Working with SparseArrays in Pandas: A Deep Dive

In this article, we will explore the world of sparse arrays in pandas and how to work with them effectively. We’ll start by understanding what sparse arrays are and why they’re useful, then dive into the details of working with them.

What are SparseArrays?

Sparse arrays are a data structure that stores only non-zero values in an array. This means that instead of storing all values, even zeros, as dense arrays do, sparse arrays store only the actual values and a pointer to their location. This results in significant memory savings for sparse datasets.

In pandas, sparse arrays are represented using the SparseDataFrame and SparseDtype classes. These classes allow us to work with large datasets that contain mostly zero values, making it an ideal data structure for big data analysis.

The Challenge of Working with SparseArrays

When working with dense arrays, we’re accustomed to using various methods like .loc[], .iloc[], and set_value() to access and modify individual elements. However, when we try to use these methods on a sparse array, we encounter errors because sparse arrays don’t support direct item assignment.

This is where the workaround comes in – converting the sparse array to dense format using to_dense(), doing the necessary modifications, and then converting it back to sparse format using to_sparse().

However, this approach has some drawbacks. For instance, it requires an extra memory allocation for the dense conversion, which can be costly for large datasets. Moreover, the resulting sparse array might not have the same sparsity properties as the original array.

The Solution: Working Directly with SparseArrays

Fortunately, pandas 0.25 introduced a new data type called SparseDtype, which allows us to work directly with sparse arrays without converting them to dense format. This means we can insert values into specific columns using .loc[] and then convert the affected columns back to sparse format.

The key to this approach is creating a custom function that applies these modifications while preserving the sparsity of the original array. Let’s explore how to do it.

Creating a Custom Function for Working with SparseArrays

We’ll create a function called sp_loc() that takes a DataFrame, index, columns, and values as input and returns the modified DataFrame. This function will convert the concerned Series to dense format using to_dense(), perform the necessary insertions using .loc[], and then convert the affected columns back to sparse format using astype().

def sp_loc(df, index, columns, val):
    """Insert data in a DataFrame with SparseDtype format

    Only applicable for pandas version > 0.25

    Args
    ----
    df : DataFrame with series formatted with pd.SparseDtype
    index: str, or list, or slice object
        Same as one would use as first argument of .loc[]
    columns: str, list, or slice
        Same one would normally use as second argument of .loc[]
    val: insert values

    Returns
    -------
    df: DataFrame
        Modified DataFrame

    """

    # Save the original sparse format for reuse later
    spdtypes = df.dtypes[columns]

    # Convert concerned Series to dense format
    df[columns] = df[columns].sparse.to_dense()

    # Do a normal insertion with .loc[]
    df.loc[index, columns] = val

    # Back to the original sparse format
    df[columns] = df[columns].astype(spdtypes)

    return df

Example Usage

Let’s create a simple DataFrame df1 with two columns, I and J, and then apply our custom function sp_loc() to insert values into the I column.

# DÉFINITION DATAFRAME SPARSE

df1 = pd.DataFrame(index=['a', 'b', 'c'], columns=['I', 'J'])
df1.loc['a', 'J'] = 0.42
df1 = df1.astype(pd.SparseDtype(float))

# INSERTION

df1 = sp_loc(df1, ['a','b'], 'I', [-1, 1])

print(df1)

Output:

   I      J
a -1.0  0.42
b  1.0   NaN
c  NaN   NaN

As we can see, our custom function successfully inserted values into the I column while preserving the sparsity of the original array.

Conclusion

Working with sparse arrays in pandas requires a deeper understanding of the underlying data structures and their limitations. By using the new SparseDtype data type introduced in pandas 0.25, we can work directly with sparse arrays without converting them to dense format. Our custom function sp_loc() demonstrates how to apply this approach by inserting values into specific columns while preserving the sparsity of the original array.