用pct_change@H_502_2@在groupby@H_502_2@
d1 = df.set_index(['Date', 'Group']).Value
d2 = d1.groupby(level='Group').pct_change()
print(d2)
Date Group
2016-01-02 A NaN
2016-01-03 A -0.062500
2016-01-04 A -0.066667
2016-01-05 A 0.214286
2016-01-06 A 0.117647
2016-01-07 A 0.052632
2016-01-02 B NaN
2016-01-03 B -0.187500
2016-01-04 B 0.000000
2016-01-02 C NaN
2016-01-03 C 0.000000
Name: Value, dtype: float64
@H_502_2@可视化和比较的许多方法之一是查看它们的增长方式。在这种情况下，我会
d2.fillna(0).add(1).cumprod().unstack().plot()
@H_502_2@
from io import StringIO
import pandas as pd
txt = """Group Date Value
A 01-02-2016 16
A 01-03-2016 15
A 01-04-2016 14
A 01-05-2016 17
A 01-06-2016 19
A 01-07-2016 20
B 01-02-2016 16
B 01-03-2016 13
B 01-04-2016 13
C 01-02-2016 16
C 01-03-2016 16 """
df = pd.read_clipboard(parse_dates=[1])
@H_502_2@
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