-navigation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5131579\n",
       "1    1932645\n",
       "2    1455823\n",
       "3     203032\n",
       "5      89876\n",
       "4      72604\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "quality-blink",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "departmental-audience",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "adjusted-distance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0c0807', '#fefefe', '#b2a684', '#fdf002', '#df232e', '#035645']"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "chinese-orange",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[89876, 5131579, 72604, 1455823, 203032, 1932645]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "liquid-lunch",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "shared-handbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[89876, 72604, 1455823, 203032, 1932645]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "grand-firewall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0c0807', '#b2a684', '#fdf002', '#df232e', '#035645']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fefefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serial-alcohol",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "sixth-silly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Restaurants and Fast Food');\n",
    "#plt.savefig('Rest4_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "paperback-evaluation",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "greatest-cardiff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_restaur.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "realistic-store",
   "metadata": {},
   "source": [
    "## Tobacco category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "excessive-emperor",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Tobacco/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "confident-indianapolis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364872, 3)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "sweet-islam",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "vulnerable-discretion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "packed-regulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "conditional-picture",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "rocky-qatar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    207240\n",
       "2     55484\n",
       "1     45889\n",
       "4     23516\n",
       "5     16751\n",
       "3     15992\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "driven-trance",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "studied-canberra",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "copyrighted-intersection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#100f15', '#fbfcfc', '#a19e8c', '#33568c', '#118c49', '#ce0e13']"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "union-tokyo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45889, 207240, 16751, 15992, 23516, 55484]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "diverse-lighting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "chicken-driving",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45889, 16751, 15992, 23516, 55484]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "integrated-float",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#100f15', '#a19e8c', '#33568c', '#118c49', '#ce0e13']"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fbfcfc')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "massive-creation",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "pharmaceutical-macedonia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Tobacco');\n",
    "#plt.savefig('Tobacco2_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "choice-introduction",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "infrared-destiny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_tobacco.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norman-commonwealth",
