get_timeseries returning incorrect time period from what was declared

pmorlen
pmorlen Newcomer
in Eikon Data APIs

I have this code:

data = ek.get_timeseries(rics, fields='CLOSE',
start_date='2019-01-01',
end_date='2019-06-30')

but it returns data starting 5/20/2019 and ignores the start_date declared in the code:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 29 entries, 2019-05-20 to 2019-06-28
Columns: 103 entries, MMM to YUM
dtypes: float64(103)
memory usage: 23.6 KB

Based on other posts, it appears to be related to the 3000 shared row limit.

Here is a snippet of what I'd like returned - with daily closing price dates going back to 1/1/2019 for 103 equity tickers in total:

Close Date MMM AFL T ABBV ABT

2019-05-28 163.35 51.30 31.93 78.03 75.71

2019-05-29 161.40 51.43 31.91 78.06 75.67

Here is my current data retrieval code:

'rics' is a list of tickers

data3 = ek.get_data(rics, ['TR.PriceClose', 'TR.PriceCloseDate'], {'Sdate':'2019-01-01', 'EDate':'2019-06-30'})

But it returns a tuple:

(      Instrument  Price Close                  Date
 0            MMM       190.95  2019-01-02T00:00:00Z
 1            MMM       183.76  2019-01-03T00:00:00Z
 2            MMM       191.32  2019-01-04T00:00:00Z
 3            MMM       190.88  2019-01-07T00:00:00Z
 4            MMM       191.68  2019-01-08T00:00:00Z
 ...          ...          ...                   ...
 12767        YUM       110.66  2019-06-24T00:00:00Z
 12768        YUM       110.31  2019-06-25T00:00:00Z
 12769        YUM       110.12  2019-06-26T00:00:00Z
 12770        YUM       110.56  2019-06-27T00:00:00Z
 12771        YUM       110.67  2019-06-28T00:00:00ZI'm having trouble setting up 'get_data()' to return a dataframe, instead of a tuple.Can you please provide some guidance to correct?Thank you
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Best Answer

  • Jirapongse
    Jirapongse ✭✭✭✭✭
    Answer ✓

    @pmorlen

    I have modified the code as shown below.

    data3 = ek.get_data(rics,['TR.PriceCloseDate','TR.PriceClose'], {'Sdate':'2019-01-01', 'EDate':'2019-06-30'}) 

    dfs = dict(tuple(data3[0].groupby('Instrument'))) 
    dfarray = [] 
    for ric, data in dfs.items(): 
       df_tmp = dfs[ric].dropna() 
       df_tmp = df_tmp.drop_duplicates() 
       df_tmp = df_tmp.set_index('Date') 
       df_tmp = df_tmp.drop(['Instrument'], axis=1) 
       df_tmp = df_tmp.rename(columns={"Price Close":ric}) 
       dfarray.append(df_tmp) 

    result = pd.concat(dfarray, axis=1, sort=False) 
    result.columns.name = 'CLOSE' 
    result

    It uses df.dropna() and df.drop_duplicates().

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