In [28]: arr = np.arange(16).reshape((2, 2, 4))
In [29]: arr
Out[29]:
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]]])
In [32]: arr.transpose((1, 0, 2))
Out[32]:
array([[[ 0, 1, 2, 3],
[ 8, 9, 10, 11]],
[[ 4, 5, 6, 7],
[12, 13, 14, 15]]])
When we pass a….
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]
a….
I’m analyzing some Python code and I don’t know what
pop = population[:]
means. Is it something like array lists in Java or like a bi-dimensional array?
….
What is a good way to split a NumPy array randomly into training and testing/validation dataset? Something similar to the cvpartition or crossvalind functions in Matlab.
….
I have a large numpy array that I need to manipulate so that each element is changed to either a 1 or 0 if a condition is met (will be used as a pixel mask later). There are about 8 million elements in the array and my current method takes too long for the reduction pipeline:
for (y,x), value in numpy.ndenumerate(mask_data):
if mask_data[y,x….
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)
….
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….
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….
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….
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