Introduction

This example demonstrates how to create an interactive plot with dropdown menus using Plotly in Python. The plot displays two lines for each unique value of stat_type in the dataset.

Requirements

• Python 3.x
• Plotly library (pip install plotly)
• pandas library (pip install pandas)

Code Explanation

The code begins by importing necessary libraries and creating a sample dataset. It then processes this data to organize it into separate dataframes for each unique value of stat_type.

Next, the code uses these dataframes to create two scatter plots: one for each sub_id across dates. These plots are added to a figure using Plotly.

To add dropdown menus to the plot, we define a list of buttons, where each button corresponds to a specific dataframe. We then update the layout of this figure with these buttons.

Finally, we display this interactive plot using fig.show().

Step-by-Step Solution

1. Import Libraries and Load Data

import pandas as pd import plotly.graph_objects as go

2.  **Create Sample Dataset and Process It**

    ```python
dfi = pd.DataFrame({
    'date': ['2020-01-01', '2020-02-01', '2020-03-01'],
    'sub_id': [1233, 1233, 3424],
    'stat_type': ['link_clicks', 'transaction', 'customer_signups'],
    'value': [12, 50, 9]
})

# change some types
dfi['date'] = pd.to_datetime(dfi['date'])
dfi['sub_id'] = dfi['sub_id'].astype(str)

3. Organize Dataframes by Stat_Type

groups = dfi[‘stat_type’].unique().tolist() dfs = {} for g in groups: dfs[str(g)] = dfi[dfi[‘stat_type’] == g]

4.  **Create Scatter Plots for Each DataFrame**

    ```python
dfp = {}
for df in dfs.values():
    dfp[df.name] = df.pivot(index='date', columns='sub_id', values='value')

5. Define Dropdown Buttons and Update Figure Layout

buttons = [] updatemenu = [] your_menu = dict() buttons.append(dict(method=‘restyle’, label=df.name, visible=True, args=[{‘y’:[dfp[df.name][‘1233’].values, dfp[df.name][‘3424’].values], ‘x’:[dfp[df.name].index], ’type’:‘scatter’}], ))

updatemenu.append(your_menu) updatemenu[0][‘buttons’] = buttons updatemenu[0][‘direction’]=‘down’ updatemenu[0][‘showactive’]=True

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

6.  **Add Annotations and Display Interactive Plot**

    ```python
fig.update_layout(
    annotations=[
        go.layout.Annotation(text="<b>stat_type:</b>",
                             x=-0.3, xref="paper",
                             y=1.1, yref="paper",
                             align="left", showarrow=False),
                          ]
)

7. Show the Interactive Plot

fig.show()

### Output

The code generates an interactive plot where you can select a stat_type from the dropdown menu and view corresponding sub_id data points over time.

When you run this example, it will display an interactive plot in your web browser or IDE.

Last modified on 2023-10-31