How to Group Consecutive Identical Values in a Pandas DataFrame?

Grouping Consecutive Values in Pandas DataFrame

In pandas, grouping data can be a crucial task for data analysis and manipulation. When dealing with sequential data, it often becomes necessary to group consecutive values that share the same characteristics.

Problem:

Given a DataFrame with a column containing consecutive values, group these values into contiguous segments where the values remain the same.

For instance, if the original column contains the following values:

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

The desired output would be:

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

Solution:

To achieve this grouping, pandas provides a flexible approach using the groupby function. However, simply using groupby on the column itself will not suffice. Instead, we need to create a custom Series that identifies the boundaries of the segments.

The following code demonstrates how to implement this solution:

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

# Create a custom Series that identifies segment boundaries
boundaries = df['a'].ne(df['a'].shift()).cumsum()

# Group data by the segment boundaries
for i, g in df.groupby(boundaries):
    print(i)
    print(g)
    print(g.a.tolist())

This approach assigns sequential numbers to consecutive segments where values remain unchanged. Using these numbers, the data is then grouped accordingly, and each group is printed out along with its corresponding consecutive values.

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