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5 Lesser-Known Python Features Every Data Scientist Should Know

Published

2 weeks ago

August 18, 2025

Jayita Gulati

5 Lesser-Known Python Features Every Data Scientist Should Know

Image by Editor | ChatGPT

# Introduction

Python is one of the most popular languages used in the data science sphere, valued for its simplicity, versatility, and powerful ecosystem of libraries, including NumPy, pandas, scikit-learn, and TensorFlow. While these tools provide much of the heavy lifting, Python itself includes a range of features that can help you write cleaner, faster, and more efficient code. Many of these capabilities go unnoticed, yet they can improve how you structure and manage your projects.

In this article, we explore five lesser-known but beneficial Python features that every data scientist should have in their toolkit.

# 1. The `else` Clause on Loops

Did you know for and while loops in Python can have an else clause?

While this may sound counterintuitive at first, the else block executes only when the loop completes without a break statement. This is useful when you search through a dataset and want to run some logic only if a specific condition was never met.

for row in dataset:
    if row['target'] == 'desired_value':
        print("Found!")
        break
else:
    print("Not found.")

In this snippet, the else block executes only when the loop finishes without encountering a break. This lets you avoid creating extra flags or conditions outside the loop.

# 2. The `dataclasses` Module

The dataclasses module, introduced in Python 3.7, provides a decorator and helper functions that automatically generate special methods like __init__(), __repr__(), and __eq__() for your classes. This is useful in data science when you need lightweight classes to store parameters, results, or configuration settings without writing repetitive boilerplate code.

from dataclasses import dataclass

@dataclass
class ExperimentConfig:
    learning_rate: float
    batch_size: int
    epochs: int

With @dataclass, you get a clean constructor, a readable string representation, and comparison capabilities.

# 3. The Walrus Operator (`:=`)

The walrus operator (:=), introduced in Python 3.8, lets you assign values to variables as part of an expression. This is useful when you want to both calculate and test a value without repeating the calculation in multiple places.

data = [1, 2, 3, 4, 5]

if (avg := sum(data) / len(data)) > 3:
    print(f"Average is {avg}")

Here, avg is assigned and checked at the same time. This removes the need for another line and makes your code easier to read.

# 4. `enumerate()` for Indexed Loops

When you need both the index and the value while iterating, enumerate() is the most Pythonic way to do it. It takes any iterable (like a list, tuple, or string) and returns pairs of (index, value) as you loop.

for i, row in enumerate(data):
    print(f"Row {i}: {row}")

This improves readability, reduces the chance of errors, and makes your intent clearer. It’s useful in data science when iterating over rows of data or results with positions that matter.

# 5. The `collections` Module

Python’s collections module provides specialized container datatypes that can be more efficient and expressive than using only lists or dictionaries. Among the most popular is Counter, which can count elements in an iterable with minimal code.

from collections import Counter

word_counts = Counter(words)
most_common = word_counts.most_common(5)

Need an ordered dictionary? Use OrderedDict. Need a dictionary with default values? Try defaultdict. These tools eliminate the need for verbose manual logic and can even improve performance in large-scale data processing.

# Conclusion

Tools like the else clause on loops, dataclasses, and the walrus operator can eliminate unnecessary boilerplate and make logic more concise. Functions like enumerate() and modules like collections help you iterate, count, and organize data with elegance and efficiency. By incorporating these lesser-known gems into your workflow, you can reduce complexity, avoid common pitfalls, and focus more on solving the actual data problem rather than wrangling your code.

Jayita Gulati is a machine learning enthusiast and technical writer driven by her passion for building machine learning models. She holds a Master’s degree in Computer Science from the University of Liverpool.

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Top Life Sciences Companies Set Up GCCs in India in Last 5 Years, says EY India

Published

8 hours ago

September 1, 2025

Shalini Mondal

India has rapidly become a key hub for life sciences global capability centres (GCCs), with nearly half of the world’s top 50 life sciences companies establishing a presence in the country—most within the past five years, according to a new EY India report.

The report, Reimagining Life Sciences GCCs, highlighted how India’s GCCs have evolved from back-office support centres into strategic engines driving drug discovery, regulatory affairs, and commercial operations.

“This isn’t about cost arbitrage anymore, it’s about India becoming indispensable to the global R&D pipeline,” said Arindam Sen, partner and GCC Sector Lead – technology, media & entertainment and telecommunications, EY India. “Life sciences multinationals are embedding their most strategic, knowledge-intensive work here, making India the epicentre for life sciences innovation, compliance, and future growth.”

According to EY, Indian life sciences GCCs now manage integrated functions across clinical trials, pharmacovigilance, supply chain analytics, biostatistics, and enabling services such as finance, HR, IT, and data analytics.

