I create a pivot table with a MultiIndex.
import pandas as pd
treatcriteria_daily_data = pd.read_csv(r"treatcriteria_evolution.csv", sep=';')
variations_data = treatcriteria_daily_data.groupby(['startdate_dayweek','startdate_weekyear'],as_index = False).sum().pivot('startdate_dayweek','startdate_weekyear').fillna(0)
print(variations_data)
print(variations_data.columns)
The result:
mc_cpu_hours ...
startdate_weekyear 27 28 ... 30 31
startdate_dayweek ...
1 527644.000731 349896.850976 ... 176007.786269 177517.591864
2 468053.338183 481313.693908 ... 364379.872622 0.000000
3 517548.838022 372385.568095 ... 366155.953075 0.000000
4 573669.325129 378069.713821 ... 375948.240935 0.000000
5 515710.534260 385937.231788 ... 366151.336263 0.000000
6 511711.421986 385856.666340 ... 395790.387672 0.000000
7 841073.028107 842468.209151 ... 700936.336812 686023.780120
[7 rows x 5 columns]
MultiIndex(levels=[['mc_cpu_hours'], [27, 28, 29, 30, 31]],
codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
names=[None, 'startdate_weekyear'])
How can I proceed to obtain this table?
27 28 ... 30 31
1 527644.000731 349896.850976 ... 176007.786269 177517.591864
2 468053.338183 481313.693908 ... 364379.872622 0.000000
3 517548.838022 372385.568095 ... 366155.953075 0.000000
4 573669.325129 378069.713821 ... 375948.240935 0.000000
5 515710.534260 385937.231788 ... 366151.336263 0.000000
6 511711.421986 385856.666340 ... 395790.387672 0.000000
7 841073.028107 842468.209151 ... 700936.336812 686023.780120
I tried a map/join ( concise way of flattening multiindex columns ) but I had this error message:
TypeError: sequence item 1: expected str instance, int found
Thanks to you.
can you try to convert the startdate_weekyear into string before your pivot and retry the map solution ? I hope it works for you