Dataset_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description \n",
    "This notebook represents a small utility tool used to perform explanatory data analysis as we suspected the posibility of class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "from shutil import copyfile\n",
    "from shutil import rmtree\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"data/train_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique labels: 80\n",
      "[21 29 17 50  8 42 68 10  9 39 11 44 49  5 65 61 56 43 54 40 23 64 58 33\n",
      " 45  3 12 46 59 77 14 13 18 69 47 63 52 66 80 30 55 53 19 67 78 24  2 25\n",
      " 37 57 36  6  1 72 27 60 31 48 74 51 15 26 75 62  4 22 41 79 34 73  7 71\n",
      " 35 38 76 28 70 16 32 20]\n",
      "      img_name  label\n",
      "0  train_1.jpg     21\n",
      "1  train_2.jpg     29\n",
      "2  train_3.jpg     17\n",
      "3  train_4.jpg     21\n",
      "4  train_5.jpg     50\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique labels:\",len(train_df.label.unique()))\n",
    "print(train_df.label.unique())\n",
    "print(train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hrvojegrgic/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118a80860>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.groupby('label')['img_name'].nunique().plot(\"bar\", figsize=(18,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1     474\n",
       "2     433\n",
       "3     412\n",
       "4     373\n",
       "5     310\n",
       "     ... \n",
       "76    302\n",
       "77    387\n",
       "78    274\n",
       "79    421\n",
       "80    369\n",
       "Name: img_name, Length: 80, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.groupby('label')['img_name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}