The Safe Harbor Method applies to "Geographic Subdivisions" as opposed to each zipcode itself.
Geographic Subdivision: All areas which share the first 3 digits of a zip code Count the total population for each geographic subdivision, storing the first 3 digits of the zip code and its corresponding population in the dictionary zip_dict. (For example, if there were 20 people whose zip code started with 090, the key-value pair in zip_dict would be {'090': 20}.)
Warning: you have to be savy with a dictionary here
To understand how a dictionary works, check the section materials, use google and go to discussion sections!
Instructions:
Create an empty dictionary: zip_dict = {}
Loop through all the zip_codes in df_zip
Create a dictionary key for the first 3 digits of a zip_code in zip_dict
Continually add population counts to the key that contains the same first 3 digits of the zip code
To extract the population you will find this code useful:

population = list(df_zip. loc[df_zip['zip']==zip_code]['popul ation'])

To extract the first 3 digits of a zip_code you will find this code useful:

int(str(zip code)[:3])
at-zıp df zip pd .read.

CSy('zip-pop . c sv ', dtype-t, zıp. : stry) =

zip population
0 01001 16769
1 01002 29049
2 01003 10372
3 01005 5079
4 01007 14649
5 01008 1263
6 01009 741
7 01010 3609
8 01011 1370
9 01012 661
10 01013 23188
11 01020 29668
12 01022 2451
13 01026 946
14 01027 17660
15 01028 15720
16 01029 789
17 01030 11669