PythonDataScienceHandbook
NumPy
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PythonDataScienceHandbook | NumPy | |
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98 | 272 | |
41,364 | 26,231 | |
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1.0 | 10.0 | |
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Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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PythonDataScienceHandbook
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About Data analyst, data scientist and data engineer, resources and experiences
Python Data Science Handbook
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Other programing options?
Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.
- Resources for Current DE Interested in Learning Data Science
- What are the best Python libraries to learn for beginners?
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Best Websites For Coders
Data Science course : Python Data Science Handbook
- What are some good useful libraries I can get the hang of?
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I learnt all the basics about python and had interest in learning about "AI development with python". and now I'm stuck cuz I don't know where to start. Can anyone give some advice to me ?
Something that helped me get started is this git repo: https://github.com/jakevdp/PythonDataScienceHandbook/tree/master/notebooks It covers all the basics of data analysis using numpy, pandas and matplotlib.
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Learn Python with CFB tutorial
Thanks! Will do. I work full time on data engineering/geospatial big data analytics, so I haven't had the energy to do this in the evenings or weekends yet. I do plenty of work with regression (but not in an MLOps sense) and dimensionality reduction (we do PCA). So in my mind my gap is (1) actual neural network work and (2) familiarity with workflows using e.g. pytorch or scikit-learn or something similar. Any pointers on where to get started resource-wise? Been thinking of starting with Ch.5 here and moving on from that: https://jakevdp.github.io/PythonDataScienceHandbook/. I have some projects in mind (including some predictive CFB model) so will start that up on the side while doing some of these tutorials.
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What programming language should I learn and what skills should I learn
Yeah! Ofc. So for R: https://r4ds.had.co.nz Python: https://jakevdp.github.io/PythonDataScienceHandbook/ SQL: I had to get a book, but this would be a good place to start: https://datascience.foundation/sciencewhitepaper/sql-for-data-science
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
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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.
- JSON dans les projets data science : Trucs & Astuces
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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:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
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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 I've decided to expand our math functionality with NumPy. pdm add is what's used to add dependencies like this to our project:
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Building an efficient sparse keyword index in Python
Large computations in pure Python can also be painfully slow. Luckily, there is a robust landscape of options for numeric processing. The most popular framework is NumPy. There is also PyTorch and other GPU-based tensor processing frameworks.
What are some alternatives?
SymPy - A computer algebra system written in pure Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
SciPy - SciPy library main repository
blaze - NumPy and Pandas interface to Big Data
Numba - NumPy aware dynamic Python compiler using LLVM
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
manim - Animation engine for explanatory math videos
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
scikit-learn - scikit-learn: machine learning in Python
Scrapy - Scrapy, a fast high-level web crawling & scraping framework for Python.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration