Sesi 2 - Python Programming for AI | rubythalib.ai

Summary

The second session introduces Python containers—structures that hold multiple values—and explains how to choose among lists, tuples, dictionaries, sets, and NumPy arrays. The instructor also reviews basic Python concepts and demonstrates them in a notebook.

1. Containers and mutability

• A container stores multiple values, which may have the same or different data types.
• Mutable containers can be changed after creation; immutable containers cannot.
• Lists, dictionaries, and sets are mutable. Tuples are immutable.
• These structures help organize and process data for tasks such as machine learning.

2. Lists

• Lists are ordered collections written with square brackets ([]) and can contain mixed data types.
• List indexing starts at zero: the first item is at index 0, and -1 refers to the last item.
• Slicing selects a range of items. The stop position is excluded, so a slice from index 1 to 3 selects the items at indices 1 and 2.
• Lists can contain other lists, allowing table-like data. To access an item in a nested list, specify its row and column indices.
• Common list operations include:
  • append() adds an item at the end.
  • insert() adds an item at a specified position.
  • remove() deletes a matching value.
  • pop() removes and returns an item at a specified index.
  • sort() orders the list.
  • len() returns the number of items.
• List comprehensions provide a concise way to process or filter a list, such as selecting scores above a threshold or transforming every score.
• Lists can support data preparation and cleaning—for example, collecting sales data and removing invalid entries.

3. Tuples

• Tuples are ordered collections, commonly written with parentheses (()), and support indexing.
• Their elements cannot be added, removed, or reassigned after creation.
• Tuples suit values intended to remain fixed, such as coordinates.
• Tuple unpacking assigns tuple elements to separate variables.
• A tuple can contain a mutable object, such as a list. That inner object can still be modified, while the tuple’s structure remains fixed.

4. Dictionaries

• Dictionaries store key–value pairs and are written with curly braces ({}).
• Values are retrieved by key rather than numerical position, and keys must be unique.
• Dictionaries are mutable: entries can be added or updated by assigning a value to a key.
• You can inspect a dictionary’s keys, values, or key–value pairs.
• Dictionaries can be nested, such as when an identifier is used as a key for a patient record.

5. Sets

• Sets are unordered collections of unique values, written with curly braces.
• Duplicate values are automatically discarded.
• Common set operations include:
  • Union: combines the elements from both sets.
  • Intersection: returns elements shared by both sets.
  • Difference: returns elements in one set but not the other.
• Sets are useful for finding unique values, checking membership, and comparing groups of data.

6. NumPy arrays

• NumPy is a library for numerical computation and a common foundation for machine-learning workflows and other Python libraries.
• Unlike ordinary lists, NumPy arrays generally contain values of a consistent data type and are designed for efficient numerical operations.
• The examples use import numpy as np and demonstrate:
  • np.array() to create an array.
  • np.zeros() and np.ones() to create arrays filled with zeros or ones.
  • np.arange() to generate a sequence of values.
• Element-wise operations apply to every array element without an explicit loop, such as adding a number, multiplying values, or testing a condition.
• The instructor demonstrates checking an array’s shape and using reshape() to change its dimensions when the number of elements permits.
• NumPy arrays support indexing and slicing. In a two-dimensional array, the first index selects a row and the second selects a column. A colon represents all items along that dimension, and negative indices count from the end.

7. Review and notebook practice

The instructor revisits earlier Python material while working in a notebook:

• Variable names cannot begin with a number, contain spaces, or use most symbols. Underscores are allowed, but Python keywords cannot be used as variable names.
• Python can infer basic types, including integers, floats, strings, and Booleans. Boolean literals are True and False.
• Variable names are case-sensitive, so capitalization should be used consistently.
• The instructor demonstrates several ways to format variables in printed output, including formatted strings.
• Values read with input() are strings by default. Convert them to a numeric type, such as int, before doing arithmetic. Conversion fails if the input is not a valid number.
• Students are reminded to make their own copy of a shared notebook before editing it and to use optional exercises for additional practice.

Speakers and sources featured

• Muhammad Rifal Alfarizi — Main instructor; introduces himself as an AI engineer with a master’s degree in AI from Gadjah Mada University.
• Deni — Participant from Tangerang with an IT background focused on security infrastructure.
• Najib — Appears at the end as the MC, thanking the instructor and participants and closing the session.
• Willy / Widi — Participants acknowledged during the session; the auto-generated subtitles vary between these names.