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arnoldpredator avatar image
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Some exported cells to csv do not have any decimal separator (I use Pandas and Python)

Hi folks,

I am trying to download data to study it with Pandas. I want to store the data in csv format.

THe problem is I see that sometimes the cells do not contain the decimal separator which means the number is 1000 higher than it should. For example:


253.456

253.634

253.83

253980


And this happens in all columns, High, Close etc.


Any ideas to solve this issue?


I think I could write a program in Python that divides by 1000 if the number is an outlier but I prefer to get the data properly in the first place.

This is the code I used to generate the csv file:

import eikon as ek 
ek.set_app_key('key')
result = ek.get_timeseries(["AAPL.O"], start_date = "2020-01-01T15:00:00", end_date = "2020-04-06T16:00:00", interval="minute",fields= "*" ) 
result.to_csv (r'C:\path\result.csv', index = True, header=True)


Thanks for your help.


eikoneikon-data-apipythonrefinitiv-dataplatform-eikonworkspaceworkspace-data-apicsv
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Jirapongse avatar image   Jirapongse

@arnoldpredator

Hi,

Thank you for your participation in the forum.

Are any of the replies below satisfactory in resolving your query? If yes please click the 'Accept' text next to the most appropriate reply.

If you have already known an answer, please share your answer, and then accept it. This will guide all community members who have a similar question.

Otherwise please post again offering further insight into your question.

Thanks,

AHS

zoya faberov avatar image   zoya faberov

Hello @arnoldpredator,

Please be informed that a reply has been verified as correct in answering the question, and has been marked as such.

Thanks,

-AHS

2 Answers

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umer.nalla avatar image
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Hi @arnoldpredator

I just tried the above snippet of code and checked the output csv file in Excel.

The only columns that have values greater than 327.9 are the COUNT and VOLUME columns. I checked this by sorting each of the HIGH, LOW, OPEN and CLOSE columns in turn and checking the highest number in each column.

I would recommend programmatically checking the raw content of result dataframe itself to see if you can see any such invalid numbers. If you can, then I recommend you raise a Content Incorrect type Ticket on MyRefinitiv with examples including the date and time stamp of the problem values.

If, however, the raw dataframe looks correct but the csv does not, then the problem is likely with the panda to_csv function. Note that I am using Pandas version 1.0.0


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umer.nalla avatar image
25.3k ●87 ●12 ●25

Hi @arnoldpredator

I followed my own advice about checking the raw dataframe (just to double-check) and got the following:

If you do the same and you are seeing different values, please raise a ticket as described above.


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