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Python for Economists Alex Bell
Python for Economists Alex Bell [email protected] http://xkcd.com/353 This version: October 2016. If you have not already done so, download the files for the exercises here. Contents 1 Introduction to Python 3 1.1 Getting Set-Up................................................. 3 1.2 Syntax and Basic Data Structures...................................... 3 1.2.1 Variables: What Stata Calls Macros ................................ 4 1.2.2 Lists.................................................. 5 1.2.3 Functions ............................................... 6 1.2.4 Statements............................................... 7 1.2.5 Truth Value Testing ......................................... 8 1.3 Advanced Data Structures .......................................... 10 1.3.1 Tuples................................................. 10 1.3.2 Sets .................................................. 11 1.3.3 Dictionaries (also known as hash maps) .............................. 11 1.3.4 Casting and a Recap of Data Types................................. 12 1.4 String Operators and Regular Expressions ................................. 13 1.4.1 Regular Expression Syntax...................................... 14 1.4.2 Regular Expression Methods..................................... 16 1.4.3 Grouping RE's ............................................ 18 1.4.4 Assertions: Non-Capturing Groups................................. 19 1.4.5 Portability of REs (REs in Stata).................................. 20 1.5 Working with the Operating System.................................... -
An Evaluation of Statistical Software for Research and Instruction
Behavior Research Methods, Instruments, & Computers 1985, 17(2),352-358 An evaluation of statistical software for research and instruction DARRELL L. BUTLER Ball State University, Muncie, Indiana and DOUGLAS B. EAMON University of Houston-Clear Lake, Houston, Texas A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruc tion and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages. Many psychologists use statistical programs to analyze ware packages available on the VAX computers at Ball research data or to teach statistics, and many are interested State University: BMDP, SPSSx, SCSS, and MINITAB. in using computer software for these purposes (e.g., Versions of these programs are widely available and are Butler, 1984; Butler & Kring, 1984). The present paper used here as points ofreference to judge the strengths and provides some program descriptions, benchmarks, and weaknesses of the microcomputer packages. Although evaluations of a wide variety of marketed statistical pro there are many programs distributed by individuals (e.g., grams that may be useful in research and/or instruction. see Academic Computer Center of Gettysburg College, This review differs in several respects from other re 1984, and Eamon, 1983), none are included in the present cent reviews of statistical programs (e. g., Carpenter, review. -
Knuthweb.Pdf
Literate Programming Donald E. Knuth Computer Science Department, Stanford University, Stanford, CA 94305, USA The author and his associates have been experimenting for the past several years with a program- ming language and documentation system called WEB. This paper presents WEB by example, and discusses why the new system appears to be an improvement over previous ones. I would ordinarily have assigned to student research A. INTRODUCTION assistants; and why? Because it seems to me that at last I’m able to write programs as they should be written. The past ten years have witnessed substantial improve- My programs are not only explained better than ever ments in programming methodology. This advance, before; they also are better programs, because the new carried out under the banner of “structured program- methodology encourages me to do a better job. For ming,” has led to programs that are more reliable and these reasons I am compelled to write this paper, in easier to comprehend; yet the results are not entirely hopes that my experiences will prove to be relevant to satisfactory. My purpose in the present paper is to others. propose another motto that may be appropriate for the I must confess that there may also be a bit of mal- next decade, as we attempt to make further progress ice in my choice of a title. During the 1970s I was in the state of the art. I believe that the time is ripe coerced like everybody else into adopting the ideas of for significantly better documentation of programs, and structured programming, because I couldn’t bear to be that we can best achieve this by considering programs found guilty of writing unstructured programs. -
Programming Pearls: a Literate Program
