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I am new to geographical data visualization, so I would like to ask if someone could recommend me some tool for easy visualization of data on map.
Particularly I have data from one European city and I need to visualize them as heat maps (eg. price based on the location, distance to transportation, etc.).
I have some experience and already prepared non geo visualizations in Python (price in time plots, etc. in matplotlib), but I can code in matlab and Java as well and I am willing to learn something new.
This is as part of my thesis and they have recommended me tools like qgis or grassgis, but it seems to me like something completely different from programming that would be worth of semestral course and I don't have any experience with this at all. I have read also something about matplotlib and basemap, but it does seem to be more for at leas visualizations for whole country (I could not find any maps with city borders and roads).
My question is which way would you go? I am especially asking people who do some geo based "Data Science", which tools do you use to visualize quickly geographical data?
People use R a lot for data visualization in Data Sciences.
More specifically, you can look at packages like ggplot2. I think it might be exactly what you are looking for.
I've been working with matlab for over 2 years now and with python for a little bit over 1 year. When it comes to geographic heatmap, I was able to do things that were much more visually appealing after a couple of days of working with R.
Just a quick introduction to what R & ggplot2 can do: http://rstudio-pubs-static.s3.amazonaws.com/10881_80cc12ceb10a433bb3de48cfc7b42de7.html
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I have a list which contains some data points which I need to plot.
The list is approximately 11,000 x 40 in size (2D Plot with numeric values).
Currently I am using matplotlib (as I moved to python from Matlab), however this is slow to plot so many points, and resizing the graph manually crashes my computer (MacBook Retina with 16Gb RAM).
The graph I would like needs to be interactive and therefore such delays are unacceptable.
Can anyone give me some advice with regards to perhaps a better (quicker) plotting library?
Thanks
EDIT: I am currently researching some tools but I am finding it difficult to work out which would be the fastest in terms of plotting...
I am looking at:
MathGL - http://mathgl.sourceforge.net/
Gnuplot.py - http://gnuplot-py.sourceforge.net/
PyQtGraph - http://www.pyqtgraph.org/
PyQwt - http://pyqwt.sourceforge.net/
guiqwt - http://pypi.python.org/pypi/guiqwt/
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I'm pretty new to the whole idea of data visualization, so I was hoping people could point me in the direction of efficient tools to use for a problem I have:
I've got a lot of numerical data (they are counts) that are tied to specific countries with a two-letter country code. Ideally, I'd like to be able to represent these counts using a sort of world heat map. In other words, if the count for India is 20 and the count for China is 5, I'd want China to be colored light red and India to be colored dark red.
What tools would be best to do something like this? I do my data manipulation and analysis using pandas and Python, so I'd love to keep things in the Python family, but I'd love any suggestions at all.
Matplotlib has something called basemap
Vincent
Databench is a tool that connects your Python analysis with an html frontend where you can use tables and interactive elements to navigate your data. You can also use d3.js or higher level JavaScript libraries that use d3.js to visualize your counts on a map.
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Can anyone recommend a good matplot tutorial. I am a complete beginner - but have used similar software (matlab, R etc), in my halcyon days at University (i.e. a long time ago).
A google search brings up a list of dubious quality, and the 'official' docs are too terse, or provide examples that are more 'edge case' (e.g. drawing dolphins swimming in a bubble), than one is likely to meet in practise.
I want a manual that provides the following information in a well structured manner:
Introduction to the data types
Introduction to 2D plotting with some simple practical examples (simple 2D graphs)
Introduction to 3D plotting with some simple practical examples (simple 2D graphs: contour and surface)
[Edit]
I'm new to both Python and matplot (but not new to programming - I have a C/C++ background)
There is a nice book called "Matplotlib for Python Developers"
It doesn't cover 3D graphics, though.
Are you a beginner with programming, Python, or just matplotlib? Because each of those will provide entirely different points of entry. I'm quite competent with Python, but I had not used matplotlib so much... however, the gallery has examples of different types of graphs and it's usually somewhat simple to convert the examples to your own use-case.
It's also a lot easier if you know PyGTK since they use a fair amount of said library in matplotlib.
Edit:
Also, Ipython in pylab mode is really good for exploring the objects, methods, and modules in pylab.
This might be useful:
Getting started with Matplotlib
http://showmedo.com/videotutorials/video?name=7200090&fromSeriesID=720
A 10-page tutorial: Raymond
Not a tutorial, but more examples:
scipy.org/Cookbook/Matplotlib
(#morpeous, if you know of any good tutorials of the kind you'd like,
please post them — might motivate someone.)
Good one for begineers:
See some sample from the below link
https://sites.google.com/site/scigraphs/tutorial#TOC-Simple-plot--font-size-2-
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There are plenty of 'pretty-printing' visualization libraries for Javascript. E.g. those listed here.
Googling for 'python visualization libraries' only turns up stuff like VTK and mayavi, which are primarily more for no-nonsense scientific use.
So, do you know of any Python libraries similar to those Javascript ones in the above link? I particularly like the Javascript Infovis Toolkit.
For Python there really isn't "one viz library to rule them all". There are different libraries and toolkits for different purposes. For graphs in Python you may find igraph useful. For other types of scientific or data visualizations matplotlib is also good.
Here's a new port of R's ggplot2 over to python. Looks very slick!
https://github.com/yhat/ggplot/
More info here:
http://blog.yhathq.com/posts/ggplot-for-python.html
Also have a look at Seaborn, described as "Improved matplotlib for statistical data visualization":
https://github.com/mwaskom/seaborn
The Seaborn examples are pretty slick:
Plotting Complex Linear Models
Visualization Distributions
Time Series Visualizations
Check out Bokeh at pydata
source code here
There's PyCha for charts.
are you looking for graph software?
Checkout http://www.graphviz.org/Gallery.php. it has python bindings.
I second matplotlib. Also Chaco. It's not Python, but for really quick heatmaps, go to OpenHeatMap.com
Networkx looks to be the best-documented of the graph libraries I've seen. I think it interfaces with Matplotlib for visualization.
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What are good python libraries which IronPython supports (current version wise) for drawing scientific plots on Win ?
By "scientific plots" I mean simple x-y plots, x-y-z surface plots and x-y-z shaded plots.
According to this it's possible to use matplotlib with IronPython. Which will at least get you 2D plots. Another way of running matplotlib.
gnuplot can generate 3D charts - http://www.resolverhacks.net/gnuplot_plotting.html might be a starting point.
If you get Resolver, then this Resolver Spreadsheet Challenge winner shows that it is quite capable of doing 3d shaded scientific plots.
http://www.voidspace.org.uk/python/weblog/arch_d7_2009_01_17.shtml#e1049
Resolver One is a sophisticated spreadsheet built entirely in IronPython which is becoming popular with scientists and people in the financial services industry due to the power of the IronPython scripting engine within it. You can dowload a trial version of it here:
http://www.resolversystems.com/
Nevertheless, if you work with colleagues who do most of their work on unix-like systems, you might want to choose matplotlib anyhow because there is more possibility of sharing code etc. Resolver One does not yet run on Mono.