A Quick Look generator for Jupyter/IPython notebooks without further dependencies

Collection of iPython notebooks with some quick demos

A presentation about topic modeling and then a quick example documented in IPython/Jupyter Notebooks

Set of basic ipython notebook commands to get up to speed quickly

A quick iPython notebook showing how to create and style Matplotlib charts with roughly same flexibility as ggplot2

This is a iPython Notebook for learning basic python and it also be a quick referece for Machine Learning

Quick Ipython notebooks

Repo of some personal notes about things I face myself on the way out of my Scientific and Python journeys

Quick and easy way to install and launch IPython's notebook feature, especially on OS X

Tutorials This is a guide to many pandas tutorials, geared mainly for new users. Internal Guides pandas own 10 Minutes to pandas More complex recipes are in the Cookbook pandas Cookbook The goal of this cookbook (by Julia Evans) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that that entails. Here are links to the v0.1 release. For an up-to-date table of contents, see the pandas-cookbook GitHub repository. To run the examples in this tutorial, you’ll need to clone the GitHub repository and get IPython Notebook running. See How to use this cookbook. A quick tour of the IPython Notebook: Shows off IPython’s awesome tab completion and magic functions. Chapter 1: Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong! Chapter 2: It’s not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns) Chapter 3: Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast. Chapter 4: Groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this. Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. Chapter 6: Strings with pandas are great. It has all these vectorized string operations and they’re the best. We will turn a bunch of strings containing “Snow” into vectors of numbers in a trice. Chapter 7: Cleaning up messy data is never a joy, but with pandas it’s easier. Chapter 8: Parsing Unix timestamps is confusing at first but it turns out to be really easy. Lessons for New pandas Users For more resources, please visit the main repository. 01 - Lesson: - Importing libraries - Creating data sets - Creating data frames - Reading from CSV - Exporting to CSV - Finding maximums - Plotting data 02 - Lesson: - Reading from TXT - Exporting to TXT - Selecting top/bottom records - Descriptive statistics - Grouping/sorting data 03 - Lesson: - Creating functions - Reading from EXCEL - Exporting to EXCEL - Outliers - Lambda functions - Slice and dice data 04 - Lesson: - Adding/deleting columns - Index operations 05 - Lesson: - Stack/Unstack/Transpose functions 06 - Lesson: - GroupBy function 07 - Lesson: - Ways to calculate outliers 08 - Lesson: - Read from Microsoft SQL databases 09 - Lesson: - Export to CSV/EXCEL/TXT 10 - Lesson: - Converting between different kinds of formats 11 - Lesson: - Combining data from various sources Practical data analysis with Python This guide is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. There are four sections covering selected topics as follows: Munging Data Aggregating Data Visualizing Data Time Series Excel charts with pandas, vincent and xlsxwriter Using Pandas and XlsxWriter to create Excel charts Various Tutorials Wes McKinney’s (pandas BDFL) blog Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 Financial analysis in python, by Thomas Wiecki Intro to pandas data structures, by Greg Reda Pandas and Python: Top 10, by Manish Amde Pandas Tutorial, by Mikhail Semeniuk indexmodules |next |previous |pandas 0.15.2 documentation » © Copyright 2008-2014, the pandas development team

An iPython Notebook for a quick 'lecture style' workshop.

Quick Python Tut for Ninjas in IPython Notebooks

quick and dirty ipython notebook samples

place to put ipython notebooks for quick displaying and sharing

Quick intro to NLTK in an ipython notebook.

Quick and dirty iPython notebook for SAR Image Classification

Quick Ipython notebooks that are used to calculate some basic stats for different tools

This is a quickly constucted demo using Bash on a ipython notebook.

A collection of iPython notebooks containing my practice work. Can be used as a quick recap

a quick demo of ipython notebook, and python packages for exploratory data science

A quick overview of how to perform statistical analysis using the Ipython notebook

A Vagrantfile and a Dockerfile to get iPython notebook up and running quickly

Quick Experiment: Interactive CoffeeScript with a notebook interface similar to Mathematica or IPython

A quick study made of airplane crashes from 1908 to 2009, using Jupyter Notebook and IPython

P0: Find the Optimal Chopstick Length An opportunity to get started with data analysis and receive some quick feedback about your progress. Set up iPython notebook and commonly used data analysis libraries on your own computer. Use them to dig into the results of an experiment testing the optimal length of chopsticks and present your findings.