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.
The data is in what I’m going to call ssv (semicolon separated values) format — each record is separated by a semicolon (;), and rows are separated by a new line. There are 1600 rows in the file, including a header row, and 12 columns.
Creating Array:
- Array creation:
import numpy as np new_array = np.array([1, 1, 1]) array([1, 1, 1]) new_arange = np.arange(10) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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 manne
Code Consoles:
provide transient scratchpads for running code interactively, with full support for rich output.
Kernel-backed documents:
enable code in any text file (Markdown, Python, R, LaTeX, etc.) to be run interactively in any Jupyter kernel.
Notebook cell: outputs can be mirrored into their own tab, side by side with the notebook, enabling simple dashboards with interactive controls backed by a kernel.
Multiple views of documents with different editors or viewers enable live editing of documents reflected in other viewers. For example, it is easy to have live preview of Markdown, Delimiter-separated Values, or Vega/Vega-Lite documents.
Resources:
Done by Omar-zoubi