Get different result when use i iteration and np.sum function - python-2.7

Question

I have a pandas Dataframe, I want to get the sum of the 'daily return' column of the data frame for every 60 days. The following is my code:
day = days() #days is a function to count business days.
for day>60:
for i in range(day-60,day):
current_x = sh600004['daily return'][i]
x_list.append(current_x)
x_sum = sum(x_list)
print x_sum
Here's what I got
To test the result, I used the following code:
y = sh600004
y.apply(lambda x: x.sum())
and I got different result.
The sum of 'daily return' column is not the same. When I print out my dataframe sh600004, I realize the data in x_sum is same as sh600004['daily return'], not the sum of it.
What do I need to do to get the sum of every 60 days of the daily return ? Can anyone help, please?
I don't know is that gonna help or not, but here's my the code I wrote so far:
#calculate daily return
daily_close = sh600004['close']
daily_pct_c = daily_close.pct_change().fillna(0)
sh600004['daily return'] = daily_pct_c
def days():
day = np.busday_count((datetime.datetime.strptime(sh600004['date'][0], '%Y/%m/%d')),pd.to_datetime(date), weekmask='1111100', holidays=holiday_list)
return day
def xn_deviation():
x_list = []
deviation_list = []
z_list = []
diff_list = []
result_list = []
day = days()
for i in range(0, 60):
current_x = sh600004['daily return'][i]
x_list.append(current_x)
x_sum = sum(x_list)
x_average = x_sum/len(x_list) #xn average
x_deviation = current_x - x_average #xn deviation
deviation_list.append(x_deviation)
dev_sum = sum(deviation_list) #calculate Z
z_list.append(dev_sum) #deviation sum list
r = max(z_list)-min(z_list) #calculate widest deviation
diff = np.square(current_x - x_average)
diff_list.append(diff)
sum_diff = sum(diff_list)
s = np.sqrt(sum_diff/len(x_list))
result_list = [r,s]
return result_list
else:
for i in range(day-60,day):
#same code as before
#loop
for date in sh600004.index:
days()
xn_deviation()