Expanding
=========

.. cpp:class:: pandas::Expanding

   Window operation class for rolling/expanding calculations.

Example
-------

.. code-block:: cpp

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

   // Use Expanding
   Expanding obj;
   // ... operations ...

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

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

   * - Signature
     - Location
     - Example
   * - ``Expanding(const Series<T>& series, size_t min_periods = 1)``
     - pd_expanding.h:45
     - 

Indexing / Selection
--------------------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``double get_value(size_t i) const``
     - double
     - pd_expanding.h:536
     - :ref:`View <example-expanding-get_value-0>`

Statistics
----------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> count() const``
     - Series<double>
     - pd_expanding.h:101
     - :ref:`View <example-expanding-count-1>`
   * - ``Series<double> kurt() const``
     - Series<double>
     - pd_expanding.h:392
     - :ref:`View <example-expanding-kurt-2>`
   * - ``Series<double> max() const``
     - Series<double>
     - pd_expanding.h:145
     - :ref:`View <example-expanding-max-3>`
   * - ``Series<double> mean() const``
     - Series<double>
     - pd_expanding.h:78
     - :ref:`View <example-expanding-mean-4>`
   * - ``Series<double> median() const``
     - Series<double>
     - pd_expanding.h:225
     - :ref:`View <example-expanding-median-5>`
   * - ``Series<double> min() const``
     - Series<double>
     - pd_expanding.h:122
     - :ref:`View <example-expanding-min-6>`
   * - ``Series<double> quantile(double q) const``
     - Series<double>
     - pd_expanding.h:441
     - :ref:`View <example-expanding-quantile-7>`
   * - ``Series<double> sem(int ddof = 1) const``
     - Series<double>
     - pd_expanding.h:479
     - :ref:`View <example-expanding-sem-8>`
   * - ``Series<double> skew() const``
     - Series<double>
     - pd_expanding.h:349
     - :ref:`View <example-expanding-skew-9>`
   * - ``Series<double> std_(int ddof = 1) const``
     - Series<double>
     - pd_expanding.h:168
     - :ref:`View <example-expanding-std_-10>`
   * - ``Series<double> sum() const``
     - Series<double>
     - pd_expanding.h:53
     - :ref:`View <example-expanding-sum-11>`
   * - ``Series<double> var(int ddof = 1) const``
     - Series<double>
     - pd_expanding.h:197
     - :ref:`View <example-expanding-var-12>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> apply(Func&& func) const``
     - Series<double>
     - pd_expanding.h:259
     - :ref:`View <example-expanding-apply-13>`

Sorting
-------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> rank() const``
     - Series<double>
     - pd_expanding.h:503
     - :ref:`View <example-expanding-rank-14>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``Series<double> corr(const Series<T>& other) const``
     - Series<double>
     - pd_expanding.h:325
     - :ref:`View <example-expanding-corr-15>`
   * - ``Series<double> cov(const Series<T>& other, int ddof = 1) const``
     - Series<double>
     - pd_expanding.h:289
     - :ref:`View <example-expanding-cov-16>`
   * - ``propagate_source_index(res)``
     - 
     - pd_expanding.h:74
     - 
   * - ``void propagate_source_index(Series<double>& result) const``
     - void
     - pd_expanding.h:547
     - 


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

The following examples are extracted from the test suite.

.. _example-expanding-get_value-0:

.. dropdown:: get_value (pd_test_2_all.cpp:17379)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 17369
      :emphasize-lines: 11

          namespace dataframe_tests_transform {
      
              // Helper to check if two doubles are approximately equal
              bool approx_equal(double a, double b, double tol = 1e-9) {
                  if (std::isnan(a) && std::isnan(b)) return true;
                  if (std::isnan(a) || std::isnan(b)) return false;
                  return std::abs(a - b) < tol;
              }
      
              // Helper to get double value from DataFrame
              double get_value(const pandas::DataFrame& df, size_t row, size_t col) {
                  return df.iloc<double>(row, col);
              }
      
              void pd_test_transform_callable() {
                  std::cout << "========= transform with callable =====================";
      
                  std::map<std::string, std::vector<double>> data = {
                      {"A", {1.0, 4.0, 9.0, 16.0}},
                      {"B", {2.0, 3.0, 4.0, 5.0}}
                  };

.. _example-expanding-count-1:

.. 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-expanding-kurt-2:

.. dropdown:: kurt (pd_test_1_all.cpp:4599)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4589
      :emphasize-lines: 11

                  std::cout << "========= Series skew/kurt ======================";
      
                  pandas::Series<double> s({1.0, 2.0, 2.0, 3.0, 9.0});
                  auto skew_val = s.skew();
                  bool passed = skew_val.has_value() && *skew_val > 0;  // Should be right-skewed
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_skew_kurt() : skew should be positive" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_series_skew_kurt failed: skew should be positive");
                  }
      
                  auto kurt_val = s.kurt();
                  passed = kurt_val.has_value();
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_skew_kurt() : kurt should have value" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_series_skew_kurt failed: kurt should have value");
                  }
      
                  // Test kurtosis alias
                  auto kurt_alias = s.kurtosis();
                  passed = kurt_alias.has_value() && std::abs(*kurt_alias - *kurt_val) < 0.0001;
                  if (!passed) {

.. _example-expanding-max-3:

.. 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-expanding-mean-4:

.. 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-expanding-median-5:

.. 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-expanding-min-6:

.. 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-expanding-quantile-7:

