Python Review Notes

February 07, 2024

2024   ·   Python   Foundation     ·   posts  

Python Review

Here are some important notes on Python regarding the reason why we use Python, the setup, the language basics, some practical tips, and some great references.

why Python?

  • Widely used
  • General purpose programming language
  • Scientific compututation functionality similar to MATLAB
  • Used by major deep learning libraries such as PyTorch and TensorFlow

Setup

  • Environment setup
    • Problem
      • Different versions of Python
      • Different versions of libraries (countless)
      • Even same library versions have different versions
    • Solution
      • Use virtual environment
      • Use conda
      • Use pipenv
      • Use Docker

Common workflow

  • Keep multiple Python environments that are isolated from each other
  • Each environment has its own Python version and libraries
  • Each environment can be easily replicated

Anaconda is a popular Python environment/package manager

  • Create environments
    • $ conda create -n // -n indicates the name of the environment
    • $ conda create -n python=3.8 // specify the version of Python
    • $ conda create -f // create environment from a file
  • Activate/deactivate environments
    • $ conda activate
    • $ conda deactivate // deactivate the current environment
  • Export environments
    • $ conda env list // list all environments
    • $ conda activate // activate the environment first
    • $ conda env export > environment.yml // export the environment to a file

Python Basics

  • Common Operations 
    # This is a comment
x = 10
y = 3
z = x + y
print(z)
x ** y # exponentiation
x / y # division, the result is 3.3333333333333335
x // y # floor division, the result is 3
x % y # modulo, the result is 1
x / float(y) # the result is 3.3333333333333335
str(x) + " + " + str(y) # convert to string
  • Built-in Values 
    a, b = True, False
x = None # Variables can be assigned None representing the absence of a value
array = [1, 2, 3, None] # List can contain None
def func():
return None # Functions can return None, used to indicate that the function does not return a value
  • Python is a strongly-typed and dynamically-typed language
    • Strongly-typed: The type of a variable can not be coerced into another type like in JavaScript
      • e.g. “1” + 2 // TypeError: can only concatenate str (not “int”) to str
    • Dynamically-typed: The type of a variable is determined at runtime, not at compile time. Variables are names of values or objects references. Variables can be reassigned to values of a different type.
      • e.g. x = 1 // x is an integer
      • x = “hello” // x is a string
  • Execution of Python code
    • Python code is first interpreted into bytecode(.pyc) and then compiled by a VM implementation into machine instructions. (Most commonly using C.)
    • Python is slower, but it can run highly optimized C/C++ subroutines which makes scientific computing (e.g. matrix multiplication) very fast.

Python Common Data Structures

Collections

  • List
    • Ordered, mutable, allows duplicate elements
    • e.g. [1, 2, 3, 4, 5] ```python names = [‘Alice’, ‘Bob’, ‘Charlie’] #index 0 1 2 #index -3 -2 -1 names[0] # ‘Alice’ print(len(names)) # get the length of the list print(names) # [‘Alice’, ‘Bob’, ‘Charlie’] names.append(‘David’) # [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’]

my_empty_list = [] my_empty_list = list()

stuff = [1, 2, [“hello”, “world”], True, -0.12, None]

List Slicing

names[-1] # ‘Charlie’ names[1:2] # [‘Bob’] numbers = [0, 1, 2, 3, 4, 5, 6] numbers[0:3] == numbers[:3] == [0, 1, 2] numbers[5:] == numbers[5:7] == [5, 6] numbers[:] == numbers == [0, 1, 2, 3, 4, 5, 6] numbers[-1] == 6 # Negative index wraps around numbers[-3:] == [4, 5, 6] numbers[3:-2] == [3, 4] # Can mix and match


