mhjensen
364 lines
24 KiB
HTML
364 lines
24 KiB
HTML
<!--
|
|
Automatically generated HTML file from DocOnce source
|
|
(https://github.com/hplgit/doconce/)
|
|
-->
|
|
<html>
|
|
<head>
|
|
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
|
|
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
|
<meta name="description" content="Week 34: Introduction to the course, Logistics and Practicalities">
|
|
|
|
<title>Week 34: Introduction to the course, Logistics and Practicalities</title>
|
|
|
|
<!-- Bootstrap style: bootstrap -->
|
|
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
|
|
<!-- not necessary
|
|
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
|
|
-->
|
|
|
|
<style type="text/css">
|
|
|
|
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
|
|
.dropdown-menu {
|
|
height: auto;
|
|
max-height: 400px;
|
|
overflow-x: hidden;
|
|
}
|
|
|
|
/* Adds an invisible element before each target to offset for the navigation
|
|
bar */
|
|
.anchor::before {
|
|
content:"";
|
|
display:block;
|
|
height:50px; /* fixed header height for style bootstrap */
|
|
margin:-50px 0 0; /* negative fixed header height */
|
|
}
|
|
</style>
|
|
|
|
|
|
</head>
|
|
|
|
<!-- tocinfo
|
|
{'highest level': 2,
|
|
'sections': [('Overview of first week', 2, None, '___sec0'),
|
|
('Thursday August 20', 2, None, '___sec1'),
|
|
('Lectures and ComputerLab', 2, None, '___sec2'),
|
|
('Course Format', 2, None, '___sec3'),
|
|
('Teachers', 2, None, '___sec4'),
|
|
('Deadlines for projects (tentative)', 2, None, '___sec5'),
|
|
('Recommended textbooks', 2, None, '___sec6'),
|
|
('Prerequisites', 2, None, '___sec7'),
|
|
('Learning outcomes', 2, None, '___sec8'),
|
|
('Topics covered in this course: Statistical analysis and '
|
|
'optimization of data',
|
|
2,
|
|
None,
|
|
'___sec9'),
|
|
('Topics covered in this course: Machine Learning',
|
|
2,
|
|
None,
|
|
'___sec10'),
|
|
('Extremely useful tools, strongly recommended',
|
|
2,
|
|
None,
|
|
'___sec11'),
|
|
('Other courses on Data science and Machine Learning at UiO',
|
|
2,
|
|
None,
|
|
'___sec12'),
|
|
('Introduction', 2, None, '___sec13'),
|
|
('What is Machine Learning?', 2, None, '___sec14'),
|
|
('Types of Machine Learning', 2, None, '___sec15'),
|
|
('Software and needed installations', 2, None, '___sec16'),
|
|
('Python installers', 2, None, '___sec17'),
|
|
('Useful Python libraries', 2, None, '___sec18'),
|
|
('Installing R, C++, cython or Julia', 2, None, '___sec19'),
|
|
('Installing R, C++, cython, Numba etc', 2, None, '___sec20'),
|
|
('Numpy examples and Important Matrix and vector handling '
|
|
'packages',
|
|
2,
|
|
None,
|
|
'___sec21'),
|
|
('Basic Matrix Features', 2, None, '___sec22'),
|
|
('Some famous Matrices', 3, None, '___sec23'),
|
|
('More Basic Matrix Features', 3, None, '___sec24'),
|
|
('Numpy and arrays', 2, None, '___sec25'),
|
|
('Matrices in Python', 2, None, '___sec26'),
|
|
('Meet the Pandas', 2, None, '___sec27'),
|
|
('Friday August 21', 2, None, '___sec28'),
|
|
('Reading Data and fitting', 2, None, '___sec29'),
|
|
('Friday August 21', 2, None, '___sec30'),
|
|
('Simple linear regression model using _scikit-learn_',
|
|
3,
|
|
None,
|
|
'___sec31'),
|
|
('To our real data: nuclear binding energies. Brief reminder on '
|
|
'masses and binding energies',
|
|
3,
|
|
None,
|
|
'___sec32'),
|
|
('Organizing our data', 3, None, '___sec33'),
|
|
('Seeing the wood for the trees', 3, None, '___sec34'),
|
|
('And what about using neural networks?', 3, None, '___sec35'),
|
|
('A first summary', 2, None, '___sec36')]}
|
|
end of tocinfo -->
|
|
|
|
<body>
|
|
|
|
|
|
|
|
<script type="text/x-mathjax-config">
|
|
MathJax.Hub.Config({
|
|
TeX: {
|
|
equationNumbers: { autoNumber: "none" },
|
|
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
|
|
}
|
|
});
|
|
</script>
|
|
<script type="text/javascript" async
|
|
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
|
|
</script>
|
|
|
|
|
|
|
|
|
|
<!-- Bootstrap navigation bar -->
|
|
<div class="navbar navbar-default navbar-fixed-top">
|
|
<div class="navbar-header">
|
|
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
|
|
<span class="icon-bar"></span>
|
|
<span class="icon-bar"></span>
|
|
<span class="icon-bar"></span>
|
|
</button>
|
|
<a class="navbar-brand" href="week34-bs.html">Week 34: Introduction to the course, Logistics and Practicalities</a>
|
|
</div>
|
|
|
|
<div class="navbar-collapse collapse navbar-responsive-collapse">
|
|
<ul class="nav navbar-nav navbar-right">
|
|
<li class="dropdown">
|
|
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
|
<ul class="dropdown-menu">
|
|
