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    <p>I think the issue is that there are two different <code>first</code> methods which share a name but act differently, one is for <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html#splitting-an-object-into-groups" rel="noreferrer">groupby objects</a> and <a href="http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.first.html" rel="noreferrer">another for a Series/DataFrame</a> (to do with timeseries).</p> <p>To replicate the behaviour of the groupby <code>first</code> method over a DataFrame using <code>agg</code> you could use <code>iloc[0]</code> (which gets the first row in each group (DataFrame/Series) by index):</p> <pre><code>grouped.agg(lambda x: x.iloc[0]) </code></pre> <p>For example:</p> <pre><code>In [1]: df = pd.DataFrame([[1, 2], [3, 4]]) In [2]: g = df.groupby(0) In [3]: g.first() Out[3]: 1 0 1 2 3 4 In [4]: g.agg(lambda x: x.iloc[0]) Out[4]: 1 0 1 2 3 4 </code></pre> <p><em>Analogously you can replicate <code>last</code> using <code>iloc[-1]</code>.</em></p> <p>Note: This will works column-wise, et al:</p> <pre><code>g.agg({1: lambda x: x.iloc[0]}) </code></pre> <p><em>In older version of pandas you could would use the irow method (e.g. <code>x.irow(0)</code>, see previous edits.</em></p> <hr> <p>A couple of updated notes:</p> <p>This is better done using the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.nth.html" rel="noreferrer"><code>nth</code></a> groupby method, which is much faster >=0.13:</p> <pre><code>g.nth(0) # first g.nth(-1) # last </code></pre> <p><em>You have to take care a little, as the default behaviour for <code>first</code> and <code>last</code> ignores NaN rows... and IIRC for DataFrame groupbys it was broken pre-0.13... there's a <code>dropna</code> option for <code>nth</code>.</em></p> <p>You can use the strings rather than built-ins (though IIRC pandas spots it's the <code>sum</code> builtin and applies <code>np.sum</code>):</p> <pre><code>grouped['D'].agg({'result1' : "sum", 'result2' : "mean"}) </code></pre>
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    1. POHow to apply "first" and "last" functions to columns while using group by in pandas?
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    1. USAndy Hayden
    1. USAndy Hayden
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    1. COJust in case it's useful to anyone, according to [the docs](http://pandas.pydata.org/pandas-docs/dev/indexing.html), `irow` is now deprecated (`x.iloc[0]` does the trick instead)
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      1. UScd98
    2. CO@cd98 Thanks for pointing that out, I've updated this with the newer syntax :)
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      1. USAndy Hayden
 

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