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Based on our record, NumPy seems to be a lot more popular than Pycoder's Weekly. While we know about 107 links to NumPy, we've tracked only 6 mentions of Pycoder's Weekly. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
PyCoder's Weekly[https://pycoders.com/] is a good newsletter for learning more about Python. - Source: Hacker News / about 1 year ago
Coincidentally, I just saw the article How to Evaluate the Quality of Python Packages on the latest PyCoder's Weekly. Source: about 1 year ago
There are many podcasts that will keep you in touch. And also https://pycoders.com/ weekly newsletter. Source: over 1 year ago
You can go to the home page of PYPI and have a look at the list of trending packages, but I'd suggest following a good podcast like Talk Python to Me or Pycoders Weekly. They often feature interesting new packages. Source: over 1 year ago
Recommend reading PyCoders weekly newsletter every Thursday. Source: almost 2 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
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
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
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
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
One Month Python - Learn to build Django apps in just one month.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Glitch Instant Database Starters - Build a database-backed full-stack app in under a minute.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Learn Python The Hard Way - One of the best guides to learn Python & coding in general
OpenCV - OpenCV is the world's biggest computer vision library
