Python Scipy stack

Works with ordinary python scripting for many publishing activities including text processing, bibliography etc.

Also shares Jupyter interface with Sage, R etc and will be main approach for Mathematical Economics Models.

Jupyter also for Haskell and other languges. Docs (and online trial) here:

http://jupyter.org/documentation

Looks like thorough recent explanation.

General

(Open source_) Blanco-Silva, Francisco J-Learning SciPy for numerical and scientific computing _ a practical tutorial that guarantees fast, accurate, and easy-to-code solutions to your numerical and s.pdf (4MB)

For solving complex problems in mathematics, science, or engineering, SciPy is the solution. Building on your basic knowledge of Python, and using a wealth of examples from many scientific fields, this book is your expert tutor.

Overview
Perform complex operations with large matrices, including eigenvalue problems, matrix decompositions, or solution to large systems of equations. Step-by-step examples to easily implement statistical analysis and data mining that rivals in performance any of the costly specialized software suites. Plenty of examples of state-of-the-art research problems from all disciplines of science, that prove how simple, yet effective, is to provide solutions based on SciPy.
In Detail

It’s essential to incorporate workflow data and code from various sources in order to create fast and effective algorithms to solve complex problems in science and engineering. Data is coming at us faster, dirtier, and at an ever increasing rate. There is no need to employ difficult-to-maintain code, or expensive mathematical engines to solve your numerical computations anymore. SciPy guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing applications.

“Learning SciPy for Numerical and Scientific Computing” unveils secrets to some of the most critical mathematical and scientific computing problems and will play an instrumental role in supporting your research. The book will teach you how to quickly and efficiently use different modules and routines from the SciPy library to cover the vast scope of numerical mathematics with its simplistic practical approach that’s easy to follow.

The book starts with a brief description of the SciPy libraries, showing practical demonstrations for acquiring and installing them on your system. This is followed by the second chapter which is a fun and fast-paced primer to array creation, manipulation, and problem-solving based on these techniques.

What you will learn from this book
Learn to store and manipulate large arrays of data in any dimension. Accurately evaluate any mathematical function in any given dimension, as well as its integration, and solve systems of ordinary differential equations with ease. Learn to deal with sparse data to perform any known interpolation, extrapolation, or regression scheme on it. Perform statistical analysis, hypothesis test design and resolution, or data mining at high level, including clustering (hierarchical or through vector quantization), and learn to apply them to real-life problems. Get to grips with signal processing — filtering audio, images, or video to extract information, features, or removing components. Effectively learn about window functions, filters, spectral theory, LTY systems theory, morphological operations, and image interpolation. Acquaint yourself with the power of distances, Delaunay triangulations, and Voronoi diagrams for computational geometry, and apply them to various engineering problems. Wrap code in other languages directly into your SciPy-based workflow, as well as incorporating data written in proprietary format (audio or image, for example), or from other software suites like Matlab/Octave.
Approach

A step-by-step practical tutorial with plenty of examples on research-based problems from various areas of science, that prove how simple, yet effective, it is to provide solutions based on SciPy.

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title = {Learning SciPy for numerical and scientific computing : a practical tutorial that guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing problems with the power of SciPy and Python},
author = {Blanco-Silva, Francisco J},
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Jaan Kiusalaas-Numerical Methods in Engineering with Python-Cambridge University Press (2010).pdf

Numerical Methods in Engineering with Python is a text for engineering students and a reference for practicing engineers, especially those who wish to explore the power and efficiency of Python. Examples and applications were chosen for their relevance to real world problems, and where numerical solutions are most efficient. Numerical methods are discussed thoroughly and illustrated with problems involving both hand computation and programming. Computer code accompanies each method and is available on the book web site. This code is made simple and easy to understand by avoiding complex bookkeeping schemes, while maintaining the essential features of the method. Python was chosen as the example language because it is elegant, easy to learn and debug, and its facilities for handling arrays are unsurpassed. Moreover, it is an open-source software package; free and available to all students and engineers. Explore numerical methods with Python, a great language for teaching scientific computation.

