Python String Processing Primer
Try this string processing primer cheatsheet to gain an understanding of using Python to manipulate and process strings at a basic level.
python
Build pipelines with Pandas using “pdpipe” - Towards Data Science
We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
10 Python Tips and Tricks You Should Learn Today - KDnuggets
Check out this collection of 10 Python snippets that can be taken as a reference for your daily work.
Martin Heinz - Personal Website & Blog
Python haters always say, that one of reasons they don't want to use it, is that it's slow. Well, whether specific program - regardle...
Learn Metaflow in 10 mins — Netflix’s Python/R Framework for Data Science
Spend more time modeling, and less time managing infrastructures. A hands-on tutorial.
SciPy 1.0: fundamental algorithms for scientific computing in Python | Natu
Nature Methods - This Perspective describes the development and capabilities of SciPy 1.0, an open source scientific computing library for the Python programming language.
HTML Parser: How to scrap HTML content | Python Central
A tutorial about a HTML parser for Python 3. Learn about the basic of a library for easily parsing web pages and extracting useful information.
Building an OCR Engine with Python and Tesseract
Dive deep into OCR with Tesseract, including Pytesseract integration, training with custom data, limitations, and comparisons with enterprise solutions.
Python Tuples and Tuple Methods
Brush up on your Python basics with this post on creating, using, and manipulating tuples.
How to Speed up Pandas by 4x with one line of code
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.
When your data doesn’t fit in memory: the basic techniques
You can process data that doesn’t fit in memory by using four basic techniques: spending money, compression, chunking, and indexing.
How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - KDnuggets
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.
5 Advanced Features of Pandas and How to Use Them
The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.
Counting FLOPS and other CPU counters in Python
On the Linux command line it is fairly easy to use the perf command to measure number of floating point operations (or other performance metrics). (See for example this old blog post ) with this approach it is not easy to get a fine grained view of how different stages of…
What’s New In Python 3.8 — Python 3.8.0 documentation
Editor, Raymond Hettinger,. This article explains the new features in Python 3.8, compared to 3.7. Python 3.8 was released on October 14, 2019. For full details, see the changelog. Summary – Releas...
PyPy's New JSON Parser
Introduction In the last year or two I have worked on and off on making PyPy's JSON faster, particularly when parsing large JSON files. I...
Partial Functions in Python: A Guide for Developers
Learn how to simplify your Python code using partial functions to create more flexible, reusable, and concise function calls
Async IO in Python: A Complete Walkthrough – Real Python
This tutorial will give you a firm grasp of Python’s approach to async IO, which is a concurrent programming design that has received dedicated support in Python, evolving rapidly from Python 3.4 through 3.7 (and probably beyond).
A Grammar of Graphics for Python – plotnine 0.13.6
PySpark Cheat Sheet: Spark in Python
This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning.
Installation — Datashader v0.16.3
Arima Model – Complete Guide to Time Series Forecasting in Python
Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend it to Seasonal ARIMA (SARIMA) and SARIMAX models. You will also see how to build autoarima models in python
Python at Netflix
By Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood
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Python Data Science Handbook | Python Data Science Handbook
Make your own Super Pandas using Multiproc
This post is a part of my series on Python Shorts. Some tips on how to use python. This post is about using the computing power we have at hand and applying it to the data structure we use most.
How to Use C Functions in Python
Did you know you can write functions in C and then call them directly from Python? Isn't that cool? L...
An Introduction to Cython, the Secret Python Extension with Superpowers
yzhao062/pyod: A Python Toolbox for Scalable Outlier Detection (Anomaly Det
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques - yzhao062/pyod
10 Simple Hacks to Speed up Your Data Analysis in Python
This article lists some curated tips for working with Python and Jupyter Notebooks, covering topics such as easily profiling data, formatting code and output, debugging, and more. Hopefully you can find something useful within.
Top 5 Tips Developers Should Know For Python Codes Optimization / Habr
Cython
Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such … - Selection from Cython [Book]
https://datawhatnow.com/things-you-are-probably-not-using-in-python-3-but-should/
Designing a RESTful API with Python and Flask - miguelgrinberg.com
In recent years REST (REpresentational State Transfer) has emerged as the standard architectural design for web services and web APIs.In this article I'm going to show you how easy it is to create a…
Talking to Python from JavaScript (and Back Again!)
Something a lot of beginners struggle with is the concept of passing data between different programmi...
Welcome to Bokeh — Bokeh 1.0.4 documentation
Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming data...
Forecasting in Python with Prophet | Reports - Mode
A guided walkthrough of how to use the Prophet python library to solve a common forecasting problem.
Dash ? – plotly – Medium
Create Reactive Web Apps in pure Python
15 Statistical Hypothesis Tests in Python (Cheat Sheet)
Quick-reference guide to the 17 statistical hypothesis tests that you need in applied machine learning, with sample code in Python. Although there are hundreds of statistical hypothesis tests that you could use, there is only a small subset that you may need to use in a machine…
Why you should be using pathlib
When I discovered Python’s new pathlib module a few years ago, I initially wrote it off as being a slightly more awkward and unnecessarily …
Python profiling with Pyflame
Profiling Python applications using Pyflame
Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - KDnuggets
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
Python Data Visualization 2018: Why So Many Libraries?
Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities.
newspaper3k · PyPI
Simplified python article discovery & extraction.
The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python c
Learn how these 12 dimensionality reduction techniques can help you extract valuable patterns and insights from high-dimensional datasets.
An A-Z of useful Python tricks
By Peter Gleeson Python is one of the world’s most popular, in-demand programming languages. This is for many reasons: it’s easy to learn it’s super versatile it has a huge range of modules and libraries I use Python daily as an integral part of my...
mukund109/word-mesh: A context-preserving word cloud generator
A context-preserving word cloud generator.
Cookbook — Bayesian Modelling with PyMC3
Recently I’ve started using PyMC3 for Bayesian modelling, and it’s an amazing piece of software! The API only exposes as much of heavy machinery of MCMC as you need — by which I mean, just the pm.sample() method (a.k.a., as Thomas Wiecki puts it, the Magic Inference Button™)…
A Feature Selection Tool for Machine Learning in Python
Using the FeatureSelector for efficient machine learning workflows
Introduction to Market Basket Analysis in Python
Using mlxtend to perform market basket analysis on online retail data set.