将 panda 列中的特定值替换为由函数确定的值

Question

我的数据如下：

import pandas as pd
import country_converter as coco
cc = coco.CountryConverter()

example = pd.DataFrame.from_dict({'Country': {0: 'Fiji', 1: 'Tanzania', 2: 'W. Sahara', 3: 'Canada', 4: 'United States of America', 5: 'Kazakhstan', 6: 'Uzbekistan', 7: 'Papua New Guinea', 8: 'Indonesia', 9: 'Argentina', 10: 'Chile', 11: 'Dem. Rep. Congo', 12: 'Somalia', 13: 'Kenya', 14: 'Sudan', 15: 'Chad', 16: 'Haiti', 17: 'Dominican Rep.', 18: 'Russia', 19: 'Bahamas', 20: 'Falkland Is.', 21: 'Norway'}, 'iso': {0: 'FJI', 1: 'TZA', 2: 'ESH', 3: 'CAN', 4: 'USA', 5: 'KAZ', 6: 'UZB', 7: 'PNG', 8: 'IDN', 9: 'ARG', 10: 'CHL', 11: 'COD', 12: 'SOM', 13: 'KEN', 14: 'SDN', 15: 'TCD', 16: 'HTI', 17: 'DOM', 18: 'RUS', 19: 'BHS', 20: 'FLK', 21: '-99'}}
)

                     Country  iso
0                       Fiji  FJI
1                   Tanzania  TZA
2                  W. Sahara  ESH
3                     Canada  CAN
4   United States of America  USA
5                 Kazakhstan  KAZ
6                 Uzbekistan  UZB
7           Papua New Guinea  PNG
8                  Indonesia  IDN
9                  Argentina  ARG
10                     Chile  CHL
11           Dem. Rep. Congo  COD
12                   Somalia  SOM
13                     Kenya  KEN
14                     Sudan  SDN
15                      Chad  TCD
16                     Haiti  HTI
17            Dominican Rep.  DOM
18                    Russia  RUS
19                   Bahamas  BHS
20              Falkland Is.  FLK
21                    Norway  -99

我希望 python 尝试：

example['iso'] = cc.convert(names = example['Country'], to = 'ISO3')

但仅如果iso=-99的值

我看到this solution，所以我尝试了：

example = example.assign(col = [(cc.convert(names = example['Country'], to = 'ISO3')) if iso = '-99' else (example['iso']) for iso in example['iso']])

但这不是正确的语法。

有人可以帮帮我吗？

Answer 1

def f(row):
    try:
        if int(row['iso']) == -99:
            return cc.convert(names=example[row['country']].strip(), to='ISO3')
    except:
        pass
    return row['iso']

example['iso'] = example.apply(f, axis=1)

如果你想提高计算效率，你可以运行

idx = example['iso'].apply(int) == -99
example['iso'][idx] = example[idx].apply(f, axis=1)

Answer 2

condition = df.iso.eq('-99')
df.iso.loc[condition] = df.Country[condition].apply(lambda x: cc.convert(x, to='ISO3'))

Answer 3

我会使用 np.select 来解决这个特殊问题

import pandas as pd
import numpy as np

example = pd.DataFrame.from_dict({'Country': {0: 'Fiji', 1: 'Tanzania', 2: 'W. Sahara', 3: 'Canada', 4: 'United States of America', 5: 'Kazakhstan', 6: 'Uzbekistan', 7: 'Papua New Guinea', 8: 'Indonesia', 9: 'Argentina', 10: 'Chile', 11: 'Dem. Rep. Congo', 12: 'Somalia', 13: 'Kenya', 14: 'Sudan', 15: 'Chad', 16: 'Haiti', 17: 'Dominican Rep.', 18: 'Russia', 19: 'Bahamas', 20: 'Falkland Is.', 21: 'Norway'}, 'iso': {0: 'FJI', 1: 'TZA', 2: 'ESH', 3: 'CAN', 4: 'USA', 5: 'KAZ', 6: 'UZB', 7: 'PNG', 8: 'IDN', 9: 'ARG', 10: 'CHL', 11: 'COD', 12: 'SOM', 13: 'KEN', 14: 'SDN', 15: 'TCD', 16: 'HTI', 17: 'DOM', 18: 'RUS', 19: 'BHS', 20: 'FLK', 21: '-99'}}
)
df = pd.DataFrame(example)
condition_list = [df['iso'] == '-99']
choice_list = ['ISO3']
#If the condition is met use the choice else keep the data as it is
df['iso'] = np.select(condition_list, choice_list, df['iso'])
df