Pandas replace/dictionary slowness

Question

Please help me understand why this \"replace from dictionary\" operation is slow in Python/Pandas:

# Series has 200 rows and 1 column
# Dictionary has 11269          


        

Answer 1

It looks like replace has a bit of overhead, and explicitly telling the Series what to do via map yields the best performance:

series = series.map(lambda x: dictionary.get(x,x))

If you're sure that all keys are in your dictionary you can get a very slight performance boost by not creating a lambda, and directly supplying the dictionary.get function. Any keys that are not present will return NaN via this method, so beware:

series = series.map(dictionary.get)

You can also supply just the dictionary itself, but this appears to introduce a bit of overhead:

series = series.map(dictionary)

Timings

Some timing comparisons using your example data:

%timeit series.map(dictionary.get)
10000 loops, best of 3: 124 µs per loop

%timeit series.map(lambda x: dictionary.get(x,x))
10000 loops, best of 3: 150 µs per loop

%timeit series.map(dictionary)
100 loops, best of 3: 5.45 ms per loop

%timeit series.replace(dictionary)
1 loop, best of 3: 1.23 s per loop