01.08-more-ipython-resources.html

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<img align="left" style="padding-right:10px;" src="/PythonDataScienceHandbook/figures/PDSH-cover-small.png">
<em>This is an excerpt from the <a href="http://shop.oreilly.com/product/0636920034919.do">Python Data Science Handbook</a> by Jake VanderPlas; Jupyter notebooks are available <a href="https://github.com/jakevdp/PythonDataScienceHandbook">on GitHub</a>.</em>
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<em>The text is released under the <a href="https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode">CC-BY-NC-ND license</a>, and code is released under the <a href="https://opensource.org/licenses/MIT">MIT license</a>. If you find this content useful, please consider supporting the work by <a href="http://shop.oreilly.com/product/0636920034919.do">buying the book</a>!</em>
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            <h1>More IPython Resources</h1>
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  /* .CodeMirror-lines */
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  border-radius: 0;
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/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
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/* class for the output area when it should be height-limited */
div.output_scroll {
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div.output_prompt {
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div.output_area {
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div.output_area .MathJax_Display {
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div.output_area 
div.output_area 
div.output_area img,
div.output_area svg {
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div.output_area img.unconfined,
div.output_area svg.unconfined {
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/* This is needed to protect the pre formating from global settings such
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.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
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@media (max-width: 540px) {
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}
div.output_area pre {
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/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
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/* all text output has this class: */
div.output_text {
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  /* This has to match that of the the CodeMirror class line-height below */
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/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
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div.output_latex {
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}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
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  /* padding + margin = 0.5em between prompt and cursor */
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}
input.raw_input:focus {
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}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
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}
div.output_unrecognized a:hover {
  color: inherit;
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.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}


.rendered_html :link {
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}
.rendered_html :visited {
  text-decoration: underline;
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.rendered_html h1:first-child {
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.rendered_html h6:first-child {
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.rendered_html * + ul {
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.rendered_html * + ol {
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.rendered_html pre,


.rendered_html tr,
.rendered_html th,

.rendered_html td,


.rendered_html * + table {
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.rendered_html * + p {
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.rendered_html * + img {
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.rendered_html img,

.rendered_html img.unconfined,

div.text_cell {
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@media (max-width: 540px) {
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div.text_cell_render {
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}
a.anchor-link:link {
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}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
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}
.text_cell.rendered .input_area {
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.text_cell.rendered 
.text_cell.unrendered .text_cell_render {
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}
.cm-header-1,
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</style>
<style type="text/css">.highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
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.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
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.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
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.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
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.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
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.highlight .kt { color: #B00040 } /* Keyword.Type */
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/* Temporary definitions which will become obsolete with Notebook release 5.0 */
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><!--NAVIGATION-->
&lt; <a href="01.07-timing-and-profiling.html">Profiling and Timing Code</a> | <a href="index.html">Contents</a> | <a href="02.00-introduction-to-numpy.html">Introduction to NumPy</a> &gt;</p>
<p><a href="https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.08-More-IPython-Resources.ipynb"><img align="left" src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab" title="Open and Execute in Google Colaboratory"></a></p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>In this chapter, we've just scratched the surface of using IPython to enable data science tasks.
Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Web-Resources">Web Resources<a class="anchor-link" href="#Web-Resources">&#182;</a></h2><ul>
<li><a href="http://ipython.org">The IPython website</a>: The IPython website links to documentation, examples, tutorials, and a variety of other resources.</li>
<li><a href="http://nbviewer.jupyter.org/">The nbviewer website</a>: This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!</li>
<li><a href="https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks/">A gallery of interesting Jupyter Notebooks</a>: This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!</li>
<li>Video Tutorials: searching the Internet, you will find many video-recorded tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Books">Books<a class="anchor-link" href="#Books">&#182;</a></h2><ul>
<li><a href="http://shop.oreilly.com/product/0636920023784.do"><em>Python for Data Analysis</em></a>: Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.</li>
<li><a href="https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization"><em>Learning IPython for Interactive Computing and Data Visualization</em></a>: This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.</li>
<li><a href="https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook"><em>IPython Interactive Computing and Visualization Cookbook</em></a>: Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics.</li>
</ul>
<p>Finally, a reminder that you can find help on your own: IPython's <code>?</code>-based help functionality (discussed in <a href="01.01-help-and-documentation.html">Help and Documentation in IPython</a>) can be very useful if you use it well and use it often.
As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer.</p>

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