交叉分析方法与实现

看一个部门的离职率的数据，简单分析一下，部门离职率与所做的工作的相关性：

因为数据太多，只能给出少量数据了，如果有想要数据的可以留言赠送

给定如下数据：

HR.csv:

satisfaction_level,last_evaluation,number_project,average_monthly_hours,time_spend_company,Work_accident,left,promotion_last_5years,department,salary
0.38,0.53,2,157,3,0,1,0,sales,low
0.8,0.86,5,262,6,0,1,0,sales,medium
0.11,0.88,7,272,4,0,1,0,sales,medium
0.72,0.87,5,223,5,0,1,0,sales,low
0.37,0.52,2,159,3,0,1,0,sales,low
0.41,0.5,2,153,3,0,1,0,sales,low
0.1,0.77,6,247,4,0,1,0,sales,low
0.92,0.85,5,259,5,0,1,0,sales,low
0.89,1,5,224,5,0,1,0,sales,low
0.42,0.53,2,142,3,0,1,0,sales,low
0.45,0.54,2,135,3,0,1,0,sales,low
0.11,0.81,6,305,4,0,1,0,sales,low
0.84,0.92,4,234,5,0,1,0,sales,low
0.41,0.55,2,148,3,0,1,0,sales,low
0.36,0.56,2,137,3,0,1,0,sales,low
0.38,0.54,2,143,3,0,1,0,sales,low
0.45,0.47,2,160,3,0,1,0,sales,low
0.78,0.99,4,255,6,0,1,0,sales,low
0.45,0.51,2,160,3,1,1,1,sales,low
0.76,0.89,5,262,5,0,1,0,sales,low
0.11,0.83,6,282,4,0,1,0,sales,low
0.38,0.55,2,147,3,0,1,0,sales,low
0.09,0.95,6,304,4,0,1,0,sales,low
0.46,0.57,2,139,3,0,1,0,sales,low
0.4,0.53,2,158,3,0,1,0,sales,low
0.89,0.92,5,242,5,0,1,0,sales,low
0.82,0.87,4,239,5,0,1,0,sales,low
0.4,0.49,2,135,3,0,1,0,sales,low
0.41,0.46,2,128,3,0,1,0,accounting,low
0.38,0.5,2,132,3,0,1,0,accounting,low
0.09,0.62,6,294,4,0,1,0,accounting,low
0.45,0.57,2,134,3,0,1,0,hr,low
0.4,0.51,2,145,3,0,1,0,hr,low
0.45,0.55,2,140,3,0,1,0,hr,low
0.84,0.87,4,246,6,0,1,0,hr,low
0.1,0.94,6,255,4,0,1,0,technical,low
0.38,0.46,2,137,3,0,1,0,technical,low
0.45,0.5,2,126,3,0,1,0,technical,low
0.11,0.89,6,306,4,0,1,0,technical,low
0.41,0.54,2,152,3,0,1,0,technical,low
0.87,0.88,5,269,5,0,1,0,technical,low
0.45,0.48,2,158,3,0,1,0,technical,low
0.4,0.46,2,127,3,0,1,0,technical,low
0.1,0.8,7,281,4,0,1,0,technical,low
0.09,0.89,6,276,4,0,1,0,technical,low
0.84,0.74,3,182,4,0,1,0,technical,low
0.4,0.55,2,147,3,0,1,0,support,low
0.57,0.7,3,273,6,0,1,0,support,low
0.4,0.54,2,148,3,0,1,0,support,low
0.43,0.47,2,147,3,0,1,0,support,low
0.13,0.78,6,152,2,0,1,0,support,low
0.44,0.55,2,135,3,0,1,0,support,low
0.38,0.55,2,134,3,0,1,0,support,low
0.39,0.54,2,132,3,0,1,0,support,low
0.1,0.92,7,307,4,0,1,0,support,low
0.37,0.46,2,140,3,0,1,0,support,low
0.11,0.94,7,255,4,0,1,0,support,low
0.1,0.81,6,309,4,0,1,0,technical,low
0.38,0.54,2,128,3,0,1,0,technical,low
0.85,1,4,225,5,0,1,0,technical,low
0.85,0.91,5,226,5,0,1,0,management,medium
0.11,0.93,7,308,4,0,1,0,IT,medium
0.1,0.95,6,244,5,0,1,0,IT,medium
0.36,0.56,2,132,3,0,1,0,IT,medium
0.11,0.94,6,286,4,0,1,0,IT,medium
0.81,0.7,6,161,4,0,1,0,IT,medium
0.43,0.54,2,153,3,0,1,0,product_mng,medium
0.9,0.98,4,264,6,0,1,0,product_mng,medium
0.76,0.86,5,223,5,1,1,0,product_mng,medium
0.43,0.5,2,135,3,0,1,0,product_mng,medium
0.74,0.99,2,277,3,0,1,0,IT,medium
0.09,0.77,5,275,4,0,1,0,product_mng,medium
0.45,0.49,2,149,3,0,1,0,product_mng,high
0.09,0.87,7,295,4,0,1,0,product_mng,low
0.11,0.97,6,277,4,0,1,0,product_mng,medium
0.11,0.79,7,306,4,0,1,0,product_mng,medium
0.1,0.83,6,295,4,0,1,0,product_mng,medium
0.4,0.54,2,137,3,0,1,0,marketing,medium
0.43,0.56,2,157,3,0,1,0,sales,low
0.39,0.56,2,142,3,0,1,0,accounting,low
0.45,0.54,2,140,3,0,1,0,support,low
0.38,0.49,2,151,3,0,1,0,technical,low
0.79,0.59,4,139,3,0,1,1,management,low
0.84,0.85,4,249,6,0,1,0,marketing,low
0.11,0.77,6,291,4,0,1,0,marketing,low
0.11,0.87,6,305,4,0,1,0,marketing,low
0.17,0.84,5,232,3,0,1,0,sales,low
0.44,0.45,2,132,3,0,1,0,sales,low
0.37,0.57,2,130,3,0,1,0,sales,low

简单 的代码实现过程：

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import scipy.stats as ss


from pandas import DataFrame,Series
#数据的路径
df = pd.read_csv("./skip_Data/HR.csv")
#我想要看各个部门的离职分布
#通过 indices 拿到分组后的索引，通过部门进行分组
df_index = df.groupby(by="department").indices
#拿到销售部门的离职值
sales_values = df['left'].iloc[df_index['sales']].values
technical_values = df['left'].iloc[df_index['technical']].values
#在python3中所有的keys加上一个list,然后才能转换为数组
dp_keys = list(df_index.keys())
#初始化一个矩阵
df_t_mat = np.zeros([len(dp_keys),len(dp_keys)])
for i in range(len(dp_keys)):
    for j in range(len(dp_keys)):
        p_value = ss.ttest_ind(df['left'].iloc[df_index[dp_keys[i]]].values,\
                  df['left'].iloc[df_index[dp_keys[j]]].values)[1]
        #对矩阵进行赋值
        if p_value<0.05:
            df_t_mat[i][j] = -1
        else :
            df_t_mat[i][j] = p_value
sns.heatmap(df_t_mat,xticklabels=dp_keys,yticklabels=dp_keys)
plt.savefig("1.jpg")
plt.show()