Creating a "Check" Column Based on Previous Rows in a Pandas DataFrame Using Groupby and Apply Functions

Creating a “Check” Column Based on Previous Rows in a Pandas DataFrame

In this article, we will explore how to create a new column in a pandas DataFrame based on previous rows. This column will contain a character (‘C’ or ‘U’) indicating whether the row’s action is preceded by ‘CREATED’ or ‘UPDATED’, respectively.

Introduction

Pandas DataFrames are powerful data structures used extensively in data analysis and scientific computing. One of their key features is the ability to manipulate and transform data using various functions and operators. In this article, we will discuss how to create a new column based on previous rows in a DataFrame.

Sample Problem

Consider a sample DataFrame with an ‘id’ column and an ‘action’ column:

   id  action
0  10   CREATED
1  10      111
2  10      222
3  10      333
4  10     DONE
5  10      222
6  10  UPDATED
7  777   CREATED
8  10      333
9  10     DONE

The task is to create a new column ‘check’ based on previous rows. The rules for creating this column are as follows:

Find the cell in the ‘action’ column with value ‘DONE’.
Search for the first row where either ‘CREATED’ or ‘UPDATED’ has the same ‘id’ as the current row, before ‘DONE’. If the first such row is ‘CREATED’, put ‘C’ in the new column; otherwise, put ‘U’.

Solution

The solution to this problem involves using pandas’ groupby and apply functions. Here’s a step-by-step explanation:

1. Define a transformer function

First, we define a lambda function transformer that takes a Series of actions as input and returns the corresponding character (‘C’ or ‘U’).

transformer = lambda s: s[(s.eq('CREATED') | s.eq('UPDATED')).cumsum().idxmax()]

This function works by selecting all rows where either ‘CREATED’ or ‘UPDATED’ appears in the Series, then finding the cumulative sum of these indices. The index of the first row with a match (i.e., idxmax) is used to determine the character to be returned.

2. Define a grouper function

Next, we define another lambda function grouper that takes a DataFrame as input and returns a new DataFrame with an additional ‘check’ column.

grouper = (
    lambda g: g.groupby(
        g['action'].eq('DONE').cumsum().shift().fillna(0))['action']
    .transform(transformer)
)

df['check'] = df.groupby('id').apply(grouper).droplevel(0).str[0]

This function works by grouping the DataFrame by the first occurrence of ‘DONE’ in each row, splitting the resulting group into separate DataFrames based on these groups. Each of these split DataFrames is then transformed using the transformer function to create a new column with the corresponding character.

3. Apply grouper function and assign result

Finally, we apply the grouper function to each group in the DataFrame using groupby, and assign the resulting ‘check’ column to the original DataFrame.

Explanation of Code

Let’s break down the code further:

The transformer function is used to determine which character should be assigned to a row. It works by finding all rows where either ‘CREATED’ or ‘UPDATED’ appears in the Series, then selecting the first such row with an index that comes before the row marked as ‘DONE’.
The grouper function groups the DataFrame by the first occurrence of ‘DONE’ in each row and applies the transformer function to each group.
By using groupby, we ensure that each unique value in the ‘id’ column is treated separately, allowing us to apply the rules for creating the new column correctly.

Example Use Cases

This technique can be applied to various real-world problems where you need to create a new column based on previous rows. Here are some examples:

Identifying Duplicate Rows: You can use this technique to identify duplicate rows in a DataFrame by comparing the values in certain columns.
Creating a “Status” Column: You can modify the transformer function to assign different statuses (e.g., ‘active’, ‘inactive’) based on conditions in the data.

Conclusion

In conclusion, creating a new column based on previous rows in a pandas DataFrame is a common requirement in data analysis and scientific computing. By using the groupby and apply functions, you can implement this technique efficiently and effectively. Whether you’re dealing with duplicate rows or need to assign statuses based on certain conditions, this approach provides a flexible solution for various real-world problems.

Additional Tips

Here are some additional tips that may be helpful:

Use cumsum() instead of idxmax(): If you want to find the first occurrence of a certain value in each group, use cumsum() instead of idxmax(). This will give you the cumulative sum of indices for all rows up to and including the row marked as ‘DONE’.
Use shift() with care: When using shift() to access previous values in a Series, be aware that it may return NaN if there are missing values. You can use fillna(0) to replace these NaN values with 0.
Experiment with different grouping orders: Depending on your data and requirements, you might need to group by different columns or apply the transformation in a different order.

By experimenting with different techniques and exploring the capabilities of pandas, you’ll become more proficient in solving complex data-related problems. Happy coding!

Last modified on 2024-08-18