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.nbytes attribute return the number of bytes required to store the underlying data of the given Index object.
Syntax: Index.nbytes
Parameter : None
Returns : number of bytes needed to store the data
Example #1: Use Index.nbytes attribute to find out the number of bytes required to store the underlying data of the given Index object.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Melbourne', 'Sanghai', 'Lisbon', 'Doha', 'Moscow', 'Rio']) # Print the index print(idx) |
Output :
Now we will use Index.nbytes attribute to find out the number of bytes required to store the data in the given Index object.
# return the number of bytes occupied # by idx object result = idx.nbytes # Print the result print(result) |
Output :
As we can see in the output, the Index.nbytes attribute has returned 48, indicating that 48 bytes are needed to store the data in the given Index object.
Example #2 : Use Index.nbytes attribute to find out the number of bytes required to store the underlying data of the given Index object.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index([900 + 3j, 700 + 25j, 620 + 10j, 388 + 44j, 900]) # Print the index print(idx) |
Output :
Now we will use Index.nbytes attribute to find out the number of bytes required to store the data in the given Index object.
# return the number of bytes occupied # by idx object result = idx.nbytes # Print the result print(result) |
Output :
As we can see in the output, the Index.nbytes attribute has returned 40, indicating that 40 bytes are needed to store the data in the given Index object.