.. dropdown:: quantile (pd_test_1_all.cpp:4540)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4530
      :emphasize-lines: 11

                      throw std::runtime_error("pd_test_aggregation_series_sem failed: sem value incorrect");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_aggregation_series_quantile() {
                  std::cout << "========= Series quantile =======================";
      
                  pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});
                  auto q50 = s.quantile(0.5);
                  bool passed = q50.has_value() && std::abs(*q50 - 3.0) < 0.001;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_quantile() : quantile(0.5) should be 3.0" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_series_quantile failed: quantile(0.5) should be 3.0");
                  }
      
                  // Test q=0 and q=1
                  auto q0 = s.quantile(0.0);
                  passed = q0.has_value() && std::abs(*q0 - 1.0) < 0.001;
                  if (!passed) {

.. _example-expanding-sem-8:

.. dropdown:: sem (pd_test_1_all.cpp:4525)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4515
      :emphasize-lines: 11

      #include "../pandas/pd_dataframe.h"
      #include "../pandas/pd_series.h"
      
      namespace dataframe_tests {
          namespace dataframe_tests_aggregation {
      
              void pd_test_aggregation_series_sem() {
                  std::cout << "========= Series sem ============================";
      
                  pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});
                  auto sem_val = s.sem();
                  // std(ddof=1) = sqrt(2.5), sem = sqrt(2.5)/sqrt(5) ≈ 0.707
                  bool passed = sem_val.has_value() && std::abs(*sem_val - 0.707) < 0.01;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_sem() : sem value incorrect" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_series_sem failed: sem value incorrect");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      

.. _example-expanding-skew-9:

.. dropdown:: skew (pd_test_1_all.cpp:4592)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4582
      :emphasize-lines: 11

                      throw std::runtime_error("pd_test_aggregation_series_mode failed: multi-mode should return 2 values");
                  }
      
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_aggregation_series_skew_kurt() {
                  std::cout << "========= Series skew/kurt ======================";
      
                  pandas::Series<double> s({1.0, 2.0, 2.0, 3.0, 9.0});
                  auto skew_val = s.skew();
                  bool passed = skew_val.has_value() && *skew_val > 0;  // Should be right-skewed
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_skew_kurt() : skew should be positive" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_series_skew_kurt failed: skew should be positive");
                  }
      
                  auto kurt_val = s.kurt();
                  passed = kurt_val.has_value();
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_series_skew_kurt() : kurt should have value" << std::endl;

.. _example-expanding-std_-10:

.. 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-expanding-sum-11:

.. 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-expanding-var-12:

.. 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-expanding-apply-13:

.. 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-expanding-rank-14:

.. dropdown:: rank (pd_test_1_all.cpp:6451)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 6441
      :emphasize-lines: 11

              // =====================================================================
              // Test: Rank
              // =====================================================================
              void pd_test_dataframe_rank() {
                  std::cout << "========= rank =============================";
      
                  // Test Series rank with default method (average)
                  {
                      std::vector<double> data = {3.0, 1.0, 4.0, 1.0, 5.0};
                      pandas::Series<double> s(data, "test");
                      auto ranked = s.rank();
      
                      // Values: 3, 1, 4, 1, 5 -> Sorted: 1, 1, 3, 4, 5
                      // Ranks (average): 1.5, 1.5, 3, 4, 5
                      // Original positions: 3->3, 1->1.5, 4->4, 1->1.5, 5->5
                      double r0 = std::stod(ranked.get_value_str(0));  // 3.0 -> rank 3
                      double r1 = std::stod(ranked.get_value_str(1));  // 1.0 -> rank 1.5
      
                      if (std::abs(r0 - 3.0) > 1e-10) {
                          std::cout << "  [FAIL] : in pd_test_dataframe_rank() : value 3.0 should have rank 3, got " << r0 << std::endl;
                          throw std::runtime_error("pd_test_dataframe_rank failed: value 3.0 rank");

.. _example-expanding-corr-15:

.. dropdown:: corr (pd_test_1_all.cpp:4655)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4645
      :emphasize-lines: 11

              }
      
              void pd_test_aggregation_dataframe_corr() {
                  std::cout << "========= DataFrame corr ========================";
      
                  std::map<std::string, std::vector<double>> data;
                  data["A"] = {1.0, 2.0, 3.0, 4.0, 5.0};
                  data["B"] = {2.0, 4.0, 6.0, 8.0, 10.0};  // Perfect correlation
                  pandas::DataFrame df(data);
      
                  auto corr_df = df.corr();
      
                  // Check dimensions
                  bool passed = corr_df.nrows() == 2 && corr_df.ncols() == 2;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_dataframe_corr() : corr should be 2x2" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_dataframe_corr failed: corr should be 2x2");
                  }
      
                  // Diagonal should be 1.0
                  std::string aa = corr_df["A"].get_value_str(0);

.. _example-expanding-cov-16:

.. dropdown:: cov (pd_test_1_all.cpp:4690)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4680
      :emphasize-lines: 11

                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_aggregation_dataframe_cov() {
                  std::cout << "========= DataFrame cov =========================";
      
                  std::map<std::string, std::vector<double>> data;
                  data["A"] = {1.0, 2.0, 3.0};
                  pandas::DataFrame df(data);
      
                  auto cov_df = df.cov();
      
                  // Check dimensions
                  bool passed = cov_df.nrows() == 1 && cov_df.ncols() == 1;
                  if (!passed) {
                      std::cout << "  [FAIL] : in pd_test_aggregation_dataframe_cov() : cov should be 1x1" << std::endl;
                      throw std::runtime_error("pd_test_aggregation_dataframe_cov failed: cov should be 1x1");
                  }
      
                  // Var(A) = 1.0 with ddof=1
                  std::string aa = cov_df["A"].get_value_str(0);