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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
5
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

Creating NumPy arrays

There is more than one way to create NumPy arrays. The following are some of the ways:

  • Transforming an existing data structure into an array
  • Creating an array from scratch and populating it with default or calculated values
  • Uploading some data from a disk into an array

If you are going to transform an existing data structure, the odds are in favor of you working with a structured list or a pandas DataFrame.

From lists to unidimensional arrays

One of the most common situations you will encounter when working with data is the transforming of a list into an array.

When operating such a transformation, it is important to consider the objects the lists contain because this will determine the dimensionality and the dtype of the resulting array.

Let's start with the first example of a list containing just integers:

In: import numpy as np
In:  # Transform a list into a uni-dimensional array
list_of_ints = [1,2,3]
Array_1 = np.array(list_of_ints)
In: Array_1
Out: array([1, 2, 3...
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Tech Concepts
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Programming languages
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Python Data Science Essentials
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