#!/usr/bin/env python

import numpy as np

a = np.array([1, 2, 3])

print type(a)

print a.shape

print a[0], a[1], a[2]

a[0] = 5

print a

b = np.array([[1,2,3], [4,5,6]])

print b.shape # (2, 3), 2 lines, 3 columns, means two vectors, each have three dimensions

print b[0,0], b[0,1], b[1,0]

a = np.zeros((2,2)) # Create an array of all zeros

print a # Prints "[[ 0. 0.]

# [ 0. 0.]]"

b = np.ones((1,2)) # Create an array of all ones

print b # Prints "[[ 1. 1.]]"

c = np.full((2,2), 7) # Create a constant array

print c # Prints "[[ 7. 7.]

# [ 7. 7.]]"

d = np.eye(2) # Create a 2x2 identity matrix

print d # Prints "[[ 1. 0.]

# [ 0. 1.]]"

e = np.random.random((2,2)) # Create an array filled with random values

print e # Might print "[[ 0.91940167 0.08143941]

# [ 0.68744134 0.87236687]]"

# Create the following rank 2 array with shape (3, 4)

# [[ 1 2 3 4]

# [ 5 6 7 8]

# [ 9 10 11 12]]

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Use slicing to pull out the subarray consisting of the first 2 rows

# and columns 1 and 2; b is the following array of shape (2, 2):

# [[2 3]

# [6 7]]

b = a[:2, 1:3]

# A slice of an array is a view into the same data, so modifying it

# will modify the original array.

print a[0, 1] # Prints "2"

b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]

print a[0, 1] # Prints "77"

# Create the following rank 2 array with shape (3, 4)

# [[ 1 2 3 4]

# [ 5 6 7 8]

# [ 9 10 11 12]]

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Two ways of accessing the data in the middle row of the array.

# Mixing integer indexing with slices yields an array of lower rank,

# while using only slices yields an array of the same rank as the

# original array:

row_r1 = a[1, :] # Rank 1 view of the second row of a

row_r2 = a[1:2, :] # Rank 2 view of the second row of a

print row_r1, row_r1.shape # Prints "[5 6 7 8] (4,)"

print row_r2, row_r2.shape # Prints "[[5 6 7 8]] (1, 4)"

# We can make the same distinction when accessing columns of an array:

col_r1 = a[:, 1]

col_r2 = a[:, 1:2]

print col_r1, col_r1.shape # Prints "[ 2 6 10] (3,)"

print col_r2, col_r2.shape # Prints "[[ 2]

# [ 6]

# [10]] (3, 1)"

a = np.array([[1,2], [3, 4], [5, 6]])

# An example of integer array indexing.

# The returned array will have shape (3,) and

print a[[0, 1, 2], [0, 1, 0]] # Prints "[1 4 5]"

# The above example of integer array indexing is equivalent to this:

print np.array([a[0, 0], a[1, 1], a[2, 0]]) # Prints "[1 4 5]"

# When using integer array indexing, you can reuse the same

# element from the source array:

print a[[0, 0], [1, 1]] # Prints "[2 2]"

# Equivalent to the previous integer array indexing example

print np.array([a[0, 1], a[0, 1]]) # Prints "[2 2]"

a = np.array([[1,2], [3, 4], [5, 6]])

bool_idx = (a > 2) # Find the elements of a that are bigger than 2;

# this returns a numpy array of Booleans of the same

# shape as a, where each slot of bool_idx tells

# whether that element of a is > 2.

print bool_idx # Prints "[[False False]

# [ True True]

# [ True True]]"

# We use boolean array indexing to construct a rank 1 array

# consisting of the elements of a corresponding to the True values

# of bool_idx

print a[bool_idx] # Prints "[3 4 5 6]"

# We can do all of the above in a single concise statement:

print a[a > 2] # Prints "[3 4 5 6]"

x = np.array([1, 2]) # Let numpy choose the datatype

print x.dtype # Prints "int64"

x = np.array([1.0, 2.0]) # Let numpy choose the datatype

print x.dtype # Prints "float64"

x = np.array([1, 2], dtype=np.int64) # Force a particular datatype

print x.dtype # Prints "int64"xample of integer array indexing.

# The returned array will have shape (3,) and

print a[[0, 1, 2], [0, 1, 0]] # Prints "[1 4 5]"

# The above example of integer array indexing is equivalent to this:

print np.array([a[0, 0], a[1, 1], a[2, 0]]) # Prints "[1 4 5]"

# When using integer array indexing, you can reuse the same

# element from the source array:

print a[[0, 0], [1, 1]] # Prints "[2 2]"

# Equivalent to the previous integer array indexing example

print np.array([a[0, 1], a[0, 1]]) # Prints "[2 2]"

a = np.array([[1,2], [3, 4], [5, 6]])

bool_idx = (a > 2) # Find the elements of a that are bigger than 2;

# this returns a numpy array of Booleans of the same

# shape as a, where each slot of bool_idx tells

# whether that element of a is > 2.

print bool_idx # Prints "[[False False]

# [ True True]

# [ True True]]"

# We use boolean array indexing to construct a rank 1 array

# consisting of the elements of a corresponding to the True values

# of bool_idx

print a[bool_idx] # Prints "[3 4 5 6]"

# We can do all of the above in a single concise statement:

print a[a > 2] # Prints "[3 4 5 6]"