pandas is a Python package providing fast, flexible, and expressive data
structures designed to make working with structured (tabular, multidimensional,
potentially heterogeneous) and time series data both easy and intuitive. 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.
pandas is well suited for many different kinds of data:
Tabular data with heterogeneously-typed columns, as in an SQL table or
Excel spreadsheet
Ordered and unordered (not necessarily fixed-frequency) time series data.
Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
column labels
Any other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, Series (1-dimensional) and DataFrame
(2-dimensional), handle the vast majority of typical use cases in finance,
statistics, social science, and many areas of engineering. For R users,
DataFrame provides everything that R’s data.frame provides and much
more. pandas is built on top of NumPy and is
intended to integrate well within a scientific computing environment with many
other 3rd party libraries.
Here are just a few of the things that pandas does well:
Easy handling of missing data (represented as NaN) in floating point as
well as non-floating point data
Size mutability: columns can be inserted and deleted from DataFrame and
higher dimensional objects
Automatic 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
computations
Powerful, flexible group by functionality to perform
split-apply-combine operations on data sets, for both aggregating and
transforming data
Make it easy to convert ragged, differently-indexed data in other
Python and NumPy data structures into DataFrame objects
Intelligent label-based slicing, fancy indexing, and subsetting
of large data sets
Intuitive merging and joining data sets
Flexible reshaping and pivoting of data sets
Hierarchical 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
format
Time series-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions,
date shifting and lagging, etc.
Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas is
the ideal tool for all of these tasks.
