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Column

Bases: Sequence[T]

A column is a named collection of values.

Parameters:

Name Type Description Default
name str

The name of the column.

required
data Sequence[T]

The data.

None

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
Source code in src/safeds/data/tabular/containers/_column.py
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class Column(Sequence[T]):
    """
    A column is a named collection of values.

    Parameters
    ----------
    name : str
        The name of the column.
    data : Sequence[T]
        The data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    """

    # ------------------------------------------------------------------------------------------------------------------
    # Creation
    # ------------------------------------------------------------------------------------------------------------------

    @staticmethod
    def _from_pandas_series(data: pd.Series, type_: ColumnType | None = None) -> Column:
        """
        Create a column from a `pandas.Series`.

        Parameters
        ----------
        data : pd.Series
            The data.
        type_ : ColumnType | None
            The type. If None, the type is inferred from the data.

        Returns
        -------
        column : Column
            The created column.

        Examples
        --------
        >>> import pandas as pd
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column._from_pandas_series(pd.Series([1, 2, 3], name="test"))
        """
        result = object.__new__(Column)
        result._name = data.name
        result._data = data
        # noinspection PyProtectedMember
        result._type = type_ if type_ is not None else ColumnType._data_type(data)

        return result

    # ------------------------------------------------------------------------------------------------------------------
    # Dunder methods
    # ------------------------------------------------------------------------------------------------------------------

    def __init__(self, name: str, data: Sequence[T] | None = None) -> None:
        """
        Create a column.

        Parameters
        ----------
        name : str
            The name of the column.
        data : Sequence[T] | None
            The data. If None, an empty column is created.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        """
        import pandas as pd

        # Enable copy-on-write for pandas dataframes
        pd.options.mode.copy_on_write = True

        if data is None:
            data = []

        self._name: str = name
        self._data: pd.Series = data.rename(name) if isinstance(data, pd.Series) else pd.Series(data, name=name)
        # noinspection PyProtectedMember
        self._type: ColumnType = ColumnType._data_type(self._data)

    def __contains__(self, item: Any) -> bool:
        return item in self._data

    def __eq__(self, other: object) -> bool:
        """
        Check whether this column is equal to another object.

        Parameters
        ----------
        other : object
            The other object.

        Returns
        -------
        equal : bool
            True if the other object is an identical column. False if the other object is a different column.
            NotImplemented if the other object is not a column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3])
        >>> column2 = Column("test", [1, 2, 3])
        >>> column1 == column2
        True

        >>> column3 = Column("test", [3, 4, 5])
        >>> column1 == column3
        False
        """
        if not isinstance(other, Column):
            return NotImplemented
        if self is other:
            return True
        return self.name == other.name and self._data.equals(other._data)

    @overload
    def __getitem__(self, index: int) -> T: ...

    @overload
    def __getitem__(self, index: slice) -> Column[T]: ...

    def __getitem__(self, index: int | slice) -> T | Column[T]:
        """
        Return the value of the specified row or rows.

        Parameters
        ----------
        index : int | slice
            The index of the row, or a slice specifying the start and end index.

        Returns
        -------
        value : Any
            The single row's value, or rows' values.

        Raises
        ------
        IndexOutOfBoundsError
            If the given index or indices do not exist in the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column[0]
        1
        """
        if isinstance(index, int):
            if index < 0 or index >= self._data.size:
                raise IndexOutOfBoundsError(index)
            return self._data[index]

        if isinstance(index, slice):
            if index.start < 0 or index.start > self._data.size:
                raise IndexOutOfBoundsError(index)
            if index.stop < 0 or index.stop > self._data.size:
                raise IndexOutOfBoundsError(index)
            data = self._data[index].reset_index(drop=True).rename(self.name)
            return Column._from_pandas_series(data, self._type)

    def __hash__(self) -> int:
        """
        Return a deterministic hash value for this column.

        Returns
        -------
        hash:
            The hash value.
        """
        return _structural_hash(self.name, self.type.__repr__(), self.number_of_rows)

    def __iter__(self) -> Iterator[T]:
        r"""
        Create an iterator for the data of this column. This way e.g. for-each loops can be used on it.

        Returns
        -------
        iterator : Iterator[Any]
            The iterator.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", ["A", "B", "C"])
        >>> string = ""
        >>> for val in column:
        ...     string += val + ", "
        >>> string
        'A, B, C, '
        """
        return iter(self._data)

    def __len__(self) -> int:
        """
        Return the size of the column.

        Returns
        -------
        n_rows : int
            The size of the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> len(column)
        3
        """
        return len(self._data)

    def __repr__(self) -> str:
        """
        Return an unambiguous string representation of this column.

        Returns
        -------
        representation : str
            The string representation.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> repr(column)
        "Column('test', [1, 2, 3])"
        """
        return f"Column({self._name!r}, {list(self._data)!r})"

    def __sizeof__(self) -> int:
        """
        Return the complete size of this object.

        Returns
        -------
        size:
            Size of this object in bytes.
        """
        return sys.getsizeof(self._data) + sys.getsizeof(self._name) + sys.getsizeof(self._type)

    def __str__(self) -> str:
        """
        Return a user-friendly string representation of this column.

        Returns
        -------
        representation : str
            The string representation.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> str(column)
        "'test': [1, 2, 3]"
        """
        return f"{self._name!r}: {list(self._data)!r}"

    # ------------------------------------------------------------------------------------------------------------------
    # Properties
    # ------------------------------------------------------------------------------------------------------------------

    @property
    def name(self) -> str:
        """
        Return the name of the column.

