@@ -671,7 +671,7 @@ Here is a plot of the firm size distribution for the largest 500 firms in 2020 t
671671``` {code-cell} ipython3
672672:tags: [hide-input]
673673
674- df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata /cross_section/forbes-global2000.csv')
674+ df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main /cross_section/forbes-global2000.csv')
675675df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']]
676676fig, ax = plt.subplots(figsize=(6.4, 3.5))
677677
@@ -693,8 +693,8 @@ The size is measured by population.
693693:tags: [hide-input]
694694
695695# import population data of cities in 2023 United States and 2023 Brazil from world population review
696- df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata /cross_section/cities_us.csv')
697- df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata /cross_section/cities_brazil.csv')
696+ df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main /cross_section/cities_us.csv')
697+ df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main /cross_section/cities_brazil.csv')
698698
699699fig, axes = plt.subplots(1, 2, figsize=(8.8, 3.6))
700700
@@ -713,7 +713,7 @@ The data is from the Forbes Billionaires list in 2020.
713713``` {code-cell} ipython3
714714:tags: [hide-input]
715715
716- df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/update_csdata /cross_section/forbes-billionaires.csv')
716+ df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main /cross_section/forbes-billionaires.csv')
717717df_w = df_w[['country', 'realTimeWorth', 'realTimeRank']].dropna()
718718df_w = df_w.astype({'realTimeRank': int})
719719df_w = df_w.sort_values('realTimeRank', ascending=True).copy()
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