Pandas: How do I groupby but only if data meets a particular condition within that group. i.e. *only if* there is a negative value in that group

Question

Please help, I need to fix a column in my df that assigns a value ( a or b ) based on its group. If the group sums roughly to 0 (anywhere between -50 to 100 accepted), it is coded 'a' . If not it is coded 'b' .

My columns are: ['Assets', 'Value']

   Asset  Value  
0  2000    500   
1  2000   -500   
2  3000    1000  
3  3000    1000  
4  4000    3000  
5  4000   -100   
6  5000   -100   
7  5000    9000  

so far I've done this:

df['Sum_Assets'] = df['Value'].groupby(df['Asset']).transform('sum')

Then I've done:

    (df['Sum_Assets'] > -50) & (df['Sum_Assets'] <= 100),
    (df['Sum_Assets'] <= -50),
    (df['Sum_Assets'] > 100 ),
    ]

# create a list of the values to assign for each condition
values = ['a', 'b', 'b']

# create new column and assign values to it
df['Action'] = np.select(conditions, values)

However, I only need to do this if there is a negative number in the 'value' column for each asset group. For instance with the expected output below: Assets grouped with asset code = 3000 (index 2 and 3) would not be assigned anything as it has no negative number under column 'values'

Expected Output:

   Asset  Value  Action
0  2000    500   a
1  2000   -500   a
2  3000    1000  
3  3000    1000  
4  4000    3000  b
5  4000   -100   b
6  5000   -100   b
7  5000    9000  b

Answer 1

It should be:

df.Asset = df.Asset.astype(int)
df.Value = df.Value.str.replace(',', '').astype(float).astype(int) 
df['Sum_Assets'] = df.groupby('Asset')['Value'].transform('sum')
conditions = [
    (df['Sum_Assets'].gt(50)) & (df['Sum_Assets'].le(100)),
    (df['Sum_Assets'].le(-50)) | (df['Sum_Assets'].gt(100))
    ]
values = ['a', 'b']

df['Action'] = np.where(df.Value<0, None,np.select(conditions, values))