Appending string Timeseries damages stored Timeseries about pystore HOT 8 OPEN

Hello capellino,
I don't have a definitive answer to your trouble, but maybe I have some clue.

Myself, I am not using the append() function because:

• well, most notably, as reported in my other tickets, I didn't succeeded to have it working,
• and also appending a dataframe to another can obey different logics depending the topic to be studied.
  More specifically, when you append a dataframe to another, it seems logical not to record values that are both in dataframe 1 and dataframe 2. But this means you are able to define an "equality" operator between this data. And which information needs to be checked to confirm there is equality is up to you only.

To give you an example, in my case, I have for instance:

• for index: timestamp of an occurence
• 1st column: value of the occurence
• 2nd column: time of recording in pystore
  In my case, equality is based only on index and 1st column

Well, after all this blabla, the question is: on which equality is based pystore append() function?
You can have a look in the source of collection.py:
combined = dd.concat([current.data, new]).drop_duplicates(keep="last")

If you have a look to 'drop_duplicates' documentation (well, I only checked out that of pandas, not dask, but I understand dask simulates pandas), you will see it identifies duplicates purely on values in columns, not index.

So you can have different index, but same value, it won't bother, it will purely suppress the 'duplicateed rows'.

Back to your ewample, maybe this can be the why.
You generate a string dataframe based on 3 letters, abc as far as I understand. I am not that surprised it keeps only 3 rows.
With float, I guess no value is ever the same.

To finish. in my own case, I only use the write() function, and operate the appending in few lines:
To give you an idea of what is my 'appending' strategy, here it is (for financial data)
` # Only removes duplicates based on key columns for given data type
# Key columns for OHLCV data are 'Timestamp', 'Open', 'High', 'Low', 'Close' & 'Volume'

# Do not keep last row of DataFrame currently in base (empty row)
# Do not keep last row of new DataFrame either (empty row)
combined = pd.concat([current[:-1].reset_index(), df[:-1].reset_index()])
combined.sort_values('Timestamp', inplace = True)
combined.drop_duplicates(subset = AC.DataTypes[cde.data_type], inplace = True)
combined.set_index(keys='Timestamp', inplace = True)
# Re-add as last row the latest timestamp
# We don't know if added data is newest data or earliest data, so let's check
if current.index[-1] > df.index[-1]:
      last_row = current.iloc[-1]
else:
      last_row = df.iloc[-1]
combined = combined.append(last_row, sort=False)
        
my_collection.write(item_ID, combined, overwrite=True) `

Hope this can give you some clues.
Bests

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Thank you very much for your suggestions. I solved not using at all the append() function as you said!

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just a naive question: why are you using remove_duplicates ? or maybe better question, should we use remove_duplicates when the index is part of the data (e.g. a datetimeindex for a timeseries) ?

from pystore.

Hi,

there is a problem with the append function:

The line:

combined = dd.concat([current.data, new]).drop_duplicates(keep="last")**

in the file collection.py should be subtituted by:

idx_name = current.data.index.name
combined = dd.concat([current.data, new]).reset_index().drop_duplicates(subset=idx_name, keep="first").set_index(idx_name)**

For further explanation, please refer to:

https://stackoverflow.com/questions/13035764/remove-rows-with-duplicate-indices-pandas-dataframe-and-timeseries

from pystore.

just a naive question: why are you using remove_duplicates ? or maybe better question, should we use remove_duplicates when the index is part of the data (e.g. a datetimeindex for a timeseries) ?

In general, pandas indexes are not unique and you can have repeated values. Therefore you need to remove duplicated indexes if unique ids are needed.

from pystore.

In the SO post you link, they suggest the simpler and more efficient alternative to remove duplicate indices;
df = df[~df.index.duplicated(keep='first')]

from pystore.

In the SO post you link, they suggest the simpler and more efficient alternative to remove duplicate indices;
df = df[~df.index.duplicated(keep='first')]

You are right. The method I mentioned is easier to understand for me, but less efficient and compact.

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from pystore.