memory_usage(self, index: 'bool' = True, deep: 'bool' = False) -> 'int'

The memory usage can optionally include the contribution of the index and of elements of :None:None:`object` dtype.

Parameters

index : bool, default True

Specifies whether to include the memory usage of the Series index.

deep : bool, default False

If True, introspect the data deeply by interrogating :None:None:`object` dtypes for system-level memory consumption, and include it in the returned value.

Returns

int

Bytes of memory consumed.

Return the memory usage of the Series.

See Also

DataFrame.memory_usage

Bytes consumed by a DataFrame.

numpy.ndarray.nbytes

Total bytes consumed by the elements of the array.

Examples

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(range(3))
... s.memory_usage()
152

Not including the index gives the size of the rest of the data, which is necessarily smaller:

This example is valid syntax, but we were not able to check execution

>>> s.memory_usage(index=False)
24

The memory footprint of :None:None:`object` values is ignored by default:

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(["a", "b"])
... s.values
array(['a', 'b'], dtype=object)

This example is valid syntax, but we were not able to check execution

>>> s.memory_usage()
144

This example is valid syntax, but we were not able to check execution

>>> s.memory_usage(deep=True)
244

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.frame.DataFrame.memory_usage pandas.core.series.Series.info

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