列数非常多的Pandas数据透视表

Question

I have a pandas dataframe df with about 1000 rows but 500 columns. The columns are named Run1, Run2, ..., Run500

The existing index is datetime .

Sample data from dataframe is as follows:

df.ix[1:4,1:4]
                       Run1    Run2    Date
2019-04-01 01:00:00  23.0263  23.0263  2019-04-01
2019-04-01 01:00:00  19.2212  19.2212  2019-04-01
2019-04-02 01:00:00  19.3694  19.3694  2019-04-02
2019-04-02 01:00:00  19.3694  19.3694  2019-04-02

I can do the trying the following:

pd.pivot_table(df, index=['Date'], values=['Run1'], aggfunc=[np.mean])['mean']

But I need to the following:

import pandas as pd
import numpy as np
pd.pivot_table(df, index=['Date'], values=['Run1', 'Run2', ...., 'Run500'], aggfunc=[np.mean])['mean']

Answer 1

我认为这是groupby +的mean

df.groupby('Date').mean()