SeriesBase

class pandas::SeriesBase

Core data container class in the pandas namespace.

Example

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

// Use SeriesBase
SeriesBase obj;
// ... operations ...

Indexing / Selection

Signature	Return Type	Location	Example
virtual std::string get_value_str(size_t idx) const = 0	virtual std::string	pd_series_base.h:140	View

Data Manipulation

Signature	Return Type	Location	Example
virtual void reset_index(bool drop = false, std::optional<int> level = std::nullopt, bool inplace = false, std::optional<int> col_level = std::nullopt, const std::string& col_fill = "", bool allow_duplicates = false, const std::optional<std::vector<std::string>>& names = std::nullopt) = 0	virtual void	pd_series_base.h:126	View
virtual void set_index(std::unique_ptr<IndexBase> new_index) = 0	virtual void	pd_series_base.h:114	View

Statistics

Signature	Return Type	Location	Example
virtual size_t count() const = 0	virtual size_t	pd_series_base.h:159	View

I/O

Signature	Return Type	Location	Example
virtual std::string to_string() const = 0	virtual std::string	pd_series_base.h:178	View
virtual std::vector<std::string> to_string_vector() const = 0	virtual std::vector<std::string>	pd_series_base.h:145	View

Other Methods

Signature	Return Type	Location	Example
size_t cache_memory_usage() const override	size_t	pd_series_base.h:202
void clear_cache() const override = 0	void	pd_series_base.h:192	View
virtual std::unique_ptr<SeriesBase> clone() const = 0	virtual std::unique_ptr<SeriesBase>	pd_series_base.h:169	View
virtual std::string dtype_name() const = 0	virtual std::string	pd_series_base.h:74	View
virtual bool empty() const = 0	virtual bool	pd_series_base.h:69	View
bool has_cached_values() const override = 0	bool	pd_series_base.h:197	View
virtual bool hasnans() const = 0	virtual bool	pd_series_base.h:154	View
virtual const IndexBase& index() const = 0	virtual const IndexBase&	pd_series_base.h:108	View
virtual std::optional<std::string> name() const = 0	virtual std::optional<std::string>	pd_series_base.h:79	View
virtual size_t nbytes() const = 0	virtual size_t	pd_series_base.h:99	View
virtual size_t ndim() const	virtual size_t	pd_series_base.h:94	View
virtual std::string repr() const = 0	virtual std::string	pd_series_base.h:183	View
virtual void set_name(const std::optional<std::string>& name) = 0	virtual void	pd_series_base.h:84	View
virtual std::vector<size_t> shape() const = 0	virtual std::vector<size_t>	pd_series_base.h:89	View
virtual size_t size() const = 0	virtual size_t	pd_series_base.h:64	View

Code Examples

The following examples are extracted from the test suite.

get_value_str (pd_test_1_all.cpp:4665)

            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);
            passed = std::abs(std::stod(aa) - 1.0) < 0.001;
            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_aggregation_dataframe_corr() : diagonal should be 1.0" << std::endl;
                throw std::runtime_error("pd_test_aggregation_dataframe_corr failed: diagonal should be 1.0");
            }

            // A-B correlation should be 1.0 (perfect correlation)
            std::string ab = corr_df["B"].get_value_str(0);
            passed = std::abs(std::stod(ab) - 1.0) < 0.001;
            if (!passed) {

reset_index (pd_test_3_all.cpp:1618)

    }

    std::cout << " -> tests passed" << std::endl;
}

// ============================================================================
// Category 10: Remaining Untested Functions
// ============================================================================

void pd_test_3_all_series_reset_index() {
    std::cout << "========= Series.reset_index() =======================";

    std::vector<double> vals = {10.0, 20.0, 30.0};
    pandas::Series<double> s(vals, "test");

    // Set a custom index
    pandas::Index<std::string> custom_idx({"a", "b", "c"});
    s.set_index(custom_idx);

