問題描述

我有熊貓系列，其中索引是整數列表(時間戳)，我如何將它們轉換為 datetime.datetime(帶時區)比以下原始轉換更有效?

I have pandas Series where index is a list of integer (timestamp), how can I convert them to datetime.datetime (with timezone) more efficient than below raw conversion?

pd.Series(data=s.values, index=map(lambda x:datetime.datetime.fromtimestamp(x,tz=utc), s.index))

推薦答案

In [49]: s = Series(range(10))

使用to_datetime，你可以提供一個單位來選擇整數的含義.

Using to_datetime, you can supply a unit to select what the meaning of the integers.

In [50]: pd.to_datetime(s,unit='s')
Out[50]: 
0   1970-01-01 00:00:00
1   1970-01-01 00:00:01
2   1970-01-01 00:00:02
3   1970-01-01 00:00:03
4   1970-01-01 00:00:04
5   1970-01-01 00:00:05
6   1970-01-01 00:00:06
7   1970-01-01 00:00:07
8   1970-01-01 00:00:08
9   1970-01-01 00:00:09
dtype: datetime64[ns]

In [51]: pd.to_datetime(s,unit='ms')
Out[51]: 
0          1970-01-01 00:00:00
1   1970-01-01 00:00:00.001000
2   1970-01-01 00:00:00.002000
3   1970-01-01 00:00:00.003000
4   1970-01-01 00:00:00.004000
5   1970-01-01 00:00:00.005000
6   1970-01-01 00:00:00.006000
7   1970-01-01 00:00:00.007000
8   1970-01-01 00:00:00.008000
9   1970-01-01 00:00:00.009000
dtype: datetime64[ns]

In [52]: pd.to_datetime(s,unit='D')
Out[52]: 
0   1970-01-01
1   1970-01-02
2   1970-01-03
3   1970-01-04
4   1970-01-05
5   1970-01-06
6   1970-01-07
7   1970-01-08
8   1970-01-09
9   1970-01-10
dtype: datetime64[ns]

創建一個系列就很簡單了

Creating a Series is then straightforward

In [54]: Series(s.values,index=pd.to_datetime(s,unit='s'))
Out[54]: 
1970-01-01 00:00:00    0
1970-01-01 00:00:01    1
1970-01-01 00:00:02    2
1970-01-01 00:00:03    3
1970-01-01 00:00:04    4
1970-01-01 00:00:05    5
1970-01-01 00:00:06    6
1970-01-01 00:00:07    7
1970-01-01 00:00:08    8
1970-01-01 00:00:09    9
dtype: int64

這篇關于在 pandas.Series 中將時間戳轉換為 datetime.datetime的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！