How to apply formula to a dataframe in pandas-CodePudding

       tr   Atr
0   0.00276 0.00276
1   0.01455 NaN
2   0.00895 NaN
3   0.00816 NaN
4   0.00596 NaN
5   0.00816 NaN
6   0.00844 NaN
7   0.01150 NaN
8   0.00473 NaN
9   0.00502 NaN

Please how to do a apply this formula to each tr

Atr = (prev_Atr * (14 - 1) tr) / 14

what i want to do is

df["Atr"] = lambda x, y: (x * (14 -1) y)/14

but i dont know how to assign

x = prev_Atr & y = tr

CodePudding user response：

for x in range(len(df)-1): df['Atr'][x 1:] = (df['Atr'][x]*(14-1) df['tr'][x 1])/14

I hope this helps. I used a for loop and offset the cell being assigned by one.

CodePudding user response：

apply doesn't you here you are doing recursive calculation. The value of the next row depends on the value of the current row, which you must calculate.

A simple for loop will do the job:

tr, atr = df[["tr", "Atr"]].to_numpy().T
for i in range(1, len(atr)):
    atr[i] = (atr[i-1] * 13   tr[i]) / 14

df["Atr"] = atr

It's clean, readable and have the same form as your formula. The performance isn't the fastest but if this is too slow for you, look at numba to JIT-compile the snippet.

CodePudding user response：

It seems you are looking for a rolling computation. But its not a simple sum() or such. You can achieve what you want with a simple for loop:

for i in df.index[1:]:
    df['Atr'].iloc[i] = (df['Atr'].iloc[i-1]*13   df['tr'].iloc[i])/14

print(df):

        tr       Atr
0  0.00276  0.002760
1  0.01455  0.003602
2  0.00895  0.003984
3  0.00816  0.004282
4  0.00596  0.004402
5  0.00816  0.004671
6  0.00844  0.004940
7  0.01150  0.005408
8  0.00473  0.005360
9  0.00502  0.005336