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I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.

import pandas as pd
import pycountry
df = pd.DataFrame({"Country":["China","US","Iceland"])


def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

But I am facing issues.Its taking too long. My dataset has 17003 rows,which is not even that large a number.

Firstly, suggestions on the code to improve its performance would be welcome.

Secondly, I would like to know if there is completely different way to do this faster.

I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.

import pandas as pd
import pycountry
df = pd.DataFrame({"Country":["China","US","Iceland"])


def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

But I am facing issues.Its taking too long. My dataset has 17003 rows,which is not even that large a number.

Firstly, suggestions on the code to improve its performance would be welcome.

Secondly, I would like to know if there is completely different way to do this faster.

I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.

import pandas as pd
import pycountry
df = pd.DataFrame({"Country":["China","US","Iceland"])


def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

But I am facing issues.Its taking too long. My dataset has 17003 rows,which is not even that large a number.

Firstly,Suggestions on the code to improve its performance would be welcome.

Secondly,I would like to know in case there is another faster way to do this.

I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.

import pandas as pd
import pycountry
df = pd.DataFrame({"Country":["China","US","Iceland"])


def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

But I am facing issues.Its taking too long. My dataset has 17003 rows,which is not even that large a number.

Firstly, suggestions on the code to improve its performance would be welcome.

Secondly, I would like to know if there is completely different way to do this faster.

I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.But I am facing issues when my dataset contains large number of rows.Its too slow.

import pycountry
def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

Firstly,Suggestions on the code to improve its performance would be welcome.

Secondly,I would like to know in case there is another faster way to do this.

I am working on a dataset which contains a column with common country names.

Task: To convert country name into standard ISO names

I have written a basic function which converts country names into country codes using pycountry library.

import pandas as pd
import pycountry
df = pd.DataFrame({"Country":["China","US","Iceland"])


def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan 

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

But I am facing issues.Its taking too long. My dataset has 17003 rows,which is not even that large a number.

Firstly,Suggestions on the code to improve its performance would be welcome.

Secondly,I would like to know in case there is another faster way to do this.