Sparse data structures¶
Note
The SparsePanel class has been removed in 0.19.0
We have implemented “sparse” versions of Series and DataFrame. These are not sparse
in the typical “mostly 0”. Rather, you can view these objects as being “compressed”
where any data matching a specific value (NaN / missing value, though any value
can be chosen) is omitted. A special SparseIndex object tracks where data has been
“sparsified”. This will make much more sense with an example. All of the standard pandas
data structures have a to_sparse method:
In [1]: ts = pd.Series(randn(10))
In [2]: ts[2:-2] = np.nan
In [3]: sts = ts.to_sparse()
In [4]: sts
Out[4]:
0 0.469112
1 -0.282863
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 -0.861849
9 -2.104569
dtype: float64
BlockIndex
Block locations: array([0, 8], dtype=int32)
Block lengths: array([2, 2], dtype=int32)
The to_sparse method takes a kind argument (for the sparse index, see
below) and a fill_value. So if we had a mostly zero Series, we could
convert it to sparse with fill_value=0:
In [5]: ts.fillna(0).to_sparse(fill_value=0)
Out[5]:
0 0.469112
1 -0.282863
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
7 0.000000
8 -0.861849
9 -2.104569
dtype: float64
BlockIndex
Block locations: array([0, 8], dtype=int32)
Block lengths: array([2, 2], dtype=int32)
The sparse objects exist for memory efficiency reasons. Suppose you had a
large, mostly NA DataFrame:
In [6]: df = pd.DataFrame(randn(10000, 4))
In [7]: df.iloc[:9998] = np.nan
In [8]: sdf = df.to_sparse()
In [9]: sdf
Out[9]:
0 1 2 3
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
... ... ... ... ...
9993 NaN NaN NaN NaN
9994 NaN NaN NaN NaN
9995 NaN NaN NaN NaN
9996 NaN NaN NaN NaN
9997 NaN NaN NaN NaN
9998 0.509184 -0.774928 -1.369894 -0.382141
9999 0.280249 -1.648493 1.490865 -0.890819
[10000 rows x 4 columns]
In [10]: sdf.density
Out[10]: 0.0002
As you can see, the density (% of values that have not been “compressed”) is extremely low. This sparse object takes up much less memory on disk (pickled) and in the Python interpreter. Functionally, their behavior should be nearly identical to their dense counterparts.
Any sparse object can be converted back to the standard dense form by calling
to_dense:
In [11]: sts.to_dense()
Out[11]:
0 0.469112
1 -0.282863
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 -0.861849
9 -2.104569
dtype: float64
SparseArray¶
SparseArray is the base layer for all of the sparse indexed data
structures. It is a 1-dimensional ndarray-like object storing only values
distinct from the fill_value:
In [12]: arr = np.random.randn(10)
In [13]: arr[2:5] = np.nan; arr[7:8] = np.nan
In [14]: sparr = pd.SparseArray(arr)
In [15]: sparr
Out[15]:
[-1.9556635297215477, -1.6588664275960427, nan, nan, nan, 1.1589328886422277, 0.14529711373305043, nan, 0.6060271905134522, 1.3342113401317768]
Fill: nan
IntIndex
Indices: array([0, 1, 5, 6, 8, 9], dtype=int32)
Like the indexed objects (SparseSeries, SparseDataFrame), a SparseArray
can be converted back to a regular ndarray by calling to_dense:
In [16]: sparr.to_dense()
Out[16]:
array([-1.9557, -1.6589, nan, nan, nan, 1.1589, 0.1453,
nan, 0.606 , 1.3342])
SparseIndex objects¶
Two kinds of SparseIndex are implemented, block and integer. We
recommend using block as it’s more memory efficient. The integer format
keeps an arrays of all of the locations where the data are not equal to the
fill value. The block format tracks only the locations and sizes of blocks
of data.
