Скачать или смотреть How to Apply Multiple Conditions for Columns in a DataFrame Using Pandas

Learn how to effectively apply multiple conditions to columns in a pandas DataFrame to generate a new cumulative sum column.
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Mastering Multiple Conditions in Pandas DataFrame Columns

When working with data in pandas, it’s often necessary to apply multiple conditions to columns in a DataFrame to derive new computed values. This is especially true when you want to achieve a specific logic that involves looking at two different columns. In this guide, we will explore how to implement multiple conditions in a DataFrame, particularly focusing on generating a new cumulative sum column based on changes in two other columns.

The Problem at Hand

Let’s take a closer look at a specific scenario. You have a DataFrame that looks like this:

itemcol1cat1cat1dog1fish1fish1fish2snake2snake2snake2Your goal is to create a new column called result that behaves as follows:

It performs a cumulative sum for the item column but increments the number only when the item changes.

The result must reset to 1 whenever there is a change in the col1 column.

Let’s visualize the desired result for clarity:

itemcol1resultcat11cat11dog12fish13fish13fish21snake22snake22snake22The Initial Attempt

You may have attempted to achieve a part of this using the following code:

[[See Video to Reveal this Text or Code Snippet]]

While this effectively computes the cumulative sum based on the item changes, it does not account for the necessary reset of the counter when the column col1 changes.

The Solution: Using groupby and transform

To address the multiple conditions for our DataFrame, we can use groupby along with transform. This method allows us to group the DataFrame by col1 and then apply a function to compute the desired cumulative sum for the item column.

Step-by-Step Implementation

Group the DataFrame: Use groupby on col1 to segregate the data based on the values in this column.

Apply Transformation:

Use the factorize function to assign a unique integer to each unique item within the group.

Add 1 to offset the zero-based index from factorize, which gives us a starting point of 1.

Here’s the complete code snippet to implement this solution:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

df.groupby('col1'): This part groups the DataFrame by the col1 column.

.item.transform(...): We're applying a transformation on the item series within each group created by the previous step.

lambda x: x.factorize()[0] + 1: This anonymous function applies factorize on each grouped item, which returns the unique values as absolute numbers, and we add one to start from 1.

Final Result

Once you run the above code, the DataFrame will look like this:

itemcol1resultcat11cat11dog12fish13fish13fish21snake22snake22snake22Conclusion

Applying multiple conditions to generate new columns in a DataFrame using pandas can seem intimidating at first. However, with the right approach and techniques such as grouping and transforming data, you can achieve complex logic with ease. We hope this solution helps you in your data manipulation tasks. Happy coding!