Method insert

pandas/core/frame.py:5572–5649

Insert column into DataFrame at specified location. Raises a ValueError if `column` is already contained in the DataFrame, unless `allow_duplicates` is set to True. Parameters ---------- loc : int Insertion index. Must verify 0 <= loc <=

(
        self,
        loc: int,
        column: Hashable,
        value: object,
        allow_duplicates: bool | lib.NoDefault = lib.no_default,
    )

        return self._mgr._get_data_subset_indices(predicate)

    def insert(
        self,
        loc: int,
        column: Hashable,
        value: object,
        allow_duplicates: bool | lib.NoDefault = lib.no_default,
    ) -> None:
        """
        Insert column into DataFrame at specified location.

        Raises a ValueError if `column` is already contained in the DataFrame,
        unless `allow_duplicates` is set to True.

        Parameters
        ----------
        loc : int
            Insertion index. Must verify 0 <= loc <= len(columns).
        column : str, number, or hashable object
            Label of the inserted column.
        value : Scalar, Series, or array-like
            Content of the inserted column.
        allow_duplicates : bool, optional, default lib.no_default
            Allow duplicate column labels to be created.

        See Also
        --------
        Index.insert : Insert new item by index.

        Examples
        --------
        >>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
        >>> df
           col1  col2
        0     1     3
        1     2     4
        >>> df.insert(1, "newcol", [99, 99])
        >>> df
           col1  newcol  col2
        0     1      99     3
        1     2      99     4
        >>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
        >>> df
           col1  col1  newcol  col2
        0   100     1      99     3
        1   100     2      99     4

        Notice that pandas uses index alignment in case of `value` from type `Series`:

        >>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
        >>> df
           col0  col1  col1  newcol  col2
        0   NaN   100     1      99     3
        1   5.0   100     2      99     4
        """
        if allow_duplicates is lib.no_default:
            allow_duplicates = False
        if allow_duplicates and not self.flags.allows_duplicate_labels:
            raise ValueError(