How to Load, Manipulate, and Insert Data from MySQL to Python Using Pandas | Full Video Tutorial

Mastering Data Connectivity with Practical Examples and Practice

Problem Statement: The Human Resources (HR) department of a Software Solutions requires a sophisticated data management system to integrate, clean, and store data from various sources. This system will automate the process of data handling, ensuring data consistency and reliability across different geographical locations and departments.

Data Integration: Develop a Python based solution to load data from multiple formats

• Employee Details - MySQL Database

• Department Details - .JSON File

• Location Details - .CSV File

• Region and Country Details - .XLSX File

We will be applying following steps and transformation

Read Data from Various Sources into Python Data Frames

• You'll need to import the necessary Python libraries and write functions to read data from each type of file into a Data Frame.

Data Cleaning and Transformation

• You'll need to create the 'Annual Salary' column and concatenate first and last names, along with any other cleaning tasks.

Join Data Frames

• Assuming you have common columns to join on, use pandas to merge these Data Frames.

Load Final Data into MySQL Database

• Finally, you need to load the cleaned and combined Data Frame into a MySQL database.

Below is the end to end flow which will help us understand how the data will flow and load in the final database

End to End Data Flow Diagram