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Data visualization is an important sub-field of data science and python programmers need to have available toolkits for them.

Is there a Python API to Tableau?

Are there any Python based data visualization toolkits?

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    $\begingroup$ Voted down as question does not show any research effort. Its not that hard. $\endgroup$ – Spacedman Jun 13 '14 at 7:26
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There is a Tablaeu API and you can use Python to use it, but maybe not in the sense that you think. There is a Data Extract API that you could use to import your data into Python and do your visualizations there, so I do not know if this is going to answer your question entirely.

As in the first comment you can use Matplotlib from Matplotlib website, or you could install Canopy from Enthought which has it available, there is also Pandas, which you could also use for data analysis and some visualizations. There is also a package called ggplot which is used in R alot, but is also made for Python, which you can find here ggplot for python.

The Tableau data extract API and some information about it can be found at this link. There are a few web sources that I found concerning it using duckduckgo at this link. Here are some samples:

Link 1

Link 2

Link 3

As far as an API like matplotlib, I cannot say for certain that one exists. Hopefully this gives some sort of reference to help answer your question.

Also to help avoid closure flags and downvotes you should try and show some of what you have tried to do or find, this makes for a better question and helps to illicit responses.

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Bokeh is an excellent data visualization library for python. NodeBox is another that comes to mind.

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There's plenty. If you've ever used ggplot2 in R and want to do that in Python.

If you want to use a similar visualisation grammar (Vega) and go via D3.

Or if you want the full-on 3d: shizzle.

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You can also checkout the seaborn package for statistical charts.

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If you know R and it's ggplot library, you could try ggplot for python:

I like it, because I do work in R and python, and both are virtually identical.

But if you are not familiar you have to deal with a very "unpythonic" syntax. But I think it's an easy library overall.

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