Python Pandas - Sparse Data

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Sparse objects are “compressed” when 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 in an example. All of the standard Pandas data structures apply the to_sparse method −

import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(10))
ts[2:-2] = np.nan
sts = ts.to_sparse()
print sts

Its output is as follows −

0   -0.810497
1   -1.419954
2         NaN
3         NaN
4         NaN
5         NaN
6         NaN
7         NaN
8    0.439240
9   -1.095910
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.

Let us now assume you had a large NA DataFrame and execute the following code −

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10000, 4))
df.ix[:9998] = np.nan
sdf = df.to_sparse()

print sdf.density

Its output is as follows −

0.0001

Any sparse object can be converted back to the standard dense form by calling to_dense −

import pandas as pd
import numpy as np
ts = pd.Series(np.random.randn(10))
ts[2:-2] = np.nan
sts = ts.to_sparse()
print sts.to_dense()

Its output is as follows −

0   -0.810497
1   -1.419954
2         NaN
3         NaN
4         NaN
5         NaN
6         NaN
7         NaN
8    0.439240
9   -1.095910
dtype: float64

Sparse Dtypes

Sparse data should have the same dtype as its dense representation. Currently, float64, int64 and booldtypes are supported. Depending on the original dtype, fill_value default changes −

• float64 − np.nan

• int64 − 0

• bool − False

Let us execute the following code to understand the same −

import pandas as pd
import numpy as np

s = pd.Series([1, np.nan, np.nan])
print s

s.to_sparse()
print s

Its output is as follows −

0   1.0
1   NaN
2   NaN
dtype: float64

0   1.0
1   NaN
2   NaN
dtype: float64