Day 4 of 14 30 minMissing valuesDuplicatesData types
Turn messy data into something you can trust. Find and handle missing values, remove duplicate rows, fix columns stored with the wrong type, and tidy up messy text.
By the end of today you can
- Count missing values per column with isna().sum()
- Decide between dropping missing values with dropna() and filling them with fillna()
- Find and remove duplicate rows with duplicated() and drop_duplicates()
- Convert columns to the right type with astype() and pd.to_numeric()
- Clean up text before converting it, such as currency symbols and stray spaces
What's inside
- 6 sections
- 3 diagrams and mind maps
- 9 code examples you can run
- 5-question quiz
- 1A messy dataset
- 2Missing values
- 3Duplicates
- 4Data types
- 5Tidying text
- 6Putting it together
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