Week 3 Python HW Instructions
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Unit 3 | Assignment - Py Me Up, Charlie
Background
Well... you've made it!
It's time to put away the Excel sheet and join the big leagues. Welcome to the world of
programming with Python. In this homework assignment, you'll be using the concepts
you've learned to complete 2 of 4 Python Challenges. Which ones you choose are
completely your choice. In fact, feel encouraged to complete them all if you can muster
the time.
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Each of these challenges encompasses a real-world situation where your newfound
Python scripting skills can come in handy. These challenges are far from easy so expect
some hard work ahead!
Before You Begin
1. Create a new GitHub repo called python-challenge . Then, clone it to your computer.
2. Inside your local git repository, create a directory for 2 of the 4 Python Challenges.
Use folder names corresponding to the 2 challenges that you have chosen to
complete: PyBank, PyPoll, PyBoss, or PyParagraph.
3. Inside of each folder that you just created, add a new file called main.py . This will be
the main script to run for each analysis.
4. Push the above changes to GitHub.
Option 1: PyBank
(/denver-coding-bootcamp/UDEN201805DATA1/raw/master/Week3%20%20Python/Python%20HW/Images/revenue-per-lead.jpg)
In this challenge, you are tasked with creating a Python script for analyzing the financial
records of your company. You will be given two sets of revenue data ( budget_data_1.csv
and budget_data_2.csv ). Each dataset is composed of two columns: Date and Revenue .
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(Thankfully, your company has rather lax standards for accounting so the records are
simple.)
Your task is to create a Python script that analyzes the records to calculate each of the
following:
The total number of months included in the dataset
The total amount of revenue gained over the entire period
The average change in revenue between months over the entire period
The greatest increase in revenue (date and amount) over the entire period
The greatest decrease in revenue (date and amount) over the entire period
As an example, your analysis should look similar to the one below:
Financial Analysis
---------------------------Total Months: 25
Total Revenue: $1241412
Average Revenue Change: $216825
Greatest Increase in Revenue: Sep-16 ($815531)
Greatest Decrease in Revenue: Aug-12 ($-652794)
Your final script must be able to handle any such similarly structured dataset in the future
(your boss is going to give you more of these -- so your script has to work for the ones to
come). In addition, your final script should both print the analysis to the terminal and
export a text file with the results.
Option 2: PyPoll
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In this challenge, you are tasked with helping a small, rural town modernize its votecounting process. (Up until now, Uncle Cleetus had been trustfully tallying them one-byone, but unfortunately, his concentration isn't what it used to be.)
You will be given two sets of poll data ( election_data_1.csv and
election_data_2.csv ). Each dataset is composed of three columns: Voter ID , County ,
and Candidate . Your task is to create a Python script that analyzes the votes and
calculates each of the following:
The total number of votes cast
A complete list of candidates who received votes
The percentage of votes each candidate won
The total number of votes each candidate won
The winner of the election based on popular vote.
As an example, your analysis should look similar to the one below:
Election Results
------------------------Total Votes: 620100
-------------------------
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Rogers: 36.0% (223236)
Gomez: 54.0% (334854)
Brentwood: 4.0% (24804)
Higgins: 6.0% (37206)
------------------------Winner: Gomez
-------------------------
Your final script must be able to handle any such similarly-structured dataset in the future
(i.e you have zero intentions of living in this hillbilly town -- so your script needs to work
without massive re-writes). In addition, your final script should both print the analysis to
the terminal and export a text file with the results.
Option 3: PyBoss
(/denver-coding-bootcamp/UDEN201805DATA1/raw/master/Week3%20%20Python/Python%20HW/Images/boss.jpg)
In this challenge, you get to be the boss. You oversee hundreds of employees across the
country developing Tuna 2.0, a world-changing snack food based on canned tuna fish.
Alas, being the boss isn't all fun, games, and self-adulation. The company recently decided
to purchase a new HR system, and unfortunately for you, the new system requires
employee records be stored completely differently.
