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monkeypox.py
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monkeypox.py
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import datetime
import pandas as pd
from shared import harmonize_countries, list_countries_in_region
SOURCE_MONKEYPOX = (
"https://frontdoor-l4uikgap6gz3m.azurefd.net/MPX/V_MPX_VALIDATED_DAILY?&$format=csv"
)
SOURCE_COUNTRY_MAPPING = "country_mapping.json"
SOURCE_POPULATION = "https://github.com/owid/covid-19-data/raw/master/scripts/input/un/population_latest.csv"
OUTPUT_FILE = "owid-monkeypox-data.csv"
WHO_REGIONS = ["EURO", "AMRO", "WPRO", "EMRO", "AFRO", "SEARO"]
def import_data() -> pd.DataFrame:
df = pd.DataFrame()
# Fetching the data for each WHO region separately
for region in WHO_REGIONS:
url = f"https://frontdoor-l4uikgap6gz3m.azurefd.net/MPX/V_MPX_VALIDATED_DAILY?&$format=csv&$filter=WHO_REGION%20eq%20%27{region}%27"
df_region = pd.read_csv(url)
df = pd.concat([df, df_region])
return df
def clean_columns(df: pd.DataFrame) -> pd.DataFrame:
return df[["COUNTRY", "DATE", "TOTAL_CONF_CASES", "TOTAL_CONF_DEATHS"]].rename(
columns={
"COUNTRY": "location",
"DATE": "date",
"TOTAL_CONF_CASES": "total_cases",
"TOTAL_CONF_DEATHS": "total_deaths",
}
)
def mpox_harmonize_countries(df: pd.DataFrame) -> pd.DataFrame:
return harmonize_countries(
df,
countries_file=SOURCE_COUNTRY_MAPPING,
country_col="location",
warn_on_missing_countries=False,
)
def clean_date(df: pd.DataFrame) -> pd.DataFrame:
df["date"] = pd.to_datetime(df.date).dt.date.astype(str)
return df
def clean_values(df: pd.DataFrame) -> pd.DataFrame:
df = df.sort_values("date", ascending=False)
df["total_cases"] = df[["location", "total_cases"]].groupby("location").cummin()
df["total_deaths"] = df[["location", "total_deaths"]].groupby("location").cummin()
return df.sort_values(["location", "date"])
def explode_dates(df: pd.DataFrame) -> pd.DataFrame:
df_range = pd.concat(
[
pd.DataFrame(
{
"location": location,
"date": pd.date_range(
start=df.date.min(), end=df.date.max(), freq="D"
).astype(str),
}
)
for location in df.location.unique()
]
)
df = pd.merge(
df, df_range, on=["location", "date"], validate="one_to_one", how="right"
)
df["report"] = df.total_cases.notnull() | df.total_deaths.notnull()
return df
def add_world(df: pd.DataFrame) -> pd.DataFrame:
df[["total_cases", "total_deaths"]] = (
df[["location", "total_cases", "total_deaths"]]
.groupby("location")
.ffill()
.fillna(0)
)
world = (
df[["date", "total_cases", "total_deaths"]]
.groupby("date", as_index=False)
.sum()
.assign(location="World", report=True)
)
world = world[world.date < str(datetime.date.today())]
return pd.concat([df, world])
def add_regions(df: pd.DataFrame) -> pd.DataFrame:
# Add region for each country
for region in [
"North America",
"South America",
"Europe",
"Asia",
"Africa",
"Oceania",
]:
df.loc[
df.location.isin(list_countries_in_region(region=region)), "region"
] = region
# Calculate regional aggregates
regions = (
df[df.region.notnull()][
["region", "date", "total_cases", "total_deaths", "report"]
]
.groupby(["region", "date"], as_index=False)
.agg({"total_cases": "sum", "total_deaths": "sum", "report": "max"})
.rename(columns={"region": "location"})
)
regions = regions[regions.date < str(datetime.date.today())]
df = df.drop(columns="region")
# Concatenate with df
return pd.concat([df, regions])
def add_population_and_countries(df: pd.DataFrame) -> pd.DataFrame:
pop = pd.read_csv(
SOURCE_POPULATION, usecols=["entity", "population", "iso_code"]
).rename(columns={"entity": "location"})
missing_locs = set(df.location) - set(pop.location)
if len(missing_locs) > 0:
raise Exception(f"Missing location(s) in population file: {missing_locs}")
df = pd.merge(pop, df, how="right", validate="one_to_many", on="location")
return df
def derive_metrics(df: pd.DataFrame) -> pd.DataFrame:
def derive_country_metrics(df: pd.DataFrame) -> pd.DataFrame:
# Add daily values
df["new_cases"] = df.total_cases.diff()
df["new_deaths"] = df.total_deaths.diff()
# Add 7-day averages
df["new_cases_smoothed"] = (
df.new_cases.rolling(window=7, min_periods=7, center=False).mean().round(2)
)
df["new_deaths_smoothed"] = (
df.new_deaths.rolling(window=7, min_periods=7, center=False).mean().round(2)
)
# Add per-capita metrics
df = df.assign(
new_cases_per_million=round(df.new_cases * 1000000 / df.population, 3),
total_cases_per_million=round(df.total_cases * 1000000 / df.population, 3),
new_cases_smoothed_per_million=round(
df.new_cases_smoothed * 1000000 / df.population, 3
),
new_deaths_per_million=round(df.new_deaths * 1000000 / df.population, 5),
total_deaths_per_million=round(
df.total_deaths * 1000000 / df.population, 5
),
new_deaths_smoothed_per_million=round(
df.new_deaths_smoothed * 1000000 / df.population, 5
),
).drop(columns="population")
min_reporting_date = df[df.report].date.min()
max_reporting_date = df[df.report].date.max()
df = df[(df.date >= min_reporting_date) & (df.date <= max_reporting_date)].drop(
columns="report"
)
return df
return df.groupby("iso_code").apply(derive_country_metrics)
def filter_dates(df: pd.DataFrame) -> pd.DataFrame:
return df[df.date >= "2022-05-01"]
def main():
(
import_data()
.pipe(clean_columns)
.pipe(mpox_harmonize_countries)
.pipe(clean_date)
.pipe(clean_values)
.pipe(explode_dates)
.pipe(add_world)
.pipe(add_regions)
.pipe(add_population_and_countries)
.pipe(derive_metrics)
.pipe(filter_dates)
.sort_values(["location", "date"])
).to_csv(f"../{OUTPUT_FILE}", index=False)
if __name__ == "__main__":
main()