累积所有其他列期望 python ML 中的日期列与 cumsum()
cumalativive the all other columns expect date column in python ML with cumsum()
我有股票数据集
**Date Open High ... Close Adj Close Volume**
0 2014-09-17 465.864014 468.174011 ... 457.334015 457.334015 21056800
1 2014-09-18 456.859985 456.859985 ... 424.440002 424.440002 34483200
2 2014-09-19 424.102997 427.834991 ... 394.795990 394.795990 37919700
3 2014-09-20 394.673004 423.295990 ... 408.903992 408.903992 36863600
4 2014-09-21 408.084991 412.425995 ... 398.821014 398.821014 26580100
我需要对列进行累加和 Open,High,Close,Adj Close, Volume
我试过了df.cumsum()
,它显示错误时间戳错误。
我认为加工贸易数据最好创建DatetimeIndex
:
#if necessary
#df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index('Date')
然后,如果需要,所有列的累计总和:
df = df.cumsum()
如果只需要某些列的累计和:
cols = ['Open','High','Close','Adj Close','Volume']
df[cols] = df.cumsum()
我有股票数据集
**Date Open High ... Close Adj Close Volume**
0 2014-09-17 465.864014 468.174011 ... 457.334015 457.334015 21056800
1 2014-09-18 456.859985 456.859985 ... 424.440002 424.440002 34483200
2 2014-09-19 424.102997 427.834991 ... 394.795990 394.795990 37919700
3 2014-09-20 394.673004 423.295990 ... 408.903992 408.903992 36863600
4 2014-09-21 408.084991 412.425995 ... 398.821014 398.821014 26580100
我需要对列进行累加和 Open,High,Close,Adj Close, Volume
我试过了df.cumsum()
,它显示错误时间戳错误。
我认为加工贸易数据最好创建DatetimeIndex
:
#if necessary
#df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index('Date')
然后,如果需要,所有列的累计总和:
df = df.cumsum()
如果只需要某些列的累计和:
cols = ['Open','High','Close','Adj Close','Volume']
df[cols] = df.cumsum()