Guides and Research
Links to the best IPython and Jupyter Notebooks.
matplotlib – 2D and 3D plotting in Python
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures .
Visualizing distributions of data
This notebook demonstrates different approaches to graphically representing distributions of data, specifically focusing on the tools provided by the seaborn package.
Neural Networks
Neural networks are one approach to machine learning that attempts to deal with the problem of large data dimensionality. The neural network approach uses a fixed number of basis functions – in contrast to methods such as support vector machines …
XKCD plots in Matplotlib
Update: the matplotlib pull request has been merged! See This post for a description of the XKCD functionality now built-in to matplotlib!
Web Scraping Indeed for Key Data Science Job Skills
As many of you probably know, being a data scientist can require a pretty large skill set . .
Python for Data Science
This short primer on Python is designed to provide a rapid “on-ramp” to enable computer programmers who are already familiar with concepts and constructs in other programming languages learn enough about Python to facilitate the effective use of open-source and ..
Automating Microsoft Office with Python
Windows applications, for many years, have provided a COM API for automation. This includes Microsoft Office as well.
Advanced Data Visualization
There have been many examples of useful and exciting data visualizations for a variety of topics and applications.
A Primer on Bayesian Methods for Multilevel Modeling
Hierarchical or multilevel modeling is a generalization of regression modeling.
9.1 Reading data from SQL databases
So far we’ve only talked about reading data from CSV files. That’s a pretty common way to store data, but there are many others! Pandas can read from HTML, JSON, SQL, Excel (!!!), HDF5, Stata, and a few other things. …
INTRODUCTION TO PYTHON FOR DATA MINING
I do most of my work from the command line, but Anaconda comes with a launcher app that can be found in the ~/anaconda directory. To get the launcher to work with a Mac, you need to do the following:
Three-Body Problem
This notebook implements a numeric simulation of the three-body problem . This is done using the Julia Language and the Sundials library.
Interactive Financial Analytics with Python & IPython
Tutorial with Examples based on the VSTOXX Volatility Index
6.4. Visualizing a NetworkX graph in the IPython notebook with d3.js
This is one of the 100 recipes of the IPython Cookbook , the definitive guide to high-performance scientific computing and data science in Python.
No title
This web site does not host notebooks, it only renders notebooks available on other websites.
Faster data processing in Python
Let’s discuss how to make these choices with the aim of running code faster.
Probabilistic Programming in Python using PyMC
Authors: Thomas V. Wiecki, John Salvatier, Christopher Fonnesbeck
1 IPython notebook hints and tips
This notebook forms part of a series on computational optical radiometry . The notebooks can be downloaded from Github . These notebooks are constantly revised and updated, please revisit from time to time.
Interactive visualisations in the browser with Python
In the past few years, the python (scientific) ecosystem has seen intense development of solutions aimed at bringing interactive data visualisation in the browser, through a set of libraries which basically interface with powerful JavaScript visualisations libraries such as D3.js …
Data Science with Hadoop – predicting airline delays – part 1
In this first blog post we will demonstrate a step by step solution to a supervised learning problem, including:
TOP 10 PYTHON IDIOMS I WISH I’D LEARNED EARLIER
By David “Prooffreader — with two f’s, that’s the joke!” Taylor
Exploring the diffusion equation with Python
Most of what follows (except the Python code and the bit on fault scarps) is based on and inspired by Slingerland and Kump (2011): Mathematical Modeling of Earth’s Dynamical Systems , Princeton University Press. The purpose is to go through …
Python Pandas Cheat Sheet
As a data analyst, these are common tasks I perform using Pandas
Embedding Matplotlib Animations in IPython Notebooks
This notebook first appeared as a blog post on Pythonic Perambulations .
Collecting Twitter data from the API using Python
Alex Hanna, University of Wisconsin-Madison alex-hanna.com @alexhanna
Putting the in IPython
Here’s a link to the abstract and you can watch the video from SciPy 2014 where this talk was presented. The rest of this document are the slides I used in the talk.
Financial market data manipulation and visualization with Python
In finance, market data is price and trade data for a given instrument like a stock, currency pair, or futures contract. Often times this data is visualized as a chart of historical data.
7.8. Analyzing data with R in the IPython notebook
This is one of the 100 recipes of the IPython Cookbook , the definitive guide to high-performance scientific computing and data science in Python.
