如何从 dataframe 中删除行，其中另一个 dataframe 的数据不匹配

Question

所以我有两个数据框：

gp_df = CCA3            Country/Territory  year       GDP_USD
2662   AFG                  Afghanistan  1970  1.748887e+09
2661   AFE  Africa Eastern and Southern  1970  4.486261e+10
2663   AFW   Africa Western and Central  1970  2.350461e+10
2665   ALB                      Albania  1970           NaN
2720   DZA                      Algeria  1970  4.863487e+09
...    ...                          ...   ...           ...
16156  PSE           West Bank and Gaza  2020  1.553170e+10
16219  WLD                        World  2020  8.490680e+13
16222  YEM                  Yemen, Rep.  2020  1.884051e+10
16224  ZMB                       Zambia  2020  1.811063e+10
16225  ZWE                     Zimbabwe  2020  1.805117e+10


pp_df = CCA3  Country/Territory Continent  2020 Population  1970 Population 
0    AFG        Afghanistan      Asia         38972230         10752971   
1    ALB            Albania    Europe          2866849          2324731   
2    DZA            Algeria    Africa         43451666         13795915   
3    ASM     American Samoa   Oceania            46189            27075   
4    AND            Andorra    Europe            77700            19860   
..   ...                ...       ...              ...              ...   
229  WLF  Wallis and Futuna   Oceania            11655             9377   
230  ESH     Western Sahara    Africa           556048            76371   
231  YEM              Yemen      Asia         32284046          6843607   
232  ZMB             Zambia    Africa         18927715          4281671   
233  ZWE           Zimbabwe    Africa         15669666          5202918   

我想找到一种方法从 gp_df 中删除所有不在 pp_df 中的国家/地区。 我已经尝试使用 .drop() 和 np.where() 来尝试找到重复项然后删除它们，但我似乎无法获得正确的语法。

Answer 1

这个有用吗...

gp_df[~gp_df['Country/Territory'].isin(pp_df['Country/Territory'])]

Answer 2

要处理的数据框

gp_df = pd.DataFrame({'CCA3': ["AFG" ,  "AFE",  "AFW", "ALB",  "DZA"], 
                    'Country/Territory': ["Afghanistan" ,  "Africa Eastern and Southern",  
                                           "Africa Western and Central", "Albania",  "Algeria"], 
                    'year': [1970, 1970, 1970, 1970, 1970], 
                    'GDP_USD':[1.748887e+09, 4.486261e+10, 2.350461e+10, pd.NA, 4.863487e+09],})

#   CCA3           Country/Territory   year        GDP_USD
# 0  AFG                  Afghanistan  1970   1748887000.0
# 1  AFE  Africa Eastern and Southern  1970  44862610000.0
# 2  AFW   Africa Western and Central  1970  23504610000.0
# 3  ALB                      Albania  1970           <NA>
# 4  DZA                      Algeria  1970   4863487000.0

pp_df = pd.DataFrame({'CCA3': ["AFG" ,  "ALB",  "DZA", "ASM",  "AND"], 
                    'Country/Territory': ["Afghanistan" ,  "Albania",  
                                           "Algeria", "American Samoa",  "Andorra"], 
                    'Continent': ["Asia", "Europe", "Africa", "Oceania", "Europe"], 
                    '2020 Population':[38972230, 2866849, 43451666, 46189, 77700],
                    '1970 Population':[10752971, 2324731, 13795915, 27075, 19860],
                    })

#   CCA3 Country/Territory Continent  2020 Population  1970 Population
# 0  AFG       Afghanistan      Asia         38972230         10752971
# 1  ALB           Albania    Europe          2866849          2324731
# 2  DZA           Algeria    Africa         43451666         13795915
# 3  ASM    American Samoa   Oceania            46189            27075
# 4  AND           Andorra    Europe            77700            19860

完整的检查脚本

import pandas as pd

gp_df = pd.DataFrame({'CCA3': ["AFG" ,  "AFE",  "AFW", "ALB",  "DZA"], 
                    'Country/Territory': ["Afghanistan" ,  "Africa Eastern and Southern",  
                                           "Africa Western and Central", "Albania",  "Algeria"], 
                    'year': [1970, 1970, 1970, 1970, 1970], 
                    'GDP_USD':[1.748887e+09, 4.486261e+10, 2.350461e+10, pd.NA, 4.863487e+09],})

pp_df = pd.DataFrame({'CCA3': ["AFG" ,  "ALB",  "DZA", "ASM",  "AND"], 
                    'Country/Territory': ["Afghanistan" ,  "Albania",  
                                           "Algeria", "American Samoa",  "Andorra"], 
                    'Continent': ["Asia", "Europe", "Africa", "Oceania", "Europe"], 
                    '2020 Population':[38972230, 2866849, 43451666, 46189, 77700],
                    '1970 Population':[10752971, 2324731, 13795915, 27075, 19860],
                    })

uKeys_1 = pp_df['Country/Territory'].unique()

r = gp_df[gp_df['Country/Territory'].isin(uKeys_1)]

print(r)

结果

#   CCA3 Country/Territory  year       GDP_USD
# 0  AFG       Afghanistan  1970  1748887000.0
# 3  ALB           Albania  1970          <NA>
# 4  DZA           Algeria  1970  4863487000.0