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<h1 id="Python-for-Data-Science">Python for Data Science<a class="anchor-link" href="#Python-for-Data-Science">&#182;</a></h1>
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<p><a href="http://interrelativity.com/joe">Joe McCarthy</a>,
<em>Data Scientist</em>, <a href="http://www.indeed.com/">Indeed</a></p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Image</span><span class="p">,</span> <span class="n">HTML</span>
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<h1 id="1.-Introduction">1. Introduction<a class="anchor-link" href="#1.-Introduction">&#182;</a></h1>
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<p><a href="http://www.python.org/"><img src="https://www.python.org/static/community_logos/python-logo.png" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="Python" alt="python-logo-master-v3-TM.png" /></a>
This short primer on <a href="http://www.python.org/">Python</a> is designed to provide a rapid "on-ramp" to enable computer programmers who are already familiar with concepts and constructs in other programming languages learn enough about Python to facilitate the effective use of open-source and proprietary Python-based machine learning and data science tools.</p>
<p><a href="http://www.nltk.org/book/"><img src="http://covers.oreilly.com/images/9780596516499/cat.gif" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="The Natural Language Toolkit (NLTK) Book" alt="nltk_book_cover.gif" /></a>
The primer is motivated, in part, by the approach taken in the <a href="http://www.nltk.org/book/">Natural Language Toolkit (NLTK) book</a>, which provides a rapid on-ramp for using Python and the open-source <a href="http://www.nltk.org/">NLTK library</a> to develop programs using natural language processing techniques (many of which involve <a href="http://www.nltk.org/book/ch06.html">machine learning</a>).</p>
<p>The <a href="http://docs.python.org/2/tutorial/">Python Tutorial</a> offers a more comprehensive primer, and opens with an excellent - if biased - overview of some of the general strengths of the Python programming language:</p>
<blockquote><p>Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.</p>
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<p><a href="http://www.amazon.com/Python-Scripting-Computational-Science-Engineering/dp/3642093159"><img src="https://images.springer.com/sgw/books/medium/9783540739159.jpg" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="Python Scripting for Computational Science, by Hans Petter Langtangen" alt="Python Scripting for Computational Science cover" /></a>
<a href="http://folk.uio.no/hpl/">Hans Petter Langtangen</a>, author of <a href="http://www.amazon.com/Python-Scripting-Computational-Science-Engineering/dp/3642093159">Python Scripting for Computational Science</a>, emphasizes the utility of Python for many of the common tasks in all areas of computational science:</p>
<blockquote><p>Very often programming is about shuffling data in and out of different tools, converting one data format to another, extracting numerical data from a text, and administering numerical experiments involving a large number of data files and directories. Such tasks are much faster to accomplish in a language like Python than in Fortran, C, C++, C#, or Java</p>
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<p><a href="http://people.stern.nyu.edu/fprovost/">Foster Provost</a>, co-author of <a href="http://data-science-for-biz.com/">Data Science for Business</a>, describes why Python is such a useful programming language for practical data science in <a href="https://docs.google.com/document/pub?id=1p6vowsEuiezLbWnFKgse70a8LxfsrRixqPF5nBg8F3A">Python: A Practical Tool for Data Science</a>, :</p>
<blockquote><p>The practice of data science involves many interrelated but different activities, including accessing data, manipulating data, computing statistics about data, plotting/graphing/visualizing data, building predictive and explanatory models from data, evaluating those models on yet more data, integrating models into production systems, etc. One option for the data scientist is to learn several different software packages that each specialize in one or two of these things, but don’t do them all well, plus learn a programming language to tie them together. (Or do a lot of manual work.)</p>
<p>An alternative is to use a general-purpose, high-level programming language that provides libraries to do all these things. Python is an excellent choice for this. It has a diverse range of open source libraries for just about everything the data scientist will do. It is available everywhere; high performance python interpreters exist for running your code on almost any operating system or architecture. Python and most of its libraries are both open source and free. Contrast this with common software packages that are available in a course via an academic license, yet are extremely expensive to license and use in industry.</p>
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<p><a href="http://scikit-learn.org/"><img src="http://scikit-learn.org/stable/_static/scikit-learn-logo-small.png" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="scikit-learn: Machine Learning for Python" alt="scikit-learn-logo-small.png" /></a>
The goal of this primer is to provide efficient and sufficient scaffolding for software engineers with no prior knowledge of Python to be able to effectively use Python-based tools for data science research and development, such as the open-source library <a href="http://scikit-learn.org/">scikit-learn</a>. There is another, more comprehensive tutorial for scikit-learn, <a href="http://scipy-lectures.github.io/index.html">Python Scientific Lecture Notes</a>, that includes coverage of a number of other useful Python open-source libraries used by scikit-learn (<a href="http://www.numpy.org/">numpy</a>, <a href="http://www.scipy.org/">scipy</a> and <a href="http://matplotlib.org">matplotlib</a>) - all highly recommended ... and, to keep things simple, all beyond the scope of this primer.</p>
<p>Using an IPython Notebook as a delivery vehicle for this primer was motivated by Brian Granger's inspiring tutorial, <a href="http://strataconf.com/strata2014/public/schedule/detail/32033">The IPython Notebook: Get Close to Your Data with Python and JavaScript</a>, one of the <a href="http://gumption.typepad.com/blog/2014/02/ipython-deep-learning-doing-good-some-highlights-from-strata-2014.html">highlights from my Strata 2014 conference experience</a>. You can run this notebook locally in a browser once you <a href="http://ipython.org/install.html">install ipython notebook</a>.</p>
