Apply Column Math Operation Utilizing Previous Row's Data, Without a For Loop?-CodePudding

I currently have this code and formula:

for i in range(len(df)):
    df['TrueRange'].iloc[i] = max(df['high'].iloc[i] - df['low'].iloc[i], df['high'].iloc[i] - df['close'].iloc[i-1], df['close'].iloc[i-1] - df['low'].iloc[i])

...that I would like to implement WITHOUT using a for loop as I have way too many rows for it to be efficient. I'd like it to be something like this:

df['TrueRange'] = max(df['high'] - df['low'].iloc[i], df['high'] - df['close'].iloc[i-1], df['close'].iloc[i-1] - df['low'])

...where the .iloc[i-1] would be replaced with whatever logic was needed to utilize the previous row, just left it there as a illustration of what I'm after. However I'm not sure how to .loc/locate the previous row's values doing it like this in the operation? Is it even possible without a for loop?

UPDATE

Using the answer below, I was getting the error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

...due to the max() function, so what I did was this:

df['H-L'] = df['high'] - df['low']
df['H-C1'] = df['high'] - df['close'].shift()
df['C1-L'] = df['close'].shift() - df['low']

df['TrueRange'] = df[['H-L','H-C1','C1-L']].max(axis=1)

...based on the top answer taken from here. Not SUPER efficient, but it's close enough to what I need, and isn't having to iterate. Thanks!

CodePudding user response：

You can use shift to reference previous or later rows in your dataframe.

Try something like this:

max(df['high'] - df['low'], df['high'] - df['close'].shift(), df['close'].shift() - df['low'])

CodePudding user response：

From the comments, you can use this:

max(df['high'] - df['low'], df['high'] - df['close'].shift(), df['close'].shift() - df['low'])