How To Find The First Quartile In A List In Python

In this tutorial, we will learn how to find the first quartile in a list using Python. Quartiles are statistical measures used to divide a data set into four equal parts, each representing 25% of the data.

The first quartile (Q1) is the value that separates the lowest 25% of the data from the remaining 75%. Knowing the first quartile can help you understand the data’s distribution and identify any potential outliers or unusual values.

Note: In this tutorial, we will stick to simple Python constructs and the built-in statistics library to calculate quartiles. However, for more advanced statistical calculations, the NumPy and Pandas libraries are recommended.

Step 1: Sort the list in ascending order

In order to calculate the first quartile, we need the data to be sorted in ascending order. We can use the built-in sorted() function to achieve this:

Step 2: Determine the position of the first quartile

The position of the first quartile (Q1) is calculated using the formula (N + 1) / 4, where N is the number of data points in the list. However, the result might not always be an integer value, so we need to account for that:

If quartile_pos is an integer, then the first quartile value is at that position. If quartile_pos is not an integer, we need to find the values at positions just before and after the quartile_pos, and then calculate the average value.

Step 3: Find the value of the first quartile

Let’s find the value of the first quartile based on the calculated position:

Example Code:

Output:

First quartile value: 15.5

Conclusion

In this tutorial, we learned how to find the first quartile of a list in Python. We discussed the three-step process of sorting the list, determining the position, and finding the quartile value. We used the built-in sorted() function to sort the data and simple if-else logic to find the quartile value. The example code provided can be adapted to find any other quartile value by changing the formula used to calculate the quartile position.