DataFrameRolling

class pandas::DataFrameRolling

Window operation class for rolling/expanding calculations.

Example

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

// Use DataFrameRolling
DataFrameRolling obj;
// ... operations ...

Constructors

Signature	Location	Example
DataFrameRolling(const DataFrame& df, size_t window, size_t min_periods = 1, bool center = false)	pd_dataframe.h:12632

Indexing / Selection

Signature	Return Type	Location	Example
const DataFrame& get_df() const	const DataFrame&	pd_dataframe.h:12654

Statistics

Signature	Return Type	Location	Example
size_t min_periods() const	size_t	pd_dataframe.h:12652

Aggregation

Signature	Return Type	Location	Example
DataFrame apply_to_columns(Func&& func) const	DataFrame	pd_dataframe.h:12690

Other Methods

Signature	Return Type	Location	Example
bool center() const	bool	pd_dataframe.h:12653	View
Rolling<double> select_column(const std::string& col) const	Rolling<double>	pd_dataframe.h:12656
void set_custom_bounds(std::vector<int64_t> start, std::vector<int64_t> end)	void	pd_dataframe.h:12645
void set_on_column(const std::string& col)	void	pd_dataframe.h:12643
void set_time_window(std::vector<int64_t> timestamps, int64_t window_seconds, int closed = 0)	void	pd_dataframe.h:12635
size_t window() const	size_t	pd_dataframe.h:12651	View

Code Examples

The following examples are extracted from the test suite.

center (pd_test_3_all.cpp:21005)

    auto alnum_result = s.str().isalnum();
    if (alnum_result[0] != true || alnum_result[1] != true || alnum_result[2] != true || alnum_result[3] != false) {
        std::cout << "  [FAIL] : isalnum() failed" << std::endl;
        throw std::runtime_error("pd_test_str_is_methods: isalnum() failed");
    }

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

// ============================================================================
// Test str().zfill(), str().center(), str().ljust(), str().rjust()
// ============================================================================

void pd_test_str_padding() {
    std::cout << "========= Series.str().zfill/center/ljust/rjust() =======";

    pandas::Series<std::string> s({"1", "22", "333"});

    auto zfill_result = s.str().zfill(5);
    if (zfill_result[0] != "00001" || zfill_result[1] != "00022" || zfill_result[2] != "00333") {
        std::cout << "  [FAIL] : zfill() failed" << std::endl;

window (pd_test_1_all.cpp:20977)

                }
                throw std::runtime_error("pd_test_expanding_corr failed: expanding corr values incorrect");
            }

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

        void pd_test_expanding_skew() {
            std::cout << "========= Expanding skew ========================";

            // Symmetric data: skew should be 0 at each expanding window (starting from 3)
            pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0});
            auto result = s.expanding().skew();

            // First 2 should be NaN (need >= 3 values)
            // Symmetric expanding windows [1,2,3], [1,2,3,4], ... all have skew = 0
            bool passed = std::isnan(result[0]) &&
                          std::isnan(result[1]) &&
                          std::abs(result[2] - 0.0) < 0.001 &&
                          std::abs(result[3] - 0.0) < 0.001 &&
                          std::abs(result[4] - 0.0) < 0.001;