Recorded: July 16, 2012Language: English
Recorded: April 12, 2014Language: English

When people hear of matplotlib, they think rudimentary graphs that will need to be touched up in photoshop. This tutorial aims to teach attendees how to exploit the functionality provided by various matplotlib libraries to create professional looking data visualizations.

Recorded: March 8, 2012Language: English

When it comes to plotting with Python many people think about matplotlib. It is widely used and provides a simple interface for creating a wide variety of plots from very simple diagrams to sophisticated animations. This tutorial is a hands-on introduction that teaches the basics of matplotlib. Students will learn how to create publication-ready plots with just a few lines of Python.

Recorded: Sept. 13, 2012Language: English

Two examples of using matplotlib: first, in Greg's PhD research in marine microbiology; second, in plotting baseball PITCHf/x data.

Recorded: March 30, 2012Language: English

In this video tutorial from the 2012 PyData Workshop, John Hunter, author of matplotlib is going to give you some advanced insight into the plotting library.

Recorded: July 2, 2013Language: English

Authors: Michael Droettboom

Track: Reproducible Science

This talk will be a general "state of the project address" for matplotlib, the popular plotting library in the scientific Python stack. It will provide an update about new features added to matplotlib over the course of the last year, outline some ongoing planned work, and describe some challenges to move into the future. The new features include a web browser backend, "sketch" style, and numerous other bugfixes and improvements. Also discussed will be the challenges and lessons learned moving to Python 3. Our new "MEP" (matplotlib enhancement proposal) method will be introduced, and the ongoing MEPs will be discussed, such as moving to properties, updating the docstrings, etc. Some of the more pie-in-the-sky plans (such as styling and serializing) will be discussed. It is hoped that this overview will be useful for those who use matplotlib, but don't necessarily follow its mailing list in detail, and also serve as a call to arms for assistance for the project.

Recorded: July 16, 2012Language: English
Recorded: July 2, 2013Language: English

Presentation of finalists for excellence in plotting using Matplotlib.

Recorded: Nov. 11, 2012Language: English

Use iPython, matplotlib, and Pandas to slice, dice, and visualise your application's behaviour through its logs.

Recorded: June 27, 2013Language: English

Presenter: Benjamin Root

Description

This tutorial will be the introduction to matplotlib, intended for users who want to become familiar with python's predominate scientific plotting package. First, the plotting functions that are available will be introduced so users will know what kinds of graphs can be done. We will then cover the fundamental concepts and terminologies, starting from the figure object down to the artists. In an organized and logical fashion, the components of a matplotlib figure are introduced, such as the axes, axis, tickers, and labels. We will explain what an Artist is for, as well as explain the purpose behind Collections. Finally, we will take an overview of the major toolkits available to use, particularly AxesGrid, mplot3d and basemap.

Outline

Outline:

Introduction

Purpose of matplotlib Online Documentation Examples Page Gallery Page FAQs API documentation Mailing Lists Github Repository Bug Reports & Feature Requests What is this "backend" thing I keep hearing about?

Plotting Functions

Graphs (plot, scatter, bar, stem, etc.) Images (imshow, pcolor, pcolormesh, contour[f], etc.) Lesser Knowns: (pie, acorr, hexbin, etc.) Brand New: streamplot() What goes in a Figure?

Axes Axis ticks (and ticklines and ticklabels) (both major & minor) axis labels axes title figure suptitle axis spines colorbars (and the oddities thereof) axis scale axis gridlines legend (Throughout the aforementioned section, I will be guiding audience members through the creation and manipulation of each of these components to produce a fully customized graph)

Introducing matplotlibrc

Hands-On: Have users try making some changes to the settings and see how a resulting figure changes What is an Artist?

Hands-On: Have audience members create some and see if they can get them displayed What is a Collection?

