在没有 for 循环的情况下，将包含 if 的函数应用于 pandas 中数据帧的每一行

Question

Given a dataframe, I want to get the nonzero values of each row and then find the minimum of absolute values. I want to have a user defined function that does this for me. Also, I do not want to use any for loop since the data is big.

My try

np.random.seed(5)
data = np.random.randn(16)
mask = np.random.permutation(16)[:6]
data[mask] = 0
df = pd.DataFrame(data.reshape(4,4))

          0         1         2         3
0  0.441227 -0.330870  2.430771  0.000000
1  0.000000  1.582481 -0.909232 -0.591637
2  0.000000 -0.329870 -1.192765  0.000000
3  0.000000  0.603472  0.000000 -0.700179


def udf(x):
  if x != 0:
    x_min = x.abs().min()
  return x_min
df.apply(udf, axis=1)

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

Question How can I solve the above?

The desired answer is the following:

0.330870
0.591637
0.329870
0.603472

Answer 1

You can use x.ne(0) as boolean indexing to filter row

res = df.apply(lambda x: x[x.ne(0)].abs().min(), axis=1)

print(res)

0    0.330870
1    0.591637
2    0.329870
3    0.603472
dtype: float64

Or use min(axis=1)

res = df[df.ne(0)].abs().min(axis=1)