Option 1: PyBank

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. (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 !list of increases and decreases month by month and it's max

• 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

In this challenge, you are tasked with helping a small, rural town modernize its vote-counting process. (Up until now, Uncle Cleetus had been trustfully tallying them one-by-one, 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
-------------------------
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

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:

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 DD/MM/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.

Option 4: PyParagraph

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 if interested in learning more):

import re
re.split("(?<=[.!?]) +", paragraph)

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