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Read the six folded datasets from data-lectures, not high_dim_data #832

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Merged
mmcky merged 1 commit into from
Aug 11, 2026
Merged

Read the six folded datasets from data-lectures, not high_dim_data #832

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2 changes: 1 addition & 1 deletion lectures/_static/lecture_specific/inequality/data.ipynb
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Expand Up @@ -34,7 +34,7 @@
"import wbgapi as wb\n",
"import plotly.express as px\n",
"\n",
"url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'\n",
"url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'\n",
"df = pd.read_csv(url)\n",
"df_income_wealth = df.dropna()"
]
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8 changes: 4 additions & 4 deletions lectures/heavy_tails.md
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Expand Up @@ -824,7 +824,7 @@ mystnb:
name: firm-size-dist
tags: [hide-input]
---
df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-global2000.csv')
df_fs = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-global2000.csv')
df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']]
fig, ax = plt.subplots(figsize=(6.4, 3.5))

Expand All @@ -851,8 +851,8 @@ mystnb:
tags: [hide-input]
---
# import population data of cities in 2023 United States and 2023 Brazil from world population review
df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_us.csv')
df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_brazil.csv')
df_cs_us = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_us.csv')
df_cs_br = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_brazil.csv')

fig, axes = plt.subplots(1, 2, figsize=(8.8, 3.6))

Expand All @@ -876,7 +876,7 @@ mystnb:
name: wealth-dist
tags: [hide-input]
---
df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-billionaires.csv')
df_w = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-billionaires.csv')
df_w = df_w[['country', 'realTimeWorth', 'realTimeRank']].dropna()
df_w = df_w.astype({'realTimeRank': int})
df_w = df_w.sort_values('realTimeRank', ascending=True).copy()
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2 changes: 1 addition & 1 deletion lectures/inequality.md
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Expand Up @@ -246,7 +246,7 @@ The following code block imports a subset of the dataset `SCF_plus` for 2016,
which is derived from the [Survey of Consumer Finances](https://en.wikipedia.org/wiki/Survey_of_Consumer_Finances) (SCF).

```{code-cell} ipython3
url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini.csv'
url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'
df = pd.read_csv(url)
df_income_wealth = df.dropna()
```
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2 changes: 1 addition & 1 deletion lectures/mle.md
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Expand Up @@ -90,7 +90,7 @@ The following code imports this data and reads it into an array called `sample`
```{code-cell} ipython3
:tags: [hide-input]

url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini_no_weights.csv'
url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini_no_weights.csv'
df = pd.read_csv(url)
df = df.dropna()
df = df[df['year'] == 2016]
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