# I wanted to add structure to my self-taught experience, and I wanted to practice learning w/o resulting to a tutorial. I asked ChatGPT4 to create a list of exercises for Python's Panda's extension.
# My answers are my own, are informed by other online guides, stack overflow posts, etc, but I wrote the code myself. 
# Here is what ChatGPT4 suggested:
# 1. Basic DataFrame Operations

# a. Create a DataFrame with 3 columns: Name, Age, and City. Add 5 rows with fictional or real data.

# b. Display the first 3 rows of the DataFrame.

# c. Display the last 2 rows of the DataFrame.

# d. Filter and display only those rows where Age is greater than 25.

# 2. Data Manipulation

# a. Sort the DataFrame based on the Age column in descending order.


# b. Create a new column called Senior which marks True if the age is above 60, and False otherwise.

# c. Replace a city name of your choice with another city name for all occurrences.

# 3. Missing Data Handling

# a. Intentionally introduce some missing values in the City and Age columns.

# b. Use the fillna() method to replace missing values in Age with the average age.

# c. Drop rows where City is missing.

# 4. Grouping and Aggregation

# a. Group the data by City and calculate the average age for each city.

# b. Group by City and get the count of names in each city. <- as of 10/29/23, I am here.

# 5. Date Handling

# a. Create a new DataFrame with the columns: Name, DateOfBirth, and Salary. Fill it with fictional data.

# b. Convert the DateOfBirth column to datetime format (if it isn't already).

# c. Create a new column Age that calculates the age of each individual based on the DateOfBirth column.

# 6. Merging DataFrames

# a. Create two DataFrames: df1 with columns ID, Name, and Role and df2 with columns ID, Salary, and Experience.

# b. Merge the two DataFrames on the ID column.

# 7. Reading and Writing

# a. Write the merged DataFrame from the previous exercise to a new CSV file.

# b. Read the CSV file back into a new DataFrame and display the first 5 rows.

# 8. Pivot Tables

# a. Using the DataFrame with Name, City, and Age, create a pivot table that shows the average age for each city.

# 9. Apply Function

# a. Use the apply() function to create a new column in a DataFrame that contains the length of each name in the Name column.

# 10. Advanced Filtering

# a. Filter the DataFrame to display rows where the name starts with the letter 'A' and age is above 30.