Introduction In this post we will consider the effect of various methods of parsing (date)time strings, and localizing and converting timezones in pandas DataFrames. We will be first and foremost interested in performance. The study Let us consider a particular dataset, the futures dataset from FirstRate Data. This dataset comes in the form of zip…

Are you looking for a faster alternative to pandas? Have a look at https://vaex.io/: a Python library for lazy Out-of-Core DataFrames. Vaex works with enormous tabular data, processes > 10^9 rows per second, computes on the fly, without wasting RAM, is memory efficient, and has built-in visualization.

Are you writing financial code in Python? Consider Saeed Amen‘s library finmarketpy. finmarketpy is a Python based library that enables you to analyze market data and also do backtesting of trading strategies using a simple to use API, which has prebuilt templates for you to define the backtest. Included in the library: Link: https://github.com/cuemacro/finmarketpy

If you are doing natural language processing (if indeed there is such a thing as a “natural” language), NLTK is likely to be your first platform of choice. NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such…

Those of you who would like to learn more about how Python works, about the internals of the language, should have a look at Python Developer’s Guide. This is a comprehensive resource for contributing to Python – for both new and experienced contributors. It’s also a great resource for those who want to understand how…