Taking subarrays from numpy array with given stride/stepsize
Lets say I have a Python Numpy array a.
vectorization
Are for-loops in pandas really bad? When should I care?
Are for loops really “bad”? If not, in what situation(s) would they be better than using a more conventional “vectorized” approach?1
Difference between map, applymap and apply methods in Pandas
Can you tell me when to use these vectorization methods with basic examples?
Is it possible to vectorize recursive calculation of a NumPy array where each element depends on the previous one?
T(i) = Tm(i) + (T(i-1)-Tm(i))**(-tau(i)) Tm and tau are NumPy vectors of the same length that have been previously calculated, and the desire is to create a new vector T. The i is included only to indicate the element index for what is desired. Is a for loop necessary for this case? Answers: Thank you … Read more
Bin elements per row – Vectorized 2D Bincount for NumPy
I have a NumPy array with integer values. Values of matrix range from 0 to max element in matrix(in other words, all numbers from 0 to max data element presented in it). I need to build effective( effective means fast fully-vectorized solution) for searching number of elements in each row and encode them according to matrix values.
Vectorized searchsorted numpy
Assume that I have two arrays A and B, where both A and B are m x n. My goal is now, for each row of A and B, to find where I should insert the elements of row i of A in the corresponding row of B. That is, I wish to apply np.digitize or np.searchsorted to each row of A and B.
NumPy version of “Exponential weighted moving average”, equivalent to pandas.ewm().mean()
How do I get the exponential weighted moving average in NumPy just like the following in pandas?
Vectorized NumPy linspace for multiple start and stop values
I need to create a 2D array where each row may start and end with a different number. Assume that first and last element of each row is given and all other elements are just interpolated according to length of the rows In a simple case let’s say I want to create a 3X3 array with same start at 0 but different end given by W below:
Vectorizing Haversine distance calculation in Python
I am trying to calculate a distance matrix for a long list of locations identified by Latitude & Longitude using the Haversine formula that takes two tuples of coordinate pairs to produce the distance:
Why np.hypot and np.subtract.outer very fast compared to vanilla broadcast ? Using Numba for speedup numpy in parallel for distance matrix calculation
I have two large sets of 2D points and I need to calculate a distance matrix.