This is a repository for notes from the Bivariate Exploration of Data lesson of the Udacity Data Analyst Nanodegree.

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In these notes a data set of engine fuel economy data is analyzed using bivariate visualizations. The data set fuel-econ.csv contains information for 3,929 vehicles including make, model, transmission type, engine displacement, and miles per gallon. The Bivariate Exploration.ipynb file contains scatter plots, heat maps, violin plots, box plots, clustered bar charts, line plots, swarm plots, and rug and strip plots, and utilizes techniques such as transparency, jitter, and faceting using matplotlib and seaborn in conducting analysis of the data. The Violin_and_Box_Plot_Practice.ipynb, Scatterplot_Practice.ipynb, Categorical_Plot_Practice.ipynb, and Additional_Plot_Practice.ipynb files contain additional examples and practice of the visualizations.

This code depends on the following libraries:

1. numpy
2. pandas
3. matplotlib.pyplot
4. seaborn

In addition to these, the Jupyter Notebooks assume that the data set fuel-econ.csv has been extracted and is in the project folder.

The Binder badge above can be used to explore an executable environment for the Bivariate Exploration.ipynb file.

• review notes
• rename files without spaces
• clean Bivariate Exploration.ipynb visualizations

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