pandas.DataFrame.memory_usage

DataFrame.memory_usage(index=True, deep=False)

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:

index : bool, 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 the first item in the output.

deep : bool, default False

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

Returns:

sizes : Series

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

See also

numpy.ndarray.nbytes

Total bytes consumed by the elements of an ndarray.

Series.memory_usage

Bytes consumed by a Series.

pandas.Categorical

Memory-efficient array for string values with many repeated values.

DataFrame.info

Concise summary of a DataFrame.

Examples

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

>>> df.memory_usage()
Index            80
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             80
int64          40000
float64        40000
complex128     80000
object        160000
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)
5168