--- title: Best NumPy posts of 2024 | daily.dev description: The most upvoted NumPy posts of 2024, curated by the daily.dev community. canonical: https://daily.dev/tags/numpy/best-of/2024 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/numpy/best-of/2024 og:type: website og:site_name: daily.dev og:title: Best NumPy posts of 2024 | daily.dev og:description: The most upvoted NumPy posts of 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of NumPy — 2024 1. 1 [](/posts/15-ds-ml-cheat-sheets-g6vzxobqd "15 DS/ML Cheat Sheets") Article Daily Dose of Data Science | Avi Chawla | Substack · 2y 15 DS/ML Cheat Sheets This post collates 15 cheat sheets covering essential data science and machine learning concepts. It includes resources on translating between different data manipulation libraries, multi-GPU training strategies, testing ML models in production, neural network optimization, and more. Detailed links are provided for further reading. 232 1 2. 2 [](/posts/machine-learning-from-zero-vsriqe7xs "Machine Learning From Zero") Article The Palindrome · 2y Machine Learning From Zero Introducing the 'mlfz' library, an educational project aimed at demystifying machine learning algorithms. Written entirely in Python + NumPy, it breaks down complex concepts into readable code. Now available on PyPI and GitHub, this library serves as both a computational tool and an informative textbook on the internal workings of machine learning. More detailed posts on neural networks are expected in the coming months. 84 2 3. 3 [](/posts/jax-in-100-seconds-owxesepqj "JAX in 100 Seconds") Video Fireship · 2y JAX in 100 Seconds Jax is an accelerated linear algebra library that allows for high-performance array computing and automatic differentiation. It enforces constraints like immutable arrays and pure functions. Jax can run on accelerated hardware like GPUs and TPUs. 76 4. 4 [](/posts/constructing-neural-networks-from-scratch-part-1-zfano8do6 "Constructing Neural Networks From Scratch: Part 1") Article DigitalOcean Community · 2y Constructing Neural Networks From Scratch: Part 1 Building neural networks from scratch provides foundational understanding of deep learning. While popular frameworks like TensorFlow and PyTorch simplify neural net implementation, they may obscure core concepts. This guide explains foundational neural network components using Python and NumPy to solve tasks like the XOR logic gate. It addresses mathematical foundations, forward and backward propagation, and outlines the steps to train a simple neural network without deep learning frameworks. 66 5. 5 [](/posts/10-python-one-liners-that-will-boost-your-data-science-workflow-l6hzkzrzb "10 Python One-Liners That Will Boost Your Data Science Workflow") Article Machine Learning Mastery · 2y 10 Python One-Liners That Will Boost Your Data Science Workflow Python offers versatile one-liners to enhance your data science workflow. Learn efficient methods to handle missing data, remove highly correlated features, apply conditional columns, find common and different elements, and use Boolean masks for filtering. Other techniques include counting occurrences in lists, extracting numbers from text, flattening nested lists, converting lists to dictionaries, and merging dictionaries efficiently. 64 6. 6 [](/posts/10-crazy-python-operators-that-i-rarely-use-qkrh5xb4y "10 Crazy Python Operators That I Rarely Use") Video Indently · 2y 10 Crazy Python Operators That I Rarely Use Discover 10 rarely used Python operators, including set operations, the matrix multiplication operator, and bitwise inversion. Learn how to perform operations like union, intersection, and symmetric difference using unique syntax. The post also covers the floor division operator and demonstrates creative use of operator combinations. 51 5 7. 7 [](/posts/simulating-wildfires-in-a-forest--yvnvofpng "Simulating Wildfires in a Forest🔥") Article Medium · 2y Simulating Wildfires in a Forest🔥 Simulating wildfires involves using a computational model called Cellular Automaton (CA), leveraging Python libraries such as NumPy and Matplotlib. The simulation describes a forest grid with cells representing trees, wildfires, and burned areas. The model applies rules to change cell states over time, incorporating additional elements like temperature, wind, and precipitation to closely mimic real-world wildfire scenarios. The more sophisticated model includes additional states (water, bedrock, grass) and weather conditions to make the simulation more realistic. 40 1 8. 8 [](/posts/mastering-python-for-data-science-beyond-the-basics-j3vmm61yj "Mastering Python for Data Science: Beyond the Basics") Article KDnuggets · 3y Mastering Python for Data Science: Beyond the Basics Learn advanced Python techniques for data science, including efficient data manipulation with Pandas, high-performance computing with NumPy, and leveraging niche libraries for elevated data analysis. 37 9. 9 [](/posts/generating-random-data-with-numpy-vxrtjwdgh "Generating Random Data with NumPy") Article KDnuggets · 2y Generating Random Data with NumPy NumPy is a powerful Python package for mathematical and statistical computations, including generating random data. It provides tools to create random data from various distributions such as uniform, normal, Poisson, binomial, and exponential distributions. The package also allows setting seeds for reproducibility and combining different distributions to create custom data sets. This versatility makes NumPy essential for tasks like data simulation, synthetic data generation for machine learning, and statistical sampling. 