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 the sum of the memory used by all the individual labels present in the Index.f

Syntax: Index.memory_usage(deep=False)
Parameters : 
deep : Introspect the data deeply, interrogate object dtypes for system-level memory consumption
Returns : bytes used
 

Example #1: Use Index.memory_usage() function to find the overall memory used by the Index object. 

# importing pandas as pd
import pandas as pd

# Creating the Index
idx = pd.Index(['Labrador', 'Beagle', 'Mastiff', 'Lhasa', 'Husky', 'Beagle'])

# Print the Index
idx

Output : 

Now we will use Index.memory_usage() function to find the memory usage of the idx object. 

# finding the memory used by the idx object
idx.memory_usage()

Output : 

The function has returned the value of 48 indicating that 48 bytes of memory are being used. 
  
Example #2: Use Index.memory_usage() function to check the memory usage of the MultiIndex object.

# importing pandas as pd
import pandas as pd

# Creating the MultiIndex
midx = pd.MultiIndex.from_arrays([['Mon', 'Tue', 'Wed', 'Thr'], [10, 20, 30, 40]],
                                                       names =('Days', 'Target'))

# Print the MultiIndex
midx

Output : 

Now we will check the amount of memory used by the midx object. 

# return the total memory used by the multi-index object
midx.memory_usage()

Output : 

As we can see in the output, the function has returned 180 indicating that the midx object is using 180 bytes of memory.