NumPy Library — Introduction

NumPy (Numerical Python) is a library for fast, memory-efficient numerical computation using arrays — the foundation of the entire Python data-science ecosystem (Pandas, SciPy, scikit-learn, TensorFlow all build on it).

Why NumPy over plain Python lists?

import numpy as np

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

print(lst * 2)     # [1, 2, 3, 1, 2, 3]  -- list repetition
print(arr * 2)      # [2 4 6]             -- element-wise multiplication

Creating One-Dimensional Arrays

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a)             # [1 2 3 4 5]
print(type(a))         # <class 'numpy.ndarray'>
print(a.dtype)           # int64 (or int32 on some systems)
print(a.shape)             # (5,)  -- 1D array of 5 elements
print(a.ndim)                # 1    -- number of dimensions
print(a.size)                  # 5    -- total number of elements

Other array-creation functions

print(np.zeros(5))            # [0. 0. 0. 0. 0.]
print(np.ones(4))              # [1. 1. 1. 1.]
print(np.arange(0, 10, 2))       # [0 2 4 6 8]  -- like range(), but returns an array
print(np.linspace(0, 1, 5))        # [0. 0.25 0.5 0.75 1.]  -- 5 evenly spaced values
print(np.full(4, 7))                 # [7 7 7 7]
print(np.array([1, 2, 3], dtype=float))   # [1. 2. 3.]

Installing NumPy

pip install numpy

Basic array info

a = np.array([10, 20, 30, 40])
print("Array:", a)
print("Data type:", a.dtype)
print("Dimensions:", a.ndim)
print("Shape:", a.shape)
print("Size:", a.size)
print("Item size (bytes):", a.itemsize)