3 Powerful Examples of the Python Re Library

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Data Science

Explore the facility of and save time in data evaluation

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Data isn’t clean and never within the required structure!!

Whether you’re starting with data science or are an experienced skilled — You won’t deny the above statement!

In an information analyst’s profession extracting actionable insights from data is a critical skill. And infrequently you face challenges with messy, inconsistent, and unstructured data.

As per my experience, traditional data cleansing methods are tedious and error-prone, especially when coping with massive amounts of information reminiscent of in an information warehouse. You spend a few hours simply to bring this data to its workable state.

But, what if I let you know a single module in Python can make your life easy?

Yes, such features exist.

Python’s re module is all you would like.

The re module in Python is a built-in library that supports Regular Expressions or . A daily expression is nothing but a pattern which is used to match character mixtures in text or string. I discovered it as a very powerful tool for text processing.

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