Selecting specific rows and columns from NumPy array
Fancy indexing requires you to provide all indices for each dimension. You are providing 3 indices for the first one, and only 2 for the second one, hence the error. You want to do something like this:
>>> a[[[0, 0], [1, 1], [3, 3]], [[0,2], [0,2], [0, 2]]]
array([[ 0, 2],
[ 4, 6],
[12, 14]])
That is of course a pain to write, so you can let broadcasting help you:
>>> a[[[0], [1], [3]], [0, 2]]
array([[ 0, 2],
[ 4, 6],
[12, 14]])
This is much simpler to do if you index with arrays, not lists:
>>> row_idx = np.array([0, 1, 3])
>>> col_idx = np.array([0, 2])
>>> a[row_idx[:, None], col_idx]
array([[ 0, 2],
[ 4, 6],
[12, 14]])
Choosing specific rows and columns from numpy array
import numpy as np
a = np.arange(676).reshape((26,26))
First we need to define which rows we want:
index = np.arange(a.shape[0]) != 14 # all rows but the 15th row
we can use the same index for columns, since we are selecting the same rows and columns, and a is a square matrix
Now we can use np.ix_ function to express that we want all selected row and columns.
a[np.ix_(index, index)] #a.shape =(25, 25)
Note that a[index, index] won't work since only the diagonal elements will be selected (the result is an array not a matrix)
how to select specific rows and columns of a double array in numpy to form a submatrix?
When you select subarrays with two broadcastable indices arrays, like array[arr_1, arr2]
, it will match each element of arr_1
to arr_2
and select corresponding element of array
. If you wish to select all rows in arr_1
and all columns in arr_2
, the most elegant way would be using np.ix_
. The code would be:
ab[np.ix_(np.array([0,2]),np.array([1,3]))]
output:
[[2 4]
[4 3]]
About np.ix_
: From numpy doc: This function takes N 1D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the nonunit shape value cycles through all N dimensions.
Which means you can extend this to ANY dimension array. For array
of N dimensions, calling np.ix_(arr_1, arr_2, ..., arr_N)
will create N indices array, each will cycle through all arr_i
rows of dimension i
in array
.
Make a numpy array selecting only certain rows and certain columns from another
Something like this
import numpy as np
first_array = np.random.rand(500,1000)
row_factor = 10
row_start = 1
col_factor = 10
col_start = 1
second_array = first_array[row_start:1:row_factor,col_start:1:col_factor]
print(second_array.shape)
You can make simple slicing where you skip row_factor or col_factor in both direction
How to extract slices and specific columns of a numpy array with one command?
As ombk suggested, you can use r_.
It is a perfect tool to concatenate slice expressions.
In your case:
A[:, np.r_[0:3, 4]]
retrieves the intended part of your array.
Just the same way you can concatenate more slice expressions.
Extracting multiple sets of rows/ columns from a 2D numpy array
IIUC, you can use numpy.r_
to generate the indices from the slice:
img[np.r_[0,2:4][:,None],2]
output:
array([[ 2],
[12],
[17]])
intermediates:
np.r_[0,2:4]
# array([0, 2, 3])
np.r_[0,2:4][:,None] # variant: np.c_[np.r_[0,2:4]]
# array([[0],
# [2],
# [3]])
How to select specific row column pairs in numpy array which have specific value?
Get the indices along first two axes that match that criteria with np.nonzero/np.where
on the mask of comparisons and then simply index with integer array indexing

r,c = np.nonzero(x>0.3)
out = x[r,c]
If you are looking to get those indices a list of tuples, zip
those indices 
zip(r,c)
To get those starting from 1
, add 1
and then zip 
zip(r+1,c+1)
On Python 3.x
, you would need to wrap it with list()
: list(zip(r,c))
and list(zip(r+1,c+1))
.
Sample run 
In [9]: x
Out[9]:
array([[ 0.11874238, 0.71885484, 0.33656161],
[ 0.69432263, 0.25234083, 0.66118676],
[ 0.77542651, 0.71230397, 0.76212491]])
In [10]: r,c = np.nonzero(x>0.3)
In [14]: zip(r,c)
Out[14]: [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1), (2, 2)]
In [18]: zip(r+1,c+1)
Out[18]: [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2), (3, 3)]
In [13]: x[r,c]
Out[13]:
array([ 0.71885484, 0.33656161, 0.69432263, 0.66118676, 0.77542651,
0.71230397, 0.76212491])
Writing indices
to file 
Use np.savetxt
with int
format, like so 
In [69]: np.savetxt("output.txt", np.argwhere(x>0.3), fmt="%d", comments='')
In [70]: !cat output.txt
0 1
0 2
1 0
1 2
2 0
2 1
2 2
With the 1
based indexing, add 1
to np.argwhere
output 
In [71]: np.savetxt("output.txt", np.argwhere(x>0.3)+1, fmt="%d", comments='')
In [72]: !cat output.txt
1 2
1 3
2 1
2 3
3 1
3 2
3 3
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