Read test_pwt.csv from data-lectures (Track D) (#612)

All four reads pointed at this repo's own raw URL — one unsplit literal
in pandas.md and three URLs split across three string literals each in
polars.md. Each collapses to a single literal of the data-lectures form
(github.com/QuantEcon/data-lectures/raw/main/lectures/test_pwt.csv);
patching the stem line alone would have left the filename appended to a
path that no longer exists in the flat published tree. pandas.md's
prose loses the stale pandas/data/ path prefix.

Data landed first in QuantEcon/data-lectures#98. The local copy at
_static/lecture_specific/pandas/data/ is deleted in a follow-up PR
only after this repoint has propagated to the three synced translation
repos (.zh-cn/.fr/.fa) and all four sites have republished — their
lectures fetch the same URL live at cell execution.

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Author: mmcky

lectures/pandas.md

@@ -153,7 +153,7 @@ In essence, a `DataFrame` in pandas is analogous to a (highly optimized) Excel s
 
 Thus, it is a powerful tool for representing and analyzing data that are naturally organized into rows and columns, often with descriptive indexes for individual rows and individual columns.
 
-Let's look at an example that reads data from the CSV file `pandas/data/test_pwt.csv`, which is taken from the [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0).
+Let's look at an example that reads data from the CSV file `test_pwt.csv`, which is taken from the [Penn World Tables](https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt-7.0).
 
 The dataset contains the following indicators 
 
@@ -169,7 +169,7 @@ The dataset contains the following indicators
 We'll read this in from a URL using the `pandas` function `read_csv`.
 
 ```{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)
 ```
 

lectures/polars.md

@@ -158,9 +158,7 @@ As in {doc}`pandas`, let's work with data from the [Penn World Tables](https://w
 We read this in using `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
 ```
@@ -344,9 +342,7 @@ Instead of executing each operation immediately, lazy mode collects the full que
 
 ```{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)
 ```
 
@@ -422,9 +418,7 @@ 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)
 ```