Pandas Dataframe - find the row with minimum value based on two columns but greater than 0

Question

I have a dataframe with 3 columns: x, y, time. There are a few thousand rows.

What I want to do is retrieve the row with the minimum time but I would like that the minim

Answer 1

You can filter out 0 values by query and get index of minimal value by idxmin, last select by loc:

s = df.loc[df.query('time != 0')['time'].idxmin()]
print (s)
x       240.0
y        19.0
time      9.7
Name: 3, dtype: float64

df = df.loc[[df.query('time != 0')['time'].idxmin()]]
print (df)
     x   y  time
3  240  19   9.7

Answer 2

Try this:

In [69]: df.loc[df.time>0, 'time'].idxmin()
Out[69]: 3

or

In [72]: df.loc[[df.loc[df.time>0, 'time'].idxmin()]]
Out[72]:
     x   y  time
3  240  19   9.7

Answer 3

You don't need groupby at all. Here's an option with mask/where + loc + idxmin;

df.loc[[df.time.mask(df.time.eq(0)).idxmin()]]

Or,

df.loc[[df.time.where(df.time.ne(0)).idxmin()]]

     x   y  time
3  240  19   9.7