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**pandas** is a Python package that provides 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, date shifting and lagging.
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.
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