Our great sponsors
| tuna | ggplot | |
|---|---|---|
| 4 | 3 | |
| 1,263 | 3,682 | |
| - | 0.2% | |
| 0.0 | 0.0 | |
| about 2 months ago | over 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tuna
Posts with mentions or reviews of tuna.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-18.
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Is AWS Lambda Cold Start Still an Issue?
Every minor detail matters and adds to the total import time as part of the cold start. We need to optimize our code and imports. If you use Python, you can analyze your code with a tool like Tuna and optimize your libraries (perhaps replace slower ones) and your imports.
- Make Python Run Faster
- Scanning Function calls in a script - is there a tool?
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Creating a Python CLI with Go(lang)-comparable startup times
I started to examine the output of python -X importtime -m gefyra 2> import.log just to check the imports. There is an awesome tool to analyze the Python imports: tuna (see: https://github.com/nschloe/tuna). tuna allows analyzing the import times from the log. Run it like so tuna import.log. It opens a browser window and visualizes the import times. With that I was able to manually move all imports to the functions in which they are needed (and bring in some other optimizations). This greatly violates PEP 8 (https://peps.python.org/pep-0008/#imports) but leads to very fast startup times.
ggplot
Posts with mentions or reviews of ggplot.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-19.
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Best tools for good looking tables and piecharts
Seaborn is based on matplotlib and quite modern. Coming from R and used to ggplot (which is also available in python) I really like it.
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Which Python visualization module to use for research-quality graphs?
If you're familiar with R, there's always ggplot.
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Plotting in R's ggplot2 vs Python's Matplotlib: Is it just me or is ggplot2 WAY smoother of an experience than Matplotlib?
I'd agree in that it's a well-specified language for defining graphics; it's not very good with rendering performance. There are packages which try to achieve similar goals in Python as well (ggplot / ggpy) and packages like Seaborn. Though, like you, I use R for lots of EDA. Hard to beat data.table and R graphics for speed and expressiveness. I prefer base graphics though; ggplot2 tends to render too slowly for any data sets I work with.
What are some alternatives?
When comparing tuna and ggplot you can also consider the following projects:
SnakeViz - An in-browser Python profile viewer
seaborn - Statistical data visualization in Python
Altair - Declarative statistical visualization library for Python
matplotlib - matplotlib: plotting with Python
vincent
plotnine - A Grammar of Graphics for Python
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
bokeh - Interactive Data Visualization in the browser, from Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
Flask JSONDash - :snake: :bar_chart: :chart_with_upwards_trend: Build complex dashboards without any front-end code. Use your own endpoints. JSON config only. Ready to go.