Skip to main content

python cheatsheet


Basics

  • Comments:
    Python
    # This is a single-line comment
"""
This is a
multi-line comment
"""
  • Variables:
    Python
    x = 5
name = "Alice"
is_valid = True
    • Dynamically typed (you don't need to declare the type).
    • Case-sensitive (myVar is different from myvar).
  • Data Types:
    • Numeric: int (integer), float (floating-point), complex (complex numbers)
    • Sequence: str (string), list, tuple, range
    • Mapping: dict (dictionary)
    • Set: set, frozenset
    • Boolean: bool (True, False)
    • NoneType: None
  • Operators:
    • Arithmetic: + (add), - (subtract), * (multiply), / (divide), // (floor division), % (modulo), ** (exponent)
    • Comparison: == (equal), != (not equal), > (greater than), < (less than), >= (greater than or equal to), <= (less than or equal to)
    • Logical: and, or, not
    • Assignment: =, +=, -=, *=, /=, //=, %=, **=
    • Identity: is, is not (check if objects are the same in memory)
    • Membership: in, not in (check if a value is present in a sequence)

Control Flow

  • Conditional Statements:
    Python
    if condition:
    # code to execute if condition is True
elif another_condition:
    # code to execute if another_condition is True
else:
    # code to execute if none of the above conditions are True
  • Loops:
    • for loop: Iterates over a sequence (list, tuple, string, range, etc.)
      Python
      for item in sequence:
    # code to execute for each item
      • range(start, stop, step): Generates a sequence of numbers.
      • enumerate(sequence): Returns pairs of (index, item).
    • while loop: Executes as long as a condition is True.
      Python
      while condition:
    # code to execute while condition is True
    • Loop Control Statements:
      • break: Exits the loop immediately.
      • continue: Skips the current iteration and proceeds to the next.
      • pass: Does nothing (placeholder).

Data Structures

  • Lists: Ordered, mutable sequences of items.
    Python
    my_list = [1, "hello", 3.14]
my_list.append(4)       # Add to the end
my_list.insert(1, "world") # Insert at a specific index
my_list.remove("hello")  # Remove the first occurrence of a value
popped_item = my_list.pop(2) # Remove and return item at index (default: last)
length = len(my_list)
sliced_list = my_list[1:3] # Slicing (start:end:step)
  • Tuples: Ordered, immutable sequences of items.
    Python
    my_tuple = (1, "hello", 3.14)
# Tuples are often used for fixed collections
  • Dictionaries: Unordered collections of key-value pairs.
    Python
    my_dict = {"name": "Bob", "age": 30}
print(my_dict["name"])   # Access value by key
my_dict["city"] = "New York" # Add a new key-value pair
del my_dict["age"]       # Delete a key-value pair
keys = my_dict.keys()
values = my_dict.values()
items = my_dict.items()   # Returns a list of (key, value) tuples
  • Sets: Unordered collections of unique elements.
    Python
    my_set = {1, 2, 3, 3, 4} # {1, 2, 3, 4} (duplicates are automatically removed)
my_set.add(5)
my_set.remove(3)
union_set = my_set.union({5, 6})
intersection_set = my_set.intersection({2, 4})

Functions

  • Defining Functions:
    Python
    def greet(name="Guest"): # Default argument
    print(f"Hello, {name}!")
    return "Greeting sent" # Optional return value

message = greet("Charlie")
greet() # Uses the default argument
  • Function Arguments:
    • Positional arguments: Passed in the order they are defined.
    • Keyword arguments: Passed with the parameter name (greet(name="David")).
    • *args: Collects extra positional arguments into a tuple.
    • **kwargs: Collects extra keyword arguments into a dictionary.
  • Lambda Functions (Anonymous Functions):
    Python
    square = lambda x: x**2
result = square(5) # result is 25

Modules and Packages

  • Importing Modules:
    Python
    import math
print(math.sqrt(16))

import random as rnd # Alias the module
print(rnd.randint(1, 10))

from datetime import date # Import specific names
today = date.today()
  • Packages: Hierarchical directory structure that organizes modules. Use dot notation to access modules within packages (e.g., my_package.my_module).

Input and Output

  • print(): Displays output to the console.
    Python
    print("Hello, world!")
print("Value:", x, "Another value:", name) # Multiple arguments
print(f"Formatted string: {x=}, {name=}") # f-strings (formatted string literals)
  • input(): Reads input from the user (returns a string).
    Python
    user_input = input("Enter something: ")
number = int(input("Enter a number: ")) # Convert to integer
  • File Handling:
    Python
    # Reading from a file
with open("my_file.txt", "r") as file:
    content = file.read()       # Read the entire file
    lines = file.readlines()    # Read all lines into a list
    for line in file:          # Iterate over lines
        print(line.strip())     # Remove leading/trailing whitespace

# Writing to a file
with open("output.txt", "w") as file: # 'w' for write (overwrites), 'a' for append
    file.write("Hello, file!\n")
    file.writelines(["Line 1\n", "Line 2\n"])

Object-Oriented Programming (OOP)

  • Classes: Blueprints for creating objects.
    Python
    class Dog:
    def __init__(self, name, breed): # Constructor
        self.name = name
        self.breed = breed

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

my_dog = Dog("Buddy", "Golden Retriever")
print(my_dog.name)
my_dog.bark()
  • Objects (Instances): Created from classes.
  • Methods: Functions defined within a class.
  • Attributes: Variables associated with an object.
  • Inheritance: Allows a class to inherit properties and methods from a parent class.
  • Polymorphism: Ability of different objects to respond to the same method in their own way.

