Hour
====

.. cpp:class:: pandas::Hour

   pandas C++ class.

Example
-------

.. code-block:: cpp

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

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

Constructors
------------

.. list-table::
   :widths: 55 25 20
   :header-rows: 1

   * - Signature
     - Location
     - Example
   * - ``Hour(int n = 1, bool normalize = false) : DateOffset(n, normalize)``
     - pd_dateoffset.h:492
     - :ref:`View <example-hour-hour-0>`

Aggregation
-----------

.. list-table::
   :widths: 40 20 15 25
   :header-rows: 1

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``numpy::datetime64 apply(const numpy::datetime64& dt) const override``
     - numpy::datetime64
     - pd_dateoffset.h:499
     - :ref:`View <example-hour-apply-1>`

Type Checking
-------------

.. list-table::
   :widths: 40 20 15 25
   :header-rows: 1

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``bool is_on_offset(const numpy::datetime64& dt) const override``
     - bool
     - pd_dateoffset.h:505
     - :ref:`View <example-hour-is_on_offset-2>`

Other Methods
-------------

.. list-table::
   :widths: 40 20 15 25
   :header-rows: 1

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``std::string freqstr() const override``
     - std::string
     - pd_dateoffset.h:494
     - :ref:`View <example-hour-freqstr-3>`
   * - ``std::string name() const override``
     - std::string
     - pd_dateoffset.h:495
     - :ref:`View <example-hour-name-4>`
   * - ``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.

.. _example-hour-hour-0:

.. dropdown:: Hour (pd_test_5_all.cpp:87061)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 87051
      :emphasize-lines: 11

          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);

.. _example-hour-apply-1:

.. dropdown:: apply (pd_test_1_all.cpp:11244)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 11234
      :emphasize-lines: 11

              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));

.. _example-hour-is_on_offset-2:

.. dropdown:: is_on_offset (pd_test_3_all.cpp:18263)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 18253
      :emphasize-lines: 11

      
      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;

.. _example-hour-freqstr-3:

.. dropdown:: freqstr (pd_test_1_all.cpp:2671)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 2661
      :emphasize-lines: 11

              }
      
              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 ======================= ";
      

.. _example-hour-name-4:

.. dropdown:: name (pd_test_1_all.cpp:295)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 285
      :emphasize-lines: 11

                  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;