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🌐 [translation-sync] [polars] Fetch the FRED CSV with requests before parsing it #97

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Merged
mmcky merged 2 commits into from
Oct 18, 2026
Merged

🌐 [translation-sync] [polars] Fetch the FRED CSV with requests before parsing it #97

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d3e3fcc
Update translation: lectures/polars.md
mmcky Oct 13, 2026
f9419bc
Update translation: .translate/state/polars.md.yml
mmcky Oct 13, 2026
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6 changes: 3 additions & 3 deletions .translate/state/polars.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
synced-at: "2026-08-18"
source-sha: b0c58ab96a9c537fcf766e4f2d9fd32f4a0fc2f7
synced-at: "2026-10-13"
model: claude-sonnet-5
mode: UPDATE
section-count: 6
tool-version: 0.26.0
tool-version: 0.29.4
8 changes: 6 additions & 2 deletions lectures/polars.md
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Expand Up @@ -561,7 +561,8 @@ Comme dans {doc}`pandas`, Python permet d'interroger facilement des bases de don

Une base de données importante pour les économistes est [FRED](https://fred.stlouisfed.org/) --- une vaste collection de séries temporelles maintenue par la Fed de St. Louis.

La méthode `read_csv` de Polars peut récupérer des données directement depuis une URL.
Nous téléchargeons le fichier CSV avec [requests](https://requests.readthedocs.io/en/latest/)
et transmettons son contenu à la méthode `read_csv` de Polars.

Nous utilisons `try_parse_dates=True` pour analyser automatiquement la colonne de date

Expand All @@ -581,7 +582,10 @@ fred_url = ('https://fred.stlouisfed.org/graph/fredgraph.csv?'
'fgst=lin&fgsnd=2020-02-01&line_index=1&'
'transformation=lin&vintage_date=2024-07-29&'
'revision_date=2024-07-29&nd=1948-01-01')
data = pl.read_csv(fred_url, try_parse_dates=True)
import requests

r = requests.get(fred_url)
data = pl.read_csv(r.content, try_parse_dates=True)
```

Examinons les premières lignes
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