My reading notes journal for Code Fellows
JupyterLab is a next-generation web-based user interface for Project Jupyter. JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. For a demonstration of JupyterLab and its features, you can view this video: https://youtu.be/A5YyoCKxEOU
Cited from: https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html
NumPy is a commonly used Python data analysis package. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. NumPy was originally developed in the mid 2000s, and arose from an even older package called Numeric. This longevity means that almost every data analysis or machine learning package for Python leverages NumPy in some way.
We can create a NumPy array using the numpy.array function. If we pass in a list of lists, it will automatically create a NumPy array with the same number of rows and columns. Because we want all of the elements in the array to be float elements for easy computation, we’ll leave off the header row, which contains strings. One of the limitations of NumPy is that all the elements in an array have to be of the same type, so if we include the header row, all the elements in the array will be read in as strings. Because we want to be able to do computations like find the average quality of the wines, we need the elements to all be floats.
Alternative NumPy Array Creation Methods
There are a variety of methods that you can use to create NumPy arrays. To start with, you can create an array where every element is zero. The below code will create an array with 3 rows and 4 columns, where every element is 0, using numpy.zeros:
import numpy as np
empty_array = np.zeros((3,4))
empty_array
It’s useful to create an array with all zero elements in cases when you need an array of fixed size, but don’t have any values for it yet.
Cited from: https://www.dataquest.io/blog/numpy-tutorial-python/
https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html
https://www.dataquest.io/blog/numpy-tutorial-python/
https://www.tutorialspoint.com/numpy/index.htm