To return the Number of elements in the underlying Index data, use the index.size property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index([15, 25, 35, 45, 55]) Display the index −print("Pandas Index...", index)Return the number of elements in the Index −print("Number of ... Read More

To return the Number of dimensions of the underlying data, use the index.ndim property.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index([15, 25, 35, 45, 55]) Display the index −print("Pandas Index...", index)Get the dimensions of the data −print("Return the dimensions...", index.ndim) ExampleFollowing is the ... Read More

To return the number of bytes in the underlying Index data, use the index.nbytes property.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index([15, 25, 35, 45, 55]) Display the index −print("Pandas Index...", index)Get the bytes in the data −print("Return the bytes...", index.nbytes) ExampleFollowing is ... Read More

To set the name of the index, use the index.set_names() and include the name of the index as an argument.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) Display the index −print("Pandas Index...", index)Set the index name −print("Index name...", index.set_names('Vehicle')) ... Read More

To return a tuple of the shape of the underlying data, use the index.shape property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) Display the index −print("Pandas Index...", index)Return a tuple of the shape of the underlying data ... Read More

To return a string of the type inferred from the values, use the index.inferred_type property in Pandas.At first, import the required libraries −import pandas as pd import numpy as npCreating the index. For NaN, we have used numpy library −index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship', None, None]) Display ... Read More

To return the dtype object of the underlying data, use the index.dtype property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Shop', 'Car', 'Airplace', 'Truck']) Display the index −print("Pandas Index...", index)Return the dtype of the data −print("The dtype object...", index.dtype) ExampleFollowing ... Read More

To check if the index has NaNs, use the index.hasnans property in Pandas.At first, import the required libraries −import pandas as pd import numpy as npCreating the index. For NaN, we have used numpy library −index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship']) Display the index −print("Pandas Index...", index)Check if ... Read More

To check if the index has duplicate values, use the index.has_duplicates property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) Display the index −print("Pandas Index...", index)Check if the index is having duplicate values −print("Is the Pandas index having ... Read More

To check if the index has unique values, use the index.is_unique.At first, import the required libraries −import pandas as pdLet us create the index −index = pd.Index([50, 40, 30, 20, 10]) Display the index −print("Pandas Index...", index)Check if the index is having unique values −print("Is the Pandas index having unique ... Read More