[英]groupby and apply on two dataframes

I have a pandas dataframe with 3 columns: key1, key2, document. All three columns are text fields with the size of document ranging from 50 characters to 5000 characters. I identify a vocabulary based on minimum frequency from the set of documents for each (key1, key2) for which I am using scikit-learn CountVectorizer and setting min_df. I am able to do this using df.groupby[['key1','key2']]['document'].apply(vocab).reset_index() where vocab is a function in which I compute and return the vocabulary (as defined above) as a set.

我有一个包含3列的pandas数据框:key1,key2,document。所有三列都是文本字段,文档大小从50个字符到5000个字符不等。我根据每个文件集(key1,key2)中的最小频率识别词汇表,我使用scikit-learn CountVectorizer并设置min_df。我可以使用df.groupby [['key1','key2']] ['document']来执行此操作.apple(vocab).reset_index()其中vocab是一个函数,我在其中计算并返回词汇表(如以上定义)作为一组。

Now, I would like to use these vocabularies (one set for each key1, key2), to filter the corresponding documents so that each document only has words which are in its vocabulary. I would appreciate any help I can get with this part.


Sample data


key1 | key2 | document
 aa  | bb   | He went home that evening. Then he had soup for dinner.
 aa  | bb   | We want to sit down and eat dinner
 cc  | mm   | Sometimes people eat in a restaurant
 aa  | bb   | The culinary skills of that chef are terrible.  Let us not go there.
 cc  | mm   | People go home after dinner and try to sleep.

Vocabulary - not using counts for the purpose of this example

key1 | key2 | vocab
 aa  | bb   | {went, evening, sit, down, culinary, chef, dinner}
 cc  | mm   | {people, restaurant, home, dinner, sleep}

Result - only use words from corresponding vocab in document

key1 | key2 | document
 aa  | bb   | went evening dinner
 aa  | bb   | sit down dinner
 cc  | mm   | people restaurant
 aa  | bb   | culinary chef
 cc  | mm   | people home dinner sleep

1 个解决方案



You can use first merge for add column vocab to first DataFrame:


import re

df = df.groupby[['key1','key2']]['document'].apply(vocab).reset_index()
df = pd.merge(df1, df2, on=['key1','key2'], how='left')

#another theoretical solution
#df['vocab'] = df.groupby[['key1','key2']]['document'].transform(vocab)

Then extract all words by findall, re.I is for ignore case and last remove column vocab:


df['document'] = df['document'].str.findall('\w+', flags=re.I)

Last get intersection between sets and convert to strings by str.join:


df['document'] = df.apply(lambda x: set(x['document']) & x['vocab'], axis=1).str.join(' ')
df = df.drop('vocab', axis=1)
print (df)
  key1 key2                  document
0   aa   bb       evening went dinner
1   aa   bb           sit down dinner
2   cc   mm         restaurant people
3   aa   bb             chef culinary
4   cc   mm  home people sleep dinner



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