- Tuple
  - Ordered, immutable, allows duplicate elements
  - e.g. (1, 2, 3, 4, 5)
```python
names = ('Alice', 'Bob', 'Charlie')
#index      0       1        2
#index     -3      -2       -1
names[0] # 'Alice'
print(len(names)) # get the length of the tuple
print(names) # ('Alice', 'Bob', 'Charlie')
names[0] = 'David' # TypeError: 'tuple' object does not support item assignment
empty_tuple = ()
empty_tuple = tuple()
single = (1,) # single element tuple, otherwise it's just a number
  • Dictionary
    • Unordered, mutable, indexed, no duplicate elements
    • e.g. {‘name’: ‘Alice’, ‘age’: 25} ```python phonebook = {} phonebook = dict() phonebook = {‘Zach’: ‘12-37’} # dictionary with one key-value pair phonebook[‘Alice’] = ‘123-456’ # add a new key-value pair

print(‘Alice’ in phonebook) # True, check if a key exists, can not check for values print(‘Bob’ in phonebook) # False print(phonebook[‘Alice’]) # ‘123-456’ print(phonebook[‘Bob’]) # KeyError: ‘Bob’

print(phonebook.get(‘Alice’)) # ‘123-456’ print(phonebook.get(‘Bob’)) # None

print(phonebook) # {‘Alice’: ‘123-456’, ‘Zach’: ‘12-37’}

phonebook[‘Alice’] = ‘321-456’ # update a value del phonebook[‘Alice’] # delete a key-value pair phonebook.clear() # remove all key-value pairs

print(phonebook) # {}


- Set
  - Unordered, mutable, no duplicate elements
  - e.g. {1, 2, 3, 4, 5}
```python
basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print(basket)                      # show that duplicates have been removed
{'orange', 'banana', 'pear', 'apple'}
'orange' in basket                 # fast membership testing
True
'crabgrass' in basket
False

# Demonstrate set operations on unique letters from two words

a = set('abracadabra')
b = set('alacazam')
a                                  # unique letters in a
{'a', 'r', 'b', 'c', 'd'}
a - b                              # letters in a but not in b
{'r', 'd', 'b'}
a | b                              # letters in a or b or both
{'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
a & b                              # letters in both a and b
{'a', 'c'}
a ^ b                              # letters in a or b but not both
{'r', 'd', 'b', 'm', 'z', 'l'}
  • Loops ```python names = [‘Alice’, ‘Bob’, ‘Charlie’] for i, name in enumerate(names): print(i, name)

phonebook = {‘Alice’: ‘123-456’, ‘Bob’: ‘456-789’, ‘Charlie’: ‘789-123’} for name, number in phonebook.items(): print(name, number)

for key, value in enumerate(phonebook): print(key, value)

for name in phonebook.keys(): print(name)

for number in phonebook.values(): print(number)


