How To Add a New Column to Using a Dictionary in Pandas Data Frame ?: Pandas Tutorial

add a new column with map in pandas

add a new column with map in pandas

Pandas library in Python has a really cool function called map that lets you manipulate your pandas data frame much easily. Pandas’ map function lets you add a new column with values from a dictionary if the data frame has a column matching the keys in the dictionary.

Adding a New Column Using keys from Dictionary matching a column in pandas

Let us say you have pandas data frame created from two lists as columns; continent and mean_lifeExp.

# derived from gapminder data set
  continent  mean_lifeExp
0      Asia        48.86
1    Europe        64.65
2    Africa        60.06
3  Americas        71.90
4   Oceania        74.32

and let us say we also have a dictionary, where the keys are “continent” as in the above data frame and values are mean population over years.

{'Europe': 24504794.99, 
'Oceania': 8874672.33, 
'Africa': 77038721.97, 
'Asia': 9916003.14, 
'Americas': 17169764.73}

Note that it is a dictionary, so the order of items do not match the continent column of the data frame.

Map function to Add a New Column to pandas with Dictionary

Let us say we want to add a new column ‘pop’ in the pandas data frame with values from the dictionary. Note the keys of the dictionary are “continents” and the column “continent” in the data frame. Pandas’ map function is here to add a new column in pandas dataframe using the keys:values from the dictionary.

gapminder_df['pop']= gapminder_df['continent'].map(pop_dict)

Voila!! here is the updated data frame with a new column from the dictionary.

  continent  mean_lifExp          pop
0      Asia        48.86   9916003.14
1    Europe        64.65  24504794.99
2    Africa        60.06  77038721.97
3  Americas        71.90  17169764.73
4   Oceania        74.32   8874672.33