DataFrameGroupByResampler

class pandas::DataFrameGroupByResampler

pandas C++ class.

Example

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

// Use DataFrameGroupByResampler
DataFrameGroupByResampler obj;
// ... operations ...

Constructors

Signature	Location	Example
DataFrameGroupByResampler(const DataFrameGroupBy& gb, const std::string& freq, const std::string& closed = "left", const std::string& label = "left")	pd_groupby.h:4877

Missing Data

Signature	Return Type	Location	Example
DataFrame bfill() const	DataFrame	pd_groupby.h:5001	View
DataFrame ffill() const	DataFrame	pd_groupby.h:4915	View

Aggregation

Signature	Return Type	Location	Example
DataFrame agg_impl_(const std::string& method) const	DataFrame	pd_groupby.h:4382
void aggregate_string_cols_( const DataFrame& sub_df, const DataFrame& result, size_t nrows, const std::string& method, const std::vector<std::string>& col_order, const std::set<std::string>& string_cols, std::map<std::string, std::vector<std::string>>& combined_str_data) const	void	pd_groupby.h:4561
const DataFrameGroupBy& groupby() const	const DataFrameGroupBy&	pd_groupby.h:4883	View
static DataFrame resample_one_(DataFrameResampler& resampler, const std::string& method)	static DataFrame	pd_groupby.h:4543
DataFrameResampler resampler(sub_df, freq_str_, closed_, label_)	DataFrameResampler	pd_groupby.h:4419

Combining

Signature	Return Type	Location	Example
static void append_group_levels_( std::vector<std::vector<std::string>>& group_level_vals, const std::string& gkey, const std::vector<std::string>& by_cols)	static void	pd_groupby.h:4723
append_group_levels_(group_level_vals, gkey, by_cols)		pd_groupby.h:4970
append_group_levels_(group_level_vals, gkey, by_cols)		pd_groupby.h:4982
append_group_levels_(group_level_vals, gkey, by_cols)		pd_groupby.h:5049

Other Methods

Signature	Return Type	Location	Example
std::vector<std::vector<std::string>> arrays(nlev + 1)	std::vector<std::vector<std::string>>	pd_groupby.h:4642	View
std::vector<std::vector<std::string>> arrays(nlev + 1)	std::vector<std::vector<std::string>>	pd_groupby.h:4693	View
DatetimeIndex build_group_grid_(const DatetimeIndex& group_idx) const	DatetimeIndex	pd_groupby.h:4334
void build_multiindex_( DataFrame& df, const std::vector<std::string>& level0_vals, const std::vector<std::string>& level1_vals, const std::vector<std::string>& by_cols, const std::vector<std::string>& group_keys) const	void	pd_groupby.h:4625
build_multiindex_presplit_(final_result, group_level_vals, level_dt_vals, by_cols, group_keys)		pd_groupby.h:4535
void build_multiindex_presplit_( DataFrame& df, const std::vector<std::vector<std::string>>& group_level_vals, const std::vector<std::string>& dt_vals, const std::vector<std::string>& by_cols, const std::vector<std::string>& group_keys) const	void	pd_groupby.h:4677
static void classify_columns_( const DataFrame& sub_df, std::vector<std::string>& col_order, std::set<std::string>& numeric_cols, std::set<std::string>& string_cols)	static void	pd_groupby.h:4821
classify_columns_(sub_df, col_order, numeric_cols, string_cols)		pd_groupby.h:4936
classify_columns_(sub_df, col_order, numeric_cols, string_cols)		pd_groupby.h:5021
const std::string& closed() const	const std::string&	pd_groupby.h:4885	View
DataFrame finalize_fill_result_( std::map<std::string, std::vector<double>>& combined_num_data, std::map<std::string, std::vector<std::string>>& combined_str_data, const std::vector<std::string>& col_order, const std::set<std::string>& string_cols, const std::vector<std::vector<std::string>>& group_level_vals, const std::vector<std::string>& level_dt_vals, const std::vector<std::string>& by_cols, const std::vector<std::string>& group_keys) const	DataFrame	pd_groupby.h:4772
std::string format_bucket_end_label_(const std::string& result_date_str) const	std::string	pd_groupby.h:4311
format_bucket_end_label_(result.index().get_value_str(r)))		pd_groupby.h:4457
std::string format_grid_label_(const DatetimeIndex& grid, size_t gi) const	std::string	pd_groupby.h:4325
const std::string& freq() const	const std::string&	pd_groupby.h:4884	View
const DateOffset& freq_offset_() const	const DateOffset&	pd_groupby.h:4275
std::vector<std::vector<std::string>> group_level_vals(by_cols.size())	std::vector<std::vector<std::string>>	pd_groupby.h:4391
std::vector<std::vector<std::string>> group_level_vals(by_cols.size())	std::vector<std::vector<std::string>>	pd_groupby.h:4920
std::vector<std::vector<std::string>> group_level_vals(by_cols.size())	std::vector<std::vector<std::string>>	pd_groupby.h:5006
DatetimeIndex idx(values, sub_df.index().name())	DatetimeIndex	pd_groupby.h:4854	View
const std::string& label() const	const std::string&	pd_groupby.h:4886	View
static GroupDateMap_ parse_index_to_datetime_index_(const DataFrame& sub_df)	static GroupDateMap_	pd_groupby.h:4848
static void push_value_( const DataFrame& sub_df, const std::string& col, size_t src_row, const std::set<std::string>& numeric_cols, std::map<std::string, std::vector<double>>& combined_num_data, std::map<std::string, std::vector<std::string>>& combined_str_data)	static void	pd_groupby.h:4747
DatetimeIndex result(out, group_idx.name())	DatetimeIndex	pd_groupby.h:4371	View
static std::string strip_time_if_day_or_larger_(const std::string& s, int64_t stride_ns)	static std::string	pd_groupby.h:4301

