verticapy.datasets.load_pop_growth¶
- verticapy.datasets.load_pop_growth(schema: str | None = None, name: str = 'pop_growth') vDataFrame¶
Ingests the population growth dataset into the Vertica database. This dataset is ideal for time series and geospatial models. If a table with the same name and schema already exists, this function creates a vDataFrame from the input relation.
Parameters¶
- schema: str, optional
Schema of the new relation. If empty, the temporary schema is used.
- name: str, optional
Name of the new relation.
Returns¶
- vDataFrame
the pop growth vDataFrame.
Examples¶
If you call this loader without any arguments, the dataset is loaded using the default schema (public).
from verticapy.datasets import load_pop_growth load_pop_growth()
123yearAbccontinentAbccountryAbccity123population🌎lat🌎lon1 1500 Asia China Beijing 672.0 39.9075 116.39723 2 1500 Asia China Guangzhou 150.0 23.11667 113.25 3 1500 Asia China Hangzhou 250.0 30.25 120.16 4 1500 Asia China Nanjing 147.0 32.05 118.766667 5 1500 Asia India Cuttack 140.0 20.46497 85.87927 6 1500 Asia India Gauda 200.0 24.866667 88.133333 7 1500 Asia India Vijayanagar 500.0 15.335 76.462 8 1500 Europe France Paris 185.0 48.85341 2.3488 9 1500 Europe Turkey Istanbul 200.0 41.005275 28.9497 10 1500 Middle East Egypt Cairo 400.0 30.04 31.24 11 1500 Middle East Iran Tabriz 250.0 38.08 46.2919 12 1500 Middle East Morocco Fez 130.0 34.033333 -5.01 13 1501 Asia China Beijing 672.36 39.9075 116.39723 14 1501 Asia China Guangzhou 150.2 23.11667 113.25 15 1501 Asia China Hangzhou 250.2 30.25 120.16 16 1501 Asia China Nanjing 147.000866666667 32.05 118.766667 17 1501 Asia India Cuttack 139.0 20.46497 85.87927 18 1501 Asia India Gauda 200.0 24.866667 88.133333 19 1501 Asia India Vijayanagar 499.6 15.335 76.462 20 1501 Europe France Paris 185.5 48.85341 2.3488 21 1501 Europe Turkey Istanbul 205.0 41.005275 28.9497 22 1501 Middle East Egypt Cairo 399.2 30.04 31.24 23 1501 Middle East Iran Tabriz 248.0 38.08 46.2919 24 1501 Middle East Morocco Fez 130.076923076923 34.033333 -5.01 25 1502 Asia China Beijing 672.72 39.9075 116.39723 26 1502 Asia China Guangzhou 150.4 23.11667 113.25 27 1502 Asia China Hangzhou 250.4 30.25 120.16 28 1502 Asia China Nanjing 147.001733333333 32.05 118.766667 29 1502 Asia India Cuttack 138.0 20.46497 85.87927 30 1502 Asia India Gauda 200.0 24.866667 88.133333 31 1502 Asia India Vijayanagar 499.2 15.335 76.462 32 1502 Europe France Paris 186.0 48.85341 2.3488 33 1502 Europe Turkey Istanbul 210.0 41.005275 28.9497 34 1502 Middle East Egypt Cairo 398.4 30.04 31.24 35 1502 Middle East Iran Tabriz 246.0 38.08 46.2919 36 1502 Middle East Morocco Fez 130.153846153846 34.033333 -5.01 37 1503 Asia China Beijing 673.08 39.9075 116.39723 38 1503 Asia China Guangzhou 150.6 23.11667 113.25 39 1503 Asia China Hangzhou 250.6 30.25 120.16 40 1503 Asia China Nanjing 147.0026 32.05 118.766667 41 1503 Asia India Cuttack 137.0 20.46497 85.87927 42 1503 Asia India Gauda 200.0 24.866667 88.133333 43 1503 Asia India Vijayanagar 498.8 15.335 