    // Reset the index
    s.reset_index(true);  // drop=true

set_index (pd_test_1_all.cpp:20318)

            // Set datetime index
            std::vector<std::string> dates = {
                "2020-01-01 00:00:00",
                "2020-01-01 12:00:00",
                "2020-01-02 00:00:00",
                "2020-01-02 12:00:00",
                "2020-01-03 00:00:00",
                "2020-01-03 12:00:00"
            };
            df.set_index(std::make_unique<pandas::Index<std::string>>(dates));

            // Resample to daily
            auto resampler = df.resample("D");
            pandas::DataFrame result = resampler.sum();

            // Check that we got aggregated results
            bool passed = (result.nrows() <= df.nrows());

            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_timeseries_resample_basic() : resample didn't reduce rows" << std::endl;

count (pd_test_1_all.cpp:66)

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

to_string (pd_test_1_all.cpp:2693)

        pandas::PeriodArray arr_m(std::vector<std::string>{
            "2020-01",
            "NaT",
            "2025-06"
        }, "M");

        // Year
        auto years = arr_m.year();
        auto y0 = years[0];
        if (!y0.has_value() || y0.value() != 2020) {
            std::cout << "  [FAIL] : year[0] should be 2020, got " << (y0.has_value() ? std::to_string(y0.value()) : "NA") << std::endl;
            throw std::runtime_error("pd_test_period_array_year_month_quarter failed: year[0]");
        }

        auto y1 = years[1];
        if (y1.has_value()) {
            std::cout << "  [FAIL] : year[1] should be NA (NaT)" << std::endl;
            throw std::runtime_error("pd_test_period_array_year_month_quarter failed: year[1] should be NA");
        }

        auto y2 = years[2];

to_string_vector (pd_test_1_all.cpp:10871)

    std::cout << " -> tests passed" << std::endl;
}

void pd_test_extension_index_to_string_vector() {
    std::cout << "========= to_string_vector =========================";

    pandas::CategoricalArray arr({"a", std::nullopt, "c"});
    pandas::CategoricalIndex idx(arr);

    auto str_vec = idx.to_string_vector();

    bool passed = (str_vec.size() == 3 &&
                   str_vec[0] == "a" && str_vec[1] == "NA" && str_vec[2] == "c");
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_extension_index_to_string_vector() : to_string_vector check failed" << std::endl;
        throw std::runtime_error("pd_test_extension_index_to_string_vector failed");
    }

    std::cout << " -> tests passed" << std::endl;
}

clear_cache (pd_test_1_all.cpp:19413)

            s.mean();
            s.min();
            s.max();

            passed = s.has_cached_values() == true;
            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_series_cache() : cache not populated" << std::endl;
                throw std::runtime_error("pd_test_series_cache failed: cache not populated");
            }

            s.clear_cache();
            passed = s.has_cached_values() == false;
            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_series_cache() : cache not cleared" << std::endl;
                throw std::runtime_error("pd_test_series_cache failed: cache not cleared");
            }

            std::cout << " -> tests passed" << std::endl;
        }

        void pd_test_series_string_repr() {

clone (pd_test_1_all.cpp:5776)

    std::cout << " -> tests passed" << std::endl;
}

void pd_test_categorical_index_clone() {
    std::cout << "========= clone =======================================";

    pandas::CategoricalArray arr({"p", "q", "r"});
    pandas::CategoricalIndex idx(arr, "original");

    std::unique_ptr<pandas::IndexBase> cloned = idx.clone();

    bool passed = (cloned != nullptr && cloned->size() == idx.size() &&
                   cloned->name() == idx.name());
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_categorical_index_clone()" << std::endl;
        throw std::runtime_error("pd_test_categorical_index_clone failed");
    }

    std::cout << " -> tests passed" << std::endl;
}

dtype_name (pd_test_1_all.cpp:10104)

