NumPy Reviews and details

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• 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 / about 1 month 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 / about 2 months ago

• Introducing Flama for Robust Machine Learning APIs

  Numpy: A library for scientific computing in Python. - Source: dev.to / 4 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 / 6 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 / 7 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 / 8 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 / 10 months ago

• Preparation for AI

  Know how to use numpy to vectorize operations and flatten (and unflatten data). Source: 11 months ago

• Python Algotrading with Machine Learning

  A super-fast backtesting engine built in NumPy and accelerated with Numba. - Source: dev.to / 11 months 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: 11 months 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: 12 months 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 / 12 months 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: about 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 / about 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: about 1 year ago

• Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service

  This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago

• Analysing AWS VPC Flow logs with Python and Pandas

  I've had some experience doing simple data analysis in Python before, specifically with Pandas, Matplotlib, Numpy, and other popular data science libraries, so it made sense that I leverage those skills rather than trying to learn something like AWS Athena. - Source: dev.to / about 1 year ago

• What are the best Python libraries to learn for beginners?

  NumPy: Scientific computing library and I know this one is the most popular especially in Data Science. Source: about 1 year ago

• Joining the Open Source Development Course

  Python is the main programming language I use nowadays. In particular numpy and pandas are of course extremely useful. I also use biopython package - a collection of software tools for biological computation written in Python by an international group of researchers and developers. - Source: dev.to / over 1 year ago

• DO YOU YAML?

  If you want to start a project from scratch, I prefer to start with a very basic virtual environment and add the packages I need as I go along. My basic framework usually consists of: Python NumPy Pandas MatplotLib & sometimes Seaborn. - Source: dev.to / over 1 year ago

• Tips for parsing inputs in Python?

  Since you mention 2D arrays, did you consider using NumPy? It has tons of useful functions for array operations. Source: over 1 year ago