Error Handling

  • try...except Blocks:
    Python
    try:
    result = 10 / 0
except ZeroDivisionError as e:
    print(f"Error: {e}")
except (TypeError, ValueError) as e: # Catch multiple exceptions
    print(f"Another error: {e}")
else:
    print("No error occurred.") # Executed if no exception
finally:
    print("This will always be executed.") # Cleanup code

List Comprehensions

  • A concise way to create lists.
    Python
    squares = [x**2 for x in range(10)] # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
even_numbers = [x for x in range(20) if x % 2 == 0]

Generator Expressions

  • Similar to list comprehensions but create iterators (more memory-efficient for large sequences).
    Python
    squares_generator = (x**2 for x in range(10))
for num in squares_generator:
    print(num)

String Formatting

  • f-strings (Formatted String Literals): (Python 3.6+) Preferred method.
    Python
    name = "Eve"
age = 25
print(f"My name is {name} and I am {age} years old.")
print(f"{2 + 2 = }") # Inline expressions
  • .format() method:
    Python
    print("My name is {} and I am {} years old.".format(name, age))
print("My name is {0} and I am {1} years old. Again, {0}.".format(name, age))
print("My name is {n} and I am {a} years old.".format(n=name, a=age))
  • Old % formatting: (Less common in modern Python)
    Python
    print("My name is %s and I am %d years old." % (name, age))

Common Built-in Functions

  • len(): Returns the length of a sequence.
  • type(): Returns the type of an object.
  • int(), float(), str(), bool(), list(), tuple(), dict(), set(): Type conversion functions.
  • sum(): Sum of elements in an iterable.
  • min(), max(): Minimum and maximum of elements.
  • sorted(): Returns a new sorted list from the items in an iterable.
  • abs(): Absolute value.
  • round(): Rounds a number.
  • pow(base, exp): Equivalent to base ** exp.
  • zip(): Returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the input iterables.
  • map(function, iterable): Applies a function to all items of an iterable.  
  • filter(function, iterable): Filters elements of an iterable based on a function.

This cheatsheet covers many fundamental aspects of Python. As you continue learning and working with Python, you'll encounter more advanced concepts and libraries. Keep practicing and referring to documentation as needed!

Comments

Post a Comment

Popular posts from this blog

Entity Relationship (ER) Diagram Model with DBMS Example

Reference :   Entity Relationship (ER) Diagram Model with DBMS Example What is ER Diagram? ER Diagram  stands for Entity Relationship Diagram, also known as ERD is a diagram that displays the relationship of entity sets stored in a database. In other words, ER diagrams help to explain the logical structure of databases. ER diagrams are created based on three basic concepts: entities, attributes and relationships. ER Diagrams contain different symbols that use rectangles to represent entities, ovals to define attributes and diamond shapes to represent relationships. At first look, an ER diagram looks very similar to the flowchart. However, ER Diagram includes many specialized symbols, and its meanings make this model unique. The purpose of ER Diagram is to represent the entity framework infrastructure. Entity Relationship Diagram Example Table of Content: What is ER Diagram? What is ER Model? History of ER models Why use ER Diagrams? Facts about ER Diagram Model ER Diagram...
Post a Comment
Read more

GCP Buckets

  CREATING BUCKET Method 1: Using GCP Console (UI) Go to the  Google Cloud Console . Navigate to  Cloud Storage . Click  Create Bucket . Enter a  unique name  for your bucket. Select a  storage class  (Standard, Nearline, Coldline, or Archive)... Choose a  location  (region/multi-region). Set  access control  (Uniform or Fine-grained). Click  Create . how do you optimise the class storage buckets - we can talk about cloud storage choosing wisely standard vs nearline vs coldline vs archive... how you managed your buckets - how did you manage? life cycle rule - regions multi regions etc
Post a Comment
Read more

Pyspark

pyspark link the above link for a sample code for Pyspark  Transformations create new datasets, actions return values, or write data. Transformations: Definition: Transformations are operations that create a new RDD or data frame from an existing one. They are "lazy," meaning they don't execute immediately. Instead, Spark builds a lineage graph (DAG - Directed Acyclic Graph) of transformations. Examples: map() : Applies a function to each element. filter() : Select elements based on a condition. flatMap() : Similar to a map, but flattens the results. groupByKey() : Groups elements by key. reduceByKey() : Reduces elements by key. join() : Joins two datasets. distinct() : Removes duplicate elements. union() : Combines two datasets. coalesce() : Reduces the number of partitions. repartition() : Increases or decreases the number of partitions. Actions: Definition: Actions trigger the execution of the lineage graph, which returns a result to the driver progra...
Post a Comment
Read more