FillSpec
========

.. cpp:class:: pandas::FillSpec

   pandas C++ class.

Example
-------

.. code-block:: cpp

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

   // Use FillSpec
   FillSpec obj;
   // ... operations ...

Construction
------------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``static FillSpec from_legacy(double fv, const std::optional<std::string>& str_fill)``
     - static FillSpec
     - pd_dataframe.h:203
     - 

Conversion
----------

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``static FillSpec boolean(bool v)``
     - static FillSpec
     - pd_dataframe.h:197
     - :ref:`View <example-fillspec-boolean-0>`

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``static FillSpec floating(double v){ FillSpec r``
     - static FillSpec
     - pd_dataframe.h:199
     - :ref:`View <example-fillspec-floating-1>`
   * - ``static FillSpec integer(int64_t v){ FillSpec r``
     - static FillSpec
     - pd_dataframe.h:198
     - :ref:`View <example-fillspec-integer-2>`
   * - ``static FillSpec nan()``
     - static FillSpec
     - pd_dataframe.h:196
     - :ref:`View <example-fillspec-nan-3>`
   * - ``static FillSpec string(std::string v){ FillSpec r``
     - static FillSpec
     - pd_dataframe.h:200
     - :ref:`View <example-fillspec-string-4>`


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

The following examples are extracted from the test suite.

.. _example-fillspec-boolean-0:

.. dropdown:: boolean (pd_test_2_all.cpp:20240)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 20230
      :emphasize-lines: 11

      
      void pd_test_getitem_dispatch_classify_bool() {
          std::cout << "pd_test_getitem_dispatch_classify_bool" << std::endl;
          pandas::DataFrame df;
          std::vector<numpy::bool_> bvals = {true, false, true};
          df.insert(0, "flag", std::make_unique<pandas::Series<numpy::bool_>>(bvals, "flag"), true);
      
          auto t = df.classify_column_access("flag");
          check(t == pandas::DataFrame::ColumnAccessType::BoolColumn, "bool -> BoolColumn");
      
          // boolean (nullable) dtype
          pandas::DataFrame df2;
          auto bs = std::make_unique<pandas::Series<numpy::bool_>>(bvals, "flag2");
          bs->set_dtype_override("boolean");
          df2.insert(0, "flag2", std::move(bs), true);
          auto t2 = df2.classify_column_access("flag2");
          check(t2 == pandas::DataFrame::ColumnAccessType::BoolColumn, "boolean -> BoolColumn");
      }
      
      void pd_test_getitem_dispatch_classify_string() {
          std::cout << "pd_test_getitem_dispatch_classify_string" << std::endl;

.. _example-fillspec-floating-1:

.. dropdown:: floating (pd_test_5_all.cpp:90472)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 90462
      :emphasize-lines: 11

      
      static void f_20_847316_df_reindex_with_spec_dup(int& local_fail) {
          std::cout << "-- f_20_847316_df_reindex_with_spec_dup\n";
          expect_value_error_duplicate("c16_df_reindex_with_spec", [] {
              std::vector<pandas::Series<std::int64_t>> cols;
              cols.emplace_back(std::vector<std::int64_t>{10, 20, 30});
              cols.emplace_back(std::vector<std::int64_t>{40, 50, 60});
              pandas::DataFrame df(cols, {"c1", "c2"});
              df.set_index(std::make_unique<pandas::Index<std::string>>(
                  std::vector<std::string>{"a", "a", "b"}));
              pandas::FillSpec spec = pandas::FillSpec::floating(0.0);
              auto r = df.reindex_with_spec({"a", "b", "c"}, /*axis=*/0, spec);
              (void)r;
          }, local_fail);
      }
      
      static void f_20_847317_series_unique_succeeds(int& local_fail) {
          std::cout << "-- f_20_847317_series_unique_succeeds\n";
          expect_no_exception("c17_series_unique", [&local_fail] {
              auto s = make_series<std::int64_t>({1, 2, 3}, {"a", "b", "c"});
              auto r = s.reindex({"a", "b", "c", "d"});

.. _example-fillspec-integer-2:

.. dropdown:: integer (pd_test_1_all.cpp:2897)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 2887
      :emphasize-lines: 11

      
          void pd_test_period_array_arithmetic() {
              std::cout << "========= PeriodArray: arithmetic ======================= ";
      
              pandas::PeriodArray arr(std::vector<std::string>{
                  "2024-01",
                  "2024-06",
                  "NaT"
              }, "M");
      
              // Add integer (shift forward)
              auto shifted = arr + 3;
              if (shifted.size() != 3) {
                  std::cout << "  [FAIL] : shifted size should be 3" << std::endl;
                  throw std::runtime_error("pd_test_period_array_arithmetic failed: size");
              }
      
              // Check ordinal shifted by 3
              auto orig0 = arr[0];
              auto shift0 = shifted[0];
              if (!orig0.has_value() || !shift0.has_value() ||

.. _example-fillspec-nan-3:

.. dropdown:: nan (pd_test_1_all.cpp:1556)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 1546
      :emphasize-lines: 11

      
              std::cout << " -> tests passed" << std::endl;
          }
      
          void pd_test_floating_array_nan_conversion() {
              std::cout << "========= FloatingArray: NaN to NA conversion ======================= ";
      
              // NaN values should be automatically converted to NA
              pandas::FloatingArray<double> arr({
                  std::optional<double>(1.0),
                  std::optional<double>(std::nan("")),  // NaN
                  std::optional<double>(3.0)
              });
      
              if (!arr.is_na(1)) {
                  std::cout << "  [FAIL] : in pd_test_floating_array_nan_conversion() : NaN should be converted to NA" << std::endl;
                  throw std::runtime_error("pd_test_floating_array_nan_conversion failed: NaN should be NA");
              }
      
              if (arr.is_na(0) || arr.is_na(2)) {
                  std::cout << "  [FAIL] : in pd_test_floating_array_nan_conversion() : non-NaN should not be NA" << std::endl;

.. _example-fillspec-string-4:

.. dropdown:: string (pd_test_1_all.cpp:941)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 931
      :emphasize-lines: 11

      
      #include "../pandas/pd_config.h"
      
      namespace dataframe_tests {
      
      namespace dataframe_tests_config {
      
          void pd_test_config_version() {
              std::cout << "========= df_config: version info ======================= ";
              const char* version = pandas::DataFrameInfo::version();
              if (version == nullptr || std::string(version).empty()) {
                  std::cout << "[FAIL] : in pd_test_config_version() : version is null or empty" << std::endl;
                  throw std::runtime_error("pd_test_config_version failed: version is null or empty");
              }
              std::cout << "-> tests passed" << std::endl;
          }
      
          void pd_test_config_na_repr() {
              std::cout << "========= df_config: NA representation ======================= ";
              const char* na_repr = pandas::DataFrameConfig::get_na_repr();
              if (na_repr == nullptr) {