pandas 大熊猫用 NaT 替换 NaN
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pandas replace NaN with NaT
提问by LateCoder
I have a series with some NaNs that I need to replace with NaTs. How can I do this?
我有一系列需要用 NaT 替换的 NaN。我怎样才能做到这一点？
Here's a simple example with what I've tried so far:
这是我迄今为止尝试过的一个简单示例：
>>> s = pd.Series([np.NaN, np.NaN])
>>> s.fillna(pd.NaT)
0 NaN
1 NaN
dtype: float64
>>> s.replace(np.NaN, pd.NaT)
0 NaN
1 NaN
dtype: float64
>>> s.where(pd.notnull(s), pd.NaT)
0 NaN
1 NaN
dtype: object
pandas version: 0.16.2
Pandas版本：0.16.2
numpy version: 1.9.2
numpy 版本：1.9.2
python version: 2.7.10
蟒蛇版本：2.7.10
回答by EdChum
Convert the dtypefirst as NaTis meaningless when the dtypeis floatwhich is the dtypeinitially:
当is是初始时，将第dtype一个 as转换NaT为毫无意义：dtypefloatdtype
In [90]:
s.astype(np.datetime64).fillna(pd.NaT)
Out[90]:
0 NaT
1 NaT
dtype: datetime64[ns]
if You have non-NaNvalues in the Series then use to_datetime:
如果NaN系列中有非值，则使用to_datetime：
In [97]:
s = pd.Series([np.NaN, np.NaN, 1.0])
pd.to_datetime(s)
Out[97]:
0 NaT
1 NaT
2 1970-01-01 00:00:00.000000001
dtype: datetime64[ns]