76.462 44 1503 Europe France Paris 186.5 48.85341 2.3488 45 1503 Europe Turkey Istanbul 215.0 41.005275 28.9497 46 1503 Middle East Egypt Cairo 397.6 30.04 31.24 47 1503 Middle East Iran Tabriz 244.0 38.08 46.2919 48 1503 Middle East Morocco Fez 130.230769230769 34.033333 -5.01 49 1504 Asia China Beijing 673.44 39.9075 116.39723 50 1504 Asia China Guangzhou 150.8 23.11667 113.25 51 1504 Asia China Hangzhou 250.8 30.25 120.16 52 1504 Asia China Nanjing 147.003466666667 32.05 118.766667 53 1504 Asia India Cuttack 136.0 20.46497 85.87927 54 1504 Asia India Gauda 200.0 24.866667 88.133333 55 1504 Asia India Vijayanagar 498.4 15.335 76.462 56 1504 Europe France Paris 187.0 48.85341 2.3488 57 1504 Europe Turkey Istanbul 220.0 41.005275 28.9497 58 1504 Middle East Egypt Cairo 396.8 30.04 31.24 59 1504 Middle East Iran Tabriz 242.0 38.08 46.2919 60 1504 Middle East Morocco Fez 130.307692307692 34.033333 -5.01 61 1505 Asia China Beijing 673.8 39.9075 116.39723 62 1505 Asia China Guangzhou 151.0 23.11667 113.25 63 1505 Asia China Hangzhou 251.0 30.25 120.16 64 1505 Asia China Nanjing 147.004333333333 32.05 118.766667 65 1505 Asia India Cuttack 135.0 20.46497 85.87927 66 1505 Asia India Gauda 200.0 24.866667 88.133333 67 1505 Asia India Vijayanagar 498.0 15.335 76.462 68 1505 Europe France Paris 187.5 48.85341 2.3488 69 1505 Europe Turkey Istanbul 225.0 41.005275 28.9497 70 1505 Middle East Egypt Cairo 396.0 30.04 31.24 71 1505 Middle East Iran Tabriz 240.0 38.08 46.2919 72 1505 Middle East Morocco Fez 130.384615384615 34.033333 -5.01 73 1506 Asia China Beijing 674.16 39.9075 116.39723 74 1506 Asia China Guangzhou 151.2 23.11667 113.25 75 1506 Asia China Hangzhou 251.2 30.25 120.16 76 1506 Asia China Nanjing 147.0052 32.05 118.766667 77 1506 Asia India Cuttack 134.0 20.46497 85.87927 78 1506 Asia India Gauda 200.0 24.866667 88.133333 79 1506 Asia India Vijayanagar 497.6 15.335 76.462 80 1506 Europe France Paris 188.0 48.85341 2.3488 81 1506 Europe Turkey Istanbul 230.0 41.005275 28.9497 82 1506 Middle East Egypt Cairo 395.2 30.04 31.24 83 1506 Middle East Iran Tabriz 238.0 38.08 46.2919 84 1506 Middle East Morocco Fez 130.461538461538 34.033333 -5.01 85 1507 Asia China Beijing 674.52 39.9075 116.39723 86 1507 Asia China Guangzhou 151.4 23.11667 113.25 87 1507 Asia China Hangzhou 251.4 30.25 120.16 88 1507 Asia China Nanjing 147.006066666667 32.05 118.766667 89 1507 Asia India Cuttack 133.0 20.46497 85.87927 90 1507 Asia India Gauda 200.0 24.866667 88.133333 91 1507 Asia India Vijayanagar 497.2 15.335 76.462 92 1507 Europe France Paris 188.5 48.85341 2.3488 93 1507 Europe Turkey Edirne 130.64 41.681808 26.562269 94 1507 Europe Turkey Istanbul 235.0 41.005275 28.9497 95 1507 Middle East Egypt Cairo 394.4 30.04 31.24 96 1507 Middle East Iran Tabriz 236.0 38.08 46.2919 97 1508 Asia China Beijing 674.88 39.9075 116.39723 98 1508 Asia China Guangzhou 151.6 23.11667 113.25 99 1508 Asia China Hangzhou 251.6 30.25 120.16 100 1508 Asia China Nanjing 147.006933333333 32.05 118.766667 Rows: 1-100 | Columns: 7