📘 Overview

• What is it?: NumPy (Numerical Python) is the foundational package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on these arrays.
• Key Features:
  • ndarray: A fast and space-efficient multidimensional array providing vectorized arithmetic operations.
  • Broadcasting: A powerful mechanism that allows operations on arrays of different shapes.
  • Vectorization: Elimination of explicit Python loops in numerical operations, leading to C-level execution speeds.
• Installation:

  pip install numpy

🧾 Core Concepts

• ndarray: An N-dimensional array object where all elements must be of the same data type (dtype).
• Vectorization: Running element-wise calculations directly in C, which avoids the overhead of standard Python loops.
• Broadcasting: Rules specifying how operations work on arrays of different shapes (e.g., adding a scalar to an array, or multiplying a 2D array by a 1D array).
• Slicing and Views: Sub-arrays created by slicing are “views” of the original data. Modifying a view modifies the original array (no copy is made unless explicitly requested with .copy()).

💻 Common Code Patterns & Cheat Sheet

• Array Creation & Properties:

  import numpy as np
   
  # Creation from list
  arr = np.array([1, 2, 3, 4])
   
  # Help helper functions
  zeros = np.zeros((2, 3))            # 2x3 matrix of zeros
  ones = np.ones((3, 3), dtype=int)   # 3x3 matrix of ones
  steps = np.arange(0, 10, 2)         # [0 2 4 6 8]
  linspace = np.linspace(0, 1, 5)     # [0. 0.25 0.5 0.75 1.]
  identity = np.eye(3)                # 3x3 Identity matrix

• Indexing, Slicing & Boolean Masking:

  grid = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
   
  # Slice: first two rows, last two columns
  sub_grid = grid[:2, 1:]
   
  # Boolean Masking (extremely fast filtering)
  mask = grid > 5
  filtered_vals = grid[mask]          # [6 7 8 9]

• Vectorized Operations & Broadcasting:

  a = np.array([1, 2, 3])
  b = np.array([[10], [20], [30]])
   
  # Broadcasting: adds (3,) to (3,1) resulting in a (3,3) matrix
  result = a + b
  # [[11 12 13],
  #  [21 22 23],
  #  [31 32 33]]

💡 Best Practices & Tips

• Avoid Explicit Loops: Never loop over NumPy arrays using Python for loops if a vectorized alternative exists.
• View vs Copy: Be careful when slicing: slice = arr[1:3] does not copy memory. Use slice = arr[1:3].copy() if you want a new independent array.
• Dtype Management: Choose specific dtypes (like np.int8, np.float32) to save memory when working with large datasets.

🔗 Navigation & Internal Links

• Parent: Python
• Related Notes: Data Science | Machine Learning | Pandas | Matplotlib | SciPy