Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
Pandas is a tool in the Development & Training Tools category of a tech stack.
Key Features
Easy handling of missing data (represented as NaN) in floating point as well as non-floating point dataSize mutability: columns can be inserted and deleted from DataFrame and higher dimensional objectsAutomatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computationsPowerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming dataMake it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objectsIntelligent label-based slicing, fancy indexing, and subsetting of large data setsIntuitive merging and joining data setsFlexible reshaping and pivoting of data setsHierarchical labeling of axes (possible to have multiple labels per tick)Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 formatTime series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Pandas
