How to add new column by getting the data from each? - pandas

Question

I have Dataframe:
teamId pts xpts
Liverpool 82 59
Man City 57 63
Leicester 53 47
Chelsea 48 55
And I'm trying to add new columns that identify the team position by each column
I wanna get this:
teamId pts xpts №pts №xpts
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 53 4 3
I tried to do something similar with the following code, but to no avail. The result is a list
df = [df.sort_values(by=i, ascending=False).assign(new_col=lambda x: range(1, len(df) + 1)) for i in df.columns]

Answer 1

You can use np.argsort:
df[["no_pts", "no_xpts"]] = df.apply(lambda x: np.argsort(-x)) + 1
We pass each column but negated so that it gives the ascending order sort's indices. Since it starts from 0, we add 1 at the end.
to get
teamId pts xpts no_pts no_xpts
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 55 4 3

Answer 2

Solutions if teamId is index:
Use DataFrame.rank with all columns, add prefix and append to original DataFrame:
df = df.join(df.rank(method='dense', ascending=False).astype(int).add_prefix('no_'))
print (df)
pts xpts no_pts no_xpts
teamId
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 55 4 3
If need specify columns for processing use list:
cols = ['pts','xpts']
df = df.join(df[cols].rank(method='dense', ascending=False).astype(int).add_prefix('no_'))