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2013-05-24T16:14:42.640
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How about this, using <code>pandas</code>: <pre><code>>>> updated = orig.append(new).groupby('Name').last().fillna(0) >>> updated Day1 Day2 Day3 Day4 Name Abe 1 2 4 0 Ben 2 2 1 0 Cat 2 3 2 0 Dan 0 0 6 3 </code></pre> <hr> First, read in the data (this will depend on the details of the format): <pre><code>>>> orig = pd.read_csv("days1.txt", delim_whitespace=True) >>> new = pd.read_csv("days2up.txt", delim_whitespace=True) >>> orig Name Day1 Day2 Day3 0 Abe 1 2 3 1 Ben 2 2 1 2 Cat 2 3 2 >>> new Name Day3 Day4 0 Abe 4 0 1 Dan 6 3 </code></pre> Then append the new data, which automatically extends the columns: <pre><code>>>> orig.append(new) Day1 Day2 Day3 Day4 Name 0 1 2 3 NaN Abe 1 2 2 1 NaN Ben 2 2 3 2 NaN Cat 0 NaN NaN 4 0 Abe 1 NaN NaN 6 3 Dan </code></pre> Combine by the Name column, and take the last valid value (originally I was worried this would lose the Day1 and Day2 information for Abe, but it doesn't): <pre><code>>>> orig.append(new).groupby("Name").last() Day1 Day2 Day3 Day4 Name Abe 1 2 4 0 Ben 2 2 1 NaN Cat 2 3 2 NaN Dan NaN NaN 6 3 </code></pre> Replace the missing values with 0: <pre><code>>>> orig.append(new).groupby("Name").last().fillna(0) Day1 Day2 Day3 Day4 Name Abe 1 2 4 0 Ben 2 2 1 0 Cat 2 3 2 0 Dan 0 0 6 3 </code></pre> And finally write out: <pre><code>>>> updated = orig.append(new).groupby("Name").last().fillna(0) >>> updated.to_csv("updated.csv") >>> !cat updated.csv Name,Day1,Day2,Day3,Day4 Abe,1.0,2.0,4,0.0 Ben,2.0,2.0,1,0.0 Cat,2.0,3.0,2,0.0 Dan,0.0,0.0,6,3.0 </code></pre>
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1. POUpdating a 2D database (CSV) with other databases in Python?
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1. COSQLite3 seems like a really good option, but I like using pandas for this. Seems like the right tool for the job. Thanks!
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