Introduction: Understanding salary distributions across different countries is crucial for various economic analyses, market insights, and policy decisions. Boxplots are an effective graphical tool that provides a clear summary of data distribution, including median, quartiles, and outliers. In this blog post, we’ll explore how to create and interpret boxplots to analyze salaries by country, incorporating both median and mean values for deeper insights.

1. Understanding Boxplots: Boxplots are a standardized way of visually summarizing the distribution of numerical data through five key statistics:

• Median: Represents the middle value of the dataset, dividing it into two equal halves.
• Quartiles: Divide the data into four equal parts, providing insights into the spread and central tendency.
• Interquartile Range (IQR): The range between the first (Q1) and third (Q3) quartiles, showing where the middle 50% of the data lies.
• Whiskers: Extend from the quartiles to indicate the data’s range, excluding outliers.
• Outliers: Individual points beyond the whiskers that may indicate unusual or extreme values in the dataset.

2. Data Preparation: Let’s start by loading our dataset containing salary information across different countries. We’ll use Python and Pandas for data manipulation and Seaborn for visualization.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset (replace 'path_to_your_dataset.csv' with your dataset path)
jobs_df = pd.read_csv('path_to_your_dataset.csv')

# Display the first few rows of the dataset to understand its structure
print(jobs_df.head())

3. Creating the Boxplot: Using Seaborn, we’ll create a basic boxplot to visualize the distribution of salaries by country.

# Create a boxplot of salaries by country
plt.figure(figsize=(14, 8))
sns.boxplot(x='SalaryUSD', y='Country', data=jobs_df)
plt.xlabel('Salary in USD')
plt.ylabel('Country')
plt.title('Boxplot of Salaries by Country')
plt.xticks(rotation=90)
plt.show()

The resulting boxplot provides a comprehensive view of salary distributions across different countries, showcasing the median and variability in salaries within each country.

4. Adding Mean to the Boxplot: To complement the boxplot with mean salary information, we’ll overlay a point plot that displays the mean salary for each country.

# Calculate the median order for countries based on median salary
median_order = jobs_df.groupby('Country')['SalaryUSD'].median().sort_values().index

# Create a boxplot with mean overlay
plt.figure(figsize=(14, 8))
sns.boxplot(x='SalaryUSD', y='Country', data=jobs_df, order=median_order)
sns.pointplot(x='SalaryUSD', y='Country', data=jobs_df, estimator='mean', color='red', markers='D', dodge=True, order=median_order)
plt.xlabel('Salary in USD')
plt.ylabel('Country')
plt.title('Boxplot of Salaries by Country with Mean')
plt.show()

In this enhanced visualization, the red diamonds represent the mean salary for each country, offering additional insights into the central tendency of salaries beyond the median.

5. Analysis and Insights: By analyzing the boxplot and mean overlay:

• Median vs. Mean: Compare the median (50th percentile) and mean (average) salaries to understand the typical salary distribution in each country.
• Outliers: Identify countries with significant outliers, which may indicate unique economic conditions or sectors.
• Variability: Assess the spread of salaries within each country using the boxplot’s interquartile range (IQR).

6. Conclusion: Boxplots are powerful tools for exploring salary distributions across different countries, providing insights into income disparities, economic trends, and market competitiveness. By combining median and mean values, stakeholders can make informed decisions and policies based on a comprehensive understanding of salary data.