by jot1 Be~~tley programming with Special Guest Oysters Don Knuth and Doug McIlroy pearls A LITERATE PROGRAM Last month‘s column introduced Don Knuth’s style of frequency; or there might not even be as many as k “Literate Programming” and his WEB system for building words. Let’s be more precise: The most common programs that are works of literature. This column pre- words are to be printed in order of decreasing fre- sents a literate program by Knuth (its origins are sketched quency, with words of equal frequency listed in al- in last month‘s column) and, as befits literature, a review. phabetic order. Printing should stop after k words So without further ado, here is Knuth’s program, have been output, if more than k words are present. retypeset in Communications style. -Jon Bentley 2. The input file is assumed to contain the given Common Words Section text. If it begins with a positive decimal number Introduction.. , . , , . , . , . , , . 1 (preceded by optional blanks), that number will be Strategic considerations . , a the value of k; otherwise we shall assume that Basic input routines . , , . 9 k = 100. Answers will be sent to the output file. Dictionary lookup . , . , . , .17 define default-k = 100 (use this value if k isn’t The frequency counts . .32 otherwise specified) Sortingatrie . ...36 Theendgame................................41 3. Besides solving the given problem, this program is Index . ...42 supposed to be an example of the WEB system, for people who know some Pascal but who have never 1. Introduction. The purpose of this program is to seen WEB before. -
Teaching Foundations of Computation and Deduction Through Literate Functional Programming and Type Theory Formalization
Teaching Foundations of Computation and Deduction Through Literate Functional Programming and Type Theory Formalization Gérard Huet Inria Paris Laboratory, France Abstract We describe experiments in teaching fundamental informatics notions around mathematical struc- tures for formal concepts, and effective algorithms to manipulate them. The major themes of lambda-calculus and type theory served as guides for the effective implementation of functional programming languages and higher-order proof assistants, appropriate for reflecting the theor- etical material into effective tools to represent constructively the concepts and formally certify the proofs of their properties. Progressively, a literate programming and proving style replaced informal mathematics in the presentation of the material as executable course notes. The talk will evoke the various stages of (in)completion of the corresponding set of notes along the years (see [1, 2, 14, 5, 6, 10, 12, 16, 7, 15, 17, 9, 3, 13, 11, 4, 8]), and tell how their elaboration proved to be essential to the discovery of fundamental results. 1998 ACM Subject Classification F.3.1 Specifying and Verifying Programs, F.4.1 Mathematical Logic and G.4 Mathematical Software Keywords and phrases Foundations, Computation, Deduction, Programming, Proofs Digital Object Identifier 10.4230/LIPIcs.FSCD.2016.3 Category Invited Talk References 1 Gérard Huet. Initiation à la logique mathématique. Notes de cours du DEA d’Informatique, Université Paris-Sud, 1977. 2 Gérard Huet. Démonstration automatique en logique de premier ordre et programmation en clauses de horn. Notes de cours du DEA d’Informatique, Université Paris-Sud, 1978. 3 Gérard Huet. Confluent reductions: Abstract properties and applications to term rewriting systems. -
Typeset MMIX Programs with TEX Udo Wermuth Abstract a TEX Macro
TUGboat, Volume 35 (2014), No. 3 297 Typeset MMIX programs with TEX Example: In section 9 the lines \See also sec- tion 10." and \This code is used in section 24." are given. Udo Wermuth No such line appears in section 10 as it only ex- tends the replacement code of section 9. (Note that Abstract section 10 has in its headline the number 9.) In section 24 the reference to section 9 stands for all of ATEX macro package is presented as a literate pro- the eight code lines stated in sections 9 and 10. gram. It can be included in programs written in the If a section is not used in any other section then languages MMIX or MMIXAL without affecting the it is a root and during the extraction of the code a assembler. Such an instrumented file can be pro- file is created that has the name of the root. This file cessed by TEX to get nicely formatted output. Only collects all the code in the sequence of the referenced a new first line and a new last line must be entered. sections from the code part. The collection process And for each end-of-line comment a flag is set to for all root sections is called tangle. A second pro- indicate that the comment is written in TEX. cess is called weave. It outputs the documentation and the code parts as a TEX document. How to read the following program Example: The following program has only one The text that starts in the next chapter is a literate root that is defined in section 4 with the headline program [2, 1] written in a style similar to noweb [7]. -
TSP 5.0 Reference Manual
TSP 5.0 Reference Manual Bronwyn H. Hall and Clint Cummins TSP International 2005 Copyright 2005 by TSP International First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International. ALL RIGHTS RESERVED Table Of Contents 1. Introduction_______________________________________________1 1. Welcome to the TSP 5.0 Help System _______________________1 2. Introduction to TSP ______________________________________2 3. Examples of TSP Programs _______________________________3 4. Composing Names in TSP_________________________________4 5. Composing Numbers in TSP _______________________________5 6. Composing Text Strings in TSP_____________________________6 7. Composing TSP Commands _______________________________7 8. Composing Algebraic Expressions in TSP ____________________8 9. TSP Functions _________________________________________10 10. Character Set for TSP ___________________________________11 11. Missing Values in TSP Procedures _________________________13 12. LOGIN.TSP file ________________________________________14 2. Command summary_______________________________________15 13. Display Commands _____________________________________15 -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Data Workflows with Stata and Python