<p>One final note on external resources: the <a href="http://legacy.python.org/dev/peps/pep-0008/">Python Style Guide (PEP-0008)</a> offers helpful tips on how best to format Python code. <a href="http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html">Code like a Pythonista</a> offers a number of additional tips on Python programming style and philosophy, several of which are incorporated into this primer.</p>
<p>We will focus entirely on using Python within the interpreter environment (as supported within an IPython Notebook). Python scripts - files containing definitions of functions and variables, and typically including code invoking some of those functions - can also be run from a command line. Using Python scripts from the command line may be the subject of a future primer.</p>
<p>To help motivate the data science-oriented Python programming examples provided in this primer, we will start off with a brief overview of basic concepts and terminology in data science.</p>
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<h2 id="2.-Data-Science:-Basic-Concepts">2. Data Science: Basic Concepts<a class="anchor-link" href="#2.-Data-Science:-Basic-Concepts">&#182;</a></h2>
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<h3 id="Data-Science-and-Data-Mining">Data Science and Data Mining<a class="anchor-link" href="#Data-Science-and-Data-Mining">&#182;</a></h3>
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<p><a href="http://data-science-for-biz.com/"><img src="http://akamaicovers.oreilly.com/images/0636920028918/cat.gif" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="Data Science for Business, by Provost and Fawcett" alt="DataScienceForBusiness_cover.jpg" /></a>
Foster Provost and <a href="http://home.comcast.net/~tom.fawcett/public_html/index.html">Tom Fawcett</a> offer succinct descriptions of data science and data mining in <a href="http://data-science-for-biz.com/">Data Science for Business</a>:</p>
<blockquote><p><strong>Data science</strong> involves principles, processes and techniques for understanding phenomena via the (automated) analysis of data.</p>
<p><strong>Data mining</strong> is the extraction of knowledge from data, via technologies that incorporate these principles.</p>
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<h3 id="Knowledge-Discovery,-Data-Mining-and-Machine-Learning">Knowledge Discovery, Data Mining and Machine Learning<a class="anchor-link" href="#Knowledge-Discovery,-Data-Mining-and-Machine-Learning">&#182;</a></h3>
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<p>Provost &amp; Fawcett also offer some history and insights into the relationship between <em>data mining</em> and <em>machine learning</em>, terms which are often used somewhat interchangeably:</p>
<blockquote><p>The field of Data Mining (or KDD: Knowledge Discovery and Data Mining) started as an offshoot of Machine Learning, and they remain closely linked. Both fields are concerned with the analysis of data to find useful or informative patterns. Techniques and algorithms are shared between the two; indeed, the areas are so closely related that researchers commonly participate in both communities and transition between them seamlessly. Nevertheless, it is worth pointing out some of the differences to give perspective.</p>
<p>Speaking generally, because Machine Learning is concerned with many types of performance improvement, it includes subfields such as robotics and computer vision that are not part of KDD. It also is concerned with issues of agency and cognition — how will an intelligent agent use learned knowledge to reason and act in its environment — which are not concerns of Data Mining.</p>
<p>Historically, KDD spun off from Machine Learning as a research field focused on concerns raised by examining real-world applications, and a decade and a half later the KDD community remains more concerned with applications than Machine Learning is. As such, research focused on commercial applications and business issues of data analysis tends to gravitate toward the KDD community rather than to Machine Learning. KDD also tends to be more concerned with the entire process of data analytics: data preparation, model learning, evaluation, and so on.</p>
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<h3 id="Cross-Industry-Standard-Process-for-Data-Mining-(CRISP-DM)">Cross Industry Standard Process for Data Mining (CRISP-DM)<a class="anchor-link" href="#Cross-Industry-Standard-Process-for-Data-Mining-(CRISP-DM)">&#182;</a></h3>
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<p>The <a href="https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining">Cross Industry Standard Process for Data Mining</a> introduced a process model for data mining in 2000 that has become widely adopted.</p>
<p><a href="https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/CRISP-DM_Process_Diagram.png/479px-CRISP-DM_Process_Diagram.png" title="Cross Industry Standard Process for Data Mining" alt="CRISP-DM_Process_Diagram" /></a></p>
<p>The model emphasizes the <strong><em>iterative</em></strong> nature of the data mining process, distinguishing several different stages that are regularly revisited in the course of developing and deploying data-driven solutions to business problems:</p>
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<li>Business understanding</li>
<li>Data understanding</li>
<li>Data preparation</li>
<li>Modeling </li>
<li>Deployment</li>
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<p>We will be focusing primarily on using Python for <strong>data preparation</strong> and <strong>modeling</strong>.</p>
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<h3 id="Data-Science-Workflow">Data Science Workflow<a class="anchor-link" href="#Data-Science-Workflow">&#182;</a></h3>
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<p><a href="http://www.pgbovine.net/">Philip Guo</a> presents a <a href="http://cacm.acm.org/blogs/blog-cacm/169199-data-science-workflow-overview-and-challenges/fulltext">Data Science Workflow</a> offering a slightly different process model emhasizing the importance of <strong>reflection</strong> and some of the meta-data, data management and bookkeeping challenges that typically arise in the data science process. His 2012 PhD thesis, <a href="http://pgbovine.net/projects/pubs/guo_phd_dissertation.pdf">Software Tools to Facilitate Research Programming</a>, offers an insightful and more comprehensive description of many of these challenges.</p>
