How To Put Values Of Pandas Dataframe Into A For Loop In Python?

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 4

Solution 1:

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={'StartDate':'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)regionsectorbrandidscoringstart_dateend_date7188     US4140000total2006-03-06  2020-03-317189     US4140345total2017-11-06  2020-03-317190     US4140123total2019-01-12  2020-03-317191     US4240145total2001-02-06  2020-03-317192     US4240185total2013-03-16  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'}

Solution 2:

Iterate the dataframe by rows using iterrows(). I believe your dataframe name is US. Then:

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

Solution 3:

Edit:Jezrael's approach is much better for what you want to do though.

To iterate through a pandas data frame by rows you can use pandas.DataFrame.iterrows

import pandas as pd

df = pd.DataFrame({
    'col1':[1,5,3,54,34,56,45],
    'col2':['foo','bar','foo','foo','bar','foo','foo'],
    'col3':[True,True,False,True,True,False,True]})

for idx, row in df.iterrows():
    if row['col3']:
        print('\n'+str(idx))
        print(row['col2']+'_'+str(row['col1']))

Output:

0
foo_1

1
bar_5

3
foo_54

4
bar_34

6
foo_45