        Returns
        -------
        name : str
            The name of the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.name
        'test'
        """
        return self._name

    @property
    def number_of_rows(self) -> int:
        """
        Return the number of elements in the column.

        Returns
        -------
        number_of_rows : int
            The number of elements.
        """
        return len(self._data)

    @property
    def type(self) -> ColumnType:
        """
        Return the type of the column.

        Returns
        -------
        type : ColumnType
            The type of the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.type
        Integer

        >>> column = Column("test", ['a', 'b', 'c'])
        >>> column.type
        String
        """
        return self._type

    # ------------------------------------------------------------------------------------------------------------------
    # Getters
    # ------------------------------------------------------------------------------------------------------------------

    def get_unique_values(self) -> list[T]:
        """
        Return a list of all unique values in the column.

        Returns
        -------
        unique_values : list[T]
            List of unique values in the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3, 2, 4, 3])
        >>> column.get_unique_values()
        [1, 2, 3, 4]
        """
        return list(self._data.unique())

    def get_value(self, index: int) -> T:
        """
        Return column value at specified index, starting at 0.

        Parameters
        ----------
        index : int
            Index of requested element.

        Returns
        -------
        value
            Value at index in column.

        Raises
        ------
        IndexOutOfBoundsError
            If the given index does not exist in the column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.get_value(1)
        2
        """
        if index < 0 or index >= self._data.size:
            raise IndexOutOfBoundsError(index)

        return self._data[index]

    # ------------------------------------------------------------------------------------------------------------------
    # Information
    # ------------------------------------------------------------------------------------------------------------------

    def all(self, predicate: Callable[[T], bool]) -> bool:
        """
        Check if all values have a given property.

        Parameters
        ----------
        predicate : Callable[[T], bool])
            Callable that is used to find matches.

        Returns
        -------
        result : bool
            True if all match.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.all(lambda x: x < 4)
        True

        >>> column.all(lambda x: x < 2)
        False
        """
        return all(predicate(value) for value in self._data)

    def any(self, predicate: Callable[[T], bool]) -> bool:
        """
        Check if any value has a given property.

        Parameters
        ----------
        predicate : Callable[[T], bool])
            Callable that is used to find matches.

        Returns
        -------
        result : bool
            True if any match.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.any(lambda x: x < 2)
        True

        >>> column.any(lambda x: x < 1)
        False
        """
        return any(predicate(value) for value in self._data)

    def none(self, predicate: Callable[[T], bool]) -> bool:
        """
        Check if no values has a given property.

        Parameters
        ----------
        predicate : Callable[[T], bool])
            Callable that is used to find matches.

        Returns
        -------
        result : bool
            True if none match.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3])
        >>> column1.none(lambda x: x < 1)
        True

        >>> column2 = Column("test", [1, 2, 3])
        >>> column2.none(lambda x: x > 1)
        False
        """
        return all(not predicate(value) for value in self._data)

    def has_missing_values(self) -> bool:
        """
        Return whether the column has missing values.

        Returns
        -------
        missing_values_exist : bool
            True if missing values exist.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3, None])
        >>> column1.has_missing_values()
        True

        >>> column2 = Column("test", [1, 2, 3])
        >>> column2.has_missing_values()
        False
        """
        import numpy as np

        return self.any(lambda value: value is None or (isinstance(value, Number) and np.isnan(value)))

    # ------------------------------------------------------------------------------------------------------------------
    # Transformations
    # ------------------------------------------------------------------------------------------------------------------

    def rename(self, new_name: str) -> Column:
        """
        Return a new column with a new name.

        The original column is not modified.

        Parameters
        ----------
        new_name : str
            The new name of the column.

        Returns
        -------
        column : Column
            A new column with the new name.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.rename("new name")
        Column('new name', [1, 2, 3])
        """
        return Column._from_pandas_series(self._data.rename(new_name), self._type)

    def transform(self, transformer: Callable[[T], R]) -> Column[R]:
        """
        Apply a transform method to every data point.

        The original column is not modified.

        Parameters
        ----------
        transformer : Callable[[T], R]
            Function that will be applied to all data points.

        Returns
        -------
        transformed_column: Column
            The transformed column.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> price = Column("price", [4.99, 5.99, 2.49])
        >>> sale = price.transform(lambda amount: amount * 0.8)
        """
        return Column(self.name, self._data.apply(transformer, convert_dtype=True))

    # ------------------------------------------------------------------------------------------------------------------
    # Statistics
    # ------------------------------------------------------------------------------------------------------------------

    def correlation_with(self, other_column: Column) -> float:
        """
        Calculate Pearson correlation between this and another column. Both columns have to be numerical.

        Returns
        -------
        correlation : float
            Correlation between the two columns.

        Raises
        ------
        NonNumericColumnError
            If one of the columns is not numerical.
        ColumnLengthMismatchError
            If the columns have different lengths.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3])
        >>> column2 = Column("test", [2, 4, 6])
        >>> column1.correlation_with(column2)
        1.0

        >>> column1 = Column("test", [1, 2, 3])
        >>> column2 = Column("test", [0.5, 4, -6])
        >>> column1.correlation_with(column2)
        -0.6404640308067906
        """
        if not self._type.is_numeric() or not other_column._type.is_numeric():
            raise NonNumericColumnError(
                f"Columns must be numerical. {self.name} is {self._type}, {other_column.name} is {other_column._type}.",
            )
        if self._data.size != other_column._data.size:
            raise ColumnLengthMismatchError(
                f"{self.name} is of size {self._data.size}, {other_column.name} is of size {other_column._data.size}.",
            )
        return self._data.corr(other_column._data)

    def idness(self) -> float:
        r"""
        Calculate the idness of this column.

