Handling Text Data

Explore key methods for handling text data in Python that are commonly tested in data science interviews. Learn to detect repeated characters, filter sentences with lambda functions, and extract clinical data using regex. Understand practical approaches for text preprocessing applied in real-world NLP and healthcare projects.

We'll cover the following...

String processing in Python
- Sample answer
Efficient filtering in Python
- Sample answer
- Bonus question
Complex text filtering and processing
- Sample answer

Text data is everywhere—from product reviews to medical records—and knowing how to process it efficiently is a key skill in interviews. In this lesson, we’ll work through a series of challenges that build from basic string operations to regex-powered information extraction. Let’s get started.

String processing in Python

Let’s assume you’re building a text editor with a feature that helps writers spot repetitive words or letters in their drafts. To get started, you need a function that checks for the first repeated character in a line of text so that it can flag unnecessary repetition. If there are no repeating characters, it should simply return “None”.

This question is frequently asked in technical interviews for roles that require strong foundational programming skills.

Write a function that takes a string as input and returns the first character that repeats. If no character repeats, return “None”.

Example:

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1.Getting Started

2.Handling Diverse Real-World Data

3. Preparing and Transforming Data for Machine Learning Pipelines

4.Understanding Supervised Learning Algorithms

5.Understanding Unsupervised Learning Algorithms

6.Advanced Machine Learning Concepts

7.ML Applications and Deployment in the Real World

8.Responsible Machine Learning: Ethics, Fairness, and Privacy

9.ML Interview Preparation and Case Studies

Handling Text Data

String processing in Python