Efficiently creating additional columns in a pandas DataFrame using .map()

Question

I am analyzing a data set that is similar in shape to the following example. I have two different types of data (abc data and xyz data):

   abc         


        

Answer 1

You can use applymap with the dictionary get method:

In [11]: df[abc_columns].applymap(categories.get)
Out[11]:
   abc1  abc2  abc3
0  Good   Bad   Bad
1   Bad  Good  Good
2   Bad   Bad  Good
3  Good   Bad  Good
4  Good  Good   Bad

And put this to the specified columns:

In [12]: abc_categories = map(lambda x: x + '_category', abc_columns)

In [13]: abc_categories
Out[13]: ['abc1_category', 'abc2_category', 'abc3_category']

In [14]: df[abc_categories] = df[abc_columns].applymap(categories.get)

Note: you can construct abc_columns relatively efficiently using a list comprehension:

abc_columns = [col for col in df.columns if str(col).startswith('abc')]