title: Find all value which satisfy multiple keys in Pandas groupby
body: <p>I generate a dataframe for a doc created from a text by spacy as follow:</p>
  <pre><code>test='We walked the walk and still walk it today. Walking brings us great joy.'
  tokens=[]
  lemma=[]
  pos=[]
  
  df=pd.DataFrame()
  
  doc=nlp(test)
  for t in doc:
      tokens.append(t.text)
      lemma.append(t.lemma_)
      pos.append(t.pos_)
  df['tokens']=tokens
  df['lemma']=lemma
  df['pos']=pos
  
  df
  </code></pre>
  <pre><code>     tokens   lemma    pos
  0        We  -PRON-   PRON
  1    walked    walk   VERB
  2       the     the    DET
  3      walk    walk   NOUN
  4       and     and  CCONJ
  5     still   still    ADV
  6      walk    walk   VERB
  7        it  -PRON-   PRON
  8     today   today   NOUN
  9         .       .  PUNCT
  10  Walking    walk   VERB
  11   brings   bring   VERB
  12       us  -PRON-   PRON
  13    great   great    ADJ
  14      joy     joy   NOUN
  15        .       .  PUNCT
  </code></pre>
  <p>And I group it by ('lemma', 'pos')</p>
  <pre><code>groups_multipe=df.groupby(['lemma','pos'])
  </code></pre>
  <p>I want to find all lemma which own both pos 'VERB' and 'NOUN'. I tried to use <code>.apply()</code> and <code>.fliter()</code>, but I'm not good at it.</p>
  <p>For example, lemma 'walk' satisfies the requirement because it has 'VERB' and 'NOUN' in the column 'pos' at the same time.</p>
  <p>How can I achieve it</p>
  <hr />
  <p>Addition:</p>
  <p>Finally, I achieve it in a stupid way: The intersection of sets verb and noun</p>
  <p>Here is my code:</p>
  <pre><code>lemma_v=set(gm[0][0] for gm in groups_multiple if gm[0][1]=='VERB')
  lemma_n=set(gm[0][0] for gm in groups_multiple if gm[0][1]=='NOUN')
  
  lemma_vn=list(lemma_v &amp; lemma_n)
  
  </code></pre>
  <p>It's so much inefficient, but I do not know any better way. Somebody has idea to improve it ?</p>