   "metadata": {},
   "source": [
    "## e-commerce category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "affiliated-factor",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/ecommerce/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "swiss-mistake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3268872, 3)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "ruled-subdivision",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "tropical-swaziland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "diverse-financing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "crazy-engine",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "increasing-particular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1113115\n",
       "4     808293\n",
       "2     781056\n",
       "3     340255\n",
       "1     114446\n",
       "5     111707\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "pharmaceutical-skiing",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "tribal-burden",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "confused-facial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#182340', '#d24d27', '#efb529', '#fafbfa', '#2e68b1', '#2ca8e0']"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "tracked-sweden",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[114446, 340255, 111707, 1113115, 781056, 808293]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "processed-county",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "sealed-oxygen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[114446, 340255, 111707, 781056, 808293]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "ancient-board",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#182340', '#d24d27', '#efb529', '#2e68b1', '#2ca8e0']"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fafbfa')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-parking",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "other-radiation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('e-commerce');\n",
    "#plt.savefig('ecommerce_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "racial-height",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "colored-career",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_ecommerce.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italic-curve",
   "metadata": {},
   "source": [
    "## Electronics category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "spanish-triple",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Electronics/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "essential-water",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1213041, 3)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "signed-imperial",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "registered-failing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "spread-footage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1, ..., 5, 4, 1], dtype=int32)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "discrete-shield",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "linear-chicago",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    950782\n",
       "0    102205\n",
       "2     56414\n",
       "3     44037\n",
       "5     33423\n",
       "4     26180\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "literary-damage",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "sapphire-career",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "infectious-occupation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#060507', '#fdfefd', '#af9e9a', '#0755a9', '#2c355c', '#ec1b1e']"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "disturbed-gossip",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[44037, 950782, 26180, 102205, 33423, 56414]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "trying-retirement",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "asian-patch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[44037, 26180, 102205, 33423, 56414]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "separate-manchester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#060507', '#af9e9a', '#0755a9', '#2c355c', '#ec1b1e']"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fdfefd')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "palestinian-census",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "reflected-cheese",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Electronics');\n",
