DatetimeArray

class pandas::DatetimeArray

Extension array type for specialized data storage.

Example

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

// Use DatetimeArray
DatetimeArray obj;
// ... operations ...

Constructors

Signature	Location	Example
DatetimeArray(const numpy::NDArray<numpy::datetime64>& data, const numpy::NDArray<numpy::bool_>& mask, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr, bool copy = false)	pd_datetime_array.h:97
DatetimeArray(const numpy::NDArray<int64_t>& data, const numpy::NDArray<numpy::bool_>& mask, numpy::DateTimeUnit unit, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr, bool copy = false)	pd_datetime_array.h:123
explicit DatetimeArray(const numpy::NDArray<numpy::datetime64>& data, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr)	pd_datetime_array.h:141
explicit DatetimeArray(const std::vector<std::optional<numpy::datetime64>>& values, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr)	pd_datetime_array.h:189
explicit DatetimeArray(const std::vector<std::string>& iso_strings, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr)	pd_datetime_array.h:210

Construction

Signature	Return Type	Location	Example
static DatetimeArray from_sequence(const std::vector<std::optional<numpy::datetime64>>& scalars, std::shared_ptr<numpy::TimezoneInfo> tz = nullptr)	static DatetimeArray	pd_datetime_array.h:486
static DatetimeArray from_timestamps( const std::vector<std::optional<Ts>>& vec, const std::string& uniform_tz = "")	DatetimeArray	pd_datetime_array.h:256	View

Indexing / Selection

Signature	Return Type	Location	Example
numpy::datetime64 at(size_t index) const	numpy::datetime64	pd_datetime_array.h:393	View
const numpy::NDArray<numpy::bool_>& mask() const	const numpy::NDArray<numpy::bool_>&	pd_datetime_array.h:373	View
DatetimeArray take(const std::vector<size_t>& indices, bool allow_fill = false, std::optional<numpy::datetime64> fill_value = std::nullopt) const	DatetimeArray	pd_datetime_array.h:440	View

Data Manipulation

Signature	Return Type	Location	Example
DatetimeArray dropna() const	DatetimeArray	pd_datetime_array.h:538	View

Missing Data

Signature	Return Type	Location	Example
DatetimeArray fillna(const numpy::datetime64& value) const	DatetimeArray	pd_datetime_array.h:523	View
numpy::NDArray<numpy::bool_> isna() const	numpy::NDArray<numpy::bool_>	pd_datetime_array.h:415	View
numpy::NDArray<numpy::bool_> notna() const	numpy::NDArray<numpy::bool_>	pd_datetime_array.h:422	View

Statistics

Signature	Return Type	Location	Example
size_t count() const	size_t	pd_datetime_array.h:586	View
std::optional<numpy::datetime64> max() const	std::optional<numpy::datetime64>	pd_datetime_array.h:893	View
std::optional<numpy::datetime64> min() const	std::optional<numpy::datetime64>	pd_datetime_array.h:884	View
IntegerArray<numpy::int32> minute() const	IntegerArray<numpy::int32>	pd_datetime_array.h:982	View

Comparison

Signature	Return Type	Location	Example
size_t len() const	size_t	pd_datetime_array.h:330	View

Sorting

Signature	Return Type	Location	Example
numpy::NDArray<size_t> argsort(bool ascending = true, const std::string& na_position = "last") const	numpy::NDArray<size_t>	pd_datetime_array.h:810	View

Combining

Signature	Return Type	Location	Example
static DatetimeArray concat(const std::vector<DatetimeArray>& arrays)	static DatetimeArray	pd_datetime_array.h:494	View

Time Series

Signature	Return Type	Location	Example
DatetimeArray tz_convert(std::shared_ptr<numpy::TimezoneInfo> tz) const	DatetimeArray	pd_datetime_array.h:1449	View
DatetimeArray tz_convert(const std::string& tz_name, const std::string& tz_display = "") const	DatetimeArray	pd_datetime_array.h:1463	View
DatetimeArray tz_localize(std::shared_ptr<numpy::TimezoneInfo> tz, const std::string& ambiguous = "raise", const std::string& nonexistent = "raise") const	DatetimeArray	pd_datetime_array.h:1299	View
DatetimeArray tz_localize(const std::string& tz_name, const std::string& ambiguous = "raise", const std::string& nonexistent = "raise", const std::string& tz_display = "") const	DatetimeArray	pd_datetime_array.h:1421	View

I/O

Signature	Return Type	Location	Example
std::string to_string() const	std::string	pd_datetime_array.h:1485	View

Conversion

Signature	Return Type	Location	Example
DatetimeArray copy() const	DatetimeArray	pd_datetime_array.h:433	View

Set Operations

Signature	Return Type	Location	Example
DatetimeArray unique() const	DatetimeArray	pd_datetime_array.h:748	View

Type Checking

Signature	Return Type	Location	Example
BooleanArray is_leap_year() const	BooleanArray	pd_datetime_array.h:1208	View
BooleanArray is_month_end() const	BooleanArray	pd_datetime_array.h:1103	View
BooleanArray is_month_start() const	BooleanArray	pd_datetime_array.h:1084	View
bool is_na(size_t index) const	bool	pd_datetime_array.h:404	View
BooleanArray is_quarter_end() const	BooleanArray	pd_datetime_array.h:1146	View
BooleanArray is_quarter_start() const	BooleanArray	pd_datetime_array.h:1125	View
bool is_tz_aware() const	bool	pd_datetime_array.h:351	View
BooleanArray is_year_end() const	BooleanArray	pd_datetime_array.h:1189	View
BooleanArray is_year_start() const	BooleanArray	pd_datetime_array.h:1170	View

