# dask.dataframe.DataFrame.memory_usage

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

  Return the memory usage of each column in bytes.

  This docstring was copied from pandas.core.frame.DataFrame.memory_usage.

  Some inconsistencies with the Dask version may exist.

  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 is 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:
    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.

  `Categorical`

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

  `DataFrame.info`

    Concise summary of a DataFrame.

  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