numpy
>>> # The standard way to import NumPy:
>>> import numpy as np
>>> # Create a 2-D array, set every second element in
>>> # some rows and find max per row:
>>> x = np.arange(15, dtype=np.int64).reshape(3, 5)
>>> x[1:, ::2] = -99
>>> x
array([[ 0, 1, 2, 3, 4],
[-99, 6, -99, 8, -99],
[-99, 11, -99, 13, -99]])
>>> x.max(axis=1)
array([ 4, 8, 13])
>>> # Generate normally distributed random numbers:
>>> rng = np.random.default_rng()
>>> samples = rng.normal(size=2500)
x = 3
print(type(x)) # Prints "<class 'int'>"
print(x) # Prints "3"
print(x + 1) # Addition; prints "4"
print(x - 1) # Subtraction; prints "2"
print(x * 2) # Multiplication; prints "6"
print(x ** 2) # Exponentiation; prints "9"
x += 1
print(x) # Prints "4"
x *= 2
print(x) # Prints "8"
y = 2.5
print(type(y)) # Prints "<class 'float'>"
print(y, y + 1, y * 2, y ** 2) # Prints "2.5 3.5 5.0 6.25"
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print(quicksort([3,6,8,10,1,2,1]))
# Prints "[1, 1, 2, 3, 6, 8, 10]"