1

有人可以帮我吗？我正进入（状态

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

从以下代码：

import pandas as pd

testdf = pd.read_csv('../../IBM.csv')

print testdf
print "------------"
testdf['NHigh'] = 0
print testdf

if testdf['Close'] > testdf['Open']:
    testdf['Nhigh'] = testdf['Close'] * testdf['High']

print "********"
print tested

我要做的是创建一个由两个现有列中的值填充的新列，但前提是条件为真。

该形状是一个带有以下列的股票数据框 -Open, High, Low, Close等等，我想添加一个新列 ( NHigh) 基于sayClose和Highif Closeis > than之间的操作High。

谢谢，如果你能帮忙......

4

1 回答 1

1

我认为您可以使用locand fillna：

print testdf
                       Open    High     Low   Close  Volume
Date_Time                                                  
1997-02-03 09:04:00  3046.0  3048.5  3046.0  3047.5     505
1997-02-03 09:27:00  3043.5  3043.5  3043.0  3043.0      56
1997-02-03 09:28:00  3043.0  3044.0  3043.0  3044.0      32
1997-02-03 09:29:00  3044.5  3044.5  3044.5  3044.5      63
1997-02-03 09:30:00  3045.0  3045.0  3045.0  3045.0      28
1997-02-03 09:31:00  3045.0  3045.5  3045.0  3045.5      75

print testdf['Close'] > testdf['Open']            
Date_Time
1997-02-03 09:04:00     True
1997-02-03 09:27:00    False
1997-02-03 09:28:00     True
1997-02-03 09:29:00    False
1997-02-03 09:30:00    False
1997-02-03 09:31:00     True
dtype: bool

testdf.loc[testdf['Close'] > testdf['Open'],'Nhigh'] = testdf['Close'] * testdf['High']
testdf['Nhigh'] = testdf['Nhigh'].fillna(0)
print testdf
                       Open    High     Low   Close  Volume       Nhigh
Date_Time                                                              
1997-02-03 09:04:00  3046.0  3048.5  3046.0  3047.5     505  9290303.75
1997-02-03 09:27:00  3043.5  3043.5  3043.0  3043.0      56        0.00
1997-02-03 09:28:00  3043.0  3044.0  3043.0  3044.0      32  9265936.00
1997-02-03 09:29:00  3044.5  3044.5  3044.5  3044.5      63        0.00
1997-02-03 09:30:00  3045.0  3045.0  3045.0  3045.0      28        0.00
1997-02-03 09:31:00  3045.0  3045.5  3045.0  3045.5      75  9275070.25

其他解决方案使用numpy.where：

testdf['Nhigh']=np.where(testdf['Close'] > testdf['Open'], testdf['Close']*testdf['High'], 0)
print testdf
                       Open    High     Low   Close  Volume       Nhigh
Date_Time                                                              
1997-02-03 09:04:00  3046.0  3048.5  3046.0  3047.5     505  9290303.75
1997-02-03 09:27:00  3043.5  3043.5  3043.0  3043.0      56        0.00
1997-02-03 09:28:00  3043.0  3044.0  3043.0  3044.0      32  9265936.00
1997-02-03 09:29:00  3044.5  3044.5  3044.5  3044.5      63        0.00
1997-02-03 09:30:00  3045.0  3045.0  3045.0  3045.0      28        0.00
1997-02-03 09:31:00  3045.0  3045.5  3045.0  3045.5      75  9275070.25
于 2016-04-18T17:42:33.383 回答