The study shows that GCCs handle 70% of finance, 75% of HR, 62% of supply chain, and 67% of IT functions for their global firms. Core functions have also grown sharply: 45% in drug discovery, 60% in regulatory affairs, 54% in medical affairs, and 50% in commercial operations.

India’s rise as a GCC hub is driven by four key factors: policy support from central and state governments, a strong talent pool of over 2.7 million life sciences professionals, access to a mature ecosystem including CROs, universities, and startups, and widespread infrastructure with scalable Grade-A office spaces.Looking ahead, the report noted that leading life sciences GCCs are positioning themselves as “twins” of their global headquarters, co-owning innovation and accelerating outcomes. Sen added, “Their evolution will be defined by future capabilities, operating model transformation, and building agile, multi-disciplinary teams skilled in areas like generative AI, bioinformatics, and digital health.”

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AI PC Shipments to Hit 77 Million Units This Year: Report

Published

11 hours ago

September 1, 2025

Ankush Das

AI PCs will make up 31% of the worldwide PC market by the end of 2025, according to Gartner. Shipments are projected to hit 77.8 million units this year, with adoption accelerating to 55% of the global market in 2026 and becoming the standard by 2029.

“AI PCs are reshaping the market, but their adoption in 2025 is slowing because of tariffs and pauses in PC buying caused by market uncertainty,” said Ranjit Atwal, senior director analyst at Gartner, in a statement. Despite this, users are expected to continue investing in AI PCs to prepare for greater edge AI integration.

AI laptop adoption is projected to outpace desktops, with 36% of laptops expected to be AI-enabled by 2025, compared to 16% of desktops. By 2026, nearly 59% of laptops will fall into this category. Businesses largely favour x86 on Windows, which is expected to represent 71% of the AI business laptop market next year, while Arm-based laptops are anticipated to see more substantial consumer traction.

To support this shift, Gartner predicts that 40% of software vendors will prioritise developing AI features for PCs by 2026, up from just 2% in 2024. Small language models (SLMs) running locally on devices are expected to drive faster, more secure, and energy-efficient AI experiences.

Looking ahead, Gartner notes that vendors must focus on software-defined, customisable AI PCs to build stronger brand loyalty. “The future of AI PCs is in customisation,” Atwal said.

Still, the rapid rise of AI PCs masks an industry-wide “TOPS race.” While Microsoft, AMD, Intel, and Qualcomm position AI PCs as the future, performance claims around neural processing units (NPUs) remain contested.

As industry leaders push for more TOPS, analysts warn that real-world AI performance may hinge less on specifications and more on practical workloads, software maturity, and user adoption.

The post AI PC Shipments to Hit 77 Million Units This Year: Report appeared first on Analytics India Magazine.

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Hexaware, Replit Partner to Bring Secure Vibe Coding to Enterprises

Published

16 hours ago

September 1, 2025

Shalini Mondal

Hexaware Technologies has partnered with Replit to accelerate enterprise software development and make it more accessible through secure Vibe Coding. The collaboration combines Hexaware’s digital innovation expertise with Replit’s natural language-powered development platform, allowing both business users and engineers to create secure production-ready applications.

The partnership aims to help companies accelerate digital transformation by enabling teams beyond IT, such as product, design, sales and operations, to develop internal tools and prototypes without relying on traditional coding skills.

Amjad Masad, CEO of Replit, said, “Our mission is to empower entrepreneurial individuals to transform ideas into software—regardless of their coding experience or whether they’re launching a startup or innovating within an enterprise.”

Hexaware said the tie-up will facilitate faster innovation while maintaining security and governance.

Sanjay Salunkhe, president and global head of digital and software services at Hexaware Technologies, noted, “By combining our vibe coding framework with Replit’s natural language interface, we’re giving enterprises the tools to accelerate development cycles while upholding the rigorous standards their stakeholders demand.”

The partnership will enable enterprises to democratise software development by allowing employees across departments to build and deploy secure applications using natural language.

It will provide secure environments with features such as SSO, SOC 2 compliance and role-based access controls, further strengthened by Hexaware’s governance frameworks to meet enterprise IT standards.

Teams will benefit from faster prototyping, with product and design groups able to test and iterate ideas quickly, reducing time-to-market. Sales, marketing and operations functions can also develop custom internal tools tailored to their workflows, avoiding reliance on generic SaaS platforms or long IT queues.

In addition, Replit’s agentic software architecture, combined with Hexaware’s AI expertise, will drive automation of complex backend tasks, enabling users to focus on higher-level logic and business outcomes.

The post Hexaware, Replit Partner to Bring Secure Vibe Coding to Enterprises appeared first on Analytics India Magazine.

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