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Created May 10, 2022 10:36
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OneHotEncoder_vs_get_dummies
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OneHotEncoder_vs_get_dummies.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "e905e1ce",
"metadata": {},
"source": [
"# OneHotEncoder Vs get_dummies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "19b73275",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5e919c45",
"metadata": {},
"outputs": [],
"source": [
"df = sns.load_dataset('tips')\n",
"df = df[['total_bill', 'tip', 'day', 'size']]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8d2caae5",
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>day</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16.99</td>\n",
" <td>1.01</td>\n",
" <td>Sun</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.34</td>\n",
" <td>1.66</td>\n",
" <td>Sun</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>21.01</td>\n",
" <td>3.50</td>\n",
" <td>Sun</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>23.68</td>\n",
" <td>3.31</td>\n",
" <td>Sun</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>24.59</td>\n",
" <td>3.61</td>\n",
" <td>Sun</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip day size\n",
"0 16.99 1.01 Sun 2\n",
"1 10.34 1.66 Sun 3\n",
"2 21.01 3.50 Sun 3\n",
"3 23.68 3.31 Sun 2\n",
"4 24.59 3.61 Sun 4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "markdown",
"id": "e2244d85",
"metadata": {},
"source": [
"## Scikit-learn OneHotEncoder"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0cecdd2c",
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('tip', axis=1)\n",
"y = df['tip']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c19f3095",
"metadata": {},
"outputs": [],
"source": [
"ohe = OneHotEncoder(handle_unknown='ignore', sparse=False, dtype='int')\n",
"ohe.fit(X_train[['day']])\n",
"\n",
"def get_ohe(df):\n",
" temp_df = pd.DataFrame(data=ohe.transform(df[['day']]), columns=ohe.get_feature_names_out())\n",
" df.drop(columns=['day'], axis=1, inplace=True)\n",
" df = pd.concat([df.reset_index(drop=True), temp_df], axis=1)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ebea7235",
"metadata": {},
"outputs": [],
"source": [
"X_train = get_ohe(X_train)\n",
"X_test = get_ohe(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a4c6a11f",
"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>total_bill</th>\n",
" <th>size</th>\n",
" <th>day_Fri</th>\n",
" <th>day_Sat</th>\n",
" <th>day_Sun</th>\n",
" <th>day_Thur</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>26.88</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>32.68</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17.89</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20.49</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>48.17</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill size day_Fri day_Sat day_Sun day_Thur\n",
"0 26.88 4 0 0 1 0\n",
"1 32.68 2 0 0 0 1\n",
"2 17.89 2 0 0 1 0\n",
"3 20.49 2 0 1 0 0\n",
"4 48.17 6 0 0 1 0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9c32e5fd",
"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>total_bill</th>\n",
" <th>size</th>\n",
" <th>day_Fri</th>\n",
" <th>day_Sat</th>\n",
" <th>day_Sun</th>\n",
" <th>day_Thur</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill size day_Fri day_Sat day_Sun day_Thur\n",
"0 25 2 0 0 1 0\n",
"1 45 4 0 0 0 0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new = pd.DataFrame( {'total_bill': [25, 45], 'day': ['Sun', 'Mon'], 'size': [2, 4]} )\n",
"get_ohe(X_test_new)"
]
},
{
"cell_type": "markdown",
"id": "148f3359",
"metadata": {},
"source": [
"### One hot encoding in pipeline"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "49542fbd",
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('tip', axis=1)\n",
"y = df['tip']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1e620741",
"metadata": {},
"outputs": [],
"source": [
"numeric_preprocessor = Pipeline(steps=[\n",
" (\"scaler\", MinMaxScaler()) \n",
"])\n",
"\n",
"categorical_preprocessor = Pipeline(steps=[ \n",
" (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")) \n",
"])\n",
"\n",
"preprocessor = ColumnTransformer([\n",
" (\"categorical\", categorical_preprocessor, [\"day\"]),\n",
" (\"numerical\", numeric_preprocessor, [\"total_bill\", \"size\"])\n",
"])\n",
"\n",
"pipe = Pipeline(steps=[\n",
" (\"preprocessor\", preprocessor), \n",
" (\"classifier\", LinearRegression())\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3f9e210c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5706168878130049"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.fit(X_train, y_train)\n",
"pipe.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "dea80c6d",
"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>total_bill</th>\n",
" <th>day</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Sun</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>Mon</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill day size\n",
"0 25 Sun 2\n",
"1 45 Mon 4"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new = pd.DataFrame( {'total_bill': [25, 45], 'day': ['Sun', 'Mon'], 'size': [2, 4]} )\n",
"X_test_new"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7770b3a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3.27325822, 5.436413 ])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.predict(X_test_new)"
]
},
{
"cell_type": "markdown",
"id": "ddb6ac19",
"metadata": {},
"source": [
"## Pandas get_dummies "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "26e6c5eb",
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('tip', axis=1)\n",
"y = df['tip']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "248ff42a",
"metadata": {},
"outputs": [],
"source": [
"X_train = pd.get_dummies(X_train, columns=['day'])\n",
"X_test = pd.get_dummies(X_test, columns=['day'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0fd105d0",
