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Learn how to effectively create new columns in a Pandas DataFrame based on conditions from another DataFrame. Enhance your data manipulation skills with this practical guide!
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Creating New Columns in Pandas DataFrame Based on Conditions

When working with data in Python using the Pandas library, you may come across scenarios where you need to create new columns based on specific conditions. This is a common task, especially when dealing with multiple DataFrames. In this guide, we’ll dive into a specific example where we want to extract values from one DataFrame to another based on partial matches. Let’s explore this problem and its solution in detail.

The Problem Statement

Suppose you have two DataFrames:

df1 - Contains various columns including col1, where you have strings with potential matches.

df2 - Contains a single column col2 with a list of words that you want to check against col1 in df1.

Given these two DataFrames, your goal is to create a new column called new_col in df1 that contains values from df2 if those values are found as partial matches within df1's col1. For example, if col2 includes "apple" and col1 has "im.apple3", then new_col should take the value "apple".

Example DataFrames

To clarify, let’s look at some sample data:

df1:

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

df2:

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

Expected Output

After processing, df1 should look something like this:

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

The Solution

To achieve the desired output, you can use the str.extract() method in Pandas, which allows you to find matches based on a regular expression. Here’s how you can do it step-by-step:

Step 1: Convert Column to Lowercase

First, to ensure that your matches are case insensitive, convert col1 to lowercase using str.lower().

Step 2: Build the Regular Expression

Next, you need to construct a regular expression pattern that matches any word in col2. You can do this by joining the list of words from df2 with the | character (which represents "or" in regex).

Step 3: Use str.extract()

Now, apply the constructed regex to your col1 and extract the matches into the new column new_col.

Implementation

Here’s the code that accomplishes the above steps:

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

Result

When you run the code, df1 will be updated as follows:

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

Finding the Position of the Second Uppercase Letter

The Problem

Additionally, if you want to find the index of the second uppercase letter in the col1, you may encounter some errors if not written correctly.

Solution

You can utilize regular expressions again to find the position of the second uppercase letter. Here’s a sample code to achieve this:

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

Make sure to adjust the regular expression pattern to correctly count occurrences as needed, and handle any exceptions that may arise.

Conclusion

In this blog, we explored how to create new columns in a Pandas DataFrame based on conditions from another DataFrame. By using string manipulation functions and regular expressions, you can efficiently manage and alter your datasets. If you've never used str.extract() or regex in Pandas before, hopefully, this example has helped demystify the process.

If you have any further questions or need additional clarification, feel free to reach out. Happy coding!