    "#plt.savefig('Electronics2_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-prison",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "decreased-doctrine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_electronics.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-incident",
   "metadata": {},
   "source": [
    "## Energy/Gas/Petrol category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "canadian-lucas",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Energy/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "regional-bottle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3817709, 3)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "dental-cargo",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "regional-moral",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "vanilla-payroll",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "violent-activity",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "contrary-condition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2455204\n",
       "1     440175\n",
       "3     393570\n",
       "2     298226\n",
       "5     187364\n",
       "4      43170\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "freelance-welcome",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "honey-suite",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "running-glucose",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#029401', '#fdfefe', '#8fc80c', '#df202d', '#fdf903', '#0d6c92']"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "adjustable-spirit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[440175, 2455204, 298226, 43170, 187364, 393570]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "subtle-motel",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "described-attribute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[440175, 298226, 43170, 187364, 393570]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "suffering-denial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#029401', '#8fc80c', '#df202d', '#fdf903', '#0d6c92']"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fdfefe')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saved-dominican",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "weighted-demand",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAFkCAYAAAB/6MMYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAq5UlEQVR4nO3deZwcZYH/8c9T3dOdzExPZnKQO5mEcCRcSUCCuhoQWUFRQFxuRdZd/WlsZb0VURTvFWntbVddxQsv7kPUxVVArjSBnCSQcARy30fnnnR3/f6oDg5xMpmjup+q6u/79erXDNMzXd8ZJt956qmqp4zruoiISP85tgOIiESFClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBGLjDHvNcY8YjuH+EOFGhHGmJeMMXuMMTs7Pf7Ldq4oM8a0G2NcY0zcdhYJBv0iRMvbXdf9v2puwBgTd123WM1tRIl+XvVFI9Q6cGC30hjzbWPMVmPMcmPMOZ2eH2SM+YkxZq0xZrUx5ivGmFinr33UGHOjMWYzcJ0xZogx5l5jTMEYM6fy+Y9UPj9njLnhoO3fY4z5jx5m/VQlxxpjzL9VRoCTKs+9zRgzr7LdlcaY6zp93QBjzM3GmM3GmG2VXMN7sL0Do8z3V7a51hjziU7PO8aYzxhjXqi89i3GmMGVp/9Webutskfw2kP8vAYZY35hjNlojHnZGPN5Y4z+7UWQ/qfWjxnAUmAo8C3gJ8YYU3nuZ0ARmARMA/4Z+LeDvvZFYDjwVSAH7AJGAFdWHgf8HLj0QGEYY4YCbwZ+fbiAxpizgY9VPn8ScPpBn7ILeA/QCrwN+KAx5vzKc1cCg4CxwBDg/wF7DrfNTs4AjsL73j9tjHlz5eNp4HxgJjAK2Ir3/QO8sfK21XXdZtd1H6/898E/r2wl28TK67wHuKoX2SQsXNfVIwIP4CVgJ7Ct0+PfK8+9F3i+0+c2Ai5eIQ4H9gEDOz1/KfBAp69d0em5GLAfOKbTx74CPNLpv58Bzqq8/2HgDz38Hm4Cvt7pvydVck46xOdngBsr7/8r8BhwYi9/bu2VbRzb6WPfAn7S6Xs5s9NzIyvff7zT18Y7Pd/Vz6sDmNLpYx8AHuz0+Y/0JrMewX1oDjVazncPPYe67sA7ruvurgxOm4HBQAOw9u8DVhxgZaev7fz+MLwyOdTz4I1SrwD+XHn73R7mHwU8eajXNcbMAL4BHA8kgCRwa+XpX