Iteration over rows (Two Loops) Automation Pandas DataFrame - loops

Question

I have the following iteration (for loop) for every row depending on the Indicator 'H' and 'G' in df1. Creates a new column with the product of the selected indicators. Now i would like that this is automatically down for all indicators (if i have more than 'H' and'G'). Unfortuntely I am struggling to put it in a dictionary.
Can anyone help with this? Thank you and have an ecellent week.
df1 =pd.DataFrame({'Country':['Armenia','Azerbaidjan','Belarus','Armenia','Azerbaidjan','Belarus'],\
'Indictaor':['G','G','G','H', 'H', 'H'],'2005':[3,4,5,6,7,4],'2006':[6,3,1,3,5,6]})
df2 = pd.DataFrame({'Year':[2005,2006,2005,2006],
'Country1':['Armenia','Armenia','Azerbaidjan','Azerbaidjan'],
'Country2': ['Belarus','Belarus','Belarus','Belarus']})
df3 = pd.DataFrame({'Year':[2005,2006,2005,2006],
'Country2': ['Belarus','Belarus','Belarus','Belarus'],
'Country1':['Armenia','Armenia','Azerbaidjan','Azerbaidjan'],
'IndictaorGProduct':[15,6,35,5],
'IndictaorHProduct':[24,18,28,30]})
gprod = []
hprod =[]
for row in df4.iterrows() :
c1 = row[1][2]
c2 = row[1][1]
yr = str(row[1][0])
g1 = df1.loc[(df1['Country']==c1)&(df1['Indictaor']=='G')]
g1val = g1[yr].values[0]
g2 = df1.loc[(df1['Country']==c2)&(df1['Indictaor']=='G')]
g2val = g2[yr].values[0]
print(g1val, g2val, g1val*g2val)
gprod.append(g1val*g2val)
df4['GProduct'] = gprod
for row in df4.iterrows() :
c1 = row[1][2]
c2 = row[1][1]
yr = str(row[1][0])
g1 = df1.loc[(df1['Country']==c1)&(df1['Indictaor']=='H')]
g1val = g1[yr].values[0]
g2 = df1.loc[(df1['Country']==c2)&(df1['Indictaor']=='H')]
g2val = g2[yr].values[0]
print(g1val, g2val, g1val*g2val)
gprod.append(g1val*g2val)
df4['HProduct'] = hprod

Answer 1

It depends where you get the Indicators from. Do you decide on them or do you get them from the column?
In case you get them from the respective column you could use the column to get a list with unique values from the column. Then you can loop over the values in a second loop. But note that, depending on your data size, this might not be very efficient.
However here is what you could do:
import pandas as pd
df1 = pd.DataFrame({'Country': ['Armenia', 'Azerbaidjan', 'Belarus', 'Armenia', 'Azerbaidjan', 'Belarus'], \
'Indictaor': ['G', 'G', 'G', 'H', 'H', 'H'], '2005': [3, 4, 5, 6, 7, 4],
'2006': [6, 3, 1, 3, 5, 6]})
df2 = pd.DataFrame({'Year': [2005, 2006, 2005, 2006],
'Country1': ['Armenia', 'Armenia', 'Azerbaidjan', 'Azerbaidjan'],
'Country2': ['Belarus', 'Belarus', 'Belarus', 'Belarus']})
df3 = pd.DataFrame({'Year': [2005, 2006, 2005, 2006],
'Country2': ['Belarus', 'Belarus', 'Belarus', 'Belarus'],
'Country1': ['Armenia', 'Armenia', 'Azerbaidjan', 'Azerbaidjan'],
'IndictaorGProduct': [15, 6, 35, 5],
'IndictaorHProduct': [24, 18, 28, 30]})
cols = ['Year', 'Country2', 'Country1']
df4 = pd.DataFrame(columns=cols)
df4['Year'] = df2['Year']
df4['Country1'] = df2['Country1']
df4['Country2'] = df2['Country2']
indicators = df1["Indictaor"].unique() # get all the unique indicators from the indicators column, you could also manually have alist with the indicators you want to loop over
for i in indicators:
prod = []
for row in df4.iterrows():
c1 = row[1][2]
c2 = row[1][1]
yr = str(row[1][0])
g1 = df1.loc[(df1['Country'] == c1) & (df1['Indictaor'] == i)] # compare to the indicator in the list
g1val = g1[yr].values[0]
g2 = df1.loc[(df1['Country'] == c2) & (df1['Indictaor'] == i)]
g2val = g2[yr].values[0]
print(g1val, g2val, g1val * g2val)
prod.append(g1val * g2val)
colname = "".join([i,"Product"])
df4[colname] = prod
print("Done")