一个基于列值的新列中对应列的添加值

Posted 2023-03-11

【中文标题】一个基于列值的新列中对应列的添加值

【英文标题】：one Column value based add value of corresponding column in new column

【发布时间】：2019-08-06 01:52:27

【问题描述】：

我有 2 个两个数据框 df1 和 df2 ，在 df2 我有 4 列。我想如果 df2 column1 值为 0 ，代码应在 df1 中添加相应的 3 列值，列名为 col2_0 、col3_0 和 col4_0 （注意：此过程也需要为值 -1、-2、-3、-4 执行， -5），如果可以解决这个问题，但我正在寻找熊猫简单快捷的方法来处理这个问题

这里是 df2

【问题讨论】：

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【参考方案1】：

我将使用一个最初为空的 df1 和一些额外的行来做这个例子：

df2 = pd.DataFrame('#timestamp':[-5,-4,-3,-2,-1,0],
                    'grid_U1':   [413.714,413.797,413.926,414.037,414.066,414.064],
                    'grid_U2':   [415.796,415.909,416.117,416.093,416.163,416.183],
                    'grid_U3':   [416.757,416.853,417.09,417.158,417.175,417.085])

df1 = pd.DataFrame(index=range(0,10), columns=['col2_0','col3_0','col4_0'])

如果你想匹配行索引（从 df2 中的给定行号复制到 df1 中的相同行号），那么你可以使用这个：

In [403]: df1[['col2_0','col3_0','col4_0']] = df2[df2['#timestamp'].isin(range(-5,1))][['grid_U1','grid_U2','grid_U3']]

In [404]: df1
Out[404]: 
    col2_0   col3_0   col4_0
0  413.714  415.796  416.757
1  413.797  415.909  416.853
2  413.926  416.117  417.090
3  414.037  416.093  417.158
4  414.066  416.163  417.175
5  414.064  416.183  417.085
6      NaN      NaN      NaN
7      NaN      NaN      NaN
8      NaN      NaN      NaN
9      NaN      NaN      NaN

我将通过选择顶部未出现的时间戳值来确认这是匹配的行号：

In [405]: df1[['col2_0','col3_0','col4_0']] = df2[df2['#timestamp'].isin([-3,-1])][['grid_U1','grid_U2','grid_U3']]

In [406]: df1
Out[406]: 
    col2_0   col3_0   col4_0
0      NaN      NaN      NaN
1      NaN      NaN      NaN
2  413.926  416.117  417.090
3      NaN      NaN      NaN
4  414.066  416.163  417.175
5      NaN      NaN      NaN
6      NaN      NaN      NaN
7      NaN      NaN      NaN
8      NaN      NaN      NaN
9      NaN      NaN      NaN

如果您想改为从 df1 的顶部填写，您可以在末尾添加对 reset_index 的调用（您需要 drop=True 以避免在其中添加额外的索引列）：

In [412]: df1[['col2_0','col3_0','col4_0']] = df2[df2['#timestamp'].isin([-3,-1])][['grid_U1','grid_U2','grid_U3']].reset_index(drop=True)

In [413]: df1
Out[413]: 
    col2_0   col3_0   col4_0
0  413.926  416.117  417.090
1  414.066  416.163  417.175
2      NaN      NaN      NaN
3      NaN      NaN      NaN
4      NaN      NaN      NaN
5      NaN      NaN      NaN
6      NaN      NaN      NaN
7      NaN      NaN      NaN
8      NaN      NaN      NaN
9      NaN      NaN      NaN