Pd.to_datetime 返回一个对象，而不是时间序列 [英] Pd.to_datetime returns an object, not a time series

问题描述

我正在尝试将 df 中的列转换为时间序列.数据集从 2015 年 3 月 23 日到 2019 年 8 月 17 日，数据集如下所示:

I am trying to convert my column in a df into a time series. The dataset goes from March 23rd 2015-August 17th 2019 and the dataset looks like this:

                        time    1day_active_users
0  2015-03-23 00:00:00-04:00              19687.0
1  2015-03-24 00:00:00-04:00              19437.0

我试图将时间列转换为日期时间系列，但它将该列作为对象返回.代码如下:

I am trying to convert the time column into a datetime series but it returns the column as an object. Here is the code:

data = pd.read_csv(data_path)
data.set_index('time', inplace=True)
data.index= pd.to_datetime(data.index)
data.index.dtype 

data.index.dtype 返回 dtype('O').我认为这就是为什么当我尝试及时索引元素时，它会返回错误.例如，当我运行这个:

data.index.dtype returns dtype('O'). I assume this is why when I try to index an element in time, it returns an error. For example, when I run this:

data.loc['2015']

它给了我这个错误

KeyError: '2015'

任何帮助或反馈将不胜感激.谢谢.

Any help or feedback would be appreciated. Thank you.

推荐答案

正如所评论的，问题可能是由于时区不同造成的.尝试将 utc=True 传递给 pd.to_datetime:

As commented, the problem might be due to the different timezones. Try passing utc=True to pd.to_datetime:

df['time'] = pd.to_datetime(df['time'],utc=True)
df['time']

测试数据

                        time  1day_active_users
0  2015-03-23 00:00:00-04:00            19687.0
1  2015-03-24 00:00:00-05:00            19437.0

输出:

0   2015-03-23 04:00:00+00:00
1   2015-03-24 05:00:00+00:00
Name: time, dtype: datetime64[ns, UTC]

然后:

df.set_index('time', inplace=True)
df.loc['2015']

给予

                           1day_active_users
time                                        
2015-03-23 04:00:00+00:00            19687.0
2015-03-24 05:00:00+00:00            19437.0

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