        We define the idness as follows:

        $$
        \frac{\text{number of different values}}{\text{number of rows}}
        $$

        Returns
        -------
        idness : float
            The idness of the column.

        Raises
        ------
        ColumnSizeError
            If this column is empty.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3])
        >>> column1.idness()
        1.0

        >>> column2 = Column("test", [1, 2, 3, 2])
        >>> column2.idness()
        0.75
        """
        if self._data.size == 0:
            raise ColumnSizeError("> 0", "0")
        return self._data.nunique() / self._data.size

    def maximum(self) -> float:
        """
        Return the maximum value of the column. The column has to be numerical.

        Returns
        -------
        max : float
            The maximum value.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.maximum()
        3
        """
        if not self._type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.max()

    def mean(self) -> float:
        """
        Return the mean value of the column. The column has to be numerical.

        Returns
        -------
        mean : float
            The mean value.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.mean()
        2.0
        """
        if not self._type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.mean()

    def median(self) -> float:
        """
        Return the median value of the column. The column has to be numerical.

        Returns
        -------
        median : float
            The median value.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3, 4])
        >>> column.median()
        2.5

        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3, 4, 5])
        >>> column.median()
        3.0
        """
        if not self._type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.median()

    def minimum(self) -> float:
        """
        Return the minimum value of the column. The column has to be numerical.

        Returns
        -------
        min : float
            The minimum value.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3, 4])
        >>> column.minimum()
        1
        """
        if not self._type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.min()

    def missing_value_ratio(self) -> float:
        """
        Return the ratio of missing values to the total number of elements in the column.

        Returns
        -------
        ratio : float
            The ratio of missing values to the total number of elements in the column.

        Raises
        ------
        ColumnSizeError
            If the column is empty.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3, 4])
        >>> column1.missing_value_ratio()
        0.0

        >>> column2 = Column("test", [1, 2, 3, None])
        >>> column2.missing_value_ratio()
        0.25
        """
        if self._data.size == 0:
            raise ColumnSizeError("> 0", "0")
        return self._count_missing_values() / self._data.size

    def mode(self) -> list[T]:
        """
        Return the mode of the column.

        Returns
        -------
        mode: list[T]
            Returns a list with the most common values.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3, 3, 4])
        >>> column1.mode()
        [3]

        >>> column2 = Column("test", [1, 2, 3, 3, 4, 4])
        >>> column2.mode()
        [3, 4]
        """
        return self._data.mode().tolist()

    def stability(self) -> float:
        r"""
        Calculate the stability of this column.

        We define the stability as follows:

        $$
        \frac{\text{number of occurrences of most common non-null value}}{\text{number of non-null values}}
        $$

        The stability is not definded for a column with only null values.

        Returns
        -------
        stability : float
            The stability of the column.

        Raises
        ------
        ColumnSizeError
            If the column is empty.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 1, 2, 3])
        >>> column1.stability()
        0.5

        >>> column2 = Column("test", [1, 2, 2, 2, 3])
        >>> column2.stability()
        0.6
        """
        if self._data.size == 0:
            raise ColumnSizeError("> 0", "0")

        if self.all(lambda x: x is None):
            raise ValueError("Stability is not definded for a column with only null values.")

        return self._data.value_counts()[self.mode()[0]] / self._data.count()

    def standard_deviation(self) -> float:
        """
        Return the standard deviation of the column. The column has to be numerical.

        Returns
        -------
        sum : float
            The standard deviation of all values.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column1 = Column("test", [1, 2, 3])
        >>> column1.standard_deviation()
        1.0

        >>> column2 = Column("test", [1, 2, 4, 8, 16])
        >>> column2.standard_deviation()
        6.099180272790763
        """
        if not self.type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.std()

    def sum(self) -> float:
        """
        Return the sum of the column. The column has to be numerical.

        Returns
        -------
        sum : float
            The sum of all values.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.sum()
        6
        """
        if not self.type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
        return self._data.sum()

    def variance(self) -> float:
        """
        Return the variance of the column. The column has to be numerical.

        Returns
        -------
        sum : float
            The variance of all values.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3, 4, 5])
        >>> column.variance()
        2.5
        """
        if not self.type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")

        return self._data.var()

    # ------------------------------------------------------------------------------------------------------------------
    # Plotting
    # ------------------------------------------------------------------------------------------------------------------

    def plot_boxplot(self) -> Image:
        """
        Plot this column in a boxplot. This function can only plot real numerical data.

        Returns
        -------
        plot: Image
            The plot as an image.

        Raises
        ------
        NonNumericColumnError
            If the data contains non-numerical data.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> boxplot = column.plot_boxplot()
        """
        import matplotlib.pyplot as plt
        import seaborn as sns

        if not self.type.is_numeric():
            raise NonNumericColumnError(f"{self.name} is of type {self._type}.")

        fig = plt.figure()
        ax = sns.boxplot(data=self._data)
        ax.set(title=self.name)
        ax.set_xticks([])
        ax.set_ylabel("")
        plt.tight_layout()

        buffer = io.BytesIO()
        fig.savefig(buffer, format="png")
        plt.close()  # Prevents the figure from being displayed directly
        buffer.seek(0)
        return Image.from_bytes(buffer.read())

    def plot_histogram(self) -> Image:
        """
        Plot a column in a histogram.