<p><a href="http://cacm.acm.org/blogs/blog-cacm/169199-data-science-workflow-overview-and-challenges/fulltext"><img src="http://cacm.acm.org/system/assets/0001/3678/rp-overview.jpg" title="Data Science Workflow, by Philip Guo" alt="pguo-data-science-overview.jpg" style="width: 500px" /></a></p>
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<p>Provost &amp; Fawcett list a number of different tasks in which data science techniques are employed:</p>
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<li>Classification and class probability estimation </li>
<li>Regression (aka value estimation) </li>
<li>Similarity matching </li>
<li>Clustering </li>
<li>Co-occurrence grouping (aka frequent itemset mining, association rule discovery, market-basket analysis) </li>
<li>Profiling (aka behavior description, fraud / anomaly detection) </li>
<li>Link prediction </li>
<li>Data reduction </li>
<li>Causal modeling </li>
</ul>
<p>We will be focusing primarily on <strong>classification</strong> and <strong>class probability estimation</strong> tasks, which are defined by Provost &amp; Fawcett as follows:</p>
<blockquote><p><em>Classification</em> and <em>class probability estimation</em> attempt to predict, for each individual in a population, which of a (small) set of classes this individual belongs to. Usually the classes are mutually exclusive. An example classification question would be: “Among all the customers of MegaTelCo, which are likely to respond to a given offer?” In this example the two classes could be called will respond and will not respond.</p>
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<p>To further simplify this primer, we will focus exclusively on <strong>supervised</strong> methods, in which the data is explicitly labeled with classes. There are also <em>unsupervised</em> methods that involve working with data in which there are no pre-specified class labels.</p>
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<h3 id="Supervised-Classification">Supervised Classification<a class="anchor-link" href="#Supervised-Classification">&#182;</a></h3>
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<p>The <a href="http://www.nltk.org/book">Natural Language Toolkit (NLTK) book</a> provides a diagram and succinct description (below, with italics and bold added for emphasis) of supervised classification:</p>
<p><a href="http://www.nltk.org/book/ch06.html"><img src="http://www.nltk.org/images/supervised-classification.png" title="Supervised Classification, from NLTK book, Chapter 6" alt="nltk_ch06_supervised-classification.png" style="width: 500px" /></a></p>
<blockquote><p><em>Supervised Classification</em>. (a) During <em>training</em>, a <strong>feature extractor</strong> is used to convert each <strong>input value</strong> to a <strong>feature set</strong>. These feature sets, which capture the basic information about each input that should be used to classify it, are discussed in the next section. Pairs of feature sets and <strong>labels</strong> are fed into the <strong>machine learning algorithm</strong> to generate a <strong>model</strong>. (b) During <em>prediction</em>, the same feature extractor is used to convert <strong>unseen inputs</strong> to feature sets. These feature sets are then fed into the model, which generates <strong>predicted labels</strong>.</p>
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<h3 id="Data-Mining-Terminology">Data Mining Terminology<a class="anchor-link" href="#Data-Mining-Terminology">&#182;</a></h3>
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<li><strong>Structured</strong> data has simple, well-defined patterns (e.g., a table or graph)</li>
<li><strong>Unstructured</strong> data has less well-defined patterns (e.g., text, images)</li>
<li><strong>Model</strong>: a pattern that captures / generalizes regularities in data (e.g., an equation, set of rules, decision tree)</li>
<li><strong>Attribute</strong> (aka <em>variable</em>, <em>feature</em>, <em>signal</em>, <em>column</em>): an element used in a model</li>
<li><strong>Instance</strong> (aka <em>example</em>, <em>feature vector</em>, <em>row</em>): a representation of a single entity being modeled</li>
<li><strong>Target attribute</strong> (aka <em>dependent variable</em>, <em>class label</em>): the class / type / category of an entity being modeled</li>
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<h3 id="Data-Mining-Example:-UCI-Mushroom-dataset">Data Mining Example: UCI Mushroom dataset<a class="anchor-link" href="#Data-Mining-Example:-UCI-Mushroom-dataset">&#182;</a></h3>
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<p>The <a href="http://cml.ics.uci.edu/">Center for Machine Learning and Intelligent Systems</a> at the University of California, Irvine (UCI), hosts a <a href="https://archive.ics.uci.edu/ml/datasets.html">Machine Learning Repository</a> containing over 200 publicly available data sets.</p>
<p><a href="https://archive.ics.uci.edu/ml/datasets/Mushroom"><img src="https://archive.ics.uci.edu/ml/assets/MLimages/Large73.jpg" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="Mushrooms from Agaricus and Lepiota Family" alt="mushroom"/></a>
We will use the <a href="https://archive.ics.uci.edu/ml/datasets/Mushroom">mushroom</a> data set, which forms the basis of several examples in Chapter 3 of the Provost &amp; Fawcett data science book.</p>
<p>The following description of the dataset is provided at the UCI repository:</p>
<blockquote><p>This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525 [The Audubon Society Field Guide to North American Mushrooms, 1981]). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like leaflets three, let it be'' for Poisonous Oak and Ivy.</p>
<p><strong>Number of Instances</strong>: 8124</p>
<p><strong>Number of Attributes</strong>: 22 (all nominally valued)</p>
<p><strong>Attribute Information</strong>: (<em>classes</em>: edible=e, poisonous=p)</p>
<ol>
<li><em>cap-shape</em>: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken=s</li>
<li><em>cap-surface</em>: fibrous=f, grooves=g, scaly=y, smooth=s</li>
<li><em>cap-color</em>: brown=n ,buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y</li>
<li><em>bruises?</em>: bruises=t, no=f</li>
<li><em>odor</em>: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p, spicy=s</li>
<li><em>gill-attachment</em>: attached=a, descending=d, free=f, notched=n</li>
<li><em>gill-spacing</em>: close=c, crowded=w, distant=d</li>
<li><em>gill-size</em>: broad=b, narrow=n</li>
