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Home > Data > Data Science > Python Data Science Essentials
Python Data Science Essentials
Python Data Science Essentials

Python Data Science Essentials: Learn the fundamentals of Data Science with Python , Second Edition

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Python Data Science Essentials

Chapter 2. Data Munging

We are just getting into action with data! In this chapter, you'll 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 more useful) one. Deep-rooted in hacker culture, munging is often described in the data science pipeline using other, almost synonymous, terms such as data wrangling or data preparation. It is a very important part of the data engineering pipeline.

Starting from this chapter, we will start mentioning more jargon and technicalities taken from the fields of probability and statistics (such as probability distributions, descriptive statistics, and hypothesis testing). Unfortunately, we cannot explain all of them in detail since our main purpose is to provide you...

The data science process

Although every data science project is different, for our illustrative purposes, we can partition an ideal data science project into a series of reduced and simplified phases.

The process starts by obtaining data (a phase know as data ingestion or data acquisition), and as such implies a series of possible alternatives, from simply uploading data to assembling it from RDBMS or NoSQL repositories, or synthetically generating it or scraping it from the web APIs or HTML pages.

Especially when faced with novel challenges, uploading data can reveal itself as a critical part of a data scientist's work. Your data can arrive from multiple sources: databases, CSV or Excel files, raw HTML, images, sound recordings, APIs (https://en.wikipedia.org/wiki/Application_programming_interface) providing JSON files, and so on. Given the wide range of alternatives, we will just briefly touch upon this aspect by offering the basic tools to get your data (even if it is too big...

Data loading and preprocessing with pandas

In the previous chapter, we discussed where to find useful datasets and examined basic import commands of Python packages. In this section, having kept your toolbox ready, you are about to learn how to structurally load, manipulate, process, and polish data using pandas and NumPy.

Fast and easy data loading

Let's start with a CSV file and pandas. The pandas library offers the most accessible and complete function to load tabular data from a file (or a URL). By default, it will store data in a specialized pandas data structure, index each row, separate variables by custom delimiters, infer the right data type for each column, convert data (if necessary), as well as parse dates, missing values, and erroneous values.

In: import pandas as pd
iris_filename = 'datasets-uci-iris.csv'
iris = pd.read_csv(iris_filename, sep=',', decimal='.', header=None,
names= ['sepal_length', 'sepal_width', 'petal_length...

Working with categorical and text data

Typically, you'll find yourself dealing with two main kinds of data: categorical and numerical. Numerical data, such as temperature, amount of money, days of usage, or house number, can be composed of either floating-point numbers (such as 1.0, -2.3, 99.99, and so on) or integers (such as -3, 9, 0, 1, and so on). Each value that the data can assume has a direct relation with others since they're comparable. In other words, you can say that a feature with a value of 2.0 is greater (actually, it is double) than a feature that assumes a value of 1.0. This type of data is very well-defined and comprehensible, with binary operators such as equal to, greater than, and less than.

Note

A key aspect of numerical data is that basic stats are meaningful for it (for example, averages). This does not apply to any other category, making it an important characteristic of this data type

The other type of data you might see in your career is the categorical...

Data processing with NumPy

Having introduced the essential pandas commands to upload and preprocess your data in memory completely, in smaller batches, or even in single data rows, at this point of the data science pipeline you'll have to work on it in order to prepare a suitable data matrix for your supervised and unsupervised learning procedures.

As a best practice, we advise that you divide the task between a phase of your work when your data is still heterogeneous (a mix of numerical and symbolic values) and another phase when it is turned into a numeric table of data. A table of data, or matrix, is arranged in rows that represent your examples, and columns that contain the characteristic observed values of your examples, which are your variables.

Following our advice, you have to wrangle between two key Python packages for scientific analysis, pandas and NumPy, and their two pivotal data structures, DataFrame and ndarray. But your data science pipeline will be more efficient and...

The data science process

Although every data science project is different, for our illustrative purposes, we can partition an ideal data science project into a series of reduced and simplified phases.

The process starts by obtaining data (a phase know as data ingestion or data acquisition), and as such implies a series of possible alternatives, from simply uploading data to assembling it from RDBMS or NoSQL repositories, or synthetically generating it or scraping it from the web APIs or HTML pages.

Especially when faced with novel challenges, uploading data can reveal itself as a critical part of a data scientist's work. Your data can arrive from multiple sources: databases, CSV or Excel files, raw HTML, images, sound recordings, APIs (https://en.wikipedia.org/wiki/Application_programming_interface) providing JSON files, and so on. Given the wide range of alternatives, we will just briefly touch upon this aspect by offering the basic tools to get your data (even if it is too big) into your...

Data loading and preprocessing with pandas

In the previous chapter, we discussed where to find useful datasets and examined basic import commands of Python packages. In this section, having kept your toolbox ready, you are about to learn how to structurally load, manipulate, process, and polish data using pandas and NumPy.

