import numpy as np

print(np.__version__)

# Similarities between lists and NumPy 1-D arrays

# Both are mutable, indexable, and sliceable
numbers = np.array([5,6,7,8])
numbers[1] = 13
print(numbers)
print(numbers[-3:])

# Both are iterable
for num in numbers:
  print(num)

# Find length using len()
print(len(numbers))

# Check membership using in/not in
print(13 in numbers)
print(14 in numbers)

# Arrays can contain only a single object type
# Check using .dtype
numbers = np.array([5,6,7,8])
print(numbers.dtype)
print(numbers.astype(str))

# Unlike lists, arrays preserve scientific notation
print([3.5e9,1.4e-3])
print(np.array([3.5e9,1.4e-3]))

# NumPy has append() [note different syntax], insert(), delete(), flip() functions
# but no option to remove, pop (as with lists)
numbers = [5,6,7,8]             # list version
numbers.append([9,10])
print(numbers)

numbers = np.array([5,6,7,8])   # array version
numbers = np.append(numbers,[9,10])
print(numbers)

# Convert between lists and arrays
my_list = [5,6,7,8]
my_array = np.array(my_list)
my_list1 = my_array.tolist()
my_list2 = list(my_array)

# Adding lists concatenates them, while adding arrays actually adds them!
a = [1,2,3,4]
b = [5,6,7,8]
print(a + b)

a = np.array([1,2,3,4])
b = np.array([5,6,7,8])
print(a + b)

a = np.array([1,2,3,4])
b = np.array([5,6,7,8])

print('a + b =',a + b)
print('a - b =',a - b)
print('a * b =',a * b)

print('a + 10 =',a + 10)
print('10 * a =',10 * a)
print('a / 10 =',a / 10)
print('a**2 =',a**2)

x = np.array([1,2,3])
y = np.array([11,12,13,14,15])

# print(x + y)   # this will produce an error

u = np.array([1,2,3,4])
v = np.array([0,2,4,6])

print(u == v)
print(u < v)
print(v != 0)
print(v <= 4)

bool1 = np.array([True,False,True])
bool2 = np.array([True,False,False])

print(np.logical_and(bool1,bool2))
print(np.logical_or(bool1,bool2))

v = np.array([10,11,12,13])

print(v[3])

print(v[[2,3]])

print(v[v >= 12])

print(v[[False,False,True,True]])

print(np.where(v >= 12))

print(np.where(v >= 12)[0])

x = np.array([10,11,12,13])

# two ways of applying functions to arrays (or lists)
print(np.sum(x))
print(x.sum())

# mathematical reductions
print(np.sum(x))
print(np.mean(x))
print(np.median(x))
print(np.max(x))
print(np.min(x))
print(np.std(x))

# constants
print(np.pi)
print(np.e)
print(np.inf)
print(np.nan)

print(5 * np.inf)
print(5 * np.nan)

# mathematical element-wise functions
print(np.absolute([-2,-1]))
print(np.round([5.23,5.29],1))
print(np.sqrt([9,16]))            # same as y**0.5
print(np.exp([0,1,2]))
print(np.sin([0,np.pi/2]))        # argument: angles in radians
print(np.cos([np.pi,2*np.pi]))

# functions to create new arrays
print(np.zeros(4))
print(np.ones(4))
print(np.full(4,2))
print(np.arange(4))
print(np.arange(0,1,0.25))
print(np.linspace(0,1,5))