如何让Pandas将包含NaT的列从timedelta转换为datetime? [英] How can I make Pandas convert a column which contains NaT from timedelta to datetime?

问题描述

我有一个pandas数据框，其列的类型为 timedelta64 [ns] ，并且我想将其转换为ot datetime64 [ns] .

I have a pandas dataframe with a column which is of type timedelta64[ns], and which I would like to convert ot datetime64[ns].

pd.to_datetime()函数据说可以做到这一点，并且过去曾奏效，但现在似乎失败了.我认为这可能与API怪癖有关，而该怪癖已经超出了我的关注范围.当前它失败并显示:

The pd.to_datetime() function purports to do just that, and has worked in the past, but appears to fail now. I would assume this might be related to an API quirk which has gone beneath my radar. Currently it fails with:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.7/site-packages/pandas/core/tools/datetimes.py", line 724, in to_datetime
    cache_array = _maybe_cache(arg, format, cache, convert_listlike)
  File "/usr/lib/python3.7/site-packages/pandas/core/tools/datetimes.py", line 152, in _maybe_cache
    cache_dates = convert_listlike(unique_dates, format)
  File "/usr/lib/python3.7/site-packages/pandas/core/tools/datetimes.py", line 363, in _convert_listlike_datetimes
    arg, _ = maybe_convert_dtype(arg, copy=False)
  File "/usr/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1916, in maybe_convert_dtype
    raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
TypeError: dtype timedelta64[ns] cannot be converted to datetime64[ns]

要尝试复制，请使用下面的MWE:

wget https://chymera.eu/ppb/61ebad.csv
python
import pandas as pd
df = pd.read_csv('61ebad.csv')
df['Animal_death_date'] = pd.to_timedelta(df['Animal_death_date'], errors='coerce')
df['Animal_death_date'] = pd.to_datetime(df['Animal_death_date'], errors='coerce')

如果我使用 errors ='ignore'，也会发生该错误.作为参考，我正在使用Pandas 1.0.1 .

The error also occurs if I am using errors='ignore'. For reference, I am using Pandas 1.0.1.

推荐答案

如果需要将时间增量转换为日期时间，请添加一些开始日期时间:

If need convert timedeltas to datetime, add some start datetime:

import pandas as pd

df = pd.read_csv('https://chymera.eu/ppb/61ebad.csv')
start = pd.to_datetime('2000-01-01')
df['Animal_death_date'] = pd.to_timedelta(df['Animal_death_date'], errors='coerce') + start
print (df['Animal_death_date'] )
0                     NaT
1                     NaT
2                     NaT
3                     NaT
4                     NaT

843                   NaT
844                   NaT
845   2000-05-12 19:00:00
846   2000-05-12 19:00:00
847   2000-05-12 19:00:00
Name: Animal_death_date, Length: 848, dtype: datetime64[ns]

或添加一些由日期时间填充的列:

Or add some column filled by datetimes:

import pandas as pd

df = pd.read_csv('https://chymera.eu/ppb/61ebad.csv')
start = pd.to_datetime(df['FMRIMeasurement_date'])
df['Animal_death_date'] = pd.to_timedelta(df['Animal_death_date'], errors='coerce') + start
print (df['Animal_death_date'] )
0                     NaT
1                     NaT
2                     NaT
3                     NaT
4                     NaT

843                   NaT
844                   NaT
845   2018-10-04 19:20:54
846   2018-10-04 19:20:54
847   2018-10-04 19:20:54
Name: Animal_death_date, Length: 848, dtype: datetime64[ns]

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