Introduced f-Strings in Section [subsec:f-Strings] as the preferred way to format strings using modern Python. The notes use f-String where possible instead of format.
Added coverage of Windowing function – rolling, expanding and ewm – to the pandas chapter.
Expanded the list of packages of interest to researchers working in statistics, econometrics and machine learning.
Expanded description of model classes and statistical tests in statsmodels that are most relevant for econometrics. Added section detailing formula support. This list represents on a small function of the statsmodels API.
Added minimize as the preferred interface for non-linear function optimization in Chapter [chap:Non-linear-Function-Optimization].
Python 2.7 support has been officially dropped, although most examples continue to work with 2.7. Do not Python 2.7 for numerical code.
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. [chap:performance-and-optimization])
Updated deprecations in pandas.
Removed hold from plotting chapter since this is no longer required.
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.
Updated syntax of pandas functions such as resample.
Added pandas Categorical.
Expanded coverage of pandas groupby.
Expanded coverage of date and time data types and functions.
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.