Python 通过从邻居中随机选择一个zipcodes来填充缺失的zipcodes

我有一个如下所示的pandas数据框，我试图通过从相似的邻域组中选择任意随机值来替换zipcode字段中缺少的值。下面是我的尝试，但效果不太好。请帮忙

zipcodes = a_df[['neighbourhood_group_cleansed','zipcode']].drop_duplicates().reset_index()
a_df['zipcode'] = a_df.apply(lambda row: np.random.choice(zipcodes[zipcodes['neighbourhood_group_cleansed'] == 
                row['neighbourhood_group_cleansed']]['zipcode']) if len(row.zipcode) == 0   else row.zipcode, axis = 1)

state   city    smart_location  neighbourhood_group_cleansed    zipcode
0   NY  New York    New York, NY    Manhattan   10029
1   NY  Brooklyn    Brooklyn, NY    Brooklyn    11221
2   NY  Brooklyn    Brooklyn, NY    Brooklyn    11206
3   NY  New York    New York, NY    Manhattan   10001
4   NY  New York    New York, NY    Manhattan   10162
... ... ... ... ... ...
6492    NY  New York    New York, NY    Manhattan   10004.0
6493    NY  Brooklyn    Brooklyn, NY    Brooklyn    11229.0
6494    NY  Queens  Queens, NY  Queens  11691.0
6495    NY  New York    New York, NY    Manhattan   10044.0
6496    NY  Brooklyn    Brooklyn, NY    Brooklyn    11234.0

这应该行得通

df['zipcode'] = df.apply(lambda x: random.choice(df[df['neighbourhood_group_cleansed'] == x['neighbourhood_group_cleansed']].zipcode.dropna().values) if np.isnan(x['zipcode']) else x['zipcode'], axis=1)

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你能告诉我你犯了什么错误吗？谢谢，这很有帮助。