Add an additional column to a panda dataframe comparing two columns

You can simply use np.where :

import numpy as np

df['Same'] = np.where(df['Column 1'] == df['Column 2'], 'Yes', 'No')

>>> print(df)

In Pandas the == operator between Series returns a new Series

So you can use:

df['Same'] = df['Column A'] == df['Column B']
df['Same'] = df['Same'].replace(True, 'Yes').replace(False, 'No')

Using vectorisation: You can make use of vectorisation to efficiently achieve the desired result. For example, you can use NumPy's where function:

df['Same'] = np.where(df['Column 1'] == df['Column 2'], 'Yes', 'No')

Using apply: If you do wish to use Pandas apply() function (e.g. as the data set is small and you wish to add additional logic) then the example below shows you how to do this to achieve the desired result.

df['Same'] = df.apply(lambda row: "Yes" if row['Column 1'] == row['Column 2'] 
                                        else "No", axis=1)

The above approach will place the value 'Yes' in the 'Same' column of the data frame if the values in 'Column 1' and 'Column 2' match, otherwise it will place the value 'No'.

Result: The result of running either of the above code examples will be as follows:

      Column 1 Column 2 Same
0        Apple   Banana   No
1      Chicken  Chicken  Yes
2  Dragonfruit      Egg   No
3         Fish     Fish  Yes

More information: You can learn more about the limitations of apply() vs approaches that utilise vectorisation by clicking here.

Use isin:

df_eq['Column1'].isin(df_eq['Column2']).replace({True:'Yes', False:'No'})

You could use the .apply(lambda) expression

df['Same'] = df.apply(lambda x: 'Yes' if x['Column 1']==x['Column 2'] else 'No', axis=1)