Build the first working study table and plot

Build only what the claim requires: CSV summaries and a saved plot file have explicit axes and a reproducible path. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

import io,pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
df=pd.read_csv(io.StringIO('hours,passed\n1,0\n3,0\n5,1\n12,1\n'))
print('mean_hours', float(df.hours.mean()), 'max_hours', int(df.hours.max()))
plt.scatter(df.hours, df.passed); plt.xlabel('hours'); plt.ylabel('passed')
plt.savefig('hours_vs_pass.png'); print('wrote hours_vs_pass.png')

Expected evidence: mean/max hours and plot path. Read each printed intermediate as part of the argument that the path works—not as decoration.

Narrate the journey from raw input to result for a single example from four-row study.csv with hours and passed columns. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the study table and plot, print or log at least three intermediates that map to the claim (CSV summaries and a saved plot file have explicit axes and a reproducible path). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with four-row study.csv with hours and passed columns twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports inspect typical hours, an extreme value, and the hours/pass relationship. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

At the implementation stage for data-tables-and-plots, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about four-row study.csv with hours and passed columns that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: hand-computed mean of the four-row fixture.

For this page specifically, success looks like a deterministic path with printed intermediates while still centering the user decision to inspect typical hours, an extreme value, and the hours/pass relationship. If you cannot point to a file, command, or assertion that proves that for the study table and plot, stay on this page instead of advancing.

Pandas glossary

Previous · Next