根据条件将新值添加到下面的行或新创建的行中

Question

考虑这个简化的 df：

import pandas as pd
data = { 'Name_Type': ["Primary", "Primary", "AKA", "Primary"],
         'Name': ["John", "Daniel", "Dan", "Bob"],
         'Surname': ["Green", "Brown", "Brown", "White"],
         'Country Type': ["Origin", "Origin", None, "Origin"],
         'Country': ["UK", "UK", None, "UK"],
         'Other': ["Info", None, None, "Info"]}
df = pd.DataFrame(data)

  Name_Type    Name Surname Country Type Country Other
0   Primary    John   Green       Origin      UK  Info
1   Primary  Daniel   Brown       Origin      UK  None
2       AKA     Dan   Brown         None    None  None
3   Primary     Bob   White       Origin      UK  Info

所以我想在 Origin 而不是 None 的每一行下添加新值。如果已经生成了一个下面没有的行（如示例中的第 2 行），我想将值“Citizenship”添加到“国家/地区类型”列中，并将值“UK”添加到该行的国家/地区中。如果没有行，我想在当前行下创建一个新行并添加相同的值。所以最终的输出会是这样的：

  Name_Type    Name Surname Country Type Country Other
0   Primary    John   Green       Origin      UK  Info
1      None    None    None  Citizenship      UK  None
2   Primary  Daniel   Brown       Origin      UK  None
3       AKA  Daniel   Brown  Citizenship      UK  None
4   Primary     Bob   White       Origin      UK  Info
5      None    None    None  Citizenship      UK  None

Answer 1

一种可能的方法如下：

import pandas as pd

data = { 
    'Name_Type': ["Primary", "Primary", "AKA", "Primary"],
    'Name': ["John", "Daniel", "Dan", "Bob"],
    'Surname': ["Green", "Brown", "Brown", "White"],
    'Country Type': ["Origin", "Origin", None, "Origin"],
    'Country': ["UK", "UK", None, "UK"],
    'Other': ["Info", None, None, "Info"]
}

df = pd.DataFrame(data)


new_rows = []

for i in range(len(df)):
    row = df.iloc[i]

    if row['Country Type'] == 'Origin':
        existing_aka = None
        for j in range(i+1, len(df)):
            if df.iloc[j]['Name_Type'] == 'AKA' and df.iloc[j]['Name'] == row['Name'] and df.iloc[j]['Surname'] == row['Surname'] and df.iloc[j]['Country Type'] is None:
                existing_aka = j
                break

        if existing_aka is not None:
            df.at[existing_aka, 'Country Type'] = 'Citizenship'
            df.at[existing_aka, 'Country'] = 'UK'
        else:
            new_row = {'Name_Type': None, 'Name': None, 'Surname': None, 'Country Type': 'Citizenship', 'Country': 'UK', 'Other': None}
            new_rows.append((i+1, new_row))

for index, new_row in reversed(new_rows):
    df = pd.concat([df.iloc[:index], pd.DataFrame([new_row]), df.iloc[index:]]).reset_index(drop=True)

print(df)

返回您的预期输出：

  Name_Type    Name Surname Country Type Country Other
0   Primary    John   Green       Origin      UK  Info
1      None    None    None  Citizenship      UK  None
2   Primary  Daniel   Brown       Origin      UK  None
3      None    None    None  Citizenship      UK  None
4       AKA     Dan   Brown         None    None  None
5   Primary     Bob   White       Origin      UK  Info
6      None    None    None  Citizenship      UK  None