Introduction to Python for Econometrics, Statistics and ...

1 Introduction to Python forEconometrics, Statistics and Data Analysis4th EditionKevin SheppardUniversity of OxfordThursday 31stDecember, 20202- 2020 Kevin SheppardSolutions and Other MaterialSolutionsSolutions for exercises and some extended examples are available on CourseA self-paced introductory course is available on GitHub in thecourse/introductionfolder. Solutions are avail-able in DemonstrationsThe introductory course is accompanied by video demonstrations of each lesson on Python for Financial EconometricsA self-paced course that shows how Python can be used in econometric analysis, with an emphasis on financialeconometrics, is also available on GitHub in thecourse/ since the Fourth Edition Added a discussion of context managers using thewithstatement. Switched examples to prefer the context manager syntax to reflect best to the Fourth EditionChanges in the Fourth Edition Python is the recommended version.

2 The notes require Python or later, and all references toPython have been removed. Removed references to NumPy smatrixclass and clarified that it should not be used. Verified that all code and examples work correctly against 2020 versions of modules. The notable pack-ages and their versions are: Python (Preferred version), (Minimum version) NumPy: SciPy: pandas: matplotlib: Expanded description of model classes and statistical tests in statsmodels that are most relevant for econo-metrics. TODO Expanded the list of packages of interest to researchers working in Statistics , econometrics and machinelearning. TODO Introduced f-Strings in Section as the preferred way to format strings using modern Python . Addedminimizeas the preferred interface for non- linear function optimization in Chapter 20. TODOC hanges since the Third Edition Verified that all code and examples work correctly against 2019 versions of modules.

3 The notable pack-ages and their versions are: Python (Preferred version) NumPy: SciPy: pandas: matplotlib: Python support has been officially dropped, although most examples continue to work with Python in 2019 for numerical Small typo fixes, thanks to Marton Huebler. Fixed direct download of FRED data due to API changes, thanks to Jesper Termansen. Thanks for Bill Tubbs for a detailed read and multiple typo reports. Updated to changes in line profiler (see Ch. 23) Updated deprecations in pandas. Removedholdfrom plotting chapter since this is no longer required. Thanks for Gen Li for multiple typo reports. Tested all code on Pyton Code has been tested against the current set of modules installed by condaas of February 2018. The notable packages and their versions are: NumPy: Pandas: to the Third EditionThis edition includes the following changes from the second edition (August 2014).

4 Changes in the Third Edition Rewritten installation section focused exclusively on using Continuum s Anaconda. Python is the default version of Python instead of Python (or newer) is well supported bythe Python packages required to analyze data and perform statistical analysis, and bring some new usefulfeatures, such as a new operator for matrix multiplication Removed distinction between integers and longs in built-in data types chapter. This distinction is onlyrelevant for Python dothas been removed from most examples and replaced with@to produce more readable code. Split Cython and Numba into separate chapters to highlight the improved capabilities of Numba. Verified all code working on current versions of core libraries using Python pandas Updated syntax of pandas functions such asresample. Added pandasCategorical. Expanded coverage of pandasgroupby. Expanded coverage of date and time data types and functions.

5 New chapter introducing statsmodels, a package that facilitates statistical analysis of data. statsmodelsincludes regression analysis, Generalized linear Models (GLM) and time-series analysis using since the Second Edition Fixed typos reported by a reader thanks to Ilya Sorvachev Code verified against Anaconda Added diagnostic tools and a simple method to use external code in the Cython section. Updated the Numba section to reflect recent changes. Fixed some typos in the chapter on Performance and Added examples of joblib and IPython s cluster to the chapter on running code in parallel. New chapter introducing object-oriented programming as a method to provide structure and organizationto related code. Added seaborn to the recommended package list, and have included it be default in the graphics chapter. Based on experience teaching Python to economics students, the recommended installation has beensimplified by removing the suggestion to use virtual environment.

