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    <p>Here's one way using a groupby to B level of the DataFrame:</p> <pre><code>In [11]: p Out[11]: V1 V2 B C c e 1870 1911 f 1887 816 d e 230 25 f 1890 1055 In [12]: g = p.groupby(level='B') </code></pre> <p>and take the percentage of each of the V2s in each group:</p> <pre><code>In [13]: g['V2'].apply(lambda s: s.astype(float) / s.sum()) Out[13]: B C c e 0.700770 f 0.299230 d e 0.023148 f 0.976852 dtype: float64 </code></pre> <p>Finally, assign this as a column:</p> <pre><code>In [14]: p['PercentOfB'] = g['V2'].apply(lambda s: s.astype(float) / s.sum()) In [15]: p Out[15]: V1 V2 PercentOfB B C c e 1870 1911 0.700770 f 1887 816 0.299230 d e 230 25 0.023148 f 1890 1055 0.976852 </code></pre> <p>To expand a little on what's going on here, during the apply the function is called on each group (in this case there are two, one for B='c' and one for B='d'), here's the c group:</p> <pre><code>In [21]: c Out[21]: B C c e 1911 f 816 Name: c, dtype: int64 </code></pre> <p>and the interested result is to divide through by the total:</p> <pre><code>In [22]: c.sum() Out[22]: 2727 </code></pre> <p>Unfortunately in python 2, integer division isn't "proper":</p> <pre><code>In [23]: c / c.sum() Out[23]: B C c e 0 f 0 Name: c, dtype: int64 </code></pre> <p>So we need to fix it by making them floats, usually I use <code>astype(float)</code> or <code>* 1.0</code> to force this:</p> <pre><code>In [24]: c.astype(float) / c.sum() Out[24]: B C c e 0.70077 f 0.29923 Name: c, dtype: float64 </code></pre> <p>Apply then outputs this together with the B='d' group to get the desired result.</p> <p><em>Note: Regarding how I got c, as I find this a very useful trick to writing applies.</em></p> <p><em>I created a dummy function and an empty list, and applied it to the groupby:</em></p> <pre><code>a = [] def f(x): a.append(x) return x g['v2'].apply(f) c = a[0] </code></pre> <p><em>Then I played with that 'til I got what I want.</em></p>
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    1. POGetting percentage of another Column in Pandas Pivot Table
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    1. USAndy Hayden
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    1. POGetting percentage of another Column in Pandas Pivot Table
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    1. COCould you walk me through this a little so I understand it a bit better? How does groupby get the lambda function to work?
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    2. CO@Jon it is a little magical! I'll add in an example group.
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    3. CO@Jon expanded quite a bit, hope it makes it less magical... :)
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