python - 如何在Python中比较两个不同DataFrame的单元格值？

Question

我有两个数据框:

Person_df

       

Name  Emplid  Country

    0  DK     123    India

    1  JS     456    India

    2  RM     789    China

    3  MS     111    China

    4  SR     222    China

 Target_df

    

Country Category    Target

    0   India   Marketing   Reduce spend by $xy.

    1   India   R&D         Increase spend by $dd.

    2   India   Infra       Reduce spend by $kn.

    3   China   Marketing   Increase spend by $eg.

    4   China   R&D         Increase spend by $cb.

    5   China   Infra       Reduce spend by $mn.

我的目标是根据每个人的国家/地区创建第三个 DataFrame，如下所示:

Individual_df

   

TargetID    Category    Target

    DK12301     Marketing   Reduce spend by $xy.

    DK12302     R&D         Increase spend by $dd.

    DK12303     Infra       Reduce spend by $kn.

    JS45601     Marketing   Reduce spend by $xy.

    JS45602     R&D         Increase spend by $dd.

    JS45603     Infra       Reduce spend by $kn.

    RM78901     Marketing   Increase spend by $eg.

    RM78902     R&D         Increase spend by $cb.

    RM78903     Infra       Reduce spend by $mn.

    MS11101     Marketing   Increase spend by $eg.

    MS11102     R&D         Increase spend by $cb.

    MS11103     Infra       Reduce spend by $mn.

    SR22201     Marketing   Increase spend by $eg.

    SR22202     R&D         Increase spend by $cb.

    SR22203     Infra       Reduce spend by $mn.

基本上，我必须从 Person_df 中选取一个人，将他/她的国家/地区与 Target_df 中提到的国家/地区相匹配，然后将每个目标分配给这个人(并存储在 individual_df 中)。

问题是，我是 python 新手，无法真正弄清楚如何进行国家/地区比较。

我编写了以下代码:

   

for index, row in Person_df.iterrows():

     

        for index1, row1 in Goals_df.iterrows():

            If Person_df['country'] == Person_df['country'] : #I know this is incorrect

                data = [] 

                #populate data[] with selected values for one person.

                #append data[] to Individual_df

我需要以下几点帮助:

1)我真的可以在这里对每个人的国家/地区进行比较。

2)即使我知道如何比较，我编写的代码也效率不高，因为我在这里进行了大量不必要的迭代。有什么建议我可以如何改进吗？

谢谢!

Answer 1

试试这个，

Individual_df = pd.merge(Person_df, Target_df2, on=['Country'], how='left')
Individual_df['TargetID'] = Individual_df['Name'] + df3['Emplid'].astype(str) + ((df3.groupby('Emplid').cumcount() + 1).astype(str).str.zfill(2))
Individual_df = Individual_df[['TargetID', 'Category', 'Target']]
print Individual_df

输出:

   TargetID   Category                  Target
0   DK12301  Marketing    Reduce spend by $xy.
1   DK12302        R&D  Increase spend by $dd.
2   DK12303      Infra    Reduce spend by $kn.
3   JS45601  Marketing    Reduce spend by $xy.
4   JS45602        R&D  Increase spend by $dd.
5   JS45603      Infra    Reduce spend by $kn.
6   RM78901  Marketing  Increase spend by $eg.
7   RM78902        R&D  Increase spend by $cb.
8   RM78903      Infra    Reduce spend by $mn.
9   MS11101  Marketing  Increase spend by $eg.
10  MS11102        R&D  Increase spend by $cb.
11  MS11103      Infra    Reduce spend by $mn.
12  SR22201  Marketing  Increase spend by $eg.
13  SR22202        R&D  Increase spend by $cb.
14  SR22203      Infra    Reduce spend by $mn.

说明:

1. 与 Person_df 和 Target_df 执行左连接
2. 然后根据姓名和员工 ID 以及员工 ID 的 cumcount 创建 TargetID
3. 提取所需列

根据用户请求通过 for 循环获取行:

unique_countries=df1['Country'].unique().tolist()

for index, row in df2.iterrows():
    if row['Country'] in unique_countries:
        print row.values
        //do operation

说明:

1. 查找 Person_df 的唯一元素

2. 通过for循环迭代Individual_df

3. 检查国家是否存在于唯一元素(国家/地区)中如果存在，则执行所需的操作。