Fix Python – Multiplying across in a numpy array

I’m trying to multiply each of the terms in a 2D array by the corresponding terms in a 1D array. This is very easy if I want to multiply every column by the 1D array, as shown in the numpy.multiply function. But I want to do the opposite, multiply each term in the row.
In other words I want to multiply:
[1,2,3] [0]
[4,5,6] * [1]
[7,8,9] [2]


Fix Python – Numpy array assignment with copy

For example, if we have a numpy array A, and we want a numpy array B with the same elements.
What is the difference between the following (see below) methods? When is additional memory allocated, and when is it not?

B = A
B[:] = A (same as B[:]=A[:]?)
numpy.copy(B, A)


Fix Python – Numpy isnan() fails on an array of floats (from pandas dataframe apply)

I have an array of floats (some normal numbers, some nans) that is coming out of an apply on a pandas dataframe.
For some reason, numpy.isnan is failing on this array, however as shown below, each element is a float, numpy.isnan runs correctly on each element, the type of the variable is definitely a numpy array.
What’s going on?!
set([type(x) for….

Fix Python – Performance of Pandas apply vs np.vectorize to create new column from existing columns

I am using Pandas dataframes and want to create a new column as a function of existing columns. I have not seen a good discussion of the speed difference between df.apply() and np.vectorize(), so I thought I would ask here.
The Pandas apply() function is slow. From what I measured (shown below in some experiments), using np.vectorize() is 25x fas….

Fix Python – Selecting specific rows and columns from NumPy array

I’ve been going crazy trying to figure out what stupid thing I’m doing wrong here.
I’m using NumPy, and I have specific row indices and specific column indices that I want to select from. Here’s the gist of my problem:
import numpy as np

a = np.arange(20).reshape((5,4))
# array([[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 1….

Fix Python – How can I reorder a list? [closed]

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Closed 2 years ago…..