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How to add a second column to pandas data frame?
I have created a data frame which looks like this:
df = pd.DataFrame({'column1': [1,2,3], 'column2': [1,2,3]})
What I want to do now is to add a third column to this data frame, which should be equal to 0 if the value of column1 is the same as column2 or 1 if they are different. 
I've tried a couple of ways by doing:
df['thirdcolumn'] = df.apply(lambda x: 1 if x['column1'] == x['column2'] else 0, axis=1)
but this gives me this error:
AttributeError: 'DataFrame' object has no attribute 'apply'
Another attempt was to convert to a dictionary which is normally the way to go but here I receive this error:
ValueError: 
KeyError: 'lambda'
I also tried using pd.concat, but then I don't know how to get rid of the two new columns that the concat function creates.
df = pd.concat([df, df.apply(lambda x: 1 if x['column1'] == x['column2'] else 0, axis=1)], axis=1)
Thanks in advance for help!
A:
Use numpy.where:
import numpy as np
df['thirdcolumn'] = np.where(df['column1'].ne(df['column2']), 1, 0)
Q:
What does "can't conjure up Monty Python" mean?
I've read the phrase "Conjure up Monty Python" while listening to a show where a presenter says the following on an episode about history:
Part of the trouble with history is that those with dubious minds cannot conjure up Monty Python.
Does this mean those people who cannot conjure up the Monty Python image in their mind can't imagine themselves talking the
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