Grouping pandas series based on condition
Question:
I have a Pandas df with one column the following values.
Data
0 A
1 A
2 B
3 A
4 A
5 A
6 B
7 A
8 A
9 B
I want to try and group these values as such, for each encounter of Value B, i want the the group value to be changed as follows
Data Group
0 A 1
1 A 1
2 B 1
3 A 2
4 A 2
5 A 2
6 B 2
7 A 3
8 A 3
9 B 3
How can this be achieved using pandas inbuilt. in some way to create any helper columns to facilitate the mentioned task.
Answers:
You can try cumsum after comparing if the series equals B and then shift 1 place to include B in the group:
df['Data'].eq('B').shift(fill_value=False).cumsum().add(1)
0 1
1 1
2 1
3 2
4 2
5 2
6 2
7 3
8 3
9 3
I notice the group here is descending. But if you only need to split the group by Data, the output should be same:
s=df.Data.eq('B').iloc[::-1].cumsum()
s
9 1
8 1
7 1
6 2
5 2
4 2
3 2
2 3
1 3
0 3
Name: Data, dtype: int64
You can also use pandas.core.groupby.GroupBy.cumcount() in combination with pandas.DataFrame.bfill() method like this.
>>> df['Group'] = (df[df.Data == 'B'].groupby('Data').Data.cumcount() + 1)
>>> df['Group'] = df.Group.bfill()
>>> print(df)
Data Group
0 A 1.0
1 A 1.0
2 B 1.0
3 A 2.0
4 A 2.0
5 A 2.0
6 B 2.0
7 A 3.0
8 B 3.0
I have a Pandas df with one column the following values.
Data
0 A
1 A
2 B
3 A
4 A
5 A
6 B
7 A
8 A
9 B
I want to try and group these values as such, for each encounter of Value B, i want the the group value to be changed as follows
Data Group
0 A 1
1 A 1
2 B 1
3 A 2
4 A 2
5 A 2
6 B 2
7 A 3
8 A 3
9 B 3
How can this be achieved using pandas inbuilt. in some way to create any helper columns to facilitate the mentioned task.
You can try cumsum after comparing if the series equals B and then shift 1 place to include B in the group:
df['Data'].eq('B').shift(fill_value=False).cumsum().add(1)
0 1
1 1
2 1
3 2
4 2
5 2
6 2
7 3
8 3
9 3
I notice the group here is descending. But if you only need to split the group by Data, the output should be same:
s=df.Data.eq('B').iloc[::-1].cumsum()
s
9 1
8 1
7 1
6 2
5 2
4 2
3 2
2 3
1 3
0 3
Name: Data, dtype: int64
You can also use pandas.core.groupby.GroupBy.cumcount() in combination with pandas.DataFrame.bfill() method like this.
>>> df['Group'] = (df[df.Data == 'B'].groupby('Data').Data.cumcount() + 1)
>>> df['Group'] = df.Group.bfill()
>>> print(df)
Data Group
0 A 1.0
1 A 1.0
2 B 1.0
3 A 2.0
4 A 2.0
5 A 2.0
6 B 2.0
7 A 3.0
8 B 3.0