Table of contents :
Contents……Page 6
Preface……Page 8
1.1 General Information……Page 10
1.2 Core Python……Page 13
1.3 Functions and Modules……Page 25
1.4 Mathematics Modules……Page 26
1.5 numarray Module……Page 28
1.6 Scoping of Variables……Page 32
1.7 Writing and Running Programs……Page 34
2.1 Introduction……Page 36
2.2 Gauss Elimination Method……Page 43
2.3 LU Decomposition Methods……Page 50
2.4 Symmetric and Banded Coefficient Matrices……Page 65
2.5 Pivoting……Page 76
∗2.6 Matrix Inversion……Page 91
∗2.7 Iterative Methods……Page 94
∗2.8 Other Methods……Page 110
3.1 Introduction……Page 112
3.2 Polynomial Interpolation……Page 113
3.3 Interpolation with Cubic Spline……Page 124
3.4 Least-Squares Fit……Page 134
3.5 Other Methods……Page 150
4.1 Introduction……Page 151
4.2 Incremental Search Method……Page 152
4.3 Method of Bisection……Page 154
4.4 Brent’s Method……Page 157
4.5 Newton–Raphson Method……Page 163
4.6 Systems of Equations……Page 167
∗4.7 Zeroes of Polynomials……Page 179
4.8 Other Methods……Page 188
5.1 Introduction……Page 190
5.2 Finite Difference Approximations……Page 191
5.3 Richardson Extrapolation……Page 196
5.4 Derivatives by Interpolation……Page 199
6.1 Introduction……Page 207
6.2 Newton–Cotes Formulas……Page 208
6.3 Romberg Integration……Page 216
6.4 Gaussian Integration……Page 225
∗6.5 Multiple Integrals……Page 242
7.1 Introduction……Page 257
7.2 Taylor Series Method……Page 258
7.3 Runge–Kutta Methods……Page 264
7.4 Stability and Stiffness……Page 281
7.5 Adaptive Runge–Kutta Method……Page 284
7.6 Bulirsch–Stoer Method……Page 292
7.7 Other Methods……Page 303
8.1 Introduction……Page 304
8.2 Shooting Method……Page 305
8.3 Finite Difference Method……Page 319
9.1 Introduction……Page 333
9.2 Jacobi Method……Page 335
9.3 Inverse Power and Power Methods……Page 352
9.4 Householder Reduction to Tridiagonal Form……Page 367
9.5 Eigenvalues of Symmetric Tridiagonal Matrices……Page 374
9.6 Other Methods……Page 389
10.1 Introduction……Page 390
10.2 Minimization Along a Line……Page 392
10.3 Conjugate Gradient Methods……Page 398
10.4 Other Methods……Page 416
Appendices……Page 418
Index……Page 428

@book{book:26153,
title = {Numerical Methods in Engineering with Python},
author = {Jaan Kiusalaas},
publisher = {Cambridge University Press},
isbn = {9780521191326,0521191327},
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series = {},
edition = {2},
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url = {http://gen.lib.rus.ec/book/index.php?md5=72EEA65783CFDE485E98CF67863FAC97}
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Wes McKinney-Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython-O’Reilly (2017).pdf OCR and bookmarked 5MB pdf.

