In this video, we continue working with the same dataset and dive deeper into salary analysis using Pandas.
You will learn how to group data, calculate summary statistics, and extract meaningful insights from numerical columns.
We explore practical Pandas techniques such as groupby, aggregation functions, sorting values, and identifying the highest and lowest salaries in a dataset.
You will also see how to create new features by categorizing salaries and comparing individual values to the overall average.
0:00 - Example 1: Average Salary by Gender (GroupBy Mean)
0:38 - Example 2: Sorted Average Salary by Gender
1:01 - Example 3: Salary Statistics by Gender (Mean, Min, Max)
1:27 - Example 4: Top 5 Highest Salaries
1:51 - Example 5: Top 5 Lowest Salaries
2:10 - Example 6: Creating Salary Levels (High vs Low)
2:35 - Example 7: Above Average Salary Analysis
This tutorial is designed to help you build real-world data analysis skills using Python and Pandas, step by step.
✅ Tutorial 5 : https://youtu.be/3JDZyR022iQ
👉 By the end of this video, you will be able to:
🔸 Analyze salary data by categories
🔸 Use groupby and aggregation functions effectively
🔸 Identify top and bottom values in a dataset
🔸 Create new columns for better insights
Github Source Code:
https://github.com/turtlecode/Data-Analysis-Tutorial/blob/main/tutorial-4.py
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🎯 This is Data Analysis Tutorial 4, and it builds directly on the concepts covered in the previous videos.
