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

Positive look-behind (?<=...) is a zero-width assertion that checks whether a pattern is preceded by another pattern without consuming those characters. It lets you match something only when it is immediately preceded by a specific context. In data science this is extremely useful for extracting numbers that come after “Price: ”, product codes that follow “SKU:”, or IDs that appear after a known label — all without including the preceding text in the final match.

TL;DR — Positive Look-Behind

(?<=...) → assert that ... must precede the match
Zero-width: the look-behind text is **not** part of the captured result
Perfect for context-sensitive extraction from logs and reports
Works seamlessly with pandas .str.extract()

1. Basic Positive Look-Behind

import re

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

# Numbers preceded by "Price: "
print(re.findall(r"(?<=Price: )d+", text))

# Product codes preceded by "SKU: "
print(re.findall(r"(?<=SKU: )([A-Z0-9-]+)", "SKU: ABC123 sold, SKU: XYZ789 in stock"))

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 order IDs only when preceded by "Order ID: "
df["order_id"] = df["log"].str.extract(r"(?<=Order ID: )(d+)")

# Example 3: Extract SKUs only when preceded by "SKU: "
df["sku"] = df["log"].str.extract(r"(?<=SKU: )([A-Z0-9-]+)")

3. Advanced Positive Look-Behind

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

# Variable-width context (Python re supports it)
print(re.findall(r"(?<=Price:s*)d+", "Price: 1250 USD"))

4. Best Practices in 2026

Use positive look-behind (?<=...) whenever you need context before the match without including it
Combine with capturing groups to extract only the part you want
Keep look-behind expressions simple and fixed-width where possible for maximum compatibility and readability
Pre-compile patterns that contain look-behind for repeated use on large datasets
Use with pandas .str.extract() for vectorized zero-width assertions across entire DataFrames

Conclusion

Positive look-behind (?<=...) is a zero-width superpower that gives you precise control over what precedes a match without consuming those characters. In 2026 data science projects it is essential for accurate, context-aware text extraction from logs, reports, and unstructured data. Master positive look-behind (together with its negative counterpart), combine it with pandas vectorized methods, and your regex pipelines will become significantly more precise and professional.

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