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Python Data Science Essentials
Python Data Science Essentials

Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques , Third Edition

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Profile Icon Alberto Boschetti Profile Icon Luca Massaron
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Paperback Sep 2018 472 pages 3rd Edition
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Paperback Sep 2018 472 pages 3rd Edition
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Python Data Science Essentials

Data Munging

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

The term mung is a technical term that was coined about half a century ago by students of at 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.

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

  • The data science process (so that you'll know what is going on and what's next)
  • Uploading data from a file
  • Selecting the data you need
  • Cleaning up any missing or wrong data
  • Adding, inserting...

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 known as data ingestion). Data ingestion implies a series of possible alternatives, from simply uploading data to assembling it from RDBMS or NoSQL repositories, or from synthetically generating it to scraping it from 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 (if you are clueless about what an API is, you can read a good tutorial about APIs with Python here: https://www.dataquest.io/blog/python-api-tutorial/) providing...

Data loading and preprocessing with pandas

In the previous chapter, we discussed where to find useful datasets and examined the 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 functionality 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...

Working with categorical and textual 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.

The other type of data you might see in your career is the categorical type. A categorical datum expresses an attribute that cannot be measured and assumes...

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...

Creating NumPy arrays

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

  • By transforming an existing data structure into an array
  • By creating an array from scratch and populating it with default or calculated values
  • By 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 transforming a list into an array.

When operating such a transformation, it is important to consider the objects the lists contain because this will determine...

NumPy fast operation and computations

When arrays need to be manipulated by mathematical operations, you just need to apply the operation on the array with respect to a numerical constant (a scalar), or an array of the same shape:

In: import numpy as np
a = np.arange(5).reshape(1,5)
a += 1
a*a

Out: array([[ 1, 4, 9, 16, 25]])

As a result, the operation is to be performed element-wise; that is, every element of the array is operated by either the scalar value or the corresponding element of the other array.

When operating on arrays of different dimensions, it is still possible to obtain element-wise operations without having to restructure the data if one of the corresponding dimensions is 1. In fact, in such a case, the dimension of size 1 is stretched until it matches the dimension of the corresponding array. This conversion is called broadcasting.

For instance:

...

Summary

In this chapter, we discussed how pandas and NumPy can provide you with all the tools to load and effectively mung your data.

We started with pandas and its data structures, DataFrames and series, and went through to the final NumPy two-dimensional arrays with a data structure suitable for subsequent experimentation and machine learning. In doing so, we touched upon subjects such as the manipulation of vectors and matrices, categorical data encoding, textual data processing, fixing missing data and errors, slicing and dicing, merging, and stacking.

pandas and NumPy surely offer many more functions than the essential building blocks we presented here, as well as the commands and procedures illustrated. You can now take any available raw data and apply all the cleaning and shaping transformations necessary for your data science project.

In the next chapter, we will take...

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Key benefits

  • •A one-stop guide to Python libraries such as pandas and NumPy
  • •Comprehensive coverage of data science operations such as data cleaning and data manipulation
  • •Choose scalable learning algorithms for your data science tasks

Description

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

Who is this book for?

If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

What you will learn

  • • Set up your data science toolbox on Windows, Mac, and Linux
  • • Use the core machine learning methods offered by the scikit-learn library
  • • Manipulate, fix, and explore data to solve data science problems
  • • Learn advanced explorative and manipulative techniques to solve data operations
  • • Optimize your machine learning models for optimized performance
  • • Explore and cluster graphs, taking advantage of interconnections and links in your data
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Table of Contents

10 Chapters
First Steps Chevron down icon Chevron up icon
Data Munging Chevron down icon Chevron up icon
The Data Pipeline Chevron down icon Chevron up icon
Machine Learning Chevron down icon Chevron up icon
Visualization, Insights, and Results Chevron down icon Chevron up icon
Social Network Analysis Chevron down icon Chevron up icon
Deep Learning Beyond the Basics Chevron down icon Chevron up icon
Spark for Big Data Chevron down icon Chevron up icon
Strengthen Your Python Foundations Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Esta serie de Packt sobre ciencia de datos ha sido muy útil por su fácil lenguaje y ejercicios entendibles.
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