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I used yahooquery to import a historical data series of 100 exchange-traded assets. I created a dataframe with this data for a period of 6 months:
df = tickers.history(period='6mo', interval="1d")
I only need the closing quotation, so I created a dataframe called dfclose with this data and used the assets as index.
dfclose = df.adjclose
dfclose = dfclose.reset_index(level=['symbol', 'date'])
I need to separate the assets from the ones not in the same column. The solution I found was to create a data frame for each asset and then merge them using the merge function.
df_AALR = dfclose[dfclose.symbol=='AALR3.SA']
df_YDUQ = dfclose[dfclose.symbol=='YDUQ3.SA']
df_PETR = dfclose[dfclose.symbol=='PETR4.SA']
pd.merge(df_AALR,df_YDUQ, on=['date']).merge(df_PETR, on='date')
I’m not sure that’s the best solution. I think it would be possible to create a function to create the dataframes without having to type each one and then to join them.
From this final dataframe, I could proceed with the analyses. Am I on the right track? How to create this function?
on pandas, when you want to create a mask to filter the data using more than one condition you can use & (for and) and | (for or). I don’t have your Dataframe to check, but I believe you could do something like
dfclose[dfclose.symbol=='AALR3.SA' | dfclose.symbol=='YDUQ3.SA']– Flavio Moraes
Failed. Error message: Typeerror: Unsupported operand type(s) for |: str and 'bool'
– MarcusMartins
Then there is something wrong because the two have to be bool. You can copy here as you tested?
– Flavio Moraes
df_final = dfclose[dfclose.Symbol=='ALR3.SA']|dfclose[dfclose.Symbol=='YDUQ3.SA']
– MarcusMartins
That’s the mistake, you did it differently than I suggested. Copy this
df_final = dfclose[dfclose.symbol=='AALR3.SA' | dfclose.symbol=='YDUQ3.SA']– Flavio Moraes
Thanks but still giving error: Cannot perform 'ror_' with a dtyped [Object] array and scalar of type [bool]
– MarcusMartins
I managed using the pd.pivot_table function.
– MarcusMartins