Python Articles - Page 385 of 1048

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 elements in the index...", index.size) ExampleFollowing is the code −import pandas as pd # Creating 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 elements in the index...", index.size) ... 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 code −import pandas as pd # Creating the index index = pd.Index([15, 25, 35, 45, 55]) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Return a tuple of the shape of the underlying data ... 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 the code −import pandas as pd # Creating the index index = pd.Index([15, 25, 35, 45, 55]) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Return a tuple of the shape of the underlying ... 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')) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Set the index name print("Index name...", index.set_names('Vehicle'))OutputThis ... 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 −print("A tuple of the shape of underlying data...", index.shape) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) ... 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 the index −print("Pandas Index...", index)Return a string of the type inferred from the values −print("The inferred type...", index.inferred_type) ExampleFollowing is the code −import pandas as pd import numpy as np # Creating the index # For NaN, we have used numpy library index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, ... 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 is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Shop', 'Car', 'Airplace', 'Truck']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Return the dtype of the data print("The ... 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 the index is having NaNs −print("Is the Pandas index having NaNs?", index.hasnans) ExampleFollowing is the code −import pandas as pd import numpy as np # Creating the index # For NaN, we have used numpy library index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship']) # Display the ... 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 duplicate values?", index.has_duplicates) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Check if the index ... 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 values?", index.is_unique) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index([50, 40, 30, 20, 10]) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Check if the index is ... Read More