我试图根据感兴趣的单词子集计算单词列中单词存在的次数。
首先我导入我的数据
products = graphlab.SFrame('amazon_baby.gl/')
products['word_count'] = graphlab.text_analytics.count_words(products['review'])
products.head(5)
数据可以在这里找到:https://drive.google.com/open?id=0BzbhZp-qIglxM3VSVWRsVFRhTWc
然后我创建我感兴趣的单词列表:
words = ['awesome', 'great', 'fantastic']
我想计算产品['word_count']中“单词”中每个单词出现的次数。
我没有结婚使用graphlab。这只是一位同事向我建议的。
好吧,我不太确定你在“词典栏目”中的意思。如果是列表:
import operator
dictionary={'texts':['red blue blue','red black','blue white white','red','white','black','blue red']}
words=['red','white','blue']
freqs=dict()
for t in dictionary['texts']:
for w in words:
try:
freqs[w]+=t.count(w)
except:
freqs[w]=t.count(w)
top_words = sorted(freqs.items(), key=operator.itemgetter(1),reverse=True)
如果只是一个文字:
import operator
dictionary={'text':'red blue blue red black blue white white red white black blue red'}
words=['red','white','blue']
freqs=dict()
for w in words:
try:
freqs[w]+=dictionary['text'].count(w)
except:
freqs[w]=dictionary['text'].count(w)
top_words = sorted(freqs.items(), key=operator.itemgetter(1),reverse=True)
如果你想计算单词的出现次数,快速的方法是使用Counter
的collections
object
例如 :
In [3]: from collections import Counter
In [4]: c = Counter(['hello', 'world'])
In [5]: c
Out[5]: Counter({'hello': 1, 'world': 1})
你能展示你的products.head(5)
命令的输出吗?
如果您坚持使用graphlab(或SFrame),请使用SArray.dict_trim_by_keys
方法。文档在这里:https://dato.com/products/create/docs/generated/graphlab.SArray.dict_trim_by_keys.html
import graphlab as gl
sf = gl.SFrame({'review': ['what a good book', 'terrible book']})
sf['word_bag'] = gl.text_analytics.count_words(sf['review'])
keywords = ['good', 'book']
sf['key_words'] = sf['word_bag'].dict_trim_by_keys(keywords, exclude=False)
print sf
+------------------+---------------------+---------------------+
| review | word_bag | key_words |
+------------------+---------------------+---------------------+
| what a good book | {'a': 1, 'good':... | {'good': 1, 'boo... |
| terrible book | {'book': 1, 'ter... | {'book': 1} |
+------------------+---------------------+---------------------+
[2 rows x 3 columns]