All Questions
19,984 questions
2
votes
2
answers
62
views
Pandas: Fill in missing values with an empty numpy array
I have a Pandas Dataframe that I derive from a process like this:
df1 = pd.DataFrame({'c1':['A','B','C','D','E'],'c2':[1,2,3,4,5]})
df2 = pd.DataFrame({'c1':['A','B','C'],'c2':[1,2,3],'c3': [np.array((...
- 289
-1
votes
0
answers
33
views
Efficiently Finding the Indices of the N Largest Values in a NumPy Array Without Sorting the Entire Array [duplicate]
I'm working with very large NumPy arrays (millions to billions of elements) and need to find the indices of the N largest values in the array. Using np.argsort() followed by slicing to get the last N ...
- 96
0
votes
1
answer
67
views
Apparently weird condition on inclusion of endpoint in np.arange() [duplicate]
The numpy.arange function takes the three parameters: start, stop, step (positional args.)
The default step is 1.
Throughout my entire Numpy experience, the last element of the resultant array is not ...
2
votes
1
answer
103
views
Why do these nearly identical functions perform very differently?
I have written four functions that modify a square 2D array in place, it reflects half of the square array delimited by two sides that meet and the corresponding 45 degree diagonal, to the other half ...
- 3,806
4
votes
1
answer
171
views
Efficient and readable way to get N-dimensional index array in C-order using NumPy
When I need to generate an N-dimensional index array in C-order, I’ve tried a few different NumPy approaches.
The fastest for larger arrays but less readable:
np.stack(np.meshgrid(*[np.arange(i, dtype=...
- 383
2
votes
2
answers
66
views
Numpy- strange behaviour of __setitem__ of array
Say we have an array:
a = np.array([
[11, 12, 13],
[21, 22, 23],
[31, 32, 33],
[41, 42, 43]
])
a[[1, 3], [0, 2]] = 0
So we want to set zeros to 0th and 2nd element at both 1st and ...
- 397
1
vote
1
answer
47
views
Pandas dataframe assign nested list not working
I'm trying to assign a dataframe cell with a nested list:
df.loc['y','A'] = [[2]]
However, the actual assigned value is [2].
It works expected for [2], [[[2]]], [[[[2]]]], but just not for [[2]]
See ...
- 13
1
vote
5
answers
98
views
Can I multiply these Numpy arrays without creating an intermediary array?
This script:
import numpy as np
a = np.linspace(-2.5, 2.5, 6, endpoint=True)
b = np.vstack((a, a)).T
c = np.array([2, 1])
print(b*c)
produces:
[[-5. -2.5]
[-3. -1.5]
[-1. -0.5]
[ 1. 0.5]
[ ...
- 8,235
1
vote
3
answers
122
views
numpy element-by-element subtract
I have two numpy 'arrays':
1st is a 2D array with shape (N, 2)
2nd is a 3D "matrix" with shape (N, M, 2) where M can be different from 0 to N-1
Code example
import numpy as np
a = np.array([...
- 28
2
votes
3
answers
221
views
What is the fastest way to generate alternating boolean sequences in NumPy?
I want to create a 1D array of length n, every element in the array can be either 0 or 1. Now I want the array to contain alternating runs of 0s and 1s, every full run has the same length as every ...
- 3,806
0
votes
0
answers
104
views
FAISS.from_texts() occur ValueError: input not a numpy array
vec_multi = embedding.embed_documents(pdf_chunks)
import numpy as np
vec_np = np.array(vec_multi)
print(vec_np.shape) # (문장 개수, 임베딩 차원)
print(vec_np.dtype)
(2119, 768)
float64
...
- 13
0
votes
2
answers
77
views
Processing satellite conjunctions with numpy efficiently
Original Post: How to populate a 2-d numpy array with values from a third dimension?
New Post:
I'm trying to analyze interference between satellites using numpy and sgp4 python libraries, and want to ...
- 45
0
votes
1
answer
54
views
Most efficient way to allocate n copies of an array to n-new dimensions?
The task is to create 361x721x11 copies of ll as fast as possible preferably using the functions in numpy. The following code works
ll = ones((13,25)) # used ones() for illustration purposes. My real ...
- 147
1
vote
1
answer
88
views
How to populate a 2-d numpy array with values from a third dimension?
New Post: Processing satellite conjunctions with numpy efficiently
Original Post:
I have a numpy array of shape n x m x r, where the n axis represents an object, the m axis represents a timestep and ...
- 45