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NumPy

NumPy is the fundamental package for scientific computing with Python

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  • NumPy Landing page
    Landing page //
    2023-05-13

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Videos

Learn NUMPY in 5 minutes - BEST Python Library!

Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks

Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

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We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about NumPy and what they use it for.
  • Python: From Beginners to Pro in 30 Mins (Part 1)
    PyCharm also integrates well with various Python frameworks and tools. It offers excellent support for web development frameworks like Django and Flask and scientific computing libraries like NumPy and Matplotlib. - Source: dev.to / 6 days ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Develop a script that iterates over the image database, preprocesses each image according to the model's requirements (e.g., resizing, normalization), and feeds them into the model for prediction. Ensure the script can handle large datasets efficiently by implementing batch processing. Use libraries like NumPy or Pandas for data management and TensorFlow or PyTorch for model inference. Include... - Source: dev.to / about 1 month ago
  • Documenting my pin collection with Segment Anything: Part 3
    NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations. - Source: dev.to / about 1 month ago
  • Awesome List
    NumPy - The fundamental package for scientific computing with Python. NumPy Documentation - Official documentation. - Source: dev.to / about 1 month ago
  • NumPy for Beginners: A Basic Guide to Get You Started
    This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / about 2 months ago
  • 2 Minutes to JupyterLab Notebook on Docker Desktop
    Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 10 months ago
  • Element-wise vs Matrix vs Dot multiplication
    In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 4 months ago
  • JSON in data science projects: tips & tricks
    Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 5 months ago
  • Introducing Flama for Robust Machine Learning APIs
    Numpy: A library for scientific computing in Python. - Source: dev.to / 7 months ago
  • A Comprehensive Guide to NumPy Arrays
    Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 9 months ago
  • Beginning Python: Project Management With PDM
    A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 9 months ago
  • Libraries vs. Frameworks: Which is right for your next web project?
    Range of tasks: Libraries provide functionality for a narrower range of challenges. They provide components that solve specific difficulties programmers might experience when creating applications. The NumPy Python library, for example, helps in manipulating data structures. We can use frameworks to perform a wide range of tasks and to build complete applications. With frameworks, developers have cohesive,... - Source: dev.to / 11 months ago
  • [Python] A Journey to Python Async - 1. Intro
    But whereas I took for granted that async syntax in JS, async in Python was quite unfamiliar to me when I saw it for the first time. I had some experiences of using Python for writing really simple scripts, without ever worrying about those async features. That was probably because many big popular libraries such as numpy, pandas, or even selenium didn’t require any async logics to be considered. And those... - Source: dev.to / about 1 year ago
  • Preparation for AI
    Know how to use numpy to vectorize operations and flatten (and unflatten data). Source: about 1 year ago
  • Python Algotrading with Machine Learning
    A super-fast backtesting engine built in NumPy and accelerated with Numba. - Source: dev.to / about 1 year ago
  • Why are physics undergrads told to "learn programming" and what does this consist of?
    NumPy: allows you to work with matrices and common math functions efficiently. Very useful for analyzing experimental data and running simulations. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 1 year ago
  • How to Develope a Hotel Price Monitoring Tool with Python?
    For this task, we are going to use Numpy. It is already installed all you have to do is import this into the file. - Source: dev.to / about 1 year ago
  • About to lose access to MATLAB, is Python a realistic replacement for DSP algorithm development?
    Python vector library. Allows you to do vector mathematics like in pythonm. Source: over 1 year ago
  • Sematic + Ray: The Best of Orchestration and Distributed Compute at your Fingertips
    Sometimes, two tools seem to “just fit” together, and you forget that you’re even working with multiple tools as the lines blur into a coherent experience. One example that every ML Engineer or Data Scientist is familiar with is numpy and pandas. Numpy enables fast and powerful mathematical computations with arrays/matrices in Python. Pandas provides higher-level data structures for manipulating tabular data.... - Source: dev.to / over 1 year ago
  • What projects / addons / libraries are you missing in 4.0?
    Fast and generic matrix / linear algebra library, like what numpy is to Python. Source: over 1 year ago

External sources with reviews and comparisons of NumPy

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack, an ecosystem of Python-based math,...

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NumPy discussion

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  1. Isn't it obvious?

  2. Dmitry avatar
    Dmitry
    · 5 months ago
    · Reply

    The most useful number crunching library for Python.

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