Text data can be messy in its own unique way. Imagine a city column with entries like 'New York', ' new york ', and 'new york'. To a computer, these are three different cities, which would ruin any analysis.
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Why is it so important for identical items to be written in the exact same way?
To clean text, we need to use string methods. In Pandas, you access these by using the .str accessor on a Series. For example, to work on a city column, you would start with df['city'].str. This tells Pandas to treat each entry as a string.
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Why do you think Pandas requires this extra .str step?
A common issue is extra whitespace. The .str.strip() method removes any spaces from the beginning and end of a string. So, ' chicago ' becomes 'chicago'. This is a crucial first step for cleaning up user-entered text.
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What kind of common data entry errors does .str.strip() help fix?
Next, we tackle inconsistent capitalization. The .str.lower() method converts every character in a string to lowercase. This ensures that 'Boston', 'boston', and 'BOSTON' are all treated as the same value: 'boston'.