- Classes

```python
class Dog:
  # Constructor `a = Dog('Fido', 3)`
  def __init__(self, name, age):
    self.name = name # refer instance variables with `self`
    self.age = age # instance variables are public by default

  def bark(self):
    print('Woof!')

  def birthday(self):
    self.age += 1

  def get_name(self):
    return self.name

  def get_age(self):
    return self.age

  def set_name(self, name):
    self.name = name

  def set_age(self, age):
    self.age = age

  # same function name, different parameters which is called method overloading
  def play(self, other_dog):
    print(self.name, 'plays with', other_dog.name)
  
  def play(self):
    print(self.name, 'plays with self')



fido = Dog('Fido', 3)
fido.bark() # Woof!
fido.birthday()
print(fido.get_age()) # 4

# Inheritance
class Husky(Dog):
  # redefine the constructor
  def __init__(self, name, age, color):
    super().__init__(name, age) # call the parent class constructor
    self.color = color
  def birthday(self):
    self.age += 5 # override the parent class method

Introduction to NumPy

  • NumPy is optimized for matrix and vector operations
  • Makes use of C/C++ subroutines and memory-efficient data structures. (Lots of computation can be efficiently represneted as vectors.)
  • NumPy arrays are homogeneous, i.e. all elements are of the same type
  • Main data structure is the numpy.ndarray. The constructor function is numpy.array()
import numpy as np
x = np.array([1, 2, 3]) # 1D array, [1, 2, 3], shape is (3,)
y = np.array([[3,4 ,5]]) # 2D array, [[3, 4, 5]], shape is (1, 3)
z = np.array([[1, 2, 3], [4, 5, 6]]) # 2D array, [[1, 2, 3], [4, 5, 6]], shape is (2, 3)
print(x.shape) # (3,)
print(y.shape) # (1, 3)
print(z.shape) # (2, 3)

# Create arrays with zeros, ones, and random numbers
a = np.zeros((2, 3)) # 2x3 array of zeros
b = np.ones((1, 2)) # 1x2 array of ones
c = np.random.random((2, 2)) # 2x2 array of random numbers, uniform distribution, [0, 1)
d = np.random.randn(2, 2) # 2x2 array of random numbers, normal distribution, mean 0, standard deviation 1
e = np.random.randint(1, 10, (2, 2)) # 2x2 array of random integers, [1, 10)
f = np.random.choice([1, 2, 3, 4, 5], (2, 2)) # 2x2 array of random integers, [1, 5]

# np.ndarray Operations
# Reductions: np.max(), np.min(), np.sum(), np.mean(), np.std(), np.var(). np.std() is the square root of np.var()

# shape (3, 2)
x = np.array([[1, 2], [3, 4], [5, 6]])
print(np.max(x)) # 6
print(np.max(x, axis=0)) # [5, 6]
print(np.max(x, axis=0, keepdims=True)) # [[5, 6]]
print(np.max(x, axis=1)) # [2, 4, 6]
print(np.max(x, axis=1, keepdims=True)) # [[2], [4], [6]]

# Matrix Operations: np.dot(), np.matmul(), np.linalg.norm, .T, etc.
# Infix operator(i.e. +, -, *, /, **) are element-wise operations
# Element-wise product of matrices A 。 B is A * B
# Element-wise division of matrices A / B is A / B
# Dot product and matrix vector product(between 1-D array vectors) are using np.dot()
np.dot(u, v) 
np.dot(x, W) # matrix vector product

# Matrix product/ multipication prefer np.matmul() over np.dot()
np.matmul(A, B) # matrix product
A @ B # matrix product

# Transpose
x.T # transpose of x
  • Indexing and Slicing ```python x = np.random.random((3, 4)) x[:] # all elements keep shape x[0, :] # first row, shape is (4,) x[:, 0] # first column, shape is (3,) x[1, 1:3] # second row, second and third column, shape is (2,)

Note: selecting with an ndarray, list, or tuple will preserve the shape

x[(1, 2), :] # second and third row, shape is (2, 4) x[np.array([1, 2]), :] # second and third row, shape is (2, 4) x[[1, 2], :] # second and third row, shape is (2, 4)

x[:, :, np.newaxis] # add a new axis, shape is (3, 4, 1)


- Broadcasting
  - allows for element-wise operations between arrays of different shapes

```python
x = np.random.random((3, 4)) # Random 3x4 matrix
y = np.random.random((3,1)) # Random 3x1 vector
z = np.random.random((1, 4)) # Random 1x4 vector

x + y # Adds y to each column of x
x * z # Multiplies each row of x by z element-wise
  • Broadcasting generalize: NumPy compares their shapes element-wise. It starts with the trailing dimensions and works its way forward. Two dimensions are compatible when
      1. they are equal, or
      1. one of them is 1 (in which case, elements on the axis are repeated along the dimension) ```python a = np.random.random((3, 4)) b = np.random.random((3, 1)) c = np.random.random((3, ))

b + b.T # works a + c # ValueError: operands could not be broadcast together with shapes (3,4) (3,) b + c # works, b is broadcasted to (3, 3), then element-wise addition


- Efficient Numpy Code
  - Avoid explicit for-loops over indices/axes at all costs. For-loops will dramatically slow down our code.(~10-100x).

```python
# Slow
x = np.random.random((1000, 1000))
y = np.zeros((1000, 1000))
for i in range(1000):
  for j in range(1000):
    y[i, j] = x[i, j] + 1

# Fast
x = np.random.random((1000, 1000))
y = x + 1
  • Use NumPy’s built-in functions and operations whenever possible. They are highly optimized and run much faster than their Python counterparts.
# Slow
x = np.random.random((1000, 1000))
y = np.zeros((1000, 1000))
for i in range(1000):
  for j in range(1000):
    y[i, j] = np.sin(x[i, j])

# Fast
x = np.random.random((1000, 1000))
y = np.sin(x)
  • Use NumPy’s broadcasting feature to avoid unnecessary duplication of data. This will save memory and speed up your code.
# Slow
x = np.random.random((1000, 1000))
y = np.random.random((1000, 1))
z = np.zeros((1000, 1000))
for i in range(1000):
  for j in range(1000):
    z[i, j] = x[i, j] + y[i, 0]

# Fast
x = np.random.random((1000, 1000))
y = np.random.random((1000, 1))
z = x + y
  • Use NumPy’s built-in linear algebra functions for matrix operations. They are highly optimized and run much faster than their Python counterparts.
# Slow
x = np.random.random((1000, 1000))
y = np.random.random((1000, 1000))
z = np.zeros((1000, 1000))
for i in range(1000):
  for j in range(1000):
    for k in range(1000):
      z[i, j] += x[i, k] * y[k, j]

# Fast
x = np.random.random((1000, 1000))
y = np.random.random((1000, 1000))
z = np.dot(x, y)

Pratical Python Tips

  • Use list comprehensions to create lists ```python

    Slow

    squares = [] for i in range(10): squares.append(i ** 2)

Fast

squares = [i ** 2 for i in range(10)]

can also use conditionals

squares = [i ** 2 for i in range(10) if i % 2 == 0]


- Convenient Syntax
```python
# Swap two variables
a, b = 1, 2
a, b = b, a

# Multiple assignment
a, b, c = 1, 2, 3
a, b, c = ('Alice', 25, True)

# Unpack a list
x = [1, 2, 3]
a, b, c = x

# Return multiple values from a function
def f():
  return 1, 2, 3
a, b, c = f()

# Joining list of strings with a delimiter
names = ['Alice', 'Bob', 'Charlie']
', '.join(names) # 'Alice, Bob, Charlie'

# String literals with both single and double quotes
s = "Hello, 'world'"
s = 'Hello, "world"'
  • Debugging Tips: use interactive shell
    • Python has no integer overflow
    • Try out syntax
    • array.shape, array.dtype, type(array)
    • import pdb; pdb.set_trace() # set a breakpoint
    • or breakpoint() # Python 3.7+, set a breakpoint
    • print(f’My name is {name}’) # f-string, Python 3.6+, string interpolation
array.shape # get the shape of the numpy array
array.dtype # get the element data type of the numpy array
type(array) # get the type of a variable
  • Common Errors
    • SyntaxError: invalid syntax
    • NameError: name ‘x’ is not defined
    • TypeError: can only concatenate str (not “int”) to str
    • ValueError: invalid literal for int() with base 10: ‘hello’
    • IndexError: list index out of range
    • KeyError: ‘Alice’
    • AttributeError: ‘list’ object has no attribute ‘append’
    • IndentationError: expected an indented block
    • ZeroDivisionError: division by zero
    • FileNotFoundError: [Errno 2] No such file or directory: ‘file.txt’
    • ModuleNotFoundError: No module named ‘numpy’

Other Great References

  • Official Python 3 documentation: https://docs.python.org/3/
  • Official Anaconda user guide: https://docs.conda.io/projects/conda/en/latest/user-guide/index.html
  • Official NumPy documentation: https://numpy.org/doc/stable/


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