arithmetic_numpy.py

# In Numpy, arithmetic operators on arrays are always applied elementwise. 
# A new array is filled and returned with the result.

# For example, if we create 2 arrays a and b, and subtract b 
# from a, we will get something like this. Remember that you 
# can NOT do such an operation with arrays of differnt sizes, 
# you'll get an error
a = np.array( [20,30,40,50] )
b = np.array( [0, 1, 2, 3] )
c = a - b
c = [20, 29, 38, 47]

# You can also perform scalar operations elementwise on the entire array
b**2
b = [0, 1, 4, 9]

# Or even apply functions
10*np.sin(a)
a = [ 9.12945251, -9.88031624,  7.4511316 , -2.62374854]

# Remember that operation between arrays are always applied elementwise
a = np.array( [20,30,40,50] )
b = np.array( [0, 1, 2, 3] )
c = a * b
c = [0, 30, 80, 150]

# There are many quick and useful functions in numpy that you will 
# use frequently like these
a = np.array( [20,30,40,50] )
a.max() # 50
a.min() # 20
a.sum() # 140

# If you have a multi-dimensional array, use the "axis" parameter
b = np.arange(12).reshape(3,4)
b = [[ 0,  1,  2,  3],
     [ 4,  5,  6,  7],
     [ 8,  9, 10, 11]]

b.sum(axis=0) # [12, 15, 18, 21]
b.min(axis=1) # [0, 4, 8]
b.cumsum(axis=1) # [[ 0,  1,  3,  6], [ 4,  9, 15, 22], [ 8, 17, 27, 38]]

# And if you need one, here are some more common ones that are a bit "mathy-er"
b = np.arange(3)
b = [0, 1, 2]

np.exp(b) # [ 1.0, 2.71828183, 7.3890561 ]
np.sqrt(b) # [ 0.0 ,  1.0, 1.41421356]
np.floor(np.exp(b)) # [ 1.0, 2.0, 7.0 ]
np.round(np.exp(b)) # [ 1.0, 3.0, 7.0 ]