From the course: Faster pandas

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Overview of alternative DataFrames

Overview of alternative DataFrames

From the course: Faster pandas

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Overview of alternative DataFrames

- [Instructor] pandas DataFrame became the tool to work with data. A lot of people use it and there's a lot of knowledge and tooling around it. However, pandas has its issues. Crossing the 10 million row boundary is challenging, missing values in other types than float is problematic and multi-core support is hard. In recent years some alternative DataFrame implementation came to light. Each of them has their own advantages and disadvantages. All of them try to be as compatible to pandas as possible, easing the switch from pandas. All of them differ from pandas in some way or another. Some methods are missing, Some have different ways of doing operation and some require extra setup. We're going to survey some of these alternative DataFrames and see what they can offer. We'll focus on performance. The pandas core team is also thinking in working towards pandas 2 which will be based upon APACHE ARROW. APACHE ARROW is a library for fast in memory sharing and already have a good success…

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