Pandas vs Facette: What are the differences?
Developers describe Pandas as "High-performance, easy-to-use data structures and data analysis tools for the Python programming language". Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. On the other hand, Facette is detailed as "Time series data visualization software". It is a web application to display time series data from various sources such as collectd, Graphite, InfluxDB or KairosDB on graphs, designed to be easy to setup and to use.
Pandas and Facette can be primarily classified as "Data Science" tools.
Pandas and Facette are both open source tools. Pandas with 22.1K GitHub stars and 8.79K forks on GitHub appears to be more popular than Facette with 1.07K GitHub stars and 75 GitHub forks.
What is Facette?
What is Pandas?
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Why do developers choose Facette?
What are the cons of using Facette?
What are the cons of using Pandas?
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Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead