Project Description

In this project, I analyzed some fictional financial data using Python. You can see the Python code and visualizations in the .ipynb file.

Analyzing financial statements with Python provides valuable insights into business performance and helps businesses make data-driven decisions. We will tackle four questions about four aspects of the data: revenue, costs, profit, and correlations.

Questions tackled:

1. Compare the sales of the first seven days (week one) to the sales of the next seven days (week two). Use a bar graph to visualize the comparison.

2. What percentage of total costs do direct costs represent on average? What is the mean cost variation change between the minimum cost and the maximum cost as a percentage of total costs?

3. What is the difference between profit values for Fridays and Mondays? Visualize the difference using the bar chart. Visualize the daily profitability trend over time (whole period) using a line plot (use the date column).

4. What are the potential correlations between sales, direct costs, and overheads? Use a heatmap to visualize the correlations.

DATASETS

• company_financials.csv

🛠 Skills

Lessons Learned

I learned about importante concepts of financial data using Python. I added new columns with existing columns in order to get more specific financial information. I created new variables based on the data provided. I learned more about correlations between variables using a heatmap.

Conclusions

• We had more sales in week 2 than week 1.
• The percentage of directs cost to total costs is 73.12%
• The mean cost variation is 82.92%
• The difference in profit between Fridays and Mondays is $1250
• There is more money coming in at the end of the week than at the beginning of the week.
• Overall, the profitability trend is upward.
• There is a strong correlation between Sale and Direct costs which means that as one variable increases, the other tends to increase as well.