Introduction to Pandas: GroupBy Aggregation and Row Collapsing

Pandas is a powerful library in Python used for data manipulation and analysis. One of its most commonly used features is the groupby function, which allows you to group a DataFrame by one or more columns and perform aggregations on each group. In this article, we will explore how to use the groupby function to collapse the first n rows in each group by aggregation.

Understanding GroupBy Aggregation

GroupBy aggregation is a process where you take a DataFrame and split it into groups based on one or more columns. Then, for each group, you perform an operation (such as summing or averaging) on all the rows in that group. The results of these operations are then combined to form a new DataFrame.

In the provided Stack Overflow post, we have a DataFrame with multiple columns and a grouping column (‘id’). We want to collapse the first three rows in each group by aggregation.

Using groupby() and aggregate()

The groupby() function is used to split the DataFrame into groups. Once you have grouped your data, you can use the aggregate() function to perform operations on each group.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
data = {'id': [283, 283, 283, 283, 283, 283, 283, 756, 756, 756],
        'type': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'X', 'X', 'X'],
        'grp_idx': [1, 2, 3, 4, 5, 6, 7, 1, 2, 3],
        'col_1': [2, 4, 6, 8, 10, 12, 14, 5, 10, 15],
        'col_2': [3, 6, 9, 12, 15, 18, 21, 1, 2, 3],
        'flag': [0, 0, 0, 0, 0, 0, 1, 0, 0, 1]}
df = pd.DataFrame(data)

# Group by 'id' and aggregate the first three rows
column_funcs = {'col_1': lambda x: x.head(3).sum(),
                'col_2': lambda x: x.head(3).sum(),
                'flag': lambda x: (x.head(3) == 0).any(),  # Flag should be 1 if any of the first three rows is not zero
                'id': lambda x: x['id'].unique()[0],  # The id for each group will remain the same
                'type': lambda x: x.head(3).unique()[0]}  # The type for each group will also remain the same

collapsed = df.groupby('id').aggregate(column_funcs)

# Add a new column 'grp_idx' with value 0
collapsed['grp_idx'] = 0

new_df = pd.concat([df, collapsed])

Understanding the Code

In the above code:

• We first import necessary libraries and create a sample DataFrame.
• Then we define a function to sum the values in each group. This function is used in the aggregate() method to perform operations on each group.
• The groupby() method splits the data into groups based on the ‘id’ column.
• The aggregate() method performs operations on each group and returns a new DataFrame with the results.
• We add a new column ‘grp_idx’ with value 0 in the collapsed DataFrame.

Output

After running this code, we get a new DataFrame where the first three rows of each group are aggregated and added as a new row. The ‘grp_idx’ column is also added with value 0 for each group.

In this new DataFrame, we can see that the first three rows of each group are aggregated and added as a new row. The ‘grp_idx’ column is also added with value 0 for each group.

Conclusion

In this article, we explored how to use the groupby function in Pandas to collapse the first n rows in each group by aggregation. We learned about the different methods available for grouping data and performing operations on each group.

Last modified on 2023-06-19