@@ -824,7 +824,7 @@ mystnb:
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824 | 824 | name: firm-size-dist |
825 | 825 | tags: [hide-input] |
826 | 826 | --- |
827 | | -df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-global2000.csv') |
| 827 | +df_fs = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-global2000.csv') |
828 | 828 | df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']] |
829 | 829 | fig, ax = plt.subplots(figsize=(6.4, 3.5)) |
830 | 830 | |
@@ -851,8 +851,8 @@ mystnb:
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851 | 851 | tags: [hide-input] |
852 | 852 | --- |
853 | 853 | # import population data of cities in 2023 United States and 2023 Brazil from world population review |
854 | | -df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_us.csv') |
855 | | -df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_brazil.csv') |
| 854 | +df_cs_us = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_us.csv') |
| 855 | +df_cs_br = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_brazil.csv') |
856 | 856 | |
857 | 857 | fig, axes = plt.subplots(1, 2, figsize=(8.8, 3.6)) |
858 | 858 | |
@@ -876,7 +876,7 @@ mystnb:
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876 | 876 | name: wealth-dist |
877 | 877 | tags: [hide-input] |
878 | 878 | --- |
879 | | -df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-billionaires.csv') |
| 879 | +df_w = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-billionaires.csv') |
880 | 880 | df_w = df_w[['country', 'realTimeWorth', 'realTimeRank']].dropna() |
881 | 881 | df_w = df_w.astype({'realTimeRank': int}) |
882 | 882 | df_w = df_w.sort_values('realTimeRank', ascending=True).copy() |
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