This has been bothering me for ages now:
Given a simple pandas DataFrame
>>> df
Timestamp Col1
2008-08-01 0.001373
2008-09-01 0.040192
2008-10-01 0.027794
2008-11-01 0.012590
2008-12-01 0.026394
2009-01-01 0.008564
2009-02-01 0.007714
2009-03-01 -0.019727
2009-04-01 0.008888
2009-05-01 0.039801
2009-06-01 0.010042
2009-07-01 0.020971
2009-08-01 0.011926
2009-09-01 0.024998
2009-10-01 0.005213
2009-11-01 0.016804
2009-12-01 0.020724
2010-01-01 0.006322
2010-02-01 0.008971
2010-03-01 0.003911
2010-04-01 0.013928
2010-05-01 0.004640
2010-06-01 0.000744
2010-07-01 0.004697
2010-08-01 0.002553
2010-09-01 0.002770
2010-10-01 0.002834
2010-11-01 0.002157
2010-12-01 0.001034
How do I separate it so that a new DataFrame equals the entries in df for the dates between 2009-05-01 and 2010-03-01
>>> df2
Timestamp Col1
2009-05-01 0.039801
2009-06-01 0.010042
2009-07-01 0.020971
2009-08-01 0.011926
2009-09-01 0.024998
2009-10-01 0.005213
2009-11-01 0.016804
2009-12-01 0.020724
2010-01-01 0.006322
2010-02-01 0.008971
2010-03-01 0.003911
If you have set the "Timestamp" column as the index , then you can simply use
df['2009-05-01' :'2010-03-01']
IIUC, a simple slicing?
from datetime import datetime
df2 = df[(df.Timestamp >= datetime(2009, 05, 01)) &
(df.Timestamp <= datetime(2010, 03, 01))]
You can do something like:
df2 = df.set_index('Timestamp')['2009-05-01' :'2010-03-01']
print(df2)
来源：https://stackoverflow.com/questions/49868647/how-to-slice-a-pandas-dataframe-based-on-datetime-index