在 for 循环中创建多个列 python

Question

I'm new to Python. I'm trying to create multiple columns in a for loop but I'm having trouble with it. I have several columns and I'm trying to create a new column that shows whether or not the elements in ohlcs is greater than elements in metrics. I can do it to create one column but I want to save time since I plan on doing the same function but for different variables.

ohlcs = ['open', 'high', 'low', 'close']
metrics = ['vwap', '9EMA', '20EMA']
wip = []
for idx, row in master_df.iterrows():
    for ohlc in ohlcs:
        for metric in metrics:
            row[f'{ohlc} above {metric}'] = np.where(row[ohlc] >= row[metric], 1, 0)

This didn't do anything. I've also done this:

ohlcs = ['open', 'high', 'low', 'close']
metrics = ['vwap', '9EMA', '20EMA']
wip = []
for idx, row in master_df.iterrows():
    for ohlc in ohlcs:
        for metric in metrics:
           if master_df[ohlc] >= master_df[metric]: 
               master_df[f'{ohlc} above {metric}'] = 1
           else:
               master_df[f'{ohlc} above {metric}'] = 0

That gave me an error.

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

I did other things but I erased those as I worked on it. At this point I'm out of ideas. Please help!

I got it now but I checked manually to see if the values lined up and it wasn't.

enter image description here

How do I fix it?

Answer 1

There is no need to iterate over the rows of the dataframe. This will give you the required result:

for ohlc in ohlcs:
    for metric in metrics:
        master_df[f'{ohlc} over {metric}'] = (master_df[ohlc] >= master_df[metric]).astype(int)

The part astype(int) is just to convert True and False into 1 and 0 , if you are okay with True and False representation, you can use just master_df[f'{ohlc} over {metric}'] = master_df[ohlc] >= master_df[metric] .

EDIT: Of course, (master_df[ohlc] >= master_df[metric]).astype(int) is equivalent to np.where(master_df[ohlc] >= master_df[metric], 1, 0) , you can use either.

Answer 2

Consider itertools.product and the functional form DataFrame.ge for all pairwise possibilities fir a flatter looping:

from itertools import product
...

ohlcs = ['open', 'high', 'low', 'close']
metrics = ['vwap', '9EMA', '20EMA']

pairs = product(ohlcs, metrics)

for ohlc, metric in pairs:
    master_df[f"{ohlc} over {metric}"] = master_df[ohlc].ge(master_df[metric])