pandas.DataFrame.to_sparse — pandas 0.24.0rc1 documentation (original) (raw)

DataFrame. to_sparse(fill_value=None, kind='block')[source]¶

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.

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.

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'>