• Data Source:
  • city_data.csv
  • PyBer_ride_data.csv
  • ride_data.csv
• Software: Python 3.7.10, Matplotlib 3.3.4, Numpy 1.20.1, Statitics 1.0.3.5, SciPy 1.6.2

Using Matplotlib, Python, Pandas, SciPy, and Jupyter Labs, I created visualizations of rideshare data for PyBer to help improve access to ride-sharing services and determine affordability for underserved neighborhoods. With Pandas and Jupyter Lab, I cleaned the data into Data Series or DataFrames, so we could use Matplotlib's features to create and annotate charts that visualize data. In this module, I created charts, scatter plots, bubble charts, pie charts, and box-and-whisker plots, and make them visually compelling and informative by adding titles, axes labels, legends, and custom colors.

Analysis Results:

• Results Notebook: PyBer_Challenge.ipynb
• Differences In Ride-Sharing Data Among Different City Types
• Line Chart:

1. Decrease Fares in Rural and Suburban Areas To Encourage More Riders
2. Shift Drivers from Urban Areas to Suburban Areas to Capture The 2nd Biggest Market
3. Decrease Drivers in Urban Areas to Increase Market Demand to Drive Up Fares