How to implement ' =' for Numpy arrays?-CodePudding

I'm trying to figure out how to use concatenate arrays on Numpy with Python by using something similar to ' ='. However I ran in to an operand error while running the program, which is quite confusing.

import numpy as np
a=np.array([])
for i in range(10):
    n=np.random.normal(1, 1, 10)
    a = a n
print(a)

What should I change in order for the thing to work? It's a really simple issue, but since I don't know how Numpy works it's been bothering me. Thanks!

CodePudding user response：

You can create zero array then insert numbers in it, like below, or only write np.random.normal(1, 1, 100) in this problem:

import numpy as np
a=np.zeros(100)
for i in range(10):
    n=np.random.normal(1, 1, 10)
    a[i*10:(i 1)*10] = n

CodePudding user response：

We really should ask for the error, with traceback, before suggesting fixes. Anyways here's your full error:

In [83]: a=np.array([])
In [84]: n = np.random.normal(1,1,10)
In [85]: a.shape
Out[85]: (0,)
In [86]: n.shape
Out[86]: (10,)
In [87]: a n
Traceback (most recent call last):
  Input In [87] in <cell line: 1>
    a n
ValueError: operands could not be broadcast together with shapes (0,) (10,)

This trying to do element-wise addition of a 0 element array and 10 element one. This isn't an operand error; it's a ValueError, a broadcasting one. It's important that you understand what's happening.

But if a starts as a list:

In [88]: a = []
In [89]: a.append(n)
In [90]: a
Out[90]: 
[array([0.73866347, 0.68341855, 1.14853292, 0.96903861, 0.28691117,
        1.20049352, 1.89670582, 0.92089883, 0.84876042, 0.79195955])]
In [91]: a.append(n)
In [92]: a
Out[92]: 
[array([0.73866347, 0.68341855, 1.14853292, 0.96903861, 0.28691117,
        1.20049352, 1.89670582, 0.92089883, 0.84876042, 0.79195955]),
 array([0.73866347, 0.68341855, 1.14853292, 0.96903861, 0.28691117,
        1.20049352, 1.89670582, 0.92089883, 0.84876042, 0.79195955])]

Now we get a list of arrays, which can be joined into one array with:

In [93]: np.stack(a)
Out[93]: 
array([[0.73866347, 0.68341855, 1.14853292, 0.96903861, 0.28691117,
        1.20049352, 1.89670582, 0.92089883, 0.84876042, 0.79195955],
       [0.73866347, 0.68341855, 1.14853292, 0.96903861, 0.28691117,
        1.20049352, 1.89670582, 0.92089883, 0.84876042, 0.79195955]])

Filling an array, row by row, is competative in speed to this list append approach:

In [94]: arr = np.zeros((3,10))
    ...: for i in range(3):
    ...:     arr[i,:] = n

But creating all rows at once is even better:

In [97]: n = np.random.normal(1,1,(3,10))

Here's how = is used with arrays:

In [99]: a = np.arange(5)
In [100]: a = np.arange(5)
In [101]: a
Out[101]: array([0, 1, 2, 3, 4])
In [102]: a  = 10
In [103]: a
Out[103]: array([10, 11, 12, 13, 14])
In [104]: a  = [1,10,100,1000,10000]
In [105]: a
Out[105]: array([   11,    21,   112,  1013, 10014])