<!-- navigation toc: --> <li><a href="._week34-bs001.html#___sec0" style="font-size: 80%;"><b>Overview of first week</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs002.html#___sec1" style="font-size: 80%;"><b>Thursday August 20</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs003.html#___sec2" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs004.html#___sec3" style="font-size: 80%;"><b>Course Format</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs005.html#___sec4" style="font-size: 80%;"><b>Teachers</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs006.html#___sec5" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs007.html#___sec6" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs008.html#___sec7" style="font-size: 80%;"><b>Prerequisites</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs009.html#___sec8" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs010.html#___sec9" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs011.html#___sec10" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs012.html#___sec11" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs013.html#___sec12" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs014.html#___sec13" style="font-size: 80%;"><b>Introduction</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs015.html#___sec14" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs016.html#___sec15" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs017.html#___sec16" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs018.html#___sec17" style="font-size: 80%;"><b>Python installers</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs019.html#___sec18" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs020.html#___sec19" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs021.html#___sec20" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs022.html#___sec21" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs023.html#___sec22" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs024.html#___sec23" style="font-size: 80%;"> Some famous Matrices</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs025.html#___sec24" style="font-size: 80%;"> More Basic Matrix Features</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs026.html#___sec25" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs027.html#___sec26" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="#___sec27" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs029.html#___sec28" style="font-size: 80%;"><b>Friday August 21</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs030.html#___sec29" style="font-size: 80%;"><b>Reading Data and fitting</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs031.html#___sec30" style="font-size: 80%;"><b>Friday August 21</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec31" style="font-size: 80%;"> Simple linear regression model using <b>scikit-learn</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec32" style="font-size: 80%;"> To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec33" style="font-size: 80%;"> Organizing our data</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec34" style="font-size: 80%;"> Seeing the wood for the trees</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec35" style="font-size: 80%;"> And what about using neural networks?</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec36" style="font-size: 80%;"><b>A first summary</b></a></li>
|
|
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</div>
|
|
</div>
|
|
</div> <!-- end of navigation bar -->
|
|
|
|
<div class="container">
|
|
|
|
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
|
|
|
<a name="part0028"></a>
|
|
<!-- !split -->
|
|
|
|
<h2 id="___sec27" class="anchor">Meet the Pandas </h2>
|
|
|
|
<p>
|
|
<br /><br /><center><p><img src="fig/pandas.jpg" align="bottom" width=600></p></center><br /><br />
|
|
|
|
<p>
|
|
Another useful Python package is
|
|
<a href="https://pandas.pydata.org/" target="_self">pandas</a>, which is an open source library
|
|
providing high-performance, easy-to-use data structures and data
|
|
analysis tools for Python. <b>pandas</b> stands for panel data, a term borrowed from econometrics and is an efficient library for data analysis with an emphasis on tabular data.
|
|
<b>pandas</b> has two major classes, the <b>DataFrame</b> class with two-dimensional data objects and tabular data organized in columns and the class <b>Series</b> with a focus on one-dimensional data objects. Both classes allow you to index data easily as we will see in the examples below.
|
|
<b>pandas</b> allows you also to perform mathematical operations on the data, spanning from simple reshapings of vectors and matrices to statistical operations.
|
|
|
|
<p>
|
|
The following simple example shows how we can, in an easy way make tables of our data. Here we define a data set which includes names, place of birth and date of birth, and displays the data in an easy to read way. We will see repeated use of <b>pandas</b>, in particular in connection with classification of data.
