Save these alternatives to your boardKeep these tools in one place and compare them anytime.

Top 10 bokeh python Alternatives & Competitors

(10)4.2 out of 5

Many buyers researching bokeh python on G2 ultimately choose a competing solution from this list of highly rated tools. In most cases, users compare alternatives based on reliability and ease of use.
The best overall alternative to bokeh python is DevExpress, rated ~4.8 stars on G2 from 112+ reviewers. It is frequently praised for customer support and product quality. Other well-known alternatives to bokeh python are:

Essential Studio – known for components and customer support (G2 star rating ~4.5)
Progress Kendo UI – highly rated for customer support and components (G2 star rating ~4.4)
Flutter – offers cross-platform compatibility and ui components (G2 star rating ~4.6)
Selenium WebDriver – best suited for teams that need automation and ease of learning (G2 star rating ~4.5)

Browse top options below. Based on G2 review data, bokeh python is most commonly used in industries like Component Libraries Software, Mobile Development Frameworks. Compare user ratings by vertical to find the best fit.

Show Less

Best Paid & Free Alternatives to bokeh python

DevExpress
Essential Studio
Progress Kendo UI
Flutter
Selenium WebDriver
Leaflet.js
Progress Telerik
pandas python

Top 10 Alternatives to bokeh python Recently Reviewed By G2 Community

Browse options below. Based on reviewer data, you can see how bokeh python stacks up to the competition, check reviews from current & previous users in industries like Internet, Information Technology and Services, and E-Learning, and find the best product for your business.

Sponsored

G2 Advertising

Get 2x conversion than Google Ads with G2 Advertising!

G2 Advertising places your product in premium positions on high-traffic pages and on targeted competitor pages to reach buyers at key comparison moments.

Learn More

You’re Seeing Part of the StoryLog in or create an account to access the full set of alternatives.

Create a Free Account

Selenium WebDriver

By Selenium

4.5/5

(100)

Product Description

Selenium WebDriver is an open-source tool that automates web browser interactions, enabling developers and testers to simulate real user actions for web application testing. It supports multiple programming languages, including Java, Python, C#, Ruby, and JavaScript, allowing users to write test scripts in their preferred language. WebDriver interacts directly with browser drivers, facilitating cross-browser testing across platforms like Chrome, Firefox, Safari, Edge, and Internet Explorer. This direct communication ensures more stable and reliable tests by mimicking real user behavior without the need for a separate server installation. Key Features and Functionality: - Cross-Browser Compatibility: Enables testing across various browsers, ensuring consistent application behavior. - Multi-Language Support: Offers bindings for languages such as Java, Python, C#, Ruby, and JavaScript, providing flexibility for testers. - Direct Browser Control: Interacts with browsers through native drivers, leading to more accurate and stable tests. - Parallel Test Execution: Integrates with Selenium Grid to run tests concurrently on multiple machines, reducing execution time. - Open-Source and Community-Driven: Freely available with a robust community, ensuring continuous improvements and support. Primary Value and Problem Solved: Selenium WebDriver addresses the need for efficient, reliable, and scalable web application testing. By automating browser interactions, it reduces manual testing efforts, accelerates the testing process, and ensures applications function correctly across different browsers and platforms. Its support for multiple programming languages and integration capabilities make it a versatile choice for teams aiming to enhance their testing frameworks and deliver high-quality web applications.

Reviewers say compared to bokeh python, Selenium WebDriver is:

More expensive

Better at support

More usable

Categories in common with bokeh python:

Component Libraries

Leaflet.js

By It's open source...

4.6/5

(29)

Product Description

Reviewers say compared to bokeh python, Leaflet.js is:

More expensive

Better at support

More usable

Categories in common with bokeh python:

Product Description

Reviewers say compared to bokeh python, Progress Telerik is:

More expensive

Better at support

More usable

Categories in common with bokeh python:

Product Description

Pandas is a powerful and flexible open-source Python library designed for data analysis and manipulation. It provides fast, efficient, and intuitive data structures, such as DataFrame and Series, which simplify handling structured (tabular, multidimensional, potentially heterogeneous) and time series data. Pandas aims to be the fundamental high-level building block for practical, real-world data analysis in Python, offering a wide range of functionalities to streamline data processing tasks. Key Features and Functionality: - Handling Missing Data: Pandas offers easy handling of missing data, represented as `NaN`, `NA`, or `NaT`, in both floating point and non-floating point data. - Size Mutability: Columns can be inserted and deleted from DataFrame and higher-dimensional objects, allowing for dynamic data manipulation. - Data Alignment: Automatic and explicit data alignment ensures that objects can be aligned to a set of labels, facilitating accurate computations. - Group By Operations: Powerful and flexible group by functionality enables split-apply-combine operations on datasets for both aggregating and transforming data. - Data Conversion: Simplifies converting differently-indexed data in other Python and NumPy data structures into DataFrame objects. - Indexing and Subsetting: Provides intelligent label-based slicing, fancy indexing, and subsetting of large datasets. - Merging and Joining: Facilitates intuitive merging and joining of datasets. - Reshaping and Pivoting: Offers flexible reshaping and pivoting of datasets. - Hierarchical Labeling: Supports hierarchical labeling of axes, allowing multiple labels per tick. - Robust I/O Tools: Includes robust tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format. - Time Series Functionality: Provides time series-specific functionality, including date range generation, frequency conversion, moving window statistics, and date shifting and lagging. Primary Value and User Solutions: Pandas addresses the challenges of data analysis by offering a comprehensive suite of tools that simplify the process of data manipulation, cleaning, and analysis. Its intuitive data structures and functions allow users to perform complex operations with minimal code, enhancing productivity and enabling efficient handling of large datasets. By providing seamless integration with other Python libraries and tools, Pandas serves as a cornerstone for data science workflows, empowering users to extract insights and make data-driven decisions effectively.

Reviewers say compared to bokeh python, pandas python is:

More expensive

Better at support

More usable

Categories in common with bokeh python:

Component Libraries