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Key benefits

Find, manipulate, and analyze your data using the Python 3.5 libraries

Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code

An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.

Description

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.

Who is this book for?

This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.

What you will learn

Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms

Prepare and clean your data, and use it for exploratory analysis

Manipulate your data with Pandas

Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5

Visualize your data with open source libraries such as matplotlib, bokeh, and plotly

Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian

Understand signal processing and time series data analysis

Get to grips with graph processing and social network analysis

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Frequently bought together

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sutha May 06, 2017

If you are into data analysis then you have to know python and this book helps to understand various libraries available to use. Also the libraries are well classified by their usage area!Recommended to read...

Amazon Verified review

AJ Apr 27, 2018

Bought this book to lay the foundations for Data Analysis and apply to Geospatial datasets- its worth it!

boots with the fur Apr 05, 2017

I was one of the technical reviewers for this book, it provides a strong foundation to begin analysing and visualizing large datasets in python. If you have wanted to analyze baseball stats and salaries, ala moneyball, or view patterns in anual solar flare events. It also explores learning algorithms for sentiment analysis and predictive patterns. While several chapters could really use a whole book all of their own, this will provide you with the tools that you need in order to begin exploring large data in python, and it even has a chapter to help you tune your performance once your data gets even bigger.

Anand Nov 25, 2017

Good but the author jumps from one topic to another without covering by leaving the user in confusion.

Python Data Analysis, Second Edition: Data manipulation and complex data analysis with Python , Second Edition

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