EIkon 1.1.4 is not backward compatible with 1.1.2
Another upgrade caused the problem with existing scripts. We've got 2 problems with existing scripts after 1.0.1. to 1.1.2 upgrade. Then we've got another problem after 1.1.2 -> 1.1.4 (1.1.5).
There is nothing on release notes page about the changes in 1.1.2. There is no release notes for 1.1.4 and 1.1.5 !
These issues makes us never upgrade the working modules at all.
Here is the problem I'm talking about :
dataFrame = ek.get_timeseries(rics='AC.MX', fields='*', start_date='2020-09-03T20:00:00', end_date='2020-09-03T20:10:00', interval='tick', normalize=False)
ricName = dataFrame.columns.name
ricName has RIC name value in 1.1.2. In 1.14 - it's nan.
This request returns the data in different formats using 1.1.2 and 1.1.4 Eikon python mode.
ts[ric] works only for a multi ric timeseries
I tested different requests et checked dataframe structure with following code:
def get_ts(rics, fields):
ts = rdp.get_timeseries(rics, fields,
start_date='2020-09-03T20:00:00',
end_date='2020-09-03T20:10:00',
interval='minute',
normalize=False, count=1)
if hasattr(ts.columns, "name"):
print("ts.columns.name: ", ts.columns.name)
else:
print("ts.columns.name: <No name>")
print(ts)
1 RIC, N fields
=> ts.columns.name contains the ric name
get_ts(["MSFT.O"], '*')
Output:
ts.columns.name: MSFT.O MSFT.O HIGH LOW OPEN CLOSE COUNT VOLUME Date 2020-09-03 20:07:00 218.70 218.30 218.53 218.45 162 11654
N RIC, 1 field
=> ts.columns.name contains the field name
get_ts(["AAPL.O", "MSFT.O"], 'HIGH')
Output:
ts.columns.name: HIGH HIGH AAPL.O MSFT.O Date 2020-09-03 20:07:00 127.67 218.70
N RIC, N fields
=> ts.columns.name contains "Security" label
get_ts(["AAPL.O", "MSFT.O"], ["HIGH", "LOW"])
Output:
ts.columns.name: None Security AAPL.O MSFT.O Field HIGH LOW HIGH LOW Date 2020-09-03 20:07:00 127.67 120.86 218.70 218.30
Hi,
We apologize if these changes cause you any inconvenience.
We noticed your remark on release note and will improve this point
Rather than using dataFrame.columns.name property which is not standard in pandas (this property was added by eikon lib), we suggest to rely on your rics request parameter.
column names should only refer to related field names.
In last 1.1.4 and 1.1.5, changes were done to align with pandas standard output.
- numpy.NaN value was change for pandas.NA
- all values are converted to the most suitable type
(in 1.1.2, all numeric values were converted in float64, other values like str keep object type)
As you'll see below, this impacts only empty data :
ts = ek.get_timeseries(rics='AC.MX', fields='*', start_date='2020-09-03T20:00:00', end_date='2020-09-03T20:10:00', interval='tick', normalize=False) print(ts) print(ts.dtypes)
1.1.2 AC.MX VALUE VOLUME Date 2020-09-03 20:00:01 NaN 4400.0 2020-09-03 20:00:01 NaN 3720.0 2020-09-03 20:00:01 NaN 5814.0 2020-09-03 20:00:01 NaN 2494.0 2020-09-03 20:00:01 NaN 2507.0 2020-09-03 20:00:01 NaN 436.0 2020-09-03 20:00:01 NaN 356.0 2020-09-03 20:00:01 NaN 16933.0 AC.MX VALUE float64 VOLUME float64 dtype: object 1.1.4 VALUE VOLUME Date 2020-09-03 20:00:01 <NA> 4400 2020-09-03 20:00:01 <NA> 3720 2020-09-03 20:00:01 <NA> 5814 2020-09-03 20:00:01 <NA> 2494 2020-09-03 20:00:01 <NA> 2507 2020-09-03 20:00:01 <NA> 436 2020-09-03 20:00:01 <NA> 356 2020-09-03 20:00:01 <NA> 16933 VALUE Int64 VOLUME Int64 dtype: object 1.1.5 VALUE VOLUME Date 2020-09-03 20:00:01 <NA> 4400 2020-09-03 20:00:01 <NA> 3720 2020-09-03 20:00:01 <NA> 5814 2020-09-03 20:00:01 <NA> 2494 2020-09-03 20:00:01 <NA> 2507 2020-09-03 20:00:01 <NA> 436 2020-09-03 20:00:01 <NA> 356 2020-09-03 20:00:01 <NA> 16933 VALUE Int64 VOLUME Int64 dtype: object
Yes, I see.
we're releasing a new version 1.1.6 and will restore it for your convenience.
About 'using dataFrame.columns.name' - yes, we rely on this field to get the RIC name. This was a workaround to solve the more general issue :
The response representation is not consistent and different for :
- if there are multiply rics received
- if there is 1 ric
- if there is 1 field requested and more than 1 ric returned
The representations of last 2 are similar , but for 3rd response instead of RIC , it has a field name.
I'm not a pro in python/pandas. But using common logic I do not understand why the data structure of the result depends on the number of requested RICs and fields. I would use the same data structure like there are multiple RICs and multiple fields.
Hi,
eikon 1.1.6 was release on pypi.org
It contains a fix for the raised issue, but I remind that dataFrame.columns.name property is not a standard from pandas but 'added value' from eikon lib for timeseries on 1 RIC.
If you call any functions that modify the dataframe, you can loose name property.
And iIf you request a timeseries for list of rics, you don't have it but you can extract dataframe for each ric by this way:
ts = ek.get_timeseries(['AC.MX', 'MSFT.O'], ...) ac_mx = ts['AC.MX'] msft = ts['MSFT.O']
(ts.columns is MultiIndex)
More generally, to get column names from a pandas.DataFrame is:
names = df.columns print([name for name in ts.columns]) ['VALUE', 'VOLUME']
Hi,
Thanks for the update. You also used that 'added field' in case I request 1 field only. Was it removed as well? That added value had a field name.
Another question is why do you use different dataframe structure if there is only 1 RIC requested?
This does not work :
ts = ek.get_timeseries(['AC.MX'], ...) ac_mx = ts['AC.MX]