title: numpy sum gives an error
body: <p>How can I resolve the following error:     dist = np.sum(train_data_features, axis=0)
    File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/fromnumeric.py", line 1711, in sum
      return sum(axis=axis, dtype=dtype, out=out)
  TypeError: sum() got an unexpected keyword argument 'dtype'</p>
  
  <p>this is my code:</p>
  
  <pre><code>import os
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.ensemble import RandomForestClassifier
  from KaggleWord2VecUtility import KaggleWord2VecUtility
  import pandas as pd
  import numpy as np
  
  if __name__ == '__main__':
      train = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'NYTimesBlogTrain.csv'), header=0)
      test = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'NYTimesBlogTest.csv'), header=0)
      train["Abstract"].fillna(0)
      print 'A sample Abstract is:'
      print train["Abstract"][0]
      #raw_input("Press Enter to continue...")
  
  
      #print 'Download text data sets. If you already have NLTK datasets downloaded, just close the Python download window...'
      #nltk.download()  # Download text data sets, including stop words
  
      # Initialize an empty list to hold the clean reviews
      clean_train_reviews = []
      # Loop over each review; create an index i that goes from 0 to the length
      # of the movie review list
      print "Cleaning and parsing the training set abstracts...\n"
      #for i in xrange( 0, len(train["Abstract"])):
      for i in xrange( 0, 10):
          if pd.isnull(train["Abstract"][i])==False:
              clean_train_reviews.append(" ".join(KaggleWord2VecUtility.review_to_wordlist(train["Abstract"][i], True)))
          else:
              clean_train_reviews.append(" ")
      print clean_train_reviews  
  
      # ****** Create a bag of words from the training set
      #
      print "Creating the bag of words...\n"
  
  
      # Initialize the "CountVectorizer" object, which is scikit-learn's
      # bag of words tool.
      vectorizer = CountVectorizer(analyzer = "word",   \
                               tokenizer = None,    \
                               preprocessor = None, \
                               stop_words = None,   \
                               max_features = 5000)
  
      # fit_transform() does two functions: First, it fits the model
      # and learns the vocabulary; second, it transforms our training data
      # into feature vectors. The input to fit_transform should be a list of
      # strings.
      print clean_train_reviews
      train_data_features = vectorizer.fit_transform(clean_train_reviews)
      print 'train_data_features'
      print train_data_features
      print train_data_features.shape
      # Take a look at the words in the vocabulary
      vocab = vectorizer.get_feature_names()
      print vocab
  
      # Sum up the counts of each vocabulary word
      dist = np.sum(train_data_features, axis=0)
  </code></pre>