Python Dry Run
Source: http://cs231n.github.io/python-numpy-tutorial/
!python --version
Python 3.5.3 :: Continuum Analytics, Inc.
print("Hello World!")
Hello World!
def add2(x,y):
return (x+y)
print(add2(2,10))
12
x = 12
print(type(x))
<class 'int'>
print(x)
12
print(x + 1)
13
print(x - 1)
11
print(x * 2)
24
print(x ** 2)
144
x += 1
print(x)
13
x *= 2
print(x)
26
y = 2.5
print(type(y))
<class 'float'>
print(y, y+1, y*2, y ** 2)
2.5 3.5 5.0 6.25
x++
File "<ipython-input-19-77f4531f2da2>", line 1
x++
^
SyntaxError: invalid syntax
Booleans:
T = True
F = False
print(type(T))
<class 'bool'>
print(T and F)
False
print(T or F)
True
print(not T)
False
print(T != F) # XOR
True
Strings:
hello = 'hello'
world = 'world'
print(hello)
hello
print(len(hello))
5
hw = hello + ' ' + world
print(hw)
hello world
hw12 = '%s %s %d' % (hello, world, 12)
print(hw12)
hello world 12
s = "hello"
print(s.capitalize())
Hello
print(s.upper())
HELLO
print(s.lower())
hello
Lists
mylist = [3, 1, 2] # Note: this is a list
print(list)
[3, 1, 'foo']
print(mylist[0])
3
print(mylist[-1]) # Negative indices count from the end of the list
2
mylist[2] = 'foo' # Lists can contain elements of different types
print(mylist)
[3, 1, 'foo']
mylist.append('CNN')
print(mylist)
[3, 1, 'foo', 'CNN']
print(mylist.pop())
CNN
print(mylist)
[3, 1, 'foo']
Slicing
seq = list(range(5))
print(seq)
[0, 1, 2, 3, 4]
print(seq[2:4]) # Get a slice from index 2 to 4 (exclusive)
[2, 3]
print(seq[2:])
[2, 3, 4]
print(seq[:2])
[0, 1]
print(seq[:])
[0, 1, 2, 3, 4]
print(seq[:-1])
[0, 1, 2, 3]
seq[2:4] = [8, 9]
print(seq)
[0, 1, 8, 9, 4]
Loops
foods = ['liempo', 'adobo', 'kare-kare']
for food in foods:
print(food)
liempo
adobo
kare-kare
foods = ['liempo', 'adobo', 'kare-kare']
for i, food in enumerate(foods):
print('%d. %s' % (i + 1, food))
1. liempo
2. adobo
3. kare-kare
List Comprehension
nums = list(range(10))
squares = []
for x in nums:
squares.append(x ** 2)
print(squares)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
cubes = [y ** 3 for y in nums]
print(cubes)
[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]
nums = list(range(15))
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print(even_squares)
[0, 4, 16, 36, 64, 100, 144, 196]
Dictionaries
d = {'cat': 'cute', 'dog': 'friend'}
print(d['cat'])
cute
print(d['dog'])
friend
print('dog' in d) # Check if a dictionary has a given key
True
d['fish'] = 'wet'
print(d)
{'dog': 'friend', 'fish': 'wet', 'cat': 'cute'}
print(d.get('monkey', 'N/A'))
N/A
print(d)
{'dog': 'friend', 'fish': 'wet', 'cat': 'cute'}
print(d.get('fish', 'N/A'))
wet
del d['fish'] # Remove an element from a dictionary
print(d)
{'dog': 'friend', 'cat': 'cute'}
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal in d:
legs = d[animal]
print('A %s has %d legs' % (animal, legs))
