def priceByManufacturer(figure, gridSpec=None, pdData=None, axes=None):
if not axes:
axes = figure.add_subplot(gridSpec[0, 0])
manufacturerIds = cf_data.getManufacturerIds(pdData)
minData = pdData.groupby("manufacturer", sort=True)["price"].min()
meanData = pdData.groupby("manufacturer", sort=True)["price"].mean()
maxData = pdData.groupby("manufacturer", sort=True)["price"].max()
axes.set_title("Price by Manufacturers", fontsize=20)
axes.plot(manufacturerIds, minData, c=colors[2], linewidth=4, alpha=0.7)
axes.plot(manufacturerIds, meanData, c=colors[3], linewidth=4, alpha=0.7)
axes.plot(manufacturerIds, maxData, c=colors[4], linewidth=4, alpha=0.7)
axes.set_xticks(range(-1, 13))
axes.set_xticklabels([" "] + cf_data.getManufacturerLabels(pdData))
axes.set_xlabel("Manufacturer", fontsize=16)
axes.set_ylabel("Price", fontsize=16)
patchLow = mpl.patches.Patch(color=colors[2], alpha=0.7, label="Low")
patchMean = mpl.patches.Patch(color=colors[3], alpha=0.7, label="Mean")
patchHigh = mpl.patches.Patch(color=colors[4], alpha=0.7, label="High")
axes.legend(handles=[patchHigh, patchMean, patchLow], loc=2)
if gridSpec:
gridSpec.tight_layout(figure)
return axes
def mpgByManufacturer(figure, gridSpec=None, pdData=None, axes=None):
if not axes:
axes = figure.add_subplot(gridSpec[0, 0])
data = cf_data.getNumericData(pdData)
axes.set_title("MPG by Manufacturers", fontsize=20)
axes.scatter(data["manufacturer"],
data["highway mpg"],
c=colors[3],
s=500,
alpha=0.4)
axes.scatter(data["manufacturer"],
data["city mpg"],
c=colors[0],
s=500,
alpha=0.4)
axes.set_xticks(range(0, 13))
axes.set_xticklabels(cf_data.getManufacturerLabels(pdData))
axes.set_xlabel("Manufacturer", fontsize=16)
axes.set_ylabel("MPG", fontsize=16)
patchCity = mpl.patches.Patch(color=colors[0], alpha=0.7, label="City")
patchHighway = mpl.patches.Patch(color=colors[3],
alpha=0.7,
label="Highway")
axes.legend(handles=[patchCity, patchHighway], loc=2)
if gridSpec:
gridSpec.tight_layout(figure)
return axes
def inverseRiskinessAndLossesCombinedByManufacturer(figure,
gridSpec=None,
pdData=None,
axes=None,
xLabel=True,
rotateTicks=False):
if not axes:
axes = figure.add_subplot(gridSpec[0, 0])
if rotateTicks:
plt.xticks(rotation=70)
manufacturerIds = cf_data.getManufacturerIds(pdData)
riskMins = pdData.groupby("manufacturer")["riskiness"].min().values
riskMeans = pdData.groupby("manufacturer")["riskiness"].mean().values
riskMaxs = pdData.groupby("manufacturer")["riskiness"].max().values
lossMins = pdData.groupby("manufacturer")["losses"].min().values
lossMeans = pdData.groupby("manufacturer")["losses"].mean().values
lossMaxs = pdData.groupby("manufacturer")["losses"].max().values
mins = riskMins + lossMins
means = riskMeans + lossMeans
maxs = riskMaxs + lossMaxs
minColor = colors[0]
meanColor = colors[3]
maxColor = colors[2]
barMins = axes.bar(manufacturerIds,
mins,
align="center",
color=minColor,
alpha=0.7)
barMeans = axes.bar(manufacturerIds,
means,
align="center",
color=meanColor,
alpha=0.7,
bottom=mins)
barMaxs = axes.bar(manufacturerIds,
maxs,
align="center",
color=maxColor,
alpha=0.7,
bottom=means + mins)
axes.set_title(
"Inverse Riskiness and Losses Combined by Manufacturers\n(Normalized Data)",
fontsize=20)
axes.set_xticks(range(0, 13))
axes.set_xticklabels(cf_data.getManufacturerLabels(pdData))
if xLabel:
axes.set_xlabel("Manufacturer", fontsize=16)
axes.set_ylabel("Inverse Riskiness", fontsize=16)
axes.legend([barMaxs, barMeans, barMins], ["Max", "Mean", "Min"], loc=2)
if gridSpec:
gridSpec.tight_layout(figure)
return axes