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pandas

Overview
Synopsis

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language

Category

Data Mining Software Free

Features

New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API’s,
Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() methodThe .ix indexer has been deprecated,
Panel has been deprecated
Addition of an IntervalIndex and Interval scalar type,
Improved user API when accessing levels in .groupby(),
Improved support for UInt64 dtypes, A new orient for JSON serialization, orient='table' that uses the Table Schema spec,
Experimental support for exporting DataFrame.style formats to Excel
Window Binary Corr/Cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated,
Support for S3 handling now uses s3fs,
Google BigQuery support now uses the pandas-gbq library
Switched the test framework to use pytest

License

Open Source

Price

Free

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

pandas

Rating
Our Rating
User Rating
Ease of use
7.6
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
Customer Support
7.6
Performance
7.6
Training
Implementation
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Bottom Line

Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.

7.6
Our Rating
User Rating
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Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas is a NUMFocus sponsored project. This will help ensure the success of development of pandas as a world-class open-source project, and makes it possible to donate to the project. Best way to get pandas is to install via conda Builds for osx-64,linux-64,linux-32,win-64,win-32 for Python 2.7, Python 3.4, and Python 3.5 are all available. This is a major release from 0.19.2 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Pandas has changed the internal structure and layout of the codebase. This can affect imports that are not from the top-level pandas.* namespace. Check the API Changes and deprecations before updating. Version 0.20.1 contains one additional change for backwards-compatibility with downstream projects using pandas’ hashing routines. The 0.19.x series includes some small regression fixes, bug fixes and performance improvements such as Compatibility with Python 3.6 and Added a Pandas Cheat Sheet. Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling users to carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R. Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate. pandas doesn’t implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment.

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Ease of use
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