How to plot subplots from a condition applied on a single column and the data available on another single column of the same dataframe?

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I have a dataframe which is much like the one following:

data = {'A':[21,22,23,24,25,26,27,28,29,30,11,12,13,14,15,16,17,18,19,20,1,2,3,4,5,6,7,8,9,10],
        'B':[8,8,8,8,8,8,8,8,8,8,5,5,5,5,5,5,5,5,5,5,3,3,3,3,3,3,3,3,3,3],
        'C':[10,15,23,17,18,26,24,30,35,42,44,42,38,36,34,30,27,25,27,24,1,0,2,3,5,26,30,40,42,50]}

data_df = pd.DataFrame(data)
data_df

I would like to have the subplots, the number of subplots should be equal to number of unique values of column 'B'. X axis = Values in column 'A' and Y axis = values in Column 'C'.

The code that I tried:

fig = px.line(data_df, 
              x='A', 
              y='C', 
              color='B',
              facet_col = 'B',
              )
fig.show()

gives output like

enter image description here

However, I would like to have the graphs in a single column, each graph autoscaled to the relevant area and resolution on the axes. Possibility: Can I somehow make use of groupby command to do it?

Since I may have other number of unique values in column 'B' (for example 5 unique values) based on other data, I would like to have this piece of code to work dynamic. Kindly help me.

PS: plotly express module is used to plot the graph.

plotly-python pandas plotly subplot python-3.x
2 Answers

You can instead use the argument facet_row = 'B' which will automatically stack the subplots by rows. Then to automatically rescale, you'll want to set all of the x data to the same array of values, which can be done by looping through fig.data and modifying fig.data[i]['x'] for each i.

import pandas as pd
import plotly.express as px

data = {'A':[21,22,23,24,25,26,27,28,29,30,11,12,13,14,15,16,17,18,19,20,1,2,3,4,5,6,7,8,9,10],
        'B':[8,8,8,8,8,8,8,8,8,8,5,5,5,5,5,5,5,5,5,5,3,3,3,3,3,3,3,3,3,3],
        'C':[10,15,23,17,18,26,24,30,35,42,44,42,38,36,34,30,27,25,27,24,1,0,2,3,5,26,30,40,42,50]}

data_df = pd.DataFrame(data)
fig = px.line(data_df, 
              x='A', 
              y='C', 
              color='B',
              facet_row = 'B',
              )
for fig_data in fig.data:
    fig_data['x'] = list(range(len(fig_data['y'])))
fig.show()

enter image description here

user_53270680

In order to stack all subplot in one column, and make sure that each xaxis is independent, just add the following in your px.line() call:

facet_col_wrap=1

And then follow up with:

fig.update_xaxes(matches=None)

Plot 1: Default setup with px.line(facet_col = 'B')

enter image description here

If you'd like to display all x-axis labels just include this:

fig.update_xaxes(showticklabels = True)

Plot 2: Show x-axes for all subplots

enter image description here

Complete code:

import plotly.express as px
import pandas as pd
data = {'A':[21,22,23,24,25,26,27,28,29,30,11,12,13,14,15,16,17,18,19,20,1,2,3,4,5,6,7,8,9,10],
        'B':[8,8,8,8,8,8,8,8,8,8,5,5,5,5,5,5,5,5,5,5,3,3,3,3,3,3,3,3,3,3],
        'C':[10,15,23,17,18,26,24,30,35,42,44,42,38,36,34,30,27,25,27,24,1,0,2,3,5,26,30,40,42,50]}

data_df = pd.DataFrame(data)
data_df

fig = px.line(data_df, 
              x='A', 
              y='C', 
              color='B',
              facet_col = 'B',
              facet_col_wrap=1
             )
fig.update_xaxes(matches=None, showticklabels = True)
fig.show()
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