Sparse Dtypes¶
Sparse data should have the same dtype as its dense representation. Currently,
float64, int64 and bool dtypes are supported. Depending on the original
dtype, fill_value default changes:
float64:np.nanint64:0bool:False
In [17]: s = pd.Series([1, np.nan, np.nan])
In [18]: s
Out[18]:
0 1.0
1 NaN
2 NaN
dtype: float64
In [19]: s.to_sparse()
Out[19]:
0 1.0
1 NaN
2 NaN
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [20]: s = pd.Series([1, 0, 0])
In [21]: s
Out[21]:
0 1
1 0
2 0
dtype: int64
In [22]: s.to_sparse()
Out[22]:
0 1
1 0
2 0
dtype: int64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [23]: s = pd.Series([True, False, True])
In [24]: s
Out[24]:
0 True
1 False
2 True
dtype: bool
In [25]: s.to_sparse()
Out[25]:
0 True
1 False
2 True
dtype: bool
BlockIndex
Block locations: array([0, 2], dtype=int32)
Block lengths: array([1, 1], dtype=int32)
You can change the dtype using .astype(), the result is also sparse. Note that
.astype() also affects to the fill_value to keep its dense representation.
In [26]: s = pd.Series([1, 0, 0, 0, 0])
In [27]: s
Out[27]:
0 1
1 0
2 0
3 0
4 0
dtype: int64
In [28]: ss = s.to_sparse()
In [29]: ss
Out[29]:
0 1
1 0
2 0
3 0
4 0
dtype: int64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [30]: ss.astype(np.float64)
Out[30]:
0 1.0
1 0.0
2 0.0
3 0.0
4 0.0
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
It raises if any value cannot be coerced to specified dtype.
In [1]: ss = pd.Series([1, np.nan, np.nan]).to_sparse()
0 1.0
1 NaN
2 NaN
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
In [2]: ss.astype(np.int64)
ValueError: unable to coerce current fill_value nan to int64 dtype
Sparse Calculation¶
You can apply NumPy ufuncs to SparseArray and get a SparseArray as a result.
In [31]: arr = pd.SparseArray([1., np.nan, np.nan, -2., np.nan])
In [32]: np.abs(arr)
Out[32]:
[1.0, nan, nan, 2.0, nan]
Fill: nan
IntIndex
Indices: array([0, 3], dtype=int32)
The ufunc is also applied to fill_value. This is needed to get
the correct dense result.
In [33]: arr = pd.SparseArray([1., -1, -1, -2., -1], fill_value=-1)
In [34]: np.abs(arr)
Out[34]:
[1.0, 1.0, 1.0, 2.0, 1.0]
Fill: 1
IntIndex
Indices: array([0, 3], dtype=int32)
In [35]: np.abs(arr).to_dense()
Out[35]: array([ 1., 1., 1., 2., 1.])
Interaction with scipy.sparse¶
SparseDataFrame¶
New in version 0.20.0.
Pandas supports creating sparse dataframes directly from scipy.sparse matrices.
In [36]: from scipy.sparse import csr_matrix
In [37]: arr = np.random.random(size=(1000, 5))
In [38]: arr[arr < .9] = 0
In [39]: sp_arr = csr_matrix(arr)
In [40]: sp_arr
Out[40]:
<1000x5 sparse matrix of type '<class 'numpy.float64'>'
with 517 stored elements in Compressed Sparse Row format>
In [41]: sdf = pd.SparseDataFrame(sp_arr)
In [42]: sdf
Out[42]:
0 1 2 3 4
0 0.956380 NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 0.999552 NaN NaN 0.956153 NaN
5 NaN NaN NaN NaN NaN
6 0.913638 NaN NaN NaN NaN
.. ... .. ... ... ..
993 NaN NaN NaN NaN NaN
994 NaN NaN NaN NaN NaN
995 NaN NaN NaN 0.998834 NaN
996 NaN NaN NaN NaN NaN
997 NaN NaN NaN NaN NaN
998 NaN NaN 0.95659 NaN NaN
999 NaN NaN NaN NaN NaN
[1000 rows x 5 columns]
All sparse formats are supported, but matrices that are not in COOrdinate format will be converted, copying data as needed.
