Pandas Index is an immutable ndarray implementing an ordered, sliceable set. It is the basic object which stores the axis labels for all pandas objects.
Pandas Index.inferred_type attribute return a string of the data type inferred from the values of the given Index object.
Syntax: Index.inferred_type
Parameter : None
Returns : inferred_type
Example #1: Use Index.inferred_type attribute to find out the inferred data type of the value in the given Index object.
# importing pandas as pd import pandas as pd   # Creating the index idx = pd.Index(['Jan', 'Feb', 'Mar', 'Apr', 'May'])   # Print the index print(idx) |
Output :
Index(['Jan', 'Feb', 'Mar', 'Apr', 'May'], dtype='object')
Now we will use Index.inferred_type attribute to find out the inferred dtype of the underlying data of the given Index object.
# return the inferred dtype result = idx.inferred_type   # Print the result print(result) |
Output :
mixed
As we can see in the output, the Index.inferred_type attribute has returned String as the inferred data type of the given Index object.
Example #2 : Use Index.inferred_type attribute to find out the inferred data type of the value in the given Index object.
# importing pandas as pd import pandas as pd   # Creating the index idx = pd.Index(['2012-12-12', None, '2002-1-10', None])   # Print the index print(idx) |
Output :
Index(['2012-12-12', None, '2002-1-10', None], dtype='object')
Now we will use Index.inferred_type attribute to find out the inferred dtype of the underlying data of the given Index object.
# return the inferred dtype result = idx.inferred_type   # Print the result print(result) |
Output :
mixed
As we can see in the output, the Index.inferred_type attribute has returned mixed as the inferred data type of the given Index object.
