如何将 Pandas 数据框列从 np.datetime64 转换为 datetime？
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How to convert a Pandas data frame column from np.datetime64 to datetime?
提问by helloB
I would like to put a Pandas Data Frame column into datetimeformat from datetime64. This works on an an individual basis. In particular the following works fine:
我想将 Pandas Data Frame 列datetime从datetime64. 这在个人基础上起作用。特别是以下工作正常：
t = dt['time'].values[0]
datetime.utcfromtimestamp(t.astype(int)/1000000000)
However, when I try to do this to the entire column
但是，当我尝试对整个列执行此操作时
dt['datetime'] = dt['time'].apply(lambda x: datetime.utcfromtimestamp(x.astype(int)/1000000000))
I get the following error:
我收到以下错误：
pandas/src/inference.pyx in pandas.lib.map_infer (pandas/lib.c:62578)()
pandas.lib.map_infer 中的 pandas/src/inference.pyx (pandas/lib.c:62578)()
<ipython-input-26-5950d82979b4> in <lambda>(x)
1 print(type(dt['time'].values[0]))
2
----> 3 dt['datetime'] = dt['time'].apply(lambda x: datetime.utcfromtimestamp(x.astype(int)/1000000000))
4 t = dt['time'].values[0]
5 print(t)
AttributeError: 'Timestamp' object has no attribute 'astype'
What am I doing wrong? How can I convert my column to datetimeand/or make a new column in datetimeformat?
我究竟做错了什么？如何将我的列转换为datetime和/或以datetime格式创建新列？
Here is the info for the dataframe:
这是数据框的信息：
回答by unutbu
You can convert Series of dtype datetime64[ns]to a NumPy array of datetime.datetimeobjects by calling the .dt.to_pydatetime()method:
您可以通过调用以下方法将系列 dtypedatetime64[ns]转换为 NumPydatetime.datetime对象数组.dt.to_pydatetime()：
In [75]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 252 entries, 0 to 251
Data columns (total 1 columns):
time 252 non-null datetime64[ns]<--the `time` column has dtype `datetime64[ns]`
dtypes: datetime64[ns](1)
memory usage: 2.0 KB
In [77]: df.head()
Out[77]:
time
0 2009-01-02
1 2009-01-05
2 2009-01-06
3 2009-01-07
4 2009-01-08
In [76]: df['time'].dt.to_pydatetime()[:5]
Out[76]:
array([datetime.datetime(2009, 1, 2, 0, 0),
datetime.datetime(2009, 1, 5, 0, 0),
datetime.datetime(2009, 1, 6, 0, 0),
datetime.datetime(2009, 1, 7, 0, 0),
datetime.datetime(2009, 1, 8, 0, 0)], dtype=object)
Note that NDFrames (such as Series and DataFrames) can only hold datetime-like objects as objects of dtype datetime64[ns]. The automatic conversion of all datetime-likes to a common dtype simplifies subsequent date computations. But it makes it impossible to store, say, Python datetime.datetimeobjects in a DataFrame column. Pandas core developer, Jeff Reback explains,
请注意，NDFrames（例如 Series 和 DataFrames）只能将类似日期时间的对象保存为 dtype 的对象datetime64[ns]。所有类似日期时间的自动转换为通用 dtype 简化了后续的日期计算。但是这使得datetime.datetime在 DataFrame 列中存储 Python对象变得不可能。Pandas 核心开发人员Jeff Reback 解释说，
"We don't allow direct conversions because its simply too complicated to keep anything other than datetime64[ns] internally (nor necessary at all)."
“我们不允许直接转换，因为它太复杂了，无法在内部保留除 datetime64[ns] 以外的任何内容（根本没有必要）。”
回答by piRSquared
Without your data set, I have to guess at some things. But, you should be able to repeat the same thing as what you demonstrated as having worked.
没有你的数据集，我不得不猜测一些事情。但是，您应该能够重复与您展示的工作相同的事情。
dt['datetime'] = datetime.utcfromtimestamp(dt['time'].values.astype(int)/1000000000))
