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Quiz: Data Visualization with matplotlib

Test your understanding of matplotlib, chart types, labels, subplots, and saving figures with these questions. Click "Show Answer" to reveal the correct answer and explanation.

1. What is the standard way to import matplotlib for chart creation?

  1. import matplotlib
  2. from matplotlib import charts as mc
  3. import matplotlib.pyplot as plt
  4. import pyplot from matplotlib
Show Answer

The correct answer is C. The standard convention is import matplotlib.pyplot as plt. The pyplot sub-module contains all the charting functions: plt.plot(), plt.show(), plt.title(), etc. The alias plt is universally used in the Python data science community — you will see it in every tutorial, textbook, and professional code example.

Concept Tested: Importing matplotlib

2. What does plt.show() do?

  1. Saves the current chart to a file named chart.png
  2. Clears all previous chart data and starts a new chart
  3. Renders and displays the chart that has been built up with previous plt.plot() calls
  4. Prints the raw data values used in the chart to the console
Show Answer

The correct answer is C. plt.show() renders and displays the chart window. You build up a chart by calling plt.plot(), plt.title(), plt.xlabel(), etc., then call plt.show() to actually display it. In Jupyter Notebooks, charts may render automatically without plt.show(), but it is always good practice to include it.

Concept Tested: plt.show()

3. Which matplotlib function creates a scatter plot?

  1. plt.dots(x, y)
  2. plt.points(x, y)
  3. plt.scatter(x, y)
  4. plt.spot(x, y)
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The correct answer is C. plt.scatter(x, y) creates a scatter plot — individual points plotted without connecting lines. Scatter plots are useful for showing the relationship (correlation) between two variables. Use plt.plot(x, y) for line charts (connecting dots). There are no dots(), points(), or spot() functions in matplotlib.

Concept Tested: Scatter Plots

4. What does plt.xlabel("Time (seconds)") do?

  1. Sets the chart title to "Time (seconds)"
  2. Adds a label to the x-axis (horizontal axis)
  3. Limits the x-axis range to 0-10 seconds
  4. Changes the x-axis tick marks to show seconds
Show Answer

The correct answer is B. plt.xlabel("text") adds a text label below the x-axis (the horizontal axis) of the chart. Similarly, plt.ylabel("text") labels the y-axis (vertical axis), and plt.title("text") sets the title at the top. Always label your axes so readers know what the data represents and what units are being used.

Concept Tested: Axis Labels and Titles

5. How do you add multiple lines to one chart?

  1. Call plt.new_line() before each plt.plot() call
  2. Pass all data as a nested list to a single plt.plot() call
  3. Call plt.plot() multiple times before calling plt.show()
  4. Create multiple figures with plt.figure() and overlay them
Show Answer

The correct answer is C. Each call to plt.plot() adds another line to the current chart. All lines are combined when you call plt.show(). Use the label= parameter on each plt.plot() call and then call plt.legend() to show a legend identifying which line is which.

Concept Tested: Multiple Lines

6. What type of chart is best for showing how a continuous value changes over time?

  1. A bar chart
  2. A histogram
  3. A pie chart
  4. A line chart
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The correct answer is D. A line chart (created with plt.plot()) is ideal for showing continuous data that changes over time — like temperature over a week, a stock price over months, or a physics experiment over seconds. Bar charts compare discrete categories. Histograms show frequency distributions. Pie charts show proportions of a whole.

Concept Tested: Chart Types

7. What does plt.grid(True) do?

  1. Divides the chart window into a grid of subplots
  2. Adds background grid lines to the chart to make values easier to read
  3. Snaps data points to the nearest grid intersection
  4. Displays the chart coordinates of every data point
Show Answer

The correct answer is B. plt.grid(True) adds light background grid lines to the chart, making it easier to read approximate values from the chart visually. Call it after your plt.plot() calls but before plt.show(). The grid does not affect the data or calculations — it is purely a visual aid for the reader.

Concept Tested: Grid Lines

8. What does plt.savefig("chart.png") do?

  1. Loads a previously saved chart from chart.png
  2. Takes a screenshot of the entire screen and saves it
  3. Saves the current chart to a file named chart.png instead of displaying it in a window
  4. Converts the chart to a PDF format automatically
Show Answer

The correct answer is C. plt.savefig("filename.png") saves the chart to an image file. Call it before plt.show() (or instead of it). You can save as PNG, PDF, SVG, or other formats by changing the file extension. This is essential for including charts in reports, websites, or sharing results without requiring the recipient to have matplotlib installed.

Concept Tested: Saving Figures

9. What does a histogram chart show?

  1. The change in one variable over time
  2. The correlation between two variables plotted as individual points
  3. The frequency distribution of a dataset — how often values fall within each range
  4. A comparison of totals across discrete categories
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The correct answer is C. A histogram (created with plt.hist()) shows how frequently values fall within ranges (called bins). For example, a histogram of student scores shows how many students scored 60-70, 70-80, 80-90, etc. It reveals the shape of the data distribution — whether it is symmetric, skewed, or has outliers. Bar charts compare distinct categories; histograms show continuous value distributions.

Concept Tested: Histograms

10. What does plt.subplots(1, 2) create?

  1. One chart with two y-axes for comparing different scales
  2. A figure with 1 row and 2 columns of chart panels side by side
  3. Two separate chart windows displayed on screen at once
  4. A single chart with two color themes applied simultaneously
Show Answer

The correct answer is B. fig, axes = plt.subplots(rows, cols) creates a figure with a grid of chart panels (subplots). plt.subplots(1, 2) creates one row and two columns — two charts side by side. You then plot to each one with axes[0].plot(...) and axes[1].plot(...). This is how you compare related datasets in a single figure.

Concept Tested: Subplots