pandas修改DataFrame中的列名&调整列的顺序-蒲公英云

pandas修改DataFrame中的列名&调整列的顺序

以你之姓@ 2023-06-16 04:52 92阅读 0赞

修改列名：

直接调用接口：

df.rename()

看一下接口中的定义：

def rename(self, *args, **kwargs):
        """
        Alter axes labels.
        Function / dict values must be unique (1-to-1). Labels not contained in
        a dict / Series will be left as-is. Extra labels listed don't throw an
        error.
        See the :ref:`user guide <basics.rename>` for more.
        Parameters
        ----------
        mapper, index, columns : dict-like or function, optional
            dict-like or functions transformations to apply to
            that axis' values. Use either ``mapper`` and ``axis`` to
            specify the axis to target with ``mapper``, or ``index`` and
            ``columns``.
        axis : int or str, optional
            Axis to target with ``mapper``. Can be either the axis name
            ('index', 'columns') or number (0, 1). The default is 'index'.
        copy : boolean, default True
            Also copy underlying data
        inplace : boolean, default False
            Whether to return a new DataFrame. If True then value of copy is
            ignored.
        level : int or level name, default None
            In case of a MultiIndex, only rename labels in the specified
            level.
        Returns
        -------
        renamed : DataFrame
        See Also
        --------
        pandas.DataFrame.rename_axis
        Examples
        --------
        ``DataFrame.rename`` supports two calling conventions
        * ``(index=index_mapper, columns=columns_mapper, ...)``
        * ``(mapper, axis={'index', 'columns'}, ...)``
        We *highly* recommend using keyword arguments to clarify your
        intent.
        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
        >>> df.rename(index=str, columns={"A": "a", "B": "c"})
           a  c
        0  1  4
        1  2  5
        2  3  6
        >>> df.rename(index=str, columns={"A": "a", "C": "c"})
           a  B
        0  1  4
        1  2  5
        2  3  6
        Using axis-style parameters
        >>> df.rename(str.lower, axis='columns')
           a  b
        0  1  4
        1  2  5
        2  3  6
        >>> df.rename({1: 2, 2: 4}, axis='index')
           A  B
        0  1  4
        2  2  5
        4  3  6
        """
        axes = validate_axis_style_args(self, args, kwargs, 'mapper', 'rename')
        kwargs.update(axes)
        # Pop these, since the values are in `kwargs` under different names
        kwargs.pop('axis', None)
        kwargs.pop('mapper', None)
        return super(DataFrame, self).rename(**kwargs)

注意：

一个*，输入可以是数组、元组，会把输入的数组或元组拆分成一个个元素。
两个*，输入必须是字典格式

示例：

>>>import pandas as pd
>>>a = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6], 'C':[7,8,9]})
>>> a 
   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9
#将列名A替换为列名a，B改为b，C改为c
>>>a.rename(columns={'A':'a', 'B':'b', 'C':'c'}, inplace = True)
>>>a
   a  b  c
0  1  4  7
1  2  5  8
2  3  6  9

调整列的顺序：

如：

>>> import pandas
>>> dict_a = {'user_id':['webbang','webbang','webbang'],'book_id':['3713327','4074636','26873486'],'rating':['4','4','4'],
'mark_date':['2017-03-07','2017-03-07','2017-03-07']}
>>> df = pandas.DataFrame(dict_a) # 从字典创建DataFrame
>>> df # 创建好的df列名默认按首字母顺序排序，和字典中的先后顺序并不一样，字典中'user_id','book_id','rating','mark_date'
 book_id mark_date rating user_id
0 3713327 2017-03-07  4 webbang
1 4074636 2017-03-07  4 webbang
2 26873486 2017-03-07  4 webbang

直接修改列名：

>>> df = df[['user_id','book_id','rating','mark_date']] # 调整列顺序为'user_id','book_id','rating','mark_date'
>>> df
 user_id book_id rating mark_date
0 webbang 3713327  4 2017-03-07
1 webbang 4074636  4 2017-03-07
2 webbang 26873486  4 2017-03-07