Python将自纪元时间以来的一系列秒转换为日期时间系列

Question

是否有任何快速方法可以将纪元时间以来的一系列秒转换为日期时间对象系列？

我使用:

for i in range(len(df)):
    df['datetime'].iloc[i] = datetime.fromtimestamp(df['epochtime'].iloc[i])

但是由于我的数据帧非常大，所以速度非常慢。有什么快速的方法可以做到这一点吗？像 pandas 函数一样？

Answer 1

您可以使用to_datetime(..., unit='s'):

df['datetime'] = pd.to_datetime(df['epochtime'], unit='s')

时间比较:

In [158]: df = pd.DataFrame({'epochtime': pd.date_range('2001-01-01', freq='1S', periods=10**5)}).astype(np.int64)//10**9

In [159]:

In [159]: df.head()
Out[159]:
   epochtime
0  978307200
1  978307201
2  978307202
3  978307203
4  978307204

In [160]:

In [160]: len(df)
Out[160]: 100000

In [161]:

In [161]: %timeit df['datetime'] = pd.to_datetime(df['epochtime'], unit='s')
100 loops, best of 3: 16.9 ms per loop

In [162]:

In [162]: %%timeit
   .....: for i in range(len(df)):
   .....:     df['datetime'].iloc[i] = datetime.fromtimestamp(df2['epochtime'].iloc[i])
   .....:
c:\envs\py35\lib\site-packages\pandas\core\indexing.py:128: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self._setitem_with_indexer(indexer, value)
1 loop, best of 3: 54.5 s per loop

结论:@内特猫，如您所见，16.9 毫秒 与 54.5 秒