Pandas: Display memory usage of a given DataFrame and every column of the DataFrame

Last update on September 06 2025 12:48:53 (UTC/GMT +8 hours)

71. Display Memory Usage of DataFrame and Columns

Write a Pandas program to display memory usage of a given DataFrame and every column of the DataFrame.

Sample Solution :

Python Code :

import pandas as pd
df = pd.DataFrame({
    'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],
    'Date_Of_Birth ': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],
    'Age': [18.5, 21.2, 22.5, 22, 23]
})
print("Original DataFrame:")
print(df)
print("\nGlobal usage of memory of the DataFrame:")
print(df.info(memory_usage = "deep"))
print("\nThe usage of memory of every column of the said DataFrame:")
print(df.memory_usage(deep = True))

Sample Output:

Original DataFrame:
             Name Date_Of_Birth    Age
0  Alberto Franco     17/05/2002  18.5
1    Gino Mcneill     16/02/1999  21.2
2     Ryan Parkes     25/09/1998  22.5
3    Eesha Hinton     11/05/2002  22.0
4    Syed Wharton     15/09/1997  23.0

Global usage of memory of the DataFrame:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
Name              5 non-null object
Date_Of_Birth     5 non-null object
Age               5 non-null float64
dtypes: float64(1), object(2)
memory usage: 801.0 bytes
None

The usage of memory of every column of the said DataFrame:
Index              80
Name              346
Date_Of_Birth     335
Age                40
dtype: int64

For more Practice: Solve these Related Problems:

Write a Pandas program to display the overall memory usage of a DataFrame and then break it down by each column.
Write a Pandas program to compute the memory footprint of each column and then output a bar chart of these values.
Write a Pandas program to get the memory usage details of a DataFrame and then compare the memory consumption before and after data type optimization.
Write a Pandas program to list the memory usage per column and then convert the results to a more human-readable format (e.g., KB, MB).

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Python-Pandas Code Editor:

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