pandas.api.types.is_datetime64_dtype() Function in Python

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. 

# importing packages
import pandas.api.types as pd

print(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.

# importing packages
import pandas.api.types as pd
import datetime
import numpy as np

date_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.

# importing packages
import pandas.api.types as pd
import numpy as np

print(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. 

# importing packages
import pandas.api.types as pd
import numpy as np

datetime_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. 

# importing packages
import pandas.api.types as pd

print(pd.is_datetime64_dtype('string'))

Output:

False

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Article Tags :

• Python
• Python-pandas
• Python pandas-methods

Practice Tags :

• python