To convert a SparseDataFrame back to sparse SciPy matrix in COO format, you can use the SparseDataFrame.to_coo() method:
In [43]: sdf.to_coo()
Out[43]:
<1000x5 sparse matrix of type '<class 'numpy.float64'>'
with 517 stored elements in COOrdinate format>
SparseSeries¶
A SparseSeries.to_coo() method is implemented for transforming a SparseSeries indexed by a MultiIndex to a scipy.sparse.coo_matrix.
The method requires a MultiIndex with two or more levels.
In [44]: s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
In [45]: s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
....: (1, 2, 'a', 1),
....: (1, 1, 'b', 0),
....: (1, 1, 'b', 1),
....: (2, 1, 'b', 0),
....: (2, 1, 'b', 1)],
....: names=['A', 'B', 'C', 'D'])
....:
In [46]: s
Out[46]:
A B C D
1 2 a 0 3.0
1 NaN
1 b 0 1.0
1 3.0
2 1 b 0 NaN
1 NaN
dtype: float64
# SparseSeries
In [47]: ss = s.to_sparse()
In [48]: ss
Out[48]:
A B C D
1 2 a 0 3.0
1 NaN
1 b 0 1.0
1 3.0
2 1 b 0 NaN
1 NaN
dtype: float64
BlockIndex
Block locations: array([0, 2], dtype=int32)
Block lengths: array([1, 2], dtype=int32)
In the example below, we transform the SparseSeries to a sparse representation of a 2-d array by specifying that the first and second MultiIndex levels define labels for the rows and the third and fourth levels define labels for the columns. We also specify that the column and row labels should be sorted in the final sparse representation.
In [49]: A, rows, columns = ss.to_coo(row_levels=['A', 'B'],
....: column_levels=['C', 'D'],
....: sort_labels=True)
....:
In [50]: A
Out[50]:
<3x4 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
In [51]: A.todense()
Out[51]:
matrix([[ 0., 0., 1., 3.],
[ 3., 0., 0., 0.],
[ 0., 0., 0., 0.]])
In [52]: rows
Out[52]: [(1, 1), (1, 2), (2, 1)]
In [53]: columns
Out[53]: [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
Specifying different row and column labels (and not sorting them) yields a different sparse matrix:
In [54]: A, rows, columns = ss.to_coo(row_levels=['A', 'B', 'C'],
....: column_levels=['D'],
....: sort_labels=False)
....:
In [55]: A
Out[55]:
<3x2 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
In [56]: A.todense()
Out[56]:
matrix([[ 3., 0.],
[ 1., 3.],
[ 0., 0.]])
In [57]: rows
Out[57]: [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]
In [58]: columns
Out[58]: [0, 1]
A convenience method SparseSeries.from_coo() is implemented for creating a SparseSeries from a scipy.sparse.coo_matrix.
In [59]: from scipy import sparse
In [60]: A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
....: shape=(3, 4))
....:
In [61]: A
Out[61]:
<3x4 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
In [62]: A.todense()
Out[62]:
matrix([[ 0., 0., 1., 2.],
[ 3., 0., 0., 0.],
[ 0., 0., 0., 0.]])
The default behaviour (with dense_index=False) simply returns a SparseSeries containing
only the non-null entries.
In [63]: ss = pd.SparseSeries.from_coo(A)
In [64]: ss
Out[64]:
0 2 1.0
3 2.0
1 0 3.0
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([3], dtype=int32)
Specifying dense_index=True will result in an index that is the Cartesian product of the
row and columns coordinates of the matrix. Note that this will consume a significant amount of memory
(relative to dense_index=False) if the sparse matrix is large (and sparse) enough.
In [65]: ss_dense = pd.SparseSeries.from_coo(A, dense_index=True)
In [66]: ss_dense
Out[66]:
0 0 NaN
1 NaN
2 1.0
3 2.0
1 0 3.0
1 NaN
2 NaN
3 NaN
2 0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
BlockIndex
Block locations: array([2], dtype=int32)
Block lengths: array([3], dtype=int32)