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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.


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.



The sparse representation of the DataFrame.

See also

Converts the DataFrame back to the its dense form.


>>> 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'>
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