This is a repository for notes from the Bivariate Exploration of Data lesson of the Udacity Data Analyst Nanodegree.
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:
numpy
pandas
matplotlib.pyplot
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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