在pandas DataFrame中查找最接近值的第一行索引

Question

So I have a dataframe containing multiple columns. For each column, I would like to get the index of the first row that is nearly equal to a user specified number (eg within 0.05 of desired number). The dataframe looks kinda like this:

ix   col1   col2   col3
0    nan    0.2    1.04
1    0.98   nan    1.5
2    1.7    1.03   1.91
3    1.02   1.42   0.97

Say I want the first row that is nearly equal to 1.0, I would expect the result to be:

• index 1 for col1 (not index 3 even though they are mathematically equally close to 1.0)
• index 2 for col2
• index 0 for col3 (not index 3 even though 0.97 is closer to 1 than 1.04)

I've tried an approach that makes use of argsort():

df.iloc[(df.col1-1.0).abs().argsort()[:1]]

This would, according to other topics, give me the index of the row in col1 with the value closest to 1.0. However, it returns only a dataframe full of nans. I would also imagine this method does not give the first value close to 1 it encounters per column, but rather the value that is closest to 1.

Can anyone help me with this?

Answer 1

Use DataFrame.sub for difference, convert to absolute values by abs , compare by lt ( < ) and last get index of first value by DataFrame.idxmax :

a = df.sub(1).abs().lt(0.05).idxmax()
print (a)
col1    1
col2    2
col3    0
dtype: int64

But for more general solution, working if failed boolean mask (no value is in tolerance) is appended new column filled by True s with name NaN :

print (df)
    col1  col2  col3
ix                  
0    NaN  0.20  1.07
1   0.98   NaN  1.50
2   1.70  1.03  1.91
3   1.02  1.42  0.87

s = pd.Series([True] * len(df.columns), index=df.columns, name=np.nan)
a = df.sub(1).abs().lt(0.05).append(s).idxmax()
print (a)
col1    1.0
col2    2.0
col3    NaN
dtype: float64

Answer 2

Suppose, you have some tolerance value tol for the nearly match threshold. You can create a mask dataframe for values below the threshold and use first_valid_index() on each column to get the index of first match occurence.

tol = 0.05
mask = df[(df - 1).abs() < tol]
for col in df:
    print(col, mask[col].first_valid_index())