Other Methods

Signature	Return Type	Location	Example
std::optional<size_t> argmax() const	std::optional<size_t>	pd_datetime_array.h:862	View
std::optional<size_t> argmin() const	std::optional<size_t>	pd_datetime_array.h:842	View
const numpy::NDArray<int64_t>& data() const	const numpy::NDArray<int64_t>&	pd_datetime_array.h:366	View
IntegerArray<numpy::int32> day() const	IntegerArray<numpy::int32>	pd_datetime_array.h:944	View
IntegerArray<numpy::int32> dayofweek() const	IntegerArray<numpy::int32>	pd_datetime_array.h:1020	View
IntegerArray<numpy::int32> dayofyear() const	IntegerArray<numpy::int32>	pd_datetime_array.h:1041	View
void detect_unit()	void	pd_datetime_array.h:1535
numpy::datetime64 dt(iso_strings[i])	numpy::datetime64	pd_datetime_array.h:224	View
dtype_type dtype() const	dtype_type	pd_datetime_array.h:288	View
bool empty() const	bool	pd_datetime_array.h:323	View
std::pair<numpy::NDArray<numpy::int64>, DatetimeArray> factorize() const	std::pair<numpy::NDArray<numpy::int64>, DatetimeArray>	pd_datetime_array.h:774	View
bool has_na() const	bool	pd_datetime_array.h:599	View
IntegerArray<numpy::int32> hour() const	IntegerArray<numpy::int32>	pd_datetime_array.h:963	View
IntegerArray<numpy::int32> month() const	IntegerArray<numpy::int32>	pd_datetime_array.h:925	View
size_t nbytes() const	size_t	pd_datetime_array.h:302	View
constexpr int ndim() const	constexpr int	pd_datetime_array.h:309	View
DatetimeArray normalize() const	DatetimeArray	pd_datetime_array.h:1272	View
IntegerArray<numpy::int32> quarter() const	IntegerArray<numpy::int32>	pd_datetime_array.h:1060	View
std::string repr() const	std::string	pd_datetime_array.h:1502	View
IntegerArray<numpy::int32> second() const	IntegerArray<numpy::int32>	pd_datetime_array.h:1001	View
std::vector<size_t> shape() const	std::vector<size_t>	pd_datetime_array.h:316	View
size_t size() const	size_t	pd_datetime_array.h:295	View
store_ns(result_data, i, wall_ns)		pd_datetime_array.h:1326
store_ns(result_data, i, utc_ns)		pd_datetime_array.h:1382
store_ns(result_data, i, utc_ns)		pd_datetime_array.h:1387
store_ns(result_data, i, utc_ns)		pd_datetime_array.h:1403
store_ns(result_data, i, valid_utc_times[0])		pd_datetime_array.h:1407
store_ns(result_data, i, utc_ns)		pd_datetime_array.h:1412
std::shared_ptr<numpy::TimezoneInfo> tz() const	std::shared_ptr<numpy::TimezoneInfo>	pd_datetime_array.h:344	View
numpy::DateTimeUnit unit() const	numpy::DateTimeUnit	pd_datetime_array.h:337	View
void validate_arrays()	void	pd_datetime_array.h:1515
IntegerArray<numpy::int32> year() const	IntegerArray<numpy::int32>	pd_datetime_array.h:906	View

Internal Methods

2 internal methods (prefixed with underscore)

Code Examples

The following examples are extracted from the test suite.

from_timestamps (pd_test_extension_array.cpp:9)

// pd_test_extension_array.cpp — L3 step L3.15 + Fix A storage-flip extensions
//
// Storage invariants and round-trip integrity for pandas::DatetimeArray and
// pandas::TimedeltaArray after the Fix A storage flip
// (do/plan_L3_fix_a_storage_flip.md, applied 2026-05-04).
//
// Verifies:
//   - sizeof(int64_t) == 8 (storage element size invariant)
//   - DatetimeArray::from_timestamps() round-trips a vector<optional<Timestamp>>
//   - NaT slots are preserved through the round-trip (mask stays consistent)
//   - tz-aware uniform-tz construction produces a non-null tz_ field
//   - Boxing reconstruction via getElementAt() recovers the input ns values
//   - Fix A storage shape: data() returns NDArray<int64_t> (8 B/elem, ns count)
//   - NaT sentinel: INT64_MIN round-trips through the boxing layer
//   - All 4 linear units (s/ms/us/ns) round-trip through ns-canonical storage
//   - Tz-aware boxing reconstruction (UTC, US/Eastern, +05:30) preserves tz

#include "../pandas/pd_datetime_array.h"
#include "../pandas/pd_timedelta_array.h"

at (pd_test_1_all.cpp:6581)

            // Test isna/notna with float data
            {
                std::map<std::string, std::vector<numpy::float64>> float_data;
                float_data["X"] = {1.0, std::nan(""), 3.0};
                float_data["Y"] = {4.0, 5.0, std::nan("")};
                pandas::DataFrame df_na(float_data);

                auto na_mask = df_na.isna();
                // Row 1, col 0 (X) should be NA
                if (!na_mask.getElementAt({1, 0})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : isna at (1,0) should be true" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_manipulation failed: isna at (1,0)");
                }
                // Row 2, col 1 (Y) should be NA
                if (!na_mask.getElementAt({2, 1})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : isna at (2,1) should be true" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_manipulation failed: isna at (2,1)");
                }
                // Row 0, col 0 should NOT be NA
                if (na_mask.getElementAt({0, 0})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : isna at (0,0) should be false" << std::endl;

mask (pd_test_1_all.cpp:9119)

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

    // Create DatetimeArray with some values
    numpy::NDArray<numpy::datetime64> data(std::vector<size_t>{3});
    data.setElementAt({0}, numpy::datetime64(1000000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2001
    data.setElementAt({1}, numpy::datetime64(1500000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2017
    data.setElementAt({2}, numpy::datetime64(1600000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2020

    numpy::NDArray<numpy::bool_> mask(std::vector<size_t>{3});
    mask.setElementAt({0}, numpy::bool_(false));
    mask.setElementAt({1}, numpy::bool_(false));
    mask.setElementAt({2}, numpy::bool_(false));

    pandas::DatetimeArray arr(data, mask);
    pandas::DatetimeTDMixin idx(arr, "timestamps");

    bool passed = (idx.size() == 3 && !idx.empty() &&
                   idx.name().has_value() && *idx.name() == "timestamps" &&
                   idx.inferred_type() == "datetime");

take (pd_test_1_all.cpp:5903)

