Sheppard K. Introduction to Python for Econometrics, Statistics and Data Analysis

3rd ed. — Oxford: University of Oxford, 2018. — 426 p.Python is a popular general purpose programming language which is well suited to a wide range of problems. Recent developments have extended Python's range of applicability to econometrics, statistics and general numerical analysis. Python – with the right set of add-ons – is comparable to domain-specific languages such as R, MatLAB or Julia. This book provides an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis. They should also be useful for students, researchers or practitioners who require a versatile platform for econometrics, statistics or general numerical analysis (e.g. numeric solutions to economic models or model simulation).This 3rd. edition includes the following changes from the second edition (August 2014):Rewritten installation section focused exclusively on using Continuum’s Anaconda.
Python 3.5 is the default version of Python instead of 2.7. Python 3.5 (or newer) is well supported by the Python packages required to analyze data and perform statistical analysis, and bring some new useful features, such as a new operator for matrix multiplication (@).
Removed distinction between integers and longs in built-in data types chapter. This distinction is only relevant for Python 2.7.
dot has 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 3.5.
pandas.
New chapter introducing statsmodels, a package that facilitates statistical analysis of data. statsmodels includes regression analysis, Generalized Linear Models (GLM) and time-series analysis using ARIMA models.