plotly_time_regression.py

import pandas as pd
import plotly.graph_objects as go
import plotly_express as px

gitcsv = 'https://raw.githubusercontent.com/justinhchae/medium/main/sample.csv'
df = pd.read_csv(gitcsv, index_col=0)

df['dates'] = pd.to_datetime(df['dates'])

freq='M' # or D or Y

df = df.groupby(['types', pd.Grouper(key='dates', freq=freq)])['types'].agg(['count']).reset_index()
df = df.sort_values(by=['dates', 'count']).reset_index(drop=True)

# group the dataframe
group = df.groupby('types')

# create a blank canvas
fig = go.Figure()

# each group iteration returns a tuple
# (group name, dataframe)
for group_name, df in group:
    fig.add_trace(
        go.Scatter(
              x=df['dates']
            , y=df['count']
            , fill='tozeroy'
            , name=group_name
        ))

    # generate a regression line with px
    help_fig = px.scatter(df, x=df['dates'], y=df['count']
                          , trendline="lowess")
    # extract points as plain x and y
    x_trend = help_fig["data"][1]['x']
    y_trend = help_fig["data"][1]['y']

    # add the x,y data as a scatter graph object
    fig.add_trace(
        go.Scatter(x=x_trend, y=y_trend
                   , name=str('trend ' + group_name)
                   , line = dict(width=4, dash='dash')))

    transparent = 'rgba(0,0,0,0)'

    fig.update_layout(
        hovermode='x',
        showlegend=True
        # , title_text=str('Court Data for ' + str(year))
        , paper_bgcolor=transparent
        , plot_bgcolor=transparent
        , title='Monthly Time Series of A and B with Regression'
    )


fig.show()