// Inherited Operations Tests
// ============================================================================

void pd_test_categorical_index_take() {
    std::cout << "========= inherited take ==============================";

    pandas::CategoricalArray arr({"a", "b", "c", "d"});
    pandas::CategoricalIndex idx(arr);

    std::vector<size_t> indices = {0, 2, 3};
    pandas::ExtensionIndex<pandas::CategoricalArray> taken = idx.take(indices);

    bool passed = (taken.size() == 3);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_categorical_index_take()" << std::endl;
        throw std::runtime_error("pd_test_categorical_index_take failed");
    }

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

dropna (pd_test_1_all.cpp:531)

        }

        // Test isna array
        numpy::NDArray<numpy::bool_> na_mask = arr.isna();
        if (na_mask.getSize() != 4) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : isna size != 4" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: isna size != 4");
        }

        // Test dropna
        pandas::CategoricalArray dropped = arr.dropna();
        if (dropped.size() != 2) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : dropna size != 2" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: dropna size != 2");
        }

        // Test fillna (fill with existing category)
        pandas::CategoricalArray filled = arr.fillna("a");  // 'a' is in categories
        if (filled.has_na()) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : fillna should have no NA" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: fillna should have no NA");

fillna (pd_test_1_all.cpp:537)

            throw std::runtime_error("pd_test_categorical_array_na_handling failed: isna size != 4");
        }

        // Test dropna
        pandas::CategoricalArray dropped = arr.dropna();
        if (dropped.size() != 2) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : dropna size != 2" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: dropna size != 2");
        }

        // Test fillna (fill with existing category)
        pandas::CategoricalArray filled = arr.fillna("a");  // 'a' is in categories
        if (filled.has_na()) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : fillna should have no NA" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: fillna should have no NA");
        }

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

    void pd_test_categorical_array_add_categories() {

isna (pd_test_1_all.cpp:524)

            throw std::runtime_error("pd_test_categorical_array_na_handling failed: has_na() should be true");
        }

        // Test count (non-NA)
        if (arr.count() != 2) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : count() != 2" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: count() != 2");
        }

        // Test isna array
        numpy::NDArray<numpy::bool_> na_mask = arr.isna();
        if (na_mask.getSize() != 4) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : isna size != 4" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: isna size != 4");
        }

        // Test dropna
        pandas::CategoricalArray dropped = arr.dropna();
        if (dropped.size() != 2) {
            std::cout << "  [FAIL] : in pd_test_categorical_array_na_handling() : dropna size != 2" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_na_handling failed: dropna size != 2");

notna (pd_test_1_all.cpp:6595)

                if (!na_mask.getElementAt({2, 1})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : isna at (2,1) should be true" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_manipulation failed: isna at (2,1)");
                }
                // Row 0, col 0 should NOT be NA
                if (na_mask.getElementAt({0, 0})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : isna at (0,0) should be false" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_manipulation failed: isna at (0,0)");
                }

                auto notna_mask = df_na.notna();
                if (notna_mask.getElementAt({1, 0})) {
                    std::cout << "  [FAIL] : in pd_test_dataframe_manipulation() : notna at (1,0) should be false" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_manipulation failed: notna at (1,0)");
                }
            }

            // Test fillna
            {
                std::map<std::string, std::vector<numpy::float64>> float_data;
                float_data["X"] = {1.0, std::nan(""), 3.0};

count (pd_test_1_all.cpp:66)

        if (arr.is_na(0)) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : is_na(0) should be false" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: is_na(0) should be false");
        }

        if (!arr.has_na()) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : has_na() should be true" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: has_na() should be true");
        }

        if (arr.count() != 2) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : count() should be 2" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: count() should be 2");
        }

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

    void pd_test_boolean_array_kleene_and() {
        std::cout << "========= BooleanArray: Kleene AND ======================= ";

max (pd_test_1_all.cpp:771)

        pandas::CategoricalArray arr = pandas::CategoricalArray::from_codes(codes, cats, true);  // ordered

        // Test min
        std::optional<std::string> min_val = arr.min();
        if (!min_val.has_value() || *min_val != "low") {
            std::cout << "  [FAIL] : in pd_test_categorical_array_ordered_operations() : min != 'low'" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_ordered_operations failed: min != 'low'");
        }

        // Test max
        std::optional<std::string> max_val = arr.max();
        if (!max_val.has_value() || *max_val != "high") {
            std::cout << "  [FAIL] : in pd_test_categorical_array_ordered_operations() : max != 'high'" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_ordered_operations failed: max != 'high'");
        }

        // Test unordered throws for min/max
        pandas::CategoricalArray unordered = arr.as_unordered();
        bool threw = false;
        try {
            unordered.min();

min (pd_test_1_all.cpp:764)

    }

    void pd_test_categorical_array_ordered_operations() {
        std::cout << "========= CategoricalArray: ordered operations (min/max) ======================= ";

        std::vector<std::string> cats = {"low", "medium", "high"};
        std::vector<numpy::int32> codes = {0, 2, 1, 0, -1};  // low, high, medium, low, NA
        pandas::CategoricalArray arr = pandas::CategoricalArray::from_codes(codes, cats, true);  // ordered

        // Test min
        std::optional<std::string> min_val = arr.min();
        if (!min_val.has_value() || *min_val != "low") {
            std::cout << "  [FAIL] : in pd_test_categorical_array_ordered_operations() : min != 'low'" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_ordered_operations failed: min != 'low'");
        }

        // Test max
        std::optional<std::string> max_val = arr.max();
        if (!max_val.has_value() || *max_val != "high") {
            std::cout << "  [FAIL] : in pd_test_categorical_array_ordered_operations() : max != 'high'" << std::endl;
            throw std::runtime_error("pd_test_categorical_array_ordered_operations failed: max != 'high'");

minute (pd_test_1_all.cpp:7505)