In [1]: from IPython.display import IFrame import ipynb_style from epstata import Stpy import pandas as pd from itertools import combinations from importlib import reload In [2]: reload(ipynb_style) ipynb_style.clean() #ipynb_style.presentation() #ipynb_style.pres2() Out[2]: Data Workflows in Stata and Python (http://www.stata.com) (https://www.python.org) Data Workflows in Stata and Python Dejan Pavlic, Education Policy Research Initiative, University of Ottawa Stephen Childs (presenter), Office of Institutional Analysis, University of Calgary (http://ucalgary.ca) (http://uottawa.ca/en) (http://socialsciences.uottawa.ca/irpe- epri/eng/index.asp) Introduction About this talk Objectives know what Python is and what advantages it has know how Python can work with Stata Please save questions for the end. Or feel free to ask me today or after the conference. Outline Introduction Overall Motivation About Python Building Blocks Running Stata from Python Pandas Python language features Workflows ETL/Data Cleaning Stata code generation Processing Stata output About Me Started using Stata in grad school (2006). Using Python for about 3 years. Post-Secondary Education sector University of Calgary - Institutional Analysis (https://oia.ucalgary.ca/Contact) Education Policy Research Initiative (http://socialsciences.uottawa.ca/irpe-epri/eng/index.asp) - University of Ottawa (a Stata shop) Motivation Python is becoming very popular in the data world. Python skills are widely applicable. Python is powerful and flexible and will help you get more done, -
Gretl User's Guide
Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases .......................................... -
Advanced Data Mining with Weka (Class 5)
Advanced Data Mining with Weka Class 5 – Lesson 1 Invoking Python from Weka Peter Reutemann Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Lesson 5.1: Invoking Python from Weka Class 1 Time series forecasting Lesson 5.1 Invoking Python from Weka Class 2 Data stream mining Lesson 5.2 Building models in Weka and MOA Lesson 5.3 Visualization Class 3 Interfacing to R and other data mining packages Lesson 5.4 Invoking Weka from Python Class 4 Distributed processing with Apache Spark Lesson 5.5 A challenge, and some Groovy Class 5 Scripting Weka in Python Lesson 5.6 Course summary Lesson 5.1: Invoking Python from Weka Scripting Pros script captures preprocessing, modeling, evaluation, etc. write script once, run multiple times easy to create variants to test theories no compilation involved like with Java Cons programming involved need to familiarize yourself with APIs of libraries writing code is slower than clicking in the GUI Invoking Python from Weka Scripting languages Jython - https://docs.python.org/2/tutorial/ - pure-Java implementation of Python 2.7 - runs in JVM - access to all Java libraries on CLASSPATH - only pure-Python libraries can be used Python - invoking Weka from Python 2.7 - access to full Python library ecosystem Groovy (briefly) - http://www.groovy-lang.org/documentation.html - Java-like syntax - runs in JVM - access to all Java libraries on CLASSPATH Invoking Python from Weka Java vs Python Java Output public class Blah { 1: Hello WekaMOOC! public static void main(String[] -
Introduction to Weka
Introduction to Weka Overview What is Weka? Where to find Weka? Command Line Vs GUI Datasets in Weka ARFF Files Classifiers in Weka Filters What is Weka? Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Where to find Weka Weka website (Latest version 3.6): – http://www.cs.waikato.ac.nz/ml/weka/ Weka Manual: − http://transact.dl.sourceforge.net/sourcefor ge/weka/WekaManual-3.6.0.pdf CLI Vs GUI Recommended for in-depth usage Explorer Offers some functionality not Experimenter available via the GUI Knowledge Flow Datasets in Weka Each entry in a dataset is an instance of the java class: − weka.core.Instance Each instance consists of a number of attributes Attributes Nominal: one of a predefined list of values − e.g. red, green, blue Numeric: A real or integer number String: Enclosed in “double quotes” Date Relational ARFF Files The external representation of an Instances class Consists of: − A header: Describes the attribute types − Data section: Comma separated list of data ARFF File Example Dataset name Comment Attributes Target / Class variable Data Values Assignment ARFF Files Credit-g Heart-c Hepatitis Vowel Zoo http://www.cs.auckland.ac.nz/~pat/weka/ ARFF Files Basic statistics and validation by running: − java weka.core.Instances data/soybean.arff Classifiers in Weka Learning algorithms in Weka are derived from the abstract class: − weka.classifiers.Classifier Simple classifier: ZeroR − Just determines the most common class − Or the median (in the case of numeric values) − Tests how well the class can be predicted without considering other attributes − Can be used as a Lower Bound on Performance.