python绘制二维核密度估计图（Kernel Density Estimation)

def kernelDensityEstimation():
    # Generate fake data
    df =  pd.read_csv("draw_picture/down_150.csv")

    x = df['time']
    y = df['score']

    # Calculate the point density
    xy = np.vstack([x,y])
    z = gaussian_kde(xy)(xy)

    # Sort the points by density, so that the densest points are plotted last
    idx = z.argsort()
    x, y, z = x[idx], y[idx], z[idx]

    fig, ax = plt.subplots()
    # reverse bar color
    plt.scatter(x, y,c=z, s=20, cmap='Spectral_r')
    # plt.title("up_60")
    cb = plt.colorbar(extend='both')
    cb.ax.yaxis.set_tick_params(labelright=False)
    plt.ylabel("Discomfort Score")
    plt.xlabel("duration")
    plt.show()