exercises

## this is a very fast very loose blocks of code for students to build on. 
## It should be relatively easy to either port it into a local machine or run and modify inside codespaces
## this is not a particularly pretty book, but it should let people work as they might wish. 



## here we've imported the first few packages for you. 
import os
import pandas as pd



## exercise 0: read in the data from the csv, hint:  pd.read_csv is your friend
## again, pandas documentation, which you can refer to frequently, is here
## https://pandas.pydata.org/docs/user_guide/index.html

pd.read_csv("Fictitious_Records.csv")



## exercise 1: Inspect the DataFrame to understand its structure and content
## hint: Use the head(), dtypes, and describe() methods to quickly inspect the DataFrame, as mentioned in the slides.



## exercise 2: Filter data based on specific conditions.
## hint: Remember how we used the > operator to filter data. Apply similar techniques here to filter and count rows.



## exercise 3: Select specific columns and rename them.
## hint: Use double brackets to select multiple columns and the rename() method to change column names.



## exercise 4: Identify and handle missing data in the DataFrame.
## hint: Use isnull(), dropna(), and fillna() to handle missing data.



## exercise 5: Translate a SQL query into Pandas code.
## code is Given SQL: SELECT Name, Age FROM your_table WHERE Age > 25 ORDER BY Age DESC;
## hint: Break down the SQL query into its components (filtering, selecting columns, sorting) and translate each part into Pandas code.



## exercise 6: Perform grouping and aggregation operations.
## hint: Use the groupby() method followed by an aggregation function like mean(), sum(), or count(). 

##Group the data by a specific column (e.g., 'State') and calculate the average age for each group.

##Group the data by ‘State’ and calculate the total amount of campaign contributions. 

##Group the data by ‘State’ and count the number of members that live in each state using the count() function.



## exercise 7: Export data from a DataFrame to a CSV or Excel file.
## hint: Use the to_csv() and to_excel() methods to save the DataFrame to a file. Refer to the Pandas documentation for more details.