Top Rated bokeh python Alternatives
I like that it's fairly easy to create dynamic html visualizations that look slick and feel good. Since I learned R before python for statistics and visualizations, I definitely prefer R's ggplot2 syntax (which plotly can then easily convert to an html version with plotly::ggplotly()). However, for the python work that I do (when my coworkers prefer python notebooks, etc.) the capability of bokeh is great! The api is fairly consistent across different types of plots which is great. Review collected by and hosted on G2.com.
While bokeh is pretty strong for creating pretty slice visualizations, I find it more difficult to customize plot themes and features as compared to some other visualization libraries. However, I also find myself more impressed with the default settings of any bokeh plot. Review collected by and hosted on G2.com.
9 out of 10 Total Reviews for bokeh python
I like how there are a several pre made interactive plot templates that allow you to generate interactive plots in one line of code, but that there is also room for customization beyond that. It's pretty easy to get started with bokeh to make simple yet useful interactive plots and web pages. Review collected by and hosted on G2.com.
I've had some issues getting certain features to work that I've ended up just giving up on. There's a learning curve when trying to make very custom things, without a lot of documentation. It's also difficult to debug and requires learning some javascript, which is useful but increases the learning curve. Review collected by and hosted on G2.com.
I like the bokeh Python package because it lets me visualize data in ways previously unachievable. This package allows me to drive analytics in a way that impresses my team. It's really changed the way we do data engineering on our team. Review collected by and hosted on G2.com.
I dislike this package because it is somewhat hard to use at times best the documentation is not the best and sometimes unclear. This package definitely needs better documentation written and shared. Review collected by and hosted on G2.com.
I love bokeh library in python because it allows me to programmatically create data visualizations for analytics work in ways not previously possible where we used slow clunky software. Review collected by and hosted on G2.com.
I dislike that bokeh python is completely open source and has no paid support. It would be great if open source products had better support. Review collected by and hosted on G2.com.
With bokeh, I can create interactive graphs which gave a whole new dimension to my performance testing reports. Not only they look beautiful, but they let me illustrate concepts in an interactive way, without generating different graphs which are only zoomed in versions or simple numbers: I can show the numbers by hovering over the generated graph instead Review collected by and hosted on G2.com.
it took me a while to get the right configuration. Matplotlib works almost out of the box, bokeh takes very little to produce a nice graph, but quite some work to get exactly what you want. Anyway, this is justified by the fact the final outcome is more captivating than Matplotlib and interactive Review collected by and hosted on G2.com.
I like bokeh python because it allows me to streamline my data analytics workflows so I can better showcase different types of data to a large audience. It makes data easier to understand with the various types of graphs. Review collected by and hosted on G2.com.
I dislike that bokeh is an open source library that is somewhat hard to understand in its documentation form, perhaps because very advanced individuals wrote it. Review collected by and hosted on G2.com.
Bokeh allows me and my team to visualize data and information in a way that previously wasnt possible with the cluncky BI tools that we used to use. I think what I love best about bokeh is how easy it is to install and use because it's open source. Review collected by and hosted on G2.com.
With open source comes the issue with support. There isn't much support available besides a developer guide which is available on their website so you better have a team of engineers ready to dive deep. Review collected by and hosted on G2.com.
Bokeh is a phenomenal visualization library in Python. As a data scientist, you're constantly looking for ways to express data in more understandable manner and Bokeh lets you do just that. Review collected by and hosted on G2.com.
I think what I dislike about this library is the learning curve. It's not easy to learn each new function and takes time. Review collected by and hosted on G2.com.
The library has a lot of potential to create a rainbow of visualizations. I like that the dashboards are interactive. Review collected by and hosted on G2.com.
The help resources or learning resources are limited. Review collected by and hosted on G2.com.
Easy to learn and use, good for basic interactive charts. Allows you to provide charts in many mediums (html, notebook and server). Good alternative to plotly and pygal. Review collected by and hosted on G2.com.
Plotly offers a much greater level of interactivity than bokeh out of the box.
bokeh has a problem with its documentation. Review collected by and hosted on G2.com.
