pandas.DataFrame.memory_usage — pandas 2.2.3 documentation (original) (raw)

DataFrame.memory_usage(index=True, deep=False)[source]#

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters:

indexbool, default True

Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.

deepbool, default False

If True, introspect the data deeply by interrogatingobject dtypes for system-level memory consumption, and include it in the returned values.

Returns:

Series

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Notes

See the Frequently Asked Questions for more details.

Examples

dtypes = ['int64', 'float64', 'complex128', 'object', 'bool'] data = dict([(t, np.ones(shape=5000, dtype=int).astype(t)) ... for t in dtypes]) df = pd.DataFrame(data) df.head() int64 float64 complex128 object bool 0 1 1.0 1.0+0.0j 1 True 1 1 1.0 1.0+0.0j 1 True 2 1 1.0 1.0+0.0j 1 True 3 1 1.0 1.0+0.0j 1 True 4 1 1.0 1.0+0.0j 1 True

df.memory_usage() Index 128 int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64

df.memory_usage(index=False) int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64

The memory footprint of object dtype columns is ignored by default:

df.memory_usage(deep=True) Index 128 int64 40000 float64 40000 complex128 80000 object 180000 bool 5000 dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

df['object'].astype('category').memory_usage(deep=True) 5244