pandasとscipyでクロス集計表のカイ2乗検定＋残差分析。調整済み標準化残差まで出せるように。

chi_sq_test.py

# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd
import scipy as sp
import scipy.stats
from pandas import DataFrame, Series


def chi_sq_test(df):
  res = {}
	#カイ2乗検定の実施→カイ2乗値、p値、自由度、期待値が戻り値
	df_chi = sp.stats.chi2_contingency(df)

	res = {
		"data" : df,
		#p値
		"p_val" : df_chi[1],
		#期待値
		"df_exp": DataFrame(df_chi[3])
	}

	#期待値のカラム名とインデックス名を基データに合わせる
	res["df_exp"].columns = df.columns
	res["df_exp"].index = df.index

	#残差
	res["df_res"] = df - res["df_exp"]
	res["df_res"].columns = df.columns
	res["df_res"].index = df.index

	#行％の計算
	arr = []
	for row in df.T:
		arr.append(df.T[row] / float(df.T[row].sum()))

	res["df_per"] = DataFrame(arr)
	res["df_per"].columns = df.columns
	res["df_per"].index = df.index
	
	
	#残差分析用前処理
	row_sum = df.T.sum()
	col_sum = df.sum()
	full_sum = float(row_sum.sum())
	
	#残差分散を算出
	arr_all = []
	for r in row_sum:
		arr = []
		for c in col_sum:
			arr.append((1-(r/full_sum))*(1-(c/full_sum)))
		arr_all.append(arr)

	res["df_res_var"] =  DataFrame(arr_all)
	res["df_res_var"].columns = df.columns
	res["df_res_var"].index = df.index

	col_size = df.columns.size
	row_size = df.index.size

	#調整済み標準化残差を算出
	arr_all = []
	for r in np.arange(row_size):
		arr = []
		for c in np.arange(col_size):
			arr.append(res["df_res"].iloc[r].iloc[c] / np.sqrt(res["df_exp"].iloc[r].iloc[c] * res["df_res_var"].iloc[r].iloc[c]))
		arr_all.append(arr)

	res["df_res_final"] = DataFrame(arr_all)
	res["df_res_final"].columns = df.columns
	res["df_res_final"].index = df.index
		
	return res

#データ出力用
def print_chisq(res):
	print "data:"
	print res["data"]

	print "\npercentile:"
	print res["df_per"]

	'''print "\nexpectation:"
	print res["df_exp"]
	print "\nresiduals:"
	print res["df_res"]
	print "\nresiduals_var:"
	print res["df_res_var"]'''

	print "\nchouseizumi_hyoujunnka_zansa:"
	print res["df_res_final"]
	print "\np_value:"
	print res["p_val"]





#データ定義
data = DataFrame([[30,6,23,42], [23,10,8,8], [32,12,2,5], [32,42,2,2], [33,33,2,3]])
data.columns = ["a","b","c","d"]
data.index = ["01","02","03","04", "05"]


res = chi_sq(data)
print_all(res)