Cleaning Column Labels 1. Drop extraneous columns Drop features that aren't consistent (not present in both datasets) or aren't relevant to our questions. Use pandas' drop function. Columns to Drop: From 2008 dataset: 'Stnd', 'Underhood ID', 'FE Cal...
Assessing Data The files all_alpha_08.csv and all_alpha_18.csv discussed in the previous pages have been provided in the workspace for you here to access. Use pandas to explore these datasets in the Jupyter Notebook below to answer the quiz questions...
Fuel Economy Data This information is provided by the U.S. Environmental Protection Agency, Office of Mobile Sources, National Vehicle and Fuel Emissions Laboratory. ^ here
Using Documentation to Understand Data Here are the files you saw in the last video. Use them to help you answer the following questions. README.txt download link PDF link
yt link Data Overview We'll learn more about these features as we go. Right now, we have a good idea of what data we're working with, so let's answer some questions. You'll see quick descriptions of each feature again in the next section to help you....
yt link Welcome To The Data Analysis Process - Case Study 2 In this second case study, you'll be analyzing fuel economy data provided by the EPA, or Environmental Protection Agency. What is Fuel Economy? Excerpt from Wikipedia page on Fuel Economy in...
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Type & Quality Plot with Matplotlib Below is the code used to create this plot with Matplotlib. (Please note the code requires you to have created and saved the 'winequality_edited.csv' file in a coding exercise earlier in this lesson.) # plot ba...
^ plot