将字典项目附加到Python中的列表

Question

I have a dict which consists

docs[infile]={'tf':{}, 'idf':{},'words':[], 'tf_idf':{}}

and I have a list that I want to pass some of the dict's items

the sub-dicts tf_idf AND idf contain data such as {(word, number),(word, number),...}

I need to store in the list both tf_idf and idf items. This code stores only one of those 2 sub-dicts.

templist=[]
for key in docs: #stores data in separate list
    TF_IDF_buffer = docs[key]['tf_idf'].items()
    templist.append(TF_IDF_buffer)

Is it possible to store both of them in the list ?

Answer 1

This joins the two sequences of items, keeping duplicated keys:

templist=[]
for key, value in docs.items():
    tf_idf = list(value['tf_idf'].items())
    idf = list(value['idf'].items())
    templist.append(tf_idf + idf)

Answer 2

I think something like this should be what you are looking for

templist=[]
for key in docs: #stores data in separate list
    for word in docs[key]['words']:
         idf = docs[key]['idf']
         tf_idf = docs[key]['tf_idf']
         temp_list.append((word, tf_idf, idf))

However, I also saw some of your other questions on this forum. I think your structure of nested lists and dicts is somewhat complicated. For instance, your list of words, is duplicated by the keys in idf and tf_idf .

You may want to consider using a more Object Oriented approach.

You could define a class like this:

class Document:
     def __init__(self, words, idf, tf_idf):
         self.words = words
         self.idf = idf
         self.tf_idf = tf_idf

Also, from my memory of using NLP, I remember that using collections.defaultdict can be quite useful (especially if your idf and tf_idf dictionaries are sparse).