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.
You can arrange multiple documents and activities side by side in the work area using tabs and splitters. Documents and activities integrate with each other, enabling new workflows for interactive computing, for example:
JupyterLab also offers a unified model for viewing and handling data formats. JupyterLab understands many file formats (images, CSV, JSON, Markdown, PDF, Vega, Vega-Lite, etc.) and can also display rich kernel output in these formats. See File and Output Formats for more information.
To navigate the user interface, JupyterLab offers customizable keyboard shortcuts and the ability to use key maps from vim, emacs, and Sublime Text in the text editor.
JupyterLab extensions can customize or enhance any part of JupyterLab, including new themes, file editors, and custom components.
JupyterLab is served from the same server and uses the same notebook document format as the classic Jupyter Notebook.
Source: 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.
Before using NumPy, we’ll first try to work with the data using Python and the csv package. We can read in the file using the csv.reader object, which will allow us to read in and split up all the content from the ssv file.
With NumPy, we work with multidimensional arrays. We’ll dive into all of the possible types of multidimensional arrays later on, but for now, we’ll focus on 2-dimensional arrays. A 2-dimensional array is also known as a matrix, and is something you should be familiar with. In fact, it’s just a different way of thinking about a list of lists. A matrix has rows and columns. By specifying a row number and a column number, we’re able to extract an element from a matrix.
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.
NumPy is a package for working with multidimensional arrays. One of the most common types of multidimensional arrays is the 1-dimensional array, or vector. As you may have noticed above, when we sliced wines, we retrieved a 1-dimensional array. A 1-dimensional array only needs a single index to retrieve an element. Each row and column in a 2-dimensional array is a 1-dimensional array. Just like a list of lists is analogous to a 2-dimensional array, a single list is analogous to a 1-dimensional array.
This doesn’t happen extremely often, but there are cases when you’ll want to deal with arrays that have greater than 3 dimensions. One way to think of this is as a list of lists of lists. Let’s say we want to store the monthly earnings of a store, but we want to be able to quickly lookup the results for a quarter, and for a year.
NumPy makes it simple to perform mathematical operations on arrays. This is one of the primary advantages of NumPy, and makes it quite easy to do computations.
If you do any of the basic mathematical operations (/, *, -, +, ^) with an array and a value, it will apply the operation to each of the elements in the array.
In addition to the common mathematical operations, NumPy also has several methods that you can use for more complex calculations on arrays. An example of this is the numpy.ndarray.sum method.
NumPy makes it possible to test to see if rows match certain values using mathematical comparison operations like <, >, >=, <=, and ==.
We can change the shape of arrays while still preserving all of their elements. This often can make it easier to access array elements. The simplest reshaping is to flip the axes, so rows become columns, and vice versa. For example, we can accomplish this with the numpy.transpose function.
With NumPy, it’s very common to combine multiple arrays into a single unified array. We can use numpy.vstack to vertically stack multiple arrays. Think of it like the second arrays’s items being added as new rows to the first array.
Source: https://www.dataquest.io/blog/numpy-tutorial-python/