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

    std::vector<std::optional<numpy::datetime64>> values = {
        make_dt(0),                    // Minute 0
        make_dt(30 * NS_PER_MIN),      // Minute 30
        make_dt(59 * NS_PER_MIN)       // Minute 59
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeIndex idx(arr);

    auto minutes = idx.minute();

    bool passed = (minutes.size() == 3);
    auto m0 = minutes[0];
    auto m1 = minutes[1];
    auto m2 = minutes[2];
    passed = passed && m0.has_value() && *m0 == 0;
    passed = passed && m1.has_value() && *m1 == 30;
    passed = passed && m2.has_value() && *m2 == 59;

    if (!passed) {

len (pd_test_3_all.cpp:20867)

    auto title_result = s.str().title();
    if (title_result[0] != "Hello World" || title_result[1] != "Hello World" || title_result[2] != "Hello World") {
        std::cout << "  [FAIL] : title() failed" << std::endl;
        throw std::runtime_error("pd_test_str_capitalize_title: title() failed");
    }

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

// ============================================================================
// Test str().len()
// ============================================================================

void pd_test_str_len() {
    std::cout << "========= Series.str().len() ============================";

    pandas::Series<std::string> s({"a", "bb", "ccc", ""});

    auto lens = s.str().len();
    if (lens[0] != 1 || lens[1] != 2 || lens[2] != 3 || lens[3] != 0) {
        std::cout << "  [FAIL] : len() failed" << std::endl;

argsort (pd_test_1_all.cpp:1304)

        std::cout << "========= DatetimeArray: sorting ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-06-15",
            "NaT",
            "2023-01-01",
            "2023-12-31"
        });

        // argsort ascending
        auto indices = arr.argsort(true, "last");
        // Expected order: 2023-01-01(2), 2023-06-15(0), 2023-12-31(3), NaT(1)
        if (indices.getElementAt({0}) != 2) {
            std::cout << "  [FAIL] : argsort: first should be index 2 (2023-01-01)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argsort first");
        }
        if (indices.getElementAt({3}) != 1) {
            std::cout << "  [FAIL] : argsort: last should be index 1 (NaT)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: NaT position");
        }

concat (pd_test_1_all.cpp:17717)

}

void pd_test_period_index_concat() {
    std::cout << "========= concat factory ==============================";

    std::vector<int64_t> ordinals1 = {0, 1};
    std::vector<int64_t> ordinals2 = {2, 3};
    pandas::PeriodIndex idx1(ordinals1, "D");
    pandas::PeriodIndex idx2(ordinals2, "D");

    pandas::PeriodIndex concatenated = pandas::PeriodIndex::concat({idx1, idx2});

    bool passed = (concatenated.size() == 4);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_period_index_concat()" << std::endl;
        throw std::runtime_error("pd_test_period_index_concat failed");
    }

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

tz_convert (pd_test_2_all.cpp:17874)

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

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

// ------------------- pd_test_tz_convert.cpp (start) -----------------------------
// dataframe_tests/pd_test_tz_convert.cpp
// Test for DataFrame.tz_convert() method

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

namespace dataframe_tests {
    namespace dataframe_tests_tz_convert {

        void pd_test_tz_convert_basic() {

tz_convert (pd_test_2_all.cpp:17874)

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

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

// ------------------- pd_test_tz_convert.cpp (start) -----------------------------
// dataframe_tests/pd_test_tz_convert.cpp
// Test for DataFrame.tz_convert() method

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

namespace dataframe_tests {
    namespace dataframe_tests_tz_convert {

        void pd_test_tz_convert_basic() {

tz_localize (pd_test_1_all.cpp:1431)

            "2023-06-15"
        });

        // Initially should be timezone-naive
        if (arr.is_tz_aware()) {
            std::cout << "  [FAIL] : array should be timezone-naive initially" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: naive");
        }

        // Localize to UTC
        auto localized = arr.tz_localize("UTC");
        if (!localized.is_tz_aware()) {
            std::cout << "  [FAIL] : localized array should be timezone-aware" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: localize");
        }

        // Verify timezone name in dtype
        auto dt = localized.dtype();
        if (!dt.is_tz_aware()) {
            std::cout << "  [FAIL] : dtype should be timezone-aware" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: dtype tz");

tz_localize (pd_test_1_all.cpp:1431)

            "2023-06-15"
        });

        // Initially should be timezone-naive
        if (arr.is_tz_aware()) {
            std::cout << "  [FAIL] : array should be timezone-naive initially" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: naive");
        }

        // Localize to UTC
        auto localized = arr.tz_localize("UTC");
        if (!localized.is_tz_aware()) {
            std::cout << "  [FAIL] : localized array should be timezone-aware" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: localize");
        }

        // Verify timezone name in dtype
        auto dt = localized.dtype();
        if (!dt.is_tz_aware()) {
            std::cout << "  [FAIL] : dtype should be timezone-aware" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: dtype tz");

to_string (pd_test_1_all.cpp:2693)

        pandas::PeriodArray arr_m(std::vector<std::string>{
            "2020-01",
            "NaT",
            "2025-06"
        }, "M");

        // Year
        auto years = arr_m.year();
        auto y0 = years[0];
        if (!y0.has_value() || y0.value() != 2020) {
            std::cout << "  [FAIL] : year[0] should be 2020, got " << (y0.has_value() ? std::to_string(y0.value()) : "NA") << std::endl;
            throw std::runtime_error("pd_test_period_array_year_month_quarter failed: year[0]");
        }

        auto y1 = years[1];
        if (y1.has_value()) {
            std::cout << "  [FAIL] : year[1] should be NA (NaT)" << std::endl;
            throw std::runtime_error("pd_test_period_array_year_month_quarter failed: year[1] should be NA");
        }

        auto y2 = years[2];

copy (pd_test_1_all.cpp:5798)

// ============================================================================
// Copy/Rename Tests
// ============================================================================

void pd_test_categorical_index_copy() {
    std::cout << "========= copy ========================================";

    pandas::CategoricalArray arr({"a", "b", "c"});
    pandas::CategoricalIndex idx(arr, "original");

    pandas::CategoricalIndex copied = idx.copy();

    bool passed = (copied.size() == idx.size() && copied.name() == idx.name() &&
                   copied.categories() == idx.categories() && copied.ordered() == idx.ordered());
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_categorical_index_copy()" << std::endl;
        throw std::runtime_error("pd_test_categorical_index_copy failed");
    }

