## Taking subarrays from numpy array with given stride/stepsize

Lets say I have a Python Numpy array `a`.

## 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.