        Returns
        -------
        plot: Image
            The plot as an image.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> histogram = column.plot_histogram()
        """
        import matplotlib.pyplot as plt
        import seaborn as sns

        fig = plt.figure()
        ax = sns.histplot(data=self._data)
        ax.set_xticks(ax.get_xticks())
        ax.set(xlabel=self.name)
        ax.set_xticklabels(
            ax.get_xticklabels(),
            rotation=45,
            horizontalalignment="right",
        )  # rotate the labels of the x Axis to prevent the chance of overlapping of the labels
        plt.tight_layout()

        buffer = io.BytesIO()
        fig.savefig(buffer, format="png")
        plt.close()  # Prevents the figure from being displayed directly
        buffer.seek(0)
        return Image.from_bytes(buffer.read())

    # ------------------------------------------------------------------------------------------------------------------
    # Conversion
    # ------------------------------------------------------------------------------------------------------------------

    def to_html(self) -> str:
        r"""
        Return an HTML representation of the column.

        Returns
        -------
        output : str
            The generated HTML.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("test", [1, 2, 3])
        >>> column.to_html()
        '<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>'
        """
        frame = self._data.to_frame()
        frame.columns = [self.name]

        return frame.to_html(max_rows=self._data.size, max_cols=1)

    # ------------------------------------------------------------------------------------------------------------------
    # IPython integration
    # ------------------------------------------------------------------------------------------------------------------

    def _repr_html_(self) -> str:
        r"""
        Return an HTML representation of the column.

        Returns
        -------
        output : str
            The generated HTML.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("col_1", ['a', 'b', 'c'])
        >>> column._repr_html_()
        '<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>col_1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>b</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>c</td>\n    </tr>\n  </tbody>\n</table>\n</div>'
        """
        frame = self._data.to_frame()
        frame.columns = [self.name]

        return frame.to_html(max_rows=self._data.size, max_cols=1, notebook=True)

    # ------------------------------------------------------------------------------------------------------------------
    # Other
    # ------------------------------------------------------------------------------------------------------------------

    def _count_missing_values(self) -> int:
        """
        Return the number of null values in the column.

        Returns
        -------
        count : int
            The number of null values.

        Examples
        --------
        >>> from safeds.data.tabular.containers import Column
        >>> column = Column("col_1", [None, 'a', None])
        >>> column._count_missing_values()
        2
        """
        return self._data.isna().sum()

name: str property

Return the name of the column.

Returns:

Name Type Description
name str

The name of the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.name
'test'

number_of_rows: int property

Return the number of elements in the column.

Returns:

Name Type Description
number_of_rows int

The number of elements.

type: ColumnType property

Return the type of the column.

Returns:

Name Type Description
type ColumnType

The type of the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.type
Integer
>>> column = Column("test", ['a', 'b', 'c'])
>>> column.type
String

__contains__(item)

Source code in src/safeds/data/tabular/containers/_column.py
def __contains__(self, item: Any) -> bool:
    return item in self._data

__eq__(other)

Check whether this column is equal to another object.

Parameters:

Name Type Description Default
other object

The other object.

required

Returns:

Name Type Description
equal bool

True if the other object is an identical column. False if the other object is a different column. NotImplemented if the other object is not a column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3])
>>> column2 = Column("test", [1, 2, 3])
>>> column1 == column2
True
>>> column3 = Column("test", [3, 4, 5])
>>> column1 == column3
False
Source code in src/safeds/data/tabular/containers/_column.py
def __eq__(self, other: object) -> bool:
    """
    Check whether this column is equal to another object.

    Parameters
    ----------
    other : object
        The other object.

    Returns
    -------
    equal : bool
        True if the other object is an identical column. False if the other object is a different column.
        NotImplemented if the other object is not a column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3])
    >>> column2 = Column("test", [1, 2, 3])
    >>> column1 == column2
    True

    >>> column3 = Column("test", [3, 4, 5])
    >>> column1 == column3
    False
    """
    if not isinstance(other, Column):
        return NotImplemented
    if self is other:
        return True
    return self.name == other.name and self._data.equals(other._data)

__getitem__(index)

Return the value of the specified row or rows.

Parameters:

Name Type Description Default
index int | slice

The index of the row, or a slice specifying the start and end index.

required

Returns:

Name Type Description
value Any

The single row's value, or rows' values.

Raises:

Type Description
IndexOutOfBoundsError

If the given index or indices do not exist in the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column[0]
1
Source code in src/safeds/data/tabular/containers/_column.py
def __getitem__(self, index: int | slice) -> T | Column[T]:
    """
    Return the value of the specified row or rows.

    Parameters
    ----------
    index : int | slice
        The index of the row, or a slice specifying the start and end index.

    Returns
    -------
    value : Any
        The single row's value, or rows' values.

    Raises
    ------
    IndexOutOfBoundsError
        If the given index or indices do not exist in the column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column[0]
    1
    """
    if isinstance(index, int):
        if index < 0 or index >= self._data.size:
            raise IndexOutOfBoundsError(index)
        return self._data[index]

    if isinstance(index, slice):
        if index.start < 0 or index.start > self._data.size:
            raise IndexOutOfBoundsError(index)
        if index.stop < 0 or index.stop > self._data.size:
            raise IndexOutOfBoundsError(index)
        data = self._data[index].reset_index(drop=True).rename(self.name)
        return Column._from_pandas_series(data, self._type)

__hash__()

Return a deterministic hash value for this column.

Returns:

Name Type Description
hash int

The hash value.

Source code in src/safeds/data/tabular/containers/_column.py
def __hash__(self) -> int:
    """
    Return a deterministic hash value for this column.

    Returns
    -------
    hash:
        The hash value.
    """
    return _structural_hash(self.name, self.type.__repr__(), self.number_of_rows)

__init__(name, data=None)

Create a column.

