Python has rapidly became a leading language for Data Science and Machine Learning. In the latest KDnuggets Poll Python leads the 11 top Data Science, Machine Learning platforms. This page brings you the latest KDnuggets Opinions and Tutorials related to Python, as well as our most popular - gold and silver-badge winning content. Enjoy!
Latest posts on Python
- KDnuggets™ News 21:n36, Sep 22: The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python - Sep 22, 2021The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python; Introduction to Automated Machine Learning; How to be a Data Scientist without a STEM degree; What Is The Real Difference Between Data Engineers and Data Scientists?
- 15 Must-Know Python String Methods - Sep 21, 2021It is not always about numbers.
- If You Can Write Functions, You Can Use Dask - Sep 21, 2021This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The first article in the series is about using LocalCluster.
- How to be a Data Scientist without a STEM degree - Sep 20, 2021Breaking into data science as a professional does require technical skills, a well-honed knack for problem-solving, and a willingness to swim in oceans of data. Maybe you are coming in as a career change or ready to take a new learning path in life--without having previously earned an advanced degree in a STEM field. Follow these tips to find your way into this high-demand and interesting field.
- Adventures in MLOps with Github Actions, Iterative.ai, Label Studio and NBDEV - Sep 16, 2021This article documents the authors' experience building their custom MLOps approach.
Most popular (badge-winning) recent posts on Python
- Do You Read Excel Files with Python? There is a 1000x Faster Way [Gold Blog]In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
- Learning Data Science and Machine Learning: First Steps After The Roadmap [Silver Blog]Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
- Automate Microsoft Excel and Word Using Python [Platinum Blog]Integrate Excel with Word to generate automated reports seamlessly.
- Django’s 9 Most Common Applications [Gold Blog]Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.
- Prefect: How to Write and Schedule Your First ETL Pipeline with Python [Gold Blog]Workflow management systems made easy — both locally and in the cloud.
- How to Query Your Pandas Dataframe [Gold Blog]A Data Scientist’s perspective on SQL-like Python functions.
- GPU-Powered Data Science (NOT Deep Learning) with RAPIDS [Gold Blog]How to utilize the power of your GPU for regular data science and machine learning even if you do not do a lot of deep learning work.
- Why and how should you learn “Productive Data Science”? [Gold Blog]What is Productive Data Science and what are some of its components?
- Top 6 Data Science Online Courses in 2021 [Platinum Blog]As an aspiring data scientist, it is easy to get overwhelmed by the abundance of resources available on the Internet. With these 6 online courses, you can develop yourself from a novice to experienced in less than a year, and prepare you with the skills necessary to land a job in data science.
- 5 Python Data Processing Tips & Code Snippets [Silver Blog]This is a small collection of Python code snippets that a beginner might find useful for data processing.
- Pandas not enough? Here are a few good alternatives to processing larger and faster data in Python [Platinum Blog]While the Pandas library remains a crucial workhorse in data processing and management for data science, some limitations exist that can impact efficiencies, especially with very large data sets. Here, a few interesting alternatives to Pandas are introduced to improve your large data handling performance.
- Managing Your Reusable Python Code as a Data Scientist [Silver Blog]Here are a few approaches that I have settled on for managing my own reusable Python code as a data scientist, presented from most to least general code use, and aimed at beginners.
- Add A New Dimension To Your Photos Using Python [Silver Blog]Read this to learn how to breathe new life into your photos with a 3D Ken Burns Effect.
- Get Interactive Plots Directly With Pandas [Silver Blog]Telling a story with data is a core function for any Data Scientist, and creating data visualizations that are simultaneously illuminating and appealing can be challenging. This tutorial reviews how to create Plotly and Bokeh plots directly through Pandas plotting syntax, which will help you convert static visualizations into interactive counterparts -- and take your analysis to the next level.
- How to Generate Automated PDF Documents with Python [Platinum Blog]Discover how to leverage automation to create dazzling PDF documents effortlessly.
- 5 Tasks To Automate With Python [Platinum Blog]Here are 5 tasks you can automate with Python, and how to do it.
- How to Make Python Code Run Incredibly Fast [Silver Blog]In this article, I have explained some tips and tricks to optimize and speed up Python code.
- Top Programming Languages and Their Uses [Gold Blog]The landscape of programming languages is rich and expanding, which can make it tricky to focus on just one or another for your career. We highlight some of the most popular languages that are modern, widely used, and come with loads of packages or libraries that will help you be more productive and efficient in your work.
- A Guide On How To Become A Data Scientist (Step By Step Approach) [Platinum Blog]Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.
- How to Determine if Your Machine Learning Model is Overtrained [Silver Blog]WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.