NUMPY Tutorial

📘 Introduction to NumPy

NumPy (short for Numerical Python) is a powerful Python library used for numerical computations. It provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them efficiently.

🧪 Installing NumPy

pip install numpy

🔧 Importing NumPy

import numpy as np

📌 Creating NumPy Arrays

Arrays are the core of NumPy. Here’s how to create them:

import numpy as np

# 1D array
a = np.array([1, 2, 3])
print(a)

# 2D array
b = np.array([[1, 2], [3, 4]])
print(b)

📏 Array Properties

• .ndim: Number of dimensions
• .shape: Shape (rows, columns)
• .size: Total number of elements
• .dtype: Data type of array elements

🧮 Useful NumPy Functions

• np.zeros((2, 3)) → Creates array of all zeros
• np.ones((2, 3)) → Creates array of all ones
• np.arange(0, 10, 2) → Array with step value
• np.linspace(0, 1, 5) → 5 values between 0 and 1
• np.random.rand(2, 2) → Random 2x2 matrix

🔄 Array Operations

NumPy makes mathematical operations easy:

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)      # [5 7 9]
print(a * b)      # [ 4 10 18]
print(np.dot(a, b)) # 32 (dot product)

📊 Basic Statistics

arr = np.array([1, 2, 3, 4, 5])

print(np.mean(arr))  # 3.0
print(np.median(arr))  # 3.0
print(np.std(arr))   # Standard deviation
print(np.max(arr))   # Max value

📱 Mobile Friendly Suggestion:

Use Jupyter Notebook or Google Colab on mobile for writing and testing NumPy code while learning on the go.

✅ Conclusion

NumPy is a must-know for anyone diving into Python-based data science, machine learning, or analytics. Mastering its basics lays a strong foundation for advanced tools like Pandas, SciPy, and TensorFlow.