Parameters:

Name Type Description Default
name str

The name of the column.

required
data Sequence[T] | None

The data. If None, an empty column is created.

None

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
Source code in src/safeds/data/tabular/containers/_column.py
def __init__(self, name: str, data: Sequence[T] | None = None) -> None:
    """
    Create a column.

    Parameters
    ----------
    name : str
        The name of the column.
    data : Sequence[T] | None
        The data. If None, an empty column is created.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    """
    import pandas as pd

    # Enable copy-on-write for pandas dataframes
    pd.options.mode.copy_on_write = True

    if data is None:
        data = []

    self._name: str = name
    self._data: pd.Series = data.rename(name) if isinstance(data, pd.Series) else pd.Series(data, name=name)
    # noinspection PyProtectedMember
    self._type: ColumnType = ColumnType._data_type(self._data)

__iter__()

Create an iterator for the data of this column. This way e.g. for-each loops can be used on it.

Returns:

Name Type Description
iterator Iterator[Any]

The iterator.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", ["A", "B", "C"])
>>> string = ""
>>> for val in column:
...     string += val + ", "
>>> string
'A, B, C, '
Source code in src/safeds/data/tabular/containers/_column.py
def __iter__(self) -> Iterator[T]:
    r"""
    Create an iterator for the data of this column. This way e.g. for-each loops can be used on it.

    Returns
    -------
    iterator : Iterator[Any]
        The iterator.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", ["A", "B", "C"])
    >>> string = ""
    >>> for val in column:
    ...     string += val + ", "
    >>> string
    'A, B, C, '
    """
    return iter(self._data)

__len__()

Return the size of the column.

Returns:

Name Type Description
n_rows int

The size of the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> len(column)
3
Source code in src/safeds/data/tabular/containers/_column.py
def __len__(self) -> int:
    """
    Return the size of the column.

    Returns
    -------
    n_rows : int
        The size of the column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> len(column)
    3
    """
    return len(self._data)

__repr__()

Return an unambiguous string representation of this column.

Returns:

Name Type Description
representation str

The string representation.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> repr(column)
"Column('test', [1, 2, 3])"
Source code in src/safeds/data/tabular/containers/_column.py
def __repr__(self) -> str:
    """
    Return an unambiguous string representation of this column.

    Returns
    -------
    representation : str
        The string representation.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> repr(column)
    "Column('test', [1, 2, 3])"
    """
    return f"Column({self._name!r}, {list(self._data)!r})"

__sizeof__()

Return the complete size of this object.

Returns:

Name Type Description
size int

Size of this object in bytes.

Source code in src/safeds/data/tabular/containers/_column.py
def __sizeof__(self) -> int:
    """
    Return the complete size of this object.

    Returns
    -------
    size:
        Size of this object in bytes.
    """
    return sys.getsizeof(self._data) + sys.getsizeof(self._name) + sys.getsizeof(self._type)

__str__()

Return a user-friendly string representation of this column.

Returns:

Name Type Description
representation str

The string representation.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> str(column)
"'test': [1, 2, 3]"
Source code in src/safeds/data/tabular/containers/_column.py
def __str__(self) -> str:
    """
    Return a user-friendly string representation of this column.

    Returns
    -------
    representation : str
        The string representation.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> str(column)
    "'test': [1, 2, 3]"
    """
    return f"{self._name!r}: {list(self._data)!r}"

all(predicate)

Check if all values have a given property.

Parameters:

Name Type Description Default
predicate Callable[[T], bool])

Callable that is used to find matches.

required

Returns:

Name Type Description
result bool

True if all match.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.all(lambda x: x < 4)
True
>>> column.all(lambda x: x < 2)
False
Source code in src/safeds/data/tabular/containers/_column.py
def all(self, predicate: Callable[[T], bool]) -> bool:
    """
    Check if all values have a given property.

    Parameters
    ----------
    predicate : Callable[[T], bool])
        Callable that is used to find matches.

    Returns
    -------
    result : bool
        True if all match.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.all(lambda x: x < 4)
    True

    >>> column.all(lambda x: x < 2)
    False
    """
    return all(predicate(value) for value in self._data)

any(predicate)

Check if any value has a given property.

Parameters:

Name Type Description Default
predicate Callable[[T], bool])

Callable that is used to find matches.

required

Returns:

Name Type Description
result bool

True if any match.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.any(lambda x: x < 2)
True
>>> column.any(lambda x: x < 1)
False
Source code in src/safeds/data/tabular/containers/_column.py
def any(self, predicate: Callable[[T], bool]) -> bool:
    """
    Check if any value has a given property.

    Parameters
    ----------
    predicate : Callable[[T], bool])
        Callable that is used to find matches.

    Returns
    -------
    result : bool
        True if any match.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.any(lambda x: x < 2)
    True

    >>> column.any(lambda x: x < 1)
    False
    """
    return any(predicate(value) for value in self._data)

correlation_with(other_column)

Calculate Pearson correlation between this and another column. Both columns have to be numerical.

Returns:

Name Type Description
correlation float

Correlation between the two columns.

Raises:

Type Description
NonNumericColumnError

If one of the columns is not numerical.

ColumnLengthMismatchError

If the columns have different lengths.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3])
>>> column2 = Column("test", [2, 4, 6])
>>> column1.correlation_with(column2)
1.0
>>> column1 = Column("test", [1, 2, 3])
>>> column2 = Column("test", [0.5, 4, -6])
>>> column1.correlation_with(column2)
-0.6404640308067906
Source code in src/safeds/data/tabular/containers/_column.py
def correlation_with(self, other_column: Column) -> float:
    """
    Calculate Pearson correlation between this and another column. Both columns have to be numerical.

