DataFrameEWM
============

.. cpp:class:: pandas::DataFrameEWM

   Window operation class for rolling/expanding calculations.

Example
-------

.. code-block:: cpp

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

   // Use DataFrameEWM
   DataFrameEWM obj;
   // ... operations ...

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

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

   * - Signature
     - Location
     - Example
   * - ``DataFrameEWM(const DataFrame& df, double span, bool adjust = true, bool ignore_na = false)``
     - pd_dataframe.h:12801
     - 

Statistics
----------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``DataFrame mean() const``
     - DataFrame
     - pd_dataframe.h:12816
     - :ref:`View <example-dataframeewm-mean-0>`
   * - ``DataFrame std(int ddof = 1) const``
     - DataFrame
     - pd_dataframe.h:12829
     - :ref:`View <example-dataframeewm-std-1>`
   * - ``DataFrame var(int ddof = 1) const``
     - DataFrame
     - pd_dataframe.h:12835
     - :ref:`View <example-dataframeewm-var-2>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``DataFrame apply_time_based(Func&& func) const``
     - DataFrame
     - pd_dataframe.h:12875
     - 
   * - ``DataFrame apply_to_columns(Func&& func) const``
     - DataFrame
     - pd_dataframe.h:12849
     - 

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``bool adjust() const``
     - bool
     - pd_dataframe.h:12843
     - 
   * - ``const DataFrame& dataframe() const``
     - const DataFrame&
     - pd_dataframe.h:12845
     - :ref:`View <example-dataframeewm-dataframe-3>`
   * - ``bool ignore_na() const``
     - bool
     - pd_dataframe.h:12844
     - 
   * - ``void set_precomputed_mean(std::map<std::string, std::vector<numpy::float64>> data, std::unique_ptr<IndexBase> idx)``
     - void
     - pd_dataframe.h:12804
     - 
   * - ``void set_time_based(std::vector<int64_t> timestamps, double halflife_seconds)``
     - void
     - pd_dataframe.h:12810
     - 
   * - ``double span() const``
     - double
     - pd_dataframe.h:12842
     - :ref:`View <example-dataframeewm-span-4>`


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

The following examples are extracted from the test suite.

.. _example-dataframeewm-mean-0:

.. 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-dataframeewm-std-1:

.. dropdown:: std (pd_test_1_all.cpp:4526)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 4516
      :emphasize-lines: 11

      #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;
              }
      
              void pd_test_aggregation_series_quantile() {

.. _example-dataframeewm-var-2:

.. 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-dataframeewm-dataframe-3:

.. dropdown:: dataframe (pd_test_2_all.cpp:11742)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 11732
      :emphasize-lines: 11

                      std::cout << "  [FAIL] : wrong dimensions" << std::endl;
                      std::remove(temp_path.c_str());
                      throw std::runtime_error("pd_test_to_hdf_mixed_types failed");
                  }
      
                  std::remove(temp_path.c_str());
                  std::cout << " -> tests passed" << std::endl;
              }
      
              void pd_test_to_hdf_empty_dataframe() {
                  std::cout << "========= to_hdf empty dataframe (real HDF5) ===================";
      
                  pandas::DataFrame df;
                  std::string temp_path = "temp/test_hdf5_empty.h5";
                  df.to_hdf(temp_path, "df", "w");
      
                  // Just verify file was created
                  std::ifstream file(temp_path);
                  if (!file.is_open()) {
                      std::cout << "  [FAIL] : file not created" << std::endl;
                      throw std::runtime_error("pd_test_to_hdf_empty_dataframe failed");

.. _example-dataframeewm-span-4:

.. dropdown:: span (pd_test_5_all.cpp:57296)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 57286
      :emphasize-lines: 11

          f_test_astype_reindex_chain_coverage_23_main_ns::f_test_astype_reindex_chain_coverage_23_main();
      }
      
      
      // --- f_test_format_helpers_coverage_9.cpp ---
      namespace f_test_format_helpers_coverage_9_main_ns {
      
      static void f_format_helpers_coverage_8b2f47_case_A01_empty_span_returns_min(int& local_fail) {
          std::cout << "-- case_A01_empty_span_returns_min\n";
          std::vector<double> v;
          int p = pandas::detail::infer_float_repr_precision(std::span<const double>(v));
          pandas_tests::check(p == 1,
              "format_helpers_coverage_8b2f47_case_A01.empty_returns_min1", local_fail);
      }
      
      static void f_format_helpers_coverage_8b2f47_case_A02_all_integer_returns_min(int& local_fail) {
          std::cout << "-- case_A02_all_integer_returns_min\n";
          std::vector<double> v = {1.0, 2.0, 3.0};
          int p = pandas::detail::infer_float_repr_precision(std::span<const double>(v));
          pandas_tests::check(p == 1,
              "format_helpers_coverage_8b2f47_case_A02.all_integer_returns_min1", local_fail);