The pandas.api.types.is_datetime64_dtype() function is used to check whether an array like object or a datatype is of the datetime64 dtype.
Syntax: pandas.api.types.is_datetime64_dtype(arr_or_dtype)
parameters:
- arr_or_dtype : array like iterable object or datatype.
function returns: a boolean value. True or False. True if object is of the type datetime64 False if not
Example 1:
pandas.api.types is imported and is_datetime64_dtype() function is used to verify whether the given array is of type datetime64. as it is of type int , false is returned.
Python3
# importing packagesimport pandas.api.types as pdprint(pd.is_datetime64_dtype([10, 20, 30])) |
Output:
False
Example 2:
In this example, a datetime array is created and np.datetime64 is given as its type. is_datetime64_dtype() function returns ‘True’ as the array is of the datetime64 type.
Python3
# importing packagesimport pandas.api.types as pdimport datetimeimport numpy as npdate_list = np.array([datetime.datetime.today() + datetime.timedelta(days=x) for x in range(10)], dtype=np.datetime64)print(pd.is_datetime64_dtype(date_list)) |
Output:
True
Example 3:
numpy.datetime64 dtype from the is directly passed in the method. ‘True’ is returned.
Python3
# importing packagesimport pandas.api.types as pdimport numpy as npprint(pd.is_datetime64_dtype(np.datetime64)) |
Output:
True
Example 4:
An empty NumPy array of type datetime64 is created and it’s passed into is_datetime64_dtype() function. ‘True’ is returned.
Python3
# importing packagesimport pandas.api.types as pdimport numpy as npdatetime_array = np.array([], dtype=np.datetime64)print(pd.is_datetime64_dtype(datetime_array)) |
Output:
True
Example 5:
A string object is passed into the is_datetime64_dtype() function and ‘False’ is returned.
Python3
# importing packagesimport pandas.api.types as pdprint(pd.is_datetime64_dtype('string')) |
Output:
False