    Returns
    -------
    correlation : float
        Correlation between the two columns.

    Raises
    ------
    NonNumericColumnError
        If one of the columns is not numerical.
    ColumnLengthMismatchError
        If the columns have different lengths.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3])
    >>> column2 = Column("test", [2, 4, 6])
    >>> column1.correlation_with(column2)
    1.0

    >>> column1 = Column("test", [1, 2, 3])
    >>> column2 = Column("test", [0.5, 4, -6])
    >>> column1.correlation_with(column2)
    -0.6404640308067906
    """
    if not self._type.is_numeric() or not other_column._type.is_numeric():
        raise NonNumericColumnError(
            f"Columns must be numerical. {self.name} is {self._type}, {other_column.name} is {other_column._type}.",
        )
    if self._data.size != other_column._data.size:
        raise ColumnLengthMismatchError(
            f"{self.name} is of size {self._data.size}, {other_column.name} is of size {other_column._data.size}.",
        )
    return self._data.corr(other_column._data)

get_unique_values()

Return a list of all unique values in the column.

Returns:

Name Type Description
unique_values list[T]

List of unique values in the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3, 2, 4, 3])
>>> column.get_unique_values()
[1, 2, 3, 4]
Source code in src/safeds/data/tabular/containers/_column.py
def get_unique_values(self) -> list[T]:
    """
    Return a list of all unique values in the column.

    Returns
    -------
    unique_values : list[T]
        List of unique values in the column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3, 2, 4, 3])
    >>> column.get_unique_values()
    [1, 2, 3, 4]
    """
    return list(self._data.unique())

get_value(index)

Return column value at specified index, starting at 0.

Parameters:

Name Type Description Default
index int

Index of requested element.

required

Returns:

Type Description
value

Value at index in column.

Raises:

Type Description
IndexOutOfBoundsError

If the given index does not exist in the column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.get_value(1)
2
Source code in src/safeds/data/tabular/containers/_column.py
def get_value(self, index: int) -> T:
    """
    Return column value at specified index, starting at 0.

    Parameters
    ----------
    index : int
        Index of requested element.

    Returns
    -------
    value
        Value at index in column.

    Raises
    ------
    IndexOutOfBoundsError
        If the given index does not exist in the column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.get_value(1)
    2
    """
    if index < 0 or index >= self._data.size:
        raise IndexOutOfBoundsError(index)

    return self._data[index]

has_missing_values()

Return whether the column has missing values.

Returns:

Name Type Description
missing_values_exist bool

True if missing values exist.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3, None])
>>> column1.has_missing_values()
True
>>> column2 = Column("test", [1, 2, 3])
>>> column2.has_missing_values()
False
Source code in src/safeds/data/tabular/containers/_column.py
def has_missing_values(self) -> bool:
    """
    Return whether the column has missing values.

    Returns
    -------
    missing_values_exist : bool
        True if missing values exist.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3, None])
    >>> column1.has_missing_values()
    True

    >>> column2 = Column("test", [1, 2, 3])
    >>> column2.has_missing_values()
    False
    """
    import numpy as np

    return self.any(lambda value: value is None or (isinstance(value, Number) and np.isnan(value)))

idness()

Calculate the idness of this column.

We define the idness as follows:

\[ \frac{\text{number of different values}}{\text{number of rows}} \]

Returns:

Name Type Description
idness float

The idness of the column.

Raises:

Type Description
ColumnSizeError

If this column is empty.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3])
>>> column1.idness()
1.0
>>> column2 = Column("test", [1, 2, 3, 2])
>>> column2.idness()
0.75
Source code in src/safeds/data/tabular/containers/_column.py
def idness(self) -> float:
    r"""
    Calculate the idness of this column.

    We define the idness as follows:

    $$
    \frac{\text{number of different values}}{\text{number of rows}}
    $$

    Returns
    -------
    idness : float
        The idness of the column.

    Raises
    ------
    ColumnSizeError
        If this column is empty.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3])
    >>> column1.idness()
    1.0

    >>> column2 = Column("test", [1, 2, 3, 2])
    >>> column2.idness()
    0.75
    """
    if self._data.size == 0:
        raise ColumnSizeError("> 0", "0")
    return self._data.nunique() / self._data.size

maximum()

Return the maximum value of the column. The column has to be numerical.

Returns:

Name Type Description
max float

The maximum value.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.maximum()
3
Source code in src/safeds/data/tabular/containers/_column.py
def maximum(self) -> float:
    """
    Return the maximum value of the column. The column has to be numerical.

    Returns
    -------
    max : float
        The maximum value.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.maximum()
    3
    """
    if not self._type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.max()

mean()

Return the mean value of the column. The column has to be numerical.

Returns:

Name Type Description
mean float

The mean value.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.mean()
2.0
Source code in src/safeds/data/tabular/containers/_column.py
def mean(self) -> float:
    """
    Return the mean value of the column. The column has to be numerical.

    Returns
    -------
    mean : float
        The mean value.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.mean()
    2.0
    """
    if not self._type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.mean()

median()

Return the median value of the column. The column has to be numerical.

Returns:

Name Type Description
median float

The median value.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3, 4])
>>> column.median()
2.5
>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3, 4, 5])
>>> column.median()
3.0
Source code in src/safeds/data/tabular/containers/_column.py
def median(self) -> float:
    """
    Return the median value of the column. The column has to be numerical.

