{ "metadata": { "name": "", "signature": "sha256:0df3d61ecf0906fc61010bb3c56c59c6f8111b67f4248087bbcd8b4a7635e130" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Plotting Libaries Overview" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Speaker: Tamara Knutsen" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Email: tamara@openmail.co" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Pretty Comprehensive List here: https://wiki.python.org/moin/NumericAndScientific/Plotting](https://wiki.python.org/moin/NumericAndScientific/Plotting)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may need to install some libraries first for this notebook to run smoothly. Edit the commands below to the subset of libraries you need to install and execute it. Skip it if you have all of these." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!pip install six, pytz, dateutil, Flask, Redis\n", "!pip install numpy, scipy, statsmodels, patsy, pandas, networkx\n", "!pip install matplotlib, mpld3, seaborn, bokeh, rpy2\n", "\n", "#git clone https://github.com/vispy/vispy.git\n", "#cd vispy\n", "#python setup.py install" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Matplotlib:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matplotlib provides a stateful scripting interface for generating graphics similar to MATLAB's syntax and appearance. Matplotlib renders to a \"back end\", which is usually a raster graphics canvas. The strength of this approach is that, once rendered, the data loads into a web page as an image and is therefore very fast. For ipython notebooks with lots of plots, this is the way to go because web browsers are optimized for displaying tons of images. \n", "\n", "Can be used to:\n", "