已解決：使用邏輯比較更新DataFrame列

根據以下建議，我已將資料框合并為一個 DF。因此也更改了標題。以下是我的合并資料框的螢屏截圖：

我現在需要將 Adj Close 與其他列進行比較，以準確了解它的位置 - 例如它在 PP 和 R1、R1 和 R2、R2 和 R3 之間等等。基于此，我需要創建一個新的狀態列，這將具有相關的狀態。我寫的功能如下

def check_status(merged):
if merged['Adj Close'] < merged['R3']:
    merged['Status'] = 'Breached R3'
elif merged['R3'] < merged['Adj Close'] < merged['R2']:
    merged['Status'] = 'Breached R2'
elif merged['R2'] < merged['Adj Close'] < merged['R1']:
    merged['Status'] = 'Breached R1'
elif merged['R1'] < merged['Adj Close'] < merged['PP']:
    merged['Status'] = 'Breached PP on lower side'
elif merged['PP'] < merged['Adj Close'] < merged['S1']:
    merged['Status'] = 'Breached PP on higher side'
elif merged['S1'] < merged['Adj Close'] < merged['S2']:
    merged['Status'] = 'Breached S1'
elif merged['S2'] < merged['Adj Close'] < merged['S3']:
    merged['Status'] = 'Breached S2'
else:
    merged['Status'] = 'Breached S3'
return merged

這給出了如下錯誤：

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

接下來我嘗試使用 np.where()。代碼片段如下：

merged['Status'] = np.where(merged['Adj Close'] < merged['R3'], 'Breached R3', merged['Adj Close'])
merged['Status'] = np.where(merged['R2'] < merged['Adj Close'] < merged['R1'], 'Breached R1', merged['Adj Close'])
merged['Status'] = np.where(merged['R1'] < merged['Adj Close'] < merged['PP'], 'Breached PP on lower side', merged['Adj Close'])
merged['Status'] = np.where(merged['PP'] < merged['Adj Close'] < merged['S1'], 'Breached PP on higher side', merged['Adj Close'])
merged['Status'] = np.where(merged['S1'] < merged['Adj Close'] < merged['S2'], 'Breached S1', merged['Adj Close'])
merged['Status'] = np.where(merged['S2'] < merged['Adj Close'] < merged['S3'], 'Breached S2', merged['Adj Close'])
merged['Status'] = np.where(merged['S3'] < merged['Adj Close'], 'Breached S3', merged['Adj Close'])

但是，我得到了同樣的錯誤。

我有什么選項可以使狀態列更新如上？

我有一個很大的 DF，所以性能可能是一個考慮因素。

uj5u.com熱心網友回復：

np.select與整個資料框一起使用。

conditions = [
    df['Adj Close'] < df['R3'],
    (df['R2'] < df['Adj Close']) & (df['Adj Close'] < df['R1']),
    (df['R1'] < df['Adj Close']) & (df['Adj Close'] < df['PP']),
    (df['PP'] < df['Adj Close']) & (df['Adj Close'] < df['S1']),
    (df['S1'] < df['Adj Close']) & (df['Adj Close'] < df['S2']),
    (df['S2'] < df['Adj Close']) & (df['Adj Close'] < df['S3']),
    df['S3'] < df['Adj Close']
]

values = [
    'Breached R3',
    'Breached R1',
    'Breached PP on lower side',
    'Breached PP on higher side',
    'Breached S1',
    'Breached S2',
    'Breached S3'
]

df['Status'] = np.select(conditions, values, df['Adj Close'])

這將顯著加快您的程式@Sachin

uj5u.com熱心網友回復：

我np.where()在for回圈中解決了這個問題：

for i in merged:
    merged['Status'] = np.where(merged['Adj Close'] > merged['R3'], 'Breached R3', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['R3'] > merged['Adj Close'], merged['Adj Close'] > merged['R2']), 'Breached R2', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['R2'] > merged['Adj Close'], merged['Adj Close'] > merged['R1']), 'Breached R1', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['R1'] > merged['Adj Close'], merged['Adj Close'] > merged['PP']), 'Breached PP on higher side', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['PP'] > merged['Adj Close'], merged['Adj Close'] > merged['S1']), 'Breached PP onn lower side', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['S1'] > merged['Adj Close'], merged['Adj Close'] > merged['S2']), 'Breached S1', merged['Status'])
    merged['Status'] = np.where(np.logical_and(merged['S2'] > merged['Adj Close'], merged['Adj Close'] > merged['S3']), 'Breached S2', merged['Status'])
    merged['Status'] = np.where(merged['S3'] > merged['Adj Close'] , 'Breached S3', merged['Status'])

非常感謝所有的建議。

標籤：Python 熊猫 数据框

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