FEAT: Nevada data scraper #108
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| variable_name="hospital_beds_in_use_covid_confirmed" | ||
| ) | ||
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| df = pd.concat([suspected, confirmed], sort=False, ignore_index=True) |
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If we're collecting suspected and confirmed then we should also make sure that we collect total.
| renamed = df.rename(columns={"Date": "dt", "Cases": "cases_total"}) | ||
| renamed.dt = pd.to_datetime(renamed.dt) | ||
| return renamed.melt(id_vars=["dt"], var_name="variable_name").assign( | ||
| vintage=pd.Timestamp.utcnow(), fips=self.state_fips |
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Please use the _retrieve_vintage method so that, if we changed how we collected vintages, then it would be a change to one method rather than hunting down every place we used it.
| renamed = df.rename(columns={"Date": "dt", "Cumulative": "tests_total"}) | ||
| renamed.dt = pd.to_datetime(renamed.dt) | ||
| return renamed.melt(id_vars=["dt"], var_name="variable_name").assign( | ||
| vintage=pd.Timestamp.utcnow(), fips=self.state_fips |
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Use _retrieve_vintage method. See below
| .melt(id_vars=["county"], var_name="variable_name") | ||
| .assign( | ||
| vintage=pd.Timestamp.utcnow(), | ||
| dt=pd.Timestamp.utcnow() |
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Please use the _retrieve_dt method rather than determine time by hand
| renamed[["county", "tests_total", "cases_total", "deaths_total"]] | ||
| .melt(id_vars=["county"], var_name="variable_name") | ||
| .assign( | ||
| vintage=pd.Timestamp.utcnow(), |
| renamed = out.rename( | ||
| columns={ | ||
| "County": "county", | ||
| "Tests": "tests_total", |
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Can we collect the "People Tested" column rather than the "Tests" column -- We prefer to report the number of people tested rather than the number of tests administered.
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