Hi @staff2,

1. S&P changed its policy for use of ratings data, you may require a direct license with S&P. Please speak with your Refinitiv representative, and they can inform you of the workflow to incorporate such S&P data in your Refinitiv package.

2.1 The 'blanks' are there to signify that there was no change. You may choose to forward fill these cells as such (for e.g.):

import pandas as pd
import numpy as np

df3, err = ek.get_data(
    instruments = ['US345370BR09','US0378331005'],
    fields = ['TR.BIDPRICE.date', 'TR.BIDPRICE', 'TR.ASKPRICE',
              'TR.GR.Rating(BondRatingSrc=MDY).date', 'TR.GR.Rating(BondRatingSrc=MDY)',
              'TR.GR.Rating(BondRatingSrc=FTC).date', 'TR.GR.Rating(BondRatingSrc=FTC)'],
    parameters = {'SDate':'-25Y','EDate':'0', 'FRQ':'Y'})
columns_of_interest = ["Moody's Rating", "Fitch Rating"]
df3.columns = df3.columns[:4].tolist() +  ["MDY Date", "Moody's Rating", "FTC Date", "Fitch Rating"]

ratings_per_inst = [
    df3[df3["Instrument"] == i][columns_of_interest # We wre only interested in the ratings.
         ].replace('', np.nan, regex=True # We want all empty cells to have the same ' <NA> ' vales to then replace them.
                  ).fillna(method='ffill') # Finally, fill-forward all <NA> cells.
    for i in df3["Instrument"].unique()] # ' .unique() ' conserves the order, ' groupby('Instrument') ' doesn't nessesarily.

df4 = ratings_per_inst[0] # Initialise a new data-frame ' df4 ' to subsequently append.
for i in ratings_per_inst[1:]:
    df4 = df4.append(i, ignore_index = True)
df3[columns_of_interest] = df4 # change relevent columns in original data-frame.

display(df3)

Note that I explicitly requested ratings dates too, and that was because:

2.2 TR.GR.RatingDate and price data (e.g.: TR.ASKPRICE ) are pulled from Eikon's Data API (EDAPI) at different frequencies. The reason for that is that it is possible for a rating's agency to change its rating more than once a year, so only the most granular data-points can be matched in this instance (i.e.: daily). To circumvent this issue, I would suggest only pulling TR.GR.Rating data; if you would like both data-sets together, you may then concatenate them. Please let me know if you would like an example of such concatenation.

@staff2 Please look at the solution on this thread. I hope this can help.

Can somebody help me with the above?

2: My favorite:

df6, err = ek.get_data(
    instruments = ['US345370BR09'],
    fields = ['TR.GR.RatingDate', 'TR.GR.Rating', 'TR.GR.Rating.RatingSource'],
    parameters = {'SDate':'-25Y','EDate':'0', 'FRQ':'Y'})
df6.columns = ['InstrumentRated', 'Date', 'Rating', 'Rating Source']
df6['Date'] = pd.to_datetime(df6["Date"], format = '%Y-%m-%dT%H:%M:%S')
df6 = df6.dropna()

ratings_dates = [i.strftime("%Y-%m-%d") for i in df6["Date"].to_list()]
price_point = pd.DataFrame(
    data = [ek.get_data(
        instruments = ['US345370BR09'],
        fields = ['TR.BIDPRICE.date',
                  'TR.BIDPRICE',
                  'TR.ASKPRICE'],
        parameters = {'SDate': i})[0].values.tolist()[0] for i in ratings_dates],
    columns = ['Instrument', 'Date', 'Bid Price', 'Ask Price']).dropna()
price_point['Date'] = pd.to_datetime(price_point["Date"], format = '%Y-%m-%dT%H:%M:%S')
df8 = df6.merge(price_point, how = 'outer', on = "Date").drop_duplicates().reset_index(drop=True)
df8

Note that there isn't necessarily price data for each of these dates. This could be because of several reasons (e.g.: it falls on a non-trading day such as a weekend, price data were not gathered at that time on the listed exchange, ...)