Code Examples

The following examples are extracted from the test suite.

bfill (pd_test_1_all.cpp:23603)

        std::cout << "====================================== [OK] pd_test_equals test suite ========================== " << std::endl;
        return 0;
    }

} // namespace dataframe_tests
// ------------------- pd_test_equals.cpp (end) -----------------------------

// ------------------- pd_test_ffill_bfill.cpp (start) -----------------------------
// dataframe_tests/pd_test_ffill_bfill.cpp
// Test file for DataFrame.ffill() and DataFrame.bfill() methods

#include <iostream>
#include <stdexcept>
#include <cmath>
#include <limits>
#include <map>
#include "../pandas/pd_dataframe.h"

// CRITICAL: No using namespace directives

ffill (pd_test_1_all.cpp:23603)

        std::cout << "====================================== [OK] pd_test_equals test suite ========================== " << std::endl;
        return 0;
    }

} // namespace dataframe_tests
// ------------------- pd_test_equals.cpp (end) -----------------------------

// ------------------- pd_test_ffill_bfill.cpp (start) -----------------------------
// dataframe_tests/pd_test_ffill_bfill.cpp
// Test file for DataFrame.ffill() and DataFrame.bfill() methods

#include <iostream>
#include <stdexcept>
#include <cmath>
#include <limits>
#include <map>
#include "../pandas/pd_dataframe.h"

// CRITICAL: No using namespace directives

groupby (pd_test_1_all.cpp:11495)

            std::cout << "========= GroupBy basic =========================";

            // Create DataFrame with category column
            std::map<std::string, std::vector<double>> data = {
                {"category", {1.0, 1.0, 2.0, 2.0, 2.0}},
                {"value", {10.0, 20.0, 30.0, 40.0, 50.0}}
            };
            pandas::DataFrame df(data);

            // Test groupby
            auto grouped = df.groupby("category");

            bool passed = grouped.ngroups() == 2;
            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_groupby_basic() : ngroups should be 2" << std::endl;
                throw std::runtime_error("pd_test_groupby_basic failed: ngroups should be 2");
            }

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

arrays (pd_test_1_all.cpp:10642)

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

// ============================================================================
// Set Operations Tests
// ============================================================================

void pd_test_extension_index_append() {
    std::cout << "========= append =========================";

    // Use same categories for both arrays (required by CategoricalArray::concat)
    std::vector<std::string> cats = {"a", "b", "c", "d"};
    pandas::CategoricalArray arr1({"a", "b"}, cats);
    pandas::CategoricalIndex idx1(arr1);

    pandas::CategoricalArray arr2({"c", "d"}, cats);
    pandas::CategoricalIndex idx2(arr2);

    auto appended = idx1.append(idx2);

    bool passed = (appended.size() == 4);

arrays (pd_test_1_all.cpp:10642)

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

// ============================================================================
// Set Operations Tests
// ============================================================================

void pd_test_extension_index_append() {
    std::cout << "========= append =========================";