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

unique (pd_test_1_all.cpp:1345)

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-01-01",
            "2023-06-15",
            "2023-01-01",
            "NaT",
            "2023-06-15",
            "NaT"
        });

        // unique
        auto uniq = arr.unique();
        // Should have: NaT, 2023-01-01, 2023-06-15 (3 unique values)
        if (uniq.size() != 3) {
            std::cout << "  [FAIL] : unique size should be 3, got " << uniq.size() << std::endl;
            throw std::runtime_error("pd_test_datetime_array_unique failed: size");
        }

        // factorize
        auto [codes, uniques] = arr.factorize();
        // Codes for NaT should be -1
        if (codes.getElementAt({3}) != -1) {

is_leap_year (pd_test_1_all.cpp:1280)

        }

        // is_month_end
        auto me = arr.is_month_end();
        if (!me[1].has_value() || !me[1].value()) {
            std::cout << "  [FAIL] : 2023-03-31 should be month end" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: month end");
        }

        // is_leap_year
        auto ly = arr.is_leap_year();
        if (!ly[2].has_value() || !ly[2].value()) {
            std::cout << "  [FAIL] : 2024 should be leap year" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: leap year");
        }
        if (!ly[0].has_value() || ly[0].value()) {
            std::cout << "  [FAIL] : 2023 should not be leap year" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: not leap year");
        }

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

is_month_end (pd_test_1_all.cpp:1273)

        }

        // is_month_start
        auto ms = arr.is_month_start();
        if (!ms[0].has_value() || !ms[0].value()) {
            std::cout << "  [FAIL] : 2023-01-01 should be month start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: month start");
        }

        // is_month_end
        auto me = arr.is_month_end();
        if (!me[1].has_value() || !me[1].value()) {
            std::cout << "  [FAIL] : 2023-03-31 should be month end" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: month end");
        }

        // is_leap_year
        auto ly = arr.is_leap_year();
        if (!ly[2].has_value() || !ly[2].value()) {
            std::cout << "  [FAIL] : 2024 should be leap year" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: leap year");

is_month_start (pd_test_1_all.cpp:1266)

        if (!ys[0].has_value() || !ys[0].value()) {
            std::cout << "  [FAIL] : 2023-01-01 should be year start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: year start");
        }
        if (!ys[1].has_value() || ys[1].value()) {
            std::cout << "  [FAIL] : 2023-03-31 should not be year start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: not year start");
        }

        // is_month_start
        auto ms = arr.is_month_start();
        if (!ms[0].has_value() || !ms[0].value()) {
            std::cout << "  [FAIL] : 2023-01-01 should be month start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: month start");
        }

        // is_month_end
        auto me = arr.is_month_end();
        if (!me[1].has_value() || !me[1].value()) {
            std::cout << "  [FAIL] : 2023-03-31 should be month end" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: month end");

is_na (pd_test_1_all.cpp:51)

    void pd_test_boolean_array_na_handling() {
        std::cout << "========= BooleanArray: NA handling ======================= ";

        pandas::BooleanArray arr({
            std::optional<bool>(true),
            std::nullopt,  // NA at index 1
            std::optional<bool>(false)
        });

        if (!arr.is_na(1)) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : is_na(1) should be true" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: is_na(1) should be true");
        }

        if (arr.is_na(0)) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : is_na(0) should be false" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: is_na(0) should be false");
        }

        if (!arr.has_na()) {

is_quarter_end (pd_test_3_all.cpp:25056)

    };
    pandas::Series<numpy::datetime64> s(dates);
    pandas::DatetimeProperties<pandas::Series<numpy::datetime64>> dt(s);
    if (dt.has_nat()) throw std::runtime_error("has_nat should be false for clean series");
    auto ms = dt.is_month_start();
    if (ms[0] != true || ms[1] != false) throw std::runtime_error("is_month_start failed");
    auto me = dt.is_month_end();
    if (me[1] != true || me[0] != false) throw std::runtime_error("is_month_end failed");
    auto qs = dt.is_quarter_start();
    if (qs[0] != true || qs[1] != false) throw std::runtime_error("is_quarter_start failed");
    auto qe = dt.is_quarter_end();
    if (qe[2] != true || qe[0] != false) throw std::runtime_error("is_quarter_end failed");
    auto ys = dt.is_year_start();
    if (ys[0] != true || ys[1] != false) throw std::runtime_error("is_year_start failed");
    auto ye = dt.is_year_end();
    if (ye[3] != true || ye[0] != false) throw std::runtime_error("is_year_end failed");
    std::cout << " -> tests passed" << std::endl;
}

void pd_test_dt_bool_na_with_nat() {
    std::cout << "========= pd_test_dt_bool_na: series with NaT ==========";

is_quarter_start (pd_test_3_all.cpp:25054)

        numpy::datetime64("2024-03-31"),
        numpy::datetime64("2024-12-31")
    };
    pandas::Series<numpy::datetime64> s(dates);
    pandas::DatetimeProperties<pandas::Series<numpy::datetime64>> dt(s);
    if (dt.has_nat()) throw std::runtime_error("has_nat should be false for clean series");
    auto ms = dt.is_month_start();
    if (ms[0] != true || ms[1] != false) throw std::runtime_error("is_month_start failed");
    auto me = dt.is_month_end();
    if (me[1] != true || me[0] != false) throw std::runtime_error("is_month_end failed");
    auto qs = dt.is_quarter_start();
    if (qs[0] != true || qs[1] != false) throw std::runtime_error("is_quarter_start failed");
    auto qe = dt.is_quarter_end();
    if (qe[2] != true || qe[0] != false) throw std::runtime_error("is_quarter_end failed");
    auto ys = dt.is_year_start();
    if (ys[0] != true || ys[1] != false) throw std::runtime_error("is_year_start failed");
    auto ye = dt.is_year_end();
    if (ye[3] != true || ye[0] != false) throw std::runtime_error("is_year_end failed");
    std::cout << " -> tests passed" << std::endl;
}

is_tz_aware (pd_test_1_all.cpp:1425)

    void pd_test_datetime_array_timezone() {
        std::cout << "========= DatetimeArray: timezone ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-01-01",
            "2023-06-15"
        });