Hands-On: Have audience members create some, manipulate the properties and display them Properties:

color (and edgecolor, linecolor, facecolor, etc...) linewidth and edgewidth and markeredgewidth (and the oddity that happens in errorbar()) linestyle zorder visible What are toolkits?

axes_grid1 mplot3d basemap Required Packages

NumPy

Matplotlib (version 1.2.1 or later is preferred, but earlier version should still be sufficient for most of the tutorial)

ipython v0.13

Documentation

https://dl.dropbox.com/u/7325604/AnatomyOfMatplotlib.ipynb

Recorded: June 27, 2013Language: English

Presenter: Benjamin Root

Description

This tutorial will be the introduction to matplotlib, intended for users who want to become familiar with python's predominate scientific plotting package. First, the plotting functions that are available will be introduced so users will know what kinds of graphs can be done. We will then cover the fundamental concepts and terminologies, starting from the figure object down to the artists. In an organized and logical fashion, the components of a matplotlib figure are introduced, such as the axes, axis, tickers, and labels. We will explain what an Artist is for, as well as explain the purpose behind Collections. Finally, we will take an overview of the major toolkits available to use, particularly AxesGrid, mplot3d and basemap.

Outline

Outline:

Introduction

Purpose of matplotlib Online Documentation Examples Page Gallery Page FAQs API documentation Mailing Lists Github Repository Bug Reports & Feature Requests What is this "backend" thing I keep hearing about?

Plotting Functions

Graphs (plot, scatter, bar, stem, etc.) Images (imshow, pcolor, pcolormesh, contour[f], etc.) Lesser Knowns: (pie, acorr, hexbin, etc.) Brand New: streamplot() What goes in a Figure?

Axes Axis ticks (and ticklines and ticklabels) (both major & minor) axis labels axes title figure suptitle axis spines colorbars (and the oddities thereof) axis scale axis gridlines legend (Throughout the aforementioned section, I will be guiding audience members through the creation and manipulation of each of these components to produce a fully customized graph)

Introducing matplotlibrc

Hands-On: Have users try making some changes to the settings and see how a resulting figure changes What is an Artist?

Hands-On: Have audience members create some and see if they can get them displayed What is a Collection?

Hands-On: Have audience members create some, manipulate the properties and display them Properties:

color (and edgecolor, linecolor, facecolor, etc...) linewidth and edgewidth and markeredgewidth (and the oddity that happens in errorbar()) linestyle zorder visible What are toolkits?

axes_grid1 mplot3d basemap Required Packages

NumPy

Matplotlib (version 1.2.1 or later is preferred, but earlier version should still be sufficient for most of the tutorial)

ipython v0.13

Documentation

https://dl.dropbox.com/u/7325604/AnatomyOfMatplotlib.ipynb

Recorded: June 27, 2013Language: English

Presenter: Benjamin Root

Description

This tutorial will be the introduction to matplotlib, intended for users who want to become familiar with python's predominate scientific plotting package. First, the plotting functions that are available will be introduced so users will know what kinds of graphs can be done. We will then cover the fundamental concepts and terminologies, starting from the figure object down to the artists. In an organized and logical fashion, the components of a matplotlib figure are introduced, such as the axes, axis, tickers, and labels. We will explain what an Artist is for, as well as explain the purpose behind Collections. Finally, we will take an overview of the major toolkits available to use, particularly AxesGrid, mplot3d and basemap.

Outline

Outline:

Introduction

Purpose of matplotlib Online Documentation Examples Page Gallery Page FAQs API documentation Mailing Lists Github Repository Bug Reports & Feature Requests What is this "backend" thing I keep hearing about?

Plotting Functions

Graphs (plot, scatter, bar, stem, etc.) Images (imshow, pcolor, pcolormesh, contour[f], etc.) Lesser Knowns: (pie, acorr, hexbin, etc.) Brand New: streamplot() What goes in a Figure?

Axes Axis ticks (and ticklines and ticklabels) (both major & minor) axis labels axes title figure suptitle axis spines colorbars (and the oddities thereof) axis scale axis gridlines legend (Throughout the aforementioned section, I will be guiding audience members through the creation and manipulation of each of these components to produce a fully customized graph)

Introducing matplotlibrc

Hands-On: Have users try making some changes to the settings and see how a resulting figure changes What is an Artist?

Hands-On: Have audience members create some and see if they can get them displayed What is a Collection?

Hands-On: Have audience members create some, manipulate the properties and display them Properties:

color (and edgecolor, linecolor, facecolor, etc...) linewidth and edgewidth and markeredgewidth (and the oddity that happens in errorbar()) linestyle zorder visible What are toolkits?

axes_grid1 mplot3d basemap Required Packages

NumPy

Matplotlib (version 1.2.1 or later is preferred, but earlier version should still be sufficient for most of the tutorial)

ipython v0.13

Documentation

https://dl.dropbox.com/u/7325604/AnatomyOfMatplotlib.ipynb

Recorded: March 17, 2013Language: English

Use iPython, matplotlib, and Pandas to slice, dice, and visualize your application's behaviour through its logs.

