statsmodels VS bokeh python

Compare statsmodels VS bokeh python and see what are their differences

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statsmodels

Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels

This Python tutorial will get you up and running with Bokeh, using examples and a real-world dataset. You'll learn how to visualize your data, customize and organize your visualizations, and add interactivity.

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2023-08-18

Landing page //
2023-08-18

statsmodels

Website: github.com

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bokeh python

Website: realpython.com

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statsmodels features and specs

No features have been listed yet.

bokeh python features and specs

Interactivity
Bokeh provides interactive plots and dashboards that can enhance the user experience by allowing them to explore data by zooming, panning, and hovering.
Web Integration
It generates outputs that are readily usable in web applications. Bokeh plots can be embedded in web pages, making it suitable for creating dashboards and web-based data visualization applications.
Versatility
Bokeh supports a wide variety of plots and chart types, which allows users to create complex and informative visualizations.
Pythonic Syntax
The library has an API that is intuitive for Python users, making it easier to learn and integrate into Python-based projects.
Server for Real-time Updates
Bokeh server allows for the creation of interactive, real-time streaming web applications, which is useful for applications requiring live data updates.

Possible disadvantages of bokeh python

Learning Curve
Despite its intuitive syntax, Bokeh's extensive capabilities and features can present a steeper learning curve, particularly for beginners in data visualization.
Rendering Performance
For very large datasets, Bokeh might encounter performance issues, such as slower rendering times in the browser compared to other digital visualization technologies.
Limited 3D Capabilities
Unlike some other visualization libraries, Bokeh’s support for 3D plotting is limited, which might be a constraint for users needing advanced 3D plotting features.
Complexity with Advanced Plots
While Bokeh is great for basic plots, creating highly customized or advanced visualizations may require more effort, with users potentially needing to write custom JavaScript callbacks.
Dependencies
Bokeh’s reliance on JavaScript and other underlying libraries might pose challenges in environments where managing dependencies is complex.

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Category Popularity

0-100% (relative to statsmodels and bokeh python)

statsmodels

bokeh python

Development Tools

35 35%

Development Tools

65% 65

Data Science And Machine Learning

38 38%

Data Science And Machine Learning

62% 62

Application Builder

37 37%

Application Builder

63% 63

Developer Tools

100 100%

Developer Tools

0% 0

User comments

Share your experience with using statsmodels and bokeh python. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, statsmodels seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

statsmodels mentions (4)

[P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post. Source: almost 3 years ago
How do you usually build your models?
Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for. Source: about 3 years ago
Can we solve serverless cold starts?
In case you're really worried about cold start latency and your application load shows high variance in the number of concurrent requests, you might want to get a bit fancier. You could use time-series forecasting to anticipate how many containers should be warmed at each point in time. StatsModels is an open-source project that offers the most common algorithms for working with time-series. Here's a good... - Source: dev.to / about 4 years ago
Advice required to choose appropriate software for an assignment
Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels. Source: over 4 years ago