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[heavy_tails] update urls for datasets used #191

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jstac merged 1 commit into from
May 4, 2023
Merged

[heavy_tails] update urls for datasets used #191

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8 changes: 4 additions & 4 deletions lectures/heavy_tails.md
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Expand Up @@ -647,7 +647,7 @@ Here is a plot of the firm size distribution for the largest 500 firms in 2020 t
```{code-cell} ipython3
:tags: [hide-input]

df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata/cross_section/forbes-global2000.csv')
df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-global2000.csv')
df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']]
fig, ax = plt.subplots(figsize=(6.4, 3.5))

Expand All @@ -669,8 +669,8 @@ The size is measured by population.
: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/update_csdata/cross_section/cities_us.csv')
df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata/cross_section/cities_brazil.csv')
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')

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

Expand All @@ -689,7 +689,7 @@ The data is from the Forbes Billionaires list in 2020.
```{code-cell} ipython3
:tags: [hide-input]

df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata/cross_section/forbes-billionaires.csv')
df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/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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