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POQuestions about pandas: expanding multivalued column, inverting and grouping
 

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  1. POQuestions about pandas: expanding multivalued column, inverting and grouping
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    <p>I was looking into pandas to do some simple calculations on NLP and text mining but I couldn't quite grasp how to do them. </p> <p>Suppose I have the following data frame, relating people's names and their gender:</p> <pre><code>import pandas people = {'name': ['John Doe', 'Mary Poppins', 'Jane Doe', 'John Cusack'], 'gender': ['M', 'F', 'F', 'M']} df = pandas.DataFrame(people) </code></pre> <p>For all rows I want to:</p> <ol> <li>determine the first name</li> <li>determine a list of 3-shingles (sequences of 3 letters contained in a word) deriving from the person name</li> <li>determine, for each shingle, how many males and females contained that shingle on their names.</li> </ol> <p>The goal is to use this as a data set to train a classifier which can determine if a given name is probably a male or a female name.</p> <p>The first two operations are quite straightforward:</p> <pre><code>def shingles(word, n = 3): return [word[i:i + n] for i in range(len(word) - n + 1)] df['firstname'] = df.name.map(lambda x : x.split()[0]) df['shingles'] = df.firstname.map(shingles) </code></pre> <p>the result is:</p> <pre><code>&gt; print df gender name firstname shingles 0 M John Doe John ['joh', 'ohn'] 1 F Mary Poppins Mary ['mar', 'ary'] 2 F Jane Doe Jane ['jan', 'ane'] 3 M John Cusack John ['joh', 'ohn'] </code></pre> <p>Now, the next step should be done by constructing a new data frame with two columns: gender and shingle, which should contain something like:</p> <pre><code> gender shingle 0 M joh 1 M ohn 2 F mar 3 F ary (...) </code></pre> <p>And then I could group by shingle and gender. Ideally, the result would be:</p> <pre><code> shingle num_males num_females 0 joh 2 0 1 ohn 2 0 2 mar 0 1 3 ary 0 1 (...) </code></pre> <p>Is there an easy way to expand the multivalued column <code>shingles</code> in a way that each row produces multiple rows, one for each value found in the list of shingles?</p> <p>Also, if I <code>groupby</code> the column <code>shingle</code>, how easy it is to produce different columns with the count for each possible value of the column <code>gender</code>?</p> <hr> <p>I managed to understand the second part. As an example, to calculate how many males and females for each <code>firstname</code>:</p> <pre><code> def countMaleFemale(df): return pandas.Series({'males': df.gender[df.gender == 'M'].count(), 'females': df.gender[df.gender == 'F'].count()}) grouped = df.groupby('first name') </code></pre> <p>And then:</p> <blockquote> <p>print grouped.apply(countMaleFemale)</p> </blockquote> <pre><code> females males first name Jane 1 0 John 0 2 Mary 1 0 </code></pre>
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    1. USRafael S. Calsaverini
    1. USRafael S. Calsaverini
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