pandas: powerful Python data analysis toolkit¶
pandas 版本: 0.19.0
Issues & Ideas: https://github.com/pydata/pandas/issues
Q&A Support: http://stackoverflow.com/questions/tagged/pandas
Developer Mailing List: http://groups.google.com/group/pydata
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.
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.
Some other notes
- pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool.
- pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python.
- pandas has been used extensively in production in financial applications.
This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first.
See the package overview for more detail about what’s in the library.
- pandas 文档中文翻译
- 版本 v0.19.0 ( 发布于2016年10月2日)
- New features
merge_asoffor asof-style time-series joining
.rolling()is now time-series aware
read_csvhas improved support for duplicate column names
- Categorical Concatenation
- Semi-Month Offsets
- New Index methods
- Google BigQuery Enhancements
- Fine-grained numpy errstate
get_dummiesnow returns integer dtypes
- Downcast values to smallest possible dtype in
- pandas development API
- Other enhancements
- API changes
Series.tolist()will now return Python types
Seriesoperators for different indexes
Seriestype promotion on assignment
- Merging changes
-no longer used for set operations
set_indexpreserve categorical dtypes
read_csvwill progressively enumerate chunks
- Sparse Changes
- Indexer dtype changes
- Other API Changes
- Removal of prior version deprecations/changes
- Performance Improvements
- Bug Fixes
- New features
- 版本 v0.19.0 ( 发布于2016年10月2日)
- Contributing to pandas
- Where to start?
- Bug reports and enhancement requests
- Working with the code
- Contributing to the documentation
- Contributing to the code base
- Contributing your changes to pandas
- 常见问题 (FAQ)