<li><em>gill-color</em>: black=k, brown=n, buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, white=w, yellow=y</li>
<li><em>stalk-shape</em>: enlarging=e, tapering=t</li>
<li><em>stalk-root</em>: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, missing=?</li>
<li><em>stalk-surface-above-ring</em>: fibrous=f, scaly=y, silky=k, smooth=s</li>
<li><em>stalk-surface-below-ring</em>: fibrous=f, scaly=y, silky=k, smooth=s</li>
<li><em>stalk-color-above-ring</em>: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y</li>
<li><em>stalk-color-below-ring</em>: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y</li>
<li><em>veil-type</em>: partial=p, universal=u</li>
<li><em>veil-color</em>: brown=n, orange=o, white=w, yellow=y</li>
<li><em>ring-number</em>: none=n, one=o, two=t</li>
<li><em>ring-type</em>: cobwebby=c, evanescent=e, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z</li>
<li><em>spore-print-color</em>: black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, white=w, yellow=y</li>
<li><em>population</em>: abundant=a, clustered=c, numerous=n, scattered=s, several=v, solitary=y</li>
<li><em>habitat</em>: grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, woods=d</li>
</ol>
<p><strong>Missing Attribute Values</strong>: 2480 of them (denoted by "?"), all for attribute #11.</p>
<p><strong>Class Distribution</strong>: -- edible: 4208 (51.8%) -- poisonous: 3916 (48.2%) -- total: 8124 instances</p>
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<p>The <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data">data file</a> associated with this dataset has one instance of a hypothetical mushroom per line, with abbreviations for the values of the class and each of the other 22 attributes separated by commas.</p>
<p>Here is a sample line from the data file:</p>
<p>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d</p>
<p>This instance represents a mushroom with the following attribute values (highlighted in <strong>bold</strong>):</p>
<p><em>class</em>: edible=e, <strong>poisonous=p</strong></p>
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<li><em>cap-shape</em>: bell=b, conical=c, convex=x, flat=f, <strong>knobbed=k</strong>, sunken=s</li>
<li><em>cap-surface</em>: <strong>fibrous=f</strong>, grooves=g, scaly=y, smooth=s</li>
<li><em>cap-color</em>: <strong>brown=n</strong> ,buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y</li>
<li><em>bruises?</em>: bruises=t, <strong>no=f</strong></li>
<li><em>odor</em>: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, <strong>none=n</strong>, pungent=p, spicy=s</li>
<li><em>gill-attachment</em>: attached=a, descending=d, <strong>free=f</strong>, notched=n</li>
<li><em>gill-spacing</em>: <strong>close=c</strong>, crowded=w, distant=d</li>
<li><em>gill-size</em>: broad=b, <strong>narrow=n</strong></li>
<li><em>gill-color</em>: black=k, brown=n, buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, <strong>white=w</strong>, yellow=y</li>
<li><em>stalk-shape</em>: <strong>enlarging=e</strong>, tapering=t</li>
<li><em>stalk-root</em>: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, <strong>missing=?</strong></li>
<li><em>stalk-surface-above-ring</em>: fibrous=f, scaly=y, <strong>silky=k</strong>, smooth=s</li>
<li><em>stalk-surface-below-ring</em>: fibrous=f, <strong>scaly=y</strong>, silky=k, smooth=s</li>
<li><em>stalk-color-above-ring</em>: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, <strong>white=w</strong>, yellow=y</li>
<li><em>stalk-color-below-ring</em>: <strong>brown=n</strong>, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y</li>
<li><em>veil-type</em>: <strong>partial=p</strong>, universal=u</li>
<li><em>veil-color</em>: brown=n, orange=o, <strong>white=w</strong>, yellow=y</li>
<li><em>ring-number</em>: none=n, <strong>one=o</strong>, two=t</li>
<li><em>ring-type</em>: cobwebby=c, <strong>evanescent=e</strong>, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z</li>
<li><em>spore-print-color</em>: black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, <strong>white=w</strong>, yellow=y</li>
<li><em>population</em>: abundant=a, clustered=c, numerous=n, scattered=s, <strong>several=v</strong>, solitary=y</li>
<li><em>habitat</em>: grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, <strong>woods=d</strong></li>
</ol>
<p>Building a model with this data set will serve as a motivating example throughout much of this primer.</p>
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<h2 id="3.-Python:-Basic-Concepts">3. Python: Basic Concepts<a class="anchor-link" href="#3.-Python:-Basic-Concepts">&#182;</a></h2>
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<h3 id="A-note-on-Python-2-vs.-Python-3"><em>A note on Python 2 vs. Python 3</em><a class="anchor-link" href="#A-note-on-Python-2-vs.-Python-3">&#182;</a></h3>
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<p>There are 2 major versions of Python in widespread use: <a href="https://docs.python.org/2/">Python 2</a> and <a href="https://docs.python.org/3/">Python 3</a>. Python 3 has some features that are not backward compatible with Python 2, and some Python 2 libraries have not been updated to work with Python 3. I have been using Python 2, primarily because I use some of those Python 2[-only] libraries, but an increasing proportion of them are migrating to Python 3, and I anticipate shifting to Python 3 in the near future.</p>
<p>For more on the topic, I recommend a very well documented IPython Notebook, which includes numerous helpful examples and links, by <a href="http://sebastianraschka.com/">Sebastian Raschka</a>, <a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb">Key differences between Python 2.7.x and Python 3.x</a>, the <a href="http://python-future.org/compatible_idioms.html">Cheat Sheet: Writing Python 2-3 compatible code</a> by Ed Schofield ... or <a href="https://www.google.com/q=python%202%20vs%203">googling Python 2 vs 3</a>.</p>
<p><a href="https://twitter.com/ncoghlan_dev">Nick Coghlan</a>, a CPython core developer, sent me an email suggesting that relatively minor changes in this notebook would enable it to run with Python 2 <em>or</em> Python 3: importing the <code>print_function</code> from the <a href="https://docs.python.org/2/library/__future__.html"><strong><code>__future__</code></strong></a> module, and changing my <a href="https://docs.python.org/2/reference/simple_stmts.html#print"><code>print</code> <em>statements</em> (Python 2)</a> to <a href="https://docs.python.org/3/library/functions.html#print"><code>print</code> <em>function calls</em> (Python 3)</a>. Although a relatively minor conceptual change, it necessitated the changing of many individual cells to reflect the Python 3 <code>print</code> syntax.</p>
