Hi @gabriel.matejka ,

I changed some fields and forward filled some cells in the code below; was this closer to what you were looking for?

# dividend data
df1 = ek.get_data(
    instruments = rics,
    fields = ['TR.DivAnnouncementDate',
              'TR.DivRecordDate',
              'TR.DivUnadjustedGross',
              'TR.DivType',
              'TR.DivPaymentType',
              'TR.DivCurr',
              'TR.DivAnnouncementDate',
              'TR.DivExDate',
              'TR.DivPayDate',
              'TR.DivEventStatus',
              'TR.DivNotes',
              'TR.DividendFrequency',
              'TR.DivSourceOfFunds'],
    parameters = {
        "SDate":"2010-01-01","EDate":"2022-09-27"
    })[0]

# df1.to_excel('stoxx_dividends_data.xlsx', sheet_name='sheet1', index=False)
print('df1:')
display(df1)

# meta-information
df2 = ek.get_data(
    rics,  # 'BAWG.VI'
    ['TR.CommonName',
     'TR.IssuerRating.Date',
     'TR.IssuerRating',
     'TR.CompanyIncorpRegion',
     'TR.ISIN',
     'TR.NAICSSector',
     'TR.NAICSIndustryGroup',
     'TR.NAICSIndustryGroupCode'],
    {
        "SDate":"2010-01-01","EDate":"2022-09-27"
    })[0]

# Some data here needs to be forward filled; see here for more info on that: https://stackoverflow.com/questions/27012151/forward-fill-specific-columns-in-pandas-dataframe
# replace field that's entirely space (or empty) with NaN (https://stackoverflow.com/questions/13445241/replacing-blank-values-white-space-with-nan-in-pandas)
import numpy as np
df2 = df2.replace('', np.nan, regex=True)

nanFilledColumns = ['Company Common Name', 'Incorp. Region Code', 'NAICS Sector Name']
df2[nanFilledColumns] = df2[nanFilledColumns].ffill()

# df2.to_excel('stoxx_meta_data.xlsx', sheet_name='sheet1', index=False)
print('df2:')
display(df2)


# financial data
df3 = ek.get_data(
    'BAWG.VI',
    ['TR.F.TotRevenue.date',
     'TR.F.TotRevenue',
     'TR.F.TotRevenue',
     'TR.TR.F.OpExpnTot',
     'TR.EBIT',
     'TR.EBITDA',
     'TR.NetIncomeBeforeTaxes',
     'TR.NetIncomeAfterTaxes',
     'TR.F.CashSTInvstTot',
     'TR.TotalAssetsReported',
     'TR.TotalLiabilities',
     'TR.F.NetDebtInclPrefEqMinIntrtoTotCap',
     'TR.TotalEquity',
     'TR.F.MktCap',
     'TR.Beta',
     'TR.FreeCashFlow'],
    {
        "SDate":"2010-01-01","EDate":"2022-09-27"
    })[0]

# df3.to_excel('stoxx_financial_data.xlsx', sheet_name='sheet1', index=False)
df3.head(10)

Hello @gabriel.matejka

What is the error or issue that you are encountering? I have tested your last request and it gets the following timeout error, is it the same issue?

According to your query, you request a lot of RICs and fields data for a wide range of time. I suggest you try to reduce the number of RICs and fields in the get_data function.

Hi @wasin.w,

yes, that was one of the issues. But I believe I could work around that by partitioning the requests.

However, even when using only a single RIC the problem with the different data-types persists (this is actually more of an issue, because I would like to have all the data for the different dates etc. together without having to check this manually).

Here is a screenshot of how it looks when I do the request:


thank you for your help.

All the best,

Gabriel

df = ek.get_data(

'BAWG.VI',

#rics,

['TR.DivAnnouncementDate',

'TR.DivRecordDate',

'TR.DivUnadjustedGross',

'TR.DivType',

'TR.DivPaymentType',

'TR.DivCurr',

'TR.DivAnnouncementDate',

'TR.DivExDate',

'TR.DivPayDate',

'TR.DivEventStatus',

'TR.DivNotes',

'TR.DividendFrequency',

'TR.DivSourceOfFunds',

'TR.CommonName',

'TR.CompanyIncorpRegion',

'TR.ISIN',

'TR.NAICSSector',

'TR.NAICSIndustryGroup',

'TR.NAICSIndustryGroupCode',

'TR.IssuerRating',

'TR.IssuerRating.Date',

'TR.Revenue',

'TR.TotalOperatingExpense',

'TR.EBIT',

'TR.EBITDA',

'TR.NetIncomeBeforeTaxes',

'TR.NetIncomeAfterTaxes',

'TR.CashAndSTInvestments',

'TR.TotalAssetsReported',

'TR.TotalLiabilities',

'TR.NetDebt',

'TR.TotalEquity',

'TR.CompanyMarketCap',

'TR.Beta',

'TR.FreeCashFlow'],

{

"SDate":"2010-01-01","EDate":"2022-09-27",

})[0]

df.to_excel('data_bawag.xlsx', sheet_name='sheet1', index=False)

df.head(10)

Hi @gabriel.matejka,

It does not look like the field 'revenue' exist for the RICs you're after, e.g.:

To find fields that exist for any one instrument, please use the Data Item Browser as per this video.

Yes, thank you for the support!!
All the best,

Gabriel