Numpy basic functions I – Python

We’re going to explore NumPy (NumPy Basic Functions I), a fundamental library for numerical analysis. I’ll guide you through the basic usage in three steps.

• Numpy basic Part I
• Numpy basic Part II
• Numpy basic Part III

NumPy란?

Numerical Python (NumPy) is Python’s core library for numerical computations, developed by Travis Oliphant in 2005. It offers a wide range of mathematical and scientific functions, optimized for handling multidimensional array objects (ndarray) efficiently.

History of NumPy

• Numeric (1995) – Python’s first numerical computation library, developed by Jim Hugunin.
• Numarray (2001) – An improved version of Numeric, designed to handle larger arrays efficiently.
• NumPy (2005) – The modern NumPy, created by merging Numeric and Numarray, developed by Travis Oliphant.

이후, NumPy는 계속 발전하면서 과학, 공학, 데이터 분석 분야에서 필수 라이브러리가 되었습니다.

Let’s take a look at the most basic usage.

Numpy basic functions I Code

import numpy as np

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

z = np.zeros((3, 4)) # 0 init
print(z)
#[[0. 0. 0. 0.]
# [0. 0. 0. 0.]
# [0. 0. 0. 0.]]

o = np.ones((2, 3)) # 1 initå
print(o)
#[[1. 1. 1.]
# [1. 1. 1.]]

e = np.empty((2, 2)) # non-init
print(e)
#[[1.5e-323 4.0e-323]
# [4.0e-323 4.0e-323]]

ar = np.arange(10)
print(ar) # [0 1 2 3 4 5 6 7 8 9]

ar_step = np.arange(0, 10, 2)
print(ar_step) # [0 2 4 6 8]

lin = np.linspace(0, 1, 5)
print(lin) # [0.   0.25 0.5  0.75 1.  ]

Output

[1 2 3]
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
[[1. 1. 1.]
 [1. 1. 1.]]
[[ 2.31584178e+077 -2.00389588e+000]
 [ 9.88131292e-324  2.78134232e-309]]
[0 1 2 3 4 5 6 7 8 9]
[0 2 4 6 8]
[0.   0.25 0.5  0.75 1.  ]

Top Uses of NumPy

1. Scientific and Engineering Calculations

• Perform mathematical operations like linear algebra, differential equations, and Fourier transforms.
• Used alongside SciPy and Matplotlib for research and simulations.

2. Data Analytics & Machine Learning

• Works with Pandas and Scikit-learn for data preprocessing and machine learning model implementation.
• Enables fast array operations to process large datasets efficiently.

3. Computer Vision & Image Processing

• Used with OpenCV for image filtering, transformation, and analysis.
• Optimizes per-pixel operations for faster computations.

4. Financial & Economic Analysis

• Applied in financial simulations, risk analysis, and equity data processing.
• Efficiently handles large financial datasets using high-speed matrix operations.

5. Big Data & AI

• Powers deep learning libraries like TensorFlow and PyTorch with NumPy-based operations.
• Plays a key role in data transformation and optimization calculations.