Pandas Series: dt.to_pydatetime() function

Series.dt.to_pydatetime() function

The to_pydatetime() function is used to get the data as an array of native Python datetime objects.
Timezone information is retained if present.

Syntax:

Series.dt.to_pydatetime(self)

Returns: numpy.ndarray
Object dtype array containing native Python datetime objects.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(pd.date_range('20190210', periods=2))
s

Output:

0   2019-02-10
1   2019-02-11
dtype: datetime64[ns]

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(pd.date_range('20190210', periods=2))
s.dt.to_pydatetime()

Output:

array([datetime.datetime(2019, 2, 10, 0, 0),
       datetime.datetime(2019, 2, 11, 0, 0)], dtype=object)

Example - pandas’ nanosecond precision is truncated to microseconds:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(pd.date_range('20190210', periods=2, freq='ns'))
s

Output:

0   2019-02-10 00:00:00.000000000
1   2019-02-10 00:00:00.000000001
dtype: datetime64[ns]

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(pd.date_range('20190210', periods=2, freq='ns'))
s.dt.to_pydatetime()

Output:

array([datetime.datetime(2019, 2, 10, 0, 0),
       datetime.datetime(2019, 2, 10, 0, 0)], dtype=object)

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