Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python Data Science Essentials
  • Table Of Contents Toc
  • Feedback & Rating feedback
Python Data Science Essentials

Python Data Science Essentials

By : Alberto Boschetti
4.2 (6)
Buy this Book
close
close
Python Data Science Essentials

Python Data Science Essentials

4.2 (6)
By: Alberto Boschetti
Buy this Book

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (8 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. First Steps
Icon
1. First Steps
Icon
Introducing data science and Python
Icon
Installing Python
Icon
Scientific distributions
Icon
Introducing IPython
Icon
Summary
2
2. Data Munging
Icon
2. Data Munging
Icon
The data science process
Icon
Data loading and preprocessing with pandas
Icon
Working with categorical and textual data
Icon
Data processing with NumPy
Icon
Creating NumPy arrays
Icon
NumPy fast operation and computations
Icon
Summary
3
3. The Data Science Pipeline
Icon
3. The Data Science Pipeline
Icon
Introducing EDA
Icon
Feature creation
Icon
Dimensionality reduction
Icon
The detection and treatment of outliers
Icon
Scoring functions
Icon
Testing and validating
Icon
Cross-validation
Icon
Hyper-parameters' optimization
Icon
Feature selection
Icon
Summary
4
4. Machine Learning
Icon
4. Machine Learning
Icon
Linear and logistic regression
Icon
Naive Bayes
Icon
The k-Nearest Neighbors
Icon
Advanced nonlinear algorithms
Icon
Ensemble strategies
Icon
A peek into Natural Language Processing (NLP)
Icon
An overview of unsupervised learning
Icon
Summary
5
5. Social Network Analysis
chevron up
Icon
5. Social Network Analysis
Icon
Introduction to graph theory
Icon
Graph algorithms
Icon
Graph loading, dumping, and sampling
Icon
Summary
6
6. Visualization
Icon
6. Visualization
Icon
Introducing the basics of matplotlib
Icon
Selected graphical examples with pandas
Icon
Advanced data learning representation
Icon
Summary
7
Index
Icon
Index

Graph algorithms

To get insights from graphs, many algorithms have been developed. In this chapter, we'll use a well-known graph in NetworkX, the Krackhardt Kite graph. It is a dummy graph containing 10 nodes, and it is typically used to proof graph algorithms. 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()
Graph algorithms

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

Note

Note that in a directed graph, you must follow the link's directions.

If at least a path exists, the shortest path between the two nodes is the one with the shortest collection of traversed nodes.

In NetworkX, checking whether a path exists between two...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Python Data Science Essentials
End of Section 5
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon