I am stuck at a problem where I have to add an additional feature (average word length) to a list of token counts created by CountVectorizer function of scikit learn. Say I have the following code:

```
#list of tweets
texts = [(list of tweets)]

#list of average word length of every tweet
average_lengths = word_length(tweets)

#tokenizer
count_vect = CountVectorizer(analyzer = 'word', ngram_range = (1,1))
x_counts = count_vect.fit_transform(texts)

```

The format should be (tokens, average word length) for every instance. My initial idea was to simply concatenate the two lists using the zip-function like this:

```
x = zip(x_counts, average_lengths)

```

but then I get an error when I try to fit my model:

```
ValueError: setting an array element with a sequence.   

```

Anyone have any idea how to solve this problem?