I am trying to add values to a column based on a couple of conditions. Here is the code example:

```
Import pandas as pd

df1 = pd.DataFrame({'Type': ['A', 'A', 'A', 'A', 'B', 'B', 'C', 'C'], 'Val': [20, -10, 20, -10, 30, -20, 40, -30]})
df2 = pd.DataFrame({'Type': ['A', 'A', 'B', 'B', 'C', 'C'], 'Cat':['p', 'n', 'p', 'n','p', 'n'], 'Val': [30, -40, 20, -30, 10, -20]})

for index, _ in df1.iterrows():  

    if df1.loc[index,'Val'] >=0:
        df1.loc[index,'Val'] = df1.loc[index,'Val'] + float(df2.loc[(df2['Type'] == df1.loc[index,'Type']) & (df2['Cat'] == 'p'), 'Val'])
    else:
        df1.loc[index,'Val'] = df1.loc[index,'Val'] + float(df2.loc[(df2['Type'] == df1.loc[index,'Type']) & (df2['Cat'] == 'n'), 'Val'])

```

For each value in the 'Val' column of df1, I want to add values from df2, based on the type and whether the original value was positive or negative.

The expected output for this example would be alternate 50 and -50 in df1. The above code does the job, but is too slow to be usable for a large data set. Is there a better way to do this?