How to Group Consecutive Values in a Pandas DataFrame Column?

Grouping Consecutive Values in a Pandas DataFrame

This question seeks a solution to group consecutive values in a DataFrame column. Consider the following DataFrame with the column 'a':

   a
0  1
1  1
2 -1
3  1
4 -1
5 -1

The goal is to group these values into sublists representing consecutive sequences, as shown below:

[1, 1]
[-1]
[1]
[-1, -1]

Solution Using Custom Series

To achieve this, we can leverage custom Series to identify consecutive value breaks. The following code demonstrates this approach:

df = pd.DataFrame({'a': [1, 1, -1, 1, -1, -1]})
print(df)

# Create a series that identifies consecutive value breaks
breaks = df['a'].ne(df['a'].shift()).cumsum()
print(breaks)

# Group the DataFrame by the breaks series
for i, g in df.groupby(breaks):
    print(i)
    print(g)
    print(g.a.tolist())

The output shows the consecutive value groupings as required:

1
   a
0  1
1  1
[1, 1]
2
   a
2 -1
[-1]
3
   a
3  1
[1]
4
   a
4 -1
5 -1
[-1, -1]

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