Wikipedia retail-interest velocity (daily)
Daily consumer-interest velocity signals for 17 shopping-relevant Wikipedia topics (shopping events, electronics, gaming, home appliances, home goods, toys, beauty, apparel, fast fashion, sporting goods), from the official keyless Wikimedia pageviews REST API (en.wikipedia, all-access, all-agents). Each day the trailing 120 UTC days of daily pageviews are fetched per topic (1.2 s pacing, polite User-Agent; API-omitted days stay explicit nulls on a full date grid, never zero-filled) and scored deterministically: views on as_of, 7-day and 30-day rolling averages, baseline_90d (median of the 90 days in [as_of-96d, as_of-7d], current week excluded) with baseline_std_90d, z_90d = (7d avg - baseline) / std (0.0 when the std is zero), wow_velocity_pct (7d avg vs the prior 7d), trend_30d_pct_per_day (OLS slope over 30d, in %/day) with trend_direction (rising >= +0.5 %/day, falling <= -0.5 %/day, else flat), spike_flag (spike: z >= 3, elevated: z >= 2, lull: z <= -2, else normal), and interest_heat = 100 * min-max-normalized z_90d within the snapshot, ranked as heat_rank (1 = hottest; ties: views desc, topic asc). Who joins this: an online shop joins per-topic daily interest heat to its category marketing calendar on (as_of, topic); a retail media planner joins on retail_category + as_of to time category campaigns around attention spikes. Primary key: (as_of, topic); join keys: as_of, topic, retail_category. Nullability: baseline_std_90d is null when fewer than 2 baseline days are observed; no country split is published (global English-Wikipedia readership). Caveats: pageview spikes can be news-driven rather than purchase intent; interest_heat is a within-snapshot relative measure, not comparable across days; the API lags ~1 day so as_of is the last complete UTC day. Sample use: filter spike_flag = 'spike' for today's surging shopping topics, or order by heat_rank for the day's hottest retail interest.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 15
- Colonnes
- 17
- Cadence de la source
- Quotidienne
- Dernière actualisation
- 2 oct. 2026
- Thème
- economy
| Colonne | Type | Description |
|---|---|---|
| as_of | string | Snapshot date (UTC, YYYY-MM-DD) — the last complete UTC day in the pageviews API at fetch time. Part of the primary key. (unit: date) |
| topic | string | Canonical topic slug from the connector's curated watchlist. Part of the primary key; the join key for a shop's category marketing calendar. (unit: text) |
| article_title | string | Exact en.wikipedia article title whose pageviews were measured. (unit: text) |
| retail_category | string | Closed retail-category vocabulary (shopping_events, electronics, gaming, home_appliances, home_goods, toys, beauty, apparel, fast_fashion, sporting_goods, seasonal). The join key for category campaign timing. (unit: category) |
| views | integer | Pageviews of the article on as_of. (unit: count) |
| views_7d_avg | float | NaN-aware mean of daily pageviews over the 7 days ending on as_of. (unit: count) |
| views_30d_avg | float | NaN-aware mean of daily pageviews over the 30 days ending on as_of. (unit: count) |
| baseline_90d | float | Median of daily pageviews over the 90 days in [as_of-96d, as_of-7d] — the trailing quarter excluding the current week. (unit: count) |
| baseline_std_90d | float | Sample standard deviation of daily pageviews over the same 90-day baseline window; null when fewer than 2 baseline days were observed. (unit: count) |
