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| from area import area | |
| import geopandas as gpd | |
| from urllib.request import urlopen | |
| bytes = urlopen("https://pastebin.com/raw/XBpJ6Hnp").read() | |
| gdf = gpd.read_file(bytes) | |
| gdf['country_name'] = ['south-africa','india','japan','france','argentina','united-states'] | |
| for reprojection in [ | |
| "epsg:3395", |
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| """ | |
| A comparison between a pure Python implementation and a Pandas-based implementation | |
| to find the max profit from buying and selling at different times. | |
| >>> n_prices= 100 | pure python: 0.0006s | pandas: 0.0088s | |
| >>> n_prices= 1000 | pure python: 0.0623s | pandas: 0.3232s | |
| >>> n_prices= 10000 | pure python: 6.5082s | pandas: 40.0255s | |
| Constructing our pd.DataFrames and using the vectorised C bindings does not improve iteration time! | |
| """ |
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| {"label":"Coverage","message":"%46","schemaVersion":1,"color":"hsl(0, 100%, 40%)"} |
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| {"label":"Coverage","message":"%64","schemaVersion":1,"color":"hsl(34, 100%, 40%)"} |
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| {"label":"Coverage","message":"%77","schemaVersion":1,"color":"hsl(65, 100%, 40%)"} |
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| import uvicorn | |
| from fastapi import FastAPI | |
| from sqlalchemy import ForeignKey | |
| from sqlalchemy.orm import Mapped, mapped_column, relationship | |
| from quickrest import Base, Resource, RouterFactory, ResourceConfig | |
| class Owner( | |
| Base, | |
| Resource, |
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| {"label":"Coverage","message":"%82","schemaVersion":1,"color":"hsl(78, 100%, 40%)"} |
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| {"label":"coverage","message":"%0","schemaVersion":1,"color":"hsl(0, 100%, 40%)"} |
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| {"label":"coverage","message":"%87","schemaVersion":1,"color":"hsl(90, 100%, 40%)"} |
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| hello |
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