7. Calculating Percentages Within Groups Using Pandas groupby
   Scenario:Imagine you have a dataset with various categories (e.g., product types) and corresponding values (e.g., sales figures). You want to find out what percentage each category contributes to the total value
8. Extracting Top Rows in Pandas Groups: groupby, head, and nlargest
   Understanding the Task:You have a DataFrame containing data.You want to identify the top n (highest or lowest) values based on a specific column within each group defined by another column
9. Handling Missing Data in Pandas GroupBy Operations: A Python Guide
   GroupBy in pandaspandas. GroupBy is a powerful tool for performing operations on subsets of a DataFrame based on one or more columns (called "group keys")
10. Supercharge Your Data Analysis: Applying Multiple Functions to Grouped Data in Python
    Here's a breakdown of the concept:GroupBy:The groupby function in pandas is used to split a DataFrame into groups based on one or more columns
11. Mastering Data Aggregation: A Guide to Group By and Count in SQLAlchemy (Python)
    Concepts:SQLAlchemy: A Python library for interacting with relational databases. It provides an object-relational mapper (ORM) that allows you to work with database objects in a Pythonic way
12. Unlocking Data Patterns: Counting Unique Values by Group in Pandas
    Importing Pandas:The import pandas as pd statement imports the Pandas library and assigns it the alias pd. This alias is then used to access Pandas functionalities throughout your code
13. From Long to Wide: Pivoting DataFrames for Effective Data Analysis (Python)
    What is Pivoting?In data analysis, pivoting (or transposing) a DataFrame reshapes the data by swapping rows and columns