#Numpy vectorize code
#Numpy vectorize trial

In general it is difficult to have a state in a vectorized approach. Secondly, not all loops can be turned into vectorized code. First of all, we have only tried it for one vectorized approach, which was obviously very easy to optimize. ConclusionДoes that mean the Numba does not pay off to use? ItвЂ™s not 2 times faster or 100 times faster as others sometimes claim.

#Numpy vectorize trial

In the trial that is graphed below, the vectorized function is 4 faster.

#Numpy vectorize code

This is not surprising, as the code in a vectorized call can be more specifically optimized than the more general purpose Numba approach. I found that the scipy vectorized function was slightly faster than the non-vectorized function as shown by the olive-colored bars in the graph below.

It is interesting that Numba is faster for small sized of the problem, while it seems like the vectorized approach outperforms Numba for bigger sizes.ДЄnd not surprisingly, the number of iterations only makes the difference bigger. It's because apply is slow.Print('Elapsed (No Numba) = %s' % (end - start)) So if the pandas apply is much slower it isn't because vectorize is magical. Direct access В¶ For performance reasons, particularly when dealing with very large arrays, it is often desirable to directly access array elements without internal checking of dimensions and bounds on every access when indices are known to be already valid. In : f=np.vectorize(clean_text, excluded=)Д®xcluded actually slows vectorize down without it, the list comprehension and vectorization perform the same. The file tests/testnumpyvectorize.cpp contains a complete example that demonstrates using vectorize() in more detail. If I make the text list longer (10000): In : timeit Changed in version 1.9: 1-D and 0-D cases are allowed. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2, xn. Return coordinate matrices from coordinate vectors. In : f=np.vectorize(clean_text, excluded=)Д«ut with only one array or dataseries to iterate over, vectorize is little more than a 1d iteration or list comprehension: In : text = shgrid(xi, copyTrue, sparseFalse, indexing'xy') source. The speed difference is probably negligible. Since we don't want the vectorized function to iterate over the 2nd argument we really should have used the excluded parameter. That's because wrapping a set in an array just creates of 0d object array: In : np.array(words) But contrary to what I feared it is passing the whole set to the function. The set is displayed twice because vectorize runs a test case to determine the dtype of the return array. With your function, and a print(stop_words) addition, I got In : words = set('one two three four five'.split()) In particular I wondered how it handled the set. It is designed to take array inputs, broadcast them against each other, and feed all elements, as scalars, to your function. I wondered how vectorize handled these inputs. What kind of 'magic' does numpy's Вґvectorize` do? I was assuming, that with vectorize, it would automatically use multiple cores to finish faster and this way gaining more speed, but it seems to do something different instead. That in itself would not be surprising, if not both methods only used one core on my machine. The computation finishes after 10 seconds (approximately).

However, when I use np.vectorize like this: dataframe = np.vectorize(clean_text)(Дataframe, set(stopwords.words('english'))) The computation needs 2 minutes to finish (approximately). Lambda text: clean_text(text, set(stopwords.words('english'))), If I simply apply this function to the dataframe like this: dataframe = dataframe.apply( # join the remaining words together to get the cleaned tweetДЄnd a dataset of 1.6 million twitter tweet in a pandas dataframe. To make sure that the code is computationally efficient, we will use vectorization. Hence, a non-computationally-optimal function can become a huge bottleneck in your algorithm and can take result in a model that takes ages to run. # removing everything which is not a letter We know that most of the application has to deal with a large number of datasets. I have a function for cleaning text of a set stopwords: def clean_text(raw_text, stopwords_set):