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Execution order appears to the left. All code runs as one file.
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https://www.dataquest.io/blog/numpy-tutorial-python/
What are the key features and benefits of Jupyter Lab, and how does it differ from Jupyter Notebook?
Jupyter Notebooks has the great ability to allow for note taking and to run code within the same document. Jupyter supports many file formats. This allows for a great level of integration with other work and code.
What are the main functionalities provided by the NumPy library, and how can it be useful in Python programming, particularly for scientific computing and data manipulation tasks?
The main functionalities is its ability to build and manipulate multi-dimentional arrays. In additon, there is a lot of high-level mathematical function support. This allows for massive data computations.
Explain the basic structure and properties of NumPy arrays, and provide examples of how to create, manipulate, and perform operations on them.
Structures of arrays that are supported are 1-D, 2-D, and N-D (multi-dimentional). The syntax is as follows to set the dimentions:
1D - shape: (4,) - Columns four cells 2D - shape: (2,3) - Rows two cells, Columns three cells 3D - shape: (4, 3, 2) - Rows four cells, Columns three cells, Slices two cells
Data is stored as matrixes, array of arrays.
import numpy as np
a = np.array([1, 2, 3]) # 1D array
b = np.array([[1, 2, 3], [4, 5, 6]]) # 2D array
g = np.reshape(a, (3, 1)) # Reshape 'a' to a 3x1 array
h = np.concatenate((a, e)) # Concatenate arrays 'a' and 'e'
Some operations include “random”, “genfromtxt”, “sum”, and more.
Some more solid use cases for Numpy.