Predictive Hacks

Count the Consecutive Events in Python

This short post is about finding an efficient way to count consecutive events. We are going to represent a straightforward practical example:

import pandas as pd
df = pd.DataFrame({'Score':['win', 'loss', 'loss', 'loss', 'win', 'win', 'win', 'win', 'win', 'loss', 'win', 'loss', 'loss']})
df
   Score
0    win
1   loss
2   loss
3   loss
4    win
5    win
6    win
7    win
8    win
9   loss
10   win
11  loss
12  loss

And with the following “hack” we are going to get the column of the “Streak”

df['Streak'] = df['Score'].groupby((df['Score'] != df['Score'].shift()).cumsum()).cumcount() + 1
df
   Score  Streak
0    win       1
1   loss       1
2   loss       2
3   loss       3
4    win       1
5    win       2
6    win       3
7    win       4
8    win       5
9   loss       1
10   win       1
11  loss       1
12  loss       2

Count the Consecutive Events within Group

Let’s say that we have the same example as above, but we want to do the same exercise within group.

df = pd.DataFrame({'Group':['A','A', 'A','A','A','A','B','B','B','B','B','B','B'],
                   'Score':['win', 'loss', 'loss', 'loss', 'win', 'win', 'win', 'win', 'win', 'loss', 'win', 'loss', 'loss']})
df
   Group Score
0      A   win
1      A  loss
2      A  loss
3      A  loss
4      A   win
5      A   win
6      B   win
7      B   win
8      B   win
9      B  loss
10     B   win
11     B  loss
12     B  loss
df['Streak'] = df['Score'].groupby((df['Score'] != df.groupby(['Group'])['Score'].shift()).cumsum()).cumcount() + 1
df
   Group Score  Streak
0      A   win       1
1      A  loss       1
2      A  loss       2
3      A  loss       3
4      A   win       1
5      A   win       2
6      B   win       1
7      B   win       2
8      B   win       3
9      B  loss       1
10     B   win       1
11     B  loss       1
12     B  loss       2

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