Recorded: July 9, 2014Language: English

We will have an open discussion about current matplotlib enhancement proposals and take calls for new ones. Anyone interested in matplotlib's future development efforts is more than welcome to attend. There will be no presentation. Current MEPs exist here: https://github.com/matplotlib/matplotlib/wiki#matplotlib-enhancement-proposals-meps

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 9, 2014Language: English

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

Recorded: July 9, 2014Language: English

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

Recorded: July 9, 2014Language: English

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

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

Presentation of finalists for excellence in plotting using Matplotlib.

Recorded: July 9, 2014Language: English

We will give an overview of the basics of the scientific computing ecosystem with Python: what does each of the fundamental packages (numpy, matplotlib, scipy, sympy and pandas) do, and how does it work? We will use the IPython Notebook in our quest to enter this wonderful world.

Recorded: Nov. 11, 2012Language: English

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

Recorded: July 14, 2014Language: English

On several issues related to the perception of colormaps...

Recorded: July 9, 2014Language: English

I will present WCSAxes, a new framework for plotting astronomical data that seamlessly handles the plotting of ticks, tick labels, and grid lines for arbitrary coordinate systems and projections. While originally written for with astronomical data, it can be used for any kind of map provided that the projection and coordinate system can be represented by a pixel-to-world transformation.

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: 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 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.