dfM = df_5y_each.copy()
year = 2016
mosaic = [[1,2],
          [3,4],]
          #[5,6]]
with cplt.Multiple(figsize=(6,6),
              dpi=300,
              mosaic=mosaic,
              sharex=True,sharey=True
              ) as mul:
    for i,year in enumerate([2016, 2017, 2018, 2019]): #, 2020]):
        with mul.Single(index=i+1, 
                        title="Title",
                        xlabel=f"Total births", 
                        ylabel=f"Off-site deliveries", 
                        ) as p:
            total_col = ("total", year)
            offsite_col = ("offsite", year)
            p.ax.scatter(dfM[total_col], dfM[offsite_col], s=6)

            # Prediction
            dfM["count"] = 1
            exog =  pd.concat((dfM["count"], dfM[total_col]),axis=1)
            res = sm.OLS(dfM[offsite_col], exog).fit()
            pred_birth = [[1, 0],[1, dfM[total_col].max()]]
            pred_offsite = res.predict(pred_birth)
            p.ax.plot([0, dfM[total_col].max()], pred_offsite, ls="--")

            for city, x,y in zip(dfM.city, 
                                 dfM[total_col], 
                                 dfM[offsite_col],
                                 ):
                if y > 40 or x >20_000 or city == "茨城県":
                    p.ax.annotate(city, (x,y), fontsize=8)