我建议建立一个参考字典来匹配邻居到邻居组。假设这是原始 Dataframe ：

import pandas as pd

data = {'resident': {0: 'John', 1: 'Mae', 2: 'Richard', 3: 'Clark', 4: 'Claire', 5: 'Susan'}, 'neighbourhoodgroup': {0: 'Brooklyn', 1: 'Brooklyn', 2: 'Manhattan', 3: 'Manhattan', 4: None, 5: None}, 'neighbourhood': {0: 'ClintonHill', 1: 'ClintonHill', 2: 'EastHarlem', 3: 'UpperWestSide', 4: 'ClintonHill', 5: 'EastHarlem'}}
df = pd.DataFrame(data)

'''
  resident neighbourhoodgroup  neighbourhood
0     John           Brooklyn    ClintonHill
1      Mae           Brooklyn    ClintonHill
2  Richard          Manhattan     EastHarlem
3    Clark          Manhattan  UpperWestSide
4   Claire               None    ClintonHill
5    Susan               None     EastHarlem
'''

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首先创建一个引用字典reference，其中键来自“neighborhood”列，值来自“neighborhoodgroup”列。

df_ref = df.dropna().drop_duplicates(['neighbourhoodgroup', 'neighbourhood'])
reference = {}
for k, v in list(zip(df_ref.neighbourhood, df_ref.neighbourhoodgroup)):
    reference[k] = v

'''
{'ClintonHill': 'Brooklyn',
 'EastHarlem': 'Manhattan',
 'UpperWestSide': 'Manhattan'}
'''

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接下来，将字典引用应用于数据框架

df['result'] = df.neighbourhood.apply(lambda x: reference[x])
print(df)

'''
  resident neighbourhoodgroup  neighbourhood     result
0     John           Brooklyn    ClintonHill   Brooklyn
1      Mae           Brooklyn    ClintonHill   Brooklyn
2  Richard          Manhattan     EastHarlem  Manhattan
3    Clark          Manhattan  UpperWestSide  Manhattan
4   Claire               None    ClintonHill   Brooklyn
5    Susan               None     EastHarlem  Manhattan
'''

型

resident neighbourhoodgroup  neighbourhood
0     John           Brooklyn    ClintonHill
1      Mae           Brooklyn    ClintonHill
2  Richard          Manhattan     EastHarlem
3    Clark          Manhattan  UpperWestSide
4   Claire               <NA>    ClintonHill
5    Susan               <NA>     EastHarlem

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我们可以通过删除重复项和NaN，并转换为字典来从邻域到邻域组进行查找：

lookup_df = df[['neighbourhoodgroup','neighbourhood']].drop_duplicates().dropna()
lookup_dict = {x.neighbourhood: x.neighbourhoodgroup for x in lookup_df.itertuples()}

{'ClintonHill': 'Brooklyn', 'EastHarlem': 'Manhattan', 'UpperWestSide': 'Manhattan'}

型
然后，我们使用DataFrame.replace()将邻域与邻域组进行匹配：

neighbourhood_fill = df.neighbourhood.replace(lookup_dict)

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然后我们用新的填充值替换neighbourhoodgroup列中的NaN，使用fillna：

df.neighbourhoodgroup.fillna(neighbourhood_fill, inplace=True)

  resident neighbourhoodgroup  neighbourhood
0     John           Brooklyn    ClintonHill
1      Mae           Brooklyn    ClintonHill
2  Richard          Manhattan     EastHarlem
3    Clark          Manhattan  UpperWestSide
4   Claire           Brooklyn    ClintonHill
5    Susan          Manhattan     EastHarlem

型

另一种可能的解决方案：

d = df.iloc[:, 1:].dropna().drop_duplicates()

(df.update(
    df['neighbourhood'][df['neighbourhood group'].isna()]
    .map(dict(zip(d.iloc[:,1], d.iloc[:,0])))
    .rename('neighbourhood group')))

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输出量：

resident neighbourhood group    neighbourhood
0     John            Brooklyn     Clinton Hill
1      Mae            Brooklyn     Clinton Hill
2  Richard           Manhattan      East Harlem
3    Clark           Manhattan  Upper West Side
4   Claire            Brooklyn     Clinton Hill
5    Susan           Manhattan      East Harlem

型