The client is currently using the code below. However, their universe contains approximately 1,000 t

Hi Team, Good day.

Query: The client is currently using the code below. However, their universe contains approximately 1,000 to 1,500 RICs, and adding each RIC manually into the universe list is time‑consuming. Is there a way where an Excel file is uploaded in code book and the RICs are automatically ingested into the code to return the results? If not please advice an work around for this it would be much appreciated.

import refinitiv.data as rd
rd.open_session()
df = rd.get_data(
universe = ['AKSA.IS'],
fields = ['TR.TRESGScore','TR.TRESGScore.date'],
parameters = {
'SDate': '2022-02-01',
'EDate': '2026-01-01',
'Frq': 'Y'
}
)

display(df)

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raksina.samasiri

Hi @Amjath ,

You can upload excel/csv file into Codebook and use Python code to import the data in the file, here's the documentation of the function https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html . Please let me know if you have any questions.

However, I'd recommend you use the newer and strategic python library, https://community.developers.lseg.com/home/leaving?allowTrusted=1&target=https%3A%2F%2Fdevelopers.lseg.com%2Fen%2Fapi-catalog%2Flseg-data-platform%2Flseg-data-library-for-python , known as the version 2 of Refinitiv Data Library, more detail can be found at https://community.developers.lseg.com/home/leaving?allowTrusted=1&target=https%3A%2F%2Fdevelopers.lseg.com%2Fen%2Farticle-catalog%2Farticle%2Fessential-guide-to-the-data-libraries

Jirapongse

@Amjath

The client can upload an Excel to Codebook by using drag and drop.

Then, use the pd.read_excel function to load that Excel file.

import pandas as pd

df_rics = pd.read_excel("rics.xlsx")

df_rics

Next, split the list into chunks. The size of each chunk could be 100.



def chunks(lst, n):

    """Yield successive n-sized chunks from lst."""

    for i in range(0, len(lst), n):

        yield lst[i:i + n]




chunk_size = 100

rics_list = df_rics['RIC'].tolist()


list_of_chunks = list(chunks(rics_list, chunk_size))


list_of_chunks

Finally, request data for each chunk.

import lseg.data as ld 

ld.open_session()

df_list = []

for chunk in list_of_chunks:

    df = ld.get_data(

        universe = chunk,

        fields = ['TR.TRESGScore','TR.TRESGScore.date'],

        parameters = {

            'SDate': '2022-02-01',

            'EDate': '2026-01-01',

            'Frq': 'Y'

            })

    df_list.append(df)


df_all = pd.concat(df_list)

df_all

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