cheatsheets
Cython
cheatsheets | Cython | |
---|---|---|
126 | 79 | |
7,245 | 8,935 | |
0.2% | 1.3% | |
7.0 | 9.8 | |
15 days ago | 5 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cheatsheets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Matplotlib - A Python 2D plotting library.
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How to retrieve and analyze crypto order book data using Python and a cryptocurrency API
Data visualization: utilizing Python's Matplotlib for visualizing order book information.
- Matplotlib
- Ask HN: What plotting tools should I invest in learning?
- Help with an array
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Getting visual studio code to work with imported library
Name: matplotlib Version: 3.7.1 Summary: Python plotting package Home-page: https://matplotlib.org Author: John D. Hunter, Michael Droettboom Author-email: [email protected] License: PSFLocation: /home/huinker/.local/lib/python3.10/site-packages
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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 without relying on expensive or proprietary software.
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What else should I complete before applying for a data analyst role?
programming language: basic python, pandas, matplotlib -- you'll probably do these in school, but if not https://cs50.harvard.edu/python/2022/ https://matplotlib.org/
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[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
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[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
Cython
- Ask HN: C/C++ developer wanting to learn efficient Python
- Ask HN: Is there a way to use Python statically typed or with any type-checking?
- Cython 3.0
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How to make a c++ python extension?
The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints.
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Never again
and again, everything that was released after using an older version of cython.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
- Slow Rust Compiler is a Feature, not a Bug.
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Any faster Python alternatives?
Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)
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What exactly is 'JIT'?
JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython.
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Python executable makers
Cython - - embed demo
What are some alternatives?
finplot - Performant and effortless finance plotting for Python
SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
manim - A community-maintained Python framework for creating mathematical animations.
PyPy
Fast-F1 - FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry
mypyc - Compile type annotated Python to fast C extensions
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
Pyston - A faster and highly-compatible implementation of the Python programming language.
Keras - Deep Learning for humans
Stackless Python
geogebra - GeoGebra apps (mirror)
Pyjion