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🌐 [translation-sync] [polars] Fetch the FRED CSV with requests before parsing it #108

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Merged
mmcky merged 2 commits into from
Oct 13, 2026
Merged

🌐 [translation-sync] [polars] Fetch the FRED CSV with requests before parsing it #108

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bd75029
Update translation: lectures/polars.md
mmcky Oct 13, 2026
1216b4f
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 @@ -555,7 +555,8 @@ print(f"Polars lazy: {lazy_time:.4f}s")

对经济学家而言，一个重要的数据库是 [FRED](https://fred.stlouisfed.org/)——由圣路易斯联储维护的庞大时间序列数据集。

Polars 的 `read_csv` 可以直接从 URL 获取数据。
我们使用 [requests](https://requests.readthedocs.io/en/latest/) 下载 CSV 文件，
并将其内容传递给 Polars 的 `read_csv`。

我们使用 `try_parse_dates=True` 来自动解析日期列

Expand All @@ -575,7 +576,10 @@ 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)
```

让我们查看前几行
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