Create table of individual float arrays as specific columns-CodePudding

I try to create a summary table with different measures over time. Therefore, I try to combine several float arrays such as measure1 into a specific column of the table with the values assigned in the correct rows (years).

This would be a simple example of what I am trying to do:

measure1:
Date
2018    3.075
2019    2.550
2020    3.325
2021    2.475
dtype: float64

Summary:
        Measure 1   Measure 2
2018    NaN         NaN
2019    NaN         NaN
2020    NaN         NaN
2021    NaN         NaN

Summary:
        Measure 1       Measure 2
2018    3.075           NaN
2019    2.550           NaN
2020    3.325           NaN
2021    2.475           NaN

For reproducibility:

Years = ['2018', '2019', '2020', '2021']
Measures = ['Measure 1', 'Measure 2']
Summary = pd.DataFrame(np.nan, index=Years, columns=Measures)
Summary

Thanks a lot!

CodePudding user response：

It is hard to say what exactly you want to do, but in any case you should be able to solve your problem with Dataframe.merge. Note that I first pack your float values and dates into a separate dataframe:

import pandas as pd

d = {'Measure 1': [3.075, 2.550, 3.325, 2.475], 'Date': ['2018', '2019', '2020', '2021']}
df = pd.DataFrame(data=d).set_index('Date')

years = ['2018', '2019', '2020', '2021']
measures = ['Measure 1', 'Measure 2']
summary = pd.DataFrame(np.nan, index=years, columns=measures).rename_axis('Date')

print(pd.merge(summary, df, on=['Date', 'Measure 1'], how='right'))

      Measure 1  Measure 2
Date                      
2018      3.075        NaN
2019      2.550        NaN
2020      3.325        NaN
2021      2.475        NaN