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 the sum of the memory used by all the individual labels present in the Index.f
Syntax: Index.memory_usage(deep=False)
Parameters :Â
deep : Introspect the data deeply, interrogate object dtypes for system-level memory consumption
Returns : bytes used
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Example #1: Use Index.memory_usage() function to find the overall memory used by the Index object.Â
Python3
# importing pandas as pdimport pandas as pdÂ
# Creating the Indexidx = pd.Index(['Labrador', 'Beagle', 'Mastiff', 'Lhasa', 'Husky', 'Beagle'])Â
# Print the Indexidx |
Output :Â
Now we will use Index.memory_usage() function to find the memory usage of the idx object.Â
Python3
# finding the memory used by the idx objectidx.memory_usage() |
Output :Â
The function has returned the value of 48 indicating that 48 bytes of memory are being used.Â
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Example #2: Use Index.memory_usage() function to check the memory usage of the MultiIndex object.
Python3
# importing pandas as pdimport pandas as pdÂ
# Creating the MultiIndexmidx = pd.MultiIndex.from_arrays([['Mon', 'Tue', 'Wed', 'Thr'], [10, 20, 30, 40]],                                                       names =('Days', 'Target'))Â
# Print the MultiIndexmidx |
Output :Â
Now we will check the amount of memory used by the midx object.Â
Python3
# return the total memory used by the multi-index objectmidx.memory_usage() |
Output :Â
As we can see in the output, the function has returned 180 indicating that the midx object is using 180 bytes of memory.
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