Pandas Quantile Tutorial (With Examples)

In data analysis and statistics, quantiles are a useful way to understand the distribution of a dataset. They divide a dataset into equal parts, allowing you to identify specific values that represent certain percentiles of the data. Pandas, a popular Python library for data manipulation and analysis, provides a convenient way to calculate and work with quantiles. In this tutorial, we’ll explore the concept of quantiles, learn how to calculate them using Pandas, and delve into practical examples to solidify our understanding.

Introduction to Quantiles
Calculating Quantiles using Pandas
- quantile() Function
- Specifying Quantile Values
Practical Examples
- Example 1: Analyzing Exam Scores
- Example 2: Income Distribution Analysis
Conclusion

1. Introduction to Quantiles

Quantiles are values that divide a dataset into equal parts, helping us understand how data is distributed across various percentiles. The most commonly used quantiles are quartiles (which divide the data into four parts) and percentiles (which divide the data into 100 parts). For instance, the median is the 50th percentile, the first quartile (Q1) is the 25th percentile, and the third quartile (Q3) is the 75th percentile.

Quantiles are particularly useful for analyzing skewed datasets, identifying outliers, and making data-driven decisions based on different levels of spread and central tendency.

2. Calculating Quantiles using Pandas

Pandas provides a convenient quantile() function that allows you to calculate quantiles of a DataFrame or Series. The basic syntax is:

DataFrame.quantile(q, axis=0, numeric_only=True, interpolation='linear')

q: The desired quantile(s) as a decimal or array of decimals (e.g., 0.25 for the first quartile).
axis: The axis along which to calculate the quantile(s) (0 for rows, 1 for columns).
numeric_only: If True, only numeric columns will be considered.
interpolation: Method used to compute quantile (e.g., 'linear', 'lower', 'higher', 'nearest', 'midpoint').

Specifying Quantile Values

When using the quantile() function, you need to specify the desired quantile value(s) using the q parameter. This can be a single value or an array of values. For example:

import pandas as pd

data = pd.Series([10, 15, 20, 25, 30, 35, 40, 45, 50])

# Calculating the median (50th percentile)
median = data.quantile(0.5)

# Calculating the first and third quartiles (25th and 75th percentiles)
quartiles = data.quantile([0.25, 0.75])

print("Median:", median)
print("First Quartile:", quartiles[0.25])
print("Third Quartile:", quartiles[0.75])

3. Practical Examples

Example 1: Analyzing Exam Scores

Let’s say we have a dataset containing exam scores of students. We want to understand the distribution of scores and identify the top 10% of scores. We’ll use Pandas quantile to achieve this.

import pandas as pd

# Sample data: Exam scores of students
scores = pd.Series([65, 78, 85, 92, 68, 72, 95, 88, 98, 60, 78, 82, 90, 84])

# Calculating the 90th percentile (top 10% of scores)
top_10_percentile = scores.quantile(0.9)

print("Top 10% Score Threshold:", top_10_percentile)

Example 2: Income Distribution Analysis

Suppose we have income data for a group of individuals, and we want to analyze the income distribution using quartiles.

import pandas as pd

# Sample data: Income of individuals
income_data = pd.Series([25000, 35000, 50000, 60000, 75000, 80000, 95000, 120000, 150000, 180000])

# Calculating quartiles
quartiles = income_data.quantile([0.25, 0.5, 0.75])

print("First Quartile (Q1):", quartiles[0.25])
print("Median (Q2):", quartiles[0.5])
print("Third Quartile (Q3):", quartiles[0.75])

4. Conclusion

Quantiles are valuable tools in data analysis for understanding the distribution of data and making informed decisions based on different percentiles. In this tutorial, we explored the concept of quantiles, learned how to calculate them using Pandas’ quantile() function, and worked through practical examples in Python. By incorporating quantile analysis into your data analysis workflow, you can gain deeper insights into your datasets and enhance your decision-making process.

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