}

void pd_test_extension_index_array_constructor() {
    std::cout << "========= array constructor =========================";

    pandas::CategoricalArray arr({"apple", "banana", "apple", "cherry"});
    pandas::CategoricalIndex idx(arr, "fruits");

    bool passed = (idx.size() == 4 && !idx.empty() &&
                   idx.name().has_value() && *idx.name() == "fruits" &&
                   idx.dtype_name() == "category");
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_extension_index_array_constructor() : array constructor check failed" << std::endl;
        throw std::runtime_error("pd_test_extension_index_array_constructor failed");
    }

    std::cout << " -> tests passed" << std::endl;
}

void pd_test_extension_index_copy_constructor() {
    std::cout << "========= copy constructor =========================";

empty (pd_test_1_all.cpp:941)

#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) {

has_cached_values (pd_test_1_all.cpp:19395)

            }

            std::cout << " -> tests passed" << std::endl;
        }

        void pd_test_series_cache() {
            std::cout << "========= cache management =========================================";

            pandas::Series<double> s({1.0, 2.0, 3.0, 4.0, 5.0});

            bool passed = s.has_cached_values() == false;
            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_series_cache() : initial cache not empty" << std::endl;
                throw std::runtime_error("pd_test_series_cache failed: initial cache not empty");
            }

            // Trigger cache
            s.sum();
            s.mean();
            s.min();
            s.max();

hasnans (pd_test_1_all.cpp:5363)

void pd_test_categorical_index_from_codes() {
    std::cout << "========= from_codes =================================";

    std::vector<numpy::int32> codes = {0, 1, 0, 2, -1};  // -1 = NA
    std::vector<std::string> categories = {"low", "medium", "high"};

    pandas::CategoricalIndex idx = pandas::CategoricalIndex::from_codes(codes, categories, true, "level");

    bool passed = (idx.size() == 5 && idx.num_categories() == 3 &&
                   idx.ordered() && idx.name().has_value() && *idx.name() == "level" &&
                   idx.hasnans());  // has NA from code -1
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_categorical_index_from_codes()" << std::endl;
        throw std::runtime_error("pd_test_categorical_index_from_codes failed");
    }

    std::cout << " -> tests passed" << std::endl;
}

void pd_test_categorical_index_simple_new() {
    std::cout << "========= _simple_new =================================";

index (pd_test_1_all.cpp:6680)

        void pd_test_dataframe_index_ops() {
            std::cout << "========= index operations =================";

            // Test set_axis (rows)
            {
                std::map<std::string, std::vector<int>> data;
                data["A"] = {1, 2, 3};
                pandas::DataFrame df(data);

                auto renamed = df.set_axis({"x", "y", "z"}, 0);
                std::string idx0 = renamed.index().get_value_str(0);
                if (idx0 != "x") {
                    std::cout << "  [FAIL] : in pd_test_dataframe_index_ops() : set_axis first label should be 'x'" << std::endl;
                    throw std::runtime_error("pd_test_dataframe_index_ops failed: set_axis");
                }
            }

            // Test set_axis (columns)
            {
                std::map<std::string, std::vector<int>> data;
                data["A"] = {1, 2};

name (pd_test_1_all.cpp:295)

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

        pandas::BooleanArray arr;
        if (arr.dtype().name() != "boolean") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype name should be 'boolean'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype name");
        }

        if (arr.dtype().kind() != "b") {
            std::cout << "  [FAIL] : in pd_test_boolean_array_dtype() : dtype kind should be 'b'" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_dtype failed: dtype kind");
        }

        std::cout << " -> tests passed" << std::endl;

nbytes (pd_test_1_all.cpp:6214)

            }

            // Test empty DataFrame
            pandas::DataFrame empty_df;
            if (!empty_df.empty()) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : should be empty" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: should be empty");
            }

            // Test nbytes > 0 for non-empty
            if (df.nbytes() == 0) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : nbytes should be > 0" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: nbytes should be > 0");
            }

