iterate to perform calculations on certain column in pandas dataframe - python-3.x

Question

I am working with a large pandas dataframe where I have created a new empty column. What I want to do si to iterate over every value within a specific column of the dataframe, do a Boolean check, and then assign a value to the new column based on the output of the value check.
I would think I need to use a for loop to check the individual contents of each cell in my specified column. The problem is that I can't seem to figure out the correct syntax to correctly write a for loop that checks values in a specific column. This is what I have so far.
call_info['% of Net Capital'] = call_info['Call Amount'] / call_info['Net Capital']
for (ColumnData) in call_info['Call Amount']:
columnSeriesObj = call_info[ColumnData]
if columnSeriesObj.any - call_info['Excess Deficit'].any > 0:
call_info['Sufficient Excess?'][ColumnData] = True
else:
call_info['Sufficient Excess?'][ColumnData] = False
I get a KeyError : 38749372
call_info is a pandas dataframe. I am trying to compare call_info['Call Amount'] against call_info['Excess Deficit'] and put a True or false value in call_info['Sufficient Excess?']
**Updated to include an example of my dataframe, and the expected output
This is a snip of a larger csv file:
I have loaded the data from this CSV file using openpyxl load_workbook
From there, I converted the data into a Pandas Dataframe using the following code :
from itertools import islice
data = sheet_ranges.values
cols = next(data)[1:]
data = list(data)
idx = [r[0] for r in data]
data = (islice(r, 1, None) for r in data)
df = pd.DataFrame(data, index=idx, columns=cols)
An example of the expected output is a column within the dataframe that looks like this:
I've been able to do this in Excel, but I am looking to automate the process

Answer 1

I made some demo data, which hopefully represents the problem.
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame(np.random.randint(1000, size = [20, 2]), columns = ['call_amount', 'excess_deficit'])
Then you can use the following code to get the result your looking for.
df['sufficient_excess'] = (df['call_amount'] - df['excess_deficit']) > 0
which gives
call_amount excess_deficit sufficient_excess
0 684 559 True
1 629 192 True
2 835 763 True
3 707 359 True
4 9 723 False
5 277 754 False
6 804 599 True
7 70 472 False
8 600 396 True
9 314 705 False
If you need the result changing to have Yes instead of True, let me now