@book{book:2123824,
title = {Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython},
author = {Wes McKinney},
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year = {2017},
series = {},
edition = {2nd},
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Fabio Nelli (auth.)-Python Data Analytics_ Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language-Apress (2015).pdf

Table of contents :
Front Matter….Pages i-xxi
An Introduction to Data Analysis….Pages 1-12
Introduction to the Python’s World….Pages 13-34
The NumPy Library….Pages 35-61
The pandas Library—An Introduction….Pages 63-101
pandas: Reading and Writing Data….Pages 103-130
pandas in Depth: Data Manipulation….Pages 131-165
Data Visualization with matplotlib….Pages 167-235
Machine Learning with scikit-learn….Pages 237-264
An Example—Meteorological Data….Pages 265-288
Embedding the JavaScript D3 Library in IPython Notebook….Pages 289-309
Recognizing Handwritten Digits….Pages 311-316
Writing Mathematical Expressions with LaTeX….Pages 317-326
Open Data Sources….Pages 327-330
Back Matter….Pages 331-337

@book{book:1406795,
title = {Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language},
author = {Fabio Nelli (auth.)},
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Pandas

Daniel Y. Chen-Pandas for Everyone. Python Data Analysis-Addison-Wesley Professional (2017).pdf (6MB OCR not bookmarked)

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Michael Heydt-Learning pandas_ Get to grips with pandas – a versatile and high-performance Python library for data manipulation, analysis, and discovery-Packt Publishing (2015).pdf

This learner’s guide will help you understand how to use the features of pandas for interactive data manipulation and analysis. This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas’ Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance. With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.

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title = {Learning pandas: Get to grips with pandas – a versatile and high-performance Python library for data manipulation, analysis, and discovery},
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Michael Heydt-Learning Pandas-Packt Publishing (2015).pdf

Key FeaturesEmploy the use of pandas for data analysis closely to focus more on analysis and less on programmingGet programmers comfortable in performing data exploration and analysis on Python using pandasStep-by-step demonstration of using Python and pandas with interactive and incremental examples to facilitate learningBook Description
This learner’s guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.

This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas’ Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.

With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
What You Will LearnInstall pandas on Windows, Mac, and Linux using the Anaconda Python distributionLearn how pandas builds on NumPy to implement flexible indexed dataAdopt pandas’ Series and DataFrame objects to represent one- and two-dimensional data constructsIndex, slice, and transform data to derive meaning from informationLoad data from files, databases, and web servicesManipulate dates, times, and time series dataGroup, aggregate, and summarize dataVisualize techniques for pandas and statistical dataAbout the Author
Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.
Table of ContentA Tour of pandasInstalling pandasNumpy for pandasThe pandas Series ObjectThe pandas Dataframe ObjectAccessing DataTidying up Your DataCombining and Reshaping DataGrouping and Aggregating DataTime-series DataVisualizationApplications to Finance

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Femi Anthony-Mastering pandas_ Master the features and capabilities of pandas, a data analysis toolkit for Python-Packt Publishing (2015).pdf

Python is a ground breaking language for its simplicity and succinctness, allowing the user to achieve a great deal with a few lines of code, especially compared to other programming languages. The pandas brings these features of Python into the data analysis realm, by providing expressiveness, simplicity, and powerful capabilities for the task of data analysis. By mastering pandas, users will be able to do complex data analysis in a short period of time, as well as illustrate their findings using the rich visualization capabilities of related tools such as IPython and matplotlib. This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.

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Michael Heydt-Mastering Pandas for Finance-Packt Publishing (2015).pdf

Master pandas, an open source Python Data Analysis Library, for financial data analysis
About This BookA single source for learning how to use the features of pandas for financial and quantitative analysis.Explains many of the financial concepts including market risk, options valuation, futures calculation, and algorithmic trading strategies.Step-by-step demonstration with interactive and incremental examples to apply pandas to financeWho This Book Is For
If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected.
What You Will Learn Modeling and manipulating financial data using the pandas DataFrame Indexing, grouping, and calculating statistical results on financial information Time-series modeling, frequency conversion, and deriving results on fixed and moving windows Calculating cumulative returns and performing correlations with index and social data Algorithmic trading and backtesting using momentum and mean reversion strategies Option pricing and calculation of Value at Risk Modeling and optimization of financial portfolios In Detail
This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve real-world financial problems.