| z_90d | float | Attention surprise: (views_7d_avg - baseline_90d) / baseline_std_90d; 0.0 when the baseline std is zero or non-finite. (unit: z-score) |
| wow_velocity_pct | float | Week-over-week velocity: 100 * (views_7d_avg - prior_7d_avg) / prior_7d_avg, where prior_7d_avg is the mean of [as_of-13d, as_of-7d]; 0.0 when the prior week is empty or zero. (unit: percent) |
| trend_30d_pct_per_day | float | 100 * OLS slope of daily pageviews over the last 30 days / views_30d_avg; 0.0 with fewer than 10 observed points or a zero mean. (unit: percent/day) |
| trend_direction | string | 'rising' (>= +0.5 %/day), 'falling' (<= -0.5 %/day), else 'flat'. (unit: category) |
| spike_flag | string | 'spike' (z_90d >= 3), 'elevated' (>= 2), 'lull' (<= -2), else 'normal'. (unit: category) |
| interest_heat | float | 0-100 composite = 100 * min-max-normalized z_90d within the snapshot. Within-snapshot relative — not comparable across days. (unit: score) |
| heat_rank | integer | Rank by interest_heat desc (1 = hottest); ties broken by views desc, then topic asc. (unit: rank) |
| row_hash | string | Deterministic 16-hex-char content hash over (as_of, topic, views, z_90d, interest_heat) — identical input yields an identical snapshot. (unit: hash) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| as_of | topic | article_title | retail_category | views | views_7d_avg | views_30d_avg | baseline_90d | baseline_std_90d | z_90d | wow_velocity_pct | trend_30d_pct_per_day | trend_direction | spike_flag | interest_heat | heat_rank | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-10-01 | black_friday | Black_Friday_(shopping) | shopping_events | 2 819 | 3 056,1 | 1 689,9 | 1 229,5 | 682,5 | 2,676 | 118,14 | 4,104 | rising | elevated | 100 | 1 | 982f03ac9743e53f |
| 2026-10-01 | christmas | Christmas | seasonal | 2 915 | 2 480,3 | 2 255,5 | 1 842,5 | 280,1 | 2,277 | 10,1 | 0,708 | rising | elevated | 88,04 | 2 | faa17b1b4684573f |
| 2026-10-01 | temu | Temu | fast_fashion | 1 838 | 1 960,3 | 1 801,6 | 1 565,5 | 188,9 | 2,09 | 2,08 | 0,867 | rising | elevated | 82,43 | 3 | c4ed4aa1b63ae1ca |
| 2026-10-01 | air_fryer | Air_fryer | home_appliances | 192 | 177 | 148,6 | 111 | 40,2 | 1,642 | -5,35 | 2,354 | rising | normal | 69 | 4 | 9d861dc8dff5fa5f |
| 2026-10-01 | skincare | Skin_care | beauty | 494 | 445,6 | 358,9 | 357 | 58,7 | 1,508 | 30,39 | 1,231 | rising | normal | 64,98 | 5 | 65f5f30ff808588e |
| 2026-10-01 | cyber_monday | Cyber_Monday | shopping_events | 319 | 308,6 | 268,5 | 221,5 | 64,3 | 1,354 | 17,97 | 0,694 | rising | normal | 60,36 | 6 | d50a80e3b67514e4 |
| 2026-10-01 | sneakers | Sneakers | apparel | 498 | 463,1 | 451,7 | 407,5 | 245,6 | 0,227 | -5,04 | 0,511 | rising | normal | 26,57 | 7 | 0cbbe3addb451837 |
| 2026-10-01 | perfume | Perfume | beauty | 746 | 733,4 | 708,2 | 722 | 125,3 | 0,091 | 4,46 | 0,117 | flat | normal | 22,49 | 8 | 64adc6c99e8bb3b9 |
| 2026-10-01 | mattress | Mattress | home_goods | 231 | 254,3 | 241,6 | 255 | 1 515,2 | -0 | 8,94 | 0,025 | flat | normal | 19,76 | 9 | a9c075f781989637 |
| 2026-10-01 | lego | Lego | toys | 3 186 | 2 532,9 | 2 583,8 | 2 692,5 | 1 081,8 | -0,148 | -1,4 | 0,355 | flat | normal | 15,32 | 10 | 81430564c4b09147 |
Profilé le 2 oct. 2026 à partir de l’instantané 20261002T031216Z-200381e14514
Mesuré- Complétude
- 100 %
- Lignes
- 15
