Python for data engineering 11 python regular expressions

python regular expressions (regex) are powerful tools for string manipulation and pattern matching. in data engineering, regular expressions are commonly used to extract, clean, and validate data. here is a brief tutorial on using regular expressions in python for data engineering:

1. import the `re` module:

2. basic patterns and functions:

3. common regex patterns:
- `.`: matches any character except a newline.
- `^`: matches the start of the string.
- `$`: matches the end of the string.
- `*`: matches zero or more occurrences of the preceding element.
- `+`: matches one or more occurrences of the preceding element.
- `?`: matches zero or one occurrence of the preceding element.
- `\d`: matches any digit.
- `\w`: matches any alphanumeric character.
- `\s`: matches any whitespace character.

4. example usage:

in this example, the regex pattern `r'\b[\w\.-]+@[\w\.-]+\.\w+\b'` matches email addresses in the text by looking for a word boundary, followed by one or more alphanumeric characters, dots, or hyphens before the `@` symbol, then the domain part, and finally the top-level domain.

regular expressions can be very complex, so it's recommended to test your patterns thoroughly to ensure they match the desired strings accurately.

this is just a basic introduction to using regular expressions in python for data engineering. regular expressions can be very powerful and versatile, allowing you to handle a wide range of string processing tasks effectively.

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