Classe « DataFrame »

Méthode pandas.DataFrame.append

Signature de la méthode append

def append(self, other, ignore_index=False, verify_integrity=False, sort=False) -> 'DataFrame' 

Description

append.__doc__

        Append rows of `other` to the end of caller, returning a new object.

        Columns in `other` that are not in the caller are added as new columns.

        Parameters
        ----------
        other : DataFrame or Series/dict-like object, or list of these
            The data to append.
        ignore_index : bool, default False
            If True, the resulting axis will be labeled 0, 1, ..., n - 1.
        verify_integrity : bool, default False
            If True, raise ValueError on creating index with duplicates.
        sort : bool, default False
            Sort columns if the columns of `self` and `other` are not aligned.

            .. versionchanged:: 1.0.0

                Changed to not sort by default.

        Returns
        -------
        DataFrame

        See Also
        --------
        concat : General function to concatenate DataFrame or Series objects.

        Notes
        -----
        If a list of dict/series is passed and the keys are all contained in
        the DataFrame's index, the order of the columns in the resulting
        DataFrame will be unchanged.

        Iteratively appending rows to a DataFrame can be more computationally
        intensive than a single concatenate. A better solution is to append
        those rows to a list and then concatenate the list with the original
        DataFrame all at once.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
        >>> df
           A  B
        0  1  2
        1  3  4
        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
        >>> df.append(df2)
           A  B
        0  1  2
        1  3  4
        0  5  6
        1  7  8

        With `ignore_index` set to True:

        >>> df.append(df2, ignore_index=True)
           A  B
        0  1  2
        1  3  4
        2  5  6
        3  7  8

        The following, while not recommended methods for generating DataFrames,
        show two ways to generate a DataFrame from multiple data sources.

        Less efficient:

        >>> df = pd.DataFrame(columns=['A'])
        >>> for i in range(5):
        ...     df = df.append({'A': i}, ignore_index=True)
        >>> df
           A
        0  0
        1  1
        2  2
        3  3
        4  4

        More efficient:

        >>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
        ...           ignore_index=True)
           A
        0  0
        1  1
        2  2
        3  3
        4  4