Datasets / Hormuz shipping crisis
Daily transits through the strait, pre-war to crisis
Traffic falls from a stable pre-war baseline to a small fraction of normal after the closure, with only a cautious recovery later.
How it decided
Hormuz shipping crisis: 125 rows, 26 columns. date, daily_ship_transits and period_type mark the baseline and crisis.
125 rows in, 125 out across 3 columns. Traffic falls from a stable pre-war baseline to a small fraction of normal after the closure, with only a cautious recovery later.
line. A time series makes the discontinuity legible; the period split keeps the closure context visible without inventing a second scale.
Palette, spacing, axis titles and legend placement applied. 0 issues found.
The transformation
This is the code, not a description of it — the chart above was drawn from what it returns, and the notebook download runs the same lines against the same file.
df["day"] = pd.to_datetime(df["date"]); result = df[["day", "daily_ship_transits", "period_type"]]What it charted
125 rows out of 125, 3 columns. First 8 shown.
| day | daily_ship_transits | period_type |
|---|---|---|
| 2026-01-01 | 103 | pre_war |
| 2026-01-02 | 108 | pre_war |
| 2026-01-03 | 108 | pre_war |
| 2026-01-04 | 108 | pre_war |
| 2026-01-05 | 98 | pre_war |
| 2026-01-06 | 98 | pre_war |
| 2026-01-07 | 106 | pre_war |
| 2026-01-08 | 97 | pre_war |