plotly histogram with filtering toolbar - python

I'm trying to generate a plotly histogram that can be filtered by values with a df column. Below, in dataset, I want to be able to filter the values in item to show the distribution of values for each unique value.

However, all I am able to display is one single value. I can't change values to show the results of differing items.

dataset = pd.DataFrame(
    {'item': ['A', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'C', 'D'],
    'value': [27, 33, 3, 26, 8, 27, 3, 13, 16, 2, 27, 17, 12, 6, 28],
    }
)

continuous_vars = ["item"]

fig = go.Figure()

fig.add_trace(
    go.Histogram(x = dataset.item,
                 xbins=go.histogram.XBins(size=5), # Change the bin size
                 marker=go.histogram.Marker(color="orange"), # Change the color
             )
)

buttons = []

for col in continuous_vars:
    buttons.append(dict(method='restyle',
                        label=col,
                        visible=True,
                        args=[{"x":[dataset[col]],
                               "type":'histogram'}, [0]],
                        )
                  )

updatemenu = []
your_menu = dict()
updatemenu.append(your_menu)

updatemenu[0]['buttons'] = buttons
updatemenu[0]['direction'] = 'down'
updatemenu[0]['showactive'] = True

fig.update_layout(showlegend=False, updatemenus=updatemenu)
fig.show()

The issue with your current approach is that you're creating a histogram of the 'item' column itself, but what you likely want is to show the distribution of 'value' filtered by each 'item'.

import pandas as pd
import plotly.graph_objects as go

# Sample dataset
dataset = pd.DataFrame(
    {'item': ['A', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'C', 'D'],
     'value': [27, 33, 3, 26, 8, 27, 3, 13, 16, 2, 27, 17, 12, 6, 28],
    }
)

# Create the figure
fig = go.Figure()

# Add an initial empty histogram
fig.add_trace(
    go.Histogram(
        x=[],
        marker=dict(color="orange"),
        name="Value Distribution"
    )
)

# Create buttons to filter by each unique item
buttons = []
unique_items = dataset['item'].unique()

# Add a button for "All" items
buttons.append(
    dict(
        method='update',
        label='All',
        args=[
            {'x': [dataset['value']]},
            {'title': 'Distribution of all values'}
        ]
    )
)

# Add a button for each unique item
for item in unique_items:
    filtered_data = dataset[dataset['item'] == item]['value']
    buttons.append(
        dict(
            method='update',
            label=f'Item {item}',
            args=[
                {'x': [filtered_data]},
                {'title': f'Distribution of values for Item {item}'}
            ]
        )
    )

# Create the dropdown menu
updatemenu = [
    dict(
        buttons=buttons,
        direction='down',
        showactive=True,
        x=0.1,
        y=1.15,
        xanchor='left',
        yanchor='top'
    )
]

# Set the layout
fig.update_layout(
    title='Distribution of values',
    xaxis_title='Value',
    yaxis_title='Count',
    updatemenus=updatemenu,
    margin=dict(t=80),
    bargap=0.1
)

# Set the initial display to show all values
fig.update_traces(x=dataset['value'])

# Show the figure
fig.show()

The Graph

<iframe src="https://bhargav-ravinuthala.github.io/plotyd/" width="700" height="450"></iframe>