将列附加到二维变量

Question

我有一个变量。该变量是二维的，但我不知道它是列表还是数组。将此变量视为大小为 n × m 的矩阵。我想向它附加一个大小为 1 的列。所以我的新变量将是 n 乘以 m+1。我就是这样做的：

train_data_features.append(train['NewsDesk'])

这是我收到的错误：

train_data_features.append(train['NewsDesk'])
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/sparse/base.py", line 440, in __getattr__
    raise AttributeError(attr + " not found")
AttributeError: append not found

这是我的全部代码：

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["Headline"].fillna(0)
    print 'A sample headline is:'
    print train["Headline"][0:10]
    #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 headlines...\n"
    for i in xrange( 0, len(train["Headline"])):
    #for i in xrange( 0, 10):
        if pd.isnull(train["Headline"][i])==False:
            clean_train_reviews.append(" ".join(KaggleWord2VecUtility.review_to_wordlist(train["Headline"][i], True)))
        else:
            clean_train_reviews.append(" ")
    print 'clean train reviews (headlines)'
    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.

    train_data_features = vectorizer.fit_transform(clean_train_reviews)
    print 'train_data_features'
    print train_data_features
    print 'train_data_features.shape'
    print train_data_features.shape
    # Take a look at the words in the vocabulary
    vocab = vectorizer.get_feature_names()
    print 'vocab'
    print vocab

    # Sum up the counts of each vocabulary word
    #dist = np.sum(train_data_features, axis=0)
    dist = train_data_features.sum (axis=0)
    print 'dist'
    print dist
    # For each, print the vocabulary word and the number of times it 
    # appears in the training set
    print 'tag+count'
    for tag, count in zip(vocab, dist):
        print count, tag
        print 'and'

#    for i in xrange( 0, len(train["NewsDesk"])):    
    for i in xrange( 0, 10):    
        if pd.isnull(train["NewsDesk"][i])==False:
            print train['NewsDesk'][i]
        else:
            print '   '

    train_data_features.append(train['NewsDesk'])

Answer 1

没有用于稀疏矩阵的 append。但是有vstack和hstack。我将用一个简单的矩阵来说明

In [121]: from scipy import sparse
In [122]: M = sparse.csr_matrix([[0,1,0],[1,0,1]])

In [123]: M.A   # show as array
Out[123]: 
array([[0, 1, 0],
       [1, 0, 1]], dtype=int32)

In [124]: M.todense()  # show a numpy matrix
Out[124]: 
matrix([[0, 1, 0],
        [1, 0, 1]], dtype=int32)

In [125]: col=np.array([[2],[3]])  # a simple column array
In [126]: col
Out[126]: 
array([[2],
       [3]])

In [128]: sparse.hstack([M,col])
Out[128]: 
<2x4 sparse matrix of type '<class 'numpy.int32'>'
    with 5 stored elements in COOrdinate format>

In [129]: sparse.hstack([M,col]).A
Out[129]: 
array([[0, 1, 0, 2],
       [1, 0, 1, 3]], dtype=int32)

In [130]: sparse.vstack([M,[1,2,3]]).A   # or add a row
Out[130]: 
array([[0, 1, 0],
       [1, 0, 1],
       [1, 2, 3]], dtype=int32)

numpy append 只是 np.concatenate 的精美包装。 vstack 和 hstack 是更简单的包装器。此外，append 不会就地更改数组（如列表追加）。最好避免它，而是用 concatenate.

来思考

将列附加到二维变量

append column to a two dimensional variable

python

numpy

list

scipy

python-2.7