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Look-Behind Assertions in Regular Expressions – Complete Guide for Data Science 2026

Look-behind assertions let you check what comes **before** a potential match without consuming those characters. They are zero-width and come in two flavors: positive look-behind (?<=...) and negative look-behind (?<!...). In data science this is extremely powerful for context-aware extraction — for example, finding numbers that are preceded by “Price: ”, product codes that come after “SKU:”, or excluding values that are preceded by unwanted text.

TL;DR — Look-Behind Assertions

(?<=...) → positive look-behind (must be preceded by ...)
(?<!...) → negative look-behind (must **not** be preceded by ...)
Zero-width: the look-behind text is **not** part of the captured result
Perfect for precise, context-sensitive extraction in logs and reports

1. Basic Look-Behind Assertions

import re

text = "Price: 1250 USD, Tax: 87 EUR, Total: 1337 USD"

# Positive look-behind: numbers preceded by "Price: "
print(re.findall(r"(?<=Price: )d+", text))

# Negative look-behind: numbers NOT preceded by "Tax: "
print(re.findall(r"(Tax: )d+", text))

2. Real-World Data Science Examples with Pandas

import pandas as pd

df = pd.read_csv("logs.csv")

# Example 1: Extract amounts only when preceded by "Price:" (positive look-behind)
df["price_amount"] = df["log"].str.extract(r"(?<=Price: )(d+(?:,d+)?(?:.d+)?)")

# Example 2: Extract product codes NOT preceded by "out of stock" (negative look-behind)
df["available_sku"] = df["log"].str.extract(r"(out of stock )([A-Z0-9-]+)")

# Example 3: Extract order IDs only when preceded by "ORD-"
df["order_id"] = df["log"].str.extract(r"(?<=ORD-)(d+)")

3. Advanced Look-Behind Combinations

# Look-behind with alternation
text = "Profit: +1250 USD, Loss: -340 EUR"
print(re.findall(r"(?<=+|-)d+", text))

# Variable context look-behind
print(re.findall(r"(?<=SKU: )([A-Z0-9-]+)", "SKU: ABC123 sold, SKU: XYZ789 in stock"))

4. Best Practices in 2026

Use positive look-behind (?<=...) when you need to “peek behind” without including the text
Use negative look-behind (?<!...) to exclude unwanted preceding patterns
Keep look-behind expressions fixed-width and simple (Python re is flexible, but readability matters)
Combine with capturing groups to extract only the part you want
Use with pandas .str.extract() for vectorized zero-width assertions across entire DataFrames

Conclusion

Look-behind assertions are zero-width superpowers that give you precise control over what precedes a match without consuming those characters. In 2026 data science projects they are essential for accurate, context-aware text extraction from logs, reports, and unstructured data. Master positive and negative look-behind, combine them with pandas vectorized methods, and your regex pipelines will become significantly more precise and professional.

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