1. NumPy For Python Statistical Analysis

Objective: Master NumPy arrays and statistical operations for efficient data analysis in Python.

1. Introduction to NumPy

Key Concepts:

• Why NumPy?: Fast numerical computations on arrays/matrices.

• Core Features:

  • Multidimensional arrays (ndarray).

  • Vectorized operations (no loops needed).

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2. Creating NumPy Arrays

Methods Covered:

• From Lists/Tuples:

• Special Arrays:

  • np.zeros((3,3)): 3×3 array of 0s.

  • np.ones((2,2)): 2×2 array of 1s.

  • np.random.rand(2,2): Random values (0–1).

Data Types:

• Specify with dtype (e.g., int, float, bool).

• Convert types: .astype('float').

3. Array Attributes & Operations

Key Attributes:

• shape: Dimensions (e.g., (3,3) for a 3×3 matrix).

• size: Total elements.

• dtype: Data type.

Mathematical Operations:

• Element-wise: +, -, *, /, **.

• Aggregations: .sum(), .mean(), .max().

4. Indexing & Slicing

Techniques:

• 1D Arrays: arr[0], arr[1:3].

• 2D Arrays: arr[0,1] (row 0, column 1).

• Boolean Indexing: arr[arr > 3].

Reshaping:

• arr.reshape(2,3): Convert 1D to 2D.

• arr.flatten(): Convert to 1D.

5. Statistical Functions

Essential Functions:

• Central Tendency:

  • np.mean(), np.median().

• Spread:

  • np.std() (standard deviation), np.var() (variance).

• Percentiles: np.percentile(arr, 50) (median).

Random Distributions:

• Normal: np.random.normal(mean, std, size).

• Uniform: np.random.rand(size).

6. Linear Algebra with NumPy

Key Operations:

• Dot Product: np.dot(a, b) or a @ b.

• Matrix Multiplication: np.matmul(a, b).

• Determinant: np.linalg.det(matrix).

7. Practical Applications

Use Cases:

• Data Cleaning: Handle missing values (np.nan).

• Simulations: Generate random data (e.g., Monte Carlo).

• Visualization: Plot histograms with matplotlib.

Access the full lesson here: NumPy For Statistical Analysis.