如何将项目附加到 Pandas 中不同列的列表中 [英] How to append item to list of different column in Pandas

问题描述

我有一个如下所示的数据框:

I have a dataframe that looks like this:

dic = {'A':['PINCO','PALLO','CAPPO','ALLOP'],
       'B':['KILO','KULO','FIGA','GAGO'],
       'C':[['CAL','GOL','TOA','PIA','STO'],
            ['LOL','DAL','ERS','BUS','TIS'],
            ['PIS','IPS','ZSP','YAS','TUS'],
            []]}
df1 = pd.DataFrame(dic)

我的目标是为每一行插入 A 的元素作为列 C 中包含的列表的第一项.同时我想将 B 的元素设置为 C 中包含的列表的最后一项.

My goal is to insert for each row the element of A as first item of the list contained in column C. At the same time I want to set the element of B as last item of the list contained in C.

我能够通过使用以下代码行来实现我的目标:

I was able to achieve my goal by using the following lines of code:

for index, row in df1.iterrows():
    try:
        row['C'].insert(0,row['A'])
        row['C'].append(row['B'])
    except:
        pass

是否有更优雅、更有效的方法来实现我的目标，也许是使用一些 Pandas 功能?我想尽可能避免 for 循环.

Is there a more elegant and efficient way to achieve my goal maybe using some Pandas function? I would like to avoid for loops possibly.

推荐答案

一个好的一般规则是尽可能避免使用 apply 和 axis=1 作为迭代在行上是昂贵的

A good general rule is to avoid using apply with axis=1 if at all possible as iterating over the rows is expenisve

您可以使用 map 将 A 列和 B 列中的每个元素转换为列表，然后对各行求和.

You can convert each element in columns A and B to a list with map and then sum across the rows.

df1['A'] = df1.A.map(lambda x: [x])  
df1['B'] = df1.B.map(lambda x: [x])
df1.sum(1)
CPU times: user 3.07 s, sys: 207 ms, total: 3.27 s

替代方法是使用轴=1 的 apply 在我的计算机上运行 100 万行时慢 15 倍

The alternative is to use apply with axis=1 which ran 15 times slower on my computer on 1 million rows

df1.apply(lambda x: [x['A']] + x['C'] + [x['B']], 1)
CPU times: user 48.5 s, sys: 119 ms, total: 48.6 s

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