A spider has 8 legs
A person has 2 legs
A cat has 4 legs
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.items():
print('A %s has %d legs' % (animal, legs))
A spider has 8 legs
A person has 2 legs
A cat has 4 legs
Sets
animals = {'cat', 'dog'}
print('cat' in animals)
True
print('fish' in animals)
False
animals.add('fish')
print(animals)
{'dog', 'fish', 'cat'}
print(len(animals))
3
animals.remove('cat')
print(animals)
{'dog', 'fish'}
animals = {'cat', 'dog', 'fish'}
for i, animal in enumerate(animals):
print('%d. %s' % (i + 1, animal))
1. dog
2. fish
3. cat
from math import sqrt
nums = {sqrt(x) for x in range(30)}
print(nums)
{0.0, 1.0, 2.0, 2.23606797749979, 1.7320508075688772, 1.4142135623730951, 2.449489742783178, 2.6457513110645907, 2.8284271247461903, 3.0, 3.1622776601683795, 3.3166247903554, 3.4641016151377544, 4.0, 5.0, 4.47213595499958, 4.795831523312719, 4.898979485566356, 5.0990195135927845, 5.196152422706632, 5.291502622129181, 3.7416573867739413, 3.872983346207417, 4.123105625617661, 4.242640687119285, 4.358898943540674, 4.58257569495584, 4.69041575982343, 5.385164807134504, 3.605551275463989}
Tuples
d = {(x, x + 1): x for x in range(10)}
print(d)
{(0, 1): 0, (1, 2): 1, (5, 6): 5, (2, 3): 2, (4, 5): 4, (6, 7): 6, (8, 9): 8, (9, 10): 9, (3, 4): 3, (7, 8): 7}
t = (5, 6)
print(type(t))
<class 'tuple'>
print(d[t])
5
print(d[(1, 2)])
1
Sample Function
def sign(x):
if x > 0:
return 'positive'
elif x < 0:
return 'negative'
else:
return 'zero'
print(sign(-1))
negative
for x in [-1, 0, 1]:
print(sign(x))
negative
zero
positive
def hello(name, loud=False):
if loud:
print('HELLO, %s!' % name.upper())
else:
print('Hello, %s!' % name)
hello('Juan')
Hello, Juan!
hello('Juan', loud=True)
HELLO, JUAN!
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([3,6,8,10,1,2,4,7,9,5]))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Classes
class Greeter(object):
# Constructor
def __init__(self, name):
self.name = name
# Instance method
def greet(self, loud=False):
if loud:
print('HELLO, %s!' % self.name.upper())
else:
print('Hello, %s!' % self.name)
g = Greeter('Melvin')
g.greet()
Hello, Melvin!
g.greet(loud=True)
HELLO, MELVIN!
NumPy
import numpy as np
a = np.array([1, 2, 3]) # Create a rank 1 array
print(type(a))
<class 'numpy.ndarray'>
print(a.shape)
(3,)
print(a)
[1 2 3]
b = np.array([[1,2,3],[4,5,6]])
print(b)
[[1 2 3]
[4 5 6]]
b[1,2] = 60
print(b)
[[ 1 2 3]
[ 4 5 60]]
a = np.zeros((3,3))
print(a)
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
b = np.ones((1,3))
print(b)
[[ 1. 1. 1.]]
c = np.full((10,10),8)
print(c)
[[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]
[8 8 8 8 8 8 8 8 8 8]]
d = np.eye(5)
print(d)
[[ 1. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0.]
[ 0. 0. 1. 0. 0.]
[ 0. 0. 0. 1. 0.]
[ 0. 0. 0. 0. 1.]]
