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I have a dataframe which has 3 columns:

import pandas as pd

d = 'A': ['left', 'right', 'east', 'west', 'south', 'north'], 'B': ['right', 'left', 'west', 'east', 'north', 'south'], 'VALUE': [0, 1, 2, 3, 4, 5]

df = pd.DataFrame(d)

Dataframe looks like this:

 A B VALUE
left right 0
right left 1
east west 2
west east 3
south north 4
north south 5

I am trying to create a new column VALUE_2 which should contain the value from the swapped row in the same Dataframe.

Eg: right - left value is 0, left - right value is 1 and I want the swapped values in the new column like this:

 A B VALUE VALUE_2
left right 0 1
right left 1 0
east west 2 3
west east 3 2
south north 4 5
north south 5 4

I tried:

for row_num, record in df.iterrows():
 A = df['A'][index]
 B = df['B'][index]
 if(pd.Series([record['A'] == B, record['B'] == A).all()):
 df['VALUE_2'] = df['VALUE']

I'm struck here, inputs will be highly appreciated.

Use map by Series:

df['VALUE_2'] = df['A'].map(df.set_index('B')['VALUE'])
print (df)
 A B VALUE VALUE_2
0 left right 0 1
1 right left 1 0
2 east west 2 3
3 west east 3 2
4 south north 4 5
5 north south 5 4

Just a more verbose answer:

import pandas as pd

d = 'A': ['left', 'right', 'east', 'west', 'south', 'north'], 'B': ['right', 'left', 'west', 'east', 'north', 'south'], 'VALUE': [0, 1, 2, 3, 4, 5]

df = pd.DataFrame(d)

pdf = pd.DataFrame()
for idx, item in df.iterrows():
 indx = list(df['B']).index(str(df['A'][idx]))
 pdf = pdf.append(pd.DataFrame('VALUE_2': df.iloc[indx][2], index=[0]), ignore_index=True)

print(pdf)
data = pd.concat([df, pdf], axis=1)
print(data)