ValueError：不再支持单位“M”和“Y”，因为它们不代表明确的时间增量值持续时间

Question

我最近将我的代码从 Python 3.3 升级到 Python 3.7，它目前抛出一个错误，内容为：

ValueError：不再支持单位“M”和“Y”，因为它们不代表明确的时间增量值持续时间

这令人费解，因为代码在升级前运行良好。

这是代码的违规部分：

df['date_modified'] = (df['date_variable']-pd.to_timedelta(df['years_variable'], unit = 'Y')).dt.date

这是完整的代码：

import pandas as pd
import numpy as np

idx = [np.array(['Jan-18', 'Jan-18', 'Feb-18', 'Mar-18', 'Mar-18', 'Mar-18','Apr-18', 'Apr-18', 'May-18', 'Jun-18', 'Jun-18', 'Jun-18','Jul-18', 'Aug-18', 'Aug-18', 'Sep-18', 'Sep-18', 'Oct-18','Oct-18', 'Oct-18', 'Nov-18', 'Dec-18', 'Dec-18',]),np.array(['A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'B', 'A', 'B', 'C','A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C'])]
data = [{'years_variable': 1}, {'years_variable': 5}, {'years_variable': 3}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 3},{'years_variable': 1}, {'years_variable': 6}, {'years_variable': 3}, {'years_variable': 5}, {'years_variable': 2}, {'years_variable': 3},{'years_variable': 1}, {'years_variable': 9}, {'years_variable': 3}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 3}, {'years_variable': 6}, {'years_variable': 8}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 9}]
df = pd.DataFrame(data, index=idx, columns=['years_variable'])
df.index.names=['date_variable','type']
df=df.reset_index()
df['date_variable'] = pd.to_datetime(df['date_variable'],format = '%b-%y') # http://strftime.org/
df=df.set_index(['date_variable','type'])
df=df.reset_index()
print(df)

df['date_modified'] = (df['date_variable']-pd.to_timedelta(df['years_variable'], unit = 'Y')).dt.date

Answer 1

这不是 Python 问题，而是与熊猫有关。

从 0.25.0 版本开始，pandas 库不再支持在 timedelta 函数中使用单位"M"（月）"Y"（年）。

https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html#other-deprecations

这特别影响pandas.to_timedelta()，pandas.Timedelta()和pandas.TimedeltaIndex()。

您可以使用它们的等效天数来指定这些。

您必须重写代码以使用天而不是年（和月）。

这是导致此弃用的 github 上问题的链接，这是解决问题并影响弃用的 PR的链接。

更新：这是您的代码的最新修改

与后面的代码块相比，这对原始代码的更改更少：

import pandas as pd
import numpy as np

idx = [np.array(['Jan-18', 'Jan-18', 'Feb-18', 'Mar-18', 'Mar-18', 'Mar-18','Apr-18', 'Apr-18', 'May-18', 'Jun-18', 'Jun-18', 'Jun-18','Jul-18', 'Aug-18', 'Aug-18', 'Sep-18', 'Sep-18', 'Oct-18','Oct-18', 'Oct-18', 'Nov-18', 'Dec-18', 'Dec-18',]),np.array(['A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'B', 'A', 'B', 'C','A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C'])]
data = [{'years_variable': 1}, {'years_variable': 5}, {'years_variable': 3}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 3},{'years_variable': 1}, {'years_variable': 6}, {'years_variable': 3}, {'years_variable': 5}, {'years_variable': 2}, {'years_variable': 3},{'years_variable': 1}, {'years_variable': 9}, {'years_variable': 3}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 3}, {'years_variable': 6}, {'years_variable': 8}, {'years_variable': 2}, {'years_variable': 7}, {'years_variable': 9}]
df = pd.DataFrame(data, index=idx, columns=['years_variable'])
df.index.names=['date_variable','type']
df=df.reset_index()
df['date_variable'] = pd.to_datetime(df['date_variable'],format = '%b-%y') # http://strftime.org/
df=df.set_index(['date_variable','type'])
df=df.reset_index()

# this is all we're touching
# multiply the values under the 'years_variable' column by 365
# to get the number of days
# and use the 'D' unit in the timedelta, to indicate that it's actually in days
df['date_modified'] = (df['date_variable']-pd.to_timedelta(df['years_variable']*365, unit = 'D')).dt.date

print(df)

输出

   date_variable type  years_variable date_modified
0     2018-01-01    A               1    2017-01-01
1     2018-01-01    B               5    2013-01-02
2     2018-02-01    B               3    2015-02-02
3     2018-03-01    A               2    2016-03-01
4     2018-03-01    B               7    2011-03-03
5     2018-03-01    C               3    2015-03-02
6     2018-04-01    A               1    2017-04-01
7     2018-04-01    B               6    2012-04-02
8     2018-05-01    B               3    2015-05-02
9     2018-06-01    A               5    2013-06-02
10    2018-06-01    B               2    2016-06-01
11    2018-06-01    C               3    2015-06-02
12    2018-07-01    A               1    2017-07-01
13    2018-08-01    B               9    2009-08-03
14    2018-08-01    C               3    2015-08-02
15    2018-09-01    A               2    2016-09-01
16    2018-09-01    B               7    2011-09-03
17    2018-10-01    C               3    2015-10-02
18    2018-10-01    A               6    2012-10-02
19    2018-10-01    B               8    2010-10-03
20    2018-11-01    A               2    2016-11-01
21    2018-12-01    B               7    2011-12-03
22    2018-12-01    C               9    2009-12-03

这是您的代码的较旧修改，可能对您有用

你应该完全忽略它。我只是为了历史目的而保留它。

   date_variable type  years_variable date_modified
0     2018-01-01    A               1    2017-01-01
1     2018-01-01    B               5    2013-01-02
2     2018-02-01    B               3    2015-02-02
3     2018-03-01    A               2    2016-03-01
4     2018-03-01    B               7    2011-03-03
5     2018-03-01    C               3    2015-03-02
6     2018-04-01    A               1    2017-04-01
7     2018-04-01    B               6    2012-04-02
8     2018-05-01    B               3    2015-05-02
9     2018-06-01    A               5    2013-06-02
10    2018-06-01    B               2    2016-06-01
11    2018-06-01    C               3    2015-06-02
12    2018-07-01    A               1    2017-07-01
13    2018-08-01    B               9    2009-08-03
14    2018-08-01    C               3    2015-08-02
15    2018-09-01    A               2    2016-09-01
16    2018-09-01    B               7    2011-09-03
17    2018-10-01    C               3    2015-10-02
18    2018-10-01    A               6    2012-10-02
19    2018-10-01    B               8    2010-10-03
20    2018-11-01    A               2    2016-11-01
21    2018-12-01    B               7    2011-12-03
22    2018-12-01    C               9    2009-12-03