If you are working with big data, distributed computing, or data pipelines, then Apache Spark is likely already on your radar. And when it comes to using Spark with Python, PySpark is the go-to library. However, with so many functions and modules (SQL, DataFrames, MLlib, Streaming), remembering everything can be overwhelming. That’s why this Cheatsheet … Read more
When working with scientific computing, mathematics, data analysis or optimization in Python, the SciPy library is one of the most powerful tools you can use. Built on top of NumPy, SciPy extends its functionality with specialized modules for linear algebra, optimization, signal processing, integration, interpolation and more. This SciPy Cheatsheet serves as a quick reference … Read more
When it comes to deep learning frameworks, two names dominate the field: TensorFlow and PyTorch. While TensorFlow has been around longer, PyTorch has quickly gained popularity due to its flexibility, dynamic computation graph and Pythonic style. Researchers, developers, and data scientists across the globe use PyTorch for everything from computer vision to natural language processing. … Read more
If you are starting your journey in machine learning or deep learning, you will likely come across TensorFlow. Built by Google, TensorFlow is one of the most popular open-source libraries for building and deploying machine learning models. From powering image recognition systems to natural language processing models, TensorFlow is the go-to framework for developers, researchers … Read more
August 23, 2025 by Vanita.ai Natural Language Processing (NLP) powers applications like chatbots, translation systems, sentiment analysis, and large language models (LLMs). But before machines can understand text, it must be cleaned, structured and normalized. This is where text preprocessing comes in. Think of preprocessing as preparing raw ingredients before cooking without it, even the … Read more
August 23, 2025 by Vanita.ai In today’s world, data is growing faster than ever, and charts are no longer just static pictures in reports, they’re interactive stories that drive decisions. Whether you’re analyzing business trends, visualizing AI model outputs or preparing a dashboard for executives, the way you present data can make or break your … Read more
In the world of data science and machine learning, visualization is the bridge between raw numbers and meaningful insights. Among Python’s visualization tools, Matplotlib stands as the most widely used and versatile library. From quick exploratory data analysis plots to highly customized publication-ready graphs, Matplotlib provides the flexibility to do it all. This Matplotlib Cheatsheet … Read more
If you’re working in machine learning with Python, Scikit-learn (sklearn) is one of the most powerful and beginner-friendly libraries you’ll ever use. It provides tools for data preprocessing, model selection, evaluation and deployment. This Scikit-learn cheatsheet is your one-stop guide, covering everything from the basics to advanced techniques. Bookmark it and you’ll never get stuck … Read more
NumPy Cheatsheet 2025: From Basics to Advanced in One Guide
Working with data often means juggling messy files, cleaning inconsistencies, and reshaping tables until they’re analysis-ready. That’s where Pandas shines it’s the backbone of data manipulation in Python. But with hundreds of methods and options, it can be overwhelming to remember them all. This cheatsheet brings together the most essential Pandas methods, organized by category, … Read more