4.2. Charts

import plotly.graph_objects as go

def plot_AvE(df, factor, min = 0, max = 0, cut_bins = None, y_min = 0, y_max = 1):

df_copy = df.copy()

if cut_bins is not None:
    df_copy[factor] = df_copy[factor].astype(float).clip(lower=min, upper=max)
    bins = pd.cut(df_copy[factor], bins=cut_bins)
    df_copy[factor] = bins.apply(lambda x: x.left)

agg_df = df_copy.groupby(factor).agg({
    'RenewalInviteFlag': 'sum',
    'RenewalFlag': 'mean',
    'base_predictions': 'mean',
    'new_predictions': 'mean',
    'reg_predictions': 'mean',
    'payment_prediction': 'mean'
}).reset_index()

fig = go.Figure()

# Add bar chart trace
fig.add_trace(go.Bar(x=agg_df[factor], y=agg_df['RenewalInviteFlag'], name='Invites', yaxis='y1'))

# Add line chart traces
fig.add_trace(go.Scatter(x=agg_df[factor], y=agg_df['RenewalFlag'], mode='lines+markers', name='Renewals', yaxis='y2'))
fig.add_trace(go.Scatter(x=agg_df[factor], y=agg_df['base_predictions'], mode='lines+markers', name='BasePredictions', yaxis='y2'))
fig.add_trace(go.Scatter(x=agg_df[factor], y=agg_df['new_predictions'], mode='lines+markers', name='NewPredictions', yaxis='y2'))
fig.add_trace(go.Scatter(x=agg_df[factor], y=agg_df['payment_prediction'], mode='lines+markers', name='PayPredictions', yaxis='y2'))




fig.update_layout(
    title='Actuals v Base Prediction v New Predictions',
    xaxis=dict(title='Category'),
    yaxis=dict(
        title='Invites',
        side='left'
    ),
    yaxis2=dict(
        title='Retention Rate',
        overlaying='y',
        side='right',
        range=[y_min, y_max]
    ),
    legend=dict(x=0.99, y=0.01)
)

return fig