Categories of Programming Languages

Machine Language: High execution efficiency but very low development productivity.

Assembly Language: Used in operating systems or performance-critical applications like game cheats; offers moderate development speed with relatively high execution performance.

High-Level Languages:

• Interpreted languages: Faster to develop but slower at runtime (e.g., Python).
• Compiled languages: Slower development cycle but faster execution (e.g., C++).

Network Bottleneck Effect: In network-dependent applications, latency dominates over local execution time. Thus, interpreted language are preferred for web apps, while compiled languages remain essential for system-level software like OS kernels.

Running Python Programs

• Interactive Mode (e.g., Jupyter Notebook): Executes code line-by-line, ideal for experimentation.
• Script Mode (e.g., PyCharm): Runs entire scripts via comand line (python script.py) or IDE execution.

Essential PyCharm Shortcuts

• Ctrl + C: Copy entire line if nothing is selected.
• Ctrl + X: Cut selected content (or full line by default).
• Ctrl + D: Duplicate current line or selection.
• Ctrl + Y: Delete current line.
• Ctrl + /: Toggle comment for selected lines.
• Ctrl + F: Find text; supports regex and batch replace.
• Ctrl + Shift + R: Global search and replace across project.
• Shift + F10: Run last executed script.
• Ctrl + Shift + F10: Run current file.
• Ctrl + Alt + L: Reformat code to conform to style guidelines.

Customize shortcuts via File → Settings → Keymap.

Variables and Constants

Programming involves writing instructions that manipulate variables—entities that store changing states from the real world.

A variable consists of:

• Name: Identifier following naming rules.
• Assignment operator (=): Binds value to name.
• Value: Actual data stored.

Naming Rules:

1. Must be descriptive.
2. Composed of letters, digits, underscores; cannot start with a digit.
3. Cannot use Python keywords (e.g., if, for).

Two common naming conventions:

• snake_case (preferred in Python)
• camelCase

Constants are conventionally written in UPPER_SNAKE_CASE. Though Python doesn’t enforce immutability, this signals intent.

Python Memory Management and Variable Inspection

When a variable is assigned, Python allocates memory for its value. The interpreter manages memory automatically using:

• Reference Counting: Tracks how many names refer to a value. When count drops to zero, memory is reclaimed.
• Garbage Collector: Handles cyclic references beyond reference counting.
• Small Integer Caching: Integers from -5 to 256 are pre-allocated and reused. PyCharm may extend this range during debugging.

Example:

# x = 100
# y = x        # Reference count for 100 becomes 2
# del x        # Reference count drops to 1
# del y        # Reference count 0 → memory freed (unless in small int pool)

Inspect variables using:

value = 42
print(value)      # Output value
print(id(value))  # Memory address
print(type(value))# Data type

Data Types

Python categorizes values into types that dictate operations and behavior.

Integer (int)

Use: Ages, IDs, counts.
Definition: age = 25 or age = int("25")
Operations: +, -, *, /, % (modulo), // (floor division), ** (exponentiation).

Floating-Point (float)

Use: Heights, salaries, measurements.
Definition: price = 19.99 or price = float("19.99")
Type Conversion:

num = float(5)     # → 5.0
truncated = int(3.9)  # → 3 (no rounding)
rounded = round(3.9)  # → 4.0

Supports same arithmetic and comparison operators as integers.

String (str)

Use: Names, labels, textual data.
Definition: Single ('text'), double ("text"), or triple quotes ('''multiline''').
Operations:

first = "Alex"
last = "Chen"
full = first + " " + last  # Concatenation
repeated = "Hi! " * 3      # → "Hi! Hi! Hi! "

Strings cannot be added to numbers directly.

Comments

• Single-line: # This is a comment — also disables code during debugging.
• Multi-line: Use triple quotes (''' or """), though primarily intended for docstrings.

Toggle comments on multiple lines with Ctrl + /.