含有缺失值数据卡方分箱Python代码

以下是一个简单的Python代码示例，用于将含有缺失值的数据进行卡方分箱：

import pandas as pd
import numpy as np
from scipy.stats import chi2_contingency

def chisq_bin(data, col, target, max_bins=5, iv_min=0.02):
    """
    卡方分箱函数
    :param data: 数据集
    :param col: 需要分箱的变量
    :param target: 目标变量
    :param max_bins: 最大分箱数
    :param iv_min: 最小IV值
    :return: 分箱结果
    """
    data = data[[col, target]].copy()
    data['missing'] = data[col].isnull().astype(int)
    data = data.groupby(['missing', target]).size().unstack().fillna(0)
    data.columns = ['neg', 'pos']
    data['tot'] = data['neg'] + data['pos']
    data['neg_prop'] = data['neg'] / data['neg'].sum()
    data['pos_prop'] = data['pos'] / data['pos'].sum()
    data['tot_prop'] = data['tot'] / data['tot'].sum()
    data['woe'] = np.log((data['neg'] / data['neg'].sum()) / (data['pos'] / data['pos'].sum()))
    data['iv'] = (data['neg_prop'] - data['pos_prop']) * data['woe']
    iv = data['iv'].sum()
    if iv < iv_min:
        return None
    cut_points = [np.nan]
    while len(cut_points) < max_bins:
        data = data.sort_values('woe')
        x = np.array(data['neg'])
        y = np.array(data['pos'])
        xy = np.array([x, y])
        chisq = chi2_contingency(xy)[0]
        p = chi2_contingency(xy)[1]
        if p < 0.05:
            cut_point = data.index[0]
            cut_points.append(cut_point)
            data = data.loc[cut_point:].copy()
            data['woe'] = np.log((data['neg'] / data['neg'].sum()) / (data['pos'] / data['pos'].sum()))
            data['iv'] = (data['neg_prop'] - data['pos_prop']) * data['woe']
            iv = data['iv'].sum()
            if iv < iv_min:
                break
        else:
            break
    cut_points.append(np.inf)
    labels = range(len(cut_points) - 1)
    result = pd.cut(data[col], cut_points, labels=labels, include_lowest=True)
    return result

这个函数将缺失值视为一个单独的类别，并根据卡方检验的结果将数据分成多个箱子，返回一个Pandas Series对象，其中每个值表示变量的分箱编号。您可以使用此结果来替换原始数据中的变量，然后使用OneHot编码或WOE编码等其他技术进一步处理。