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Anofelesoff
Asked:2020-06-09 05:15:53 +0000 UTC2020-06-09 05:15:53 +0000 UTC

如何以“年”格式将浮点数据类型转换为日期？

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有一个 DataFrame，其中年份表示为浮点数据类型。它必须转换为年份格式的日期数据类型，例如 - 2006-01-01。

0        2006.0
1        1985.0
2        2008.0
3        2009.0
4        1996.0

尝试过：

data['year_of_release'] = pd.to_datetime(data['year_of_release'], format='%d.%m.%Y %H:%M:%S')

出现以下错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
    431             try:
--> 432                 values, tz = conversion.datetime_to_datetime64(arg)
    433                 return DatetimeIndex._simple_new(values, name=name, tz=tz)

pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()

TypeError: Unrecognized value type: <class 'int'>

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
4 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
    398                 try:
    399                     result, timezones = array_strptime(
--> 400                         arg, format, exact=exact, errors=errors
    401                     )
    402                     if "%Z" in format or "%z" in format:

pandas/_libs/tslibs/strptime.pyx in pandas._libs.tslibs.strptime.array_strptime()

ValueError: time data '2006' does not match format '%d.%m.%Y %H:%M:%S' (match)
python

1 个回答

  1. Best Answer

    如果我正确理解了这个问题：

    df["year_of_release"] = pd.to_datetime(df["year_of_release"].astype(int).astype(str) + "-01-01")

    结果：

    In [208]: df
Out[208]:
  year_of_release
0      2006-01-01
1      1985-01-01
2      2008-01-01
3      2009-01-01
4      1996-01-01
    • 2

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