6 The discussion of virtual environmentsas been moved to the appendix. Rewrote parts of the pandas chapter. Changed the Anaconda install to use both create and install, which shows how to install additional pack-ages. Fixed some missing packages in the direct install. Changed the configuration of IPython to reflect best practices. Added subsection covering IPython profiles. Small section about Spyder as a good starting to the Second EditionThis edition includes the following changes from the first edition (March 2012).Changes in the Second Edition The preferred installation method is now Continuum Analytics Anaconda. Anaconda is a completescientific stack and is available for all major platforms. New chapter on pandas. pandas provides a simple but powerful tool to manage data and perform prelim-inary analysis. It also greatly simplifies importing and exporting data. New chapter on advanced selection of elements from an array.

7 Numba provides just-in-time compilation for numeric Python code which often produces large perfor-mance gains when pure NumPy solutions are not available ( looping code). Dictionary, set and tuple comprehensions Numerous typos All code has been verified working against Anaconda Background.. Conventions.. Important Components of the Python Scientific Stack.. Setup.. Using Python .. Exercises.. Additional Installation Issues..122 Built-in Data Variable Names.. Core Native Data Types.. Additional Container Data Types in the Standard Library.. Python and Memory Management.. Exercises..273 Array.. 1-dimensional Arrays.. 2-dimensional Arrays.. Multidimensional Arrays.. Concatenation.. Accessing Elements of an Array.. Slicing and Memory Management.. Modules.. Calling Functions.. Exercises..414 Basic Operators.

8 Broadcasting.. Addition (+) and Subtraction (-).. Multiplication ( ).. Matrix Multiplication .. Array and Matrix Division (/).. Exponentiation (**).. Parentheses.. Transpose.. Operator Precedence.. Exercises..475 Basic Functions and Numerical Generating Arrays.. Rounding.. Mathematics.. Complex Values.. Set Functions.. Sorting and Extreme Values.. Nan Functions.. Functions and Methods/Properties.. Exercises..586 Special Exercises..627 Array Shape Information and Transformation.. linear Algebra Functions.. Views.. Exercises..728 Importing and Exporting Importing Data using pandas.. Importing Data without pandas.. Saving or Exporting Data using pandas.. Saving or Exporting Data without pandas.. Exercises..829 Inf,NaNand Numeric .. Floating point precision.. Exercises.

9 8410 Logical Operators and >,>=,<,<=,==,!=.. and, or, not and xor.. Multiple tests.. Exercises..88 CONTENTS xiii11 Advanced Selection and Numerical Indexing.. Logical Indexing.. Performance Considerations and Memory Management.. Assignment with Broadcasting.. Exercises..10112 Flow Control, Loops and Exception Whitespace and Flow Control.. List Comprehensions.. Tuple, Dictionary and Set Comprehensions.. Exercises..11013 Dates and Creating Dates and Times.. Dates Mathematics.. Numpy..11414 seaborn.. 2D Plotting.. Advanced 2D Plotting.. 3D Plotting.. General Plotting Functions.. Exporting Plots.. Exercises..13515 Data Structures.. Statistical Functions.. Time-series Data.. Importing and Exporting Data.. Graphics.. Examples..16816 Structured Mixed Arrays with Column Names.. Record Arrays..17817 Custom Function and Functions.. Variable Scope.

10 Example: Least Squares with Newey-West Covariance.. Anonymous Functions.. Modules.. Packages.. PYTHONPATH.. Python Coding Conventions.. Exercises.. Listing of ..19118 Probability and Statistics Simulating Random Variables.. Simulation and Random Number Generation.. Statistics Functions.. Continuous Random Variables.. Select Statistics Functions.. Select Statistical Tests.. Exercises..20919 Statistical Analysis regression .. Generalized linear Models.. Other Notable Models.. Time-series Analysis.. Generalized linear Models..21420 Non- linear Function Unconstrained Optimization.. Derivative-free Optimization.. Constrained Optimization.. Scalar Function Minimization.. Nonlinear Least Squares.. Exercises..22521 String String Building.. String Functions.. Formatting Numbers.. Regular Expressions.. Safe Conversion of Strings..23722 File System Changing the Working Directory.