Hour

class pandas::Hour

pandas C++ class.

Example

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

// Use Hour
Hour obj;
// ... operations ...

Constructors

Signature	Location	Example
Hour(int n = 1, bool normalize = false) : DateOffset(n, normalize)	pd_dateoffset.h:492	View

Aggregation

Signature	Return Type	Location	Example
numpy::datetime64 apply(const numpy::datetime64& dt) const override	numpy::datetime64	pd_dateoffset.h:499	View

Type Checking

Signature	Return Type	Location	Example
bool is_on_offset(const numpy::datetime64& dt) const override	bool	pd_dateoffset.h:505	View

Other Methods

Signature	Return Type	Location	Example
std::string freqstr() const override	std::string	pd_dateoffset.h:494	View
std::string name() const override	std::string	pd_dateoffset.h:495	View
int64_t total_nanoseconds() const override { return static_cast<int64_t>(n_) \* 3600000000000LL	int64_t	pd_dateoffset.h:497

Code Examples

The following examples are extracted from the test suite.

Hour (pd_test_5_all.cpp:87061)

    pandas::DataFrame df;
    std::vector<int64_t> v(idx.size(), 0);
    df.add_column<int64_t>("v", v);
    df.set_index(std::make_unique<pandas::DatetimeIndex>(idx));
    return df;
}

void f_core_05_upsample_05f4ab_case_1_hourly_of_daily(int& local_fail) {
    std::cout << "-- case_1_hourly_of_daily\n";
    auto idx = mk_idx({"2020-01-01", "2020-01-02", "2020-01-03"});
    auto up = idx.upsample(pandas::Hour(1));
    pandas_tests::check(up.size() == 49,
                        "case_1.hourly_of_daily.size==49", local_fail);
}

void f_core_05_upsample_05f4ab_case_2_minute_of_hourly(int& local_fail) {
    std::cout << "-- case_2_minute_of_hourly\n";
    auto idx = mk_idx({"2020-01-01T00:00:00", "2020-01-01T02:00:00"});
    auto up = idx.upsample(pandas::Minute(1));
    pandas_tests::check(up.size() == 121,
                        "case_2.minute_of_hourly.size==121", local_fail);

apply (pd_test_1_all.cpp:11244)

        void pd_test_func_apply_dataframe_apply_axis0() {
            std::cout << "========= DataFrame apply axis=0 ======================";

            std::map<std::string, std::vector<double>> data = {
                {"A", {1.0, 2.0, 3.0}},
                {"B", {4.0, 5.0, 6.0}}
            };
            pandas::DataFrame df(data);

            // apply axis=0 applies function to each column
            auto result = df.apply([](const std::vector<double>& col) {
                return std::accumulate(col.begin(), col.end(), 0.0);
            }, 0);

            bool passed = true;

            // Plan F·dtype: axis=0 reduce now returns a single "result" column
            // with the original column names ("A", "B") as the row index.
            // Sum of A: 1+2+3=6, Sum of B: 4+5+6=15
            const auto& result_col = result["result"];
            double sum_a = std::stod(result_col.get_value_str(0));

is_on_offset (pd_test_3_all.cpp:18263)

void pd_test_business_day_offset() {
    std::cout << "========= BusinessDay offset ===========================";

    pandas::BusinessDay offset(1);
    if (offset.freqstr() != "B") {
        std::cout << "  [FAIL] : BusinessDay freqstr() failed" << std::endl;
        throw std::runtime_error("pd_test_business_day_offset: freqstr() failed");
    }

    // Test is_on_offset (Friday = weekday)
    numpy::datetime64 friday("2020-01-17");  // Friday
    if (!offset.is_on_offset(friday)) {
        std::cout << "  [FAIL] : BusinessDay is_on_offset(Friday) failed" << std::endl;
        throw std::runtime_error("pd_test_business_day_offset: is_on_offset(Friday) failed");
    }

    // Test is_on_offset (Saturday = weekend)
    numpy::datetime64 saturday("2020-01-18");  // Saturday
    if (offset.is_on_offset(saturday)) {
        std::cout << "  [FAIL] : BusinessDay is_on_offset(Saturday) should be false" << std::endl;

freqstr (pd_test_1_all.cpp:2671)

        }

        pandas::PeriodDtype dtype_y("Y");
        if (dtype_y.name() != "period[Y]") {
            std::cout << "  [FAIL] : dtype_y.name() should be 'period[Y]'" << std::endl;
            throw std::runtime_error("pd_test_period_array_freq_validation failed: dtype name Y");
        }

        // Test frequency string
        pandas::PeriodArray arr(std::vector<std::string>{"2024-01-15"}, "D");
        if (arr.freqstr() != "D") {
            std::cout << "  [FAIL] : arr.freqstr() should be 'D'" << std::endl;
            throw std::runtime_error("pd_test_period_array_freq_validation failed: freqstr");
        }

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

    void pd_test_period_array_year_month_quarter() {
        std::cout << "========= PeriodArray: year/month/quarter components ======================= ";

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;