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Python Data Science Essentials

Python Data Science Essentials

By : Alberto Boschetti
4.2 (6)
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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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. First Steps
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1. First Steps
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Introducing data science and Python
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Installing Python
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Scientific distributions
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Introducing IPython
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Summary
2
2. Data Munging
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2. Data Munging
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The data science process
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Data loading and preprocessing with pandas
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Working with categorical and textual data
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Data processing with NumPy
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Creating NumPy arrays
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NumPy fast operation and computations
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Summary
3
3. The Data Science Pipeline
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3. The Data Science Pipeline
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Introducing EDA
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Feature creation
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Dimensionality reduction
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The detection and treatment of outliers
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Scoring functions
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Testing and validating
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Cross-validation
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Hyper-parameters' optimization
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Feature selection
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Summary
4
4. Machine Learning
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4. Machine Learning
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Linear and logistic regression
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Naive Bayes
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The k-Nearest Neighbors
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Advanced nonlinear algorithms
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Ensemble strategies
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A peek into Natural Language Processing (NLP)
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An overview of unsupervised learning
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Summary
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5. Social Network Analysis
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5. Social Network Analysis
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Introduction to graph theory
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Graph algorithms
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Graph loading, dumping, and sampling
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Summary
6
6. Visualization
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6. Visualization
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Introducing the basics of matplotlib
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Selected graphical examples with pandas
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Advanced data learning representation
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Summary
7
Index
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Index

Chapter 2. Data Munging

We are just getting into action! In this new chapter, you'll essentially learn how to munge data. What does munging data imply?

The term munge is a technical term coined about half a century ago by the students of the Massachusetts Institute of Technology (MIT). Munging means to change, in a series of well-specified and reversible steps, a piece of original data to a completely different (and hopefully a more useful) one. Deep-rooted in hacker culture, munging is often heard in data science processes in parallel with other almost completely synonymous terms, such as data wrangling and data preparation.

Given such premises, in this chapter, the following topics will be covered:

  • The data science process (so you'll know what is going on and what's next)
  • Uploading data from a file
  • Selecting the data you need
  • Cleaning up missing or wrong data
  • Adding, inserting, and deleting data
  • Grouping and transforming data to obtain new, meaningful information
  • Managing...
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Tech Concepts
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Programming languages
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Python Data Science Essentials
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