        // Initially should be timezone-naive
        if (arr.is_tz_aware()) {
            std::cout << "  [FAIL] : array should be timezone-naive initially" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: naive");
        }

        // Localize to UTC
        auto localized = arr.tz_localize("UTC");
        if (!localized.is_tz_aware()) {
            std::cout << "  [FAIL] : localized array should be timezone-aware" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_timezone failed: localize");
        }

is_year_end (pd_test_3_all.cpp:25060)

    auto ms = dt.is_month_start();
    if (ms[0] != true || ms[1] != false) throw std::runtime_error("is_month_start failed");
    auto me = dt.is_month_end();
    if (me[1] != true || me[0] != false) throw std::runtime_error("is_month_end failed");
    auto qs = dt.is_quarter_start();
    if (qs[0] != true || qs[1] != false) throw std::runtime_error("is_quarter_start failed");
    auto qe = dt.is_quarter_end();
    if (qe[2] != true || qe[0] != false) throw std::runtime_error("is_quarter_end failed");
    auto ys = dt.is_year_start();
    if (ys[0] != true || ys[1] != false) throw std::runtime_error("is_year_start failed");
    auto ye = dt.is_year_end();
    if (ye[3] != true || ye[0] != false) throw std::runtime_error("is_year_end failed");
    std::cout << " -> tests passed" << std::endl;
}

void pd_test_dt_bool_na_with_nat() {
    std::cout << "========= pd_test_dt_bool_na: series with NaT ==========";
    std::vector<numpy::datetime64> dates = {
        numpy::datetime64("2024-01-01"),
        numpy::datetime64(),  // NaT
        numpy::datetime64("2024-12-31")

is_year_start (pd_test_1_all.cpp:1255)

        std::cout << "========= DatetimeArray: boolean properties ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-01-01",   // year start, month start
            "2023-03-31",   // quarter end, month end
            "2024-02-29",   // leap year (2024 is leap year)
            "2023-12-31"    // year end, month end
        });

        // is_year_start
        auto ys = arr.is_year_start();
        if (!ys[0].has_value() || !ys[0].value()) {
            std::cout << "  [FAIL] : 2023-01-01 should be year start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: year start");
        }
        if (!ys[1].has_value() || ys[1].value()) {
            std::cout << "  [FAIL] : 2023-03-31 should not be year start" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_boolean_props failed: not year start");
        }

        // is_month_start

argmax (pd_test_1_all.cpp:1323)

        }

        // argmin
        auto min_idx = arr.argmin();
        if (!min_idx.has_value() || min_idx.value() != 2) {
            std::cout << "  [FAIL] : argmin should be 2 (2023-01-01)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argmin");
        }

        // argmax
        auto max_idx = arr.argmax();
        if (!max_idx.has_value() || max_idx.value() != 3) {
            std::cout << "  [FAIL] : argmax should be 3 (2023-12-31)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argmax");
        }

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

    void pd_test_datetime_array_unique() {
        std::cout << "========= DatetimeArray: unique/factorize ======================= ";

argmin (pd_test_1_all.cpp:1316)

        if (indices.getElementAt({0}) != 2) {
            std::cout << "  [FAIL] : argsort: first should be index 2 (2023-01-01)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argsort first");
        }
        if (indices.getElementAt({3}) != 1) {
            std::cout << "  [FAIL] : argsort: last should be index 1 (NaT)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: NaT position");
        }

        // argmin
        auto min_idx = arr.argmin();
        if (!min_idx.has_value() || min_idx.value() != 2) {
            std::cout << "  [FAIL] : argmin should be 2 (2023-01-01)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argmin");
        }

        // argmax
        auto max_idx = arr.argmax();
        if (!max_idx.has_value() || max_idx.value() != 3) {
            std::cout << "  [FAIL] : argmax should be 3 (2023-12-31)" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_sorting failed: argmax");

data (pd_test_1_all.cpp:9114)

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

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

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

    // Create DatetimeArray with some values
    numpy::NDArray<numpy::datetime64> data(std::vector<size_t>{3});
    data.setElementAt({0}, numpy::datetime64(1000000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2001
    data.setElementAt({1}, numpy::datetime64(1500000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2017
    data.setElementAt({2}, numpy::datetime64(1600000000000000000LL, numpy::DateTimeUnit::Nanosecond));  // ~2020

    numpy::NDArray<numpy::bool_> mask(std::vector<size_t>{3});
    mask.setElementAt({0}, numpy::bool_(false));
    mask.setElementAt({1}, numpy::bool_(false));
    mask.setElementAt({2}, numpy::bool_(false));

    pandas::DatetimeArray arr(data, mask);

day (pd_test_1_all.cpp:1193)

            std::cout << "  [FAIL] : month[0] should be 3" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: month[0]");
        }
        auto m1 = months[1];
        if (!m1.has_value() || m1.value() != 12) {
            std::cout << "  [FAIL] : month[1] should be 12" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: month[1]");
        }

        // Day
        auto days = arr.day();
        auto d0 = days[0];
        if (!d0.has_value() || d0.value() != 15) {
            std::cout << "  [FAIL] : day[0] should be 15" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: day[0]");
        }
        auto d1 = days[1];
        if (!d1.has_value() || d1.value() != 25) {
            std::cout << "  [FAIL] : day[1] should be 25" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: day[1]");
        }

dayofweek (pd_test_1_all.cpp:7565)

    // 1970-01-01 was a Thursday (day 3)
    std::vector<std::optional<numpy::datetime64>> values = {
        make_dt(0),                // Thursday (3)
        make_dt(NS_PER_DAY),       // Friday (4)
        make_dt(2 * NS_PER_DAY),   // Saturday (5)
        make_dt(3 * NS_PER_DAY)    // Sunday (6)
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeIndex idx(arr);

    auto dow = idx.dayofweek();

    bool passed = (dow.size() == 4);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_datetime_index_dayofweek()" << std::endl;
        throw std::runtime_error("pd_test_datetime_index_dayofweek failed");
    }

