eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = 'data/car_evaluation.csv'\n",
    "df = pd.read_csv(data, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>high</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1  2  3      4     5      6\n",
       "0  vhigh  vhigh  2  2  small   low  unacc\n",
       "1  vhigh  vhigh  2  2  small   med  unacc\n",
       "2  vhigh  vhigh  2  2  small  high  unacc\n",
       "3  vhigh  vhigh  2  2    med   low  unacc\n",
       "4  vhigh  vhigh  2  2    med   med  unacc"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1728 entries, 0 to 1727\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   0       1728 non-null   object\n",
      " 1   1       1728 non-null   object\n",
      " 2   2       1728 non-null   object\n",
      " 3   3       1728 non-null   object\n",
      " 4   4       1728 non-null   object\n",
      " 5   5       1728 non-null   object\n",
      " 6   6       1728 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing column names with their real names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['buying', 'meant', 'doors', 'persons', 'lug_boot', 'safety', 'class'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# real names\n",
    "real_cols_names = ['buying', 'meant', 'doors', 'persons', 'lug_boot', 'safety', 'class']\n",
    "\n",
    "# replacing column names\n",
    "df.columns = real_cols_names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1728 entries, 0 to 1727\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   buying    1728 non-null   object\n",
      " 1   meant     1728 non-null   object\n",
      " 2   doors     1728 non-null   object\n",
      " 3   persons   1728 non-null   object\n",
      " 4   lug_boot  1728 non-null   object\n",
      " 5   safety    1728 non-null   object\n",
      " 6   class     1728 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>buying</th>\n",
       "      <th>meant</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buying  meant doors persons lug_boot safety  class\n",
       "count    1728   1728  1728    1728     1728   1728   1728\n",
       "unique      4      4     4       3        3      3      4\n",
       "top     vhigh  vhigh     2       2    small    low  unacc\n",
       "freq      432    432   432     576      576    576   1210"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying      0\n",
       "meant       0\n",
       "doors       0\n",
       "persons     0\n",
       "lug_boot    0\n",
       "safety      0\n",
       "class       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['safety'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "safety\n",
       "high    Axes(0.125,0.11;0.775x0.77)\n",
       "low     Axes(0.125,0.11;0.775x0.77)\n",
       "med     Axes(0.125,0.11;0.775x0.77)\n",
       "Name: buying, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('safety')['buying'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export the processed data to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df to csv\n",
    "df.to_csv('data/car_evaluation_processed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlops",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}