DataFrameGroupByRollingWindow
=============================

.. cpp:class:: pandas::DataFrameGroupByRollingWindow

   pandas C++ class.

Example
-------

.. code-block:: cpp

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

   // Use DataFrameGroupByRollingWindow
   DataFrameGroupByRollingWindow obj;
   // ... operations ...

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

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

   * - Signature
     - Location
     - Example
   * - ``DataFrameGroupByRollingWindow(const DataFrameGroupBy& gb, size_t window, size_t min_periods = 0)``
     - pd_groupby.h:6058
     - 

Statistics
----------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``DataFrame count() const``
     - DataFrame
     - pd_groupby.h:6190
     - :ref:`View <example-dataframegroupbyrollingwindow-count-0>`
   * - ``DataFrame max() const``
     - DataFrame
     - pd_groupby.h:6178
     - :ref:`View <example-dataframegroupbyrollingwindow-max-1>`
   * - ``DataFrame mean() const``
     - DataFrame
     - pd_groupby.h:6172
     - :ref:`View <example-dataframegroupbyrollingwindow-mean-2>`
   * - ``DataFrame median() const``
     - DataFrame
     - pd_groupby.h:6187
     - :ref:`View <example-dataframegroupbyrollingwindow-median-3>`
   * - ``DataFrame min() const``
     - DataFrame
     - pd_groupby.h:6175
     - :ref:`View <example-dataframegroupbyrollingwindow-min-4>`
   * - ``DataFrame std_(int ddof = 1) const``
     - DataFrame
     - pd_groupby.h:6184
     - :ref:`View <example-dataframegroupbyrollingwindow-std_-5>`
   * - ``DataFrame sum() const``
     - DataFrame
     - pd_groupby.h:6169
     - :ref:`View <example-dataframegroupbyrollingwindow-sum-6>`
   * - ``DataFrame var(int ddof = 1) const``
     - DataFrame
     - pd_groupby.h:6181
     - :ref:`View <example-dataframegroupbyrollingwindow-var-7>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> apply_per_column(const std::string& col_name, RollingOp op) const``
     - Series<double>
     - pd_groupby.h:6064
     - 
   * - ``DataFrame apply_per_group(RollingOp op) const``
     - DataFrame
     - pd_groupby.h:6118
     - 
   * - ``const DataFrameGroupBy& groupby() const``
     - const DataFrameGroupBy&
     - pd_groupby.h:6061
     - :ref:`View <example-dataframegroupbyrollingwindow-groupby-8>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> column_max(const std::string& col) const``
     - Series<double>
     - pd_groupby.h:6204
     - 
   * - ``Series<double> column_mean(const std::string& col) const``
     - Series<double>
     - pd_groupby.h:6198
     - 
   * - ``Series<double> column_min(const std::string& col) const``
     - Series<double>
     - pd_groupby.h:6201
     - 
   * - ``Series<double> column_std(const std::string& col, int ddof = 1) const``
     - Series<double>
     - pd_groupby.h:6207
     - 
   * - ``Series<double> column_sum(const std::string& col) const``
     - Series<double>
     - pd_groupby.h:6195
     - 
   * - ``Series<double> column_var(const std::string& col, int ddof = 1) const``
     - Series<double>
     - pd_groupby.h:6210
     - 


Code Examples
-------------

The following examples are extracted from the test suite.

.. _example-dataframegroupbyrollingwindow-count-0:

.. dropdown:: count (pd_test_1_all.cpp:66)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 56
      :emphasize-lines: 11

              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 ======================= ";
      

.. _example-dataframegroupbyrollingwindow-max-1:

.. dropdown:: max (pd_test_1_all.cpp:771)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 761
      :emphasize-lines: 11

              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();

.. _example-dataframegroupbyrollingwindow-mean-2:

.. dropdown:: mean (pd_test_1_all.cpp:282)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 272
      :emphasize-lines: 11

                  std::optional<bool>(true),
                  std::optional<bool>(true)
              });
      
              auto s = arr.sum();
              if (!s.has_value() || s.value() != 3) {
                  std::cout << "  [FAIL] : in pd_test_boolean_array_reductions() : sum should be 3" << std::endl;
                  throw std::runtime_error("pd_test_boolean_array_reductions failed: sum");
              }
      
              auto m = arr.mean();
              if (!m.has_value() || std::abs(m.value() - 0.75) > 0.001) {
                  std::cout << "  [FAIL] : in pd_test_boolean_array_reductions() : mean should be 0.75" << std::endl;
                  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 ======================= ";

