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

Lets say I have a Python Numpy array `a`

.

Lets say I have a Python Numpy array `a`

.

**Are for loops really “bad”? If not, in what situation(s) would they be better than using a more conventional “vectorized” approach? ^{1}**

Can you tell me when to use these vectorization methods with basic examples?

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

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.

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`

.

How do I get the exponential weighted moving average in NumPy just like the following in pandas?

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:

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:

I have two large sets of 2D points and I need to calculate a distance matrix.