Python | Pandas Series.memory_usage() Last Updated : 11 Feb, 2019 Comments Improve Suggest changes Like Article Like Report 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.memory_usage() function return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of object dtype. Syntax: Series.memory_usage(index=True, deep=False) Parameter : index : Specifies whether to include the memory usage of the Series index. deep : If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned value. Returns : Bytes of memory consumed. Example #1: Use Series.memory_usage() function to find the memory usage of the given series object. Python3 # importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([10, 25, 3, 25, 24, 6]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) Output : Now we will use Series.memory_usage() function to find the memory usage of the given series object. Python3 # return the memory usage result = sr.memory_usage() # Print the result print(result) Output : As we can see in the output, the Series.memory_usage() function has successfully returned the memory usage of the given series object. Example #2: Use Series.memory_usage() function to find the memory usage of the given series object. Python3 # importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, 16.8, 20.124, None, 18.1002, 19.5]) # Print the series print(sr) Output : Now we will use Series.memory_usage() function to find the memory usage of the given series object. Python3 # return the memory usage result = sr.memory_usage() # Print the result print(result) Output : As we can see in the output, the Series.memory_usage() function has successfully returned the memory usage of the given series object. Comment More infoAdvertise with us Next Article Python | Pandas Series.memory_usage() S Shubham__Ranjan Follow Improve Article Tags : Python Pandas Python-pandas Python pandas-series-methods AI-ML-DS With Python +1 More Practice Tags : python Similar Reads Python | Pandas TimedeltaIndex.memory_usage 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 TimedeltaIndex.memory_usage() function return the memory usage of the given Tim 2 min read Python | Pandas Series.shape 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 un 2 min read Python | Pandas Index.memory_usage() 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 Index.memory_usage() function return the memory usage of the Index. It returns 2 min read Python | Pandas Series.size 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 un 2 min read Python | Pandas Series.item() 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.item() function return the fi 2 min read Like