Starting with a focus on pandas data structures, you will learn to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations using fixed- and moving-windows. This leads into correlating time-series data to both index and social data to build simple trading algorithms. From there, you will learn about more complex trading algorithms and implement them using open source back-testing tools. Then, you will examine the calculation of the value of options and Value at Risk. This then leads into the modeling of portfolios and calculation of optimal portfolios based upon risk. All concepts will be demonstrated continuously through progressive examples using interactive Python and IPython Notebook.

By the end of the book, you will be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance.

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Statistics

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publisher = {Packt},
isbn = {978-1-78862-119-9},
year = {2017},
series = {},
edition = {2nd},
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.rar archive:

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publisher = {Packt},
isbn = {978-1-78862-119-9},
year = {2017},
series = {},
edition = {2nd},
volume = {},
url = {http://gen.lib.rus.ec/book/index.php?md5=3d7f9e27add6ede6ffc4d905ccc05ecd}
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Pratap Dangeti-Statistics for Machine Learning_ Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R-Packt Publishing (2017).epub (12MB)

Key FeaturesLearn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Book Description
Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.

By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.
What you will learnUnderstand the Statistical and Machine Learning fundamentals necessary to build modelsUnderstand the major differences and parallels between the statistical way and the Machine Learning way to solve problemsLearn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packagesAnalyze the results and tune the model appropriately to your own predictive goalsUnderstand the concepts of required statistics for Machine LearningIntroduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning modelsLearn reinforcement learning and its application in the field of artificial intelligence domainAbout the Author
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master’s degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Table of ContentsJourney from Statistics to Machine LearningParallelism of Statistics and Machine LearningLogistic Regression vs. Random ForestTree-Based Machine Learning modelsK-Nearest Neighbors & Naive BayesSupport Vector Machines & Neural NetworksRecommendation EnginesUnsupervised LearningReinforcement Learning

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(Statistics and Computing) Thomas Haslwanter (auth.)-An Introduction to Statistics with Python_ With Applications in the Life Sciences-Springer International Publishing (2016).pdf

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.
Table of contents :
Front Matter….Pages i-xvii
Front Matter….Pages 1-1
Why Statistics?….Pages 3-4
Python….Pages 5-42
Data Input….Pages 43-49
Display of Statistical Data….Pages 51-71
Front Matter….Pages 73-73
Background….Pages 75-88
Distributions of One Variable….Pages 89-120
Hypothesis Tests….Pages 121-137
Tests of Means of Numerical Data….Pages 139-157
Tests on Categorical Data….Pages 159-173
Analysis of Survival Times….Pages 175-180
Front Matter….Pages 181-181
Linear Regression Models….Pages 183-220
Multivariate Data Analysis….Pages 221-225
Tests on Discrete Data….Pages 227-236
Bayesian Statistics….Pages 237-243
Back Matter….Pages 245-278

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title = {An Introduction to Statistics with Python: With Applications in the Life Sciences},
author = {Thomas Haslwanter (auth.)},
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year = {2016},
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José Unpingco (auth.)-Python for Probability, Statistics, and Machine Learning-Springer International Publishing (2016).pdf (7MB)

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Table of contents :
Front Matter….Pages i-xv
Getting Started with Scientific Python….Pages 1-33
Probability….Pages 35-100
Statistics….Pages 101-196
Machine Learning….Pages 197-273
Back Matter….Pages 275-276

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title = {Python for Probability, Statistics, and Machine Learning},
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year = {2016},
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edition = {1},
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Allen B. Downey-Think Bayes_ Bayesian Statistics in Python-O’Reilly Media (2013).pdf (12MB)

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners.

@book{book:1528743,
title = {Think Bayes: Bayesian Statistics in Python},
author = {Allen B. Downey},
publisher = {O’Reilly Media},
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year = {2013},
series = {},
edition = {},
volume = {},
url = {http://gen.lib.rus.ec/book/index.php?md5=af5b9b5edca5c669d5ce022d4e65d3a5}
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