Recorded: July 24, 2011Language: English

[EuroPython 2011] Stefano Cotta Ramusino - 23 June 2011 in "Track Ravioli"

Recorded: July 13, 2011Language: English

[EuroPython 2011] Stefano Cotta Ramusino - 23 June 2011 in "Track Ravioli"

Recorded: July 27, 2013Language: English

PyEphem is a powerful astronomy library. This talk covers some simple things you can do with PyEphem that relate to your everyday life, such as matching your sleep cycle to the sunrise and seeing what the moon is up to tonight. This will also include plotting data about the sky using matplotlib.

Recorded: Sept. 12, 2013Language: English

Python + SQL/CSV + matplotlib + HTML make it possible to create flexible and sophisticated analyses. If you want to express something about your data, there is probably a way of doing it using these tools. This talk will be about some lessons learned.

Recorded: July 21, 2011Language: English

[EuroPython 2011] Erik Janssens,Jeroen Dierckx - 22 June 2011 in "Track Ravioli "

Recorded: July 13, 2011Language: English

[EuroPython 2011] Erik Janssens,Jeroen Dierckx - 22 June 2011 in "Track Ravioli "

Recorded: Sept. 13, 2012Language: English

Ben recently discovered event driven concurrency. He will be talking about some of the basics as well as trying to compare it with other concurrency options. The focus will be on its application in a few recent projects as well as a comparison of Python's gevent and node.js. Some of the demos will also be using matplotlib in honor of John Hunter's passing.

Recorded: March 11, 2012Language: English

Python has provided a suite of tools required for our Lagrangian Particle Modeling Framework that is used for simulation of atmospheric transport. The Python language is used for generating input files for our FORTRAN codebase, using f2py to control several aspects of running the model and reading output, and dynamically generating content with matplotlib for web-display using Plone.

Recorded: July 2, 2013Language: English

Iris & Cartopy: Open source Python packages for Atmospheric and Oceanographic science

Authors: Elson, Philip, UK Met Office;

Track: Meteorology, Climatology, Atmospheric and Oceanic Science

As the capabilities of Python packages valuable to the Atmospheric and Oceanographic Sciences (AOS) such as matplotlib, scipy and numpy have developed, so the UK Met Office's use of Python has expanded. The open source scientific Python stack is strategically important to the Met Office as it strives to meet the increasing need to collaborate freely and openly in academic and commercial partnerships. Python's easy to develop, dynamically typed syntax is ideally suited for data assimilation and model post-processing type tasks, and in recent years the Met Office has sustained funding for a team of software engineers to simplify, develop and improve its scientific capabilities by contributing to the the open source AOS community.

The focus of much of this effort has been on a new open source Python package, Iris 1, which implements a generalised n-dimensional gridded data model to isolate analysis and visualisation code from file format specifics. The Iris data model is a result of close collaboration with the CF Data Model community and currently has read/write support for a variety of file formats including NetCDF and GRIB. In order to deliver a component of the core visualisation functionality, a new mapping library called Cartopy 2 has also been developed on top of matplotlib. Cartopy exposes an intuitive interface for the transformation and visualisation of geospatial vector and raster data.

This talk will outline some of the Met Office's involvement in the open source community, including demonstrations of Iris and Cartopy; highlights of recent matplotlib contributions; and an outline of future developments.

Recorded: March 17, 2013Language: English

Nodality has pioneered a novel flow cytometry-based technology in the areas of oncology and autoimmunity to reveal underlying disease biology. We present a custom framework written in Python that uses Django, Matplotlib, MongoDB and Pandas to join this experimental data with clinical facts such as individual patient disease outcomes to develop actionable biological and clinical information.

Recorded: July 28, 2012Language: English

What you need to know about datetimes: time, datetime, and calendar from the standard library are a bit messy. Find out what to use where and how (particularly when you have users in many timezones), and what extra modules you might want to look into.

Log analysis for web applications: Use iPython, matplotlib, and some custom functions to slice, dice, and visualise your app through its logs.