Exemplo n.º 1
0
def make_autos_losses_plot(
    figure, gs=None, pddata=None, axes=None, legend=True, labels=True):
    if not axes:
        axes = figure.add_subplot(gs[0, 0])
    loss_mins = pddata.groupby("make")["losses"].min().values
    loss_means = pddata.groupby("make")["losses"].mean().values
    loss_maxs = pddata.groupby("make")["losses"].max().values
    make_ids = geradados.get_make_ids(pddata)
    min_color = colors[0]
    mean_color = colors[3]
    max_color = colors[2]
    axes.set_title("Inverse Losses", fontsize=14)
    mins_bar = axes.bar(make_ids, loss_mins, width=0.5, align="center",
                        color=min_color, alpha=0.7)
    means_bar = axes.bar(make_ids, loss_means, width=0.5, align="center",
                         bottom=loss_mins, color=mean_color, alpha=0.7)
    maxs_bar = axes.bar(make_ids, loss_maxs, width=0.5, align="center",
                        bottom=loss_means + loss_mins, color=max_color, alpha=0.7)
    if labels:
        axes.set_xticks(range(0, 13))
        axes.set_xticklabels(geradados.get_make_labels(pddata))
        axes.set_xlabel("Make", fontsize=12)
        axes.set_ylabel("Inverse Losses", fontsize=12)
    else:
        axes.set_xticklabels([])
        axes.set_yticklabels([])
    if legend:
        axes.legend([mins_bar, means_bar, maxs_bar], ["Min", "Mean", "Max"], loc=2)
    if gs:
        gs.tight_layout(figure)
    return axes
Exemplo n.º 2
0
def make_autos_loss_and_risk_plot(figure,
                                  gs=None,
                                  pddata=None,
                                  axes=None,
                                  x_label=True,
                                  rotate_ticks=False):
    if not axes:
        axes = figure.add_subplot(gs[0, 0])
    if rotate_ticks:
        plt.xticks(rotation=70)
    risk_mins = pddata.groupby("make")["riskiness"].min().values
    risk_means = pddata.groupby("make")["riskiness"].mean().values
    risk_maxs = pddata.groupby("make")["riskiness"].max().values
    loss_mins = pddata.groupby("make")["losses"].min().values
    loss_means = pddata.groupby("make")["losses"].mean().values
    loss_maxs = pddata.groupby("make")["losses"].max().values
    mins = risk_mins + loss_mins
    means = risk_means + loss_means
    maxs = risk_maxs + loss_maxs
    make_ids = geradados.get_make_ids(pddata)
    min_color = colors[0]
    mean_color = colors[3]
    max_color = colors[2]
    axes.set_title(("Combined Losses and Riskiness Data\n"
                    "(Inverted, Normalized)"),
                   fontsize=16)
    mins_bar = axes.bar(make_ids,
                        mins,
                        align="center",
                        color=min_color,
                        alpha=0.7)
    means_bar = axes.bar(make_ids,
                         means,
                         align="center",
                         bottom=mins,
                         color=mean_color,
                         alpha=0.7)
    maxs_bar = axes.bar(make_ids,
                        maxs,
                        align="center",
                        bottom=means + mins,
                        color=max_color,
                        alpha=0.7)
    axes.set_xticks(range(0, 13))
    axes.set_xticklabels(geradados.get_make_labels(pddata))
    if x_label:
        axes.set_xlabel("Make", fontsize=12)
    axes.set_ylabel("Risco", fontsize=12)
    axes.legend([mins_bar, means_bar, maxs_bar], ["Min", "Mean", "Max"], loc=2)
    if gs:
        gs.tight_layout(figure)
    return axes
Exemplo n.º 3
0
def make_autos_mpg_plot(figure, gs = None, pddata = None, axes = None):
    if not axes:
        axes = figure.add_subplot(gs[0, 0])
    data = geradados.get_numeric_data(pddata)
    axes.set_title("Ranges of Cidades e Velocidade", fontsize = 20)
    axes.scatter(data["make"], data["highway mpg"], c = colors[3], s = 500, alpha = 0.4)
    axes.scatter(data["make"], data["city mpg"], c = colors[0], s = 500, alpha = 0.4)
    axes.set_xticks(range(0, 13))
    axes.set_xticklabels(geradados.get_make_labels(pddata))
    axes.set_xlabel("Make", fontsize = 16)
    axes.set_ylabel("MPG", fontsize = 16)
    city_patch = mpl.patches.Patch(color = colors[0], alpha = 0.7, label = "City")
    highway_patch = mpl.patches.Patch(color=colors[3], alpha=0.7, label = "Highway")
    axes.legend(handles=[city_patch, highway_patch], loc = 2)
    if gs:
        gs.tight_layout(figure)
    return axes
Exemplo n.º 4
0
def make_autos_price_plot(figure, gs=None, pddata=None, axes=None):
    if not axes:
        axes = figure.add_subplot(gs[0, 0])
    min_data = pddata.groupby("make", sort=True)["price"].min()
    max_data = pddata.groupby("make", sort=True)["price"].max()
    mean_data = pddata.groupby("make", sort=True)["price"].mean()
    make_ids = geradados.get_make_ids(pddata)
    axes.set_title("Auto Price Ranges", fontsize=20)
    axes.plot(make_ids, min_data, c=colors[2], linewidth=4, alpha=0.7)
    axes.plot(make_ids, mean_data, c=colors[3], linewidth=4, alpha=0.7)
    axes.plot(make_ids, max_data, c=colors[4], linewidth=4, alpha=0.7)
    axes.set_xticks(range(-1, 13))
    axes.set_xticklabels([" "] + geradados.get_make_labels(pddata))
    axes.set_xlabel("Make", fontsize=16)
    axes.set_ylabel("Price", fontsize=16)
    high_patch = mpl.patches.Patch(color=colors[4], alpha=0.7, label="High")
    mean_patch = mpl.patches.Patch(color=colors[3], alpha=0.7, label="Mean")
    low_patch = mpl.patches.Patch(color=colors[2], alpha=0.7, label="Low")
    axes.legend(handles=[high_patch, mean_patch, low_patch], loc=2)
    if gs:
        gs.tight_layout(figure)
    return axes