Develop Reference

How to concatenate numba lists

I replaced a Python list with a Numba int32 list and its concatenation behaviour wasn't as expected.
For example in Python: if a = [1,2,3] and b = [4,5,6] then a += b gives [1, 2, 3, 4, 5, 6].
In Numba, on the other hand:
if a = numba.int32([1,2,3]) and b = numba.int32([4,5,6]) then a += b gives array([5, 7, 9], dtype=int32).
Is there a way I can easily concatenate Numba lists as we do in Python? or is the only solution to write a function that loops over the two arrays and creates another one?
Thanks.

use the concatenate function. https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
import numpy as np
a = numba.int32([1,2,3])
b = numba.int32([4,5,6])
c = np.concatenate((a,b))

Use list()
a = numba.int32(list(a)+list(b))

Python sum values from multiple lists (more than two)

Looking for a pythonic way to sum values from multiple lists:
I have got the following list of lists:
a = [0,5,2]
b = [2,1,1]
c = [1,1,1]
d = [5,3,4]
my_list = [a,b,c,d]
I am looking for the output:
[8,10,8]
I`ve used:
print ([sum(x) for x in zip(*my_list )])
but zip only works when I have 2 elements in my_list.
Any idea?

zip works for an arbitrary number of iterables:
>>> list(map(sum, zip(*my_list)))
[8, 10, 8]
which is, of course, roughly equivalent to your comprehension which also works:
>>> [sum(x) for x in zip(*my_list)]
[8, 10, 8]

Numpy has a nice way of doing this, it is also able to handle very large arrays. First we create the my_list as a numpy array as such:
import numpy as np
a = [0,5,2]
b = [2,1,1]
c = [1,1,1]
d = [5,3,4]
my_list = np.array([a,b,c,d])
To get the sum over the columns, you can do the following
np.sum(my_list, axis=0)
Alternatively, the sum over the rows can be retrieved by
np.sum(my_list, axis=1)

I'd make it a numpy array and then sum along axis 0:
my_list = numpy.array([a,b,c,d])
my_list.sum(axis=0)
Output:
[ 8 10 8]

Concatenate 2d list in python

I'm trying to create a 2d list with shape of [n,784] (the same shape as the MNIST image batches) using multiple [1,784] lists.
mylist.append(element) doesn't give me what I'm looking for, where mylist is the 2d [n,784] list and element is the [1,784] lists. It would return a list with shape [n,1,784].
I've also tried mylist[index].append(element), and I got a [784] 1d list instead.
Any idea how to solve my problem?
Thanks a lot

import numpy as np
myarray = np.array(mylist)
newarray = np.concatenate((myarray, element))
And if you want to turn it back into a list:
newlist = newarray.tolist()

a = [[1,1],[2,2]]
b = np.concatenate([a, a], axis=1).tolist()
The output will be:
[[1, 1, 1, 1], [2, 2, 2, 2]]

Appending a new row to a numpy array

I am trying to append a new row to an existing numpy array in a loop. I have tried the methods involving append, concatenate and also vstack none of them end up giving me the result I want.
I have tried the following:
for _ in col_change:
if (item + 2 < len(col_change)):
arr=[col_change[item], col_change[item + 1], col_change[item + 2]]
array=np.concatenate((array,arr),axis=0)
item+=1
I have also tried it in the most basic format and it still gives me an empty array.
array=np.array([])
newrow = [1, 2, 3]
newrow1 = [4, 5, 6]
np.concatenate((array,newrow), axis=0)
np.concatenate((array,newrow1), axis=0)
print(array)
I want the output to be [[1,2,3][4,5,6]...]

The correct way to build an array incrementally is to not start with an array:
alist = []
alist.append([1, 2, 3])
alist.append([4, 5, 6])
arr = np.array(alist)
This is essentially the same as
arr = np.array([ [1,2,3], [4,5,6] ])
the most common way of making a small (or large) sample array.
Even if you have good reason to use some version of concatenate (hstack, vstack, etc), it is better to collect the components in a list, and perform the concatante once.

If you want [[1,2,3],[4,5,6]] I could present you an alternative without append: np.arange and then reshape it:
>>> import numpy as np
>>> np.arange(1,7).reshape(2, 3)
array([[1, 2, 3],
[4, 5, 6]])
Or create a big array and fill it manually (or in a loop):
>>> array = np.empty((2, 3), int)
>>> array[0] = [1,2,3]
>>> array[1] = [4,5,6]
>>> array
array([[1, 2, 3],
[4, 5, 6]])
A note on your examples:
In the second one you forgot to save the result, make it array = np.concatenate((array,newrow1), axis=0) and it works (not exactly like you want it but the array is not empty anymore). The first example seems badly indented and without know the variables and/or the problem there it's hard to debug.

Remove duplicates for 2d array Python

I have an 2d array. When I'm adding more values to the array, some values are duplicates. How can I remove these? My aray, named a: looks like this:
[[u'82', Button], [u'67', Button], [u'23', Button], [u'19', Button], [u'23', Button]]
I have tried
import numpy as np
def unique(a):
order = np.lexsort(a.T)
a = a[order]
diff = np.diff(a, axis=0)
ui = np.ones(len(a), 'bool')
ui[1:] = (diff != 0).any(axis=1)
return a[ui]
And
[list(t) for t in set(tuple(element) for element in a)]
And
from pandas import *
import numpy as np
a = np.array([[1, 1], [2, 3], [1, 1], [5, 4], [2, 3]])
DataFrame(a).drop_duplicates().values
But none of them work. How can i delete duplicates from the 2d array?

The problem is you are trying to do everything in one step. You need to break it up (EdChum was almost there)
df = pd.DataFrame(data=a)
df = df.drop_duplicates(subset=a)
Per EdChum's comment, this will not work unless the dataframe (df) has been created, otherwise we cannot reference it as the subset.