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 Calc Appr', 'Unadj Cmb MPG'
From 2018 dataset: 'Stnd', 'Stnd Description', 'Underhood ID', 'Comb CO2'

2. Rename Columns

Change the "Sales Area" column label in the 2008 dataset to "Cert Region" for consistency.
Rename all column labels to replace spaces with underscores and convert everything to lowercase. (Underscores can be much easier to work with in Python than spaces. For example, having spaces wouldn't allow you to use df.column_name instead of df['column_name'] to select columns or use query() . Being consistent with lowercase and underscores also helps make column names easy to remember.)

Workspace

This section contains either a workspace (it can be a Jupyter Notebook workspace or an online code editor work space, etc.) and it cannot be automatically downloaded to be generated here. Please access the classroom with your account and manually download the workspace to your local machine. Note that for some courses, Udacity upload the workspace files onto https://github.com/udacity , so you may be able to download them there.

Workspace Information:

Default file path:
Workspace type: jupyter
Opened files (when workspace is loaded): n/a

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