jupyter   4003

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Binder (beta)
Turn a Git repo into a collection of interactive notebooks
Have a repository full of Jupyter notebooks? With Binder, open those notebooks in an executable environment, making your code immediately reproducible by anyone, anywhere.
jupyter  python 
21 minutes ago by micktwomey
IHaskell on Windows! – Techscape
If you ever have wanted IHaskell, and also wanted that on Windows, the first thing that can disappoint you are these wordings in the installation page: Of course, a VirtualBox is a good choice. On…
haskell  jupyter  windows  linux 
14 hours ago by ianweatherhogg
Jupyter Notebook for Beginners: A Tutorial
a sample analysis, to answer a real-life question, so you can see how the flow of a notebook makes the task intuitive to work through
jupyter 
20 hours ago by ltalley
Jupyter Notebook Tutorial | plotly
Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code
jupyter 
20 hours ago by ltalley
Fun with NFL Stats, Bokeh, and Pandas
Posted by J253 on Sun 11 November 2018
Updated on Mon 12 November 2018

Cruising through Kaggle last week, I found a CSV of NFL play-by-play statistics. I get particularly excited about sports data so I started digging into this one right away. The data I found was compiled by Maksim Horowitz, Ron Yurko, and Sam Ventura. I originally found the CSV posted on Maksim's Kaggle page.

After reading a bit more I learned that they've created a really cool NFL API scraping tool called nflscrapR (written in R) that not only scrapes, cleans, parses, and outputs the CSV, but they also built expected point and win probability models for the NFL and have included this information in the CSV. Thanks to them for the work and sharing the data

[...]

As you can seen I'm going to be using [Bokeh](https://bokeh.pydata.org/en/latest/) for plotting, Counter from the collections module, and itertools as well. As is also obvious, I originally did this in a Jupyter Notebook that I've made available in [my repository here](https://github.com/j253/blog/blob/master/fun_with_NFL_stats_rel_1.1.ipynb).

Summary

In this post, I explored some NFL play-by-play data. You can find the data here. Since the data was already in such good condition, I didn't really have to do any cleaning. It was more of an exercise in Pandas slicing and filtering and plotting in Bokeh.
Jupyter  python  pandas  datavis  dataviz 
yesterday by rcyphers
Tracking Jupyter
RT : Tony Hirst () is doing a great job reporting on developments in . Thanks Tony!
Jupyter  from twitter
2 days ago by mshook
Tracking Jupyter
Tony Hirst () is doing a great job reporting on developments in . Thanks Tony!
Jupyter  from twitter_favs
2 days ago by psychemedia
zekelabs/data-science-complete-tutorial
Lesson 1 : Introduction to Numpy (Video) Lesson 2 : Data Wrangling using Pandas Lesson 3 : Plotting in Python Lesson 4 : Linear Models for Regression & Classification Lesson 5 : Preprocessing Data Lesson 6 : Decision Trees Lesson 7 : Naive Bayes Lesson 8 : Composite Estimators Lesson 9 : Model Selection and Evaluation Lesson 10 : Feature Selection Techniques Lesson 11 : Nearest Neighbors Lesson 12 : Clustering Techniques Lesson 13 : Anomaly Detection Lesson 14 : Support Vector Machines Lesson 15 : Dealing with Imbalanced Classes Lesson 16 : Ensemble Methods
Lesson 1 : Introduction to Numpy (Video) Lesson 2 : Data Wrangling using Pandas Lesson 3 : Plotting in Python Lesson 4 : Linear Models for Regression & Classification Lesson 5 : Preprocessing Data Lesson 6 : Decision Trees Lesson 7 : Naive Bayes Lesson 8 : Composite Estimators Lesson 9 : Model Selection and Evaluation Lesson 10 : Feature Selection Techniques Lesson 11 : Nearest Neighbors Lesson 12 : Clustering Techniques Lesson 13 : Anomaly Detection Lesson 14 : Support Vector Machines Lesson 15 : Dealing with Imbalanced Classes Lesson 16 : Ensemble Methods
jupyter  python  education  machinelearning 
4 days ago by michaelfox

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