.. _example-dataframegroupbyrollingwindow-median-3:

.. dropdown:: median (pd_test_1_all.cpp:20910)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 20900
      :emphasize-lines: 11

                      throw std::runtime_error("pd_test_expanding_var failed: expanding var values incorrect");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_expanding_median() {
                  std::cout << "========= Expanding median ======================";
      
                  pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});
                  auto result = s.expanding().median();
      
                  // Expanding median: 1, 1.5, 2, 2.5, 3
                  bool passed = std::abs(result[0] - 1.0) < 0.001 &&
                                std::abs(result[1] - 1.5) < 0.001 &&
                                std::abs(result[2] - 2.0) < 0.001 &&
                                std::abs(result[3] - 2.5) < 0.001 &&
                                std::abs(result[4] - 3.0) < 0.001;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_expanding_median() : expanding median values incorrect" << std::endl;
                      throw std::runtime_error("pd_test_expanding_median failed: expanding median values incorrect");

.. _example-dataframegroupbyrollingwindow-min-4:

.. dropdown:: min (pd_test_1_all.cpp:764)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 754
      :emphasize-lines: 11

          }
      
          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'");

.. _example-dataframegroupbyrollingwindow-std_-5:

.. dropdown:: std_ (pd_test_1_all.cpp:20752)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 20742
      :emphasize-lines: 11

                      throw std::runtime_error("pd_test_rolling_min_periods failed: with min_periods=1, idx 1 should be 3.0");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_rolling_std() {
                  std::cout << "========= Rolling std ===========================";
      
                  pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});
                  auto result = s.rolling(3).std_();
      
                  // std([1,2,3]) = 1.0 (ddof=1)
                  // std([2,3,4]) = 1.0
                  // std([3,4,5]) = 1.0
                  bool passed = std::abs(result[2] - 1.0) < 0.001;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_rolling_std() : rolling std should be 1.0" << std::endl;
                      throw std::runtime_error("pd_test_rolling_std failed: rolling std should be 1.0");
                  }
      

.. _example-dataframegroupbyrollingwindow-sum-6:

.. dropdown:: sum (pd_test_1_all.cpp:276)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 266
      :emphasize-lines: 11

              }
      
              // Test sum/mean
              pandas::BooleanArray arr({
                  std::optional<bool>(true),
                  std::optional<bool>(false),
                  std::optional<bool>(true),
                  std::optional<bool>(true)
              });
      
              auto s = arr.sum();
              if (!s.has_value() || s.value() != 3) {
                  std::cout << "  [FAIL] : in pd_test_boolean_array_reductions() : sum should be 3" << std::endl;
                  throw std::runtime_error("pd_test_boolean_array_reductions failed: sum");
              }
      
              auto m = arr.mean();
              if (!m.has_value() || std::abs(m.value() - 0.75) > 0.001) {
                  std::cout << "  [FAIL] : in pd_test_boolean_array_reductions() : mean should be 0.75" << std::endl;
                  throw std::runtime_error("pd_test_boolean_array_reductions failed: mean");
              }

.. _example-dataframegroupbyrollingwindow-var-7:

.. dropdown:: var (pd_test_1_all.cpp:20890)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 20880
      :emphasize-lines: 11

                      throw std::runtime_error("pd_test_expanding_std failed: expanding std values incorrect");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_expanding_var() {
                  std::cout << "========= Expanding var =========================";
      
                  pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});
                  auto result = s.expanding().var();
      
                  // Expanding var (ddof=1): NaN, 0.5, 1.0, 1.6667, 2.5
                  bool passed = std::isnan(result[0]) &&
                                std::abs(result[1] - 0.5) < 0.001 &&
                                std::abs(result[2] - 1.0) < 0.001 &&
                                std::abs(result[3] - 1.6667) < 0.001 &&
                                std::abs(result[4] - 2.5) < 0.001;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_expanding_var() : expanding var values incorrect" << std::endl;
                      throw std::runtime_error("pd_test_expanding_var failed: expanding var values incorrect");

.. _example-dataframegroupbyrollingwindow-groupby-8:

.. dropdown:: groupby (pd_test_1_all.cpp:11495)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 11485
      :emphasize-lines: 11

                  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;
              }