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这篇文章主要讲解了python实现卡方值分箱算法的代码详解，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。

原理很简单，初始分20箱或更多，先确保每箱中都含有0，1标签，对不包含0，1标签的箱向前合并，计算各箱卡方值，对卡方值最小的箱向后合并，代码如下

import pandas as pd
import numpy as np
import scipy
from scipy import stats
def chi_bin(DF,var,target,binnum=5,maxcut=20):
  '''
  DF:data
  var:variable
  target:target / label
  binnum: the number of bins output
  maxcut: initial bins number 
  '''
  
  data=DF[[var,target]]
  #equifrequent cut the var into maxcut bins
  data["cut"],breaks=pd.qcut(data[var],q=maxcut,duplicates="drop",retbins=True)
  #count 1,0 in each bin
  count_1=data.loc[data[target]==1].groupby("cut")[target].count()
  count_0=data.loc[data[target]==0].groupby("cut")[target].count()
  #get bins value: min,max,count 0,count 1
  bins_value=[*zip(breaks[:maxcut-1],breaks[1:],count_0,count_1)]
  #define woe
  def woe_value(bins_value):
    df_woe=pd.DataFrame(bins_value)
    df_woe.columns=["min","max","count_0","count_1"]
    df_woe["total"]=df_woe.count_1+df_woe.count_0
    df_woe["bad_rate"]=df_woe.count_1/df_woe.total
    df_woe["woe"]=np.log((df_woe.count_0/df_woe.count_0.sum())/(df_woe.count_1/df_woe.count_1.sum()))
    return df_woe
  #define iv
  def iv_value(df_woe):
    rate=(df_woe.count_0/df_woe.count_0.sum())-(df_woe.count_1/df_woe.count_1.sum())
    iv=np.sum(rate * df_woe.woe)
    return iv
  #make sure every bin contain 1 and 0
  ##first bin merge backwards
  for i in range(len(bins_value)):
    if 0 in bins_value[0][2:]:
      bins_value[0:2]=[(
        bins_value[0][0],
        bins_value[1][1],
        bins_value[0][2]+bins_value[1][2],
        bins_value[0][3]+bins_value[1][3])]
      continue
  ##bins merge forwards
    if 0 in bins_value[i][2:]:
      bins_value[i-1:i+1]=[(
        bins_value[i-1][0],
        bins_value[i][1],
        bins_value[i-1][2]+bins_value[i][2],
        bins_value[i-1][3]+bins_value[i][3])]
      break
    else:
      break
  
  #calculate chi-square merge the minimum chisquare    
  while len(bins_value)>binnum:
    chi_squares=[]
    for i in range(len(bins_value)-1):
      a=bins_value[i][2:]
      b=bins_value[i+1][2:]
      chi_square=scipy.stats.chi2_contingency([a,b])[0]
      chi_squares.append(chi_square)
  #merge the minimum chisquare backwards
    i = chi_squares.index(min(chi_squares))
               
    bins_value[i:i+2]=[(
      bins_value[i][0],
      bins_value[i+1][1],
      bins_value[i][2]+bins_value[i+1][2],
      bins_value[i][3]+bins_value[i+1][3])]
    
    df_woe=woe_value(bins_value)
    
  #print bin number and iv
    print("箱数：{},iv:{:.6f}".format(len(bins_value),iv_value(df_woe)))
  #return bins and woe information 
  return woe_value(bins_value)             

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