lift chart for predictions - should work for matching vectors, similar to a residuals plots. This can also be used for plotting variables against each other, similar to a scatter graph.

lift_chart.py

import numpy as np
import pandas as pd
import seaborn as sns
import scipy


def lift_chart(y_true, y_pred, bins=10, ax=None, normalize=False, labels=None):
    '''Given matched vectors of true versus predicted
    targets, plot them against each other.
    
    This can be useful for comparing predictions vs targets
    across the whole spectrum. Often a model fits well to
    the mid-range, however, does poorly in the extremes. This
    plot can help spot these problems.
    
    The plot is inspired by Datarobot's lift chart visualization:
    https://twitter.com/hackathorn/status/868136907301146624    
    
    Parameters:
    -----------
    y_true : target values for model
    y_pred : predicted values
    bins : number of bins
    ax : axis, if the lift chart is to be included
      in an existing figure
    normalize : if y_true and y_pred should be (z-)normalized. This
      can be useful to look at variable correlations, where the
      variables can have a different scale.
    labels : the labels to show in the legend/colorbar.
    
    Returns:
    --------
    corr : the Spearman correlation between predicted and actual targets.
    
    Example:
    --------
    >> a = np.random.randn(10000)
    >> lift_chart(a, np.random.randn(10000) * 0.9 + a, bins=100)
    '''    
    if isinstance(y_true, (pd.Series, pd.DataFrame)):
        y_true = y_true.values
    if isinstance(y_pred, (pd.Series, pd.DataFrame)):
        y_pred = y_pred.values

    if normalize:
        y_pred = scipy.stats.zscore(y_pred)
        y_true = scipy.stats.zscore(y_true)
    means, _, _ = scipy.stats.binned_statistic(
        y_true, [y_true, y_pred], bins=bins
    )
    for i, l_mean in enumerate(means):
        if labels is None:
            label='target' if i == 0 else 'pred %d' % i
        else:
            label = labels[i]
        ax = sns.lineplot(
            range(bins), l_mean, label=label,
            ax=ax
        )
    ax.set(xlabel='Target Ranking', ylabel='Target')
    box = ax.get_position()
    ax.legend(
        bbox_to_anchor=(0.3, 0.99),
        ncol=1,     
    )
    print(scipy.stats.spearmanr(y_true, y_pred))
    return ax