问题描述
如何在 Pandas 数据框中进行累积连接?我发现在 R 中有很多解决方案,但在 python 中找不到.
How to do a cumulative concatenate in pandas dataframe?I found there are a number of solutions in R, but can't find it in python.
问题在于:假设我们有一个数据框:列:date
和 name
:
Here is the problem: suppose we have a dataframe: with columns: date
and name
:
import pandas as pd
d = {'date': [1,1,2,2,3,3,3,4,4,4], 'name':['A','B','A','C','A','B','B','A','B','C']}
df = pd.DataFrame(data=d)
我想获取CUM_CONCAT
,这是一个累积连接分组日期:
I want to get CUM_CONCAT
, which is a cumulative concatenate groupby date:
date name CUM_CONCAT
0 1 A [A]
1 1 B [A,B]
2 2 A [A]
3 2 C [A,C]
4 3 A [A]
5 3 B [A,B]
6 3 B [A,B,B]
7 4 A [A]
8 4 B [A,B]
9 4 C [A,B,C]
到目前为止我已经尝试过:
so far i've tried:
temp = df.groupby(['date'])['name'].apply(list)
df = df.join(temp, 'date', rsuffix='_cum_concat')
我得到的是:
date name CUM_CONCAT
0 1 A [A,B]
1 1 B [A,B]
2 2 A [A,C]
3 2 C [A,C]
4 3 A [A,B,B]
5 3 B [A,B,B]
6 3 B [A,B,B]
7 4 A [A,B,C]
8 4 B [A,B,C]
9 4 C [A,B,C]
我知道有 .rolling
和 cumsum
函数,它们与我需要的类似,但它们主要用于累积和而不是用于 concat.
I know there are .rolling
and cumsum
functions, which are similar to what i need, but they are mainly for cumulative sum not for concat.
任何帮助将不胜感激!!!
Any help will be appreciated!!!
推荐答案
我想出了如下解决方案:
I have came up with a solution as follow:
就运行时间而言,两种解决方案(我和@Wen-Ben)看起来相似,他的代码更短
In terms of time taken to run, both solutions (me and @Wen-Ben) seem similar, his code is shorter tho
from itertools import accumulate
def cum_concat(x):
return list(accumulate(x))
f = lambda x: cum_concat([[i] for i in x])
b =df.groupby(['date'])['name'].apply(f)
df['CUM_CONCAT']=[item for sublist in b for item in sublist]
df
Out:
date name CUM_CONCAT
0 1 A [A]
1 1 B [A, B]
2 2 A [A]
3 2 C [A, C]
4 3 A [A]
5 3 B [A, B]
6 3 B [A, B, B]
7 4 A [A]
8 4 B [A, B]
9 4 C [A, B, C]
这篇关于python: pandas 数据框中的累积连接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!