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