    Returns
    -------
    median : float
        The median value.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3, 4])
    >>> column.median()
    2.5

    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3, 4, 5])
    >>> column.median()
    3.0
    """
    if not self._type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.median()

minimum()

Return the minimum value of the column. The column has to be numerical.

Returns:

Name Type Description
min float

The minimum value.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3, 4])
>>> column.minimum()
1
Source code in src/safeds/data/tabular/containers/_column.py
def minimum(self) -> float:
    """
    Return the minimum value of the column. The column has to be numerical.

    Returns
    -------
    min : float
        The minimum value.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3, 4])
    >>> column.minimum()
    1
    """
    if not self._type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.min()

missing_value_ratio()

Return the ratio of missing values to the total number of elements in the column.

Returns:

Name Type Description
ratio float

The ratio of missing values to the total number of elements in the column.

Raises:

Type Description
ColumnSizeError

If the column is empty.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3, 4])
>>> column1.missing_value_ratio()
0.0
>>> column2 = Column("test", [1, 2, 3, None])
>>> column2.missing_value_ratio()
0.25
Source code in src/safeds/data/tabular/containers/_column.py
def missing_value_ratio(self) -> float:
    """
    Return the ratio of missing values to the total number of elements in the column.

    Returns
    -------
    ratio : float
        The ratio of missing values to the total number of elements in the column.

    Raises
    ------
    ColumnSizeError
        If the column is empty.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3, 4])
    >>> column1.missing_value_ratio()
    0.0

    >>> column2 = Column("test", [1, 2, 3, None])
    >>> column2.missing_value_ratio()
    0.25
    """
    if self._data.size == 0:
        raise ColumnSizeError("> 0", "0")
    return self._count_missing_values() / self._data.size

mode()

Return the mode of the column.

Returns:

Name Type Description
mode list[T]

Returns a list with the most common values.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3, 3, 4])
>>> column1.mode()
[3]
>>> column2 = Column("test", [1, 2, 3, 3, 4, 4])
>>> column2.mode()
[3, 4]
Source code in src/safeds/data/tabular/containers/_column.py
def mode(self) -> list[T]:
    """
    Return the mode of the column.

    Returns
    -------
    mode: list[T]
        Returns a list with the most common values.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3, 3, 4])
    >>> column1.mode()
    [3]

    >>> column2 = Column("test", [1, 2, 3, 3, 4, 4])
    >>> column2.mode()
    [3, 4]
    """
    return self._data.mode().tolist()

none(predicate)

Check if no values has a given property.

Parameters:

Name Type Description Default
predicate Callable[[T], bool])

Callable that is used to find matches.

required

Returns:

Name Type Description
result bool

True if none match.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3])
>>> column1.none(lambda x: x < 1)
True
>>> column2 = Column("test", [1, 2, 3])
>>> column2.none(lambda x: x > 1)
False
Source code in src/safeds/data/tabular/containers/_column.py
def none(self, predicate: Callable[[T], bool]) -> bool:
    """
    Check if no values has a given property.

    Parameters
    ----------
    predicate : Callable[[T], bool])
        Callable that is used to find matches.

    Returns
    -------
    result : bool
        True if none match.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3])
    >>> column1.none(lambda x: x < 1)
    True

    >>> column2 = Column("test", [1, 2, 3])
    >>> column2.none(lambda x: x > 1)
    False
    """
    return all(not predicate(value) for value in self._data)

plot_boxplot()

Plot this column in a boxplot. This function can only plot real numerical data.

Returns:

Name Type Description
plot Image

The plot as an image.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> boxplot = column.plot_boxplot()
Source code in src/safeds/data/tabular/containers/_column.py
def plot_boxplot(self) -> Image:
    """
    Plot this column in a boxplot. This function can only plot real numerical data.

    Returns
    -------
    plot: Image
        The plot as an image.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> boxplot = column.plot_boxplot()
    """
    import matplotlib.pyplot as plt
    import seaborn as sns

    if not self.type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")

    fig = plt.figure()
    ax = sns.boxplot(data=self._data)
    ax.set(title=self.name)
    ax.set_xticks([])
    ax.set_ylabel("")
    plt.tight_layout()

    buffer = io.BytesIO()
    fig.savefig(buffer, format="png")
    plt.close()  # Prevents the figure from being displayed directly
    buffer.seek(0)
    return Image.from_bytes(buffer.read())

plot_histogram()

Plot a column in a histogram.

Returns:

Name Type Description
plot Image

The plot as an image.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> histogram = column.plot_histogram()
Source code in src/safeds/data/tabular/containers/_column.py
def plot_histogram(self) -> Image:
    """
    Plot a column in a histogram.

    Returns
    -------
    plot: Image
        The plot as an image.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> histogram = column.plot_histogram()
    """
    import matplotlib.pyplot as plt
    import seaborn as sns

    fig = plt.figure()
    ax = sns.histplot(data=self._data)
    ax.set_xticks(ax.get_xticks())
    ax.set(xlabel=self.name)
    ax.set_xticklabels(
        ax.get_xticklabels(),
        rotation=45,
        horizontalalignment="right",
    )  # rotate the labels of the x Axis to prevent the chance of overlapping of the labels
    plt.tight_layout()

    buffer = io.BytesIO()
    fig.savefig(buffer, format="png")
    plt.close()  # Prevents the figure from being displayed directly
    buffer.seek(0)
    return Image.from_bytes(buffer.read())

rename(new_name)

Return a new column with a new name.

The original column is not modified.

Parameters:

Name Type Description Default
new_name str

The new name of the column.

required

Returns:

Name Type Description
column Column

A new column with the new name.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.rename("new name")
Column('new name', [1, 2, 3])
Source code in src/safeds/data/tabular/containers/_column.py
def rename(self, new_name: str) -> Column:
    """
    Return a new column with a new name.

