Numpy Primer

import numpy as np

# a list of numbers
x_list = [1,2,3,4,5]
print type(x_list)

# an array of numbers
x = np.array(x_list)
y = np.array([6,7,8,9])
print type(x), type(y)

print x, x.shape, x.dtype

# nested lists
nes_list = [ [1,2,3], [4,5,6], [7,8,9] ]
nes_array = np.array(nes_list)

# zero matrix
zero_mat = np.zeros([4,3])
print zero_mat

# one matrix
ones_mat = np.ones([3,5]) 
print ones_mat

# constant matrix
const_mat =  np.full([3,3], 6) 
print const_mat

# identity matrix
id_mat = np.eye(3)
print id_mat

# random matrix
rand_mat = np.random.random( [3,3] )
print rand_mat

# random integers
rand_ints = np.random.randint(1,10,[3,3])
print rand_ints

# continuous integers
nums = np.arange(2,18)
nums_mat = nums.reshape([4,4])
print nums_mat
# 0 to 9 reshaped to 3x3 matrix
print np.arange(9).reshape([3,3])

# lets create a 4x4 matrix
x = np.arange(16).reshape(4,4)
print x

# To get the element 4 at position (2,2); 
print x[1,1] # index starts from 0

# To get the element 10 at position (3,3); 
print x[2,2] 

# To get row 2
print x[1]

# To get column 3
print x[:,2]

# To get elements 6,10,14
print x[1:,2]

# To get elements 13,14,15
print x[-1,1:]

# Boolean indexing / Conditional
print x>7
print x%2 == 0
print x[x<7]
print x[x%2 != 0]

# default type np.int64
x = np.array([ [1,2], [3,4] ])
print x, x.dtype

# type set to np.int32
x1 = np.array([ [1,2], [3,4] ], dtype=np.int32)
print x1, x1.dtype

# default type np.float64
y = np.array([ [1.,2], [3,4] ])
print y, y.dtype

# set to np.float32
y1 = y.astype(np.float32)
print y1, y1.dtype

# the boolean type
z = x>2
print x, x.dtype

# elementwise operations
x = np.full([3,3],3)
y = np.eye(3)
z = np.ones(3)

# Add
print x + y
# Subtract
print x-y
# Multiply
print x * (x+z)
# Divide
print (x + 1) / (z + 1)

# Dot product
a = np.arange(9).reshape(3,3)
b = np.arange(12).reshape(3,4)
print np.dot(a,b) # or a.dot(b)
# try b.dot(a)

# transpose for dot product
x = np.random.randint(0,9,[3,3])
y = np.random.randint(0,9,[1,3])
# x.dot(y) wont work as x.shape[1] != y.shape[0]
print x.dot(y.T) # transpose y to shape[3,1]
# gives an output of shape [3,1]

a = np.arange(9)
# sum
print a.sum()
# mean
print a.mean()
# stddev, variance
print a.std(), a.var()
# min and max
print a.min(), a.max()
# id of min and max elements
print a.argmin(), a.argmax()

b = np.arange(9).reshape([3,3])
# row-wise sum
b.sum(axis=1)
# column-wise sum
b.sum(axis=0)

x = np.arange(16).reshape([4,4])
# array[1] is broadcasted to shape [4,4] of x
print x + 1
# consider 2 arrays of shapes : [4,4] and [4,]
a = np.random.randint(0,10,[4,4])
b = np.arange(4)
print a*b
# here b is broadcasted to all the rows of a

'''
Array masking is the name of a special method of selection available in numpy by means of a boolean mask, it allows to extract data out of an array based on certain condition.
'''
arr = np.arange(9).reshape([3,3])
print arr
# create a random boolean array
mask1 = np.random.choice([True,False],[3,3]).astype(np.bool)
print mask1
# mask over array
print arr[mask1]

# using conditions to create mask
div_4_mask = (arr%4 == 0)
print div_4_mask
print arr[div_4_mask]

# Efficient masking using np.putmask() function
#	  using a <divisible by 3> mask
np.putmask(arr,arr%3 == 0,0)
print arr
# there is an option to operate on the elements that satisfy
#	  the condition <divisible by 3>
#	    enter the operation as parameter 3; say multiply by 10
arr = np.arange(9).reshape([3,3])
print arr
# if divisible by 3 multiply by 10
np.putmask(arr, arr%3 == 0, arr*10) 
print arr