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  1. PO
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    <p>This is pretty easy if you use pandas.</p> <pre><code>import pandas as pd def sorted_df(df, ascending=False): grouped = df.groupby([0,1]) data = [] for g in grouped: d = g[1] d[4] = d[2].rank(ascending=ascending) d = d.sort(4) data.append(d) return pd.concat(data) # load our dataframe from a csv string import StringIO f = StringIO.StringIO("""uniquedata1,uniquecell1,42,data,1,data uniquedata1,uniquecell1,32,data,2,data uniquedata1,uniquecell1,13,data,3,data uniquedata2,uniquecell2,41,data,2,data uniquedata2,uniquecell2,39,data,3,data uniquedata2,uniquecell2,45,data,1,data uniquedata2,uniquecell2,22,data,4,data uniquedata1,uniquecell2,36,data,3,data uniquedata1,uniquecell2,66,data,1,data uniquedata1,uniquecell2,40,data,2,data""") df = pd.read_csv(f, header=None) # sort descending sorted_df(df) =&gt; 0 1 2 3 4 5 0 uniquedata1 uniquecell1 42 data 1 data 1 uniquedata1 uniquecell1 32 data 2 data 2 uniquedata1 uniquecell1 13 data 3 data 8 uniquedata1 uniquecell2 66 data 1 data 9 uniquedata1 uniquecell2 40 data 2 data 7 uniquedata1 uniquecell2 36 data 3 data 5 uniquedata2 uniquecell2 45 data 1 data 3 uniquedata2 uniquecell2 41 data 2 data 4 uniquedata2 uniquecell2 39 data 3 data 6 uniquedata2 uniquecell2 22 data 4 data # sort ascending sorted_df(df, ascending=True) =&gt; 0 1 2 3 4 5 2 uniquedata1 uniquecell1 13 data 1 data 1 uniquedata1 uniquecell1 32 data 2 data 0 uniquedata1 uniquecell1 42 data 3 data 7 uniquedata1 uniquecell2 36 data 1 data 9 uniquedata1 uniquecell2 40 data 2 data 8 uniquedata1 uniquecell2 66 data 3 data 6 uniquedata2 uniquecell2 22 data 1 data 4 uniquedata2 uniquecell2 39 data 2 data 3 uniquedata2 uniquecell2 41 data 3 data 5 uniquedata2 uniquecell2 45 data 4 data # add some NA values from numpy import nan df.ix[1,2] = nan df.ix[4,2] = nan df.ix[5,2] = nan # sort ascending sorted_df(df, ascending=True) =&gt; 0 1 2 3 4 5 2 uniquedata1 uniquecell1 13 data 1 data 0 uniquedata1 uniquecell1 42 data 2 data 1 uniquedata1 uniquecell1 NaN data NaN data 7 uniquedata1 uniquecell2 36 data 1 data 9 uniquedata1 uniquecell2 40 data 2 data 8 uniquedata1 uniquecell2 66 data 3 data 6 uniquedata2 uniquecell2 22 data 1 data 3 uniquedata2 uniquecell2 41 data 2 data 4 uniquedata2 uniquecell2 NaN data NaN data 5 uniquedata2 uniquecell2 NaN data NaN data </code></pre> <p>I think the behavior I have shown here to handle NA values (ranking them as NA) is probably more appropriate than the behavior you have shown in your hypothetical example, but you can fill NA values with whatever you want within each group using <code>fillna</code>.</p>
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    1. COI was working on a version after I found http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.rank.html but yours is much better than my attempt, I was making it pretty hacky. Surely I can set the NA cells with NaN (from numpy?)
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    2. COYes, that is exactly what I have done here. See the four lines starting with `from numpy import nan`.
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