D598 Task 2 Report
A. Python Program
The Python program developed for Task 2 successfully performs all required data analysis as
outlined in Task 1. The program begins by importing the Excel file from the same directory
without using a hardcoded path. It removes duplicate entries to ensure data accuracy. The script
then groups businesses by the 'Business State' column and calculates descriptive statistics (mean,
median, minimum, and maximum) for all numeric fields. Businesses with negative debt-to-equity
ratios are filtered for risk analysis. Finally, the program calculates the debt-to-income ratio using
long-term debt divided by revenue, while replacing any zero revenue values with 0.01 to avoid
divide-by-zero errors. This computation is merged back into the original DataFrame to maintain
data structure. All operations execute without error and adhere to Python best practices.
GitLab Link: https://gitlab.com/wgu-gitlab-environment/student-repos/shay281/d598-analytics-
programming
B. APA Sources
Sources:
The only sources used were the official course materials from WGU.
C. Professional Communication
This report is structured professionally, using clear grammar, spelling, and sentence structure
throughout. The explanation of the code is logically sequenced and easy to understand, with
consistent formatting and terminology. Code comments and in-line documentation are included in
the Python script to explain each functional block. No spelling or grammatical issues were
identified, and the tone is appropriate for academic and professional submission standards as
required by the rubric.
A. Python Program
The Python program developed for Task 2 successfully performs all required data analysis as
outlined in Task 1. The program begins by importing the Excel file from the same directory
without using a hardcoded path. It removes duplicate entries to ensure data accuracy. The script
then groups businesses by the 'Business State' column and calculates descriptive statistics (mean,
median, minimum, and maximum) for all numeric fields. Businesses with negative debt-to-equity
ratios are filtered for risk analysis. Finally, the program calculates the debt-to-income ratio using
long-term debt divided by revenue, while replacing any zero revenue values with 0.01 to avoid
divide-by-zero errors. This computation is merged back into the original DataFrame to maintain
data structure. All operations execute without error and adhere to Python best practices.
GitLab Link: https://gitlab.com/wgu-gitlab-environment/student-repos/shay281/d598-analytics-
programming
B. APA Sources
Sources:
The only sources used were the official course materials from WGU.
C. Professional Communication
This report is structured professionally, using clear grammar, spelling, and sentence structure
throughout. The explanation of the code is logically sequenced and easy to understand, with
consistent formatting and terminology. Code comments and in-line documentation are included in
the Python script to explain each functional block. No spelling or grammatical issues were
identified, and the tone is appropriate for academic and professional submission standards as
required by the rubric.
