How to Create a New DataFrame in Pandas by Filtering Rows Based on Multiple Conditions

Discover how to effectively filter rows in a Pandas DataFrame using multiple conditions to create new, smaller DataFrames for specific ranges of data.
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How to Create a New DataFrame in Pandas by Filtering Rows Based on Multiple Conditions

Working with data often involves filtering and segmenting datasets to draw insights from various ranges. If you're using Python's Pandas library, you may find yourself needing to create new DataFrames based on specific conditions. In this post, we will address a common hurdle: how to filter a DataFrame based on multiple conditions effectively.

The Problem: Filtering Rows in a DataFrame

Let's say you have a DataFrame containing pressure and depth data, and you wish to divide this data into three separate subsets based on different depth ranges:

Shallow Depth: Less than 2900 meters

Mid Depth: Between 2901 to 3500 meters

Deep Depth: Greater than 3501 meters

The challenge lies in applying multiple conditions when filtering the DataFrame, particularly if you're accustomed to single-condition filtering—that works seamlessly. A common mistake arises when coding multiple conditions in one line, leading to confusion and syntax errors.

The Solution: Using Logical Operators Correctly

To correctly filter the DataFrame with multiple conditions, follow these steps:

Step 1: Sample Data Creation

First, let’s set up some sample data. We can create a DataFrame that contains a range of depth values:

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

Step 2: Filtering DataFrame Based on Multiple Conditions

When you're applying multiple conditions, you can combine them using logical operators.

Use & for AND conditions

Use | for OR conditions

However, it's important to wrap each condition within parentheses to avoid syntax errors. For example, to filter the mid-depth range, the code should look like this:

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

Step 3: Results of the Filtering

Executing the above command will yield:

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

Step 4: Using Alternative Methods

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

Accessing the Results

You can access the subsets as follows:

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

The output gives you three distinct DataFrames categorized by depth range, effectively organizing your dataset.

Conclusion

Now you can sift through your data like a pro, effortlessly creating smaller DataFrames that perfectly fit your analytical needs!