Print two arrays side by side using numpy

Question

I'm trying to create a table of cosines using numpy in python. I want to have the angle next to the cosine of the angle, so it looks something like this:

0.0    1.000  5.0    0.996  10.0    0.985  15.0 0.966
20.0   0.940  25.0   0.906 and so on. 

I'm trying to do it using a for loop but I'm not sure how to get this to work. Currently, I have .

Any suggestions?

Answer 1

Let's say you have:

>>> d = np.linspace(0, 360, 10, endpoint=False)
>>> c = np.cos(np.radians(d))

If you don't mind having some brackets and such on the side, then you can simply concatenate column-wise using np.c_, and display:

>>> print(np.c_[d, c])
[[  0.00000000e+00   1.00000000e+00]
 [  3.60000000e+01   8.09016994e-01]
 [  7.20000000e+01   3.09016994e-01]
 [  1.08000000e+02  -3.09016994e-01]
 [  1.44000000e+02  -8.09016994e-01]
 [  1.80000000e+02  -1.00000000e+00]
 [  2.16000000e+02  -8.09016994e-01]
 [  2.52000000e+02  -3.09016994e-01]
 [  2.88000000e+02   3.09016994e-01]
 [  3.24000000e+02   8.09016994e-01]]

But if you care about removing them, one possibility is to use a simple regex:

>>> import re
>>> print(re.sub(r' *\n *', '\n',
                 np.array_str(np.c_[d, c]).replace('[', '').replace(']', '').strip()))
0.00000000e+00   1.00000000e+00
3.60000000e+01   8.09016994e-01
7.20000000e+01   3.09016994e-01
1.08000000e+02  -3.09016994e-01
1.44000000e+02  -8.09016994e-01
1.80000000e+02  -1.00000000e+00
2.16000000e+02  -8.09016994e-01
2.52000000e+02  -3.09016994e-01
2.88000000e+02   3.09016994e-01
3.24000000e+02   8.09016994e-01

I'm removing the brackets, and then passing it to the regex to remove the spaces on either side in each line.

np.array_str also lets you set the precision. For more control, you can use np.array2string instead.

Answer 2

Pandas is very convenient module for such tasks:

In [174]: import pandas as pd
     ...:
     ...: x = pd.DataFrame({'angle': np.linspace(0, 355, 355//5+1),
     ...:                   'cos': np.cos(np.deg2rad(np.linspace(0, 355, 355//5+1)))})
     ...:
     ...: pd.options.display.max_rows = 20
     ...:
     ...: x
     ...:
Out[174]:
    angle       cos
0     0.0  1.000000
1     5.0  0.996195
2    10.0  0.984808
3    15.0  0.965926
4    20.0  0.939693
5    25.0  0.906308
6    30.0  0.866025
7    35.0  0.819152
8    40.0  0.766044
9    45.0  0.707107
..    ...       ...
62  310.0  0.642788
63  315.0  0.707107
64  320.0  0.766044
65  325.0  0.819152
66  330.0  0.866025
67  335.0  0.906308
68  340.0  0.939693
69  345.0  0.965926
70  350.0  0.984808
71  355.0  0.996195

[72 rows x 2 columns]

Answer 3

Side-by-Side Array Comparison using Numpy

A built-in Numpy approach using the column_stack((...)) method.

numpy.column_stack((A, B)) is a column stack with Numpy which allows you to compare two or more matrices/arrays.

Use the numpy.column_stack((A, B)) method with a tuple. The tuple must be represented with () parenthesizes representing a single argument with as many matrices/arrays as you want.

import numpy as np

A = np.random.uniform(size=(10,1))
B = np.random.uniform(size=(10,1))
C = np.random.uniform(size=(10,1))

np.column_stack((A, B, C)) ## <-- Compare Side-by-Side

The result looks like this:

array([[0.40323596, 0.95947336, 0.21354263],
       [0.18001121, 0.35467198, 0.47653884],
       [0.12756083, 0.24272134, 0.97832504],
       [0.95769626, 0.33855075, 0.76510239],
       [0.45280595, 0.33575171, 0.74295859],
       [0.87895151, 0.43396391, 0.27123183],
       [0.17721346, 0.06578044, 0.53619146],
       [0.71395251, 0.03525021, 0.01544952],
       [0.19048783, 0.16578012, 0.69430883],
       [0.08897691, 0.41104408, 0.58484384]])

Numpy column_stack is useful for AI/ML applications when comparing the predicted results with the expected answers. This determines the effectiveness of the Neural Net training. It is a quick way to detect where errors are in the network calculations.

Answer 4

You can use python's zip function to go through the elements of both lists simultaneously.

import numpy as np
degreesVector = np.linspace(0.0, 360.0, 73.0)
cosinesVector = np.cos(np.radians(degreesVector))
for d, c in zip(degreesVector, cosinesVector):
    print d, c

And if you want to make a numpy array out of the degrees and cosine values, you can modify the for loop in this way:

table = []
for d, c in zip(degreesVector, cosinesVector):
    table.append([d, c])
table = np.array(table)

And now on one line!

np.array([[d, c] for d, c in zip(degreesVector, cosinesVector)])