Numpy is a fundamental library for scientific computing in Python. If you're new to data science, this guide will help you get started with its core features. Let's dive in!

🔑 Key Features of Numpy

  • High-performance array operations 🚀
    Numpy arrays are faster than Python lists for numerical tasks.

    numpy_performance

  • Multi-dimensional data structures 📊
    Work with 1D, 2D, and N-dimensional arrays seamlessly.

    numpy_array_structure

  • Integrated mathematical functions 📈
    Perform operations like summation, broadcasting, and linear algebra directly on arrays.

    numpy_math_functions

🧠 Basic Operations

import numpy as np
# Create a 1D array
arr = np.array([1, 2, 3])  # <center><img src="https://cloud-image.ullrai.com/q/numpy_array_creation/" alt="numpy_array_creation"/></center>
# Create a 2D array
matrix = np.array([[1, 2], [3, 4]])
# Access elements
print(matrix[0, 1])  # Output: 2

📌 Array Creation Methods

Method Description Example
np.array() Convert a list to an array np.array([1, 2, 3])
np.zeros() Create an array filled with 0s np.zeros((2, 3))
np.arange() Generate a sequence np.arange(0, 10, 2)
np.linspace() Create evenly spaced values np.linspace(0, 1, 5)

📚 Advanced Topics (Recommended Reading)

🌟 Why Learn Numpy?

  • Efficiency: Leverage C-level speed for numerical computations
  • Integration: Works effortlessly with Matplotlib, Pandas, and Scikit-learn
  • Community: 2.5M+ developers use Numpy globally 🌍
python_data_science

For hands-on practice, try this interactive Numpy demo ⚙️!