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

dayofyear (pd_test_3_all.cpp:18582)

    auto seconds = s.dt().second();
    if (seconds[0] != 45 || seconds[1] != 30 || seconds[2] != 59) {
        std::cout << "  [FAIL] : second() failed" << std::endl;
        throw std::runtime_error("pd_test_dt_time_components: second() failed");
    }

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

// ============================================================================
// Test dt().dayofweek(), dt().dayofyear(), dt().quarter()
// ============================================================================

void pd_test_dt_derived_properties() {
    std::cout << "========= Series.dt().dayofweek/dayofyear/quarter() ======";

    // 2020-01-01 is a Wednesday (dayofweek=2), dayofyear=1, Q1
    // 2020-07-04 is a Saturday (dayofweek=5), dayofyear=186, Q3
    pandas::Series<std::string> s({"2020-01-01", "2020-07-04"});

    auto dow = s.dt().dayofweek();

dt (pd_test_3_all.cpp:18239)

    if (offset.freqstr() != "D") {
        std::cout << "  [FAIL] : Day freqstr() failed" << std::endl;
        throw std::runtime_error("pd_test_day_offset: freqstr() failed");
    }
    if (offset.name() != "Day") {
        std::cout << "  [FAIL] : Day name() failed" << std::endl;
        throw std::runtime_error("pd_test_day_offset: name() failed");
    }

    // Test apply
    numpy::datetime64 dt("2020-01-15");
    auto result = offset.apply(dt);
    std::tm tm = result.toTm();
    if (tm.tm_mday != 20) {
        std::cout << "  [FAIL] : Day apply() failed, got day " << tm.tm_mday << std::endl;
        throw std::runtime_error("pd_test_day_offset: apply() failed");
    }

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

dtype (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;

empty (pd_test_1_all.cpp:941)

#include "../pandas/pd_config.h"

namespace dataframe_tests {

namespace dataframe_tests_config {

    void pd_test_config_version() {
        std::cout << "========= df_config: version info ======================= ";
        const char* version = pandas::DataFrameInfo::version();
        if (version == nullptr || std::string(version).empty()) {
            std::cout << "[FAIL] : in pd_test_config_version() : version is null or empty" << std::endl;
            throw std::runtime_error("pd_test_config_version failed: version is null or empty");
        }
        std::cout << "-> tests passed" << std::endl;
    }

    void pd_test_config_na_repr() {
        std::cout << "========= df_config: NA representation ======================= ";
        const char* na_repr = pandas::DataFrameConfig::get_na_repr();
        if (na_repr == nullptr) {

factorize (pd_test_1_all.cpp:1353)

        // unique
        auto uniq = arr.unique();
        // Should have: NaT, 2023-01-01, 2023-06-15 (3 unique values)
        if (uniq.size() != 3) {
            std::cout << "  [FAIL] : unique size should be 3, got " << uniq.size() << std::endl;
            throw std::runtime_error("pd_test_datetime_array_unique failed: size");
        }

        // factorize
        auto [codes, uniques] = arr.factorize();
        // Codes for NaT should be -1
        if (codes.getElementAt({3}) != -1) {
            std::cout << "  [FAIL] : factorize: NaT code should be -1" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_unique failed: NaT code");
        }
        // Same values should have same codes
        if (codes.getElementAt({0}) != codes.getElementAt({2})) {
            std::cout << "  [FAIL] : factorize: 2023-01-01 values should have same code" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_unique failed: same code");
        }

has_na (pd_test_1_all.cpp:61)

        if (!arr.is_na(1)) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : is_na(1) should be true" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: is_na(1) should be true");
        }

        if (arr.is_na(0)) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : is_na(0) should be false" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: is_na(0) should be false");
        }

        if (!arr.has_na()) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : has_na() should be true" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: has_na() should be true");
        }

        if (arr.count() != 2) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_na_handling() : count() should be 2" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_na_handling failed: count() should be 2");
        }

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

hour (pd_test_1_all.cpp:7476)

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

    std::vector<std::optional<numpy::datetime64>> values = {
        make_dt(0),                    // Hour 0
        make_dt(6 * NS_PER_HOUR),      // Hour 6
        make_dt(23 * NS_PER_HOUR)      // Hour 23
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeIndex idx(arr);

    auto hours = idx.hour();

    bool passed = (hours.size() == 3);
    auto h0 = hours[0];
    auto h1 = hours[1];
    auto h2 = hours[2];
    passed = passed && h0.has_value() && *h0 == 0;
    passed = passed && h1.has_value() && *h1 == 6;
    passed = passed && h2.has_value() && *h2 == 23;

    if (!passed) {

month (pd_test_1_all.cpp:1180)

    void pd_test_datetime_array_component_month_day() {
        std::cout << "========= DatetimeArray: month/day components ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-03-15",
            "2023-12-25",
            "NaT"
        });

        // Month
        auto months = arr.month();
        auto m0 = months[0];
        if (!m0.has_value() || m0.value() != 3) {
            std::cout << "  [FAIL] : month[0] should be 3" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: month[0]");
        }
        auto m1 = months[1];
        if (!m1.has_value() || m1.value() != 12) {
            std::cout << "  [FAIL] : month[1] should be 12" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_month_day failed: month[1]");
        }

nbytes (pd_test_1_all.cpp:6214)

            }

            // Test empty DataFrame
            pandas::DataFrame empty_df;
            if (!empty_df.empty()) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : should be empty" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: should be empty");
            }

            // Test nbytes > 0 for non-empty
            if (df.nbytes() == 0) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : nbytes should be > 0" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: nbytes should be > 0");
            }

            // Test columns index
            if (df.columns().size() != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : columns size != 3" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: columns size != 3");
            }

ndim (pd_test_1_all.cpp:6195)

            pandas::DataFrame df(data);

            // Test shape
            auto shape = df.shape();
            if (shape.size() != 2 || shape[0] != 4 || shape[1] != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : shape mismatch" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: shape mismatch");
            }

            // Test ndim
            if (df.ndim() != 2) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : ndim != 2" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: ndim != 2");
            }

