Use apply function to simpify nested for loop?

Question

I have a dataframe with a column of values (column 'One'). For each pair of values given by (product(sma1, sma2)), I want to:

• calculate 2 simple moving average using SMA1, and SMA2
• When condition A or B is met, append to results the value of SMA1, SMA2, and price

The code below shows my approach. Is it possible to do the same thing removing the nested for loop (with apply maybe)? would that be faster / better?

from itertools import product 
import pandas as pd 


data = pd.DataFrame( [3,2,5,8,5,12,7,8,9,10,11,12,13,14,17,19,15,18] ,columns=['One'])
sma1 = range(2, 5)  
sma2 = range(5, 8)
results=pd.DataFrame()

for SMA1, SMA2 in product(sma1, sma2): #for each pair SMA1, SMA2, perfomr the following
    data['SMA1'] = data['One'].rolling(SMA1).mean()
    data['SMA2'] = data['One'].rolling(SMA2).mean()
    
    data.dropna(inplace=True)
    
    for i in range( len(data)):
        #Condition A:
        if data.iloc[i, 1] > data.iloc[i, 2] and data.iloc[i-1, 1] < data.iloc[i-1,2]: 
          #Buy
          price = data.iloc[i, 0]
        #Condition B
        elif data.iloc[i, 1] < data.iloc[i, 2] and data.iloc[i-1, 1] > data.iloc[i-1,2]:
          #Sell
          price = data.iloc[i, 0] 
        #if neither condition is true, dont append anything 
        else: 
          continue
          
        results = results.append(pd.DataFrame(
              {'SMA1': SMA1,
               'SMA2': SMA2,
               'price': price,
               
              },
               index=[0]), ignore_index = True)

    
results

Out:

    SMA1 SMA2   price
0   2    5      5
1   2    5      8
2   2    5      9
3   2    5      18

EIDT from Hugolmn's answer I see the issue with using [i-1]. Howver with your approach I am not getting the right answer, I have simpliefied the probelm to just finding if the conditions A, B are satisfied for 2 columns.

data = pd.DataFrame({'col1': [3,2,6,8,6,11], 'col2': [3,3,5,8,5,12]})

#columns with shifted data
data['col1_shift']=data['col1'].shift(1)
data['col2_shift']=data['col2'].shift(1)

#condition A  
data['Con1_col1>col2'] = data['col1']>data['col2']
data['Con1_col1<col2_shift'] = data['col1_shift'] < data['col2_shift']
data['ConA'] = data['Con1_col1>col2'] & data['Con1_col1<col2_shift']

#condition B
data['Con2_col1<col2'] = data['col1']<data['col2']
data['Con2_col1>col2_shift'] = data['col1_shift'] > data['col2_shift']
data['ConB'] = data['Con2_col1<col2'] & data['Con2_col1>col2_shift']


# data['part1']= ((data.col1 - data.col2) > 0)
data['Hugolmn_method']= ((data.col1 - data.col2) >= 0).diff() > 0
data['expected']= data['ConA'] | data['ConB']

data

Out:

   col1  col2  col1_shift  col2_shift  Con1_col1>col2  Con1_col1<col2_shift  \
0     3     3         NaN         NaN           False                 False   
1     2     3         3.0         3.0           False                 False   
2     6     5         2.0         3.0            True                  True   
3     8     8         6.0         5.0           False                 False   
4     6     5         8.0         8.0            True                 False   
5    11    12         6.0         5.0           False                 False   

    ConA  Con2_col1<col2  Con2_col1>col2_shift   ConB  Hugolmn_method  \
0  False           False                 False  False           False   
1  False            True                 False  False            True   
2   True           False                 False  False            True   
3  False           False                  True  False           False
4  False           False                 False  False           False
5  False            True                  True   True            True   

   expected  
0     False  
1     False  
2      True  
3     False  
4     False  
5      True  

Answer 1

I inspected your code and come up with these comments:

• You don't need a loop to iterate over data
• You can append a dict to a dataframe directly
• You're result is wrong for price=5, it was comparing the index 0 with the index -1, so between the first and last row of data. This is probably not an expected behavior

Instead, I would suggest you to use .shift() to do tests with previous rows.

data.iloc[i, 1] > data.iloc[i, 2] and data.iloc[i-1, 1] < data.iloc[i-1,2]

# would become
(data.SMA1 > data.SMA2) & (data.SMA1.shift() < data.SMA2.shift())