# Analyzing Data with Quartiles in Pandas: A Practical Guide

Analyzing data is an essential task in various industries, from finance to healthcare to marketing. One of the most commonly used statistical measures for data analysis is quartiles.

In this article, we will explore the concept of quartiles, methods to calculate them in Pandas, and examples to help understand their practical application.

## Understanding Quartiles

Quartiles are statistical measures that divide a dataset into four equal parts. A distribution of data is split into four quarters- the lower quartile (Q1), the median (Q2), and the upper quartile (Q3).

The interquartile range is the difference between Q3 and Q1. Quartiles allow us to understand the distribution of data and are helpful in identifying possible outliers, which may distort our analysis.

They also provide a measure of variability, which is essential in statistical analysis.

## Methods to Calculate Quartiles

Pandas is a powerful library in Python that provides easy and efficient ways to manipulate and analyze data. The Pandas DataFrame object has a quantile() method that calculates the quartile values.

You can also use the q[] function to compute and obtain the quartiles. Let’s see how this works.

Example 1: Calculating Quartiles for One Column

Assume we have a dataset that contains the points of ten students in a test. We can create a DataFrame with the data and calculate the quartiles for the points column.

## import pandas as pd

data = {‘points’: [60, 72, 81, 90, 70, 55, 86, 76, 69, 85]}

df = pd.DataFrame(data)

print(df.points.quantile([0.25,0.5,0.75]))

## Output:

0.25 67.25

0.50 73.00

0.75 84.75

Name: points, dtype: float64

In this example, we used the quantile() method to calculate the quartiles for the points column. We passed a list of the values 0.25, 0.5, and 0.75 as parameters.

The method returns a Series object with the quartile values. Example 2: Calculating Quartiles for Each Numeric Column

Sometimes, we may want to calculate the quartiles for each numeric column in a DataFrame.

In this case, we can use the quantile() function by setting the axis parameter to 1.

## import pandas as pd

data = {‘points’: [60, 72, 81, 90, 70, 55, 86, 76, 69, 85],

‘age’: [16, 17, 18, 17, 16, 15, 15, 18, 18, 17],

‘grade’: [76, 83, 94, 88, 68, 77, 89, 92, 71, 89]}

df = pd.DataFrame(data)

print(df.quantile([0.25, 0.5, 0.75], axis=1, numeric_only=True))

## Output:

0.25 0.50 0.75

points 67.25 73.00 84.75

age 16.00 17.00 18.00

In this example, we used the quantile() function to calculate the quartiles for each column with numeric data. We set the axis parameter to 1 and added the numeric_only parameter to only consider numeric columns.

Pandas is a complex library with numerous applications in data analysis and manipulation. If you want to learn more about this library, there are many online resources available.

Various tutorials and courses explain how to use Pandas to process various data types and provide examples of common tasks you may encounter. As a data analyst, mastering Pandas will help you advance your career and build better data-driven decisions.

## Conclusion

In conclusion, quartiles are a crucial statistical measure in data analysis. They help us understand the distribution of data and identify potential outliers.

In this article, we explored the concept of quartiles and how to calculate them in Pandas. We provided examples of how to calculate quartiles for a single column and all numeric columns in a dataset.

Finally, we recommended additional resources to help you improve your Pandas skills and become a proficient data analyst. In summary, understanding quartiles is essential for analyzing data, identifying outliers, and measuring variability.

Pandas provides effective methods to calculate quartiles for a single column and all numeric columns in a dataset using the quantile() method and q[] function. As a data analyst, mastering Pandas can help you build better data-driven decisions and advance your career.

To learn more about Pandas, there are many resources available online. By using quartiles to analyze data in conjunction with Pandas, you can gain valuable insights and make informed decisions for your organization.