dataPreprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex\n",
       "0    Adelie  Torgersen            39.1  ...              181.0       3750.0    Male\n",
       "1    Adelie  Torgersen            39.5  ...              186.0       3800.0  Female\n",
       "2    Adelie  Torgersen            40.3  ...              195.0       3250.0  Female\n",
       "3    Adelie  Torgersen             NaN  ...                NaN          NaN     NaN\n",
       "4    Adelie  Torgersen            36.7  ...              193.0       3450.0  Female\n",
       "..      ...        ...             ...  ...                ...          ...     ...\n",
       "339  Gentoo     Biscoe             NaN  ...                NaN          NaN     NaN\n",
       "340  Gentoo     Biscoe            46.8  ...              215.0       4850.0  Female\n",
       "341  Gentoo     Biscoe            50.4  ...              222.0       5750.0    Male\n",
       "342  Gentoo     Biscoe            45.2  ...              212.0       5200.0  Female\n",
       "343  Gentoo     Biscoe            49.9  ...              213.0       5400.0    Male\n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins = sns.load_dataset('penguins')\n",
    "penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species               0\n",
       "island                0\n",
       "bill_length_mm        2\n",
       "bill_depth_mm         2\n",
       "flipper_length_mm     2\n",
       "body_mass_g           2\n",
       "sex                  11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']\n"
     ]
    }
   ],
   "source": [
    "num_cols = penguins.select_dtypes('number').columns\n",
    "print(num_cols.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['species', 'island', 'sex']\n"
     ]
    }
   ],
   "source": [
    "cat_cols = penguins.select_dtypes(exclude='number').columns\n",
    "print(cat_cols.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_imp = SimpleImputer()\n",
    "cat_imp = SimpleImputer(strategy='most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.10000</td>\n",
       "      <td>18.70000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.50000</td>\n",
       "      <td>17.40000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.30000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>3250.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>43.92193</td>\n",
       "      <td>17.15117</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.70000</td>\n",
       "      <td>19.30000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>3450.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>43.92193</td>\n",
       "      <td>17.15117</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.80000</td>\n",
       "      <td>14.30000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>4850.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.40000</td>\n",
       "      <td>15.70000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>5750.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.20000</td>\n",
       "      <td>14.80000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.90000</td>\n",
       "      <td>16.10000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex\n",
       "0    Adelie  Torgersen        39.10000  ...         181.000000  3750.000000    Male\n",
       "1    Adelie  Torgersen        39.50000  ...         186.000000  3800.000000  Female\n",
       "2    Adelie  Torgersen        40.30000  ...         195.000000  3250.000000  Female\n",
       "3    Adelie  Torgersen        43.92193  ...         200.915205  4201.754386     NaN\n",
       "4    Adelie  Torgersen        36.70000  ...         193.000000  3450.000000  Female\n",
       "..      ...        ...             ...  ...                ...          ...     ...\n",
       "339  Gentoo     Biscoe        43.92193  ...         200.915205  4201.754386     NaN\n",
       "340  Gentoo     Biscoe        46.80000  ...         215.000000  4850.000000  Female\n",
       "341  Gentoo     Biscoe        50.40000  ...         222.000000  5750.000000    Male\n",
       "342  Gentoo     Biscoe        45.20000  ...         212.000000  5200.000000  Female\n",
       "343  Gentoo     Biscoe        49.90000  ...         213.000000  5400.000000    Male\n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins[num_cols] = num_imp.fit_transform(penguins[num_cols])\n",
    "penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.10000</td>\n",
       "      <td>18.70000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.50000</td>\n",
       "      <td>17.40000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.30000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>3250.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>43.92193</td>\n",
       "      <td>17.15117</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.70000</td>\n",
       "      <td>19.30000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>3450.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>43.92193</td>\n",
       "      <td>17.15117</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.80000</td>\n",
       "      <td>14.30000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>4850.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.40000</td>\n",
       "      <td>15.70000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>5750.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.20000</td>\n",
       "      <td>14.80000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.90000</td>\n",
       "      <td>16.10000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex\n",
       "0    Adelie  Torgersen        39.10000  ...         181.000000  3750.000000    Male\n",
       "1    Adelie  Torgersen        39.50000  ...         186.000000  3800.000000  Female\n",
       "2    Adelie  Torgersen        40.30000  ...         195.000000  3250.000000  Female\n",
       "3    Adelie  Torgersen        43.92193  ...         200.915205  4201.754386    Male\n",
       "4    Adelie  Torgersen        36.70000  ...         193.000000  3450.000000  Female\n",
       "..      ...        ...             ...  ...                ...          ...     ...\n",
       "339  Gentoo     Biscoe        43.92193  ...         200.915205  4201.754386    Male\n",
       "340  Gentoo     Biscoe        46.80000  ...         215.000000  4850.000000  Female\n",
