我在 Pandas 中有一个 DataFrame,它有日期、股票代码(即“MSFT”)以及该股票在该特定日期的开盘价和收盘价以及其他数据点。因此,我的数据集中每个股票代码基本上都有一份日期副本。
我想转换我的数据帧:
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
变成类似:
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 ...
我正在使用我编写的代码手动执行此操作:
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)
但不幸的是,这段代码未经优化,需要几个小时才能运行。我一直在研究各种不同的 Pandas 操作,但我不知道该怎么做。
答案 0 :(得分:1)
使用:
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]