You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: INTRO.md
+3-7Lines changed: 3 additions & 7 deletions
Original file line number
Diff line number
Diff line change
@@ -28,20 +28,16 @@ Many excellent plotting tools are built on top of Matplotlib.
28
28
29
29
There are several tools that can make the kinds of plots described here. At present, I have little experience with them. If anyone would like to help add examples, please [get in touch](https://github.com/tdhopper/pythonplot.com).
30
30
31
-
"[Altair](https://altair-viz.github.io/"Declarative Visualization in Python") is a declarative statistical visualization library for Python, based on [Vega-Lite](https://vega.github.io/vega-lite/"Vega-Lite: A High-Level Visualization Grammar for Interactive Graphics")." According to Jake Vanderplas, "Declarative visualization lets you think about data and relationships, rather than incidental details."[^jake]
32
-
33
-
Fundamentally, Altair renders JSON-descriptions of plots that are rendered by Vega-Lite; this JSON could be rendered by other backends as well, and I'm told a Matplotlib backend is under development.
34
-
35
-
Altair is new on the scene and offers a lot of promise. I hope to add Altair examples here now that 2.0 is released.
31
+
"[Altair](https://altair-viz.github.io/"Declarative Visualization in Python") is a declarative statistical visualization library for Python, based on [Vega-Lite](https://vega.github.io/vega-lite/"Vega-Lite: A High-Level Visualization Grammar for Interactive Graphics")." According to Jake Vanderplas, "Declarative visualization lets you think about data and relationships, rather than incidental details."[^jake] I provide Altair examples rendered as static images.
36
32
37
33
[^jake]: See [here](https://speakerdeck.com/jakevdp/visualization-in-python-with-altair).
38
34
35
+
"[plotly](https://plot.ly/"Plotly - Make charts and dashboards online")'s Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts." I provide plotly examples rendered as static images.
36
+
39
37
"[Bokeh](http://bokeh.pydata.org/en/latest/"Python interactive visualization library") is a Python interactive visualization library that targets modern web browsers for presentation."
40
38
41
39
"[bqplot](https://github.com/bloomberg/bqplot) is a Grammar of Graphics-based interactive plotting framework for the Jupyter notebook."
42
40
43
-
"[plotly](https://plot.ly/"Plotly - Make charts and dashboards online")'s Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts."
44
-
45
41
### The Python Plotting Landscape
46
42
47
43
If you're interested in the breadth of plotting tools available for Python, I commend Jake Vanderplas's Pycon 2017 talk called the [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ). Similarly, the blogpost [A Dramatic Tour through Python's Data Visualization Landscape (including ggplot and Altair)](https://dsaber.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/) by Dan Saber is worth your time.
0 commit comments