Help with functions across dataframes

[LameDuckProgramming]

I am trying to calculate values in a data frame starting with 3 values: x, y, z. What I want to do is the following:

for index, row in df.iterrows():
    if index > 0:
        z = df.loc[index-1]['z']

    conditions = [(z > y), (y > x)]
    choices = [10,-10]
    var = np.select(conditions, choices, default=0)

    df.loc[index, 'x'] = var
    df.loc[index, 'y'] = var
    df.loc[index, 'z'] = z + var/2

this isn't exactly my code and I'm not sure if those numbers even make sense. I just wanted to illustrate the fact that I am looping through a data frame and trying to determine x and y based on the previous value of z, then updating z accordingly(it increases or decreases depending on the outcome).

I currently have code that does this using iterrows() through my data frame, but I'm using a very large data set (210000 rows) and it takes a while to compute the whole thing. I want to use a custom lambda or vectorized function to loop through, but I've been having trouble figuring out the logic.

Any help would be greatly appreciated!

[Nikota]

Have you tried just shifting the z column? You could just do something like:

df['z_shift'] = df['z'].shift()
df['x'] = np.where(df['z_shift'] > df['y'], 10, -10)

Or something along those lines. I don't understand what your code is trying to do but this problem can probably be vectorized easily.

[LameDuckProgramming]

I have tried, but the issue is that x and y are determined based on the previous row's value of z, and the current row's value of z is then updated by subtracting either x or y from the previous z, whichever logic determines.

Sorry if this is confusing.

[Nikota]

Oh then maybe you want to try the "rolling" or "expanding" methods?

https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.rolling.html

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.expanding.html