ApplyResultInference
====================

.. cpp:class:: pandas::ApplyResultInference

   pandas C++ class.

Example
-------

.. code-block:: cpp

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

   // Use ApplyResultInference
   ApplyResultInference obj;
   // ... operations ...

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

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

   * - Signature
     - Return Type
     - Location
     - Example
   * - ``static ApplyResultKind classify(const ProbeDescriptor& probe, const std::string& source_dtype)``
     - static ApplyResultKind
     - pd_apply_result.h:519
     - :ref:`View <example-applyresultinference-classify-0>`
   * - ``static void finalize_numeric(SeriesT& res, const std::string& source_dtype, bool all_results_int)``
     - void
     - pd_apply_result.h:531
     - 
   * - ``static void observe_probe(ScalarKindHistogram& hist, const ProbeDescriptor& d)``
     - static void
     - pd_apply_result.h:401
     - :ref:`View <example-applyresultinference-observe_probe-1>`
   * - ``static std::string pandas_apply_widen_narrow(const std::string& a, const std::string& b)``
     - static std::string
     - pd_apply_result.h:347
     - 


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

The following examples are extracted from the test suite.

.. _example-applyresultinference-classify-0:

.. dropdown:: classify (pd_test_5_all.cpp:126920)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 126910
      :emphasize-lines: 11

      
      // --- f_test_is_numeric_extension_dtypes_2.cpp ---
      
      namespace f_test_is_numeric_extension_dtypes_2_ns {
      
      static bool inline_is_numeric_two_term(const std::string& dt) {
          return (dt.find("float") != std::string::npos ||
                  dt.find("int")   != std::string::npos);
      }
      
      static void classify(const std::string& dt,
                           bool expect_two_term,
                           bool expect_central,
                           const std::string& label_prefix,
                           int& local_fail) {
          bool two_term = inline_is_numeric_two_term(dt);
          bool central  = pandas::DataFrame::is_numeric_dtype(dt);
          pandas_tests::check(two_term == expect_two_term,
                label_prefix + ".two_term_classifies_as_expected (dt=" + dt + ")",
                local_fail);
          pandas_tests::check(central == expect_central,

.. _example-applyresultinference-observe_probe-1:

.. dropdown:: observe_probe (pd_test_5_all.cpp:97995)
   :class-title: example-dropdown

   .. code-block:: cpp
      :linenos:
      :lineno-start: 97985
      :emphasize-lines: 11

                                         std::string("c")});
          // Match pandas' Series<string> short dtype convention.
          s.set_dtype_override("object");
          return s;
      }
      
      static Cb cb_int(Hist& hist) {
          return [&hist](const pandas::MapCellInput& in) {
              pandas::ApplyResultInference::ProbeDescriptor d;
              d.is_int = true;
              pandas::ApplyResultInference::observe_probe(hist, d);
              (void)in;
              return pandas::ApplyCellResult::of_int(42);
          };
      }
      
      static Cb cb_bool(Hist& hist) {
          return [&hist](const pandas::MapCellInput& in) {
              pandas::ApplyResultInference::ProbeDescriptor d;
              d.is_bool = true;
              pandas::ApplyResultInference::observe_probe(hist, d);