I have 1 Dataframe with some rows of `Sold-To Country Name` column in the value of `Not: XX XX XX`,which means the rest of `Sold-To Country Codes` except `XX XX XX` will be reporting to the mapped `Reporting Country`.

Another requirement is that if `Sold-To Country Code` is `null` (or `NaN`), it will capture all revenue from all country codes in that SalesOrg.

```
df_mapping = pd.DataFrame({'SalesOrg Code':['0001','0002','0002','0002','0002'],
                           'Reporting Country':['Spain','UK','UK','UK','Netherlands'],
                           'Sold-To Country Code':[np.nan,'IE','FR','IT','Ex:'],
                           'Sold-To Country Name':[np.nan,'Ireland','France','Italy','NOT: FR IE IT']})

```

```
SalesOrg Code   Reporting Country   Sold-To Country Code    Sold-To Country Name
0001            Spain                null                   null
0002            UK                   IE                     Ireland
0002            UK                   FR                     France
0002            UK                   IT                     Italy
0002            Netherlands          Ex:                    NOT: FR IE IT
.......

```

There will be another Dataframe with a full list of global country codes, where we can search for the rest of the country codes. 

Example of the Dataframe:

```
df_countrylist = pd.DataFrame(["AF", "AX", "AL", "DZ", "AS", "AD", "AO", "AI", "AQ", "AG", "AR",
"AM", "AW", "AU", "AT", "AZ", "BS", "BH", "BD", "BB", "BY", "BE",
"BZ", "BJ", "BM", "BT", "BO", "BQ", "BA", "BW", "BV", "BR", "IO",
"BN", "BG", "BF", "BI", "CV", "KH", "CM", "CA", "KY", "CF", "TD",
"CL", "CN", "CX", "CC", "CO", "KM", "CG", "CD", "CK", "CR", "CI",
"HR", "CU", "CW", "CY", "CZ", "DK", "DJ", "DM", "DO", "EC", "EG",
"SV", "GQ", "ER", "EE", "ET", "FK", "FO", "FJ", "FI", "FR", "GF",
"PF", "TF", "GA", "GM", "GE", "DE", "GH", "GI", "GR", "GL", "GD",
"GP", "GU", "GT", "GG", "GN", "GW", "GY", "HT", "HM", "VA", "HN",
"HK", "HU", "IS", "IN", "ID", "IR", "IQ", "IE", "IM", "IL", "IT",
"JM", "JP", "JE", "JO", "KZ", "KE", "KI", "KP", "KR", "KW", "KG",
"LA", "LV", "LB", "LS", "LR", "LY", "LI", "LT", "LU", "MO", "MK",
"MG", "MW", "MY", "MV", "ML", "MT", "MH", "MQ", "MR", "MU", "YT",
"MX", "FM", "MD", "MC", "MN", "ME", "MS", "MA", "MZ", "MM", "NA",
"NR", "NP", "NL", "NC", "NZ", "NI", "NE", "NG", "NU", "NF", "MP",
"NO", "OM", "PK", "PW", "PS", "PA", "PG", "PY", "PE", "PH", "PN",
"PL", "PT", "PR", "QA", "RE", "RO", "RU", "RW", "BL", "SH", "KN",
"LC", "MF", "PM", "VC", "WS", "SM", "ST", "SA", "SN", "RS", "SC",
"SL", "SG", "SX", "SK", "SI", "SB", "SO", "ZA", "GS", "SS", "ES",
"LK", "SD", "SR", "SJ", "SZ", "SE", "CH", "SY", "TW", "TJ", "TZ",
"TH", "TL", "TG", "TK", "TO", "TT", "TN", "TR", "TM", "TC", "TV",
"UG", "UA", "AE", "GB", "US", "UM", "UY", "UZ", "VU", "VE", "VN",
"VG", "VI", "WF", "EH", "YE", "ZM", "ZW"])

```

Ultimately, I want to have like this:

```
SalesOrg Code   Reporting Country   Sold-To Country Code    Sold-To Country Name
0001            Spain                null (all)             null
0002            UK                   IE                     Ireland
0002            UK                   FR                     France
0002            UK                   IT                     Italy
0002            Netherlands          AT                     Austria 
0002            Netherlands          DK                     Denmark 
0002            Netherlands          NL                     Netherlands 
0002            Netherlands          BE                     Belgium 
0002            Netherlands          LT                     Lithuania 
0002            Netherlands          LX                     Latvia      
.......

```

For SalesOrg #0002, if the `Sold-To Country Code` are not `FR IE IT`, the rest will be reporting to Netherlands. So I want to create rows for the rest of the country codes. 

Is there any better way to create rows and expand into the existing Dataframe?