<p>I decided to import the <code>division</code> module from the <code>future</code>, as I find <a href="https://www.python.org/dev/peps/pep-0238/">the use of <code>/</code> for "true division"</a> - and the use of <code>//</code> for "floor division" - to be more aligned with my intuition. I also needed to replace a few functions that are no longer available in Python 3 with related functions that are available in both versions; I've added notes in nearby cells where the incompatible functions were removed explaining why they are related ... and no longer available.</p>
<p>The differences are briefly illustrated below, with print statements or function calls before and after the importing of the Python 3 versions of the print function and division operator.</p>
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<pre>1 / 2 = 0
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s">&quot;/&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s">&quot;=&quot;</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
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<pre>(1, &apos;/&apos;, 2, &apos;=&apos;, 0)
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="mi">1</span><span class="p">,</span> <span class="s">&quot;/&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s">&quot;=&quot;</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">2</span>
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<span class="ansicyan"> File </span><span class="ansigreen">&quot;&lt;ipython-input-5-168a26d8ec56&gt;&quot;</span><span class="ansicyan">, line </span><span class="ansigreen">1</span>
<span class="ansired"> print 1, &quot;/&quot;, 2, &quot;=&quot;, 1 / 2</span>
^
<span class="ansired">SyntaxError</span><span class="ansired">:</span> invalid syntax
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<pre>1 / 2 = 0.5
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<h3 id="Names-(identifiers),-strings-&amp;-binding-values-to-names-(assignment)">Names (identifiers), strings &amp; binding values to names (assignment)<a class="anchor-link" href="#Names-(identifiers),-strings-&amp;-binding-values-to-names-(assignment)">&#182;</a></h3>
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<p>The sample instance of a mushroom shown above can be represented as a string.</p>
<p>A Python <strong><em>string</em> (<a href="http://docs.python.org/2/tutorial/introduction.html#strings"><code>str</code></a>)</strong> is a sequence of 0 or more characters enclosed within a pair of single quotes (<code>'</code>) or a pair of double quotes (<code>"</code>).</p>
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<div class=" highlight hl-ipython2"><pre><span class="s">&#39;p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d&#39;</span>
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<pre>&apos;p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d&apos;</pre>
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<p>Python <a href="http://docs.python.org/2/reference/lexical_analysis.html#identifiers"><em>identifiers</em></a> (or <a href="https://docs.python.org/2/reference/executionmodel.html#naming-and-binding"><em>names</em></a>) are composed of letters, numbers and/or underscores ('<code>_</code>'), starting with a letter or underscore. Python identifiers are case sensitive. Although camelCase identifiers can be used, it is generally considered more <a href="http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html">pythonic</a> to use underscores. Python variables and functions typically start with lowercase letters; Python classes start with uppercase letters.</p>
<p>The following <a href="http://docs.python.org/2/reference/simple_stmts.html#assignment-statements">assignment statement</a> binds the value of the string shown above to the name <code>single_instance_str</code>. Typing the name on the subsequent line will cause the intepreter to print the value bound to that name.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">single_instance_str</span> <span class="o">=</span> <span class="s">&#39;p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d&#39;</span>
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<pre>&apos;p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d&apos;</pre>
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<p>The <a href="https://docs.python.org/3/library/functions.html#print"><strong><code>print</code></strong></a> function writes the value of its comma-delimited arguments to <a href="http://docs.python.org/2/library/sys.html#sys.stdout"><strong><code>sys.stdout</code></strong></a> (typically the console). Each value in the output is separated by a single blank space.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">,</span> <span class="s">&#39;C&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;Instance 1:&#39;</span><span class="p">,</span> <span class="n">single_instance_str</span><span class="p">)</span>
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<pre>A B C 1 2 3
Instance 1: p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
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<p>The print function has an optional keyword argument, <strong><code>end</code></strong>. When this argument is used and its value does not include <code>'\n'</code> (newline character), the output cursor will not advance to the next line.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">)</span> <span class="c"># no end argument</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;C&#39;</span><span class="p">)</span>
<span class="k">print</span> <span class="p">(</span><span class="s">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s">&#39;...</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">)</span> <span class="c"># end includes &#39;\n&#39; --&gt; output cursor advancees to next line</span>
<span class="k">print</span> <span class="p">(</span><span class="s">&#39;C&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;A&#39;</span><span class="p">,</span> <span class="s">&#39;B&#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s">&#39; &#39;</span><span class="p">)</span> <span class="c"># end=&#39; &#39; --&gt; use a space rather than newline at the end of the line</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;C&#39;</span><span class="p">)</span> <span class="c"># so that subsequent printed output will appear on same line</span>
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<pre>A B
C
A B...