|
|
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
|
|
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
|
|
data <span style="color: #666666">=</span> {<span style="color: #BA2121">'First Name'</span>: [<span style="color: #BA2121">"Frodo"</span>, <span style="color: #BA2121">"Bilbo"</span>, <span style="color: #BA2121">"Aragorn II"</span>, <span style="color: #BA2121">"Samwise"</span>],
|
|
<span style="color: #BA2121">'Last Name'</span>: [<span style="color: #BA2121">"Baggins"</span>, <span style="color: #BA2121">"Baggins"</span>,<span style="color: #BA2121">"Elessar"</span>,<span style="color: #BA2121">"Gamgee"</span>],
|
|
<span style="color: #BA2121">'Place of birth'</span>: [<span style="color: #BA2121">"Shire"</span>, <span style="color: #BA2121">"Shire"</span>, <span style="color: #BA2121">"Eriador"</span>, <span style="color: #BA2121">"Shire"</span>],
|
|
<span style="color: #BA2121">'Date of Birth T.A.'</span>: [<span style="color: #666666">2968</span>, <span style="color: #666666">2890</span>, <span style="color: #666666">2931</span>, <span style="color: #666666">2980</span>]
|
|
}
|
|
data_pandas <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(data)
|
|
display(data_pandas)
|
|
</pre></div>
|
|
<p>
|
|
In the above we have imported <b>pandas</b> with the shorthand <b>pd</b>, the latter has become the standard way we import <b>pandas</b>. We make then a list of various variables
|
|
and reorganize the aboves lists into a <b>DataFrame</b> and then print out a neat table with specific column labels as <em>Name</em>, <em>place of birth</em> and <em>date of birth</em>.
|
|
Displaying these results, we see that the indices are given by the default numbers from zero to three.
|
|
<b>pandas</b> is extremely flexible and we can easily change the above indices by defining a new type of indexing as
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>data_pandas <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(data,index<span style="color: #666666">=</span>[<span style="color: #BA2121">'Frodo'</span>,<span style="color: #BA2121">'Bilbo'</span>,<span style="color: #BA2121">'Aragorn'</span>,<span style="color: #BA2121">'Sam'</span>])
|
|
display(data_pandas)
|
|
</pre></div>
|
|
<p>
|
|
Thereafter we display the content of the row which begins with the index <b>Aragorn</b>
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>display(data_pandas<span style="color: #666666">.</span>loc[<span style="color: #BA2121">'Aragorn'</span>])
|
|
</pre></div>
|
|
<p>
|
|
We can easily append data to this, for example
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>new_hobbit <span style="color: #666666">=</span> {<span style="color: #BA2121">'First Name'</span>: [<span style="color: #BA2121">"Peregrin"</span>],
|
|
<span style="color: #BA2121">'Last Name'</span>: [<span style="color: #BA2121">"Took"</span>],
|
|
<span style="color: #BA2121">'Place of birth'</span>: [<span style="color: #BA2121">"Shire"</span>],
|
|
<span style="color: #BA2121">'Date of Birth T.A.'</span>: [<span style="color: #666666">2990</span>]
|
|
}
|
|
data_pandas<span style="color: #666666">=</span>data_pandas<span style="color: #666666">.</span>append(pd<span style="color: #666666">.</span>DataFrame(new_hobbit, index<span style="color: #666666">=</span>[<span style="color: #BA2121">'Pippin'</span>]))
|
|
display(data_pandas)
|
|
</pre></div>
|
|
<p>
|
|
Here are other examples where we use the <b>DataFrame</b> functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix
|
|
of dimensionality \( 10\times 5 \) and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations.