+KNTn9rjGkFbgaucV13fw+33XlbLwMnVN4fD9xpjCl3er6E94eoJ681FO/n+/JBrz+6h7kkRLTLLyvxRqhDXddtrTxaXNc9rtPndF6SbCPe9MCYTh8be9Br3gycZ4w5CZgM3NXDLGsP87q/Bu4BxrquOwj4AWAAXNfd77rul1zXnQK8DjgXb9e6pzpvaxywpvL+SuCcTj+bVtd1B7iuu5pX/1w66/zxTXgj2vEHvf7qXmSTkFCh1jnXddcC9wM3GGNaKgdhjjTGzDzE55eAO/AOtjQaY47loOJyXXcVMAdv1Hi767qvzGUaY35mjPnZIeLcAlxljJlsjGkErj3o+RSwxXXdvcaYU4HLOr3uGcaYEyoH0wp4JVauPHedMebBw/worq18P8fhzW/+rvLxHwBfNcaMr7zWMGPMeZXnNla2MfFQL1r5ed1SeY1U5XU+hvdHRyJGhRot9x50HuqdPfy69+DtQi/BO+hyG95c4aF8GO8gyzq80vwN3ii3s5/j7Tb/8qCPjwUe7epFXdf9I/A94AHgeWB25akDr/0h4MvGmB3AF/CK6oARldwFvHnPhzpt+5Db7OShyjb/Anzbdd37Kx//Lt6o+P7KdmfjHXTCdd3deAedHq2cWXDaIV47jXdA7UXgEbyR9k2HySMhZFxXC0xL/xhjvgmMcF33yk4feyPeKGy8W/klM8YkgAV4B44OO7dpjJkMPA0k3X6cy2mMmY93YGlzF8+1A8uBhv5sQwQ0QpU+MMYca4w50XhOBd4H3Nnp+Qbgo8CP3U5/sV3X7XBdd3J3ZWqMucAYkzTGtAHfBO7tb9G5rju1qzIV8ZsKVfoihTePugtvrvEG4G54ZVS5DW/KINOH1/4AsAF4Ae9o+gf7nVakRrTLLyLiE41QRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfGJClVExCcqVBERn6hQRUR8okIVEfFJ3HYAqS+xnJMAhnbzaAEa8H43u3oYYC+wu/LYBRQqj+3ANmAtsBJYWZpV3lOb70wEjOu6tjNIxMRyTgtwVOUx6aD3h9U4zmZgFZWCrTyeAxYCz5dmlcs1ziMRpkKVfonlnAnAa4BTgVOAKdS+NPtqD7AEr1wXVR4LS7PKG6ymktBSoUqPxXLOUOA0vAI98BhqNVR1rAT+Vnk8VJpVXmo5j4SEClUOqTLf+Xrgn4G3AFPx5jDrzXrgYeAhvJJdVJpV1j8c+QcqVHmVWM45hr8X6Eyg2W6iQFoH3AvcBfylNKu8z24cCQoVqhDLOScAlwAX4R04kp7bAfwJuBu4rzSrvM1uHLFJhVqnYjlnEl6JXgIcZzlOVOzHmxb4DXBLaVZ5p+U8UmMq1DoSyzkjgMuBS4GTLceJul3ArcBNpVnlh22HkdpQodaBWM6ZCXwIuADvpHmpreeAnwI/L80qr7EdRqpHhRpRsZzTDLwbr0iPtxxHPCW8+dZMaVb5/2yHEf+pUCMmlnMm45Xoe/Au45Rgmgd8G2+utWg7jPhDhRoRsZwzA7gGOJf6PFc0rF4GMsCPdRAr/FSoITfllw2vX1oofQk403YW6ZetwA+A75ZmldfbDiN9o0INqUw+9Xrg+v1ld8xn5u6ehEalUbELuBH4z9KscsF2GOkdFWrIZPKpU4DrgbMPfOz2l/fNfmxj8TR7qaQKNgFfBb5fmlXusB1GekaFGhKZfKod+A7eqU+v0lFyl3523u6j0Sg1il4GvgDcrKUGg0+FGnCZfCoBfBLvgNPAQ33erS/ty8/eVJxRs2BSa4uAT5dmlf9oO4gcmgo1wDL51FnAfwFHH+5z95XcZz83b/ex1U8llt0DfKQ0q/yy7SDyj1SoAZTJp0bj7d5f1Juv++3yfU/M2Vw8tTqpJEB2482vflvzq8GiQg2QTD4VBz4KXEcfls3bW3KXXDNv9xS/c0lgLQHeX5pVftR2EPHorqcBkcmn3sDfr57p0xqkA2JmysmD43N8DSZBNgV4OJZz/rtyHy+xTCNUyzL51ADgBrzLRfttb8ldfM283VqOr/6sAq4szSr/1XaQeqYRqkWZfGoK8AQ+lSnAgJg5btrg2FN+vZ6Exhjgz7Gc881YztGKYpaoUC3J5FP/BswBTvD7tS8cl0z4/ZoSCg7wKWB25VY2UmPa5a+xTD41CPgRvTyC31u/eGHv3AVbS9OruQ0JtN3A1aVZ5f+xHaSeaIRaQ5l8agbegaeqlinAu8Yn49XehgRaI/CjWM65I5ZzhtgOUy80Qq2BTD5l8HbFvgLUrOh++vze+U9vK02t1fYksF4Gzi/NKs+3HSTqNEKtskw+dQTwv8A3qGGZAlzUnqzl5iS4xgOPxnJO1feM6p0KtYoy+dSxQB44y8b2m+Jm6pRBsQU2ti2B0wj8LpZzvhrLOVpEp0pUqFWSyadmAo8B7TZzXNye1ApF0tnngLtjOSdlO0gUqVCrIJNPXQbcD7TZztLcYKYd2xJbaDuHBMrb8U6tmmQ7SNSoUH2Wyac+B9wMBOZc0EsmJPbbziCBMwV4IpZzXmc7SJToKL9PMvlUDPg+8H7bWbryw2V7Fi0rlH2/iEBCbzdwYWlW+U+2g0SBRqg+yORTzcC9BLRMAS5pT+6znUECqRG4J5ZzLrEdJApUqP2UyadGAn8DzrGdpTuDEs4pR6acxbZzSCA1AL+K5ZwP2g4SdirUfsjkU2PxjuRPs52lJy6bkNxtO4MElgN8P5ZzrrUdJMxUqH1UGZn+FcunRfVGa8J5zcRmZ4ntHBJoX47lnBtthwgrFWofZPKpYcBfgNCddnLZhORO2xkk8K6O5ZwbbIcIIxVqL2XyqTbgz8Bk21n6oi3pnNre5DxrO4cE3sdiOedLtkOEjQq1FzL5VAveCfsn2c7SH5dNTG63nUFC4QuxnPNJ2yHCRIXaQ5VTo/4InGI7S38NTphTxzY5y2znkFD4lo7+95wKtQcy+dRAvPNMI3FViTHGXDEhucV2DgmNXCznvMd2iDBQoR5GJp9KAncCp1uO4qshSTNjTKPznO0cEgoGuCmWcy60HSToVKiH93PgLbZD+M0YY66YmNxkO4eERgy4OZZzZtgOEmQq1G5UFjq52HaOahmaNDNGDXResJ1DQmMAcFcs54y1HSSoVKiHkMmnzgWut52jmowxzhUTk+tt55BQGQHcG8s5TbaDBJEKtQuVlfZ/RR38fI4YYE4bMdAst51DQuUkvGv/I//vo7f0AzlIJp9qBe4GWixHqQljjPPuiQPW2M4hoXMe8HXbIYJGhdpJJp9ygN8AR9vOUkvDB5jThg8wL9nOIaHzqVjOea/tEEGiQn21bwBn2w5Ra8aY2BUTk6ts55BQ+mEs50y3HSIoVKgVlftA1e1ldiMHOqcNG2Betp1DQieBdzdV3fQPFSoAmXxqOvBj2zlsMsbEr5iQXGE7h4TSJOC/bYcIgrov1Mplpb8BBtrOYtvoRue1Q5JGu/7SF5fHcs5VtkPYVveFijdvWlcHoQ7FGBO/YmJSp1BJX2VjOedY2yFsqutCzeRTpwNp2zmCZGyjc9rghFltO4eEUhPefOoA20FsqdtCzeRTKeCneAs/SIUxpuHyiUldjip9dSJQt6v9122h4v1Pb7cdIojGNzmntSWMTvaXvvpQLOecYTuEDXVZqJl86mzg323nCCpjTOLyCcnnbeeQUPtRLOfU3YHeuivUyj2hfmI7R9C1NzszWhNmre0cElqTgOtsh6i1uitUIAuMsh0i6IwxycsmJLUAtfTHx+vtKqq6KtRMPvVO4HLbOcJiYrNz6qAGo+X9pK9iwI9jOSduO0it1E2hVm6yl7OdI0yMMQMunZDULaelP6YBH7cdolbqplCBT+Mtjiu9MCnlnJpqMBtt55BQ+2Is5xxpO0Qt1EWhZvKpMdTRX0k/GWMGXtqeWGI7h4TaQOA7tkPUQl0UKvA1dK1+nx3dEntNcxzd0E/64x2xnHOm7RDVFvlCzeRTJwNX2M4RZsaYxksmJBfbziGhd2Ms58Rsh6imyBcq3hVRury0n45tiZ3cFGeL7RwSaicA77MdopoiXaiZfOp8YKbtHFFgjGm+uD25yHYOCb3rYjmn0XaIaolsoWbyqQbgW7ZzRMmUQbHpjTG22s4hoTYS+JjtENUS2UIFPgQcZTtElBhjUhe1JxfYziGh98lYzhlmO0Q1RLJQK9frf8F2jig6vjU2fWCM7bZzSKi1ENHTGCNZqMDVwGDbIaLIGNPyL+OT82znkND7YCzntNoO4bfIFWomn2oCPmw7R5Sd2BabNkCjVOmfFiL47zRyhYq3zqlGp1VkjBn0rnEapUq/fTRqR/wjVaiVI/uRPYIYJFMHx6YmHXbYziGhNpSILfQeqULFW5pvrO0Q9cAY0/rO8cmnbOeQ0PtELOc02A7hl6gVaiSPHAbVyYNjJyYcdtrOIaE2Bni37RB+iUyhZvKpM4HjbeeoJ8aYwReMSzxpO4eE3qdiOScSl4dHplCBj9oOUI9OGRI/PuGwy3YOCbVjgEjcJTUShZrJpyYBb7Odox45xgw9b6xGqdJv77cdwA+RKFQgTXS+l9A5dWh8SoPDbts5JNQuiMLlqKEvoUw+NQB4r+0c9cwxZtg7xiTm2M4hoZYgAv+OQ1+owLl4V12IRacNi09uMOyxnUNCLfTnpEahUC+zHUDAMeaIczVKlf45KpZzQn1wKtSFmsmnBgFvtZ1DPK87In50zLDPdg4JtQ/YDtAfoS5U4EIgaTuEeBxjRrxtTCJvO4eE2vmxnBPaKbywF6p29wPmDUfEj4oZOmznkNBKAu+wHaKvQluomXxqBBE5GThKHGNGnjO6QaNU6Y932Q7QV6EtVOASwp0/st44vGGiRqnSD2+J5Zxm2yH6IsyFdKntANK1mDGj/3mURqnSZwPwTocMnVAWaiafOhI41XYOObQzRjRMcAz7beeQ0Arlbn8oCxWNTgMvZsyYs0ZqlCp9dk4YV/MPa6FqIZQQOHNEwzgHirZzSCg1AufYDtFboSvUTD6VAk6xnUMOL+aYcWeObJhtO4eEVugGTqErVOANQNx2COmZN49sGGugZDuHhNKZtgP0VhgL9U22A0jPxR0z/owRGqVKn4yL5ZwjbYfoDRWqVN1bRjWMMlC2nUNCKVT/3kNVqJl8ajBwku0c0jtxx0yYObzhcds5JJRCtdsfqkIFZhK+zAKcPbphhEap0gehurw8bOUUquG//F2DY478pyPimkuV3joilnNCczdjFarUzNvGJI4AXNs5JHRCs9sfmkLN5FPDgSm2c0jfNThm0uuHaZQqvfZa2wF6KjSFCpxuO4D037ljEkPQKFV6Z7rtAD0VpkKdajuA9F8iZo5+7bC4rvGX3pgUyzkp2yF6IkyFepztAOKPd4xJtKFRqvScISSnS6pQpeYSMXPMjKFx3SFVeiMUu/2hKNRMPtUItNvOIf45b2wiFLtwEhjTbAfoiVAUKnAs4ckqPZCMmcmnDNEoVXpMI1QfaXc/gi4Yl2iynUFCY0os5wT+lvEqVLFmQMxMmT449qTtHBIKceBo2yEOR4UqVr1zXHKA7QwSGu22AxyOClWsGhg3x09ti821nUNCYYLtAIcT+ELVEf7ou3B8UndgkJ5QofrgWLwTeyWiGuPmxBNaY/Ns55DAa7cd4HDCUKijbQeQ6vuX9mQYfhfFLo1QfTDUdgCpvqa4Oem41tgC2zkk0NptBzicMBTqMNsBpDYuGp/Uiv7SnUGxnNNmO0R3wlCoGqHWieYGM23yII1SpVtjbQfoThgKVSPUOnJxu0ap0i2NUPtJhVpHUg1m2tEtsUW2c0hgtdoO0J0wFKp2+evMpe2JDtsZJLA0Qu0njVDrTEvCOXlSyllsO4cEUqvtAN0JQ6FqhFqHLpuQ3GM7gwRSq+0A3Ql0oWbyqQTQYjuH1N6ghHPKkc3OEts5JHC0y98PQ2wHEHsunZjcZTuDBE6r7QDdCXqhBn5BWametoTzmvZm5xnbOSRQBtkO0J2gF6rujFnnLp+Q3GE7gwRKg+0A3VGhSqC1JcxrxjU5S23nkMAIdGcFOhygq2bqnDHGXDExudV2DgmMmO0A3Qn6wr4aoQojSs6kMfsnP7h2d6NuPV3vTGmN7QjdUaFK4Dj72TdibuPicY81bW9/5/Y9w87dfvw1d77/uAGuo4s8JNB7K0EvVO3y1wMXt+355LLxDzSvG/FUY3NylzOpNb1xV8tNK44yccYsXD36hbLrjLMdUwIh0J0Q9ELVCDWiBmyOrRv/YOqFMY810bym4WjjmmOIuxMHfWTD7Jb3bd5h4rzhwOfe8tTJq4EjLcaV4FCh9oMKNSJie8zOUU80PTPuoebdg59Ljontd44ERgAQd/cP+sj6h1vet3lC5yI94A+Ljw/0qTJSU/ttB+hO0As10H+NpBtlSkOXDHh2/AOpjcMXDGxr2OlMMZjXvOpzGsodrR/dmE9dtXliV0V6wEubh0yqel4Ji0Cfl6xCFd80r25YMe7B5pdG55sSjRvikw3muC4/0SvS2amrNh/ZXZECLFo96sWy60ysSmAJo+22A3Qn6IWqa7kDrGGns23MY03Pjv1bc0fr8mS7UzLjgEMfPGood7RevTGfeu/mI02cN/ZkG7fOPXk1oEKVAwq2A3Qn0IV69Ywd+zL51DYCviBCvTBF9g9f0Lh4/APN24YuHjgsvsdMNpjTDvuFDeWO1v/YODt15eZJhxuRHuy+p48P9O+o1JxGqP20HhWqNYOWJ54f/0Bq9cg5jY0DtsamGMzUHn9xoryv9eoN+dSVWyb1dER6sOWbh2r+VDrTCLWf1gHH2A5RL5JbYxvHPdy8bMwjzbSsbDjSuGYS0LtSS5T3tf7Hhnzqyi1HmVjfihTg6TUjl5ddZ0Jfv14iSYXaT+ttB4gyZ5/ZM/LJxsXjH0ztGrw0OTLWYY4ymL5dkZQo72v9+IZ86t39K9IDbp178ipAhSqdaZe/n1SofnJxhyxNPjv+r6n1w+c3DkoUnCkGc0q/XtMr0tmpd2852o8iPeC+p08Iw++n1NY62wG6E4ZfWBVqPzWuj68a/2Dz8tGPN8eb1sePMa6ZDEzu9wsnyvtaP7Ehn7piy1Emxsz+J321FzcN1dVRcrAVtgN0JwyFGui/SEEU320Kox9vembc31L7Wl9Ijo0VzQRgjG8bSJb3tn1iwxPNl/s7Iu1s8VrNn8o/6CDgA6wwFGqgf4BBYEoUhy0auGT8A6ktwxYNGNKw25lsMDN831ANivSAW+dO1/ypHGx1IZsO9OXoKtSQalnRsHzcA6kVo+Y0DRy4KXaswZxYtY0ly3vbPrn+iebLth5T7SI94L6nTwj0QsJixUrbAQ4nDIWqXX4gsd3ZPPbR5qVjH24utbycmOiUzQSqPYJLlve2fWr9E82X1q5ID3hh4zDNn8rBAj1/CuEo1LV4cycJ20Fq6ZVFlh9IFYY+M2B4bJ851mBeV4ttm2R5b6ulIgV4Zu0IzZ9KVzRC7a+rZ+woZvKpZ4Hq7dIGwcGLLG+PTTGY6bWMYJLlPa2fWf9E88VbJ9so0gNunaf5U+nSi7YDHE7gC7ViIREs1C4XWbZwVZhJlve0fnb9E80XbZ1cjdOfeuv3i07U/Kl05WnbAQ4nTIUaet0usmzBK0V68dYpxrFfpAc8v3GYVpeSriy2HeBwVKjV1JNFli0wA8q7Wz+7bk7zRdsCVaQAz6wb8VLZddpt55DAWVHIpgO9uDSEp1AX2Q7QUz1eZNkCM6C8u+1z6+Y0/UvwivSA2+ZOXwm0284hgRP43X0ISaFePWPHmkw+tQkYajvLwXq9yLIFYSjSA36v80+laypUny0CzrAdwuynY/jCxiW9XmTZAjOgvLvtmnVzmt4V/CI94PkNR+jovnRFheqzhVgq1H4tsmyBGVje1XbNujlNF247LixFCvDsuuEvl1xnvO0cEkgLbAfoibAVak0kt8Y2jvtb83NjHmkut6xqmNSnRZYtMAPLu9o+v+7JpnduO844nG47