Pandas DataFrame mean of data in columns occurring before certain date time

I have a dataframe with ID’s of clients and their expenses for 2014-2018. What I want is to have the mean of the expenses per ID but only the years before a certain date can be taken into account when calculating the mean value (so column ‘Date’ dictates which columns can be taken into account for the mean).

Example: for index 0 (ID: 12), the date states ‘2016-03-08’, then the mean should be taken from the columns ‘y_2014’ and ‘y_2015’, so then for this index, the mean is 111.0. If the date is too early (e.g. somewhere in 2014 or earlier in this case), then NaN should be returned (see index 6 and 9).

Desired output:

   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

The code below is what I tried.

Tried code:

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

Answer

Solved: one possible answer to my own question

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)