Example Gallery#
This page shows different scenarios implemented with ipyprogressivis
NYC Taxis / Precipitations scatterplot#
The notebook shown below is downloadable here. You can display this example in a new tab.
NYC Taxis / Precipitations line chart#
The notebook shown below is downloadable here. You can display this example in a new tab.
NYC Taxis / Correlation between the number of courses per day and precipitations#
The notebook shown below is downloadable here. You can display this example in a new tab.
A scenario using progressivis snippets#
The notebook shown below is downloadable here. You can display this example in a new tab.
A scenario using duckdb in a snippet#
The notebook shown below is downloadable here. You can display this example in a new tab.
Note
This example requires the duckdb package which is not part of progressivis dependencies.
A multi-class density map for NYC Taxis data.#
There are two classes represented here:
A: represents the pickup points density
B: represents the dropoff points density
Pickup/dropoff sample points are also represented as red/blue dots.
The notebook shown below is downloadable here. You can display this example in a new tab.
Progressive KMeans#
This implémentation is based on scikit-learn mini-batch KMeans
The notebook shown below is downloadable here. You can display this example in a new tab.
TSNE 2D#
The implementation used here is based on PANENE, an algorithm for the k-nearest neighbor (KNN) problem and more specificaly on this example.
The algorithm is described in the paper: J. Jo, J. Seo and J. -D. Fekete, “PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors,” in IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 2, pp. 1347-1360, 1 Feb. 2020, doi: 10.1109/TVCG.2018.2869149.
The PANENE implementation for python is called pynene.
As pynene is not part of progressivis you have to install it before running this example.
In an environment containing progressivis, ipyprogressivis and a C++ compiler you can install pynene via pip this way:
pip install git+https://github.com/progressivis/PANENE.git@progressivis
The notebook shown below is downloadable here. You can display this example in a new tab.