diff --git a/.translate/state/pandas.md.yml b/.translate/state/pandas.md.yml
index 32920b0..322727c 100644
--- a/.translate/state/pandas.md.yml
+++ b/.translate/state/pandas.md.yml
@@ -1,6 +1,6 @@
-source-sha: d35eb831da23a763f43d403e96f20d43529cae53
-synced-at: "2026-08-12"
+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
diff --git a/.translate/state/polars.md.yml b/.translate/state/polars.md.yml
index 4f87853..81b3af0 100644
--- a/.translate/state/polars.md.yml
+++ b/.translate/state/polars.md.yml
@@ -1,6 +1,6 @@
-source-sha: d35eb831da23a763f43d403e96f20d43529cae53
-synced-at: "2026-08-12"
+source-sha: 55c87c9fdbdb522866c5b6bbdc65c073941d7f16
+synced-at: "2026-08-18"
 model: claude-sonnet-5
 mode: UPDATE
 section-count: 6
-tool-version: 0.25.0
+tool-version: 0.26.0
diff --git a/lectures/pandas.md b/lectures/pandas.md
index b0761ff..43cbd21 100644
--- a/lectures/pandas.md
+++ b/lectures/pandas.md
@@ -165,7 +165,7 @@ s
 
 因此，它是表示和分析自然组织成行和列的数据的强大工具，通常具有用于单个行和单个列的描述性索引。
 
-让我们看一个从 CSV 文件 `pandas/data/test_pwt.csv` 读取数据的示例，该文件取自 [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0)。
+让我们看一个从 CSV 文件 `test_pwt.csv` 读取数据的示例，该文件取自 [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0)。
 
 该数据集包含以下指标：
 
@@ -181,7 +181,7 @@ s
 我们将使用 `pandas` 函数 `read_csv` 从 URL 读取数据。
 
 ```{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)
 ```
 
diff --git a/lectures/polars.md b/lectures/polars.md
index 5dd234a..ef40703 100644
--- a/lectures/polars.md
+++ b/lectures/polars.md
@@ -174,9 +174,7 @@ df
 我们使用 `pl.read_csv` 来读取数据
 
 ```{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
 ```
@@ -359,9 +357,7 @@ Polars 最强大的特性之一是**惰性求值（lazy evaluation）**。
 
 ```{code-cell} ipython3
 # 重新加载数据集
-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)
 ```
 
@@ -436,9 +432,7 @@ import pandas as pd
 import time
 
 # 小数据集 -- Penn World Tables（约 8 行）
-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)
 ```