AI Python Lab

NumPy: The Foundation of Scientific Computing

NumPy (Numerical Python) is the core library for scientific computing. Its most important object is the `ndarray`, a multi-dimensional array that is significantly faster and more memory-efficient than Python's built-in lists for numerical operations. In AI/ML, these arrays are used to store and manipulate data, from images to the weights of a neural network.

For more information, see the official NumPy documentation: docs.numpy.org.

Key Concepts:

• N-dimensional Arrays (`ndarray`): The primary data structure for efficient, vectorized operations.
• Vectorized Operations: Performing element-wise operations without explicit loops, which is a major performance benefit.
• Array Creation: Functions like `np.empty()`, `np.full()`, `np.zeros()`, and `np.array()` for creating arrays.

import numpy as np

# Create an array of zeros
zeros_array = np.zeros((2, 3), dtype=int)
print("Array with zeros:\n", zeros_array)

# Perform a vectorized operation
arr = np.array([1, 2, 3])
squared_arr = arr ** 2
print("\nSquared array:", squared_arr)

Pandas: The Data Analyst's Best Friend

Built on top of NumPy, Pandas is a data manipulation and analysis library. It introduces two powerful data structures: `Series` (a one-dimensional labeled array) and `DataFrame` (a two-dimensional labeled data table, like a spreadsheet). In AI/ML, Pandas is crucial for data preprocessing, cleaning, and exploratory data analysis (EDA).

For more information, see the official Pandas documentation: docs.pandas.pydata.org.

Key Concepts:

• Series: A single column of data with an index.
• DataFrame: The primary data structure for structured data, consisting of columns and rows.
• Data Manipulation: Functions for filtering, sorting, merging, and grouping data.

import pandas as pd
import numpy as np

exam_data = {
    'name': ['A', 'B', 'C'],
    'score': [12.5, np.nan, 16.5],
    'qualify': ['yes', 'no', 'yes']
}
df = pd.DataFrame(exam_data, index=['a', 'b', 'c'])
print("Original DataFrame:\n", df)

# Select rows where score is greater than 10
qualified_students = df[df['score'] > 10]
print("\nStudents with score > 10:\n", qualified_students)

Conditional Logic: `if`, `elif`, `else`

Essential for building algorithms, conditionals allow your program to make decisions. In AI, this is used in everything from simple rules-based systems to complex control flow in machine learning models.

if score > 90:
    action = "Excellent"
elif score > 70:
    action = "Good"
else:
    action = "Needs Improvement"

Loops & Iteration: `for`, `while`, `range()`

Loops are used to perform repetitive tasks, such as processing a large dataset, training a model over multiple epochs, or iterating through a list of students. The `range()` function is a powerful tool for controlling the number of iterations.

for student in student_list:
    process_data(student)

for i in range(1, 101):  # Loop for 100 students
    print(f"Processing student {i}")