hanyang2019 / the_power_of_plots

Python Pandas & Matplotlib Analysis of Ride Sharing Data and Pharmaceutical Data

Project Pymber

Background

The ride sharing bonanza continues! Seeing the success of notable players like Uber and Lyft, you've decided to join a fledgling ride sharing company of your own. As Chief Data Strategist for the company, you'll be expected to offer data-backed guidance on new opportunities for market differentiation.

You've since been given access to the company's complete recordset of rides. This contains information about every active driver and historic ride, including details like city, driver count, individual fares, and city type.

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Objectives

1. Build a Bubble Plot that showcases the relationship between four key variables:

• Average Fare ($) Per City
• Total Number of Rides Per City
• Total Number of Drivers Per City
• City Type (Urban, Suburban, Rural)

2. Produce the following three pie charts:

• % of Total Fares by City Type
• % of Total Rides by City Type
• % of Total Drivers by City Type

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Results

• Buble Plot of Sharing Data
• Total Fares by City Type

• Total Rides by City Type

• Total Drivers by City Type

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Conclusion

1. The prevalence of Pyber in a city correlates with its extent of urbanization. People in urban cities are more likely to take Pyber rides than those in suburban and rural cities.
2. There are much more Pyber drivers in urban cities than the other two city types combined.
3. Rural riders tend to spend more money on a single ride than riders in urban and suburban cities.

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Project Pymerceuticals

Background

Pymaceuticals Inc. is a burgeoning pharmaceutical company specializing in drug-based, anti-cancer pharmaceuticals. In their most recent efforts,they've since begun screening for potential treatments to squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.

As their Chief Data Analyst, you've been given access to the complete data from their most recent animal study. In this study, 250 mice were treated through a variety of drug regimes over the course of 45 days. Their physiological responses were then monitored over the course of that time.

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Objectives

Analyze the data to show how four treatments (Capomulin, Infubinol, Ketapril, and Placebo) compare.

• Create a scatter plot that shows how the tumor volume changes over time for each treatment.
• Create a scatter plot that shows how the number of metastatic (cancer spreading) sites changes over time for each treatment.
• Create a scatter plot that shows the number of mice still alive through the course of treatment (Survival Rate)
• Create a bar graph that compares the total % tumor volume change for each drug across the full 45 days.

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Results

• Tumor Response to Treatment
• Metastatic Response to Treatment
• Survival Rates
• Summary Bar Graph

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Conclusion

1. Only two, capomulin and ramicane, out of 10 drugs have successfully reduced the total tumor volume in mice.
2. Ramicane, followed by capomulin, showed the strongest capacity to reduce metastasis in comparison to other treatments.
3. Both capomulin and ramicane out-ranked other treatments in terms of survival rate.