This is a part of a Python API Connection program

Here is the DataFrame Sample

      Region  Sector  Brand ID  Start Date
7188  US      41      40000     2006-03-06
7189  US      41      40345     2017-11-06
7190  US      41      40123     2019-01-12
7191  US      42      40145     2001-02-06
7192  US      42      40185     2013-03-16

Here is the working function as a part of API python program which usually takes a fixed parameter values i.e; Region, Sector, Brand ID, Start Date, End Date="05-04-2020", Scoring ="total".(End Date and Scoring are fixed values, rest of them are keep changing).

#This code is working if manually enter
#details of a Dataframe one by one which is inefficient.
#This code generates me a URL of a dataset using the parametersquerySingleBrandTimeline('db.csv', {'region':'us','sector':'41', 'brand_id':'40000', 'scoring':'total', 'start_date':'2006-03-06','end_date':'2020-03-31'})

What I am trying to achieve is a for loop which takes values from the pandas DtaFrame and put it in the function parameters accordingly.

Here is the sample code i've tried:

for id in US['Sector']:for brandid in US['Brand ID']:querySingleBrandTimeline('db.csv', {'region':{}.format(US['Region']),'sector':{}.format(US['Category ID']), 'brand_id':{}.format(US['Brand ID']), 'scoring':'total'}, 'start_date':{}.format{US['Start Date']},'end_date':'2020-03-31'})

I'm quite not sure how to pass values of the DataFrame to the function dynamically

I think iterrows here is not necessary, because slowiest iterate solution in pandas (and output is Series, here are necessary dicts).

First add scalar columns to DataFrame and rename columns names:

df1 = df.rename(columns={'Start Date':'start_date'}).rename(columns=str.lower)
df1.insert(3, 'scoring', 'total')
df1['end_date'] = '2020-03-31'
df1['start_date'] = df1['start_date'].dt.strftime('%Y-%m-%d')print (df1)region  sector  brand id scoring  start_date    end_date
7188     US      41     40000   total  2006-03-06  2020-03-31
7189     US      41     40345   total  2017-11-06  2020-03-31
7190     US      41     40123   total  2019-01-12  2020-03-31
7191     US      42     40145   total  2001-02-06  2020-03-31
7192     US      42     40185   total  2013-03-16  2020-03-31

And then convert to list of dicts by DataFrame.to_dict and loop:

for d in df1.to_dict('record'):print (d){'region': 'US', 'sector': 41, 'brand id': 40000, 'scoring': 'total', 'start_date': '2006-03-06', 'end_date': '2020-03-31'}
{'region': 'US', 'sector': 41, 'brand id': 40345, 'scoring': 'total', 'start_date': '2017-11-06', 'end_date': '2020-03-31'}
{'region': 'US', 'sector': 41, 'brand id': 40123, 'scoring': 'total', 'start_date': '2019-01-12', 'end_date': '2020-03-31'}
{'region': 'US', 'sector': 42, 'brand id': 40145, 'scoring': 'total', 'start_date': '2001-02-06', 'end_date': '2020-03-31'}
{'region': 'US', 'sector': 42, 'brand id': 40185, 'scoring': 'total', 'start_date': '2013-03-16', 'end_date': '2020-03-31'}