I have a simple piece of code. Essentially, I want to speed up my loop that creates a dataframe using dataframes. I haven't found an example and would appreciat
I have four (nx1) dimensional arrays named a, b, c, and F. I want to run this algorithm without any loops. for i in range(n): if a[i] < b[i]: F[i
I have a 3D array of image such as [ [ [225, 0, 0], [225, 225, 0], ... ], [ [225, 0, 0], [225, 225, 0],
I have made a resume parser but to parse my resumes, I am using a for loop to run my parse function over each resume. Is there a way to vectorize this approach?
Please I need someone to explain the function of excluded argument in Numpy vectorize function in a simple way.
I want to know which elements of list_1 are in list_2. I need the output as an ordered list of booleans. But I want to avoid for loops, because both lists have
I have a Pandas dataframe sorted by a datetime column. Several rows will have the same datetime, but the "report type" column value is different. I need to se
Is there a fast way to take 2.0 to some floating-point degree x? I mean something faster than pow(2.0, x) and preferrably what vectorizes well with AVX2. The c
I'm trying to align in memory the following type of data: type foo real, allocatable, dimension(:) :: bar1, bar2 !dir$ attributes align:64 :: bar1 !di
I have a dataframe like this: date value 0 2018-05-15 06:00:00 100.86 1 2018-05-15 07:00:00 101.99 2 2018-05-15 08:00:00 110.00 3 201
I am trying to vectorize the following code: for i in xrange(s.shape[0]): a[i] = np.argmax(np.random.multinomial(1,s[i,:])) s.shape = 400 x 100 [
I'm looking for an approximation of the natural exponential function operating on SSE element. Namely - __m128 exp( __m128 x ). I have an implementation whic
Given a self-indexing (not sure if this is the correct term) numpy array, for example: a = np.array([3, 2, 0, 1]) This represents this permutation (=> is
I am following this document clustering tutorial. As an input I give a txt file which can be downloaded here. It's a combined file of 3 other txt files divided