20 2 10. 10 [](/posts/python-news-roundup-july-2024-real-python-d6633whn0 "Python News Roundup: July 2024 – Real Python") Article Real Python · 2y Python News Roundup: July 2024 – Real Python July 2024 saw significant updates in the Python data science ecosystem. NumPy released version 2.0, featuring improved string handling and cleaner namespaces but also introducing breaking changes. Polars, a high-performance DataFrame library, reached its 1.0 milestone, indicating its readiness for production use. Highlights from PyCon US included keynotes on diverse tech topics and PSF’s coverage of the Python Language Summit. Additionally, there's an ongoing discussion about shifting Python's versioning scheme to calendar versioning (CalVer). 20 11. 11 [](/posts/what-is-numpy-and-why--hptin5yhd "What is Numpy and Why?") Video Telusko · 2y What is Numpy and Why? Numpy, short for Numerical Python, is a powerful library used in AI, machine learning, and data science for numerical computing. It addresses Python's limitations with arrays, supporting multi-dimensional arrays and offering performance benefits through optimized C code. Numpy is foundational to many other libraries like Pandas and Scikit-Learn, making it a crucial tool for scientific and data-intensive computations. 18 1 12. 12 [](/posts/using-astropy-for-astronomy-with-python-real-python-257cv6ebl "Using Astropy for Astronomy With Python – Real Python") Article Real Python · 2y Using Astropy for Astronomy With Python – Real Python This course introduces foundational astronomy concepts and teaches you how to use Python libraries such as Astropy, NumPy, Matplotlib, and pandas. Learn how to find planet conjunctions and graph the best viewing times for a star. The course includes 19 lessons, video subtitles, full transcripts, downloadable resources, and a certificate of completion. 21 1 13. 13 [](/posts/rounding-numbers-in-python-real-python-ekg2ajbwv "Rounding Numbers in Python – Real Python") Article Real Python · 2y Rounding Numbers in Python – Real Python Learn about the importance of rounding numbers in Python, various rounding strategies, how rounding affects data, and how to round numbers in NumPy arrays and pandas DataFrames. 18 14. 14 [](/posts/fast-multidimensional-matrix-multiplication-on-cpu-from-scratch-ycvu9op1b "Fast Multidimensional Matrix Multiplication on CPU from Scratch") Article Hacker News · 2y Fast Multidimensional Matrix Multiplication on CPU from Scratch Recreating fast multidimensional matrix multiplication from scratch using C++ can achieve significant performance boosts through various optimization techniques. These include using compiler flags, loop reordering, tiling, and multithreading. By leveraging these methods, performance can approach the optimized levels achieved by libraries such as Intel’s MKL used in Numpy. Understanding CPU architecture and effective benchmarking are crucial for successful optimization. 18 15. 15 [](/posts/numpy-practical-examples-useful-techniques-real-python-xlt2mphtq "NumPy Practical Examples: Useful Techniques – Real Python") Article Real Python · 2y NumPy Practical Examples: Useful Techniques – Real Python This tutorial covers practical examples and useful techniques for using the NumPy library in Python. Topics include creating multidimensional arrays from files, identifying and removing duplicate data, using structured arrays to reconcile datasets, analyzing hierarchical data, and creating vectorized functions. It also includes instructions on setting up the working environment, and some exercises to practice the skills learned. 17 16. 16 [](/posts/vectorization-in-practice-gzqzp0edv "Vectorization in practice") Article The Palindrome · 2y Vectorization in practice This post explores the concept of vectorization and the use of data structures in computer science. It compares tuples and lists in Python as representations of vectors and explains why NumPy arrays are faster than Python lists for scientific computation. 17 17. 17 [](/posts/visualizing-data-in-python-with-matplotlib-tfc1felx5 "Visualizing Data in Python With Matplotlib") Article The New Stack · 2y Visualizing Data in Python With Matplotlib Learn to effectively communicate complex datasets using Matplotlib, a versatile Python library for data visualization. This guide covers the basics of creating line plots, bar charts, scatter plots, and subplots, as well as how to customize these visualizations to enhance data comprehension and support data-driven decisions. 16 18. 18 [](/posts/broadcasting-in-numpy-so1fl0yxn "Broadcasting in Numpy") Video Telusko · 2y Broadcasting in Numpy Learn how to perform broadcasting in Numpy, a powerful operation that allows you to add arrays of different shapes and dimensions. Understand the rules and logic behind broadcasting and see practical examples that demonstrate how Numpy handles various scenarios to make mathematical operations simpler. 11 19. 19 [](/posts/what-s-new-in-tensorflow-2-18-cwg64xuge "What's new in TensorFlow 2.18") Article TensorFlow · 2y What's new in TensorFlow 2.18 TensorFlow 2.18 has been released with significant updates, including support for NumPy 2.0, which may affect edge cases in API usage. The transition from TFLite to LiteRT has begun, and developers are encouraged to switch over. TensorFlow builds will now include specific versions of CUDA for more reproducible builds and added performance for Ada-Generation GPUs. 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