Mastering pandas : master the features and capabilities of pandas, a data analysis toolkit for Python / Femi Anthony.Material type: TextSeries: Community experience distilledPublisher: Birmingham, UK : Packt Publishing, 2015Description: 1 online resource (1 volume) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781783981977; 1783981970Subject(s): Python (Computer program language) | Data structures (Computer science) | Statistics -- Computer programs | Quantitative research | COMPUTERS -- Programming Languages -- C# | COMPUTERS -- Programming Languages -- Java | COMPUTERS -- Programming Languages -- Pascal | Data structures (Computer science) | Python (Computer program language) | Quantitative research | Statistics -- Computer programsGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Mastering pandas : master the features and capabilities of pandas, a data analysis toolkit for Python.DDC classification: 005.133 LOC classification: QA76.73.P98Online resources: Click here to access online
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Online resource; title from cover (Safari, viewed July 6, 2015).
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to pandas and Data Analysis; Motivation for data analysis; We live in a big data world; 4 V's of big data; Volume of big data; Velocity of big data; Variety of big data; Veracity of big data; So much data, so little time for analysis; The move towards real-time analytics; How Python and pandas fit into the data analytics mix; What is pandas; Benefits of using pandas; Summary; Chapter 2: Installation of pandas and Supporting Software
Selecting a version of Python to usePython installation; Linux; Installing Python from compressed tarball; Windows; Core Python installation; Third-party Python software install; Mac OS/X; Installation using a package manager; Installation of Python and pandas from a third-party vendor; Continuum Analytics Anaconda; Installing Anaconda; Linux; Mac OS/X; Windows; Final step for all platforms; Other numeric or analytics-focused Python distributions; Downloading and installing pandas; Linux; Ubuntu/Debian; Red Hat; Ubuntu/Debian; Fedora; OpenSuse; Mac; Source installation; Binary installation
WindowsBinary Installation; Source installation; IPython; IPython Notebook; IPython installation; Linux; Windows; Mac OS/X; Install via Anaconda (for Linux/Mac OS/X); Wakari by Continuum Analytics; Virtualenv; Virtualenv installation and usage; Summary; Chapter 3: The pandas Data Structures; NumPy ndarrays; NumPy array creation; NumPy arrays via numpy.array; NumPy array via numpy.arange; NumPy array via numpy.linspace; NumPy array via various other functions; NumPy datatypes; NumPy indexing and slicing; Array slicing; Array masking; Complex indexing; Copies and views; Operations
Basic operationsReduction operations; Statistical operators; Logical operators; Broadcasting; Array shape manipulation; Flattening a multi-dimensional array; Reshaping; Resizing; Adding a dimension; Array sorting; Data structures in pandas; Series; Series creation; Operations on Series; DataFrame; DataFrame Creation; Operations; Panel; Using 3D NumPy array with axis labels; Using a Python dictionary of DataFrame objects; Using the DataFrame.to_panel method; Other operations; Summary; Chapter 4: Operations in Pandas, Part I -- Indexing and Selecting; Basic indexing
Accessing attributes using dot operatorRange slicing; Label, integer, and mixed indexing; Label-oriented indexing; Selection using a Boolean array; Integer-oriented indexing; The .iat and .at operators; Mixed indexing with the .ix operator; Multi-indexing; Swapping and re-ordering levels; Cross-sections; Boolean indexing; The is in and any all methods; Using the where() method; Operations on indexes; Summary; Chapter 5: Operations in pandas, Part II -- Grouping, Merging, and Reshaping of Data; Grouping of data; The groupby operation; Using groupby with a MultiIndex; Using the aggregate method
This book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth.