I have a problem. I have a dataframe, that contains ISO-codes of countries. I want to change these ISO-codes to country names. But unfortunately I do not know how I could use these functions inside a .apply(lambda x:)
function.
Dataframe
id country
0 1 DE
1 2 DE
2 3 CN
3 4 BG
4 3 CN
5 4 BG
6 5 BG
Code
import pandas as pd
import pycountry
input_countries = ['BG', 'CN', 'DE']
countries = {}
for country in pycountry.countries:
countries[country.alpha_2] = country.name
codes = [countries.get(country, 'Unknown code') for country in input_countries]
import pandas as pd
d = {'id': [1, 2, 3, 4, 3, 4, 5], 'country': ['DE', 'DE', 'CN', 'BG', 'CN', 'BG', 'BG']}
df = pd.DataFrame(data=d)
# print(df)
df['country'] = df['country'].apply(lambda x: ...)
What I want
id country
0 1 Germany
1 2 Germany
2 3 China
3 4 Bulgaria
4 3 China
5 4 Bulgaria
6 5 Bulgaria
CodePudding user response:
I think you should use df.apply
instead of df['country'].apply
to create new function from the given value in country
column
import pandas as pd
import pycountry
input_countries = ['BG', 'CN', 'DE']
countries = {}
for country in pycountry.countries:
countries[country.alpha_2] = country.name
codes = [countries.get(country, 'Unknown code') for country in input_countries]
import pandas as pd
d = {'id': [1, 2, 3, 4, 3, 4, 5], 'country': ['DE', 'DE', 'CN', 'BG', 'CN', 'BG', 'BG']}
df = pd.DataFrame(data=d)
# print(df)
df['country'] = df.apply(lambda x: countries[x['country']], axis=1)
CodePudding user response:
Have a dictionary:
d ={
'DE': 'Germany'.
...
}
Then do this:
df['country'] = df['country'].apply(lambda x: d[x['country']])
CodePudding user response:
The most suitable function to use here is probably map
instead, for the 'country' column. Pseudo code as below:
country_map = dict(zip(country_ids, country_names))
df['country'] = df['country'].map(country_map)
where country_ids and country_names are the lists or columns of the input codes and the desired output country names.