How to handle the multiple columns in pandas with symbols

jaydoubleu79
Newcomer
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!
Tagged:
0
Best Answer
-
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
0
Answers
-
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]0 -
Thank you very much! that is awesome
0
Categories
- All Categories
- 6 AHS
- 36 Alpha
- 166 App Studio
- 6 Block Chain
- 4 Bot Platform
- 18 Connected Risk APIs
- 47 Data Fusion
- 33 Data Model Discovery
- 682 Datastream
- 1.4K DSS
- 613 Eikon COM
- 5.2K Eikon Data APIs
- 10 Electronic Trading
- Generic FIX
- 7 Local Bank Node API
- 3 Trading API
- 2.9K Elektron
- 1.4K EMA
- 248 ETA
- 552 WebSocket API
- 37 FX Venues
- 14 FX Market Data
- 1 FX Post Trade
- 1 FX Trading - Matching
- 12 FX Trading – RFQ Maker
- 5 Intelligent Tagging
- 2 Legal One
- 23 Messenger Bot
- 3 Messenger Side by Side
- 9 ONESOURCE
- 7 Indirect Tax
- 60 Open Calais
- 275 Open PermID
- 44 Entity Search
- 2 Org ID
- 1 PAM
- PAM - Logging
- 6 Product Insight
- Project Tracking
- ProView
- ProView Internal
- 22 RDMS
- 1.8K Refinitiv Data Platform
- 625 Refinitiv Data Platform Libraries
- 4 LSEG Due Diligence
- LSEG Due Diligence Portal API
- 4 Refinitiv Due Dilligence Centre
- Rose's Space
- 1.2K Screening
- 18 Qual-ID API
- 13 Screening Deployed
- 23 Screening Online
- 12 World-Check Customer Risk Screener
- 1K World-Check One
- 46 World-Check One Zero Footprint
- 45 Side by Side Integration API
- 2 Test Space
- 3 Thomson One Smart
- 10 TR Knowledge Graph
- 151 Transactions
- 143 REDI API
- 1.8K TREP APIs
- 4 CAT
- 26 DACS Station
- 121 Open DACS
- 1.1K RFA
- 104 UPA
- 191 TREP Infrastructure
- 228 TRKD
- 915 TRTH
- 5 Velocity Analytics
- 9 Wealth Management Web Services
- 83 Workspace SDK
- 11 Element Framework
- 5 Grid
- 18 World-Check Data File
- 1 Yield Book Analytics
- 46 中文论坛