    The original column is not modified.

    Parameters
    ----------
    new_name : str
        The new name of the column.

    Returns
    -------
    column : Column
        A new column with the new name.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.rename("new name")
    Column('new name', [1, 2, 3])
    """
    return Column._from_pandas_series(self._data.rename(new_name), self._type)

stability()

Calculate the stability of this column.

We define the stability as follows:

\[ \frac{\text{number of occurrences of most common non-null value}}{\text{number of non-null values}} \]

The stability is not definded for a column with only null values.

Returns:

Name Type Description
stability float

The stability of the column.

Raises:

Type Description
ColumnSizeError

If the column is empty.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 1, 2, 3])
>>> column1.stability()
0.5
>>> column2 = Column("test", [1, 2, 2, 2, 3])
>>> column2.stability()
0.6
Source code in src/safeds/data/tabular/containers/_column.py
def stability(self) -> float:
    r"""
    Calculate the stability of this column.

    We define the stability as follows:

    $$
    \frac{\text{number of occurrences of most common non-null value}}{\text{number of non-null values}}
    $$

    The stability is not definded for a column with only null values.

    Returns
    -------
    stability : float
        The stability of the column.

    Raises
    ------
    ColumnSizeError
        If the column is empty.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 1, 2, 3])
    >>> column1.stability()
    0.5

    >>> column2 = Column("test", [1, 2, 2, 2, 3])
    >>> column2.stability()
    0.6
    """
    if self._data.size == 0:
        raise ColumnSizeError("> 0", "0")

    if self.all(lambda x: x is None):
        raise ValueError("Stability is not definded for a column with only null values.")

    return self._data.value_counts()[self.mode()[0]] / self._data.count()

standard_deviation()

Return the standard deviation of the column. The column has to be numerical.

Returns:

Name Type Description
sum float

The standard deviation of all values.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column1 = Column("test", [1, 2, 3])
>>> column1.standard_deviation()
1.0
>>> column2 = Column("test", [1, 2, 4, 8, 16])
>>> column2.standard_deviation()
6.099180272790763
Source code in src/safeds/data/tabular/containers/_column.py
def standard_deviation(self) -> float:
    """
    Return the standard deviation of the column. The column has to be numerical.

    Returns
    -------
    sum : float
        The standard deviation of all values.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column1 = Column("test", [1, 2, 3])
    >>> column1.standard_deviation()
    1.0

    >>> column2 = Column("test", [1, 2, 4, 8, 16])
    >>> column2.standard_deviation()
    6.099180272790763
    """
    if not self.type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.std()

sum()

Return the sum of the column. The column has to be numerical.

Returns:

Name Type Description
sum float

The sum of all values.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.sum()
6
Source code in src/safeds/data/tabular/containers/_column.py
def sum(self) -> float:
    """
    Return the sum of the column. The column has to be numerical.

    Returns
    -------
    sum : float
        The sum of all values.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.sum()
    6
    """
    if not self.type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")
    return self._data.sum()

to_html()

Return an HTML representation of the column.

Returns:

Name Type Description
output str

The generated HTML.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3])
>>> column.to_html()
'<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>'
Source code in src/safeds/data/tabular/containers/_column.py
def to_html(self) -> str:
    r"""
    Return an HTML representation of the column.

    Returns
    -------
    output : str
        The generated HTML.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3])
    >>> column.to_html()
    '<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>'
    """
    frame = self._data.to_frame()
    frame.columns = [self.name]

    return frame.to_html(max_rows=self._data.size, max_cols=1)

transform(transformer)

Apply a transform method to every data point.

The original column is not modified.

Parameters:

Name Type Description Default
transformer Callable[[T], R]

Function that will be applied to all data points.

required

Returns:

Name Type Description
transformed_column Column

The transformed column.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> price = Column("price", [4.99, 5.99, 2.49])
>>> sale = price.transform(lambda amount: amount * 0.8)
Source code in src/safeds/data/tabular/containers/_column.py
def transform(self, transformer: Callable[[T], R]) -> Column[R]:
    """
    Apply a transform method to every data point.

    The original column is not modified.

    Parameters
    ----------
    transformer : Callable[[T], R]
        Function that will be applied to all data points.

    Returns
    -------
    transformed_column: Column
        The transformed column.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> price = Column("price", [4.99, 5.99, 2.49])
    >>> sale = price.transform(lambda amount: amount * 0.8)
    """
    return Column(self.name, self._data.apply(transformer, convert_dtype=True))

variance()

Return the variance of the column. The column has to be numerical.

Returns:

Name Type Description
sum float

The variance of all values.

Raises:

Type Description
NonNumericColumnError

If the data contains non-numerical data.

Examples:

>>> from safeds.data.tabular.containers import Column
>>> column = Column("test", [1, 2, 3, 4, 5])
>>> column.variance()
2.5
Source code in src/safeds/data/tabular/containers/_column.py
def variance(self) -> float:
    """
    Return the variance of the column. The column has to be numerical.

    Returns
    -------
    sum : float
        The variance of all values.

    Raises
    ------
    NonNumericColumnError
        If the data contains non-numerical data.

    Examples
    --------
    >>> from safeds.data.tabular.containers import Column
    >>> column = Column("test", [1, 2, 3, 4, 5])
    >>> column.variance()
    2.5
    """
    if not self.type.is_numeric():
        raise NonNumericColumnError(f"{self.name} is of type {self._type}.")

    return self._data.var()