Rolling Calculation of Slope in Python

Question

I am trying to calculate Slope for the rolling window of 5 and 20 periods and append it to the existing data frame. The length of the total dataset would be let's say 30 days. I have two columns "Volume" and "Vpt", I have tried sklearn (linregress) and numpy (polyfit) but in both the scenario, I am getting an error message "IndexError: iloc cannot enlarge its target object". Please see below code and help to resolve this issue.

for j in range(len(temp)):
   x = np.array(temp['Volume'][j:j+5])
   y = np.array(temp['vpt'][j:j+5])
   slope = np.polyfit(x,y,1)
   # slope, intercept, r_value, p_value, std_err = linregress(x, y)
   temp['slope_5'].iloc[j+5] = slope[0]
   a = np.array(temp['Volume'][j:j+20])
   b = np.array(temp['vpt'][j:j+20])
   slope_1 = np.polyfit(a,b,1)
   # slope, intercept, r_value, p_value, std_err = linregress(a, b)
   temp['slope_20'].iloc[j+20] = slope_1[0]

Answer 1

Actually, I found an answer to my question above.

def np_slope(data):
    return np.polyfit(np.arange(data.shape[0]),data.values,1)[0]
 
temp.index = temp['Volume']
temp['slope_5'] = temp['vpt'].rolling(5,min_periods=5).apply(np_slope,raw=False)        
temp['slope_20']= temp['vpt'].rolling(20,min_periods=20).apply(np_slope,raw=False)        

Answer 2

this is a similar solution, but using scipy.stats.linregress

from scipy.stats import linregress

def slope(data):
    return linregress(np.arange(data.shape[0]),data).slope

temp.index = temp['Volume']
temp['slope_5'] = temp['vpt'].rolling(5,min_periods=5).apply(slope,raw=True)        
temp['slope_20']= temp['vpt'].rolling(20,min_periods=20).apply(slope,raw=True)        

as per pandas apply documentation, i'm passing raw=True in order to pass a numpy array, which can improve performance, specially on reduction functions