This is a senario where we have a dataframe representing states and their geo-political zones. The geo-political zone column is however abbreviated as seen below, so we want to have a new column that will hold the full meaning of the corresponding abbreviation.
look_up_dict = {'NC': 'North Central', 'NE': 'North East', 'NW': 'North West', 'SE': 'South East', 'SW': 'South West', 'SS': 'South South'}The lookup dictionary could come from a different dataframe, that is to say the dataframe can be converted to dictionary for this lookup purpose.
A solution here was to use the map() function to create a new column by mapping the dictionar keys to the values on geopolitical zone column 'GeoPZ'
df['Geo Political Zones'] = df['GeoPZ'].map(look_up_dict)
How about if we just want to replace the existing 'GeoPZ' column without creating a new one? There are couple of ways to get this done by using replace() or update() methods or simply by overriding the existing column.
df['GeoPZ'] = df['GeoPZ'].map(look_up_dict)
df['GeoPZ'] = df['GeoPZ'].replace(look_up_dict)
df.GeoPZ.update(pd.Series(look_up_dict))Lambda function
You can use a Lamda function to perform operations on the fly while creating a new column. Lets say, we want a new column that will hold character count/lenght of each state's name. Then a Lamda function will come in handy as seen below;-
df['State_LCount'] = df['State'].map(lambda x: len(str(x)))
Thank you for following!
