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Getting Started with Python Data Analysis (Paperback)
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Getting Started with Python Data Analysis (Paperback)
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Learn to use powerful Python libraries for effective data
processing and analysis About This Book * Learn the basic
processing steps in data analysis and how to use Python in this
area through supported packages, especially Numpy, Pandas, and
Matplotlib * Create, manipulate, and analyze your data to extract
useful information to optimize your system * A hands-on guide to
help you learn data analysis using Python Who This Book Is For If
you are a Python developer who wants to get started with data
analysis and you need a quick introductory guide to the python data
analysis libraries, then this book is for you. What You Will Learn
* Understand the importance of data analysis and get familiar with
its processing steps * Get acquainted with Numpy to use with arrays
and array-oriented computing in data analysis * Create effective
visualizations to present your data using Matplotlib * Process and
analyze data using the time series capabilities of Pandas *
Interact with different kind of database systems, such as file,
disk format, Mongo, and Redis * Apply the supported Python package
to data analysis applications through examples * Explore predictive
analytics and machine learning algorithms using Scikit-learn, a
Python library In Detail Data analysis is the process of applying
logical and analytical reasoning to study each component of data.
Python is a multi-domain, high-level, programming language. It's
often used as a scripting language because of its forgiving syntax
and operability with a wide variety of different eco-systems.
Python has powerful standard libraries or toolkits such as Pylearn2
and Hebel, which offers a fast, reliable, cross-platform
environment for data analysis. With this book, we will get you
started with Python data analysis and show you what its advantages
are. The book starts by introducing the principles of data analysis
and supported libraries, along with NumPy basics for statistic and
data processing. Next it provides an overview of the Pandas package
and uses its powerful features to solve data processing problems.
Moving on, the book takes you through a brief overview of the
Matplotlib API and some common plotting functions for DataFrame
such as plot. Next, it will teach you to manipulate the time and
data structure, and load and store data in a file or database using
Python packages. The book will also teach you how to apply powerful
packages in Python to process raw data into pure and helpful data
using examples. Finally, the book gives you a brief overview of
machine learning algorithms, that is, applying data analysis
results to make decisions or build helpful products, such as
recommendations and predictions using scikit-learn. Style and
approach This is an easy-to-follow, step-by-step guide to get you
familiar with data analysis and the libraries supported by Python.
Topics are explained with real-world examples wherever required.
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