Visualization

EDA is a crucial step in any Data Science project. For large datasets, EDA can be time-consuming.

PyGWalker simplifies the process of creating visualizations by allowing users to drag and drop variables to create charts without writing much code.

You can use PyGWalker without changing your existing workflow. For example, you can call up PyGWalker with the Dataframe loaded in this way:

import pygwalker as pyg
import pandas as pd

df = pd.read_csv("https://kanaries-app.s3.ap-northeast-1.amazonaws.com/public-datasets/bike_sharing_dc.csv", parse_dates=['date'])
df.head(10)

Output:

date month season hour year holiday temperature feeling_temp \
0 2011-01-01 1 winter 0 2011 no 3.28 3.0014
1 2011-01-01 1 winter 1 2011 no 2.34 1.9982
2 2011-01-01 1 winter 2 2011 no 2.34 1.9982
3 2011-01-01 1 winter 3 2011 no 3.28 3.0014
4 2011-01-01 1 winter 4 2011 no 3.28 3.0014

humidity winspeed casual registered count work yes or not am or pm \
0 81.0 0.0 3 13 16 0 am
1 80.0 0.0 8 32 40 0 am
2 80.0 0.0 5 27 32 0 am
3 75.0 0.0 3 10 13 0 am
4 75.0 0.0 0 1 1 0 am

Day of the week
0 6
1 6
2 6
3 6
4 6

And then just walk around!

Link to PygWalker.

Run in Google Colab.
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Knowledge graphs visually represent the relationships between entities, allowing users to understand complex connections more easily. To effortlessly convert text or URLs into insightful knowledge graphs, use InstaGraph.

Link to Instagraph.
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If you want to transform your matplotlib plots into nice figures for scientific papers or presentations, try LovelyPlots.

To use LovelyPlots, simply add plt.style.use(ipynb) to your code.

Link to LovelyPlots.

Code to create the figures
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If you want to effortlessly animate your matplotlib plot in Python, use gif.

The animation above is created using gif.