Clean data
Get your data ready for analysis.
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Get your data ready for analysis.
Last updated
Was this helpful?
Cleaning data in Quadratic is more seamless than you may be used to, as your data is viewable in the sheet as you step through your DataFrame. Every change to your DataFrame can be reflected in the sheet in real-time. Some data cleaning steps you may be interested in taking (very much non-exhaustive!):
Assume DataFrame named df
. With df.head()
you can display the first x rows of your spreadsheet. With this as your last line the first x rows will display in the spreadsheet. You can do the same except with the last x rows via df.tail()
Deleting columns point and click can be done by highlighting the entire column and pressing Delete
. Alternatively, do this programmatically with the code below.
There are many ways to make field-specific changes, but this list will give you some ideas.
Going column by column to clean specific things is best done programmatically.
With the beauty of Quadratic, feel free to delete rows via point and click; in other cases, you may need to do this programmatically.
Identifying empty rows should be intuitive in the spreadsheet via point-and-click; in other cases, you may need to do this programmatically.
By default, Quadratic inputs will be read as strings by Python code. Manipulate these data types as you see fit in your DataFrame.
Duplicates are likely best removed programmatically, not visually. Save some time with the code below.