Avoiding loops in python/pandas-CodePudding

I can do python/pandas to basic stuff, but I still struggle with the "no loops necessary" world of pandas. I tend to fall back to converting to lists and doing loops like in VBA and then just bring those list back to dfs. I know there is a simpler way, but I can't figure it out.

I simple example is just a very basic strategy of creating a signal of -1 if a series is above 70 and keep it -1 until the series breaks below 30 when the signal changes to 1 and keep this until a value above 70 again and so on.

I can do this via simple list looping, but I know this is far from "Pythonic"! Can anyone help "translating" this to some nicer code without loops?

#rsi_list is just a list from a df column of numbers. Simple example:
rsi={'rsi':[35, 45, 75, 56, 34, 29, 26, 34, 67. 78]}
rsi=pd.DataFrame(rsi)
rsi_list=rsi['rsi'].tolist()

signal_list=[]
hasShort=0
hasLong=0
for i in range(len(rsi_list)-1):           
    if rsi_list[i] >= 70 or hasShort==1:
        signal_list.append(-1)
    
        if rsi_list[i 1] >= 30:
            hasShort=1
        else:
            hasShort=0
    
    elif rsi_list[i] <= 30 or hasLong==1:
        signal_list.append(1)
        
        if rsi_list[i 1] <= 70:
            hasLong=1
        else:
            hasLong=0
    else:
        signal_list.append(0)

#last part just for the list to be the same lenght of the original df as I put it back as a column    
if rsi_list[-1]>=70:
    signal_list.append(-1)
else:
    signal_list.append(1)

CodePudding user response：

First clip the values to 30 in lower and 70 in upper bound, use where to change to nan all the values that are not 30 or 70. replace by 1 and -1 and propagate these values with ffill. fillna with 0 the values before the first 30 or 70.

rsi['rsi_cut'] = (
    rsi['rsi'].clip(lower=30,upper=70)
       .where(lambda x: x.isin([30,70]))
       .replace({30:1, 70:-1})
       .ffill()
       .fillna(0)
)
print(rsi)
   rsi  rsi_cut
0   35      0.0
1   45      0.0
2   75     -1.0
3   56     -1.0
4   34     -1.0
5   29      1.0
6   26      1.0
7   34      1.0
8   67      1.0
9   78     -1.0

Edit: maybe a bit easier, use ge (greater or equal) and le (less or equal) and do a subtraction, then replace the 0s with the ffill method

print((rsi['rsi'].le(30).astype(int) - rsi['rsi'].gt(70))
       .replace(to_replace=0, method='ffill'))