10/21/2015 · I can do it with np.where as follows: N = 300 shape = (1000,1000,10) data = np.random.randint(0,N+1,shape) indx = [np.where(data == i_id) for i_id in.
Faster alternative to np.where for a sorted array. Given a large array a which is sorted along each row, is there faster alternative to numpy’s np.where to find the indices where min_v <= a <= max_v ? I would imagine that leveraging the sorted nature of the array should be able to speed things up.I have 2 pandas datasets which have 'RF' field in common. The optionsdata variable is ~60K, which map to at most the histdata fields of 5K. Using np.where is incredibly slow: for j in range (0,len (optionsdata)): optionsmap [j] = np.where (histdata.ix ['RF'].str.match (optionsdata.RF [j])) [0] [0], 11/2/2017 · np.where(arr> 0.05) and (arr > 0.05).nonzero() are ~22-25% faster in my tests. For example: while exit_code == No: index_of_pixels_with_excess_volume = numpy.where(ds2 > 0.05) if not index_of_pixels_with_excess_volume[0].size: exit_code = Yes else: for pixel in zip(*index_of_pixels_with_excess_volume):, 3/13/2018 · Alternative to nested np.where in Pandas DataFrame. I have this code (which works) – a bunch of nested conditional statements to set the value in the ‘paragenesis1’ row of a dataframe ( myOxides [‘cpx’] ), depending on the values in various other rows of the frame. I’m very new to python and programming in general.
11/1/2019 · Using np.where() to loop np.where() is sometimes case specific, but if you have an if-else loop, it can almost always be used as the best alternative for faster code.
2/14/2021 · Let’s try another one with an array. We’ll build a Numpy array of size 1000×1000 with a value of 1 at each and again try to multiple each element by a float 1.0000001. The code is shown below. On the same machine, multiplying those array values by 1.0000001 in a regular floating point loop took 1.28507 seconds.
9/12/2018 · Select using at() and iat() is faster than loc() Location-based indexing on numpy array is faster than locating-based indexing on a pandas dataframe What are surprising: zip() is relatively fast for small dataset – even faster than apply() for N faster .
8/2/2017 · Like Pandas , NumPy operates on array objects (referred to as ndarrays) however, it leaves out a lot of overhead incurred by operations on Pandas series, such as indexing, data type checking, etc. As a result, operations on NumPy arrays can be significantly faster than operations on Pandas series.
