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.itemsize attribute return the size of the dtype of the item of the underlying data for the given Series object.
Syntax:Series.itemsize
Parameter : None
Returns : size
Example #1: Use Series.itemsize attribute to check the size of the underlying data for the given Series object.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon'])
# Creating the row axis labels sr.index = ['City 1', 'City 2', 'City 3', 'City 4']
# Print the series print(sr)
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Output :
Now we will use Series.itemsize attribute to check the size of the underlying data in the given Series object.
# return the size sr.itemsize |
Output :
As we can see in the output, the Series.itemsize attribute has returned 8 indicating that the size of the underlying data for the given Series object is 8 bytes.
Example #2 : Use Series.itemsize attribute to check the size of the underlying data for the given Series object.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series(['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018'])
# Creating the row axis labels sr.index = ['Day 1', 'Day 2', 'Day 3', 'Day 4']
# Print the series print(sr)
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Output :
Now we will use Series.itemsize attribute to check the size of the underlying data in the given Series object.
# return the size sr.itemsize |
Output :
As we can see in the output, the Series.itemsize attribute has returned 8 indicating that the size of the underlying data for the given Series object is 8 bytes.