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.
How do I fix it?
CodePudding user response:
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.
CodePudding user response:
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])