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POWhy were pandas merges in python faster than data.table merges in R in 2012?
 

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  1. POWhy were pandas merges in python faster than data.table merges in R in 2012?
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    <p>I recently came across the <a href="http://pandas.sourceforge.net/" rel="noreferrer">pandas</a> library for python, which according to <a href="http://wesmckinney.com/blog/some-pandas-database-join-merge-benchmarks-vs-r-basemerge/" rel="noreferrer">this benchmark</a> performs very fast in-memory merges. It's even faster than the <a href="http://cran.r-project.org/web/packages/data.table/index.html" rel="noreferrer">data.table</a> package in R (my language of choice for analysis).</p> <p>Why is <code>pandas</code> so much faster than <code>data.table</code>? Is it because of an inherent speed advantage python has over R, or is there some tradeoff I'm not aware of? Is there a way to perform inner and outer joins in <code>data.table</code> without resorting to <code>merge(X, Y, all=FALSE)</code> and <code>merge(X, Y, all=TRUE)</code>?</p> <p><img src="https://i.stack.imgur.com/0pCvh.png" alt="Comparison"></p> <p>Here's the <a href="https://github.com/wesm/pandas/blob/master/bench/bench_merge.R" rel="noreferrer">R code</a> and the <a href="https://github.com/wesm/pandas/blob/master/bench/bench_merge.py" rel="noreferrer">Python code</a> used to benchmark the various packages.</p>
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    1. COMy hypothesis: because data.table is based on data.frame and data.frames are slow. And I think most of the pandas merge code is in Cython.
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      1. POWhy were pandas merges in python faster than data.table merges in R in 2012?
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    2. CO@JoshuaUlrich: IIRC `data.table` just inherits from `data.frame`, but it relies on C-code under the hood.
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    3. CO@digEmAll: data.frames are slow even if you manipulate them in C, but I've never looked at the data.table source.
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