pandas.DataFrame.memory_usage — pandas 0.24.2 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: | 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 interrogatingobject 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. |
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