根据唯一值创建 pandas DataFrame 的新列？

Question

I have a DataFrame in pandas that has a date, a stock symbol (ie 'MSFT'), and the Open and Close and other datapoints of that stock on that particular day. Thus, there is essentially a copy of the dates for each stock symbol in my dataset.

I want to convert my DataFrame:

    Open    High    Low Close   Adj Close   Volume  Name
Date                            
2006-12-04  0.06508 0.06508 0.06508 0.06508 -0.098360   193352.0    AAIT
2006-12-05  0.06464 0.06464 0.06464 0.06464 -0.097695   81542.0 AAIT
2006-12-06  0.06596 0.06596 0.06552 0.06596 -0.099690   158115.0    AAIT
2006-12-07  0.06596 0.06596 0.06596 0.06596 -0.099690   65731.0 AAIT
2006-12-11  0.06596 0.06596 0.06596 0.06596 -0.099690   542561.0    AAIT

into something like:

    ADBE_Adj Close  ADBE_Close  ADBE_High   ADBE_Low    ADBE_Open   ADBE_Volume ADXS_Adj Close  ADXS_Close  ADXS_High   ADXS_Low    ... 
2019-12-19  327.630005  327.630005  327.959991  324.26001   324.380005  2561400.0   0.581   0.581   0.59    0.550   ...
2020-11-17  467.950012  467.950012  469.910004  460.00000   461.660004  2407600.0   0.393   0.393   0.40    0.383   ...

I'm doing it manually with the code that I wrote:

df = pd.DataFrame() # init empty dataframe
dates_set = set(stocks_df.index)
print('Going through {} days of data.'.format(len(dates_set)))
for _date in tqdm(dates_set):
    row = {}
    for symbol in filtered_stock_list:
        stock_at_date = stocks_df.loc[(stocks_df['Name']==symbol) &
                                     (stocks_df.index==_date)]
        for attribute in ['Open','High','Low','Close','Adj Close','Volume']:
            try:
                row[symbol + '_' + attribute] = float(stock_at_date[attribute])
            except Exception as e:
                row[symbol + '_' + attribute] = None
    #print(row)
    ser = pd.Series(data=row, name=_date)
    df = df.append(ser)

but unfortunately, this code is very unoptimized and will take hours to run. I've been looking at all kinds of different pandas operations, but I can't figure out how to do it.

Answer 1

Use:

new_df = (df.set_index('Name', append=True)
            .loc[:, ['Open','High','Low','Close','Adj Close','Volume']]
            .unstack('Name'))
new_df.columns = [f'{x}_{y}' for x, y in new_df.columns]