Visualizing Your Study Log

This chapter is the project checkpoint. You will add a small visualization layer to the study log.

The project already knows how to load sessions, create a DataFrame, and compute pandas summaries. Do not replace that structure. Add plotting helpers beside it.

The required DataFrame shape is still:

subject, minutes, date

The required charts are:

• total minutes by subject,
• study minutes over time,
• distribution of session lengths.

Keep the Layers Separate

The project should have one function that creates the DataFrame from sessions:

df = sessions_dataframe(log.sessions)

Plotting helpers accept that DataFrame. They should not read JSON, ask for input, or save the study log. Each helper prepares the summary it needs and draws one chart.

This separation keeps the project readable:

JSON file -> Session objects -> DataFrame -> summary table -> chart

Each arrow has one job.

The examples below use this six-row checkpoint fixture:

Subject Totals

Before plotting, the subject helper should produce this summary:

Use this implementation checklist:

1. Group the rows by subject and sum minutes.
2. Put the totals in descending order.
3. Create a figure and axes, then draw one bar per subject.
4. Use the required title and axis labels.
5. Return (fig, ax).

Daily Minutes

The daily helper should prepare one row per date in chronological order:

Use this checklist:

1. Work without changing the input DataFrame.
2. Convert date with pd.to_datetime.
3. Group by date and sum minutes.
4. Put the dates in chronological order.
5. Draw a line chart, set the required labels, and return (fig, ax).

Session Lengths

The histogram uses the six original session lengths:

45, 30, 60, 25, 90, 40

It should use five bins. Do not group the rows first: every original session contributes one value to the distribution.

Use this checklist:

1. Create a figure and axes.
2. Draw a five-bin histogram from the minutes column.
3. Use the required title and axis labels.
4. Return (fig, ax).

Saving the Charts

The plotting helpers return their figures so the caller controls what happens next. For example, the caller can save and close one chart:

fig, ax = plot_subject_totals(df)
fig.savefig("subject_totals.png")
plt.close(fig)

Use the same pattern for the other two helpers when you connect them to the full project.

Empty Data

If the DataFrame is empty, the functions should still return a labeled chart. It may have no bars, no line points, or no histogram bars. That is acceptable for the helper layer.

In the user-facing program, you can choose to skip saving charts when there are no sessions:

if len(df) == 0:
    print("No sessions to visualize yet.")
else:
    save_visualizations(df)

That keeps the user experience friendly without making every plotting helper responsible for printing messages.

Testing the Helpers

Start with the six-row fixture above instead of connecting the helpers to your real file immediately. Compare the prepared values and charts with the tables and images in this chapter.

Ask the same questions you have used throughout this material:

1. Does the chart answer the intended question?
2. Did the helper prepare the right summary first?
3. Are the labels readable?
4. Can you explain what the chart shows and what it does not show?

The exercise below practices the plotting layer with a plain DataFrame. In your full project, connect these helpers to the DataFrame produced from your StudyLog.

df = pd.DataFrame([
    {"subject": "math", "minutes": 45, "date": "2026-05-12"},
    {"subject": "history", "minutes": 30, "date": "2026-05-10"},
    {"subject": "math", "minutes": 60, "date": "2026-05-10"},
    {"subject": "programming", "minutes": 25, "date": "2026-05-11"},
    {"subject": "programming", "minutes": 90, "date": "2026-05-13"},
    {"subject": "writing", "minutes": 40, "date": "2026-05-11"},
])

Finally, do what the charts are for: read them. Write a short interpretation of each of your three project charts.

Before Moving On

By the end of this checkpoint, your project should still have the textual summaries from Using Libraries. The new charts are an addition, not a replacement.

The study log has grown carefully across the course: interaction, repetition, functions, files, summaries, JSON, classes, pandas, and now visualizations. Keeping the layers separate is what lets the program keep growing without becoming one giant script.