pandas: powerful Python data analysis toolkit¶
Date: November 08, 2014 Version: 0.15.1
Binary Installers: http://pypi.python.org/pypi/pandas
Source Repository: http://github.com/pydata/pandas
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.
Note
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.
- What’s New
- v0.15.1 (November 9, 2014)
- v0.15.0 (October 18, 2014)
- v0.14.1 (July 11, 2014)
- v0.14.0 (May 31 , 2014)
- v0.13.1 (February 3, 2014)
- v0.13.0 (January 3, 2014)
- v0.12.0 (July 24, 2013)
- v0.11.0 (April 22, 2013)
- v0.10.1 (January 22, 2013)
- v0.10.0 (December 17, 2012)
- v0.9.1 (November 14, 2012)
- v0.9.0 (October 7, 2012)
- v0.8.1 (July 22, 2012)
- v0.8.0 (June 29, 2012)
- v.0.7.3 (April 12, 2012)
- v.0.7.2 (March 16, 2012)
- v.0.7.1 (February 29, 2012)
- v.0.7.0 (February 9, 2012)
- v.0.6.1 (December 13, 2011)
- v.0.6.0 (November 25, 2011)
- v.0.5.0 (October 24, 2011)
- v.0.4.3 through v0.4.1 (September 25 - October 9, 2011)
- Installation
- Frequently Asked Questions (FAQ)
- Package overview
- 10 Minutes to pandas
- Tutorials
- Cookbook
- Intro to Data Structures
- Series
- DataFrame
- From dict of Series or dicts
- From dict of ndarrays / lists
- From structured or record array
- From a list of dicts
- From a dict of tuples
- From a Series
- Alternate Constructors
- Column selection, addition, deletion
- Indexing / Selection
- Data alignment and arithmetic
- Transposing
- DataFrame interoperability with NumPy functions
- Console display
- DataFrame column attribute access and IPython completion
- Panel
- Panel4D (Experimental)
- PanelND (Experimental)
- Essential Basic Functionality
- Working with Text Data
- Options and Settings
- Indexing and Selecting Data
- Different Choices for Indexing
- Deprecations
- Basics
- Attribute Access
- Slicing ranges
- Selection By Label
- Selection By Position
- Setting With Enlargement
- Fast scalar value getting and setting
- Boolean indexing
- Indexing with isin
- The where() Method and Masking
- The query() Method (Experimental)
- Duplicate Data
- Dictionary-like get() method
- The select() Method
- The lookup() Method
- Index objects
- Set / Reset Index
- Returning a view versus a copy
- MultiIndex / Advanced Indexing
- Computational tools
- Working with missing data
- Group By: split-apply-combine
- Merge, join, and concatenate
- Reshaping and Pivot Tables
- Time Series / Date functionality
- Time Deltas
- Categorical Data
- Plotting
- IO Tools (Text, CSV, HDF5, ...)
- CSV & Text files
- Specifying column data types
- Handling column names
- Filtering columns (usecols)
- Ignoring line comments and empty lines
- Dealing with Unicode Data
- Index columns and trailing delimiters
- Specifying Date Columns
- Specifying method for floating-point conversion
- Date Parsing Functions
- Inferring Datetime Format
- International Date Formats
- Thousand Separators
- NA Values
- Infinity
- Comments
- Returning Series
- Boolean values
- Handling “bad” lines
- Quoting and Escape Characters
- Files with Fixed Width Columns
- Files with an “implicit” index column
- Reading an index with a MultiIndex
- Reading columns with a MultiIndex
- Automatically “sniffing” the delimiter
- Iterating through files chunk by chunk
- Specifying the parser engine
- Writing to CSV format
- Writing a formatted string
- JSON
- HTML
- Excel files
- Clipboard
- Pickling
- msgpack (experimental)
- HDF5 (PyTables)
- Read/Write API
- Fixed Format
- Table Format
- Hierarchical Keys
- Storing Mixed Types in a Table
- Storing Multi-Index DataFrames
- Querying a Table
- Indexing
- Query via Data Columns
- Iterator
- Advanced Queries
- Multiple Table Queries
- Delete from a Table
- Compression
- Notes & Caveats
- DataTypes
- String Columns
- External Compatibility
- Backwards Compatibility
- Performance
- Experimental
- SQL Queries
- Google BigQuery (Experimental)
- STATA Format
- Performance Considerations
- CSV & Text files
- Remote Data Access
- Enhancing Performance
- Sparse data structures
- Caveats and Gotchas
- rpy2 / R interface
- pandas Ecosystem
- Comparison with R / R libraries
- Comparison with SQL
- API Reference
- Input/Output
- General functions
- Series
- Constructor
- Attributes
- Conversion
- Indexing, iteration
- Binary operator functions
- Function application, GroupBy
- Computations / Descriptive Stats
- Reindexing / Selection / Label manipulation
- Missing data handling
- Reshaping, sorting
- Combining / joining / merging
- Time series-related
- Datetimelike Properties
- String handling
- Categorical
- Plotting
- Serialization / IO / Conversion
- DataFrame
- Constructor
- Attributes and underlying data
- Conversion
- Indexing, iteration
- Binary operator functions
- Function application, GroupBy
- Computations / Descriptive Stats
- Reindexing / Selection / Label manipulation
- Missing data handling
- Reshaping, sorting, transposing
- Combining / joining / merging
- Time series-related
- Plotting
- Serialization / IO / Conversion
- Panel
- Constructor
- Attributes and underlying data
- Conversion
- Getting and setting
- Indexing, iteration, slicing
- Binary operator functions
- Function application, GroupBy
- Computations / Descriptive Stats
- Reindexing / Selection / Label manipulation
- Missing data handling
- Reshaping, sorting, transposing
- Combining / joining / merging
- Time series-related
- Serialization / IO / Conversion
- Panel4D
- Index
- DatetimeIndex
- TimedeltaIndex
- GroupBy
- General utility functions
- Contributing to pandas
- Internals
- Release Notes
- pandas 0.15.1
- pandas 0.15.0
- pandas 0.14.1
- pandas 0.14.0
- pandas 0.13.1
- pandas 0.13.0
- pandas 0.12.0
- pandas 0.11.0
- pandas 0.10.1
- pandas 0.10.0
- pandas 0.9.1
- pandas 0.9.0
- pandas 0.8.1
- pandas 0.8.0
- pandas 0.7.3
- pandas 0.7.2
- pandas 0.7.1
- pandas 0.7.0
- pandas 0.6.1
- pandas 0.6.0
- pandas 0.5.0
- pandas 0.4.3
- pandas 0.4.2
- pandas 0.4.1
- pandas 0.4.0
- pandas 0.3.0