"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>total_bill</th>\n",
" <th>size</th>\n",
" <th>day_Thur</th>\n",
" <th>day_Fri</th>\n",
" <th>day_Sat</th>\n",
" <th>day_Sun</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>26.88</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>32.68</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>17.89</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>20.49</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>48.17</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill size day_Thur day_Fri day_Sat day_Sun\n",
"7 26.88 4 0 0 0 1\n",
"83 32.68 2 1 0 0 0\n",
"176 17.89 2 0 0 0 1\n",
"106 20.49 2 0 0 1 0\n",
"156 48.17 6 0 0 0 1"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0c048ce6",
"metadata": {},
"outputs": [],
"source": [
"cols = X_test.columns.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "015e66a3",
"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>total_bill</th>\n",
" <th>size</th>\n",
" <th>day_Mon</th>\n",
" <th>day_Sun</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill size day_Mon day_Sun\n",
"0 25 2 0 1\n",
"1 45 4 1 0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new = pd.DataFrame( {'total_bill': [25, 45], 'day': ['Sun', 'Mon'], 'size': [2, 4]} )\n",
"pd.get_dummies(X_test_new, columns=['day']).head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "75cd1330",
"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>total_bill</th>\n",
" <th>day</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Sun</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>Mon</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill day size\n",
"0 25 Sun 2\n",
"1 45 Mon 4"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "6abea7c5",
"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",
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"\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>total_bill</th>\n",
" <th>size</th>\n",
" <th>day_Thur</th>\n",
" <th>day_Fri</th>\n",
" <th>day_Sat</th>\n",
" <th>day_Sun</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill size day_Thur day_Fri day_Sat day_Sun\n",
"0 25 2 0.0 0.0 0.0 1\n",
"1 45 4 0.0 0.0 0.0 0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new = pd.get_dummies(X_test_new, columns=['day'])\n",
"X_test_new.reindex(columns=cols).fillna(0) "
]
},
{
"cell_type": "markdown",
"id": "8c3be6c6",
"metadata": {},
"source": [
"### get_dummies in Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "72a6c0fc",
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('tip', axis=1)\n",
"y = df['tip']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "391df006",
"metadata": {},
"outputs": [],
"source": [
"cols = [\"total_bill\", \"size\", \"day_Fri\", \"day_Sat\", \"day_Sun\", \"day_Thur\"]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fafc567b",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"\n",
"class PreprocessorTransformer(BaseEstimator, TransformerMixin):\n",
" def __init__(self, cols):\n",
" self.cols = cols\n",
" \n",
" def fit(self, X, y = None):\n",
" return self\n",
" \n",
" def transform(self, X, y = None):\n",
" X = pd.get_dummies(X, columns=['day'])\n",
" X = X.reindex(columns=self.cols).fillna(0) \n",
" return X[self.cols]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ae0ec1a3",
"metadata": {},
"outputs": [],
"source": [
"preprocessor = Pipeline(steps=[ \n",
" (\"preprocessor\", PreprocessorTransformer(cols)) \n",
"])\n",
"\n",
"pipe = Pipeline(steps=[\n",
" (\"preprocessor\", preprocessor), \n",
" (\"classifier\", LinearRegression())\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "4e363485",
"metadata": {},
"outputs": [],
"source": [
"pipe.fit(X_train, y_train);"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "612e202f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5706168878130053"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "05518bcc",
"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>total_bill</th>\n",
" <th>day</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Sun</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>Mon</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill day size\n",
"0 25 Sun 2\n",
"1 45 Mon 4"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test_new = pd.DataFrame( {'total_bill': [25, 45], 'day': ['Sun', 'Mon'], 'size': [2, 4]} )\n",
"X_test_new"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e475f927",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3.27325822, 5.436413 ])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipe.predict(X_test_new)"
]
},
{
"cell_type": "markdown",
"id": "a49f3eaf",
"metadata": {},
"source": [
"## Example"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "2930f647",
"metadata": {},
"outputs": [],
"source": [
"data = {\"Airline\": [\"American Airlines\", \"Delta Air Lines\", \"United Airlines\"]}\n",
"df = pd.DataFrame(data=data)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f0fd2cd9",
"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>Airline_American Airlines</th>\n",
" <th>Airline_Delta Air Lines</th>\n",
" <th>Airline_United Airlines</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Airline_American Airlines Airline_Delta Air Lines Airline_United Airlines\n",
"0 1 0 0\n",
"1 0 1 0\n",
"2 0 0 1"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.get_dummies(df)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "1f34ab08",
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>American Airlines</th>\n",
" <th>Delta Air Lines</th>\n",
" <th>United Airlines</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" American Airlines Delta Air Lines United Airlines\n",
"0 1 0 0\n",
"1 0 1 0\n",
"2 0 0 1"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ohe = OneHotEncoder(sparse=False)\n",
"pd.DataFrame(ohe.fit_transform(df), columns=ohe.categories_, dtype='int8')"
]
}
],
"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
}
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