Python | Pandas Series.nbytes
Last Updated :
28 Jan, 2019
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.nbytes
attribute is return the number of bytes required to store the underlying data in the given Series object.
Syntax:Series.nbytes
Parameter : None
Returns : number of bytes
Example #1: Use Series.nbytes
attribute is used to find the number of bytes required to store the underlying data of the given Series object.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
print (sr)
|
Output :
Now we will use Series.nbytes
attribute to find the number of bytes required to store the underlying data of the given Series object.
Output :
As we can see in the output, the Series.nbytes
attribute has returned 40 indicating that the memory needed to store the given series object is 40 bytes.
Example #2 : Use Series.nbytes
attribute is used to find the number of bytes required to store the underlying data of the given Series object.
import pandas as pd
sr = pd.Series([ '1/1/2018' , '2/1/2018' , '3/1/2018' , '4/1/2018' ])
sr.index = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' ]
print (sr)
|
Output :
Now we will use Series.nbytes
attribute to find the number of bytes required to store the underlying data of the given Series object.
Output :
As we can see in the output, the Series.nbytes
attribute has returned 32 indicating that the memory needed to store the given series object is 32 bytes.
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