T2/dNm/6CkCFKgcrEIIj/BCuQq3agSlfF1m2wAws72q7dt2TTReEa0R6MJ1/Kocwu5BNl2yH6ImwFeo+INnvVypTHrIsudTXRZYtMI2lnW2fX/dk0wXbTwhzkR7wnOZPpWuP2g7QU6Ep1Ktn7NibyadmQ9+Ko2qLLFtgGks7265d91TT+duPD+OufVeWrj9iRcl1AnV2hASGCrVKHqCHhVr1RZYtMI2lnW1fWPdU03nRGJF2duvck1cQsNPNJBBKQN52iJ4KY6Fe19UTNVtk2QLTVNrRdu26p5rO235i1Ir0gN8vOsHYziCBtKCQTe+0HaKnwlaos4E9wECo8SLLFpim0o7BX1g3t/Ed20+Iyq79oTy3UfOn0qVHbAfojVAV6tUzdnTc9ZVRv27/S+rolhWJI2uyyLIFpqm0Y/AX181tfHt0R6SdLdtwxIpSOabdfenKn2wH6I1QFSrA1J8MfRZ4n+0c1WCaSjsGX7fuqcZzt59UD0V6wG1zp2v+VLqyB2+aLzQc2wH64A+2A/jNNJcKQ7696qExTy4tNr1j++nGoc12plq6d9GJmj+Vrvy1kE3vtR2iN0JXqO3LFy4BXradww+muVQYcsOqh8bMWVpuenthZr0V6QHLNhzRbjuDBNJ9tgP0VugKteKPtgP0x6uK9NzCTOPQajuTLc9tGLayVI6Ntp1DAil0e6NhLdTQ/aABTKq0fch3VKSd3TZveiT2NsR3iwvZdOh+N0J3UKriL/h1GWoNmFRp++Avr53feE5hqjH1c7CpJ+5dqPlT6VLodvchpCPU9uULdxOC3X6TKm0fcuOqh8bMWUrTWwszjWGQ7UxBs3TD8HbbGSSQbrEdoC/COkIFuBk433aIrpiWUmHIl9fOG3i2RqTdeWHj0FWlcizUlwNLVSwtZNNP2Q7RF2Eu1N8D2yA485CmpbR9yPVr5g98yw4VaQ/cNm/6S4R8fQWpil/bDtBXodzlB2hfvnAfcKvtHOAV6dDvrnxozBNLaTx7h3bte+iehSdp/lS6okK15GabG3cGlbYN/Z6KtK+Wrh+u1fnlYE8UsunnbYfoqzDv8gM8jHeSf03/YTqDStsGf3XNgoFv3jFNu/Z98+KmoauLmj+VfxTa0SmEfITavnyhC/ysVttzBpW2Df2vlQ+Nzi91Gs/aMdMYWmq17ai5be70l2xnkMApAr+zHaI/Ql2oFT8BytXcgNNa3Do0pyL10z0LI7XSovjj7kI2vc52iP4IfaG2L1+4kiot8eW0FrcO/f6Kh0bPXhZvfLOK1E/Prh/RbjuDBE7OdoD+Cvsc6gE/At7q14s5rcWtg7+2ZuHAN+2crjlS/y3fNGSV5k/lIEsK2XSolurrSlQK9ffAaqBfi2w4bcUtg7+2ZtHAM1Sk1VS5fl+FKp2FfnQKEdjlB2hfvrAE3NTXr3failuG/veKB0c/vqyh8U07ZxpDysd4cpB7Fp7k2s4ggbID+KXtEH6IRKFW/ABvwZQec9qKW4b9YMVDox9flmh8087TVaS18cw6zZ/Kq/yikE3vsB3CD5Ep1PblC9cAP+/J5zqDi5uH/dAr0oFn7JxpDM1VjicVL20erPNPpTOXiOzuQ4QKteKbePfx7lKlSB8c/diy5MDTVaQ23D5P55/Kq9xTyKafsR3CL5Eq1PblC18Efnvwx53Bxc3DfrTiodGPLRsw8PSdp6tI7blb86fyal+zHcBPUTnK39nXgcsA4wwpbhryjdWLB7xh1yk6ah8MS9aO1PX7csBfCtn0E7ZD+Clyhdq+fOHila8/9peDv7R2vIo0WF7eMnhNsRwbazuHBMb1tgP4LXKFCjD6gedvBOYCWh4uQG6fN205MMp2DgmEBwvZ9EO2Q/gtUnOoBziJ4nzgTts55NXuXqD5U3nFdbYDVEMkC7Xii3inZEhALFk7cpztDBIIf47i6BQiXKhOovg0lheglr97ecvgNfvLcRWqlIGP2w5RLZEt1IrPArtthxC4Y97U5bYzSCDcVMimF9kOUS2RLlQnUVwNfMt2DoG7F07V9IvsBK61HaKaIl2oFf8JrLIdot4t1vypwDfDvoD04US+UJ1EcTferr9YsmJL29r9Jc2f1rlVwA22Q1Rb5Au14ldApK7ICJM75k970XYGse5zhWx6j+0Q1VYXheokii6Qpsr3npKu3b3gJP3c69tfC9l0JNY7PZy6KFQAJ1F8AsjazlGPnl47Srv79Wsv8AHbIWqlbgq14hrgZdsh6snKra1r95fiWhClfn2pkE0/bztErdRVoTqJ4i7q6K9lENzhXb8v9WkB8G3bIWqprgoVwEkU/xddQVUzdy2cesgFvyXSysC/F7Lpou0gtVR3hVpxNbDRdoh6sHjNKC3XV5++V8im59gOUWt1WahOorgZ+H+2c0Td6m2t6zpK8XbbOaTmnsE7XlF36rJQAZxE8Q7gx7ZzRNnt83T+aR3aB1xayKbrcg2Nui3Uio8Cz9oOEVV3LThJ86f15zOFbHqB7RC21HWhVi5LvRTosJ0lip5eM1q3i64vfwS+azuETXVdqPDK6v6fsZ0jatZsG7S+oxSfYDuH1Mx64L2FbLquVxWr+0KtyAB/sh0iSu6YP+0F2xmkZly8Mt1gO4htKlReudb/CkAnofvkzgU6/7SOXF/IpjUgQYX6isqpVOejFf59sWi15k/rxN1E9IZ7faFC7cRJFBcCV9nOEXZrt7ds0PxpXVgCvLve5007U6EexEkUbwG+YTtHmGn+tC5sBc4rZNM7bAcJEhVq164B/mA7RFjdOX9qXV2/XYdKwCX1tIpUT6lQu+AkimXgMmCx7SxhtHDN6NG2M0hVfbqQTd9vO0QQqVAPwUkUtwNnoxv89cq6QsvGjmLDRNs5pGoyhWw68veG6isVajecRHEV8Ba8+SLpgTvmT9VuYHT9GviY7RBBpkI9DCdRXAK8He9WDnIYd86fpvnTaLofXQl1WCrUHnASxUeBS/Am46UbC1dr/jSC5gAXFrLp/baDBJ0KtYecRPFu4IN4l9lJF9YVWjbu0/xp1CwD3lrIpnfaDhIGKtRecBLF/wE+hEq1S3ctOEnzp9HyInBWIZveZDtIWKhQe8lJFH+ASrVLd8ybpl3C6HgOmFnIplfYDhImKtQ+qJSqdv8PsmD1GM2fRsNS4PRCNq1TBntJhdpHTqL4Q7z7UqlUgfWF1MZ9xYYjbeeQfluCV6ZrbAcJIxVqPziJ4o+A9+PdMreu3bVgqq7fD7+ngTMK2fQ620HCSoXaT06i+GPgQur8PNU75k/VbWTC7Qm8Mq37RaL7Q4XqAydRvAt4M3V8RdX8VWM1fxpe9+CVqY7m95MK1SeVk/9fD9TdUdENO5o3af40tHLABfV622e/xW0HiBInUXym3BF/Hd79qY63nadW7low9TlgqO0c0isu3qpR/2k7SJRohOozJ1FcDfwT8L+2s9TKHfN1/mnI7AMuU5n6T4VaBZWl/94GfMd2llqYv3LsKNsZpMfWA2cWsunf2g4SRSrUKnESxZKTKH4cuJIInwGwYUfzpr3FuOZPwyEPnFzIph+1HSSqVKhV5iSKv8A7WPWS5ShVcc/Ck54HY2znkMP6H+CNhWx6te0gUaaDUjXgJIpzyx3xU4Bf4S1YHRm3z5um80+DbTfwwUI2/QvbQeqBRqg14iSKm4FzgE8AkSmheavGjrSdQQ5pKTBDZVo7KtQachJF10kUbwBOA561nae/Nu5s3rx3f8Mk2zmkSzlgeiGbftp2kHqiXX4LnERxXrkjfjJwI95aAKF094KTngNzmu0c8iprgH8tZNN1c9pekGiEaomTKO52EsUPABcAobzk74750/bZziCvcgtwgsrUHo1QLXMSxbvKHfFHgAxwueU4vTJP558GxTbgw4Vs+le2g9Q7jVADwEkUNzmJ4hV4B61eshynRzbtbNqyR/OnQfA7YIrKNBg0Qg0QJ1H8U7kjfjxwPfARIGY50iHdvfCk58DMsJ2jjj0HzCpk03+2HUT+TiPUgHESxV1OovgxvDMBnrSd51DumDctsld/Bdxe4It4c6Uq04DRCDWgnETxyXJH/FTgCuDrQKDWG527cpzOP629P+HNleruCAGlQg0wJ1F0gV+WO+K3410Q8CmgyW4q2Lyrceue/Q1H2c5RR+YBnylk0/fbDiLd0y5/CFROsfoycDTwMyzfGPCehSct0/X7NfEicBnegiYq0xDQCDVEnERxDXBVuSN+A3At8C4s/FG8bd50nX9aXRuArwA/KGTTWms2RFSoIeQkik8DF5c74lPwivUialisc1eMG16rbdWZDcD3gO8VsukdtsNI76lQQ8xJFJcAl5Y74l8GPg9cQpWLdfOuxm179jccXc1t1KHlwLeBnxay6T22w0jfqVAjwEkUnwEuL3fEr8M7f/VKIFWNbd278MRlYE6txmvXoQXAN4FbCtl0yXYY6T8VaoQ4ieJzQLrcEb8GuAr4MODr1Uy3zZuu80/7p4x3v7HvFbLpP9kOI/4yrmv1gLFUUbkjboC34o1azwL6fWR+1Ge+sXT3/uQx/X2dOrQauAn4cSGbrrtbjdcLjVAjrHIe633AfeWOeDvw7sqjT+eQbtnVuG33/oTOP+25MvBH4EfAfdqtjz4Vap1wEsWX8NYIuL7cEX8t8B7gYqCtp69x7yLNn/bQk3hL6f22kE2vtB1GakeFWoecRPFx4PFyR/xqvNtdX1B522253j5vuo5AH9pTeCV6ayGbXm47jNihQq1jTqK4D7gDuKPcEY8D/wScV3lMOPjzn1wxbkRtEwbafmA23pTKrYVs+kXLeSQAdFBKulTuiJ8AnAucCbxu2+6BHe3XfjUFpp4vV34OuL/yeEAn38vBNEKVLjmJ4iJgEfD1ckc8+fALk04BcybwRrylBa0v0lIDLwKPA38D7i9k0y/ZjSNBpxGq9FpLOhsHpgLTOr09kXCX7Ca8g0lPAnOA2YVseoPdSBI2KlTxRUs66+CdjjW18pgETMSbi+3xmQRV5gIr8W7hvbTz20I2vdpmMIkGFapUXUs624pXrBMrj5HAkC4ebfTt4oO9wC68m9WtA9Z2envg/dXA84VsenffvxOR7qlQJTAqo9wWoKHyiHfxdh+wu/OjkE3rl1gCQYUqIuKTej4FRkTEVypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGfqFBFRHyiQhUR8YkKVUTEJypUERGf/H+FhmfgVkpSZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Energy, gas, petrol');\n",
    "#plt.savefig('Energy2_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-mississippi",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "promotional-drawing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAAM9CAYAAAAhIwUbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAGmUlEQVR4nO3awUlDQRhGUV8Mghtr0AbcWpQErMVgXYINaA0uRTMWIBcGRPMI56y/xXD5IYuXZYxxxk+bYz9grYQJwgRhgjBhOzt8u7k9iZ+v69eXZWbnYoIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYcIy+/+Yw8f2JL5Ebi4+fYn8DWGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggThAnCBGGCMEGYIEwQJggTtrPDx+fLv3zHv3m4m9u5mCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMWMYYU8Or3X5uuHLv+90ys3MxQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZggTBAmCBOECcIEYYIwQZiwjDGmhudPm7nhyn3dH5aZnYsJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYIE4QJwgRhgjBBmCBMECYsY4xjv2GVXEwQJggThAnCBGHCN4UGJ3wInKcWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_energy.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respiratory-marriage",
   "metadata": {},
   "source": [
    "## Finance category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "lesbian-aquarium",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_images = np.zeros(shape= (1,3))\n",
    "logos = glob('./Logos/Finance/*.jpg', recursive=True)\n",
    "\n",
    "for j in logos:\n",
    "    img = Image.open(j)\n",
    "    img = asarray(img)\n",
    "    img = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    final_images = np.concatenate((final_images,img), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "expected-rehabilitation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4869132, 3)"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "molecular-vulnerability",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_init = final_images.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "contemporary-catholic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=6)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "\n",
    "# 1. Instantiate the model\n",
    "kmeans = KMeans(n_clusters = k) \n",
    "\n",
    "# 2.Build the model with the image\n",
    "kmeans.fit(final_images) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "roman-blocking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 1, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "genetic-clark",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_ = pd.DataFrame(kmeans.labels_, columns=['cluster_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "amateur-proof",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2126600\n",
       "0    1296742\n",
       "2     771311\n",
       "3     427325\n",
       "5     133199\n",
