数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -192,7 +192,7 @@ def chi_cal(data):
     data['共计'] = data.apply(lambda x: x.sum(), axis=1)
     # print(data)
     data.loc['共计'] = data.apply(lambda x: x.sum())
-    # print(data)
+    print(data)
     t1=data.iloc[0]
     t2=data.iloc[1]
     t11_count=t1[0]
@@ -207,8 +207,8 @@ def chi_cal(data):
     temp2=data.iloc[2]
     t11_theory=temp2[0]*rate1
     t12_theory=temp2[1]*rate1
-    t21_theory = temp2[0] * rate2
-    t22_theory = temp2[1] * rate2
+    t21_theory = temp2[0]*rate2
+    t22_theory = temp2[1]*rate2
     #计算卡方值
     X=(((t11_count-t11_theory)**2)/t11_theory)+(((t12_count-t12_theory)**2)/t12_theory)+(((t21_count-t21_theory)**2)/t21_theory)+(((t22_count-t22_theory)**2)/t22_theory)
     print("卡方值为：{}".format(X))