Visualize & Explain the Dataset

Choose a chart for a question, label it clearly and turn the analysis into a repeatable report.

By the end of this lesson

You will be able to

Create line, bar, histogram and scatter plots.
Label axes and units and include a useful title.
Save figures and a summary report.
Explain findings without claiming causation.

Match the chart to the question

Use a line chart when order or time matters, a bar chart to compare categories, a histogram to inspect a distribution and a scatter plot to inspect the relationship between two numeric variables. The chart type does not prove why a pattern exists.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(by_hour["hr"], by_hour["mean_rentals"])
ax.set(title="Average hourly rentals", xlabel="Hour of day", ylabel="Mean rentals")
fig.tight_layout()
fig.savefig("reports/average-rentals-by-hour.png", dpi=160)

Make the analysis reproducible

Keep data loading, auditing, transformation and plotting in small functions. Save generated files to a known output directory and write down the dataset version or file hash. A report should distinguish observations from interpretations.

from pathlib import Path

REPORT_DIR = Path("reports")
REPORT_DIR.mkdir(exist_ok=True)
summary.to_csv(REPORT_DIR / "summary.csv", index=False)

# Keep a short report with:
# - source file name and checksum
# - row/column counts
# - cleaning decisions
# - charts and observations

Apply what you learned

Exercise

Finish the Bike Sharing Dataset Explorer

Produce a small, reproducible report that explains what is in the hourly bike-sharing data and what patterns are visible.

Create a report folder and save at least three charts: hourly rentals, rental-count distribution and rentals versus temperature.
Include the downloaded file name, checksum and exact row count.
Report dtypes, missing values, duplicate count and key column ranges.
Create a grouped summary by hour and weekday.
Write 3–5 observations and clearly separate observations from explanations.

Done when

A fresh run recreates the same summary files and charts from the same source file. Each chart has a title, labeled axes and a clear question it answers.

Free references

Study resources

1 links

01
Matplotlib quick start guide Article
Read the simple example and learn the Figure/Axes pattern.

Done

Check your understanding

Self-test

2 questions

Question 1

Which chart is a natural first choice for the distribution of a numeric column?
Question 2

Why record a checksum for the source CSV?

Completion check

Move on when

A fresh run recreates a compact dataset report with traceable inputs, validated summaries and clearly labeled plots.

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