在 Pandas 中寻找最接近的值

Question

X     A    B   C    D 
10  10.2  16   28  30 
20  27    15   14  16
30  30.4  34   35  45

i want to create a new column which shows values from A,B,C or D in case they are 2% close to 'X'.

Intended Result:

X     A    B   C    D   result
10  10.2  16   28  30   10.2
20  27    15   14  16   NaN
30  30.4  34   35  45   30.4

I can identify values that are close to "X" with the following code but now sure how to bring out values from A,B,C,D columns:

cond = np.isclose(df.X, df['A'], rtol=0.02) | np.isclose(df.X, df['B'], rtol=0.02) | np.isclose(df.X, df['C'], rtol=0.02) | np.isclose(df.X, df['D'], rtol=0.02)

df['result'] = np.where(cond,#See note,np.nan)

#note: how do I put column value that meets the criteria?

Little help will be appreciated. THANKS!

Edit:

ID  OP     X   Vl  Ch  A    B   C    D   result
          10           10.2  16   28  30   10.2
          20           27    15   14  16   NaN
          30           30.4  34   35  45   30.4

Blank values have some data in them. ID is string, others are float.

Answer 1

In [1]: df = pd.DataFrame([[10, 10.2, 16, 28, 30], [20, 27, 15, 14, 16], [30, 30.4, 34, 25, 35]], columns=["X", "A", "B", "C", "D"])

In [2]: def f(x):
     ...:     # If there are multiple values that meet the conditions, please modify here
     ...:     c = None  # []
     ...:     for i in x[1:]:
     ...:         if i/x[0] >=1.0 and i/x[0] <=1.02:
     ...:             c = i
     ...:             break
     ...:             # c.append(i)
     ...:     return c
     ...:

In [3]: df['result'] = df.apply(lambda x: f(x), axis=1)

In [4]: df
Out[5]:
    X     A   B   C   D  result
0  10  10.2  16  28  30    10.2
1  20  27.0  15  14  16     NaN
2  30  30.4  34  25  35    30.4

Answer 2

val = [[10,  10.2,  16,   28,  30], [20, 27,  15,  14, 16], [30, 30.4,  34,  35, 45]]
df = pd.DataFrame(val, columns=list('XABCD'))

# split df for convenience:
target_X = df['X']
df2 = df.drop('X', axis=1)
tol = 0.02

# get the closest value to X within the tolerance tol:
df['Result'] = df2[df2.subtract(target_X, axis=0).abs().apply(lambda x: x <= target_X * tol)].min(1)
print(df)