这应该相对容易。我有一个pandas数据帧(日期):
A B C
1/8/2017 1/11/2017 1/20/2017 1/25/2017
1/9/2017 1/11/2017 1/20/2017 1/25/2017
1/10/2017 1/11/2017 1/20/2017 1/25/2017
1/11/2017 1/20/2017 1/25/2017 1/31/2017
1/12/2017 1/20/2017 1/25/2017 1/31/2017
1/13/2017 1/20/2017 1/25/2017 1/31/2017
我想区分Dates.index和Dates。输出就像这样:
A B C
1/8/2017 3 12 17
1/9/2017 2 11 16
1/10/2017 1 10 15
1/11/2017 9 14 20
1/12/2017 8 13 19
1/13/2017 7 12 18
当然,我试过这个:
Dates - Dates.index
但是我收到了这个可爱的TypeError:
TypeError: Could not operate DatetimeIndex...with block values ufunc subtract cannot use operands with types dtype('<M8[ns]') and dtype('O')
相反,我已经写了一个循环逐列,但这看起来很傻。任何人都可以建议采用pythonic方法吗?
修改
In [1]: import pandas as pd
import numpy as np
import datetime
dates = pd.date_range('20170108',periods=6)
df = pd.DataFrame(np.empty([len(dates),3]),index=dates,columns=list('ABC'))
df['A'].loc[0:3] = datetime.date(2017, 1, 11)
df['B'].loc[0:3] = datetime.date(2017, 1, 20)
df['C'].loc[0:3] = datetime.date(2017, 1, 25)
df['A'].loc[3:6] = datetime.date(2017, 1, 20)
df['B'].loc[3:6] = datetime.date(2017, 1, 25)
df['C'].loc[3:6] = datetime.date(2017, 1, 31)
In [2]: print(df)
A B C
2017-01-08 2017-01-11 2017-01-20 2017-01-25
2017-01-09 2017-01-11 2017-01-20 2017-01-25
2017-01-10 2017-01-11 2017-01-20 2017-01-25
2017-01-11 2017-01-20 2017-01-25 2017-01-31
2017-01-12 2017-01-20 2017-01-25 2017-01-31
2017-01-13 2017-01-20 2017-01-25 2017-01-31
In [3]: df = df.sub(df.index.to_series(),axis=0)
ValueError: operands could not be broadcast together with shapes (18,) (6,)
答案 0 :(得分:2)
您需要先转换所有列to_datetime
,然后使用sub
:
#if dtypes of all columns are datetime, omit it
date_cols = list('ABC')
for col in df.columns:
df[col] = pd.to_datetime(df[col])
df = df.sub(df.index.to_series(),axis=0)
print (df)
A B C
2017-01-08 3 days 12 days 17 days
2017-01-09 2 days 11 days 16 days
2017-01-10 1 days 10 days 15 days
2017-01-11 9 days 14 days 20 days
2017-01-12 8 days 13 days 19 days
2017-01-13 7 days 12 days 18 days
您需要dtypes
datetime64
:
dates = pd.date_range('20170108',periods=6)
df = pd.DataFrame(index=dates)
df.loc[0:3, 'A'] = pd.Timestamp(2017, 1, 11)
df.loc[0:3, 'B'] = pd.Timestamp(2017, 1, 20)
df.loc[0:3, 'C'] = pd.Timestamp(2017, 1, 25)
df.loc[3:6, 'A'] = pd.Timestamp(2017, 1, 20)
df.loc[3:6, 'B'] = pd.Timestamp(2017, 1, 25)
df.loc[3:6, 'C'] = pd.Timestamp(2017, 1, 31)
print (df)
A B C
2017-01-08 2017-01-11 2017-01-20 2017-01-25
2017-01-09 2017-01-11 2017-01-20 2017-01-25
2017-01-10 2017-01-11 2017-01-20 2017-01-25
2017-01-11 2017-01-20 2017-01-25 2017-01-31
2017-01-12 2017-01-20 2017-01-25 2017-01-31
2017-01-13 2017-01-20 2017-01-25 2017-01-31
print (df.dtypes)
A datetime64[ns]
B datetime64[ns]
C datetime64[ns]
dtype: object
df = df.sub(df.index.to_series(),axis=0)
print (df)
A B C
2017-01-08 3 days 12 days 17 days
2017-01-09 2 days 11 days 16 days
2017-01-10 1 days 10 days 15 days
2017-01-11 9 days 14 days 20 days
2017-01-12 8 days 13 days 19 days
2017-01-13 7 days 12 days 18 days
答案 1 :(得分:0)
我认为更明确和优雅的方法是简单地使用apply
。
df = df.apply(pd.to_datetime, axis="columns") # just to make sure values are datetime
df.apply(lambda x: x - df.index.to_series(), axis="rows)