[英]How to merge rows in a Dataframe based on a previous row?
I have a sequentially ordered dataframe that represent two events measured over time - the measurements are the start and end times of the event.我有一个按顺序排列的数据帧,它表示随时间测量的两个事件 - 测量值是事件的开始时间和结束时间。 They should be ordered in an ABABAB sequence, but in some cases I may have consecutive events of the same type (ie ABABAABABB).
它们应该按 ABABAB 序列排序,但在某些情况下,我可能有相同类型的连续事件(即 ABABAABABB)。 I am looking for a way to check the event label (A or B) in each row with the previous event label, and if they are the same to merge the rows in such a way that I maintain the start time of the first event and the end time of the second event.
我正在寻找一种方法来检查每一行中的事件标签(A 或 B)与前一个事件标签,如果它们相同,则以保持第一个事件的开始时间的方式合并行,并且第二个事件的结束时间。 Consider the following:
考虑以下:
myDF = pd.DataFrame({"Event": ["A","B","A","A","B","B","A"],
"Start": [1,3,5,7,9,11,13],
"End": [2,4,6,8,10,12,14]})
What I currently have...
我目前拥有的...
==============================
Event Start End
==============================
A 1 2
B 3 4
A 5 6
A 7 8
B 9 10
B 11 12
A 13 14
==============================
What I need...
我需要的...
Note: The two A events at index position 2-3 have been merged into one, as have the two B events originally at positions 4-5.
注意:索引位置 2-3 处的两个 A 事件已合并为一个,就像最初位于位置 4-5 处的两个 B 事件一样。
==============================
Event Start End
==============================
A 1 2
B 3 4
A 5 8
B 9 12
A 13 14
==============================
I had initially thought to use groupby
but I don't think this right as this will group over the entire dataframe.我最初想使用
groupby
但我认为这不正确,因为这将对整个数据帧进行分组。 Similarly I have tried using iteritems
but have not had any success.同样,我尝试使用
iteritems
但没有任何成功。 Apologies for the lack of code but I'm at a loss as to how to approach the problem.为缺少代码道歉,但我不知道如何解决这个问题。
You can use GroupBy.agg
with first
and last
.您可以将
GroupBy.agg
与first
和last
。
g = df["Event"].ne(df["Event"].shift()).cumsum()
df.groupby(g, as_index = False).agg({
"Event": "first",
"Start": "first",
"End": "last"
})
Event Start End
0 A 1 2
1 B 3 4
2 A 5 8
3 B 9 12
4 A 13 14
Another way can be另一种方式可以是
for i in range(1,myDF.shape[0]):
if myDF['Event'][i] == myDF['Event'][i-1]:
myDF.loc[i, ('Start')]= min(myDF['Start'][i],myDF['Start'][i-1])
myDF.loc[i, ('End')]= max(myDF['End'][i],myDF['End'][i-1])
myDF.drop([i-1],inplace=True)
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