pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

The User Guide covers all of pandas by topic area. Each of the subsections introduces a topic (such as “working with missing data”), and discusses how pandas approaches the problem, …

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data.

class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None) [source] # Two-dimensional, size-mutable, potentially heterogeneous tabular data.

pandas provides various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of …

For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the PyData stack (SciPy, NumPy, Matplotlib, and more) is with Anaconda, a …

How do I read and write tabular data? How do I select a subset of a DataFrame? How do I create plots in pandas? How to create new columns derived from existing columns How to calculate …

These classes are not to be confused with classes from the pandas-stubs package which has classes in addition to those that occur in pandas for type-hinting. In addition, public functions in …

We’ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, …

Top-level dealing with Interval data #Top-level evaluation #