BooleanDtype

class pandas::BooleanDtype

Data type class for pandas extension types.

Example

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

// Use BooleanDtype
BooleanDtype obj;
// ... operations ...

Constructors

Signature	Location	Example
BooleanDtype() = default	pd_boolean_dtype.h:25	View

Iteration

Signature	Return Type	Location	Example
size_t itemsize() const override	size_t	pd_boolean_dtype.h:46

Other Methods

Signature	Return Type	Location	Example
std::string kind() const override	std::string	pd_boolean_dtype.h:54	View
bool matches(const std::string& dtype_name) const	bool	pd_boolean_dtype.h:70	View
std::string name() const override	std::string	pd_boolean_dtype.h:32	View
numpy::DType numpy_dtype() const override	numpy::DType	pd_boolean_dtype.h:39
std::string repr() const override	std::string	pd_boolean_dtype.h:81	View
const std::type_info& type() const override	const std::type_info&	pd_boolean_dtype.h:61	View

Code Examples

The following examples are extracted from the test suite.

BooleanDtype (pd_test_4_all.cpp:2174)

// ============================================================================
// Case 1: na_iloc.slice_with_na (2 x 4, Index<int64_t>{1, 2})
//
// Source: pandasPython_tests/test_pandas_indexing_compare_full.py L1058
//   compare_all(pd_df.iloc[1:3], pc_df.iloc[1:3], "na_iloc.slice_with_na")
// where pd_df was:
//   pd.DataFrame({
//       "int_na":   pd.array([1, None, 3, None], dtype=pd.Int64Dtype()),
//       "float_na": [1.0, np.nan, 3.0, np.nan],
//       "bool_na":  pd.array([True, None, False, None], dtype=pd.BooleanDtype()),
//       "str_na":   ["a", None, "c", None],
//   })
// The slice yields row labels {1, 2}: row 1 is all-NA, row 2 is
// {3, 3.0, False, "c"}.
//
// Formatter bug (three distinct issues bundled under one label):
//   - header gutter is 2 spaces wide instead of 3
//   - bool_na column is 7-wide instead of 8
//   - a trailing newline is emitted
//

kind (pd_test_1_all.cpp:300)

    void pd_test_boolean_array_dtype() {
        std::cout << "========= BooleanArray: dtype ======================= ";

        pandas::BooleanArray arr;
        if (arr.dtype().name() != "boolean") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype name should be 'boolean'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype name");
        }

        if (arr.dtype().kind() != "b") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype kind should be 'b'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype kind");
        }

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

int pd_test_boolean_array_main() {
    std::cout << "====================================== running pd_test_boolean_array ==================================== " << std::endl;

matches (pd_test_5_all.cpp:15741)

    for (size_t i = 0; i < 5; ++i) {
        // DatetimeIndex serializes individual entries via get_value_str()
        bdate_first5.push_back(idx.get_value_str(i));
    }
    std::cout << "  case_4 bdate_range first 5: ";
    for (auto& s : bdate_first5) std::cout << s << " ";
    std::cout << "\n  case_4 expected first 5:    ";
    for (auto& s : expected_visible_head) std::cout << s << " ";
    std::cout << "\n";

    // First date matches (both start at 2023-01-04)
    pandas_tests::check(contains(bdate_first5[0], "2023-01-04"),
          "case_4.first_bdate_is_2023-01-04", local_fail);

    // Second date should differ: bdate gives 2023-01-05, fixture wants 2023-01-20
    bool second_matches_expected = contains(bdate_first5[1], "2023-01-20");
    pandas_tests::check(!second_matches_expected,
          "case_4.bdate_NOT_matching_filtered_fixture", local_fail);
    // The bdate second is the next business day:
    pandas_tests::check(contains(bdate_first5[1], "2023-01-05"),
          "case_4.bdate_second_is_2023-01-05_sequential", local_fail);

name (pd_test_1_all.cpp:295)

            throw std::runtime_error("pd_test_boolean_array_reductions failed: mean");
        }

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

    void pd_test_boolean_array_dtype() {
        std::cout << "========= BooleanArray: dtype ======================= ";

        pandas::BooleanArray arr;
        if (arr.dtype().name() != "boolean") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype name should be 'boolean'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype name");
        }

        if (arr.dtype().kind() != "b") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype kind should be 'b'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype kind");
        }

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

repr (pd_test_1_all.cpp:10906)

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

void pd_test_extension_index_repr() {
    std::cout << "========= repr =========================";

    pandas::CategoricalArray arr({"a", "b", "c"});
    // Use ExtensionIndex<CategoricalArray> directly to test base class repr
    pandas::ExtensionIndex<pandas::CategoricalArray> idx(arr, "test");

    std::string repr_str = idx.repr();

    bool passed = (!repr_str.empty() && repr_str.find("ExtensionIndex") != std::string::npos);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_extension_index_repr() : repr check failed" << std::endl;
        throw std::runtime_error("pd_test_extension_index_repr failed");
    }

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

type (pd_test_3_all.cpp:15450)

/**
 * Test Series.convert_dtypes() parameter flags
 */
void pd_test_series_convert_dtypes_flags() {
    std::cout << "========= Series.convert_dtypes() flags =================";

    // Test convert_integer=false - with floats disabled too, so it becomes string/object
    pandas::Series<std::string> s({"1", "2", "3"}, "numbers");
    auto converted = s.convert_dtypes(true, true, false, true, false);  // convert_integer=false, convert_floating=false

    // Should remain object type (Series<std::string> has dtype_name()="object")
    // When integer and floating are both disabled for integer-like strings, it falls back to string type
    if (converted->dtype_name() != "object") {
        std::cout << "  [FAIL] : dtype should be object when convert_integer=false and convert_floating=false, got " << converted->dtype_name() << std::endl;
        throw std::runtime_error("pd_test_series_convert_dtypes_flags failed: convert_integer");
    }

    // Test convert_boolean=false - strings stay as object/string type
    pandas::Series<std::string> s_bool({"true", "false"}, "bools");
    auto converted_bool = s_bool.convert_dtypes(true, true, true, false, true);  // convert_boolean=false