Statistical Data Modeling
Some or most of you have probably taken some undergraduate- or graduate-level statistics courses.
Python tutorial : Multivariate linear regression to predict house prices
This tutorial , uses multivariate regression to predict house price. The high level goal is the use multiple features …
Celebs vs. Mortals: Facial Recognition in Python
After watching way too much Game of Thrones and Ink Master, I started wondering what it is that makes a celebrity standout from the rest of us.
Up and Down the Python Data and Web Visualization Stack
USGS dataset listing every wind turbine in the United States:
Practical Data Science in Python
This notebook accompanies my talk on “Data Science with Python”. Questions & comments welcome @RadimRehurek .
Lightning and the IPython notebook
Lightning is a framework for data visualization providing API-based access to reproducible, web-based, interactive visualizations.
Weather Changes: an Introduction to Data Analysis with Python
This is a basic introduction to real-world data analysis and visualization with Python. To see the finished product and a bit more analysis, read my blog .
Jupyter Notebook Viewer
After seeing this post I wanted to perform the same analsysis using SciKit-Learn . The classifiers used are given below with their paramters. I do not have a Neural Network like that shown in the post, but added a few …
Mining Twitter Data Using Python / Tweepy
This web site does not host notebooks, it only renders notebooks available on other websites.
Kalman and Bayesian Filters in Python
Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.
Graph Gmail inbox data with IPython notebook
This web site does not host notebooks, it only renders notebooks available on other websites.
Intro to Pandas
A DataFrame is a 2D array of data, with row and column indices. A Series is a 1D array, with a single index. Each row and column of a DataFrame can be viewed as a Series .
Fast Lomb-Scargle Periodograms in Python
The Lomb-Scargle Periodogram is a well-known method of finding periodicity in irregularly-sampled time-series data. The common implementation of the periodogram is relatively slow: for $N$ data points, a frequency grid of $\\sim N$ frequencies is required and the computation scales …
Introduction to Data Analysis with Python
Source: http://www.nutanix.com/2013/09/16/the-cup-has-been-flipped/
Differential Equations in Data Science
The ordinary differential equation (ODE) is a tool often overlooked in data science. It’s not listed in the top 10 data science algorithms , Google’s Chief Economist’s tricks for big data or by the numerious texts in data science. However, …
A Gentle Introduction to Machine Learning with Python and Scikit-learn
In this notebook, we show (using very simple examples) the general procedures for performing machine learning using scikit-learn. We will address classification, regression and clustering. We will use the Iris flower dataset, introduced in 1936 by Sir Ronald Fisher for …
Using Sumatra with Pandas in IPython
This notebook demonstrates how to use Sumatra to capture simulation input data and meta data and then export these records into a Pandas data frame. Sumatra has a stand alone web interface built with Django which allows users to view …
Expresiones Regulares con Python
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Mi blog sobre Python . El contenido esta bajo la licencia BSD.
FP idioms in Python
A whirlwind tour of the available, the usable, and the undesirable.
building a reading map based on a reduced graph of co-edited pages
The drawing of the graph of pages linked when their share editors is very messy. It also show a non-pertinent structure to achieve our goal. On an another hand, the hyperlink graph is also not very pertinent because it is …
Jupyter Notebook Viewer
This web site does not host notebooks, it only renders notebooks available on other websites.
Jupyter Notebook Viewer
DataPhilly is a local data meetup group I started back in 2012. I had attended a few data science conferences and I was really disappointed about the lack of a local meetup group for people interested in data science. And …
Python data visualization with matplotlib and stylin’ like ggplot2
At the end of the guide, you’ll see a few ways to achieve a ggplot2-like aesthetic using the matplotlib API:
Convert A CSV Into Python Code To Recreate It
This might seem like a strange bit of code, but it serves a very valuable (though niche) function. As a rule, I like all the snippits in my well-documented Python snippits series to not rely on outside data to run. …
Jupyter Notebook Viewer
” An estimator is any object that learns from data; it may be a classification, regression or clustering algorithm or a transformer that extracts/filters useful features from raw data. “
IPython 2013 Progress Report – Sloan Foundation
Submitted by: Fernando Perez and Brian Granger.
What is Bayes Theorem?
Bayes theorem is what allows us to go from a sampling (or likelihood) distribution and a prior distribution to a posterior distribution .
