Change multiple column entries in a pandas dataframe

I have a large pandas dataframe

df.head()

        Year	Average Elo	Club	Country	Level
0   	2017	1283.267334	Kukesi	ALB	    0
1    	2018	1263.912195	Kukesi	ALB	    0
2   	2019	1212.714661	Kukesi	ALB	    0
3   	2020	1231.063379	Kukesi	ALB	    0
4   	2018	1213.269553	Laci	ALB	    0

The Country column contains abbreviated country codes for 55 countries. I want to change each of these country codes to the full name of the country (for example, ALB to Albania)

df['Country'].unique()

array(['ALB', 'AND', 'ARM', 'AUT', 'AZE', 'BEL', 'BHZ', 'BLR', 'BUL',
       'CRO', 'CYP', 'CZE', 'DEN', 'ENG', 'ESP', 'EST', 'FAR', 'FIN',
       'FRA', 'GEO', 'GER', 'GIB', 'GRE', 'HUN', 'IRL', 'ISL', 'ISR',
       'ITA', 'KAZ', 'KOS', 'LAT', 'LIE', 'LIT', 'LUX', 'MAC', 'MLT',
       'MNT', 'MOL', 'NED', 'NIR', 'NOR', 'POL', 'POR', 'ROM', 'RUS',
       'SCO', 'SLK', 'SMR', 'SRB', 'SUI', 'SVN', 'SWE', 'TUR', 'UKR',
       'WAL'], dtype=object)

I have written code that does this for ALB specifically:

df.Country[df.Country=='ALB'] = 'Albania'

Is there a way to do this for all 55 countries?

You could use pycountry to craft a dictionary to map the names from the 3 letter code:

import pycountry

mapper = {c.alpha_3: c.name for c in pycountry.countries}
# {'ABW': 'Aruba', 'AFG': 'Afghanistan', 'AGO': 'Angola',
#  'AIA': 'Anguilla', 'ALA': 'Åland Islands', 'ALB': 'Albania',
#  'AND': 'Andorra', ...}

df['Country'] = df['Country'].map(mapper)

However your format is not exactly standard, so you could also map from the first 3 letters as a failback:

mapper = ({c.name[:3].upper(): c.name for c in pycountry.countries}
         |{c.alpha_3: c.name for c in pycountry.countries}
         )

Yes, you can automate the process of replacing country codes with full names for all 55 countries. One way to achieve this is by using a dictionary that maps each country code to its corresponding full name. Here's an example:

import pandas as pd

# Sample data
data = {
    'Year': [2017, 2018, 2019, 2020, 2018],
    'Average Elo Club': [1283.267334, 1263.912195, 1212.714661, 1231.063379, 1213.269553],
    'Country': ['ALB', 'AND', 'ARM', 'AUT', 'AZE'],
    'Level': [0, 0, 0, 0, 0]
}

df = pd.DataFrame(data)

# Country code to full name mapping
country_mapping = {
    'ALB': 'Albania',
    'AND': 'Andorra',
    'ARM': 'Armenia',
    # Add more country code to full name mappings here
    # For all 55 countries
}

# Replace country codes with full names
df['Country'] = df['Country'].replace(country_mapping)

print(df.head())

In the "country_mapping" dictionary, you can add mappings for all 55 countries, specifying the country code as the key and the corresponding full name as the value. When you call "df['Country'].replace(country_mapping)", it will replace the country codes in the "Country" column with the respective full names for all countries.