01_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "584cc0c7",
   "metadata": {},
   "source": [
    "# Initial exploration of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14da7e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6de6057e",
   "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",
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       "    }\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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>4341.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>4354.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4312.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4425.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5   \n",
       "5  15.0          8         429.0       198.0  4341.0          10.0   \n",
       "6  14.0          8         454.0       220.0  4354.0           9.0   \n",
       "7  14.0          8         440.0       215.0  4312.0           8.5   \n",
       "8  14.0          8         455.0       225.0  4425.0          10.0   \n",
       "9  15.0          8         390.0       190.0  3850.0           8.5   \n",
       "\n",
       "   Model Year Origin  \n",
       "0          70  India  \n",
       "1          70  India  \n",
       "2          70  India  \n",
       "3          70  India  \n",
       "4          70  India  \n",
       "5          70  India  \n",
       "6          70  India  \n",
       "7          70  India  \n",
       "8          70  India  \n",
       "9          70  India  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/auto_mpg.csv\",\n",
    "                 header=0,\n",
    "                 names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower','Weight',\\\n",
    "                          'Acceleration', 'Model Year', 'Origin'])\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be324e7",
   "metadata": {},
   "source": [
    "## Initial exploration of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f63cbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "828de12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MPG',\n",
       " 'Cylinders',\n",
       " 'Displacement',\n",
       " 'Horsepower',\n",
       " 'Weight',\n",
       " 'Acceleration',\n",
       " 'Model Year',\n",
       " 'Origin']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f815a4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=398, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4834ca39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   MPG           398 non-null    float64\n",
      " 1   Cylinders     398 non-null    int64  \n",
      " 2   Displacement  398 non-null    float64\n",
      " 3   Horsepower    392 non-null    float64\n",
      " 4   Weight        398 non-null    float64\n",
      " 5   Acceleration  398 non-null    float64\n",
      " 6   Model Year    398 non-null    int64  \n",
      " 7   Origin        398 non-null    object \n",
      "dtypes: float64(5), int64(2), object(1)\n",
      "memory usage: 25.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7fe07f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78c15702",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb5c8bc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d6dcb67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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