Class Info

https://github.com/xie1989/intermediate-python

Instructor

• Name: Henry Xie
• Email: [email protected]
• Website: simplefractal.com

Topics

• range/xrange
• List Comprehensions
• List Slicing
• Lambda functions
• Sorting
• Exception Handling
• Debugging with pdb and ipdb
• Reading in CSV files
• Data Analysis Techniques

Python Exercises

Objectives

• Practice/Review data structures, control flow and functions.
• Learn intermediate Python techniques
• Prepare for Data Analysis section

1. Write a function that prints all the even numbers between 1 and 10,000.

2. Write a function that returns a list of the numbers between 1 and 10,000 that are divisible by 3.

3. The same as 2, but use Python list comprehensions

4. Write a function get_max that takes a list of numbers and returns the max of those numbers, don't use the builtin max() function. Afterward, try using max()

5. Write a function is_odd_or_div_by_7 that returns True if a number is odd or divisble by 7 and False otherwise.

6. Use is_odd_or_div_by_7 and list comprehensions to write a function get_sublist_of_numbers_odd_or_div_by_7 that takes in a list and returns a sublist of those numbers that are either odd or divisible by 7.

7. Write a division function divide(a, b) that catch exceptions and return an error string if the arguments do not make sense.

8. Given a list of food orders, e.g. ["burger", "fries", "burger", "tenders", "apple pie"], write a function get_aggregate_order_counts that takes the list and returns a dictionary with the different dishes as keys and the number of times they appear in the list as the values. For example, it takes ["burger", "fries", "burger", "tenders", "apple pie"] and outputs { "burger": 2, "fries": 1, "tenders": 1, "apple pie": 1 }

9. Write a function get_most_popular_order_data that takes a list of orders but instead of returning a dictionary with the counts, it just outputs a tuple: the dish that appears the most in the list and the number of times it appears in the list. So the output given the example would be ("burger", 2)

Data Analysis

Objectives

• use csv library to read in data
• use Python techniques to extract insights about the data

1. Using csv library, read in data from rock.csv, which you can download here: https://www.dropbox.com/s/cbffxkqq0ujru58/rock.csv?dl=0

2. How many songs are from 1981?

3. How many songs are from before 1984

4. What is the earliest release year in the data? (HINT: You might have to account for/clean up dirty data)

5. What are the top 20 songs by play count (HINT: use builtin sorted() function, documentation here: https://wiki.python.org/moin/HowTo/Sorting)

6. Who are the top 10 most prolific artists in the data along with the number of their songs that appear in the data?

7. How many different artists appear in the data?

8. How many songs does 'Rock' appear in the title of?