How to get two coaxial pieces of data to be displayed together instead of divided into two views? about pygwalker HOT 5 CLOSED

This feature is in the draft stage in Graphic-Walker, the developer of Graphic-Walker told me it will be released soon.

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For now, you can make it with pandas APIs.

Suppose we have a dataframe df with fields A, B and date, and now we want an overlay view of A & date and B & date.

To make this, you can use df = df.melt(id_vars='date', value_vars=['A', 'B']) to unpivot A and B into one column , run pyg.walk(df) and then drag the merged 'variable' field onto the color channel to distinguish A and B in the value & date view.

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Hi, I am interested in contributing to this Issue.

However, I am new to this repository.

I understand the modifications described, but I am not sure where I should write the procedure. Could you please give me a guide here?

from pygwalker.

Hi, I am interested in contributing to this Issue.

However, I am new to this repository.

I understand the modifications described, but I am not sure where I should write the procedure. Could you please give me a guide here?

@d505 Sorry for the late reply, I didn't notice this comment. it's now a feature of https://github.com/Kanaries/Graphic-Walker, you can view it here Kanaries/graphic-walker#61

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Hi there, I think I am having the same issue with a dataset of mine.
What I would like to achieve is multiple lines for the same X and Y Axis without creating new plots for each of this "categories". When I try to put this dataset into a single plot with a single attribute for the X-Axis and the Y-Axis and I try to make the distinction of two categories with the Color and the Shape view, the coloring and shaping looks fine but only a single line is plotted.

When I put another item on the X-Axis I get two plots with one line plot in each and this is kind of the plot I would like to have, but in a single plot. Maybe there should be a toggle to select either of those two variants?

I hope this description makes sense, thanks for any help!

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