1. Basic Syntax and Data Types
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Variable Declaration: No need for
var,let, orconst. Just name the variable.
x = 10 name = "Python" -
Primitive Types:
-
int(Integer) -
float(Floating Point) -
str(String) -
bool(Boolean)
-
-
Data Structures:
- Lists (like arrays in JS):
numbers = [1, 2, 3] numbers.append(4)- Tuples (immutable lists):
point = (10, 20)- Dictionaries (like JS objects):
person = {"name": "Alice", "age": 30} person["name"] # Accessing value- Sets (unique, unordered elements):
unique_numbers = {1, 2, 3, 2}
2. Control Structures
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Conditionals:
if x > 5: print("Greater") elif x == 5: print("Equal") else: print("Lesser") -
Loops:
- For Loop (works with iterable objects):
for num in [1, 2, 3]: print(num)- While Loop:
i = 0 while i < 5: i += 1
3. Functions
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Function definition and return syntax:
def greet(name): return f"Hello, {name}" -
Lambda Functions (like JS arrow functions):
square = lambda x: x * x
4. List Comprehensions and Generators
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List Comprehensions (efficient way to create lists):
squares = [x * x for x in range(10)] -
Generators (yielding values one by one):
def generate_numbers(n): for i in range(n): yield i
5. Error Handling
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Try/Except Blocks:
try: result = 10 / 0 except ZeroDivisionError: print("Cannot divide by zero")
6. Classes and OOP
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Class Definition:
class Animal: def __init__(self, name): self.name = name def speak(self): return f"{self.name} makes a sound" -
Inheritance:
class Dog(Animal): def speak(self): return f"{self.name} barks"
7. Common Built-In Functions
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len(),max(),min(),sum(),sorted() - Type conversions:
int(),float(),str(),list(),dict()
8. Working with Files
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Reading and Writing:
with open("file.txt", "r") as file: data = file.read()
9. Important Libraries
- NumPy for numerical operations, Pandas for data manipulation, and Matplotlib for plotting.
10. Differences from JavaScript
- No need for semicolons.
- Indentation is mandatory for defining blocks.
- No
switchstatement (useif-elifinstead). -
Noneis used instead ofnull.
This summary should provide the essentials to begin coding in Python efficiently.
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