Python data analysis : data manipulation and complex data analysis with python / Armando Fandango.

Format:

Book

Author/Creator:

Fandango, Armando, author.

Language:

English

Subjects (All):

Electronic data processing.

Information visualization--Data processing.

Information visualization.

Punched card systems.

Physical Description:

1 online resource (320 pages) : illustrations (some color), graphs

Edition:

Second edition.

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

text file

Biography/History:

Idris Ivan: Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1. 5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

Summary:

Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book 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. Who This Book Is 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 In Detail 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. Style and approach The book takes a very comprehensive approach to enha...

Contents:

Cover

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Getting Started with Python Libraries

Installing Python 3

Installing data analysis libraries

On Linux or Mac OS X

On Windows

Using IPython as a shell

Reading manual pages

Jupyter Notebook

NumPy arrays

A simple application

Where to find help and references

Listing modules inside the Python libraries

Visualizing data using Matplotlib

Summary

Chapter 2: NumPy Arrays

The NumPy array object

Advantages of NumPy arrays

Creating a multidimensional array

Selecting NumPy array elements

NumPy numerical types

Data type objects

Character codes

The dtype constructors

The dtype attributes

One-dimensional slicing and indexing

Manipulating array shapes

Stacking arrays

Splitting NumPy arrays

NumPy array attributes

Converting arrays

Creating array views and copies

Fancy indexing

Indexing with a list of locations

Indexing NumPy arrays with Booleans

Broadcasting NumPy arrays

References

Chapter 3: The Pandas Primer

Installing and exploring Pandas

The Pandas DataFrames

The Pandas Series

Querying data in Pandas

Statistics with Pandas DataFrames

Data aggregation with Pandas DataFrames

Concatenating and appending DataFrames

Joining DataFrames

Handling missing values

Dealing with dates

Pivot tables

Chapter 4: Statistics and Linear Algebra

Basic descriptive statistics with NumPy

Linear algebra with NumPy

Inverting matrices with NumPy

Solving linear systems with NumPy

Finding eigenvalues and eigenvectors with NumPy

NumPy random numbers

Gambling with the binomial distribution

Sampling the normal distribution.

Performing a normality test with SciPy

Creating a NumPy masked array

Disregarding negative and extreme values

Chapter 5: Retrieving, Processing, and Storing Data

Writing CSV files with NumPy and Pandas

The binary .npy and pickle formats

Storing data with PyTables

Reading and writing Pandas DataFrames to HDF5 stores

Reading and writing to Excel with Pandas

Using REST web services and JSON

Reading and writing JSON with Pandas

Parsing RSS and Atom feeds

Parsing HTML with Beautiful Soup

Reference

Chapter 6: Data Visualization

The matplotlib subpackages

Basic matplotlib plots

Logarithmic plots

Scatter plots

Legends and annotations

Three-dimensional plots

Plotting in Pandas

Lag plots

Autocorrelation plots

Plot.ly

Chapter 7: Signal Processing and Time Series

The statsmodels modules

Moving averages

Window functions

Defining cointegration

Autocorrelation

Autoregressive models

ARMA models

Generating periodic signals

Fourier analysis

Spectral analysis

Filtering

Chapter 8: Working with Databases

Lightweight access with sqlite3

Accessing databases from Pandas

SQLAlchemy

Installing and setting up SQLAlchemy

Populating a database with SQLAlchemy

Querying the database with SQLAlchemy

Pony ORM

Dataset - databases for lazy people

PyMongo and MongoDB

Storing data in Redis

Storing data in memcache

Apache Cassandra

Chapter 9: Analyzing Textual Data and Social Media

Installing NLTK

About NLTK

Filtering out stopwords, names, and numbers

The bag-of-words model

Analyzing word frequencies

Naive Bayes classification

Sentiment analysis

Creating word clouds

Social network analysis

Chapter 10: Predictive Analytics and Machine Learning.

Preprocessing

Classification with logistic regression

Classification with support vector machines

Regression with ElasticNetCV

Support vector regression

Clustering with affinity propagation

Mean shift

Genetic algorithms

Neural networks

Decision trees

Chapter 11: Environments Outside the Python Ecosystem and Cloud Computing

Exchanging information with Matlab/Octave

Installing rpy2 package

Interfacing with R

Sending NumPy arrays to Java

Integrating SWIG and NumPy

Integrating Boost and Python

Using Fortran code through f2py

PythonAnywhere Cloud

Chapter 12: Performance Tuning, Profiling, and Concurrency

Profiling the code

Installing Cython

Calling C code

Creating a process pool with multiprocessing

Speeding up embarrassingly parallel for loops with Joblib

Comparing Bottleneck to NumPy functions

Performing MapReduce with Jug

Installing MPI for Python

IPython Parallel

Appendix A: Key Concepts

Appendix B: Useful Functions

Matplotlib

NumPy

Pandas

Scikit-learn

SciPy

scipy.fftpack

scipy.signal

scipy.stats

Appendix C: Online Resources

Index.

Notes:

Includes index.

Includes bibliographical references.

Description based on online resource; title from PDF title page (ebrary, viewed April 11, 2017).

OCLC:

980850611