Filter rows of pandas dataframe whose values are lower than 0

Question

I have a pandas dataframe like this

df = pd.DataFrame(data=[[21, 1],[32, -4],[-4, 14],[3, 17],[-7,NaN]], columns=['a', 'b'])
df

I want to be able to remove all rows with negative values in a list of columns and conserving rows with NaN.

In my example there is only 2 columns, but I have more in my dataset, so I can't do it one by one.

Answer 1

If you want to apply it to all columns, do df[df > 0] with dropna():

>>> df[df > 0].dropna()
    a   b
0  21   1
3   3  17

If you know what columns to apply it to, then do for only those cols with df[df[cols] > 0]:

>>> cols = ['b']
>>> df[cols] = df[df[cols] > 0][cols]
>>> df.dropna()
    a   b
0  21   1
2  -4  14
3   3  17

Answer 2

I've found you can simplify the answer by just doing this:

>>> cols = ['b']
>>> df = df[df[cols] > 0]

dropna() is not an in-place method, so you have to store the result.

>>> df = df.dropna()