Calling functions

Calling functions in Python, especially when working with regular expressions via the re module, is a powerful way to search, match, extract, substitute, and manipulate text patterns efficiently. The re module provides a set of functions (not methods on strings) that operate on strings using pattern strings — functions like findall(), search(), match(), finditer(), sub(), split(), and more. In 2026, regular expression function calls remain essential — used constantly in data cleaning, log parsing, input validation, NLP preprocessing, URL extraction, and pandas/Polars string column transformations where vectorized .str.extract() or .str.replace() scales to millions of rows. Mastering these calls lets you handle complex text patterns with precision and performance.

Here’s a complete, practical guide to calling regular expression functions in Python: core re functions with examples, flags for customization, real-world patterns, pandas/Polars equivalents, and modern best practices with type hints, compilation, error handling, and performance.

The most common function is re.findall(pattern, string, flags=0) — returns all non-overlapping matches as a list of strings.


import re

text = "The quick brown fox jumps over the lazy dog."

# Find all 5-letter words
pattern = r'\b\w{5}\b'
matches = re.findall(pattern, text)
print(matches)
# ['quick', 'brown', 'jumps']

# Case-insensitive with flag
matches_insensitive = re.findall(pattern, text, re.IGNORECASE)
print(matches_insensitive)   # same result (already lowercase in text)

re.search(pattern, string, flags=0) finds the first match — returns a Match object or None; use .group() to get the matched text.


match = re.search(r'brown fox', text)
if match:
    print(match.group())     # brown fox
    print(match.start(), match.end())   # 10 19 (positions)
else:
    print("Not found")

re.sub(pattern, repl, string, count=0, flags=0) replaces matches — repl can be string or function; count limits replacements.


cleaned = re.sub(r'\bthe\b', 'THE', text, flags=re.IGNORECASE)
print(cleaned)
# THE quick brown fox jumps over THE lazy dog.

# Replace digits with X
masked = re.sub(r'\d', 'X', "Phone: 123-456-7890")
print(masked)   # Phone: XXX-XXX-XXXX

Real-world pattern: cleaning and extracting from text columns in pandas — vectorized .str methods call regex under the hood efficiently.


import pandas as pd

df = pd.DataFrame({
    'log': [
        "ERROR: connection failed at 2023-03-15",
        "INFO: data loaded successfully",
        "WARNING: low memory at 14:30"
    ]
})

# Extract timestamps
df['timestamp'] = df['log'].str.extract(r'(\d{4}-\d{2}-\d{2})')
df['level'] = df['log'].str.extract(r'^(ERROR|INFO|WARNING)')

# Replace words case-insensitively
df['clean'] = df['log'].str.replace(r'\berror\b', 'ERROR', regex=True, case=False)

print(df)

Best practices make regex function calls safe, readable, and performant. Compile patterns with re.compile() for repeated use — faster and clearer. Use flags like re.IGNORECASE, re.MULTILINE, re.DOTALL — pass as third argument or via compiled pattern. Modern tip: use Polars for large text columns — pl.col("text").str.extract(r'pattern') or .str.replace_all(...) is 10–100× faster than pandas .str. Add type hints — str or pd.Series[str] — improves static analysis. Use raw strings r'pattern' — avoids double-escaping backslashes. Handle no-match cases — check if match is not None or matches or []. For complex patterns, use verbose mode re.VERBOSE — allows comments and whitespace. Avoid overusing regex — simple string methods (split(), replace()) are faster when sufficient. Combine with pandas.str — df['col'].str.contains(r'pattern', regex=True) for vectorized boolean checks.

Calling regex functions like findall(), search(), sub() unlocks powerful pattern matching and text transformation. In 2026, compile patterns, use flags, vectorize in pandas/Polars, add type hints, and prefer raw strings. Master regex calls, and you’ll search, extract, replace, and validate text data with precision and efficiency.

Next time you need to find or manipulate patterns in text — reach for re functions. It’s Python’s cleanest way to say: “Match this pattern and do something with it.”

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