Key features and benefits of Jupyter Lab
- Jupyter Lab is a web-based interactive development environment (IDE) that combines the features of Jupyter Notebook and other tools.
- It provides a more integrated experience, with a single window for managing files, running code, and viewing outputs.
- It also includes a number of extensions that can be used to add additional functionality, such as code linting, debugging, and data visualization.
Differences between Jupyter Lab and Jupyter Notebook
- Jupyter Lab is a more recent development than Jupyter Notebook, and it includes a number of new features and improvements.
- For example, Jupyter Lab has a more integrated user interface, and it supports a wider range of extensions.
- However, Jupyter Notebook is still a popular choice for many users, as it is a simpler and more lightweight application.
Main functionalities provided by the NumPy library
- NumPy provides a powerful set of tools for working with multidimensional arrays in Python.
- These tools can be used for a variety of tasks, such as scientific computing, data analysis, and machine learning.
- NumPy also provides a number of functions for performing mathematical operations on arrays, such as addition, subtraction, multiplication, and division.
Basic structure and properties of NumPy arrays
- NumPy arrays are objects that store data in a multidimensional format.
- The data in a NumPy array can be of any type, such as integers, floats, or strings.
- NumPy arrays can be created in a number of ways, such as by using the
array() function or by importing data from a file.
- NumPy arrays can be manipulated using a variety of methods, such as slicing, indexing, and sorting.
- NumPy arrays can also be used to perform mathematical operations, such as addition, subtraction, multiplication, and division.