{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f8c6593-fa93-44c3-bbc2-7191da00ed1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf716145-8bf5-41d5-a8dc-ecad0802ed28",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_df = pd.read_csv(\"train.csv\", header=None)\n",
    "labels_df.columns = [\"path1\", \"path2\", \"path3\", \"is_collected\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82b36668-d24c-42fb-a956-6db50cf93c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "items = []\n",
    "for is_collected in labels_df[\"is_collected\"].values:\n",
    "    item = \"True\" if is_collected else \"False\"\n",
    "    items.append(item)\n",
    "items = np.array(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1401cdb8-6fec-4635-b1f4-dd96becd81eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gSJKagSBJAgwESVIzECRJwOL/PwRJ+lBaf+8fLduxX//yZy/Ifr1CkCQBBoIkqRkIkiTAQJAkNQNBkgQYCJKkZiBIkgADQZLUDARJEmAgSJKagSBJAgwESVIzECRJgIEgSWoXTSAk2Zbk1STjSe5d7vFI0ofNRREISVYA/wH4J8CNwF1JblzeUUnSh8tFEQjAZmC8qv68qv4f8CSwfZnHJEkfKhfL/5i2Bnhj4P0x4O9Pb5RkF7Cr376X5NUFHu8a4K8W2HdRcv9yHFXSpST3L+oz7JfOteFiCYQMqdVZhaq9wN5FHywZq6pNi92PJC2HC/UZdrFMGR0D1g28XwscX6axSNKH0sUSCN8DNiS5PskvAKPAgWUekyR9qFwUU0ZVdSrJ54E/BlYAj1bV0Qt4yEVPO0nSMrogn2GpOmuqXpL0IXSxTBlJkpaZgSBJAi6SewjnQ5LTwJGB0u1V9fo52r5XVR9bkoFJ0hwkuRo42G//HnAamOj3m/tLuxd2DJfKPYT5fMgbCJIuZkm+BLxXVf9+oLayqk5dyONeslNGST6W5GCSHyQ5kuSsn8JIsjrJd5McTvJSkn/Q9V9Lcqj7/n4Sw0PSkkvyWJKvJvkOcH+SLyX5VwPbX0qyvtf/eZIX+/PsP/ZvxM3LpRQIq/of4nCSbwHvA5+rqk8BnwG+kmT6N6L/GfDHVbUR+ARwOMk1wBeBX+2+Y8C/XLKzkKQz/QqTn0dfOFeDJB8H/ilwa3+enQZ+Y74HumTuIQD/t/8hAEjyUeDfJvmHwM+Y/L2k64D/NdDne8Cj3fYPq+pwkn/E5C+u/vfOj18ADi3NKUjSWX6/qk7P0mYrcAvwvf7cWgWcnO+BLqVAmO43gBHglqr6aZLXgV8cbFBV3+3A+CzwRJJ/B7wNPFdVdy31gCVpiP8zsH6KM2d2pj7TAuyrqvsWc6BLacpouiuAkx0Gn2HIL/wl+aVu8zXgEeBTwP8Abk1yQ7f5W0l+ZQnHLUnn8jqTn1Mk+RRwfdcPAnckuba3XdWfb/NyKV8hfB34z0nGgMPAj4a0+TTwr5P8FHgPuLuqJpL8C+AbSS7rdl8E/uyCj1iSZvZN4O4kh5mc8v4zgKp6OckXgT9J8hHgp8Bu4C/ms/NL5rFTSdLiXMpTRpKkeTAQJEmAgSBJagaCJAkwECRJzUCQJAEGgiSp/X+HnPkIxXtoiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(items)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bca04b79-e9e7-4e45-965c-f94a6cd1cf2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2577"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(items == \"True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4405f10a-9c73-4d5c-b530-7e01870f2a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29796"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(items == \"False\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}