DataFrameSparseAccessor

class pandas::DataFrameSparseAccessor

pandas C++ class.

Example

#include <pandas/pandas.h>
using namespace pandas;

// Use DataFrameSparseAccessor
DataFrameSparseAccessor obj;
// ... operations ...

Constructors

Signature	Location	Example
explicit DataFrameSparseAccessor(const DataFrame& parent)	pd_sparse_accessor.h:181

Construction

Signature	Return Type	Location	Example
static DataFrame from_spmatrix( const std::vector<double>& data, const std::vector<size_t>& row_idx, const std::vector<size_t>& col_idx, size_t nrows, size_t ncols, const std::vector<std::string>& index = {}, const std::vector<std::string>& columns = {})	static DataFrame	pd_sparse_accessor.h:208

I/O

Signature	Return Type	Location	Example
to_coo() const		pd_sparse_accessor.h:202	View
DataFrame to_dense() const	DataFrame	pd_sparse_accessor.h:194	View

Other Methods

Signature	Return Type	Location	Example
double density() const	double	pd_sparse_accessor.h:188	View

Code Examples

The following examples are extracted from the test suite.

to_coo (pd_test_3_all.cpp:21918)

        throw std::runtime_error("SparseAccessor.to_dense(): wrong size");
    }
    if (dense[0] != 0.0 || dense[1] != 1.0 || dense[2] != 0.0 || dense[3] != 2.0) {
        throw std::runtime_error("SparseAccessor.to_dense(): wrong values");
    }

    std::cout << " -> tests passed" << std::endl;
}

void test_sparse_to_coo() {
    std::cout << "========= SparseAccessor.to_coo() ==================";

    pandas::Series<numpy::float64> s({0.0, 1.0, 0.0, 2.0, 0.0});
    auto sparse = s.sparse();

    auto [data, rows, cols, shape] = sparse.to_coo();
    if (data.size() != 2) {
        throw std::runtime_error("SparseAccessor.to_coo(): expected 2 data points");
    }
    if (data[0] != 1.0 || data[1] != 2.0) {
        throw std::runtime_error("SparseAccessor.to_coo(): wrong data values");

to_dense (pd_test_1_all.cpp:3272)

        std::cout << " -> tests passed" << std::endl;
    }

    void pd_test_sparse_array_to_dense() {
        std::cout << "========= SparseArray: to_dense ======================= ";

        std::vector<numpy::float64> data = {0.0, 1.0, 0.0, 2.0, 0.0};
        pandas::SparseArray<numpy::float64> arr(data, 0.0);

        auto dense = arr.to_dense();
        if (dense.getSize() != 5) {
            std::cout << "  [FAIL] : in pd_test_sparse_array_to_dense() : dense size != 5" << std::endl;
            throw std::runtime_error("pd_test_sparse_array_to_dense failed: dense size != 5");
        }

        if (dense.getElementAt({0}) != 0.0 ||
            dense.getElementAt({1}) != 1.0 ||
            dense.getElementAt({2}) != 0.0 ||
            dense.getElementAt({3}) != 2.0 ||
            dense.getElementAt({4}) != 0.0) {

density (pd_test_1_all.cpp:3247)

            std::cout << "  [FAIL] : in pd_test_sparse_array_fill_value_property() : default float fill_value should be NaN" << std::endl;
            throw std::runtime_error("pd_test_sparse_array_fill_value_property failed: default float fill_value should be NaN");
        }

        std::cout << " -> tests passed" << std::endl;
    }

    void pd_test_sparse_array_density() {
        std::cout << "========= SparseArray: density ======================= ";

        // 20% density (2 non-fill out of 10)
        std::vector<numpy::float64> data = {0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0};
        pandas::SparseArray<numpy::float64> arr(data, 0.0);

        double density = arr.density();
        if (std::abs(density - 0.2) > 0.001) {
            std::cout << "  [FAIL] : in pd_test_sparse_array_density() : density != 0.2, got " << density << std::endl;
            throw std::runtime_error("pd_test_sparse_array_density failed: density != 0.2");
        }

        double sparsity = arr.sparsity();