Using keys from a dict to look for the same "keys" in a pandas dataframe, then assign value from dict to dataframe empty column

Question

I have a pandas dataframe with zipcodes. I also have a dictionary where keys = zipkode and values = regions

The dictionary

my_regions = {8361: 'Central region', 8381: 'Central region', 8462: 'North region', 8520: 'South region', 8530: 'Central region', 8541: 'South region'}

The dataframe has a col name

df["zipcode"]= [8462, 8361, 8381,8660,8530,8530]

I want to add a new col to the dataframe df with the dict values (region name), when the loop sees that zip code in dataframe is == to zipkode in dict.keys

I have tried this

my_regions_list = []

for keyname in my_regions:
    for zipcode in df.zipcode:
        if zipcode == my_regions.keys():
            my_regions_list.append(my_regions.values())
            # df["region"] = df.append(my_regions.values())
            df =df.insert(column="region", value = my_r)

The list is empty and this is not adding the new row to the existing dataframe...

I also tried to convert it to a dataframe but it makes no sense

df1 = pd.DataFrame(list(my_regions.items()),columns = ['ZIPCODE','REGIONNAME'])

Answer 1

You can use .map():

df = pd.DataFrame({"zipcode": [8462, 8361, 8381, 8660, 8530, 8530]})

my_regions = {
    8361: "Central region",
    8381: "Central region",
    8462: "North region",
    8520: "South region",
    8530: "Central region",
    8541: "South region",
}


df["name"] = df["zipcode"].map(my_regions)
print(df)

Prints:

   zipcode            name
0     8462    North region
1     8361  Central region
2     8381  Central region
3     8660             NaN
4     8530  Central region
5     8530  Central region