Simple way to find 3 largest values for a given column with corresponding value in another column in my own format

Let’s say I have a DataFrame…

data = {'PVOL': [190, 105, 100, 150, 100, 170], 'STKS': [2000, 2500, 3000, 3500, 4000, 4500],
'CVOL': [64, 179, 98, 281, 86, 90]}
df = pd.DataFrame(data)

Now I want to find 3 largest values for all other columns(PVOL, CVOL…etc as my df may have multiple other columns too) and their corresponding value of column STKS in following manner (as string/print):

PVOL - 2000[190], 4500[170], 3500[150]
CVOL - 3500[281], 2500[179], 3000[98]

I tried following to get 2 largest values in df format …

columns_name = list(df.columns)
columns_name.remove('STKS')
data_dict = {}
for col in columns_name:
      temp=[]
      data=df.sort_values(col, ascending=False)[:2][[col,'STKS']].values
      for row in data:
        temp.append(row[1])
        temp.append(row[0])
      data_dict[col]=temp

new_df1=pd.DataFrame(data_dict,index="STK VOL STK VOL".split())
new_df1.set_axis(["PVOL", "CVOL"], axis='columns', inplace=True)
Vol_df = new_df1[["PVOL", "CVOL"]]
print(Vol_df)

Is there any simple method to do that plz ??? I also read about..

df.nlargest()

Thanks.

>Solution :

Using a custom function with nlargest:

def f(s, n=3):
    x = s.nlargest(n)
    return ', '.join(f'{a}[{b}]' for a,b in zip(x.index, x))

df.set_index('STKS').apply(f)

Output:

PVOL    2000[190], 4500[170], 3500[150]
CVOL     3500[281], 2500[179], 3000[98]
dtype: object

If you want strings:

for key, col in df.set_index('STKS').items():
    x = col.nlargest(3)
    s = ', '.join(f'{a}[{b}]' for a,b in zip(x.index, x))
    print(f'{key} - {s}')

Output:

PVOL - 2000[190], 4500[170], 3500[150]
CVOL - 3500[281], 2500[179], 3000[98]

only for a subset of columns:

cols = ['CCHOI', 'PIV']

for key, col in df.set_index('STKS')[cols].items():
    x = col.nlargest(3)
    s = ', '.join(f'{a}[{b}]' for a,b in zip(x.index, x))
    print(f'{key} - {s}')