Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lära Arrays Operations | Introduction to NumPy
Introduction to Data Analysis in Python
course content

Kursinnehåll

Introduction to Data Analysis in Python

Introduction to Data Analysis in Python

1. Basics
2. Data Types
3. Control Flow
4. Functions and Modules
5. Introduction to NumPy

book
Arrays Operations

NumPy arrays not only perform faster, but also support different operations, that are not available for built-in lists.

For example, you can add/subtract/multiply/divide by a certain number all the array elements. Or you can multiply two arrays element-by-element.

123456789
# Import the library import numpy as np # Creating two arrays arr1 = np.array([1, 2, 3]) arr2 = np.array([10, 20, 30]) # Perform some arrays operations print(arr1 + 3) print(arr1 * arr2)
copy

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 5. Kapitel 3

Fråga AI

expand
ChatGPT

Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal

course content

Kursinnehåll

Introduction to Data Analysis in Python

Introduction to Data Analysis in Python

1. Basics
2. Data Types
3. Control Flow
4. Functions and Modules
5. Introduction to NumPy

book
Arrays Operations

NumPy arrays not only perform faster, but also support different operations, that are not available for built-in lists.

For example, you can add/subtract/multiply/divide by a certain number all the array elements. Or you can multiply two arrays element-by-element.

123456789
# Import the library import numpy as np # Creating two arrays arr1 = np.array([1, 2, 3]) arr2 = np.array([10, 20, 30]) # Perform some arrays operations print(arr1 + 3) print(arr1 * arr2)
copy

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 5. Kapitel 3
Vi beklagar att något gick fel. Vad hände?
some-alt