Fast and easy data loading

Let's start with a CSV file and pandas. The pandas library offers the most accessible and complete function to load tabular data from a file (or a URL). By default, it will store data in a specialized pandas data structure, index each row, separate variables by custom delimiters, infer the right data type for each column, convert data (if necessary), as well as parse dates, missing values, and erroneous values.

In: import pandas as pd
iris_filename = 'datasets-uci-iris.csv'
iris = pd.read_csv(iris_filename, sep=',', decimal='.', header=None,
names= ['sepal_length', 'sepal_width', 'petal_length',
    'petal_width',
'target'])

You can specify...

Working with categorical and text data

Typically, you'll find yourself dealing with two main kinds of data: categorical and numerical. Numerical data, such as temperature, amount of money, days of usage, or house number, can be composed of either floating-point numbers (such as 1.0, -2.3, 99.99, and so on) or integers (such as -3, 9, 0, 1, and so on). Each value that the data can assume has a direct relation with others since they're comparable. In other words, you can say that a feature with a value of 2.0 is greater (actually, it is double) than a feature that assumes a value of 1.0. This type of data is very well-defined and comprehensible, with binary operators such as equal to, greater than, and less than.

Note

A key aspect of numerical data is that basic stats are meaningful for it (for example, averages). This does not apply to any other category, making it an important characteristic of this data type

The other type of data you might see in your career is the categorical type (also...

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Key benefits

  • Quickly get familiar with data science using Python 3.5
  • Save time (and effort) with all the essential tools explained
  • Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience

Description

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of 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.

Who is this book for?

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

What you will learn

  • * Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
  • * Get data ready for your data science project
  • * Manipulate, fix, and explore data in order to solve data science problems
  • * Set up an experimental pipeline to test your data science hypotheses
  • * Choose the most effective and scalable learning algorithm for your data science tasks
  • * Optimize your machine learning models to get the best performance
  • * Explore and cluster graphs, taking advantage of interconnections and links in your data

Product Details

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Last updated date : Feb 11, 2025
Publication date : Oct 28, 2016
Length: 378 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786462831
Category :
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Languages :
Python
Concepts :
Data Science

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Last updated date : Feb 11, 2025
Publication date : Oct 28, 2016
Length: 378 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786462831
Category :
Data
Languages :
Python
Concepts :
Data Science

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Table of Contents

7 Chapters
1. First Steps Chevron down icon Chevron up icon
1. First Steps
Introducing data science and Python
Installing Python
Introducing Jupyter
Datasets and code used in the book
Summary
2. Data Munging Chevron down icon Chevron up icon
2. Data Munging
The data science process
Data loading and preprocessing with pandas
Working with categorical and text data
Data processing with NumPy
Creating NumPy arrays
NumPy's fast operations and computations
Summary
3. The Data Pipeline Chevron down icon Chevron up icon
3. The Data Pipeline
Introducing EDA
Building new features
Dimensionality reduction
The detection and treatment of outliers
Validation metrics
Testing and validating
Cross-validation
Hyperparameter optimization
Feature selection
Wrapping everything in a pipeline
Summary
4. Machine Learning Chevron down icon Chevron up icon
4. Machine Learning
Preparing tools and datasets
Linear and logistic regression
Naive Bayes
K-Nearest Neighbors
Nonlinear algorithms
Ensemble strategies
Dealing with big data
Approaching deep learning
A peek at Natural Language Processing (NLP)
An overview of unsupervised learning
Summary
5. Social Network Analysis Chevron down icon Chevron up icon
5. Social Network Analysis
Introduction to graph theory
Graph algorithms
Graph loading, dumping, and sampling
Summary
6. Visualization, Insights, and Results Chevron down icon Chevron up icon
6. Visualization, Insights, and Results
Introducing the basics of matplotlib
Wrapping up matplotlib's commands
Interactive visualizations with Bokeh
Advanced data-learning representations
Summary
1. Strengthen Your Python Foundations Chevron down icon Chevron up icon
1. Strengthen Your Python Foundations
Your learning list
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Customer reviews

Rating distribution
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(3 Ratings)
5 star 66.7%
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2 star 33.3%
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Daniel Jul 31, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for those studying data science. Great for the undergraduate.
Amazon Verified review Amazon
David E. Mar 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very informative. I'm still reading and working some of your examples; learning curve is less high with your book. If other people are interested in Data Science your book should be required reading.
Amazon Verified review Amazon
Daniele Jun 05, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
The content is very poor. Some chapteres even just tell you to find a video online... then why did I get the book?
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About the authors

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Profile icon Alberto Boschetti
Alberto Boschetti
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Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
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Profile icon Luca Massaron
Luca Massaron
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Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.
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Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.

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