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Create MultiIndex DataFrame from pipeline output in IDE
Pier-Olivier Marquis
posted

This has probably been asked before, but I can't find any post on that.

I want to create a DataFrame containing several days of pipeline output. I want to have the date as the index level 0 and the equity as the index level 1. Since the equities might change every day, I can't set the level 1 index in advance.

I would need something like this. Any suggestion?

    if not 'full_data' in context:  
        columns           = [context.output.columns.values]  
        multi_index       = pd.MultiIndex(levels=[[],[]], labels=[[],[]])  
        context.full_data = pd.DataFrame(columns=columns, index=multi_index)  

    date         = get_datetime('US/Eastern')  
    equity_index = context.output.index.values  
    context.full_data.loc[date, equity_index] = context.output.values

Right now I get the following:

KeyError: '[Equity(2 [ARNC]) Equity(24 [AAPL]) Equity(31 [ABAX]) ...,\n Equity(50758 [OKTA]) Equity(50763 [SNDR]) Equity(50782 [UPL])] not in index'

1 response
Pier-Olivier Marquis

It seems that context.full_data.loc[date, equity_index] = context.output.value is not supported at the moment.

https://github.com/pandas-dev/pandas/issues/15959

This works:

    columns       = [context.output.columns.values]  
    date          = get_datetime('US/Eastern')  
    partial_index = context.output.index.values  
    partial_index_length = len(partial_index)  


    partial_index = pd.MultiIndex.from_product([[date], list(partial_index)])  
    context.partial_data = pd.DataFrame(context.output.values, columns=columns, index=partial_index)  

    if not 'full_data' in context:  
        context.full_data = context.partial_data

    else:  
        context.full_data = pd.concat([context.full_data, context.partial_data])
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