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Hi!
when I request timeseries data for a list of RICS instead of 1, the DF returned has an extra layer on top of the usual OHLC-V data with the symbol names. So the DF is horizontally built.
I cant seem to transform this to something I can easily work with.
Could anyone tell me how to get this:
into this:
thanks!
Hello @jaydoubleu79
The above answer although valid, uses a loop to handle a dataframe, which is usually not a good deal.
This would be the genuine Pandas style way to do it.
import eikon as ek
ek.set_app_key('<<your_api_key_here>>')
symbols = ['RDSa.AS', 'NESN.S', 'RO.S']
df = ek.get_timeseries(symbols)
df = df.stack(0).reset_index().set_index('Date')
display(df)
result
You can flatten the multiindex and append the dataframes like this:
apd = pd.DataFrame() for sec in df.columns.levels[0]: dd = df[sec].copy().reset_index() dd['Security'] = sec apd = apd.append(dd, sort=True) display(apd)
result:
>>> apd CLOSE COUNT Date HIGH LOW OPEN Security VOLUME 0 1215.56000 112119.0 2020-03-09 1254.7599 1200.0000 1205.300 GOOG.O 3365365.0 1 1280.39000 68048.0 2020-03-10 1281.1500 1218.7700 1260.000 GOOG.O 2611373.0 2 1215.41000 78221.0 2020-03-11 1260.9600 1196.0700 1249.700 GOOG.O 2611229.0 3 1114.91000 133400.0 2020-03-12 1193.8700 1113.3000 1126.000 GOOG.O 4226748.0 .. ... ... ... ... ... ... ... ... 66 126.66380 -1.0 2020-06-11 132.3800 125.9400 131.480 VOD.L 76129921.0 67 126.10000 -1.0 2020-06-12 127.9000 122.2400 124.660 VOD.L 62619296.0 68 124.46000 -1.0 2020-06-15 125.0800 122.7000 123.760 VOD.L 129273352.0 69 129.18000 14970.0 2020-06-16 130.5600 126.2000 126.940 VOD.L 32893872.0 [140 rows x 8 columns]
