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pandas - a powerful data analysis and manipulation library for Python
|
=====================================================================
|
|
**pandas** is a Python package providing fast, flexible, and expressive data
|
structures designed to make working with "relational" or "labeled" 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.
|
|
Main Features
|
-------------
|
Here are just a few of the things that pandas does well:
|
|
- Easy handling of missing data 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, date shifting and lagging.
|
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