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POHow can I create new rows in a pandas data frame containing the words in a string of an existing row?
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copied!<p>I have a <code>DataFrame</code> in <code>pandas</code> with a column called <code>df.strings</code> with strings of text. I would like to get the individual words of those strings on their own rows with identical values for the other columns. For example if I have 3 strings (and an unrelated column, Time):</p> <pre><code> Strings Time 0 The dog 4Pm 1 lazy dog 2Pm 2 The fox 1Pm </code></pre> <p>I want new rows containing the words from the string, but with otherwise identical columns</p> <pre><code>Strings --- Words ---Time "The dog" --- "The" --- 4Pm "The dog" --- "dog" --- 4Pm "lazy dog"--- "lazy"--- 2Pm "lazy dog"--- "dog" --- 2Pm "The fox" --- "The" --- 1Pm "The fox" --- "fox" --- 1Pm </code></pre> <p>I know how to split the words up from the strings: </p> <pre><code> string_list = '\n'.join(df.Strings.map(str)) word_list = re.findall('[a-z]+', Strings) </code></pre> <p>But how can I get these into the dataframe while preserving the index & other variables? I'm using Python 2.7 and pandas 0.10.1.</p> <p>EDIT: I now understand how to expand rows using groupby found in <a href="https://stackoverflow.com/questions/13050003/pandas-apply-function-to-dataframe-that-can-return-multiple-rows">this question</a>:</p> <pre><code>def f(group): row = group.irow(0) return DataFrame({'words': re.findall('[a-z]+',row['Strings'])}) df.groupby('class', group_keys=False).apply(f) </code></pre> <p>I would still like to preserve the other columns. Is this possible?</p>

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