       "4     113955\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_.cluster_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "senior-lover",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(kmeans.labels_)\n",
    "center_colors = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "naval-stationery",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_colors = center_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "experimental-lender",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0f070c', '#fbfbfb', '#41a3d0', '#085c80', '#f69c20', '#db1728']"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts]\n",
    "\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "worse-freeze",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[113955, 2126600, 133199, 771311, 427325, 1296742]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = list(counts.values())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "explicit-rebecca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the cluster with a higher number of pixels\n",
    "counts.remove(max(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "combined-knock",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[113955, 133199, 771311, 427325, 1296742]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "beneficial-marking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#0f070c', '#41a3d0', '#085c80', '#f69c20', '#db1728']"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove white color from the list of hex_colors\n",
    "hex_colors.remove('#fbfbfb')\n",
    "hex_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aboriginal-particular",
   "metadata": {},
   "source": [
    "### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "distributed-interface",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(counts,  \n",
    "        colors = hex_colors);\n",
    "plt.title('Financial Services');\n",
    "#plt.savefig('Finance2_colors_pie.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finite-history",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "daily-produce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "new_df = pd.DataFrame(counts,hex_colors)\n",
    "new_df.columns =  ['counts']\n",
    "new_df\n",
    "\n",
    "new_df['percentage'] = new_df['counts']/sum(new_df['counts'])*100\n",
    "new_df = new_df.sort_values(by= 'percentage', ascending=False)\n",
    "new_df\n",
    "\n",
    "\n",
    "new_df = new_df.T\n",
    "\n",
    "new_df\n",
    "\n",
    "first_c = round(new_df.iloc[1,0],2)\n",
    "second_c = round(new_df.iloc[1,1],2)\n",
    "third_c = round(new_df.iloc[1,2],2)\n",
    "fourth_c = round(new_df.iloc[1,3],2)\n",
    "fifth_c = round(new_df.iloc[1,4],2)\n",
    "\n",
    "second_c\n",
    "\n",
    "final_colors = new_df.columns\n",
    "\n",
    "final_colors\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (1,15)\n",
    "\n",
    "barWidth = 1\n",
    "\n",
    "r = [0]\n",
    "# Create first color Bar\n",
    "plt.bar(r,first_c, color=final_colors[0], width=barWidth)\n",
    "\n",
    "# Create second color bar\n",
    "plt.bar(r,second_c, bottom = first_c,color=final_colors[1],  width=barWidth)\n",
    "\n",
    "# Create third color bar\n",
    "plt.bar(r,third_c,\n",
    "        color=final_colors[2], bottom = first_c + second_c, width=barWidth)\n",
    "\n",
    "# Create fourth color bar\n",
    "plt.bar(r,fourth_c,\n",
    "        color=final_colors[3], bottom = first_c + second_c+ third_c,  width=barWidth)\n",
    "\n",
    "# Create fifth color bar\n",
    "plt.bar(r,fifth_c,\n",
    "        color=final_colors[4], bottom = first_c + second_c +third_c +fourth_c , width=barWidth)\n",
    "\n",
    "plt.ylim(0,100)\n",
    "\n",
    "# Custom x axis\n",
    "plt.axis('off')\n",
    "#plt.savefig('bar_finance.png')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "light-dominant",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.7.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}