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  • Book Overview & Buying Python Data Science Essentials
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Python Data Science Essentials

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron
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Python Data Science Essentials

Python Data Science Essentials

5 (2)
By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
Table of Contents (11 chapters)
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Graph algorithms

To get insights from graphs, many algorithms have been developed. In this chapter, we'll use a well-known graph in NetworkX, that is, the Krackhardt Kite graph. It is a dummy graph containing 10 nodes, and it is typically used to proof graph algorithms. David Krackhardt is the creator of the structure, which has the shape of a kite. It's composed of two different zones. In the first zone (composed of nodes 0 to 6), the nodes are interlinked; in the other zone (nodes 7 to 9), they are connected as a chain:

In: G = nx.krackhardt_kite_graph()
nx.draw_networkx(G)
plt.show()

In the following plot, you can examine the Krackhardt Kite's graph structure:

Let's start with connectivity. Two nodes of a graph are connected if there is at least a path (that is, a sequence of nodes) between them.

If at least one path exists, the shortest path between...

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Python Data Science Essentials
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