- Colonnes
- 17
- Colonnes incomplètes
- 0
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| as_ofvarchar | 0 % | 1 | — |
|
| topicvarchar | 0 % | 16 | — |
|
| article_titlevarchar | 0 % | 17 | — |
|
| retail_categoryvarchar | 0 % | 11 | — |
|
| viewsbigint | 0 % | 16 | 132 → 4 158médiane 888 | 2 hors du 1er–99e centile |
| views_7d_avgdouble | 0 % | 17 | 139,1 → 4 268médiane 897,9 | 2 hors du 1er–99e centile |
| views_30d_avgdouble | 0 % | 13 | 148,6 → 4 924médiane 915,8 | 2 hors du 1er–99e centile |
| baseline_90ddouble | 0 % | 13 | 111 → 4 774médiane 935,5 | 2 hors du 1er–99e centile |
| baseline_std_90ddouble | 0 % | 17 | 40,2 → 1 515médiane 245,6 | 2 hors du 1er–99e centile |
| z_90ddouble | 0 % | 15 | -0,659 → 2,68médiane 0,091 | 2 hors du 1er–99e centile |
| wow_velocity_pctdouble | 0 % | 13 | -14,56 → 118,14médiane -1,4 | 2 hors du 1er–99e centile |
| trend_30d_pct_per_daydouble | 0 % | 17 | -1,45 → 4,1médiane 0,355 | 2 hors du 1er–99e centile |
| trend_directionvarchar | 0 % | 3 | — |
|
| spike_flagvarchar | 0 % | 2 | — |
|
| interest_heatdouble | 0 % | 13 | 0 → 100médiane 22,49 | 2 hors du 1er–99e centile |
| heat_rankbigint | 0 % | 17 | 1 → 15médiane 8 | 2 hors du 1er–99e centile |
| row_hashvarchar | 0 % | 15 | — |
|
Dernier changement
20261001T160120Z-d28aea9e5b7a → 20261002T031216Z-200381e14514
- Lignes actuelles
- 15 (-1)
- Nouvelles lignes
- 15
- Lignes retirées
- 16
- Lignes inchangées
- 0
Les lignes sont comparées comme des enregistrements entiers sur les colonnes communes aux deux versions ; une ligne modifiée compte pour une retirée et une nouvelle.
Mêmes colonnes et mêmes types que la version précédente.
- Actuelle
20261002T031216Z-200381e14514 · sha256 200381e14514…
15 lignes · -1 lignes par rapport à la précédente
20261001T160120Z-d28aea9e5b7a · sha256 d28aea9e5b7a…
16 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/wiki_retail_interest_intel/wiki_retail_interest_daily" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/wiki_retail_interest_intel/wiki_retail_interest_daily").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/wiki_retail_interest_intel/wiki_retail_interest_daily
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20261002T031216Z-200381e14514 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Wikipedia retail-interest velocity (agent-curated). (2026). Wikipedia retail-interest velocity (daily) [Data set, snapshot 20261002T031216Z-200381e14514, sha256 200381e14514]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/fr/datasets/wiki_retail_interest_intel/wiki_retail_interest_daily?snapshot=20261002T031216Z-200381e14514
@misc{dz_wiki_retail_interest_intel_wiki_retail_i_200381e1,
title = {{Wikipedia retail-interest velocity (daily)}},
author = {{Wikipedia retail-interest velocity (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/wiki_retail_interest_intel/wiki_retail_interest_daily?snapshot=20261002T031216Z-200381e14514}},
note = {Snapshot 20261002T031216Z-200381e14514, sha256 200381e145142711ce88244c12f74e50fe8da66799e7f18a5860cbf06cc94a49; accessed 2026-10-02}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=wiki_retail_interest_intel%2Fwiki_retail_interest_daily&lang=fr&theme=auto&snapshot=20261002T031216Z-200381e14514&x=as_of&y=views&agg=avg" title="Wikipedia retail-interest velocity (daily)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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