I\'d like to search a pandas DataFrame
for minimum values. I need the min in the entire dataframe (across all values) analogous to df.min().min()
.
Based on your revised update:
In [209]:
keys = ['x', 'y', 'z']
vals = [[1,2,-1], [3,5,-1], [4,2,3]]
data = dict(zip(keys,vals))
df = pd.DataFrame(data)
df
Out[209]:
x y z
0 1 3 4
1 2 5 2
2 -1 -1 3
Then the following would work:
In [211]:
df[df==df.min().min()].dropna(axis=1, thresh=1).dropna()
Out[211]:
x y
2 -1.0 -1.0
So this uses the boolean mask on the df:
In [212]:
df[df==df.min().min()]
Out[212]:
x y z
0 NaN NaN NaN
1 NaN NaN NaN
2 -1.0 -1.0 NaN
and we call dropna
with param thresh=1
this drops columns that don't have at least 1 non-NaN value:
In [213]:
df[df==df.min().min()].dropna(axis=1, thresh=1)
Out[213]:
x y
0 NaN NaN
1 NaN NaN
2 -1.0 -1.0
Probably safer to call again with thresh=1
:
In [214]:
df[df==df.min().min()].dropna(axis=1, thresh=1).dropna(thresh=1)
Out[214]:
x y
2 -1.0 -1.0