Select Column With Only One Negative Value

I'd like to select the columns that only have one negative value or none. How can I construct this looking at this example? I've been searching for something similar, didn't succee

Solution 1:

Use np.sign to get the signs. Look for negative signs. Get the count of negative numbers for each column. Compare against the threshold of 1 to get a mask. Select the column names from the mask.

Hence, the implementation -

df1.columns[(np.sign(df1)<0).sum(0)<=1].tolist()

Or directly compare against 0 to replace the use of np.sign -

df1.columns[(df1<0).sum(0)<=1].tolist()

This gives us the column names. To select entire columns, other solutions I think have covered it.

Solution 2:

You can use iloc to do that i.e

df1.iloc[:,((df1<0).sum(0) <=1).values]

or (Thanks Jon)

df1.loc[:,df1.lt(0).sum() <= 1]

Output:

          X         Z
0  0.292881  1.750350
1  1.291136  0.173370
2  0.616580  0.408267
3  0.269299  2.553580
4 -0.458071 -2.573784

Solution 3:

Or You can try :

df1.columns[(df1<0).sum(0).lt(1)]
Out[338]: Index(['X', 'Z'], dtype='object')