This question already has answers here:
Transposing a 1D NumPy array
(15 answers)
numpy's transpose method can't convert 1D row ndarray to a column one [duplicate]
(2 answers)
Numpy transpose of 1D array not giving expected result
(4 answers)
Closed last month.
I know the simple/worked solution to this question is reshape (-1, 1) for turning row vector (numpy.array) into a column vector (numpy.array).
Specifically, I want to understand why numpy.transpose(a) won't work.
Say,
vector_of_1 = np.transpose(np.ones(N)) # statement 1
And if I define a column vector b, and use the following statement:
V = b + vector_of_1
I would get a weird matrix V.
My fix is to use
vector_of_1 = np.ones(N).reshape(-1,1)
And it works as expected (V being a column vector).
But I want to understand why the transpose method (i.e., statement 1) won't work. Detailed explanation is appreciated.
This question already has answers here:
Python Numpy Sort rows [duplicate]
(2 answers)
Closed 2 years ago.
I have an numpy array arr with shape (1500,10) where each element is a digit from 0 to 9. I'd like to sort the array as each row's elements are concatenated to form a single number and then sort these numbers in ascending order.Let a simple array be like:
arr = ([[3,4,1,5,1,2,3,4,5,6],
[1,2,3,5,6,2,9,2,1,2],
[0,3,1,4,2,1,6,8,2,1],
[0,1,3,5,1,2,9,2,1,7],
[2,3,5,7,1,2,5,7,1,5]])
it should return
arr = ([[0,1,3,5,1,2,9,2,1,7],
[0,3,1,4,2,1,6,8,2,1],
[1,2,3,5,6,2,9,2,1,2],
[2,3,5,7,1,2,5,7,1,5],
[3,4,1,5,1,2,3,4,5,6]])
You can do the following:
arr[np.lexsort(np.flip(arr.transpose(), axis=0))]
This question already has answers here:
Convert Python sequence to NumPy array, filling missing values
(8 answers)
Convert list of lists with different lengths to a numpy array [duplicate]
(3 answers)
Closed 3 years ago.
all
I have a numpy array, the dimension of the element is different. for example:
[
[1,2,3],
[2,3,4,5,6]
[1,2]
]
I want to adjust the element dimension, set the dimension as the largest one, and fill with 0, how can I do it?
You can create a placeholder with the desired shape first, then fill the placeholder with the data list
data = [[1,2,3], [2,3,4,5,6], [1,2]]
# create a placeholder
tmp = np.zeros((len(data), max([len(item) for item in data])))
# fill the placeholder with data
for ind, line in enumerate(data):
tmp[ind, :len(line)] = line
However, this may not be super fast when the size of the data list is large.
This question already has an answer here:
how to calculate the dot product of two arrays of vectors in python? [duplicate]
(1 answer)
Closed 4 years ago.
I need a simple and fast function to multiply each row of numpy array 'a' to array 'b'
a , b have same 2d dimention
like the result of this is example(c):
but I want a numpy function insted of this loop
a=np.arange(6).reshape(3,2)
b=np.arange(6,12).reshape(3,2)
c=np.array([[a[i,:]#b[i,:]]for i in range(a.shape[0])])
An easy way would be to write it yourself with vectorized numpy methods:
np.sum(a*b,axis=1,keepdims=True)
array([[ 8],
[ 48],
[104]])
This question already has answers here:
Concatenating two one-dimensional NumPy arrays
(6 answers)
Closed 6 years ago.
I have two numpy arrays that have the same shape(4,1,2).
How can I combine them and get a new array of size(8,1,2) with minimum lines of python code? Not changing values just put them together with A on the top B at the bottom.
A=numpy.array([[[1,1]],
[[2,2]],
[[3,3]],
[[4,4]]]);
B=numpy.array([[[5,5]],
[[6,6]],
[[7,7]],
[[8,8]]]);
numpy.concatenate() should do what you want:
numpy.concatenate((A, B))
Use numpy.vstack()
numpy.vstack([A,B])