Unlock the Power of Data Segmentation with Pandas’ cut() Method
When working with large datasets, it’s essential to have a way to segment and sort data values into meaningful bins. This is where Pandas’ cut() method comes in – a powerful tool that helps you categorize your data into specific groups based on predefined criteria.
Understanding the cut() Method
The cut() method takes in several arguments, including the input array to be binned, the criteria to bin by, and several optional parameters that allow you to customize the binning process. These optional arguments include:
- right: Indicates whether the bins include the rightmost edge
- labels: Specifies the labels for the returned bins
- retbins: Specifies whether to return the bins or not
- precision: Precision at which to store and display the bins labels
- include_lowest: Whether the first interval should be left-inclusive or not
Categorizing Data with cut()
Let’s dive into some examples to see how the cut() method works in practice. In our first example, we’ll create a list of exam scores and categorize them into different grading ranges using the cut() method.
Example 1: Grading Ranges
| Scores | Bin |
| — | — |
| 40 | 0-60 |
| 75 | 71-80 |
| 92 | 91-100 |
In this example, we’ve defined the bins to represent different grading ranges: 0-60, 61-70, 71-80, 81-90, and 91-100. The cut() method then categorizes each score into the corresponding grading bin.
Customizing Bin Boundaries
But what if you want to control the bin boundaries? That’s where the right argument comes in. By setting right=True, the bins include the rightmost edge, while setting right=False means the left edge is included.
Example 2: Bin Boundaries
| Values | Bin (right=True) | Bin (right=False) |
| — | — | — |
| 3 | (0, 5] | [0, 5) |
| 7 | (5, 10] | [5, 10) |
Naming Your Bins
Want to give your bins custom labels? The labels argument allows you to do just that. Simply define your custom labels and pass them to the cut() method.
Example 3: Custom Labels
| Scores | Bin |
| — | — |
| 40 | Low |
| 75 | Medium |
| 92 | Very High |
Extracting Bin Information
Sometimes, you need more than just the binned categories – you need the actual bin edges. That’s where the retbins argument comes in. By setting retbins=True, the cut() method returns both the binned categories and the array of bin edges.
Example 4: Bin Edges
| Categories | Bin Edges |
| — | — |
| Low | [0, 60) |
| Medium | [60, 80) |
| High | [80, 100] |
Precision Matters
When working with numerical data, precision is crucial. The precision argument allows you to specify the precision at which to store and display the bins labels.
Example 5: Precision
| Scores | Bin |
| — | — |
| 40 | 0.00-60.00 |
| 75 | 60.00-80.00 |
| 92 | 80.00-100.00 |
Inclusive Intervals
Finally, the include_lowest argument allows you to specify whether the first interval should be left-inclusive or not.
Example 6: Inclusive Intervals
| Scores | Bin (includelowest=False) | Bin (includelowest=True) |
| — | — | — |
| 20 | NaN | [20, 25] |
| 22 | [20, 25] | [20, 25] |
With these examples, you’ve seen the power of Pandas’ cut() method in action. By mastering this method, you’ll be able to segment and sort your data with ease, unlocking new insights and possibilities in your data analysis journey.