    // Use same categories for both arrays (required by CategoricalArray::concat)
    std::vector<std::string> cats = {"a", "b", "c", "d"};
    pandas::CategoricalArray arr1({"a", "b"}, cats);
    pandas::CategoricalIndex idx1(arr1);

    pandas::CategoricalArray arr2({"c", "d"}, cats);
    pandas::CategoricalIndex idx2(arr2);

    auto appended = idx1.append(idx2);

    bool passed = (appended.size() == 4);

closed (pd_test_1_all.cpp:1903)

// ============================================================================
void test_constructors() {
    std::cout << "========= IntervalArray: constructors ======================= ";

    // Default constructor
    pandas::IntervalArrayFloat64 empty;
    if (empty.size() != 0) {
        std::cout << "[FAIL] : in test_constructors() : default constructor size" << std::endl;
        return;
    }
    if (empty.closed() != pandas::IntervalClosed::Right) {
        std::cout << "[FAIL] : in test_constructors() : default closure" << std::endl;
        return;
    }

    // Constructor from left/right arrays
    numpy::NDArray<numpy::float64> left(std::vector<size_t>{3});
    numpy::NDArray<numpy::float64> right(std::vector<size_t>{3});
    left.setElementAt({0}, 0.0);  right.setElementAt({0}, 1.0);
    left.setElementAt({1}, 1.0);  right.setElementAt({1}, 2.0);
    left.setElementAt({2}, 2.0);  right.setElementAt({2}, 3.0);

freq (pd_test_1_all.cpp:8233)

    std::cout << "========= freq property ===============================";

    std::vector<std::optional<numpy::datetime64>> values = {
        numpy::datetime64(0LL, numpy::DateTimeUnit::Nanosecond),
        numpy::datetime64(86400000000000LL, numpy::DateTimeUnit::Nanosecond)  // 1 day
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeMixinIndex idx(arr);

    // Default freq is nullopt or inferred
    auto f = idx.freq();
    std::string fs = idx.freqstr();

    bool passed = true;  // freq may or may not be set
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_datetime_mixin_freq()" << std::endl;
        throw std::runtime_error("pd_test_datetime_mixin_freq failed");
    }

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

idx (pd_test_1_all.cpp:5266)

        throw std::runtime_error("pd_test_categorical_index_default_constructor failed");
    }

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

void pd_test_categorical_index_array_constructor() {
    std::cout << "========= array constructor ===========================";

    pandas::CategoricalArray arr({"apple", "banana", "apple", "cherry"});
    pandas::CategoricalIndex idx(arr, "fruits");

    bool passed = (idx.size() == 4 && !idx.empty() &&
                   idx.name().has_value() && *idx.name() == "fruits" &&
                   idx.inferred_type() == "categorical");
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_categorical_index_array_constructor()" << std::endl;
        throw std::runtime_error("pd_test_categorical_index_array_constructor failed");
    }

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

label (pd_test_4_all.cpp:4935)

// Helper: compare and report
// ----------------------------------------------------------------------------
static void check_str(const std::string& label,
                      const std::string& expected,
                      const std::string& actual) {
    int _f = 0;
    pandas_tests::check_str_ws(label, expected, actual, _f);
    if (_f > 0) throw std::runtime_error(label + ": str mismatch");
}

// Slugify a python compare-test label ("a.b.c" → "a_b_c") matching the
// scheme in scripts/gen_repr_mismatch_fixtures.py.
static std::string slugify_label(const std::string& label) {
    std::string out = label;
    for (char& ch : out) {
        if (ch == '.') ch = '_';
    }
    return out;
}

// Load a captured pandas-generated expected output file. The file is written

result (pd_test_1_all.cpp:15406)

    data.setElementAt({0}, numpy::datetime64(100LL, numpy::DateTimeUnit::Nanosecond));
    data.setElementAt({1}, numpy::datetime64(200LL, numpy::DateTimeUnit::Nanosecond));

    numpy::NDArray<numpy::bool_> mask(std::vector<size_t>{2});
    mask.setElementAt({0}, numpy::bool_(false));
    mask.setElementAt({1}, numpy::bool_(false));

    pandas::DatetimeArray arr(data, mask);
    pandas::DatetimeIndexBase idx(arr, "original");

    // Create join result (int64 values)
    numpy::NDArray<numpy::int64> join_result(std::vector<size_t>{3});
    join_result.setElementAt({0}, numpy::int64(500LL));
    join_result.setElementAt({1}, numpy::int64(600LL));
    join_result.setElementAt({2}, numpy::int64(700LL));

    auto new_idx = idx._from_join_target(join_result);

    bool passed = (new_idx.size() == 3 &&
                   new_idx.name().has_value() && *new_idx.name() == "original");
    if (!passed) {