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Python Data Analytics [electronic resource] : Data Analysis and Science using pandas, matplotlib and the Python Programming Language / by Fabio Nelli.

Author/Creator:
Nelli, Fabio. author., Author,
Other Title:
Data analysis and science using Pandas, Matplotlib and the Python programming language
Edition:
1st ed. 2015.
Publication:
Berkeley, CA : Apress : Imprint: Apress, 2015.
Series:
Expert's voice in Python.
Expert's Voice in Python
Format/Description:
Book
1 online resource (350 p.)
Subjects:
Python (Computer program language).
Computer science.
Information systems.
Local subjects:
Python.
Programming Languages, Compilers, Interpreters.
Computer Appl. in Administrative Data Processing.
Form/Genre:
Electronic books.
Language:
English
System Details:
text file
Summary:
Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics.  This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs.
Contents:
Contents at a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: An Introduction to Data Analysis; Data Analysis; Knowledge Domains of the Data Analyst; Computer Science ; Mathematics and Statistics ; Machine Learning and Artificial Intelligence ; Professional Fields of Application ; Understanding the Nature of the Data; When the Data Become Information ; When the Information Becomes Knowledge ; Types of Data ; The Data Analysis Process ; Problem Definition ; Data Extraction ; Data Preparation ; Data Exploration/Visualization
Predictive Modeling Model Validation ; Deployment ; Quantitative and Qualitative Data Analysis ; Open Data ; Python and Data Analysis ; Conclusions; Chapter 2: Introduction to the Python's World; Python-The Programming Language; Python-The Interpreter; Cython; Jython; PyPy; Python 2 and Python 3; Installing Python; Python Distributions; Anaconda; Enthought Canopy; Python(x,y); Using Python; Python Shell; Run an Entire Program Code; Implement the Code Using an IDE; Interact with Python; Writing Python Code; Make Calculations; Import New Libraries and Functions
Data Structure Functional Programming (Only for Python 3.4); Indentation; IPython; IPython Shell; IPython Qt-Console; IPython Notebook; The Jupyter Project; PyPI-The Python Package Index; The IDEs for Python; IDLE (Integrated DeveLopment Environment); Spyder; Eclipse (pyDev); Sublime; Liclipse; NinjaIDE; Komodo IDE; SciPy; NumPy; Pandas; matplotlib; Conclusions; Chapter 3: The NumPy Library; NumPy: A Little History; The NumPy Installation; Ndarray: The Heart of the Library; Create an Array; Types of Data; The dtype Option; Intrinsic Crea tion of an Array
Basic Operations Arit hmetic Operators; The M atrix Product; Increm ent and Decrement Operators; Universal Functions (ufunc); Aggregat e Functions; Indexing, Slicing, and Iterating; Indexing; Slicing; Iterating an Array; Conditions an d Boolean Arrays; Shape Manipulation; Array Manipulation; Joining Arrays; Splitting Arrays; General Concepts; Copies or Views of Objects; Vectorization; Broadcasting; Structured Arrays; Reading and Writing Array Data on Files; Loading and Saving Data in Binary Files; Reading File with T abular Data; Conclusions
Chapter 4: The pandas Library-An Introduction pandas: The Python Data Analysis Library; Installation; Installation from Anaconda; Installation from PyPI; Installation on Linux ; Installation from Source; A Module Repository for Windows ; Test Your pandas Installation; Getting Started with pandas; Introduction to pandas Data Structures; The Series ; Declaring a Series ; Selecting the Internal Elements ; Assigning Values to the Elements; Defining Series from NumPy Arrays and Other Series; Filtering Values ; Operations and Mathematical Functions ; Evaluating Values ; NaN Values
Series as Dictionaries
Notes:
Includes index.
ISBN:
1-4842-0958-3
Publisher Number:
10.1007/978-1-4842-0958-5 doi
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