C
A B C
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<h3 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h3>
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<p>The Python <strong><em>comment</em></strong> character is <strong><code>'#'</code></strong>: anything after <code>'#'</code> on the line is ignored by the Python interpreter. PEP8 style guidelines recommend using at least 2 blank spaces before an inline comment that appears on the same line as any code.</p>
<p><strong><em>Multi-line strings</em></strong> can be used within code blocks to provide multi-line comments.</p>
<p>Multi-line strings are delimited by pairs of triple quotes (<strong><code>'''</code></strong> or <strong><code>"""</code></strong>). Any newlines in the string will be represented as <code>'\n'</code> characters in the string.</p>
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<span class="sd">This is</span>
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<pre>&apos;\nThis is\na mult-line\nstring&apos;</pre>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Before comment&#39;</span><span class="p">)</span> <span class="c"># this is an inline comment</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="sd">This is</span>
<span class="sd">a multi-line</span>
<span class="sd">comment</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;After comment&#39;</span><span class="p">)</span>
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<pre>Before comment
After comment
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<p>Multi-line strings can be printed, in which case the embedded newline (<code>'\n'</code>) characters will be converted to newlines in the output.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;&#39;&#39;</span>
<span class="s">This is</span>
<span class="s">a mult-line</span>
<span class="s">string&#39;&#39;&#39;</span><span class="p">)</span>
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This is
a mult-line
string
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<h3 id="Lists">Lists<a class="anchor-link" href="#Lists">&#182;</a></h3>
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<p>A <a href="http://docs.python.org/2/tutorial/introduction.html#lists"><strong><code>list</code></strong></a> is an ordered <strong><em>sequence</em></strong> of 0 or more comma-delimited elements enclosed within square brackets ('<code>[</code>', '<code>]</code>'). The Python <a href="http://docs.python.org/2/library/stdtypes.html#str.split"><strong><code>str.split(sep)</code></strong></a> method can be used to split a <code>sep</code>-delimited string into a corresponding list of elements.</p>
<p>In the following example, a comma-delimited string is split using <code>sep=','</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">single_instance_list</span> <span class="o">=</span> <span class="n">single_instance_str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;,&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
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<pre>[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
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<p>Python lists are <em>heterogeneous</em>, i.e., they can contain elements of different types.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">mixed_list</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">2.3</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">]]</span>
<span class="k">print</span><span class="p">(</span><span class="n">mixed_list</span><span class="p">)</span>
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<pre>[&apos;a&apos;, 1, 2.3, True, [1, &apos;b&apos;]]
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<p>The Python <strong><code>+</code></strong> operator can be used for addition, and also to concatenate strings and lists.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">3</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;a&#39;</span> <span class="o">+</span> <span class="s">&#39;b&#39;</span> <span class="o">+</span> <span class="s">&#39;c&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">([</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mf">2.3</span><span class="p">,</span> <span class="bp">True</span><span class="p">]</span> <span class="o">+</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">]])</span>
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<pre>6
abc
[&apos;a&apos;, 1, 2.3, True, [1, &apos;b&apos;]]
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<h3 id="Accessing-sequence-elements-&amp;-subsequences">Accessing sequence elements &amp; subsequences<a class="anchor-link" href="#Accessing-sequence-elements-&amp;-subsequences">&#182;</a></h3>
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<p>Individual elements of <a href="http://docs.python.org/2/library/stdtypes.html#typesseq"><em>sequences</em></a> (e.g., lists and strings) can be accessed by specifying their <em>zero-based index position</em> within square brackets ('<code>[</code>', '<code>]</code>').</p>
<p>The following statements print out the 3rd element - at zero-based index position 2 - of <code>single_instance_str</code> and <code>single_instance_list</code>.</p>
<p>Note that the 3rd elements are not the same, as commas count as elements in the string, but not in the list created by splitting a comma-delimited string.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
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<pre>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
k
[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
f
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<p><em>Negative index values</em> can be used to specify a position offset from the end of the sequence.</p>
<p>It is often useful to use a <code>-1</code> index value to access the last element of a sequence.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
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<pre>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
d
,
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
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<pre>[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
d
v
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<p>The Python <strong><em>slice notation</em></strong> can be used to access subsequences by specifying two index positions separated by a colon (':'); <code>seq[start:stop]</code> returns all the elements in <code>seq</code> between <code>start</code> and <code>stop - 1</code> (inclusive).</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span>
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<pre>k,
[&apos;f&apos;, &apos;n&apos;]
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<p>Slices index values can be negative.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
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<pre>,v
[&apos;e&apos;, &apos;w&apos;]
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<p>The <code>start</code> and/or <code>stop</code> index can be omitted. A common use of slices with a single index value is to access all but the first element or all but the last element of a sequence.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c"># all but the last </span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="c"># all but the last 2 </span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="c"># all but the first</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span> <span class="c"># all but the first 2</span>
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<pre>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v
,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
</pre>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