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
|
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
|
|
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
|
|
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">100</span>)
|
|
<span style="color: #408080; font-style: italic"># setting up a 10 x 5 matrix</span>
|
|
rows <span style="color: #666666">=</span> <span style="color: #666666">10</span>
|
|
cols <span style="color: #666666">=</span> <span style="color: #666666">5</span>
|
|
a <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(rows,cols)
|
|
df <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(a)
|
|
display(df)
|
|
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>mean())
|
|
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>std())
|
|
display(df<span style="color: #666666">**2</span>)
|
|
</pre></div>
|
|
<p>
|
|
Thereafter we can select specific columns only and plot final results
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>df<span style="color: #666666">.</span>columns <span style="color: #666666">=</span> [<span style="color: #BA2121">'First'</span>, <span style="color: #BA2121">'Second'</span>, <span style="color: #BA2121">'Third'</span>, <span style="color: #BA2121">'Fourth'</span>, <span style="color: #BA2121">'Fifth'</span>]
|
|
df<span style="color: #666666">.</span>index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(<span style="color: #666666">10</span>)
|
|
|
|
display(df)
|
|
<span style="color: #008000; font-weight: bold">print</span>(df[<span style="color: #BA2121">'Second'</span>]<span style="color: #666666">.</span>mean() )
|
|
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>info())
|
|
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>describe())
|
|
|
|
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">pylab</span> <span style="color: #008000; font-weight: bold">import</span> plt, mpl
|
|
plt<span style="color: #666666">.</span>style<span style="color: #666666">.</span>use(<span style="color: #BA2121">'seaborn'</span>)
|
|
mpl<span style="color: #666666">.</span>rcParams[<span style="color: #BA2121">'font.family'</span>] <span style="color: #666666">=</span> <span style="color: #BA2121">'serif'</span>
|
|
|
|
df<span style="color: #666666">.</span>cumsum()<span style="color: #666666">.</span>plot(lw<span style="color: #666666">=2.0</span>, figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">6</span>))
|
|
plt<span style="color: #666666">.</span>show()
|
|
|
|
|
|
df<span style="color: #666666">.</span>plot<span style="color: #666666">.</span>bar(figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">6</span>), rot<span style="color: #666666">=15</span>)
|
|
plt<span style="color: #666666">.</span>show()
|
|
</pre></div>
|
|
<p>
|
|
We can produce a \( 4\times 4 \) matrix
|
|
<p>
|
|
|
|
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
|
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>b <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(<span style="color: #666666">16</span>)<span style="color: #666666">.</span>reshape((<span style="color: #666666">4</span>,<span style="color: #666666">4</span>))
|
|
<span style="color: #008000; font-weight: bold">print</span>(b)
|
|
df1 <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(b)
|
|
<span style="color: #008000; font-weight: bold">print</span>(df1)
|
|
</pre></div>
|
|
<p>
|
|
and many other operations.
|
|
|
|
<p>
|
|
The <b>Series</b> class is another important class included in
|
|
<b>pandas</b>. You can view it as a specialization of <b>DataFrame</b> but where
|
|
we have just a single column of data. It shares many of the same features as _DataFrame. As with <b>DataFrame</b>,
|
|
most operations are vectorized, achieving thereby a high performance when dealing with computations of arrays, in particular labeled arrays.
|
|
As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in.
|
|
For multidimensional arrays, we recommend strongly <a href="http://xarray.pydata.org/en/stable/" target="_self">xarray</a>. <b>xarray</b> has much of the same flexibility as <b>pandas</b>, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both <b>pandas</b> and <b>xarray</b>.
|
|
|
|
<p>
|
|
<p>
|
|
<!-- navigation buttons at the bottom of the page -->
|
|
<ul class="pagination">
|
|
<li><a href="._week34-bs027.html">«</a></li>
|
|
<li><a href="._week34-bs000.html">1</a></li>
|
|
<li><a href="">...</a></li>
|
|
<li><a href="._week34-bs020.html">21</a></li>
|
|
<li><a href="._week34-bs021.html">22</a></li>
|
|
<li><a href="._week34-bs022.html">23</a></li>
|
|
<li><a href="._week34-bs023.html">24</a></li>
|
|
<li><a href="._week34-bs024.html">25</a></li>
|
|
<li><a href="._week34-bs025.html">26</a></li>
|
|
<li><a href="._week34-bs026.html">27</a></li>
|
|
<li><a href="._week34-bs027.html">28</a></li>
|
|
<li class="active"><a href="._week34-bs028.html">29</a></li>
|
|
<li><a href="._week34-bs029.html">30</a></li>
|
|
<li><a href="._week34-bs030.html">31</a></li>
|
|
<li><a href="._week34-bs031.html">32</a></li>
|
|
<li><a href="._week34-bs032.html">33</a></li>
|
|
<li><a href="._week34-bs029.html">»</a></li>
|
|
</ul>
|
|
<!-- ------------------- end of main content --------------- -->
|
|
|
|
</div> <!-- end container -->
|
|
<!-- include javascript, jQuery *first* -->
|
|
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
|
|
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
|
|
|
|
<!-- Bootstrap footer
|
|
<footer>
|
|
<a href="http://..."><img width="250" align=right src="http://..."></a>
|
|
</footer>
|
|
-->
|
|
|
|
|
|
<center style="font-size:80%">
|
|
<!-- copyright only on the titlepage -->
|
|
</center>
|
|
|
|
|
|
</body>
|
|
</html>
|
|
|
|
|