01_04_begin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "584cc0c7",
   "metadata": {},
   "source": [
    "# Create new features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14da7e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6de6057e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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": 2,
     "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": 3,
   "id": "3f63cbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f815a4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=398, step=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": "markdown",
   "id": "1a72620f",
   "metadata": {},
   "source": [
    "## Summary Statistics and Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78c15702",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MPG   Cylinders  Displacement  Horsepower       Weight  \\\n",
       "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
       "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
       "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
       "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       Acceleration  Model Year  \n",
       "count    398.000000  398.000000  \n",
       "mean      15.568090   76.010050  \n",
       "std        2.757689    3.697627  \n",
       "min        8.000000   70.000000  \n",
       "25%       13.825000   73.000000  \n",
       "50%       15.500000   76.000000  \n",
       "75%       17.175000   79.000000  \n",
       "max       24.800000   82.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb5c8bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Origin\n",
       "count     398\n",
       "unique      3\n",
       "top     India\n",
       "freq      249"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d6dcb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India      249\n",
       "Germany     79\n",
       "USA         70\n",
       "Name: Origin, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "679a80f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Origin'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc7e7da",
   "metadata": {},
   "source": [
    "## Create new features from existing ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4edbf71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c87081ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7e48fd0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}