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<pre>[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;]
[&apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
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<p>Slice notation includes an optional third element, <code>step</code>, as in <code>seq[start:stop:step]</code>, that specifies the steps or increments by which elements are retrieved from <code>seq</code> between <code>start</code> and <code>step - 1</code>:</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[::</span><span class="mi">2</span><span class="p">])</span> <span class="c"># print elements in even-numbered positions</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span> <span class="c"># print elements in odd-numbered positions</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c"># print elements in reverse order</span>
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<pre>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
pkfnfnfcnwe?kywnpwoewvd
,,,,,,,,,,,,,,,,,,,,,,
d,v,w,e,o,w,p,n,w,y,k,?,e,w,n,c,f,n,f,n,f,k,p
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<p>The <a href="http://docs.python.org/2/tutorial/introduction.html">Python tutorial</a> offers a helpful ASCII art representation to show how positive and negative indexes are interpreted:</p>
<pre>
+---+---+---+---+---+
| H | e | l | p | A |
+---+---+---+---+---+
0 1 2 3 4 5
-5 -4 -3 -2 -1
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<h3 id="Splitting-/-separating-statements">Splitting / separating statements<a class="anchor-link" href="#Splitting-/-separating-statements">&#182;</a></h3>
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<p>Python statements are typically separated by newlines (rather than, say, the semi-colon in Java). Statements can extend over more than one line; it is generally best to break the lines after commas, parentheses, braces or brackets. Inserting a backslash character ('\') at the end of a line will also enable continuation of the statement on the next line, but it is generally best to look for other alternatives.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">attribute_names</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;class&#39;</span><span class="p">,</span>
<span class="s">&#39;cap-shape&#39;</span><span class="p">,</span> <span class="s">&#39;cap-surface&#39;</span><span class="p">,</span> <span class="s">&#39;cap-color&#39;</span><span class="p">,</span>
<span class="s">&#39;bruises?&#39;</span><span class="p">,</span>
<span class="s">&#39;odor&#39;</span><span class="p">,</span>
<span class="s">&#39;gill-attachment&#39;</span><span class="p">,</span> <span class="s">&#39;gill-spacing&#39;</span><span class="p">,</span> <span class="s">&#39;gill-size&#39;</span><span class="p">,</span> <span class="s">&#39;gill-color&#39;</span><span class="p">,</span>
<span class="s">&#39;stalk-shape&#39;</span><span class="p">,</span> <span class="s">&#39;stalk-root&#39;</span><span class="p">,</span>
<span class="s">&#39;stalk-surface-above-ring&#39;</span><span class="p">,</span> <span class="s">&#39;stalk-surface-below-ring&#39;</span><span class="p">,</span>
<span class="s">&#39;stalk-color-above-ring&#39;</span><span class="p">,</span> <span class="s">&#39;stalk-color-below-ring&#39;</span><span class="p">,</span>
<span class="s">&#39;veil-type&#39;</span><span class="p">,</span> <span class="s">&#39;veil-color&#39;</span><span class="p">,</span>
<span class="s">&#39;ring-number&#39;</span><span class="p">,</span> <span class="s">&#39;ring-type&#39;</span><span class="p">,</span>
<span class="s">&#39;spore-print-color&#39;</span><span class="p">,</span>
<span class="s">&#39;population&#39;</span><span class="p">,</span>
<span class="s">&#39;habitat&#39;</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="n">attribute_names</span><span class="p">)</span>
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<pre>[&apos;class&apos;, &apos;cap-shape&apos;, &apos;cap-surface&apos;, &apos;cap-color&apos;, &apos;bruises?&apos;, &apos;odor&apos;, &apos;gill-attachment&apos;, &apos;gill-spacing&apos;, &apos;gill-size&apos;, &apos;gill-color&apos;, &apos;stalk-shape&apos;, &apos;stalk-root&apos;, &apos;stalk-surface-above-ring&apos;, &apos;stalk-surface-below-ring&apos;, &apos;stalk-color-above-ring&apos;, &apos;stalk-color-below-ring&apos;, &apos;veil-type&apos;, &apos;veil-color&apos;, &apos;ring-number&apos;, &apos;ring-type&apos;, &apos;spore-print-color&apos;, &apos;population&apos;, &apos;habitat&apos;]
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">,</span> <span class="s">&#39;c&#39;</span><span class="p">,</span> <span class="c"># no &#39;\&#39; needed when breaking after comma</span>
<span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
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<pre>a b c 1 2 3
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span> <span class="c"># no &#39;\&#39; needed when breaking after parenthesis, brace or bracket</span>
<span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">,</span> <span class="s">&#39;c&#39;</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
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<pre>a b c 1 2 3
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> \
<span class="o">+</span> <span class="mi">3</span><span class="p">)</span>
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<pre>6
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<h3 id="Processing-strings-&amp;-other-sequences">Processing strings &amp; other sequences<a class="anchor-link" href="#Processing-strings-&amp;-other-sequences">&#182;</a></h3>
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<p>The <a href="http://docs.python.org/2/library/stdtypes.html#str.strip"><strong><code>str.strip([chars]</code></strong>)</a> method returns a copy of <code>str</code> in which any leading or trailing <code>chars</code> are removed. If no <code>chars</code> are specified, it removes all leading and trailing whitespace. [<em>Whitespace</em> is any sequence of spaces, tabs (<code>'\t'</code>) and/or newline (<code>'\n'</code>) characters.]</p>
<p>Note that since a blank space is inserted in the output after every item in a comma-delimited list, the second asterisk below is printed after a leading blank space is inserted on the new line.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;*&#39;</span><span class="p">,</span> <span class="s">&#39;</span><span class="se">\t</span><span class="s">p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">,</span> <span class="s">&#39;*&#39;</span><span class="p">)</span>
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<pre>* p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
*
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;*&#39;</span><span class="p">,</span> <span class="s">&#39;</span><span class="se">\t</span><span class="s">p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d</span><span class="se">\n</span><span class="s">&#39;</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span> <span class="s">&#39;*&#39;</span><span class="p">)</span>
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<pre>* p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d *
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;*&#39;</span><span class="p">,</span> <span class="s">&#39;</span><span class="se">\t</span><span class="s">p,k,f,n,f,n,f,c,n,w,e, ?,k,y</span><span class="se">\t</span><span class="s">,w,n,p,w</span><span class="se">\n</span><span class="s">,o,e,w,v,d</span><span class="se">\n</span><span class="s">&#39;</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span> <span class="s">&#39;*&#39;</span><span class="p">)</span>
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<pre>* p,k,f,n,f,n,f,c,n,w,e, ?,k,y ,w,n,p,w
