How to Group Consecutive Values in a Pandas DataFrame?

Grouping Consecutive Values in a Pandas DataFrame

In data analysis, we often encounter situations where data is ordered and there's a need to group consecutive values together. This task can be achieved in pandas using customized grouping techniques.

Suppose we have a DataFrame with a column named 'a' containing the following values:

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

Our goal is to group these values into consecutive blocks, like so:

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

To accomplish this, we can employ the following steps:

1. Create a custom Series: We create a new Series using the ne and shift functions. This Series returns a Boolean value indicating whether the current value is different from the previous value.
2. Use the Series for grouping: We pass the custom Series to the groupby function. This groups the data by the consecutive blocks.
3. Iterate over the grouped data: We iterate over the grouped data and print the index, the grouped DataFrame, and a list of the values in the 'a' column for each group.

Here's the code implementing these steps:

import pandas as pd

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

custom_series = df['a'].ne(df['a'].shift()).cumsum()
print(custom_series)

for i, g in df.groupby(custom_series):
    print(i)
    print(g)
    print(g.a.tolist())

This outputs the desired grouping:

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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