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import requests
from pandas import DataFrame, read_csv, to_datetime
def get_public_holidays(url: str, year: str) -> DataFrame:
"""
Get public holidays for the given year for Brazil
Args:
url (str): The url to get the public holidays
year (str): The year to get the public holidays
Raises:
SystemExit: If the request fails
Returns:
DataFrame: The public holidays
"""
url = "{}/{}/BR".format(url, year)
try:
response = requests.get(url)
response.raise_for_status() # Raise error if something went wrong
data = DataFrame(response.json())
# Drop the columns types and countries
df = data.drop(["types", "counties"], axis=1) # Miss spelling in the API
# Convert the date column to datetime
df["date"] = to_datetime(df["date"])
return df
except requests.exceptions.HTTPError:
raise SystemExit
def extract(
csv_folder: str, csv_table_mapping: dict[str, str], public_holidays_url: str
) -> dict[str, DataFrame]:
"""
Extract the data from the csv files and load them into a dictionary of dataframes
Args:
csv_folder (str): The folder where the csv files are
csv_table_mapping (dict[str, str]): The mapping between the csv files and the table names
public_holidays_url (str): The url to get the public holidays
Returns:
Dict[str, DataFrame]: A dictionary with keys as the table names and values as the dataframes
"""
dataframes = {
table_name: read_csv("{}/{}".format(csv_folder, csv_file))
for csv_file, table_name in csv_table_mapping.items()
}
public_holidays = get_public_holidays(url=public_holidays_url, year="2017")
dataframes["public_holidays"] = public_holidays
return dataframes
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