How to fill in a dataframe column based on another column of the same dataframe using a dictionary

I am working with a dataframe in Python with a 'Country Name' and a 'Region Name' column. The Country Name does not have any nans, while the Region Name has.

I have created a dictionary:

dict = {
    "Central and Eastern Europe": [
        "Albania",
        "Bosnia and Herzegovina",
        "Bulgaria",
        "Croatia",
        "Czech Republic",
        "Estonia",
        "Hungary",
        "Kosovo",
        "Latvia",
        "Lithuania",
        "Montenegro",
        "North Macedonia",
        "Poland",
        "Romania",
        "Serbia",
        "Slovakia",
        "Slovenia",
    ],
    "East Asia": [
        "China",
        "Hong Kong S.A.R. of China",
        "Japan",
        "Mongolia",
        "South Korea",
        "Taiwan Province of China",
    ],
    ...
}

and I want to fill the nans of the 'Region Name' column based on the 'Country Name' of the same row by using this dictionary.

Can you provide me with a solution ?

I have tried the following line of code but it didn't work:

df.loc[df['Country name'].isnull(), 'Country name'] = df['Regional indicator'].map(dict)

If I were you, I would transform my backup lookup dict to an inverted_index structure.
This means that I would map the country names to regions, instead of the other way around, and then I would simply apply the map to the nans only. This would be much faster than the need to check all regions and then check if the country lies in this region or not.

import pandas as pd
country = {"egypt": "Africa", "Libia": "Africa", "China": "Asia"}
df = pd.DataFrame({
    'Country Name': ['Albania', 'Japan', 'United States', 'China'],
    'Region Name': ['Central and Eastern Europe', 'East Asia', pd.NA, pd.NA]
})
df['Region Name'] = df['Region Name'].fillna(df['Country Name'].map(country))
print(df)

Before the fillna line:

Country Name                 Region Name
0        Albania  Central and Eastern Europe
1          Japan                   East Asia
2  United States                        <NA>
3          China                        <NA>

and after it, you can see China was mapped to Asia:

Country Name                 Region Name
0        Albania  Central and Eastern Europe
1          Japan                   East Asia
2  United States                         NaN
3          China                        Asia

Any country that doesn't exist in the country to regions map, would be left as NaN.

The dict you have is not directly usable for mapping. As @MinaAshraf correctly said, you need to invert the definitions you have. Here is a way to do this (also, please do not override the keyword dict):

dct = {
    "Central and Eastern Europe": [
        "Albania",
        "Bosnia and Herzegovina",
        # ...
    ],
    "East Asia": [
        "China",
        "Japan",
        # ...
    ],
    # ...
}

revdct = {c: r for r, lst in dct.items() for c in lst}

Now, there are several ways to fill in the missing values. A simple one is as follows. But first, let's write a reproducible example:

nan = float('NaN')
df = pd.DataFrame({
    'Country Name': ['Albania', 'Japan'],
    'Region Name': [nan, nan],
})

Now:

newdf = df.set_index('Country Name')['Region Name'].fillna(revdct).reset_index()

>>> newdf
  Country Name                 Region Name
0      Albania  Central and Eastern Europe
1        Japan                   East Asia