pandas.DataFrame.memory_usage¶
-
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: 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