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However, when i use unique() it gives me another datatype: However, there are some differences in how these operations are. >>> import numpy as np >>> np.dtype('datetime64[ns]') == np.dtype('<m8[ns]') true.

高強度ボルト用鋼(黒染加工) [極低頭] NSローヘッド パワーエイト(Power8) M8 (太さ=8mm)×長さ=30mm 【 バラ売り

Numpy arrays with datetime64[ns] can be seamlessly used within pandas dataframes. Datetime64[ns] is a general dtype, while <m8[ns] is a specific dtype. General dtypes map to specific dtypes, but may be different from one installation of numpy to the next.

Datetime64[ns] is a general dtype, while <m8[ns] is a specific dtype.

<m8[ns] is a synonym for datetime64[ns]. Also, you don't need np.isnat if you are dealing with pandas datetime: The data type is called datetime64, so named because datetime is already taken. Starting in numpy 1.7, there are core array data types which natively support datetime functionality.

Pandas series with timestamps internally use the <m8[ns] representation. Both the datetime64[ns] and <m8[ns] data types can be compared and converted to other data types. I have a dataframe where one column is a datetime in <m8[ns] datatype. If in doubt, you may verify that the following statement returns.

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高強度ボルト用鋼(黒染加工) [極低頭] NSローヘッド パワーエイト(Power8) M8 (太さ=8mm)×長さ=30mm 【 バラ売り

But if you really need to convert, just use astype like you would for any other conversion:

General dtypes map to specific dtypes, but may be different from one installation of numpy to the next.

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PYTHON Difference between data type 'datetime64[ns]' and ' M8[ns

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