            // Test empty
            if (df.empty()) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : should not be empty" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: should not be empty");
            }

normalize (pd_test_1_all.cpp:8723)

void pd_test_datetime_mixin_normalize() {
    std::cout << "========= normalize ===================================";

    // Create datetime with time component
    std::vector<std::optional<numpy::datetime64>> values = {
        numpy::datetime64(86400000000000LL + 3600000000000LL, numpy::DateTimeUnit::Nanosecond)  // 1 day + 1 hour
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeMixinIndex idx(arr);

    pandas::DatetimeMixinIndex normalized = idx.normalize();

    bool passed = (normalized.size() == 1);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_datetime_mixin_normalize()" << std::endl;
        throw std::runtime_error("pd_test_datetime_mixin_normalize failed");
    }

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

quarter (pd_test_1_all.cpp:1218)

    void pd_test_datetime_array_quarter() {
        std::cout << "========= DatetimeArray: quarter ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2023-01-15",  // Q1
            "2023-05-20",  // Q2
            "2023-09-10",  // Q3
            "2023-11-25"   // Q4
        });

        auto quarters = arr.quarter();

        auto q0 = quarters[0];
        if (!q0.has_value() || q0.value() != 1) {
            std::cout << "  [FAIL] : quarter[0] should be 1" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_quarter failed: quarter[0]");
        }
        auto q1 = quarters[1];
        if (!q1.has_value() || q1.value() != 2) {
            std::cout << "  [FAIL] : quarter[1] should be 2" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_quarter failed: quarter[1]");

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;
}

second (pd_test_1_all.cpp:7534)

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

    std::vector<std::optional<numpy::datetime64>> values = {
        make_dt(0),                    // Second 0
        make_dt(30 * NS_PER_SEC),      // Second 30
        make_dt(59 * NS_PER_SEC)       // Second 59
    };
    pandas::DatetimeArray arr(values);
    pandas::DatetimeIndex idx(arr);

    auto seconds = idx.second();

    bool passed = (seconds.size() == 3);
    auto s0 = seconds[0];
    auto s1 = seconds[1];
    auto s2 = seconds[2];
    passed = passed && s0.has_value() && *s0 == 0;
    passed = passed && s1.has_value() && *s1 == 30;
    passed = passed && s2.has_value() && *s2 == 59;

    if (!passed) {

shape (pd_test_1_all.cpp:6188)

            std::cout << "========= properties =======================";

            std::map<std::string, std::vector<numpy::float64>> data;
            data["A"] = {1.0, 2.0, 3.0, 4.0};
            data["B"] = {5.0, 6.0, 7.0, 8.0};
            data["C"] = {9.0, 10.0, 11.0, 12.0};

            pandas::DataFrame df(data);

            // Test shape
            auto shape = df.shape();
            if (shape.size() != 2 || shape[0] != 4 || shape[1] != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : shape mismatch" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: shape mismatch");
            }

            // Test ndim
            if (df.ndim() != 2) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : ndim != 2" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: ndim != 2");
            }

size (pd_test_1_all.cpp:22)

#include "../pandas/pd_boolean_array.h"

namespace dataframe_tests {

namespace dataframe_tests_boolean_array {
    void pd_test_boolean_array_constructors() {
        std::cout << "========= BooleanArray: constructors ======================= ";

        // Default constructor
        pandas::BooleanArray arr1;
        if (arr1.size() != 0) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_constructors() : default constructor size != 0" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_constructors failed: default constructor size != 0");
        }

        // Initializer list constructor
        pandas::BooleanArray arr2({
            std::optional<bool>(true),
            std::optional<bool>(false),
            std::nullopt,
            std::optional<bool>(true)

tz (pd_test_2_all.cpp:17914)

            pandas::DataFrame df(data);
            df.set_index(std::make_unique<pandas::DatetimeIndex>(tz_aware_idx));

            // Verify the index is timezone-aware
            const pandas::DatetimeIndex* original_idx = dynamic_cast<const pandas::DatetimeIndex*>(&df.index());
            if (!original_idx) {
                std::cout << "  [FAIL] : in pd_test_tz_convert_basic() : index is not DatetimeIndex" << std::endl;
                throw std::runtime_error("pd_test_tz_convert_basic failed: index is not DatetimeIndex");
            }

            std::string original_tz = original_idx->tz();
            if (original_tz.empty()) {
                std::cout << "  [FAIL] : in pd_test_tz_convert_basic() : original index is not timezone-aware" << std::endl;
                throw std::runtime_error("pd_test_tz_convert_basic failed: original index is not timezone-aware");
            }

            // Convert to Asia/Shanghai timezone
            pandas::DataFrame df_shanghai = df.tz_convert("Asia/Shanghai");

            // Verify result has a DatetimeIndex
            const pandas::DatetimeIndex* converted_idx = dynamic_cast<const pandas::DatetimeIndex*>(&df_shanghai.index());

unit (pd_test_1_all.cpp:9284)

    data.setElementAt({0}, numpy::datetime64(1000LL, numpy::DateTimeUnit::Nanosecond));
    data.setElementAt({1}, numpy::datetime64(2000LL, 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::DatetimeTDMixin idx(arr);

    std::string unit = idx.unit();

    bool passed = (unit == "ns");  // nanosecond
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_datetime_unit_property() : unit property check failed, got '" << unit << "'" << std::endl;
        throw std::runtime_error("pd_test_datetime_unit_property failed");
    }

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

year (pd_test_1_all.cpp:1147)

    void pd_test_datetime_array_component_year() {
        std::cout << "========= DatetimeArray: year component ======================= ";

        pandas::DatetimeArray arr(std::vector<std::string>{
            "2020-01-15",
            "NaT",
            "2025-06-20"
        });

        auto years = arr.year();

        auto y0 = years[0];
        if (!y0.has_value() || y0.value() != 2020) {
            std::cout << "  [FAIL] : year[0] should be 2020" << std::endl;
            throw std::runtime_error("pd_test_datetime_array_component_year failed: year[0]");
        }

        auto y1 = years[1];
        if (y1.has_value()) {
            std::cout << "  [FAIL] : year[1] should be NA (NaT)" << std::endl;