You can call abs() on multiple columns too.

df[['col1', 'col2']] = df[['col1', 'col2']].abs()

or use numpy abs() as well, which interestingly returns a pandas object, not a numpy array:

df['col1'] = np.abs(df['col1'])

On a tangential note, if you get SettingWithCopyWarning when you convert column values into absolute values, that means your dataframe is probably created by filtering another dataframe. Turn on copy-on-write mode to turn it off. See this post for more info.

pd.options.mode.copy_on_write = True

df['count'] = df['count'].abs()

or use assign() to make a copy.

df = df.assign(count=df['count'].abs())

I would go for

df["Discrepancy_Num"] = (
    -1 + df.Impressions_Source2/df.Impressions_Source
).abs()/100

since you can avoid accessing df.Impressions_Source twice. Indeed, the result is the same as yours, stemming from a rearranged formulation.