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🌐 [translation-sync] Read test_pwt.csv from data-lectures (Track D) #35

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mmcky merged 4 commits into from
Aug 18, 2026
+7 −13
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🌐 [translation-sync] Read test_pwt.csv from data-lectures (Track D) #35

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1a0f3a4
Update translation: lectures/pandas.md
mmcky Aug 18, 2026
4d1834e
Update translation: .translate/state/pandas.md.yml
mmcky Aug 18, 2026
72bdf87
Update translation: lectures/polars.md
mmcky Aug 18, 2026
172068b
Update translation: .translate/state/polars.md.yml
mmcky Aug 18, 2026
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2 changes: 1 addition & 1 deletion .translate/state/pandas.md.yml
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source-sha: d35eb831da23a763f43d403e96f20d43529cae53
source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
synced-at: "2026-08-18"
model: claude-sonnet-5
mode: UPDATE
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2 changes: 1 addition & 1 deletion .translate/state/polars.md.yml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
source-sha: d35eb831da23a763f43d403e96f20d43529cae53
source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
synced-at: "2026-08-18"
model: claude-sonnet-5
mode: UPDATE
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4 changes: 2 additions & 2 deletions lectures/pandas.md
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Expand Up @@ -161,7 +161,7 @@ En substance, un `DataFrame` dans pandas est analogue à une feuille de calcul E

Ainsi, c'est un outil puissant pour représenter et analyser des données naturellement organisées en lignes et en colonnes, souvent avec des indices descriptifs pour les lignes et les colonnes individuelles.

Regardons un exemple qui lit des données à partir du fichier CSV `pandas/data/test_pwt.csv`, tiré des [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0).
Regardons un exemple qui lit des données à partir du fichier CSV `test_pwt.csv`, tiré des [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0).

Le jeu de données contient les indicateurs suivants

Expand All @@ -177,7 +177,7 @@ Le jeu de données contient les indicateurs suivants
Nous allons le lire depuis une URL en utilisant la fonction `read_csv` de `pandas`.

```{code-cell} ipython3
df = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/lecture-python-programming/main/lectures/_static/lecture_specific/pandas/data/test_pwt.csv')
df = pd.read_csv('https://github.com/QuantEcon/data-lectures/raw/main/lectures/test_pwt.csv')
type(df)
```

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12 changes: 3 additions & 9 deletions lectures/polars.md
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Expand Up @@ -175,9 +175,7 @@ Comme dans {doc}`pandas`, travaillons avec les données des [Penn World Tables](
Nous les lisons à l'aide de `pl.read_csv`

```{code-cell} ipython3
url = ('https://raw.githubusercontent.com/QuantEcon/'
'lecture-python-programming/main/lectures/_static/'
'lecture_specific/pandas/data/test_pwt.csv')
url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/test_pwt.csv'
df = pl.read_csv(url)
df
```
Expand Down Expand Up @@ -361,9 +359,7 @@ Au lieu d'exécuter chaque opération immédiatement, le mode paresseux collecte

```{code-cell} ipython3
# Rechargement du jeu de données
url = ('https://raw.githubusercontent.com/QuantEcon/'
'lecture-python-programming/main/lectures/_static/'
'lecture_specific/pandas/data/test_pwt.csv')
url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/test_pwt.csv'
df_full = pl.read_csv(url)
```

Expand Down Expand Up @@ -439,9 +435,7 @@ import pandas as pd
import time

# Petit jeu de données -- Penn World Tables (~8 lignes)
url = ('https://raw.githubusercontent.com/QuantEcon/'
'lecture-python-programming/main/lectures/_static/'
'lecture_specific/pandas/data/test_pwt.csv')
url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/test_pwt.csv'
small_pd = pd.read_csv(url)
small_pl = pl.read_csv(url)
```
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