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> start with seaborn, then modify the underlying matplotlib figure Maybe this is the heart of my complaint: last I remember trying Seaborn, I found it hard to

> start with seaborn, then modify the underlying matplotlib figure Maybe this is the heart of my complaint: last I remember trying Seaborn, I found it hard to get access to the underlying Axes objects, and I also found it hard/impossible to add elements incrementally to an existing Axes. This might be a PEBCAK issue so I'm willing to try it again.

magnio 3y ago
Understandable. A reason why I keep going back and forth between matplotlib and seaborn in the past is due to not knowing how to customize seaborn. Now that I know they are just regular matplotlib objects, it's very easy to mix and match them when seaborn is just too opinionated. You can read more here https://seaborn.pydata.org/tutorial/introduction.html#opinionated-defaults-and-flexible-customization https://seaborn.pydata.org/tutorial/introduction.html#opinio... Some examples I did recently: to set the layout engine of a seaborn plot g = sns.relplot(df, x="value", y="y", col="variable", col_wrap=3) g.figure.set_layout_engine("constrained") g.figure.set_size_inches(16, 12) Set the subplot titles g = sns.displot(df, x="value", col="model") g.figure.set_size_inches(16, 8) for (ax, title) in zip(g.axes[0], titles): ax.set_title(f"Title: {title}") Multiple seaborn plots with two lines, error band, and scatter placed onto existing matplotlib subplots and custom colors colors = sns.color_palette() fig, axs = plt.subplots(1, len(ys), sharey=True, figsize=(16, 6), constrained_layout=True) for (X, y, ax, title) in zip(Xs, ys, axs, titles): sns.lineplot(x=X, y=y, color=colors[0], ax=ax) sns.lineplot(x=X, y=y_predict, color=colors[1], ax=ax) ax.fill_between(X, y_predict - std, y_predict + std, color=colors[1], alpha=0.3) sns.scatterplot(x=X_train, y=y_train, alpha=0.7, color=colors[7], ax=ax) ax.set_title(title) ax.tick_params(rotation=0) (You can also prepare the data in pandas the way seaborn like it and create faceted plot declaratively; the point is you can mix and match them freely and intuitively.)

magnio 3y ago

Understandable. A reason why I keep going back and forth between matplotlib and seaborn in the past is due to not knowing how to customize seaborn. Now that I know they are just regular matplotlib objects, it's very easy to mix and match them when seaborn is just too opinionated. You can read more here https://seaborn.pydata.org/tutorial/introduction.html#opinionated-defaults-and-flexible-customization https://seaborn.pydata.org/tutorial/introduction.html#opinio... Some examples I did recently: to set the layout engine of a seaborn plot g = sns.relplot(df, x="value", y="y", col="variable", col_wrap=3) g.figure.set_layout_engine("constrained") g.figure.set_size_inches(16, 12) Set the subplot titles g = sns.displot(df, x="value", col="model") g.figure.set_size_inches(16, 8) for (ax, title) in zip(g.axes[0], titles): ax.set_title(f"Title: {title}") Multiple seaborn plots with two lines, error band, and scatter placed onto existing matplotlib subplots and custom colors colors = sns.color_palette() fig, axs = plt.subplots(1, len(ys), sharey=True, figsize=(16, 6), constrained_layout=True) for (X, y, ax, title) in zip(Xs, ys, axs, titles): sns.lineplot(x=X, y=y, color=colors[0], ax=ax) sns.lineplot(x=X, y=y_predict, color=colors[1], ax=ax) ax.fill_between(X, y_predict - std, y_predict + std, color=colors[1], alpha=0.3) sns.scatterplot(x=X_train, y=y_train, alpha=0.7, color=colors[7], ax=ax) ax.set_title(title) ax.tick_params(rotation=0) (You can also prepare the data in pandas the way seaborn like it and create faceted plot declaratively; the point is you can mix and match them freely and intuitively.)