Appending string Timeseries damages stored Timeseries

[fracorallo]

Hi everyone. I'm using the useful pystore library to store also very long timeseries with string values. I noticed a very strange behaviour when I try to append to an existing string Timeseries item, as in the following example code. In particular when appending to an item in the database, the resulting dataframe in the databases loses many of its values.

#import
import pystore
import pandas as pd
import numpy as np

pystore.set_path('./pystore_test')
store = pystore.store('store')
collection = store.collection('collection')

#define first dataframe (string values)
index_1 = pd.date_range('2013-1-1 00:00', '2014-1-1  00:00', freq='1m')
data_1= pd.DataFrame(np.random.choice(list('abc'), [len(index_1),1]),index=index_1, columns=['test']) 

#write dataframe to database
collection.write('test', data_1)

#read from database
item = collection.item('test')
data_DB = item.to_pandas() # contains 12 dates

#define second dataframe (string values)
index_2 = pd.date_range('2014-1-2 00:00', '2015-1-1  00:00', freq='1m')
data_2= pd.DataFrame(np.random.choice(list('abc'), [len(index_2),1]),index=index_2, columns=['test']) 

#append to database
item = collection.item('test')
collection.append('test', data_2, npartitions=item.data.npartitions)

#read appending result from database
item = collection.item('test')
data_DB = item.to_pandas()  #<-- Contains only 3 of the 12 initial and 12 appended values

Instead it works perfectly if I replace strings with floats:

data_1['test'] = np.random.rand(len(index_1),1) 
data_2['test'] = np.random.rand(len(index_2),1)

Is pystore library able to store also string values? Thank you

[yohplala]

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

[fracorallo]

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

[sdementen]

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) ?

[st4ck3]

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

[st4ck3]

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.

[sdementen]

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')]

[st4ck3]

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.

[sdementen]

Yet I see that there is already the logic to avoid duplicates values in DatetimeIndex a bit above in the code. So the drop_duplicates should probably only be applied when the index is not a DatetimeIndex

On Thu, Dec 3, 2020, 14:43 cstocker73 notifications@github.com wrote:

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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