Bokeh Reviews & Product Details

When I think of Bokeh, the first word that comes to my mind is intuitive.

Python has a large charting ecosystem which rarely is actually useful. Bokeh improve the useful of the Python ecosystem and then takes leaps of improvement with its interactive features.

The Bokeh community also is very useful and can solve most problems that arise with bokeh. Its truly a pleasure to work with bokeh Review collected by and hosted on G2.com.

Complex charting is not Bokeh's strong suit. Often it takes more effort than reasonable to make Bokeh work on date which is not in Pandas dataframes.

Large datasets are not something Bokeh is good either. It tends to lag to the point of unusability Review collected by and hosted on G2.com.

I like the most about Bokeh is that it manage my large amout of data and categorize data very easily and I also like the display of plot and easy to use and understand. Review collected by and hosted on G2.com.

It is fine for me but the only thing I don't like about Bokeh is that sometime i have to spend more time to read its documentation in order to understand it better. Review collected by and hosted on G2.com.

As a company we have large amount of data to render and categorize according to the needs, but with Bokeh, all this made ease just because of its visual tools that we can use for identifying common trends and the lags we're going along with. Review collected by and hosted on G2.com.

Sometimes it's hard to understand different terms if totally new person gonna use that. Review collected by and hosted on G2.com.

- Interactive visuals within Python.

- Large data handling.

Integration with other tools.

- Beautiful visualizations. Compared to other libraries like Matplotlib and Seaborn, Bokeh's defaults make it a BEAUTIFUL choice. Review collected by and hosted on G2.com.

- Took more time to learn, as compared to Seaborn for example.

- Performance is slightly lower. This is natural, as it provides more than an average Python visualization library.

- Does not offer some specialized chart types like heatmaps. Review collected by and hosted on G2.com.

There are several pre-made interactive plots. The display of plots are in high quality. We can generate the plots with one line of code compared to matplot library. We can also customize the the plot according to the requirement.It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Review collected by and hosted on G2.com.

The visualization will look unnatural when used poorly. We need to spend little more time in understanding the documentations. Also if any error comes up, it would be difficult to debug the issue. Review collected by and hosted on G2.com.

-intuitive

-helps manage large amount of data

-easy to use and understand

-categorises data accordingly

-visual tools are very helpful

-coustomisable codes .

-cross platform compatability Review collected by and hosted on G2.com.

- more efforts are required

- lagging occurs

- needs a lot of understanding which is time consuming

- sometimes difficult to understand

- difficult to install and connect to server Review collected by and hosted on G2.com.

Such a helpful tool for visualization and presenting the data in the form of interactive graphs, which help a lot to draw inferences. A lot better then other tools available such poltly Review collected by and hosted on G2.com.

Sometimes it is not easy to install and connect it with the server. However the problem is resolved, so no dislikes. Review collected by and hosted on G2.com.

It has amazing interactive graphs with lots of variety. Much fast in loading dashboards and rendering data while hovering on the graph. With pyspark availability, it can handle larger datasets. Review collected by and hosted on G2.com.

Nothing such as of now. while searching for errors lots of questions were asked, but few answers were available online (like on StackOverflow). Should add some more troubleshooting elements. Review collected by and hosted on G2.com.

- Seamless Integration of Python for Interactive Visualizations

- Cross-platform Compatability

- Support for Real-Time and Streaming Data

- Support for Multiple Rendering Platforms Review collected by and hosted on G2.com.

- Poor performance for large datasets on interactive visualizations

- Lack of documentation in terms of examples for drawing complex visualizations

- Dependency on Javascript for rendering makes it somewhat difficult for python developers Review collected by and hosted on G2.com.

I have been using Bokeh for quite some time now and it helps me present data to clients in interactive format . Being in healthcare industry , i find Bokeh Python very useful ! It is very simple and interactive :) Review collected by and hosted on G2.com.

Only downside is it is little hard to debug and takes little more time than required. Review collected by and hosted on G2.com.