Your task is to help bridge the gap by creating a Python script able to convert your
employee records to the required format. Your script will need to do the following:
Import the employee_data1.csv and employee_data2.csv files, which currently
holds employee records like the below:
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Emp ID,Name,DOB,SSN,State
214,Sarah Simpson,1985-12-04,282-01-8166,Florida
15,Samantha Lara,1993-09-08,848-80-7526,Colorado
411,Stacy Charles,1957-12-20,658-75-8526,Pennsylvania
Then convert and export the data to use the following format instead:
Emp ID,First Name,Last Name,DOB,SSN,State
214,Sarah,Simpson,12/04/1985,***-**-8166,FL
15,Samantha,Lara,09/08/1993,***-**-7526,CO
411,Stacy,Charles,12/20/1957,***-**-8526,PA
In summary, the required conversions are as follows:
The Name column should be split into separate First Name and Last Name
columns.
The DOB data should be re-written into MM/DD/YYYY format.
The SSN data should be re-written such that the first five numbers are hidden
from view.
The State data should be re-written as simple two-letter abbreviations.
Special Hint: You may find this link to be helpful—Python Dictionary for State
Abbreviations (https://gist.github.com/afhaque/29f0f4f37463c447770517a6c17d08f5).
Option 4: PyParagraph
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In this challenge, you get to play the role of chief linguist at a local learning academy. As
chief linguist, you are responsible for assessing the complexity of various passages of
writing, ranging from the sophomoric Twilight novel to the nauseatingly high-minded
research article. Having read so many passages, you've since come up with a fairly simple
set of metrics for assessing complexity.
Your task is to create a Python script to automate the analysis of any such passage using
these metrics. Your script will need to do the following:
Import a text file filled with a paragraph of your choosing.
Assess the passage for each of the following:
Approximate word count
Approximate sentence count
Approximate letter count (per word)
Average sentence length (in words)
As an example, this passage:
“Adam Wayne, the conqueror, with his face flung back and his mane like a lion's,
stood with his great sword point upwards, the red raiment of his office flapping
around him like the red wings of an archangel. And the King saw, he knew not
how, something new and overwhelming. The great green trees and the great red
robes swung together in the wind. The preposterous masquerade, born of his
own mockery, towered over him and embraced the world. This was the normal,
this was sanity, this was nature, and he himself, with his rationality, and his
detachment and his black frock-coat, he was the exception and the accident - a
blot of black upon a world of crimson and gold.”
...would yield these results:
Paragraph Analysis
----------------Approximate Word Count: 122
Approximate Sentence Count: 5
Average Letter Count: 4.56557377049
Average Sentence Length: 24.4
Special Hint: You may find this code snippet helpful when determining sentence
length (look into regular expressions
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(https://en.wikipedia.org/wiki/Regular_expression) if interested in learning more):
import re
re.split("(?<=[.!?]) +", paragraph)
Hints and Considerations
Consider what we've learned so far. To date, we've learned how to import modules
like csv ; to read and write files in various formats; to store contents in variables, lists,
and dictionaries; to iterate through basic data structures; and to debug along the way.
Using what we've learned, try to break down you tasks into discrete mini-objectives.
This will be a much better course of action than attempting to Google Search for a
miracle.
As you will discover, for some of these challenges, the datasets are quite large. This
was done purposefully, as it showcases one of the limits of Excel-based analysis.
While our first instinct, as data analysts, is often to head straight into Excel, creating
scripts in Python can provide us with more robust options for handling "big data".
Your scripts should work for each dataset provided. Run your script for each dataset
separately to make sure that the code works for different data.
Feel encouraged to work in groups, but don't shortchange yourself by copying
someone else's work. You get what you put in, and the art of programming is
extremely unforgiving to smoochers. Dig your heels in, burn the nigh oil, and learn
this while you can! These are skills that will pay dividends in your future career.
Start early, and reach out for help often! Challenge yourself to identify specific
questions for your instructors and TAs. Don't resign yourself to simply saying, "I'm
totally lost." Come prepared to show your effort and thought patterns, we'll be happy
to help along the way.
Always commit your work and back it up with GitHub pushes. You don't want to lose
hours of your work because you didn't push it to GitHub every half hour or so.
Commit often.
Don't spend too much time trying to choose the "right" challenge. Each of these is
fairly comparable in difficulty level, and you'll need to use most of the same
techniques in each. Waffling won't get you anywhere. Pick your battle, and stick with
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it! Good luck!
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Coding Boot Camp © 2016. All Rights Reserved.
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