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What is pandas?

Pandas is a versatile open-source library for data analysis and manipulation that excels in speed and power while maintaining a user-friendly interface within the Python ecosystem. It supports a wide range of data formats for both importing and exporting, such as CSV, text documents, Microsoft Excel, SQL databases, and the efficient HDF5 format. The library stands out with its intelligent data alignment features and its adept handling of missing values, allowing for seamless label-based alignment during calculations, which greatly aids in the organization of chaotic datasets. Moreover, pandas includes a sophisticated group-by engine that facilitates complex aggregation and transformation tasks, making it simple for users to execute split-apply-combine operations on their data. In addition to these capabilities, pandas is equipped with extensive time series functions that allow for the creation of date ranges, frequency conversions, and moving window statistics, as well as managing date shifting and lagging. Users also have the flexibility to define custom time offsets for specific applications and merge time series data without losing any critical information. Ultimately, the comprehensive array of features offered by pandas solidifies its status as an indispensable resource for data professionals utilizing Python, ensuring they can efficiently handle a diverse range of data-related tasks.

What is Bokeh?

Bokeh streamlines the creation of standard visualizations while also catering to specific and unique needs. It provides users the ability to share plots, dashboards, and applications either on web platforms or directly within Jupyter notebooks. The Python ecosystem is rich with a variety of powerful analytical tools, such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV, among many others. Featuring an extensive array of widgets, plotting options, and user interface events that activate real Python callbacks, the Bokeh server is essential for linking these tools to dynamic and interactive visualizations displayed in web browsers. Moreover, the Microscopium initiative, led by researchers at Monash University, harnesses Bokeh's interactive features to assist scientists in uncovering new functionalities of genes or drugs by allowing them to explore extensive image datasets. Another significant tool in this ecosystem is Panel, which focuses on producing polished data presentations and operates on the Bokeh server, enjoying support from Anaconda. Panel simplifies the process of building custom interactive web applications and dashboards by effortlessly connecting user-defined widgets to a variety of components, including plots, images, tables, or text. This seamless integration not only enhances the overall user experience but also cultivates an atmosphere that promotes effective data-driven decision-making and thorough exploration of complex datasets. Ultimately, the combination of these tools empowers users to engage with their data in innovative and meaningful ways.

Media

Media

Integrations Supported

ApertureDB
Cleanlab
Codédex
Coiled
DagsHub
Dagster
Flyte
GLM-5.1
GLM-5.3
Giskard
Kedro
MLJAR Studio
Netdata
RunCode
Sliq
Spyder
ThinkData Works
Union Pandera
skills.ai

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

pandas

Date Founded

2008

Company Website

pandas.pydata.org

Company Facts

Organization Name

Bokeh

Company Website

bokeh.org

Categories and Features

Component Libraries

Not specified

Data Analysis

Not specified

Categories and Features

Component Libraries

Not specified

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