Plot grid of histograms based on group variable using plotly

Question

I have a data frame that contains multiple variables where each variable is logically connected to a factor level of an additional group variable. I would like to plot a histogram of each variable in such a way that it is possible to show a grid of multiple histograms 'group-wise'.

Here's an example data frame df_melt (the variables var_1,var_2,var_3,var_4 are logically connected to the factor level 'foo', the variables var_5,var_6,var_7 belong to factor level 'bar'):

import numpy as np
import pandas as pd

# simulate data and create plot-ready dataframe
np.random.seed(42)
var_values = np.random.randint(low=1,high=100,size=(100,7))
var_names = ['var_1','var_2','var_3','var_4','var_5','var_6','var_7']
group_names = ['foo','foo','foo','foo','bar','bar','bar']

df = pd.DataFrame(var_values,columns=var_names)
multi_index = pd.MultiIndex.from_arrays([df.columns,group_names],names=['variable','group'])
df.columns = multi_index
df_melt = pd.melt(df)

The output should look like this:

These stackoverflow posts might help to provide an answer, but I was not able to come up with a solution on my own:

Plotting a grouped pandas data in plotly

Plotly equivalent for pd.DataFrame.hist

You say you weren't able to come up with a solution on your own, but can you show what you habe tried? — vestland
– vestland, Commented Nov 24, 2020 at 16:20

Hans Bambel · Accepted Answer · 2020-11-24 23:09:59Z

Best I came up with is the following. Sadly, this is not in the nicely plotted format that you wanted, but I think/hope you can start with this.

import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# simulate data and create plot-ready dataframe
np.random.seed(42)
var_values = np.random.randint(low=1,high=100,size=(100,7))
var_names = ['var_1','var_2','var_3','var_4','var_5','var_6','var_7']
group_names = ['foo','foo','foo','foo','bar','bar','bar']

df = pd.DataFrame(var_values,columns=var_names)
multi_index = pd.MultiIndex.from_arrays([df.columns,group_names],names=['variable','group'])
df.columns = multi_index
df_melt = pd.melt(df)


uniq_cols = set(group_names)
for col in uniq_cols:
    rows = df_melt[df_melt['group']==col]['variable'].unique()
#     print(list(rows))
    num_vars = len(rows)
    
    fig = make_subplots(rows=1, cols=len(rows), column_titles=list(rows))
    for i, row in enumerate(rows):
        fig.add_trace(go.Histogram(x=df_melt[(df_melt['group']==col) & (df_melt['variable']==row)]['value']), 
                      row=1, col=i+1)
    
    fig.show()

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