pandas :按列A分组，并从其他列中列出元组? [英] Pandas: groupby column A and make lists of tuples from other columns?

问题描述

我想将用户交易汇总到熊猫列表中.我不知道如何制作一个包含多个字段的列表.例如，

I would like to aggregate user transactions into lists in pandas. I can't figure out how to make a list comprised of more than one field. For example,

df = pd.DataFrame({'user':[1,1,2,2,3], 
                   'time':[20,10,11,18, 15], 
                   'amount':[10.99, 4.99, 2.99, 1.99, 10.99]})

看起来像

    amount  time  user
0   10.99    20     1
1    4.99    10     1
2    2.99    11     2
3    1.99    18     2
4   10.99    15     3

如果我愿意

print(df.groupby('user')['time'].apply(list))

我知道

user
1    [20, 10]
2    [11, 18]
3        [15]

但如果我这样做

df.groupby('user')[['time', 'amount']].apply(list)

我知道

user
1    [time, amount]
2    [time, amount]
3    [time, amount]

多亏了下面的答案，我才知道我可以做到

Thanks to an answer below, I learned I can do this

df.groupby('user').agg(lambda x: x.tolist()))

获得

             amount      time
user                         
1     [10.99, 4.99]  [20, 10]
2      [2.99, 1.99]  [11, 18]
3           [10.99]      [15]

但是我要按照相同的顺序对时间和金额进行排序-因此我可以按顺序处理每个用户的交易.

but I'm going to want to sort time and amounts in the same order - so I can go through each users transactions in order.

我一直在寻找一种产生这种方式的方法:

I was looking for a way to produce this:

             amount-time-tuple
user                         
1     [(20, 10.99), (10, 4.99)]
2     [(11,  2.99), (18, 1.99)]
3     [(15, 10.99)]

但是也许有一种方法可以在不纠缠"两列的情况下进行排序?

but maybe there is a way to do the sort without "tupling" the two columns?

推荐答案

apply(list)将考虑系列索引而不是值.我认为您正在寻找

apply(list) will consider the series index not the values .I think you are looking for

df.groupby('user')[['time', 'amount']].apply(lambda x: x.values.tolist())

user
1    [[23.0, 2.99], [50.0, 1.99]]
2                  [[12.0, 1.99]]

这篇关于 pandas :按列A分组，并从其他列中列出元组?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！