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Python Data Analysis, Second Edition
Python Data Analysis, Second Edition

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

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Python Data Analysis, Second Edition

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 27, 2017
Length: 330 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787127487
Category :
Languages :
Concepts :

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Product Details

Publication date : Mar 27, 2017
Length: 330 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787127487
Category :
Languages :
Concepts :

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Table of Contents

15 Chapters
1. Getting Started with Python Libraries Chevron down icon Chevron up icon
2. NumPy Arrays Chevron down icon Chevron up icon
3. The Pandas Primer Chevron down icon Chevron up icon
4. Statistics and Linear Algebra Chevron down icon Chevron up icon
5. Retrieving, Processing, and Storing Data Chevron down icon Chevron up icon
6. Data Visualization Chevron down icon Chevron up icon
7. Signal Processing and Time Series Chevron down icon Chevron up icon
8. Working with Databases Chevron down icon Chevron up icon
9. Analyzing Textual Data and Social Media Chevron down icon Chevron up icon
10. Predictive Analytics and Machine Learning Chevron down icon Chevron up icon
11. Environments Outside the Python Ecosystem and Cloud Computing Chevron down icon Chevron up icon
12. Performance Tuning, Profiling, and Concurrency Chevron down icon Chevron up icon
A. Key Concepts Chevron down icon Chevron up icon
B. Useful Functions Chevron down icon Chevron up icon
C. Online Resources Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(4 Ratings)
5 star 50%
4 star 25%
3 star 0%
2 star 25%
1 star 0%
sutha May 06, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
AJ Apr 27, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Bought this book to lay the foundations for Data Analysis and apply to Geospatial datasets- its worth it!
Amazon Verified review Amazon
boots with the fur Apr 05, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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.
Amazon Verified review Amazon
Anand Nov 25, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Good but the author jumps from one topic to another without covering by leaving the user in confusion.
Amazon Verified review Amazon
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