Creating a new column by referencing other columns from a data frame in python

Question

I want to create a new column labeled Region which associates ranges of integers with Regions as displayed through a dictionary. However, there is a condition: the sub-segment needs to be Australia.

import pandas as pd
import numpy as np

df = pd.read_excel(r'/Users/Desktop/dictionary.xlsx')
mydict = {"NSW": range(1000,1209)}

if df['Sub-Segment'] == "Australia":
    df['Region'] = df['Postal Code'].map(mydict) 

The data frame looks like this :

Sub-Segment Postal Code
Australia   1001
Australia   1002
Australia   1209
Mexico      1004

The desired data frame is this:

Sub-Segment Postal Code  Region
Australia   1001         NSW
Australia   1002         NSW
Australia   1209         NSW
Mexico      1004         Other

I tried the above and got the following error message:

The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

mydict={
    "NSW": range(1000, 1210),
    "NHL": range(2000, 2099),
}

def region(df):
    if df['Sub-Segment'] == 'Australia':
        result = [key for (key, value) in mydict.items() if df['Postal Code'] in value]
        if result:
            return result[0]
    return 'Other'

df['Region'] = df.apply(lambda row: region(row), axis=1)

Answer 2

I'm not sure where you're going with the dictionary. Since if you use dictionary you'll have to have tons of keys, I will prefer using a function instead.
You can use pandas.DataFrame.loc to get the rows you want and use pandas.DataFrame.apply to apply the function.

import pandas as pd
import numpy as np

df=pd.read_excel(r'/Users/Desktop/dictionary.xlsx' )

def func(x):
    if 1000<=x<=1209:
        return 'NSW'
    else:
        # some other stuff maybe?
        pass

df.loc[df['Sub-Segment']=="Australia",'Region']=df['PostalCode'].apply(func)
df = df.fillna('Other')
print(df)