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🌐 [translation-sync] Read test_pwt.csv from data-lectures (Track D) #153

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Merged
mmcky merged 4 commits into from
Aug 18, 2026
Merged

🌐 [translation-sync] Read test_pwt.csv from data-lectures (Track D) #153

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dc2382f
Update translation: lectures/pandas.md
mmcky Aug 18, 2026
77229b8
Update translation: .translate/state/pandas.md.yml
mmcky Aug 18, 2026
8c1481a
Update translation: lectures/polars.md
mmcky Aug 18, 2026
b81ad41
Update translation: .translate/state/polars.md.yml
mmcky Aug 18, 2026
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6 changes: 3 additions & 3 deletions .translate/state/pandas.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: aacff3815993e8bea8db7b63df72a0a038e220c1
synced-at: "2026-08-05"
source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
synced-at: "2026-08-18"
model: claude-sonnet-5
mode: UPDATE
section-count: 5
tool-version: 0.25.0
tool-version: 0.26.0
6 changes: 3 additions & 3 deletions .translate/state/polars.md.yml
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@@ -1,6 +1,6 @@
source-sha: fb389e7722b4f70fce7835bd773df8eea2de9744
synced-at: "2026-08-04"
source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
synced-at: "2026-08-18"
model: claude-sonnet-5
mode: UPDATE
section-count: 6
tool-version: 0.24.0
tool-version: 0.26.0
4 changes: 2 additions & 2 deletions lectures/pandas.md
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Expand Up @@ -159,7 +159,7 @@ s

بنابراین، یک ابزار قدرتمند برای نمایش و تحلیل داده‌هایی است که به طور طبیعی در سطرها و ستون‌ها سازماندهی شده‌اند، اغلب با اندیس‌های توصیفی برای سطرها و ستون‌های فردی.

بیایید به مثالی نگاه کنیم که داده را از فایل CSV `pandas/data/test_pwt.csv` می‌خواند، که از [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0) گرفته شده است.
بیایید به مثالی نگاه کنیم که داده را از فایل CSV `test_pwt.csv` می‌خواند، که از [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0) گرفته شده است.

مجموعه داده شامل شاخص‌های زیر است

Expand All @@ -174,7 +174,7 @@ s
ما این را از یک URL با استفاده از تابع `read_csv` در `pandas` خواهیم خواند.

```{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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24 changes: 9 additions & 15 deletions lectures/polars.md
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Expand Up @@ -174,9 +174,7 @@ df
این را با `pl.read_csv` می‌خوانیم

```{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 @@ -360,9 +358,7 @@ plt.show()

```{code-cell} ipython3
# Reload the dataset
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 @@ -438,9 +434,7 @@ import pandas as pd
import time

# Small dataset -- Penn World Tables (~8 rows)
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)
```
Expand Down Expand Up @@ -480,13 +474,13 @@ print(f"Small data -- pandas: {pd_small:.4f}s | Polars eager: {pl_small:.4f}s"

```{code-cell} ipython3
n = 5_000_000
np.random.seed(42)
rng = np.random.default_rng(42)

groups = np.random.choice(['A', 'B', 'C', 'D'], n)
values = np.random.randn(n)
weights = np.random.rand(n)
extra1 = np.random.randn(n)
extra2 = np.random.randn(n)
groups = rng.choice(['A', 'B', 'C', 'D'], n)
values = rng.standard_normal(n)
weights = rng.random(n)
extra1 = rng.standard_normal(n)
extra2 = rng.standard_normal(n)

big_pd = pd.DataFrame({
'group': groups, 'value': values,
Expand Down
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