Python Data Analysis 2nd (Packt)

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Table of contents Python Data Analysis- Second Edition Credits about the author About the reviewers www.Packtpub.com Why subscribe? Customer feedback Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata rac Questions l. 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 upyter Notebook NumPy arrays A simple application Where to find help and references Listing modules inside the Python libraries Visualizing data using Matplotlib S ummar NumPy Arrays The NumPy array object Advantages of NumPy arrays Creating a mul tidimensional array Selecting numpy array elements NumPy numerical types Data type obiects 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 ummary References 3. The pandas primer Installing and exploring Pandas The Pandas data frames The pandas series Querying data in Pandas Statistics with pandas data frames Data aggregation with Pandas DataFrames Concatenating and appending dataframes Joining dataframes Handling missing values Dealing with dates Pivot tables Summary References 4. Statistics and Linear Algebra Basic descriptive statistics with NumPy Linear algebra with NumPy Inverting matrices with NumP 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 Scip Creating a NumPy masked array Disregarding negative and extreme values Summar 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 Data Frames 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 Summar Reference 6. Data visualization The matplotlib subpackages Basic matplotlib plots Logarithmic plots Scatter plots Legends and annotations Three-dimensional plots Plotting in pandas Lag plots ots Autocorrelation plot Plot. I Summary 7. Signal Processing and Time Series The statsmodels modules Moving averages Window functions Defining cointegration Autocorrelation Autoregressive models ARMA mode Generating periodic signals Fourier analysis Spectral analysis Filtering ummary 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 △ pache Cassandra Summary 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 Summar 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 S ummar 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 Python Anyw here Cloud Summary 12. Performance Tuning, Profiling, and Concurrency Profiling the code Installing Cython Calling 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 Summary K ey concepts B. Useful functions Matplotlib NumPy andas Scikit-learn SciP scipy. ffupack scipy signal scipystats C. Online resources Python data analysis- Second edition Python data analysis- Second edition Copyright C 2017 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: March 2017 Production reference: 1230317 Published by packt Publishing ltd avery place 35 Livery Street Birmingham B3 2PB. UK ISBN978-1-78712-748-7 www.packtpub.com Credits Author Copy Editor Armando fandango Safis Editing Revie wers Project Coordinator Joran Basle Shweta H Birwatkar Ratan Kumar Commis sioning Editor Proofreader Amey Varangoankar Safis Editing Acquisition editor Indexer Tushar Gupta A ishwarya Gangawane Content Development EditorGraphics ∧ mrita noronha Tania dutta Technical Editor Production Coordinator Deepti tuscano Arvindkumar Gupta

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