import numpy as np
a = range(5)
b = [0,1,2,3,4]
print 'a is {} and b is {}'.format(a,b)
c = np.arange(5)
d = np.array([0,1,2,3,4])
print 'c is {} and d is {}'.format(c, d)
print a+b
print c+d
let’s say that we want b to follow a, we just need to add 5 to each element of b
b = [i+5 for i in b]
print 'this is a slow b', a+b
with numpy it’s one operation
d+=5
print 'this is a fast d', np.append(c,d)
this also works with multiplication and division
c * 20
d/-2
aaah what happened there? truncation?
a = np.arange(5)
print a.dtype
b = np.arange(5, dtype='float64')
print b.dtype
print (a*b).dtype
available types are:
a = range(5)
b = np.asarray(a)
c = np.array(a)
print "a, b and c are", a, b, c
d = np.array([a,b,c])
e = np.vstack([a,b,c])
f = np.hstack([a,b,c])
print "d is", d
print "e is", e
print "f is", f
hstack seen here is roughly equivalent to np.append seen before and also np.concatenate if you’re more into c style