更快地在 Pandas 中循环

Question

I need to make a loop in pandas faster. It's a time series. Below code works pretty well but it is slow for massive df.

It iterates through a df and at each first value 0 'zero' of column A (it needs to be only the first zero of a serie; df has many 0 series) calculates the delta (in absolute value) of column B values at one period before and after of the initial value 0 'zero' of column A. Then it stores the results in a new df with column called 'Delta'

I bet I can do something with loc. but I cannot figure out how.

deltas=[]
indexes = []
i=0
for idx, row in df.iterrows():

    if df.A[i] == 0 and df.A[i-1] !=0:
    
        deltas.append(abs(df.B.shift(periods=1)[i] - df.B.shift(periods=-1)[i]))
        indexes.append(idx)
        
    i+=1
s_delta = pd.Series(deltas, name="Delta", index = indexes)
df_delta = s_delta.to_frame()

Answer 1

Use assign function to process df in series not per row:

df = df.assign(
    n = lambda x: x.B.shift(1),
    p = lambda x: x.B.shift(-1),
    s_delta= np.abs(x.n-x.p)
)

Then you can modify it using np.where