import pandas as pd
# constants
ROOT_URL = "http://nefin.com.br/Cost%20of%20Capital/"
FILE_EXT = ".xls"
SECTORS_FILES = {
"Basic": ROOT_URL + "Basic%20Products" + FILE_EXT,
"Basic Products": ROOT_URL + "Basic%20Products" + FILE_EXT,
"Basic_Products": ROOT_URL + "Basic%20Products" + FILE_EXT,
"Basic_products": ROOT_URL + "Basic%20Products" + FILE_EXT,
"Construction": ROOT_URL + "Construction" + FILE_EXT,
"Consumer": ROOT_URL + "Consumer" + FILE_EXT,
"Energy": ROOT_URL + "Energy" + FILE_EXT,
"Finance": ROOT_URL + "Finance" + FILE_EXT,
"Manufacturing": ROOT_URL + "Manufacturing" + FILE_EXT,
"Other": ROOT_URL + "Other" + FILE_EXT,
"Others": ROOT_URL + "Other" + FILE_EXT,
}
# dict with aggregation name and pandas resampling frequency
RESAMPLE_FREQ = {
"year": "BY",
"yearly": "BY",
}
# Identity aggregations
IDENTITY_AGG = ["day", "daily", "month", "monthly", None]
# functions
[docs]def get_sector_cost_of_capital(sector, agg=None, agg_func=None):
"""Download data of an industrial sector cost of capital from NEFIN's website.
Data is available for 1, 5, 10 and 20-year projects.
Args:
sector (str): Industry sector to download. Options are: 'Basic', 'Construction', 'Consumer', 'Energy', 'Finance', 'Manufacturing' and 'Other'.
agg (str, optional): Frequency to aggregate. Either 'year' or 'yearly'). Defaults to None.
agg_func (str, optional): Function to apply at aggreagtion (e.g. 'last' or 'mean'). If it is None, then it defaults to 'last'.
Returns:
pandas.core.dataframe: Pandas dataframe with (aggregated) time series of chosen sector
"""
# get url
url = SECTORS_FILES[sector]
# read xls
print(f"Getting data of {sector} sector...")
df = pd.read_excel(url)
# set index as a datetime from columns year, month, day
df["datetime"] = pd.to_datetime(df["Month/Year"], format="%m/%Y")
df.set_index(["datetime"], inplace=True)
df.drop(["Month/Year"], axis=1, inplace=True)
# aggregate if desired
if agg not in IDENTITY_AGG:
if agg_func is None:
print(
"\n\nWARNING: aggregation function not provided. Using last() by default\n"
)
agg_func = "last"
# resample
df = df.resample(RESAMPLE_FREQ[agg]).apply(agg_func)
print("Done!")
return df