在特定日期时间之前出现的列中数据的Pandas DataFrame平均值

问题描述

我有一个数据框，里面有2014-2018年的客户ID和他们的费用。我想要的是每个ID的费用的平均值，但在计算平均值时，只能考虑某个日期之前的年份(因此，列‘Date’表示可以考虑哪些列作为平均值)。

例如：对于索引0(ID：12)，日期为‘2016-03-08’，则平均值应取自列‘y_2014’和‘y_2015’，因此对于此索引，平均值为111.0。如果日期太早(例如，在这种情况下为2014年或更早)，则应返回NaN(请参阅索引6和9)。

所需输出：

   y_2014  y_2015  y_2016  y_2017  y_2018        Date  ID    mean
0   100.0   122.0     324     632     NaN  2016-03-08  12   111.0
1   120.0   159.0      54     452   541.0  2015-04-09  96   120.0
2     NaN   164.0     687     165   245.0  2016-02-15  20   164.0
3   180.0   421.0     512     184   953.0  2018-05-01  73  324.25
4   110.0   654.0     913     173   103.0  2017-08-04  84   559.0
5   130.0     NaN     754     124   207.0  2016-07-03  26   130.0
6   170.0   256.0     843      97   806.0  2013-02-04  87     NaN
7   140.0   754.0      95     101   541.0  2016-06-08  64     447
8    80.0   985.0     184      84    90.0  2019-03-05  11   284.6
9    96.0    65.0     127     130   421.0  2014-05-14  34     NaN

下面的代码是我尝试的代码。

已尝试代码：

import pandas as pd

import numpy as np




df = pd.DataFrame({"ID":   [12,96,20,73,84,26,87,64,11,34],
  
               "y_2014": [100,120,np.nan,180,110,130,170,140,80,96],
  
               "y_2015": [122,159,164,421,654,np.nan,256,754,985,65],
  
               "y_2016": [324,54,687,512,913,754,843,95,184,127],
    
               "y_2017": [632,452,165,184,173,124,97,101,84,130],
  
               "y_2018": [np.nan,541,245,953,103,207,806,541,90,421],
   
                 "Date": ['2016-03-08', '2015-04-09', '2016-02-15', '2018-05-01', '2017-08-04',
                          '2016-07-03', '2013-02-04', '2016-06-08', '2019-03-05', '2014-05-14']})



print(df)



# the years from columns

data = df.filter(like='y_')

data_years = data.columns.str.extract('(d+)')[0].astype(int)



# the years from Date

years = pd.to_datetime(df.Date).dt.year.values



df['mean'] = data.where(data_years<years[:,None]).mean(1)

print(df)

-> ValueError: Lengths must match to compare

---

解决方案

已解决：我自己的问题有一个可能的答案

import pandas as pd

import numpy as np



df = pd.DataFrame({"ID":   [12,96,20,73,84,26,87,64,11,34],
                 
               "y_2014": [100,120,np.nan,180,110,130,170,140,80,96],
   
               "y_2015": [122,159,164,421,654,np.nan,256,754,985,65],
                 
               "y_2016": [324,54,687,512,913,754,843,95,184,127],
  
               "y_2017": [632,452,165,184,173,124,97,101,84,130],
                 
               "y_2018": [np.nan,541,245,953,103,207,806,541,90,421],
  
                 "Date": ['2016-03-08', '2015-04-09', '2016-02-15', '2018-05-01', '2017-08-04',
               
                          '2016-07-03', '2013-02-04', '2016-06-08', '2019-03-05', '2014-05-14']})

#Subset from original df to calculate mean
subset = df.loc[:,['y_2014', 'y_2015', 'y_2016', 'y_2017', 'y_2018']]


#an expense value is only available for the calculation of the mean when that year has passed, therefore 2015-01-01 is chosen for the 'y_2014' column in the subset etc. to check with the 'Date'-column
subset.columns = ['2015-01-01', '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01']


s = subset.columns[0:].values < df.Date.values[:,None]

t = s.astype(float)
t[t == 0] = np.nan


df['mean'] = (subset.iloc[:,0:]*t).mean(1)


print(df)

#Additionally: (gives the sum of expenses before a certain date in the 'Date'-column
df['sum'] = (subset.iloc[:,0:]*t).sum(1)


print(df)