       "341  Gentoo     Biscoe        50.40000  ...         222.000000  5750.000000    Male\n",
       "342  Gentoo     Biscoe        45.20000  ...         212.000000  5200.000000  Female\n",
       "343  Gentoo     Biscoe        49.90000  ...         213.000000  5400.000000    Male\n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins[['sex']] = cat_imp.fit_transform(penguins[['sex']])\n",
    "penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_enc = LabelEncoder()\n",
    "penguins['sex'] = sex_enc.fit_transform(penguins['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      0\n",
      "2      0\n",
      "3      1\n",
      "4      0\n",
      "      ..\n",
      "339    1\n",
      "340    0\n",
      "341    1\n",
      "342    0\n",
      "343    1\n",
      "Name: sex, Length: 344, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(penguins['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_enc = OneHotEncoder(drop='first')\n",
    "dummy_cols = cat_enc.fit_transform(penguins[cat_cols]).toarray()\n",
    "dummy_df = pd.DataFrame(dummy_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Adelie       152\n",
       "Gentoo       124\n",
       "Chinstrap     68\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "island\n",
       "Biscoe       168\n",
       "Dream        124\n",
       "Torgersen     52\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins['island'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4\n",
       "0    0.0  0.0  0.0  1.0  1.0\n",
       "1    0.0  0.0  0.0  1.0  0.0\n",
       "2    0.0  0.0  0.0  1.0  0.0\n",
       "3    0.0  0.0  0.0  1.0  1.0\n",
       "4    0.0  0.0  0.0  1.0  0.0\n",
       "..   ...  ...  ...  ...  ...\n",
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       "340  0.0  1.0  0.0  0.0  0.0\n",
       "341  0.0  1.0  0.0  0.0  1.0\n",
       "342  0.0  1.0  0.0  0.0  0.0\n",
       "343  0.0  1.0  0.0  0.0  1.0\n",
       "\n",
       "[344 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
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       "      <td>-1.422488</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>-8.134940e-01</td>\n",
       "      <td>1.265563e-01</td>\n",
       "      <td>-1.065352</td>\n",
       "      <td>-0.503168</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>-6.663195e-01</td>\n",
       "      <td>4.317192e-01</td>\n",
       "      <td>-0.422507</td>\n",
       "      <td>-1.192003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>-1.307172e-15</td>\n",
       "      <td>1.806927e-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>-1.328605e+00</td>\n",
       "      <td>1.092905e+00</td>\n",
       "      <td>-0.565361</td>\n",
       "      <td>-0.941517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>-1.307172e-15</td>\n",
       "      <td>1.806927e-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>5.294731e-01</td>\n",
       "      <td>-1.450118e+00</td>\n",
       "      <td>1.006038</td>\n",
       "      <td>0.811880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>1.191758e+00</td>\n",
       "      <td>-7.380718e-01</td>\n",
       "      <td>1.506028</td>\n",
       "      <td>1.939064</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>2.351241e-01</td>\n",
       "      <td>-1.195816e+00</td>\n",
       "      <td>0.791756</td>\n",
       "      <td>1.250229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>1.099774e+00</td>\n",
       "      <td>-5.346299e-01</td>\n",
       "      <td>0.863183</td>\n",
       "      <td>1.500715</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g  sex\n",
       "0    Adelie  Torgersen   -8.870812e-01  ...          -1.422488    -0.565789    1\n",
       "1    Adelie  Torgersen   -8.134940e-01  ...          -1.065352    -0.503168    0\n",
       "2    Adelie  Torgersen   -6.663195e-01  ...          -0.422507    -1.192003    0\n",
       "3    Adelie  Torgersen   -1.307172e-15  ...           0.000000     0.000000    1\n",
       "4    Adelie  Torgersen   -1.328605e+00  ...          -0.565361    -0.941517    0\n",
       "..      ...        ...             ...  ...                ...          ...  ...\n",
       "339  Gentoo     Biscoe   -1.307172e-15  ...           0.000000     0.000000    1\n",
       "340  Gentoo     Biscoe    5.294731e-01  ...           1.006038     0.811880    0\n",
       "341  Gentoo     Biscoe    1.191758e+00  ...           1.506028     1.939064    1\n",
       "342  Gentoo     Biscoe    2.351241e-01  ...           0.791756     1.250229    0\n",
       "343  Gentoo     Biscoe    1.099774e+00  ...           0.863183     1.500715    1\n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "penguins[num_cols] = scaler.fit_transform(penguins[num_cols])\n",
    "penguins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PIPELINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "df = sns.load_dataset('penguins')\n",
    "num_cols = df.select_dtypes('number').columns\n",
    "cat_cols = df.select_dtypes(exclude='number').columns\n",
    "\n",
    "num_pipeline = Pipeline(\n",
    "    steps=[\n",
    "        ('imputer', SimpleImputer()),\n",
    "        ('scaler', StandardScaler())\n",
    "    ]\n",
    ")\n",
    "\n",
    "cat_pipeline = Pipeline(\n",
    "    steps = (\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('encoder', OneHotEncoder(drop='first'))\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = ColumnTransformer(\n",
    "    transformers = [\n",
    "        ('num', num_pipeline, num_cols),\n",
    "        ('cat', cat_pipeline, cat_cols)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.88708123,  0.78774251, -1.42248782, ...,  0.        ,\n",
       "         1.        ,  1.        ],\n",
       "       [-0.81349399,  0.12655633, -1.06535169, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [-0.66631952,  0.43171918, -0.42250666, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       ...,\n",
       "       [ 1.1917582 , -0.73807176,  1.50602843, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.23512413, -1.19581604,  0.79175618, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.09977416, -0.53462985,  0.8631834 , ...,  0.        ,\n",
       "         0.        ,  1.        ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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