Pandas aren’t only cute cute bears; they’re also a data analysis/manipulation library for Python. Pandas is designed to make it easier to work with relational and labeled data.

“It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language. It is already well on its way toward this goal,” according to its GitHub page.

According to the team, while Python is good for data munging and preparation, it lacks in its ability to do data analysis and data modeling. Pandas aims to solve this problem by allowing developers to do data analysis workflows in Python without having to switch programming languages.

The open-source project’s main features include the ability to handle missing data; size mutability capabilities; automatic and explicit data alignment; grouping by functionality; the ability to convert differently indexed data; intelligent label-based slicing, fancy indexing and subsetting of large datasets; the ability to merge, join reshape and pivot datasets; hierarchical labeling of axes; IO tools for loading data; and time series-specific functionality.

“We use pandas to process time-series data on our production servers. The simplicity and elegance of its API, and its high level of performance for high-volume datasets, made it a perfect choice for us,” said Olivier Pomel, CEO of Datadog, in a statement.

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About Christina Cardoza

Christina Cardoza, formerly known as Christina Mulligan, is the Online & Social Media Editor of SD Times. She covers agile, DevOps, AI, machine learning, mixed reality and software security. Follow her on Twitter at @chriscatdoza!