NumPy: Array Manipulation

NumPy: Array Manipulation

Hendrik Speleers

NumPy: Array Manipulation

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Overview

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1D and 2D arrays

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Creation, indexing and slicing

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Memory structure

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Shape manipulation

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Basic mathematical operations

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Arithmetic and logic operations

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Reduction and linear algebra operations

Other operations

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Polynomial manipulation

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Input and output

Lab Calc

2023-2024

NumPy: Array Manipulation

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NumPy

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Numerical Python

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Python extension for multi-dimensional arrays

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Suited for creation and manipulation of numerical data

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Closer to hardware: more efficient

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Designed for scientific computation: more intuitive

Import convention

import numpy as np

Lab Calc

2023-2024

NumPy: Array Manipulation

¡ñ

NumPy array

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A NumPy array is a collection of objects of the same type

In [1]:

In [2]:

Out[2]:

In [3]:

Out[3]:

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a = np.array([0, 1, 2, 3])

a

array([0, 1, 2, 3])

a.size

4

Default object types of an array

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boolean (bool), integer (int, int64)

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float (float, float64), complex (complex, complex128)

Lab Calc

2023-2024

NumPy: Array Manipulation

¡ñ

NumPy array

¨C

More compact and more efficient operations than list

In [1]: L = 100000

In [2]: a = range(L)

In [3]: %timeit [i**2 for i in a]

16.4 ms ¡À 8.6 ?s per loop (mean ¡À std. dev. of 7

runs, 100 loops each)

In [4]: b = np.arange(L)

In [5]: %timeit b**2

33.7 ?s ¡À 43.4 ns per loop (mean ¡À std. dev. of 7

runs, 10000 loops each)

Lab Calc

2023-2024

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