Getting access
arr = np.array(
[[1,2,3,6],
[4,5,6,2],
[7,8,9,4]]
)
arr[0] # [1,2,3,6]
arr[0][0] # 1
arr[0][1] # 2
arr[0,1] # 2 (same notation!)
""" Getting cols"""
arr[:,0] # arr[1,4,7]
: means select everything in that dimension, in our case, select everything in that row
Transpose
arr.T
# [[1 4 7]
# [2 5 8]
# [3 6 9]
# [6 2 4]]
np.exp()
- computes the exponential of all elements in the input array
- calculates ex for each element xxx in x the array
np.exp(arr)
#[[2.71828183e+00 7.38905610e+00 2.00855369e+01 4.03428793e+02]
# [5.45981500e+01 1.48413159e+02 4.03428793e+02 #7.38905610e+00]
# [1.09663316e+03 2.98095799e+03 8.10308393e+03 5.45981500e+01]]
np.exp(random_list)
- So many functions allows you to put a list (not a np array) and it will still worka dn return a np array
Matrix multiplication
A = np.array([[1,2],[3,4]])
B = np.array([[1,2,3],[4,5,6]])
A.dot(B)
# [[9,12,15],
# [19,26,33]]