How would I loop this code to include all the columns in python?

below is how the data is structured

I'm sure this is simple, but as a newbie to python, I'm having trouble figuring out how to iterate over variables.

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 35 entries, 2021-02-25 to 2021-04-15
Data columns (total 11 columns):
dtypes: float64(11)

suppose I wanted to loop the code below so it performs the function on all the columns and outputs df. How would I do that?

n_days = 6
n=len(prices)- n_days
dnum = np.arange(1,n, 1).tolist()

a_list = []
b_list = []
c_list = []
d_list = []
e_list = []
f_list = []


for x in dnum:
  s = x 
  i = n_days+x
  new_prices = prices[s:i]
  
  returns = new_prices['fngu'].to_returns().dropna()
  sharp_1 = returns.calc_sharpe_ratio(nperiods=365)
  
  returns = new_prices['soxl'].to_returns().dropna()
  sharp_2 = returns.calc_sharpe_ratio(nperiods=365)
  
  returns = new_prices['spxl'].to_returns().dropna()
  sharp_3 = returns.calc_sharpe_ratio(nperiods=365)
  
  returns = new_prices['fas'].to_returns().dropna()
  sharp_4 = returns.calc_sharpe_ratio(nperiods=365)
  
  returns = new_prices['utsl'].to_returns().dropna()
  sharp_5 = returns.calc_sharpe_ratio(nperiods=365)

  date = prices.index[i]
  
  a_list.append(date)
  b_list.append(sharp_1)
  c_list.append(sharp_2)
  d_list.append(sharp_3)
  e_list.append(sharp_4)
  f_list.append(sharp_5)

df = pd.DataFrame({'Date': a_list, 'fngu': b_list, 'soxl': c_list, 'spxl': d_list, 'fas': e_list, 'utsl': f_list})
del a_list, b_list, c_list, d_list, e_list, f_list

I tried using concat and iteritems function but I am too much of a noob for that

EDIT:: I sort of came up with solution from other posts,

r_list = []

for x in dnum:
  s = x 
  i = n_days+x
  new_prices = prices[s:i]
  returns = new_prices.to_returns().dropna()
  sharp = returns.calc_sharpe_ratio(nperiods=365)
  sharp['Date'] = prices.index[i]
  s = pd.DataFrame(sharp)
  s= s.transpose()
  r_list.append(s)
df = pd.concat(r_list)
df.Date = pd.to_datetime(df.Date)
df = df.set_index('Date')

This works for what I need but I would like to learn the most efficient way to do this type of loop or something else.