Pandas DataFrame.Assign参数

Question

问题

如何使用assign返回添加了多个新列的原始数据帧的副本？

df = pd.DataFrame({'A': range(1, 5), 'B': range(11, 15)})
>>> df.assign({'C': df.A.apply(lambda x: x ** 2), 'D': df.B * 2})
   A   B   C   D
0  1  11   1  22
1  2  12   4  24
2  3  13   9  26
3  4  14  16  28

背景

Pandas中的assign函数获取连接到新分配列的相关数据帧的副本，例如。

df = df.assign(C=df.B * 2)
>>> df
   A   B   C
0  1  11  22
1  2  12  24
2  3  13  26
3  4  14  28

该函数的0.19.2文档暗示可以向DataFrame添加多个列。

关键字是列名。

函数的源代码声明它接受字典：

def assign(self, **kwargs):
    """
    .. versionadded:: 0.16.0
    Parameters
    ----------
    kwargs : keyword, value pairs
        keywords are the column names. If the values are callable, they are computed 
        on the DataFrame and assigned to the new columns. If the values are not callable, 
        (e.g. a Series, scalar, or array), they are simply assigned.

    Notes
    -----
    Since ``kwargs`` is a dictionary, the order of your
    arguments may not be preserved. The make things predicatable,
    the columns are inserted in alphabetical order, at the end of
    your DataFrame. Assigning multiple columns within the same
    ``assign`` is possible, but you cannot reference other columns
    created within the same ``assign`` call.
    """

    data = self.copy()

    # do all calculations first...
    results = {}
    for k, v in kwargs.items():

        if callable(v):
            results[k] = v(data)
        else:
            results[k] = v

    # ... and then assign
    for k, v in sorted(results.items()):
        data[k] = v

    return data

Answer 1

可以通过将每个新列作为关键字参数提供来创建多个列：

df = df.assign(C=df['A']**2, D=df.B*2)

我使用**将字典作为关键字参数解包，从而使示例字典工作：

df = df.assign(**{'C': df.A.apply(lambda x: x ** 2), 'D': df.B * 2})

看起来assign应该能够获取字典，但是根据您发布的源代码，它看起来不受支持。

结果输出：

   A   B   C   D
0  1  11   1  22
1  2  12   4  24
2  3  13   9  26
3  4  14  16  28