,o,e,w,v,d *
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<p>A common programming pattern when dealing with CSV (comma-separated value) files, such as the mushroom dataset file mentioned above, is to repeatedly:</p>
<ol>
<li>read a line from a file</li>
<li>strip off any leading and trailing whitespace</li>
<li>split the values separated by commas into a list</li>
</ol>
<p>We will get to repetition control structures (loops) and file input and output shortly, but here is an example of how <code>str.strip()</code> and <code>str.split()</code> be chained together in a single instruction for processing a line representing a single instance from the mushroom dataset file. Note that chained methods are executed in left-to-right order.</p>
<p><em>[Python providees a <strong><a href="https://docs.python.org/2/library/csv.html"><code>csv</code></a></strong> module to facilitate the processing of CSV files, but we will not use that module here]</em></p>
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<div class=" highlight hl-ipython2"><pre><span class="n">single_instance_str</span> <span class="o">=</span> <span class="s">&#39;p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d</span><span class="se">\n</span><span class="s">&#39;</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">)</span>
<span class="c"># first strip leading &amp; trailing whitespace, then split on commas</span>
<span class="n">single_instance_list</span> <span class="o">=</span> <span class="n">single_instance_str</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;,&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
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<pre>p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
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<p>The <a href="http://docs.python.org/2/library/string.html#string.join"><strong><code>str.join(words)</code></strong></a> method is the inverse of <code>str.split()</code>, returning a single string in which each string in the sequence of <code>words</code> is separated by <code>str</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">))</span>
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<pre>[&apos;p&apos;, &apos;k&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;n&apos;, &apos;f&apos;, &apos;c&apos;, &apos;n&apos;, &apos;w&apos;, &apos;e&apos;, &apos;?&apos;, &apos;k&apos;, &apos;y&apos;, &apos;w&apos;, &apos;n&apos;, &apos;p&apos;, &apos;w&apos;, &apos;o&apos;, &apos;e&apos;, &apos;w&apos;, &apos;v&apos;, &apos;d&apos;]
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
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<p>A number of Python methods can be used on strings, lists and other sequences.</p>
<p>The <a href="http://docs.python.org/2/library/functions.html#len"><strong><code>len(s)</code></strong></a> function can be used to find the length of (number of items in) a sequence <code>s</code>. It will also return the number of items in a <em>dictionary</em>, a data structure we will cover further below.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">single_instance_str</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">single_instance_list</span><span class="p">))</span>
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<pre>46
23
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<p>The <strong><code>in</code></strong> operator can be used to determine whether a sequence contains a value.</p>
<p>Boolean values in Python are <strong><code>True</code></strong> and <strong><code>False</code></strong> (note the capitalization).</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;,&#39;</span> <span class="ow">in</span> <span class="n">single_instance_str</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;,&#39;</span> <span class="ow">in</span> <span class="n">single_instance_list</span><span class="p">)</span>
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<pre>True
False
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<p>The <a href="http://docs.python.org/2/library/stdtypes.html#str.count"><strong><code>s.count(x)</code></strong></a> ormethod can be used to count the number of occurrences of item <code>x</code> in sequence <code>s</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s">&#39;,&#39;</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s">&#39;f&#39;</span><span class="p">))</span>
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<pre>22
3
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<p>The <a href="http://docs.python.org/2/library/stdtypes.html#str.index"><strong><code>s.index(x)</code></strong></a> method can be used to find the first zero-based index of item <code>x</code> in sequence <code>s</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span><span class="p">(</span><span class="n">single_instance_str</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s">&#39;,&#39;</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">single_instance_list</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s">&#39;f&#39;</span><span class="p">))</span>
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<pre>1
2
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<p>Note that an <a href="https://docs.python.org/2/library/exceptions.html#exceptions.ValueError"><code>ValueError</code></a> exception will be raised if item <code>x</code> is not found in sequence <code>s</code>.</p>
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<span class="ansired">---------------------------------------------------------------------------</span>
<span class="ansired">ValueError</span> Traceback (most recent call last)
<span class="ansigreen">&lt;ipython-input-38-062ca5cc211d&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansigreen">print</span><span class="ansiblue">(</span>single_instance_list<span class="ansiblue">.</span>index<span class="ansiblue">(</span><span class="ansiblue">&apos;,&apos;</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
<span class="ansired">ValueError</span>: &apos;,&apos; is not in list</pre>
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<h3 id="Mutability">Mutability<a class="anchor-link" href="#Mutability">&#182;</a></h3>
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<p>One important distinction between strings and lists has to do with their <a href="http://docs.python.org/2/reference/datamodel.html"><em>mutability</em></a>.</p>
<p>Python strings are <em>immutable</em>, i.e., they cannot be modified. Most string methods (like <code>str.strip()</code>) return modified <em>copies</em> of the strings on which they are used.</p>
<p>Python lists are <em>mutable</em>, i.e., they can be modified.</p>
<p>The examples below illustrate a number of <a href="http://docs.python.org/2/tutorial/datastructures.html#more-on-lists"><code>list</code></a> methods that modify lists.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">list_1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">list_2</span> <span class="o">=</span> <span class="n">list_1</span> <span class="c"># list_2 now references the same object as list_1</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;list_1: &#39;</span><span class="p">,</span> <span class="n">list_1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;list_2: &#39;</span><span class="p">,</span> <span class="n">list_2</span><span class="p">)</span>
<span class="k">print</span><span class="p">()</span>
<span class="n">list_1</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c"># remove [only] the first occurrence of 1 in list_1</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;list_1.remove(1): &#39;</span><span class="p">,</span> <span class="n">list_1</span><span class="p">)</span>
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