Matplotlib Mastery Quiz — Quiz

Test your command of Figures, Axes, formatting, date axes, seaborn and chart export. · 10 questions

Question 1 of 10

In Matplotlib, what does a single `Axes` object represent?

aThe x-axis ruler onlybOne plotting panel with its own x and y axiscThe entire saved imagedA colour map

Question 2 of 10

Which call is the recommended way to create a figure and one Axes in object-oriented style?

aplt.figure().plot()bplt.axes_create()cfig, ax = plt.subplots()dax = plt.Axes()

Question 3 of 10

Which file format is best for a chart that must stay crisp at any zoom level on a website?

aSVGbJPGcBMPdLow-DPI PNG

Question 4 of 10

Why should you avoid using only red vs green to separate profit and loss series?

aMatplotlib cannot draw redbGreen lines are slower to rendercIt increases file sizedMany readers have red-green colour vision deficiency

Question 5 of 10

What will the y-axis tick for 2500000 display?

from matplotlib.ticker import FuncFormatter
ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f'₹{x/1e5:.0f}L'))

a₹2500000b₹25Lc₹2.5Ld₹25Cr

Question 6 of 10

By default, what do the bar heights in `sns.barplot(data=df, x='city', y='sales')` show?

aThe sum of sales per citybThe count of rows per citycThe mean of sales per city with a confidence intervaldThe median of sales

Question 7 of 10

Which formatter gives compact, non-repetitive multi-level date labels automatically?

amdates.ConciseDateFormatterbticker.PercentFormattercmdates.DayLocatordticker.NullFormatter

Question 8 of 10

Why is `sharey=True` important in a small-multiples dashboard?

aIt makes the figure render fasterbIt hides the y-axis completelycIt lets seaborn compute confidence intervalsdIt puts every panel on the same scale so heights are comparable

Question 9 of 10

A Django view generates a chart on every request and memory keeps growing. What is the most likely fix?

def chart(request):
    fig, ax = plt.subplots()
    ax.plot(get_data())
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    return HttpResponse(buf.getvalue(), content_type='image/png')

aCall plt.close(fig) after savingbIncrease the dpicUse plt.show() instead of savefigdSwitch to JPG

Question 10 of 10

You plot daily sales where the date column holds strings like '05-04-2025'. Ticks are crowded and the spacing ignores missing days. What is the best fix?

aRotate the labels 90 degreesbConvert with pd.to_datetime(col, dayfirst=True) and sort before plottingcUse a bar chart insteaddSet plt.xticks([]) to hide labels