argsort() only positive and negative values separately and add a new pandas column-CodePudding

I have a dataframe that has column , 'col', with both positive and negative numbers. I would like run a ranking separately on both the positive and negative numbers only with 0 excluded not to mess up the ranking. My issue is that my code below is updating the 'col' column. I must be keeping a reference it but not sure where?

data = {'col':[random.randint(-1000, 1000) for _ in range(100)]}
df = pd.DataFrame(data)

pos_idx = np.where(df.col > 0)[0]
neg_idx = np.where(df.col < 0)[0]
p = df[df.col > 0].col.values
n = df[df.col < 0].col.values
p_rank = np.round(p.argsort().argsort()/(len(p)-1)*100,1)
n_rank = np.round((n*-1).argsort().argsort()/(len(n)-1)*100,1)
pc = df.col.values
pc[pc > 0] = p_rank
pc[pc < 0] = n_rank
df['ranking'] = pc

CodePudding user response：

was able to figure it out on my own.

created a new column of zeros then used .loc to update te value at their respective index locations.

df['ranking'] = 0 
df[df.col > 0, 'ranking'] = pos_rank
df[df.col < 0, 'ranking'] = neg_rank

CodePudding user response：

One way to do it is to avoid mutating the original dataframe by replacing this line in your code:

pc = df.col.values

with:

pc = df.copy().col.values

So that:

print(df)
# Output
    col  ranking
0  -492       49
1   884       93
2  -355       36
3   741       77
4  -210       24
..  ...      ...
95  564       57
96  683       63
97 -129       18
98 -413       44
99  810       81

[100 rows x 2 columns]