            // Test columns index
            if (df.columns().size() != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : columns size != 3" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: columns size != 3");
            }

ndim (pd_test_1_all.cpp:6195)

            pandas::DataFrame df(data);

            // Test shape
            auto shape = df.shape();
            if (shape.size() != 2 || shape[0] != 4 || shape[1] != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : shape mismatch" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: shape mismatch");
            }

            // Test ndim
            if (df.ndim() != 2) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : ndim != 2" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: ndim != 2");
            }

            // Test empty
            if (df.empty()) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : should not be empty" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: should not be empty");
            }

repr (pd_test_1_all.cpp:10906)

    std::cout << " -> tests passed" << std::endl;
}

void pd_test_extension_index_repr() {
    std::cout << "========= repr =========================";

    pandas::CategoricalArray arr({"a", "b", "c"});
    // Use ExtensionIndex<CategoricalArray> directly to test base class repr
    pandas::ExtensionIndex<pandas::CategoricalArray> idx(arr, "test");

    std::string repr_str = idx.repr();

    bool passed = (!repr_str.empty() && repr_str.find("ExtensionIndex") != std::string::npos);
    if (!passed) {
        std::cout << "  [FAIL] : in pd_test_extension_index_repr() : repr check failed" << std::endl;
        throw std::runtime_error("pd_test_extension_index_repr failed");
    }

    std::cout << " -> tests passed" << std::endl;
}

set_name (pd_test_1_all.cpp:11798)

                throw std::runtime_error("pd_test_index_vector_constructor failed");
            }

            std::cout << " -> tests passed" << std::endl;
        }

        void pd_test_index_copy_constructor() {
            std::cout << "========= copy constructor ============================";

            pandas::Index<numpy::int64> idx1{1, 2, 3};
            idx1.set_name("original");

            pandas::Index<numpy::int64> idx2(idx1);

            bool passed = (idx2.size() == 3);
            passed = passed && (idx2.name().value() == "original");
            passed = passed && idx2.equals(idx1);

            if (!passed) {
                std::cout << "  [FAIL] : in pd_test_index_copy_constructor() : copy failed" << std::endl;
                throw std::runtime_error("pd_test_index_copy_constructor failed");

shape (pd_test_1_all.cpp:6188)

            std::cout << "========= properties =======================";

            std::map<std::string, std::vector<numpy::float64>> data;
            data["A"] = {1.0, 2.0, 3.0, 4.0};
            data["B"] = {5.0, 6.0, 7.0, 8.0};
            data["C"] = {9.0, 10.0, 11.0, 12.0};

            pandas::DataFrame df(data);

            // Test shape
            auto shape = df.shape();
            if (shape.size() != 2 || shape[0] != 4 || shape[1] != 3) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : shape mismatch" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: shape mismatch");
            }

            // Test ndim
            if (df.ndim() != 2) {
                std::cout << "  [FAIL] : in pd_test_dataframe_properties() : ndim != 2" << std::endl;
                throw std::runtime_error("pd_test_dataframe_properties failed: ndim != 2");
            }

size (pd_test_1_all.cpp:22)

#include "../pandas/pd_boolean_array.h"

namespace dataframe_tests {

namespace dataframe_tests_boolean_array {
    void pd_test_boolean_array_constructors() {
        std::cout << "========= BooleanArray: constructors ======================= ";

        // Default constructor
        pandas::BooleanArray arr1;
        if (arr1.size() != 0) {
            std::cout << "  [FAIL] : in pd_test_boolean_array_constructors() : default constructor size != 0" << std::endl;
            throw std::runtime_error("pd_test_boolean_array_constructors failed: default constructor size != 0");
        }

        // Initializer list constructor
        pandas::BooleanArray arr2({
            std::optional<bool>(true),
            std::optional<bool>(false),
            std::nullopt,
            std::optional<bool>(true)