matplotlib. matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it's still the most widely used library for plotting in the Python community.
Plotly is a library that brings the power of interactive plotting with JavaScript to languages like Python and R. It is built with JavaScript and takes advantage of the powerful D3. js library. Using Plotly, we can create interactive plots in Python with only a few lines of code.
Matplotlib can create any plot because it is a low-level visualization library. Bokeh can be both used as a high-level or low-level interface; thus, it can create many sophisticated plots that Matplotlib creates but with fewer lines of code and higher resolution.
Plotly lets you make graphs using a Python API, matplotlib, and pandas. Their IPython gallery has some example scientific graphs with the Python scripts that generated them.
Here's a sample:
(source: fastly.net)
There is a list of Python-based plotting tools on the Python wiki.
You can always use the object-oriented framework for Matplotlib instead of the pylab interface.
I have used Gnuplot.py with great success.
I was in need of gnuplot interface for Python 3 but have found nothing. So I have ported Gnuplot.py to it. See more info in the 'readme' of the [repository]. Hope this will help someone else in future.
There is also a old program for automating the reading, filtering, processing, interpolation and plot n-dimensional values from different sources (like libreOffice- or csv-files) and variable size: diaGrabber.
You have to use some simple python-commands to create a case. After this you can manipulate the graphical output in an interactive GUI.
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