How Can I Efficiently Group Consecutive Values in a Pandas DataFrame Column?

Grouping Consecutive Values in Pandas DataFrames

In a DataFrame, you may encounter a column containing consecutive values that you need to group together. For instance, consider the following column with values:

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

To efficiently group these values into desired groups like:

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

follow these steps using the Pandas library:

Solution using Custom Series Grouping

You can leverage a custom Series to achieve this grouping. Here's the approach:

import pandas as pd

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

# Use ne() and cumsum() to create grouping indicator
ind = df['a'].ne(df['a'].shift()).cumsum()

# Group by this indicator
for i, g in df.groupby(ind):
    # Print grouping key
    print(i)
    
    # Print rows in group
    print(g)
    
    # Convert values to list for display
    print(g.a.tolist())

This code will output the desired groupings and values:

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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