import pandas as pd
# constants
ROOT_URL = "http://nefin.com.br/Predictability/"
FILENAME = "dividend_yield"
FILE_EXT = ".xls"
# dict with aggregation name and pandas resampling frequency
RESAMPLE_FREQ = {
"month": "BM",
"monthly": "BM",
"year": "BY",
"yearly": "BY",
}
# Identity aggregations
IDENTITY_AGG = ["day", "daily", None]
# functions
[docs]def get_dividend_yield(agg=None, agg_func=None):
"""Download Dividend Yield data from NEFIN's website.
Args:
agg (str, optional): Frequency to aggregate. Either 'year' (or 'yearly') and 'month' (or 'monhtly'). 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
"""
# get url
url = ROOT_URL + FILENAME + FILE_EXT
# read xls
print(f"Getting Dividend Yield data...")
df = pd.read_excel(url)
# set index as a datetime from columns year, month, day
dt = [f"{y}-{m}-{d}" for y, m, d in zip(df.year, df.month, df.day)]
df["datetime"] = pd.to_datetime(dt, format="%Y-%m-%d")
df.set_index(["datetime"], inplace=True)
df.drop(["year", "month", "day"], 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