如何调节一列的循环以填充熊猫数据框中的另一列值

所以你需要1if not missing value else 0，所以可能的解决方案是 test not missing values bySeries.notna并将其转换为0, 1：

#if possible `NaN` is string convert to missing values
a = a.replace('NaN', np.nan)

a['Disease'] = a['Beat'].notna().astype(int)

a['Disease'] = a['Beat'].notna().view('i1')

a['Disease'] = np.where(a['Beat'].notna(), 1, 0)

a['Disease'] = np.where(a['Beat'].isna(), 0, 1)

样品：

a = pd.DataFrame({'Beat': [np.nan, 5, np.nan]})

#e.g. this solution, all working same
a['Disease'] = np.where(a['Beat'].isna(), 0, 1)
print (a)
   Beat  Disease
0   NaN        0
1   5.0        1
2   NaN        0