CC3-Week-2-Introduction - (Part 1 and 2)

WEEK 2 CC3 - PYTHON PROGRAMMING

• Instructor: Kim Shaira B. Casimiro

HISTORY OF PYTHON

• Conceived in the late 1980s, implementation started in December 1989.

• Creator: Guido van Rossum, developed at the Centre for Mathematics and Computer Science (CWI) in the Netherlands.

• Designed as a successor to the ABC programming language.

• Features included exception handling and interfacing with the Amoeba operating system.

• Guido van Rossum's role: Principal author, continues to direct Python as its "Benevolent Dictator For Life" (BDFL).

THRUST AREAS OF PYTHON

MARKETABILITY OF PYTHON

• Known for simplicity and developer-friendliness.

• One of the fastest-growing programming languages; consistently ranked in the top ten since 2003 (TIOBE).

• As of April 2018, Python ranked 4th in the TIOBE Index.

PYTHON'S RANKINGS

• 1st by IEEE Spectrum in 2017.

• 3rd by RedMonk in 2018.

• Spectrum ranking shows Python outperforming other languages including C, Java, and C++.

ACADEMIA

• Commonly used as an introductory programming language in American universities.

• Competes with Matlab for research; advantages include being a true programming language with scientific tools almost equivalent to Matlab modules.

• Matlab is not considered a real programming language

CORE SCIENTIFIC TOOLS IN PYTHON

• SciPy: Numerical integration and optimization.

• NumPy: Provides N-dimensional array objects, linear algebra, Fourier transforms.

• Jupyter: Changes how programming is done in Python.

• Tools available under the Berkeley Software Distribution (BSD) license.

HTTP LIBRARY

• Requests library: Simplifies HTTP operations, supports verbs like GET, POST, PUT, DELETE.

• Known for thread safety, cookie persistence, and connection timeouts.

MACHINE LEARNING

• Machine Learning: Effective and adaptive tool to learn from experience and it originates from Computer Science and Statistics.

• Scikit-Learn - Well-known Machine Learning tool built on top of other
  Python scientific tools like NumPy, SciPy, and Matplotlib

DATABASE CONNECTORS

• Enable querying from programming languages.

• Popular open-source databases include MySQL and PostgreSQL.

• MySQL-Python-Connector is widely used in Python.

OBJECT RELATIONAL MAPPING (ORM)

• Connects object-oriented programming to relational databases.

• Popular frameworks: Django (full-fledged) and Flask (microframework).

CLOUD COMPUTING

• OpenStack: Entirely written in Python for creating clouds, overseen by the OpenStack Foundation.

• Hosted on platforms like Google App Engine, AWS, Heroku, Microsoft Azure.

GAME DEVELOPMENT

• Simplifies game development and rapid prototyping via libraries like Pygame.

WHY PYTHON?

• Cross-platform: works on Windows, Mac, Linux, Raspberry Pi.

• Syntax similar to English for better readability.

• Interpreter system allows for immediate execution of code, facilitating faster prototyping.

• Supports procedural, object-oriented, and functional programming.

PYTHON SYNTAX

GENERAL CHARACTERISTICS

• Designed for readability; blocks defined using indentation.

• Uses line breaks, not semicolons or braces, to terminate commands.

INTERPRETED LANGUAGE

• Source code is converted to bytecode for execution by the Python virtual machine.

• No need for compiling like in C or C++.

INDENTATION IN PYTHON

• Critical for defining code scopes and structure; avoids use of symbols or braces.

• Minimum one space indentation required, consistency is crucial.

COMMENTS IN PYTHON

• Used to improve code readability and document thought processes.

• Comments start with # and are ignored by the interpreter.

VARIABLES IN PYTHON

• Containers for storing data values, created upon assignment.

• No declaration command is needed; variable types can change dynamically.

EXAMPLES AND TYPES

• Common data types: int, float, string, char, bool.

• Example of assignments:

  • number = 5

  • name = "Mary"

STRING HANDLING IN PYTHON

STRING CHARACTERISTICS

• Strings are sequences of characters with powerful built-in processing capabilities.

• Declare strings with single or double quotes.

STRING OPERATIONS

CONCATENATION

• Combining strings using +. Example:

  • print(str0 + " " + str1 + " " + str2).

ACCESSING CHARACTERS

• Use indexing; negative indices access from the end of the string.

• Example: mystr = 'I am a String'; print(mystr[-8:]).

STRING FORMATTING

• Placeholders: format() function or f-strings for dynamic content.

• Example using format:

  • print("My name is {}".format(name)).

NUMBER FORMATTING FUNCTIONS

COMMON FUNCTIONS

• round(): Rounds numbers to the nearest whole/decimal place.

• math.ceil(): Rounds up to the nearest whole number.

• math.floor(): Rounds down to the nearest integer.

• pow(): Raises a number to a power.

INPUT AND OUTPUT FUNCTIONS

OUTPUT

• Use print() to display messages or variable values.

INPUT

• Capture user input via input(). Example:

  • name = input("Enter Full Name: ").

OPERATORS IN PYTHON

ARITHMETIC OPERATORS

• Basic operations: +, -, *, /, %, **.

• Example: x + y.

ASSIGNMENT OPERATORS

• Assign values to variables: = and compound assignments like +=.

COMPARISON AND LOGICAL OPERATORS

• Comparison operators like ==, !=, >, and logical operators like and, or, not.

CONTROL STRUCTURES IN PYTHON

TYPES OF CONTROL STRUCTURES

1. Sequential: Default flow of execution.

2. Selection: Decision-making using conditional statements.

3. Iteration: Looping constructs to repeat code.

EXAMPLES

IF STATEMENTS

• Used to check conditions and branch execution paths.

FOR AND WHILE LOOPS

• for to iterate over a range, while to repeat actions until a condition fails.

CONCLUSION

• Questions invited, and wish for continued learning!