RoshniRanaDS27/Python_for_Financial_Analysis_and_Election_results_Analysis

Python for Financial Analysis for Banking records and 2. Election Results Analysis (For Rural area).

Python

Python for Financial Analysis and Election results Analysis

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Analysis Background

The world of programming with Python. In these Analysis, have used the concepts i have learned to complete two Python challenges, PyBank and PyPoll. Both tasks present a real-world situation where newly developed Python scripting skills come in handy.

Important

Key information users need to know to achieve their goal.

PyBank Instructions

In this Analysis, created a Python script to analyze the financial records of Banking company. Financial dataset was given called budget_data.csv. The dataset Was composed of two columns: "Date" and "Profit/Losses".

Created a Python script that analyzes the records to calculate each of the following values:

The total number of months included in the dataset, The net total amount of "Profit/Losses" over the entire period, The changes in "Profit/Losses" over the entire period, and then the average of those changes, The greatest increase in profits (date and amount) over the entire period, The greatest decrease in profits (date and amount) over the entire period

In addition, final script has both print the analysis to the terminal and export a text file with the results at Analysis folder.

PyPoll Instructions

In this Analysis, with helping a small, rural _town modernize_ its vote-counting process, a set of poll data was given called `election_data.csv`. The dataset was composed of three columns: "`Voter ID`", "`County`", and "`Candidate`". My task was to create a `Python` script that analyzes the votes and calculates each of the following values:

• 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.

In addition, final script has both print the analysis to the terminal and export a text file with the results at Analysis folder.

Note

Useful information that users should know, even when skimming content.

Lesson learned

Considering what i have learned so far.

• how to import modules like csv.
• have learned how to read and write files in various formats.
• how to store content in variables lists, and dictionaries.
• learned how to iterate through basic data structures.
• learned how to debug along the way. Using all that i have learned, try to break down my tasks into discrete mini-objectives.

The datasets for these Challenges were quite large. This was done purposefully to showcase one of the limits of Excel-based analysis.

Tip

Helpful advice for doing things better or more easily.

In conclusion, As a data analysts; our first instinct is often to go straight to Excel, but creating scripts in Python can provide us with more powerful options for handling big data.

Note

Useful information that users should know, even when skimming content.

Repo Instructions

• Inside my local Git repository, created a folder for each Analysis by Python and name them PyBank and PyPoll.

In each folder, have created, added the following content:

• A new file called main.py. This will be the main script to run for each analysis.
• A Resources folder that contains the CSV files i have used. Assured that my script has the correct path to the CSV file.
• An analysis folder that contains text file that has the results from each analysis.
• Pushed these changes to GitHub profile by bash terminal.