e = np.random.random((3,3))
print(e)
[[ 0.08245566 0.23666261 0.1054603 ]
[ 0.71728521 0.81500598 0.65669296]
[ 0.68904601 0.16632103 0.90048958]]
Indexing
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print(a)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
b = a[:2,1:3]
print(b)
[[2 3]
[6 7]]
row_r1 = a[1, :]
print(row_r1)
[5 6 7 8]
row_r2 = a[1:2, :]
print(row_r2)
[[5 6 7 8]]
print(row_r1, row_r1.shape)
[5 6 7 8] (4,)
print(row_r2, row_r2.shape)
[[5 6 7 8]] (1, 4)
col_r1 = a[:, 1]
print(col_r1)
[ 2 6 10]
col_r2 = a[:, 1:2]
print(col_r2, col_r2.shape)
[[ 2]
[ 6]
[10]] (3, 1)
a = np.array([[1,2], [3, 4], [5, 6]])
print(a)
[[1 2]
[3 4]
[5 6]]
print(a[[0, 1, 2], [0, 1, 1]])
[1 4 6]
print(np.array([a[0, 0], a[1, 1], a[2, 1]]))
[1 4 6]
print(a[[0, 0], [1, 1]])
[2 2]
print(np.array([a[0, 1], a[0, 1]]))
[2 2]
a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
print(a)
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
b = np.array([0, 2, 0, 1])
print(b)
[0 2 0 1]
print(a[np.arange(4), b])
[ 1 6 7 11]
a[np.arange(4), b] += 10
print(a)
[[11 2 3]
[ 4 5 16]
[17 8 9]
[10 21 12]]
Boolean Indexing
a = np.array([[1,2], [3, 4], [5, 6]])
bool_idx = (a > 2)
print(bool_idx)
[[False False]
[ True True]
[ True True]]
print(a[bool_idx])
[3 4 5 6]
print(a[a > 2])
[3 4 5 6]
Datatypes
x = np.array([1, 2])
print(x.dtype)
int32
x = np.array([1.0, 2.0])
print(x.dtype)
float64
x = np.array([1, 2], dtype=np.int64)
print(x.dtype)
int64
Array math
x = np.array([[1,2],[3,4]], dtype=np.float64)
print(x)
[[ 1. 2.]
[ 3. 4.]]
y = np.array([[5,6],[7,8]], dtype=np.float64)
print(y)
[[ 5. 6.]
[ 7. 8.]]
print(x + y)
[[ 6. 8.]
[ 10. 12.]]
print(np.add(x,y))
[[ 6. 8.]
[ 10. 12.]]
print(x - y)
[[-4. -4.]
[-4. -4.]]
print(np.subtract(x,y))
[[-4. -4.]
[-4. -4.]]
Element-wise multiplication
print(x * y)
[[ 5. 12.]
[ 21. 32.]]
print(np.multiply(x, y))
[[ 5. 12.]
[ 21. 32.]]
print(x / y)
[[ 0.2 0.33333333]
[ 0.42857143 0.5 ]]
print(np.divide(x, y))
[[ 0.2 0.33333333]
[ 0.42857143 0.5 ]]
print(np.sqrt(x))
[[ 1. 1.41421356]
[ 1.73205081 2. ]]
x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
print(v.dot(w))
219
print(np.dot(v, w))
219
print(x.dot(v))
[29 67]
print(np.dot(x, v))
[29 67]
print(x.dot(y))
[[19 22]
[43 50]]
print(np.dot(x, y))
[[19 22]
[43 50]]
x = np.array([[1,2], [3,4]])
print(x)
[[1 2]
[3 4]]
print(x.T)
[[1 3]
[2 4]]
v = np.array([1,2,3])
print(v)
[1 2 3]
print(v.T)
[1 2 3]
Broadcasting
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
print(x)
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
v = np.array([1, 0, 1])
y = np.empty_like(x) # Create an empty matrix with the same shape as x
for i in range(4):
y[i, :] = x[i, :] + v
print(y)
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1))
print(vv)
[[1 0 1]
[1 0 1]
[1 0 1]
[1 0 1]]
y = x + vv
print(y)
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v
print(y)
[[ 2 2 4]
[ 5 5 7]
[ 8 8 10]
[11 11 13]]
v = np.array([1,2,3]) # v has shape (3,)
w = np.array([4,5]) # w has shape (2,)
print(np.reshape(v, (3, 1)) * w)
[[ 4 5]
[ 8 10]
[12 15]]
x = np.array([[1,2,3], [4,5,6]])
print(x + v)
[[2 4 6]
[5 7 9]]
print((x.T + w).T)
[[ 5 6 7]
[ 9 10 11]]
print(x + np.reshape(w, (2, 1)))
[[ 5 6 7]
[ 9 10 11]]
print(x * 2)
[[ 2 4 6]
[ 8 10 12]]
-mkc
Written on September 26, 2017