pandas.DataFrame.to_sparse

DataFrame.to_sparse(fill_value=None, kind='block')

Convert to SparseDataFrame.

Implement the sparse version of the DataFrame meaning that any data matching a specific value it’s omitted in the representation. The sparse DataFrame allows for a more efficient storage.

Parameters:

fill_value : float, default None

The specific value that should be omitted in the representation.

kind : {‘block’, ‘integer’}, default ‘block’

The kind of the SparseIndex tracking where data is not equal to the fill value:

• ‘block’ tracks only the locations and sizes of blocks of data.
• ‘integer’ keeps an array with all the locations of the data.

In most cases ‘block’ is recommended, since it’s more memory efficient.

Returns:

SparseDataFrame

The sparse representation of the DataFrame.

See also

DataFrame.to_dense

Converts the DataFrame back to the its dense form.

Examples

>>> df = pd.DataFrame([(np.nan, np.nan),
...                    (1., np.nan),
...                    (np.nan, 1.)])
>>> df
     0    1
0  NaN  NaN
1  1.0  NaN
2  NaN  1.0
>>> type(df)
<class 'pandas.core.frame.DataFrame'>

>>> sdf = df.to_sparse()
>>> sdf
     0    1
0  NaN  NaN
1  1.0  NaN
2  NaN  1.0
>>> type(sdf)
<class 'pandas.core.sparse.frame.SparseDataFrame'>