pandas 群使用时间频率 [英] Pandas Groupby using time frequency
问题描述
我的问题是关于大熊猫数据框的分组依据.样本数据集如下所示:
My question is regarding a groupby of pandas dataframe. A sample dataset would look like this:
cust_id | date | category
A0001 | 20/02/2016 | cat1
A0001 | 24/02/2016 | cat2
A0001 | 02/03/2016 | cat3
A0002 | 03/04/2015 | cat2
现在,我要对cust_id进行分组,然后查找彼此之间30天内发生的事件,并为这些事件编制类别列表.到目前为止,我已经想到的是按以下方式使用pd.grouper.
Now I want to groupby cust_id and then find events that occur within 30days of each other and compile the list of categories for those. What I have figured so far is to use pd.grouper in the following manner.
df.groupby(['cust_id', pd.Grouper(key='date', freq='30D')])['category'].apply(list)
但是,这并未将[cat1,cat2,cat3]放在A0001的同一列表中.对于我在做错事情或如何去做自己需要做的事情的任何帮助,将不胜感激.
But this isn't putting [cat1, cat2, cat3] in the same list for A0001. Any help on what I'm doing wrong or how I can go about doing what I need would be most appreciated.
我想要的结果应如下所示:
The results I want should look something like this:
A0001 | [cat1, cat2, cat3]
A0002 | [cat2]
预先感谢
按照Wen的回答,我尝试了并将其用于此最低限度的示例,这对提供不具有代表性的最低限度的示例不利.此示例可以针对0.20.3和0.23.0版本的熊猫重新创建.
Following Wen's answer, I tried and it worked for this minimum example, my bad for providing a minimum example that wasn't representative. This can be recreated with this example for both 0.20.3 and 0.23.0 versions of pandas.
cust_id date category
0 A0001 2015-02-02 cat5
1 A0002 2015-02-03 cat1
2 A0001 2016-02-20 cat1
3 A0001 2016-02-24 cat2
4 A0001 2016-03-02 cat3
5 A0003 2016-09-09 cat2
6 A0003 2016-08-21 cat5
我得到的答案是:
cust_id
A0001 [cat5]
A0001 [cat1, cat2]
A0001 [cat3]
A0002 [cat1]
A0003 [cat5]
Name: category, dtype: object
对于最初的困惑,我深表歉意!
My apologies for the initial confusion!
推荐答案
您的代码对我有用
df.date=pd.to_datetime(df.date)
df.groupby(['cust_id', pd.Grouper(key='date', freq='30D')])['category'].apply(list).reset_index(level=1,drop=True)
Out[215]:
cust_id
A0001 [ cat1, cat2, cat3]
A0002 [ cat2]
Name: category, dtype: object
这篇关于 pandas 群使用时间频率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!