altair-viz/altair

@machinelearnbot

WARNING: As of 2/10/2018, the master branch of this repository is under construction as we reorganize and refactor for the upcoming 2.0 release. The 2.x series of Altair will have full support for VegaLite 1.0/2.0 and Vega 2.0/3.0, We have created a 1.x branch for maintenance of the 1.x series. Altair is a declarative statistical visualization library for Python. Altair is developed by Brian Granger and Jake Vanderplas in close collaboration with the UW Interactive Data Lab.


Top 5 Python Libraries For Data Visualization

#artificialintelligence

Data visualization gives many insights that data alone cannot. Python has some of the most interactive data visualisation tools. The most basic plot types are shared between multiple libraries, but others are only available in certain libraries. Data journalist and information designer, David McCandless, talking about the significance of data visualization in his TED talk had said, "By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful."


On Education Python 3 Data Science - NumPy, Pandas, and Time Series - all courses

#artificialintelligence

Understand the Scientific Python Ecosystem Understand Data Science, Pandas, and Plotly Learn basics of NumPy Fundamentals Learn Advanced Data Visualization Learn Data Acquisition Techniques Linear Algebra and Matrices Time Series with Pandas Time Series with Plotly, Matplotlib, Altair, and Seaborn Requirements Windows PC/ Raspberry Pi with Internet Connection Zeal and enthusiasm to learn new things a burning desire to take your career to the next level Basic Programming and Python Programming Basics basic mathematics knowledge will be greatly appreciated Become a Master in Data Acquisition, Visualization, and Time Series Analysis with Python 3 and acquire employers' one of the most requested skills of 21st Century! An expert level Data Science professional can earn minimum $100000 (that's five zeros after 1) in today's economy. This is the most comprehensive, yet straight-forward course for the Data Science and Time Series with Python 3 on Udemy! Whether you have never worked with Data Science before, already know basics of Python, or want to learn the advanced features of Pandas Time Series with Python 3, this course is for you! In this course we will teach you Data Science and Time Series with Python 3, Jupyter, NumPy, Pandas, Matplotlib, and Plotly .


Intermediate Streamlit

#artificialintelligence

Streamlit is a great tool to give your data science work an interface. I've been using it as a lightweight dashboard tool to display simple visualizations, wrapping python packages in a UI, and exploring model evaluations for NLP models (live examples 1 and 2). It's allowed me to better understand my data and created an interface that can help translate between code/data/analysis and communication with stakeholders and subject matter experts. However, today's prototypes become tomorrow's production apps. There's a risk in making things too easy -- before you know it you've set up expectations that the next iteration will happen just as quickly, or that your app is robust and well tested, or that deploying it company-wide is around the corner, or that the new intern can take it from here to save some money.


Viz Playground - basic data viz with Vega

@machinelearnbot

Oh no:-/ I haven't written a post in so long! For my second technical post, I had in mind something related to the talk I gave at PyCon Sweden 2017 back in September 2017 (here's a link to my slides on Github). My idea then was to share my experience of trying a new data visualisation tool. This tool called Altair isn't another data visualisation library as such, but a Python API to access the powerful and simple Vega-Lite JSON like grammar for interactive graphics (itself build on top of Vega). While it was fun and interesting to try out Altair (I used it for a personal project), it is still in the process of supporting Vega-Lite 2.0.