{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Author：马肖\n",
"#### E-Mail：maxiaoscut@aliyun.com\n",
"#### GitHub：https://github.com/Albertsr"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd \n",
"\n",
"\n",
"rdg = np.random.RandomState(2017)\n",
"age = rdg.randint(1, 78, 20)\n",
"fare = rdg.uniform(10, 100, 20)\n",
"df = pd.DataFrame({'Age':age, 'Fare':fare}).round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 离散化后的特征对异常数据有很强的鲁棒性：比如一个特征是年龄>30是1，否则0。\n",
"- 如果特征没有离散化，一个异常数据“年龄300岁”会给模型造成很大的干扰；"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. 等距分箱"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 方法一：运用pd.cut()\n",
"- [pandas.cut官方文档](http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.cut.html)\n",
"- pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(0.928, 19.0] 8\n",
"(55.0, 73.0] 5\n",
"(37.0, 55.0] 5\n",
"(19.0, 37.0] 2\n",
"Name: Age, dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.cut(df['Age'], 4).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"中年 8\n",
"少年 8\n",
"老年 3\n",
"青年 1\n",
"Name: Age, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_catogary = pd.cut(df['Age'], bins=[0, 17, 35, 59, 100], labels=['少年', '青年', '中年', '老年'])\n",
"age_catogary.value_counts()"
]
},
{
"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>Age</th>\n",
" <th>Fare</th>\n",
" <th>Age_少年</th>\n",
" <th>Age_青年</th>\n",
" <th>Age_中年</th>\n",
" <th>Age_老年</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>60</td>\n",
" <td>76.21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>71.77</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>71</td>\n",
" <td>24.76</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>14</td>\n",
" <td>71.50</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>43</td>\n",
" <td>43.19</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": [
" Age Fare Age_少年 Age_青年 Age_中年 Age_老年\n",
"0 60 76.21 0 0 0 1\n",
"1 10 71.77 1 0 0 0\n",
"2 71 24.76 0 0 0 1\n",
"3 14 71.50 1 0 0 0\n",
"4 43 43.19 0 0 1 0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_dummies = pd.get_dummies(age_catogary, prefix='Age')\n",
"df = df.join(age_dummies)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 方法二：运用np.digitize进行等距分段"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 25.33333333, 49.66666667, 74. ])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 将年龄分为3个区间\n",
"bins = np.linspace(df['Age'].min(), df['Age'].max()+1, 4)\n",
"bins"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 1, 3, 1, 2, 1, 3, 3, 1, 2, 1, 1, 3, 1, 2, 2, 2, 1, 3, 2],\n",
" dtype=int64)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_bins = np.digitize(df['Age'], bins)\n",
"age_bins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 等频分箱qcut\n",
"- [pandas.qcut官方文档](http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html)\n",
"- Discretize variable into equal-sized buckets based on rank or based on sample quantiles.\n",
"- cut将根据值本身来选择箱子均匀间隔，qcut是根据这些值的频率来选择箱子的均匀间隔"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"age_bins = pd.qcut(df['Age'], 4)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(53.0, 73.0] 5\n",
"(37.5, 53.0] 5\n",
"(9.75, 37.5] 5\n",
"(0.999, 9.75] 5\n",
"Name: Age, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.qcut(df['Age'], 4).value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. 自定义区间对费用分段"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Fare</th>\n",
" <th>Age_少年</th>\n",
" <th>Age_青年</th>\n",
" <th>Age_中年</th>\n",
" <th>Age_老年</th>\n",
" <th>Fare_high</th>\n",
" <th>Fare_low</th>\n",
" <th>Fare_middle</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>60</td>\n",
" <td>76.21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>71.77</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</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>71</td>\n",
" <td>24.76</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>14</td>\n",
" <td>71.50</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>43</td>\n",
" <td>43.19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Fare Age_少年 Age_青年 Age_中年 Age_老年 Fare_high Fare_low \\\n",
"0 60 76.21 0 0 0 1 0 1 \n",
"1 10 71.77 1 0 0 0 0 0 \n",
"2 71 24.76 0 0 0 1 1 0 \n",
"3 14 71.50 1 0 0 0 0 0 \n",
"4 43 43.19 0 0 1 0 1 0 \n",
"\n",
" Fare_middle \n",
"0 0 \n",
"1 1 \n",
"2 0 \n",
"3 1 \n",
"4 0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def fare_rate_func(x):\n",
" if x <= np.percentile(df['Fare'], 25):\n",
" return 'high'\n",
" elif np.percentile(df['Fare'],25) < x <= np.percentile(df['Fare'], 75):\n",
" return 'middle'\n",
" else:\n",
" return 'low'\n",
" \n",
"df['fare_rate'] = df['Fare'].apply(fare_rate_func)\n",
"# df['fare_rate'] = df['Fare'].map(fare_rate_func)\n",
"fare_dummies = pd.get_dummies(df['fare_rate'], prefix='Fare')\n",
"df.drop(['fare_rate'], axis=1, inplace=True)\n",
"df = df.join(fare_dummies)\n",
"df.head()"
]
}
],
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