cudf.DataFrame.memory_usage

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

Return the memory usage of an object.

Parameters

indexbool, default True

Specifies whether to include the memory usage of the index.

deepbool, default False

The deep parameter is ignored and is only included for pandas compatibility.

Returns

Series or scalar

For DataFrame, a Series whose index is the original column names and whose values is the memory usage of each column in bytes. For a Series the total memory usage.

Examples

DataFrame

>>> dtypes = ['int64', 'float64', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
...              for t in dtypes])
>>> df = cudf.DataFrame(data)
>>> df.head()
   int64  float64  object  bool
0      1      1.0     1.0  True
1      1      1.0     1.0  True
2      1      1.0     1.0  True
3      1      1.0     1.0  True
4      1      1.0     1.0  True
>>> df.memory_usage(index=False)
int64      40000
float64    40000
object     40000
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)
5008

Series >>> s = cudf.Series(range(3), index=[‘a’,’b’,’c’]) >>> s.memory_usage() 43

Not including the index gives the size of the rest of the data, which is necessarily smaller:

>>> s.memory_usage(index=False)
24