![]() It has been s.ġ I bought this a few times for my older son and.Ģ This is great for basics, but I wish the space.ģ This book is perfect! I'm a first time new mo.Ĥ During your postpartum stay at the hospital th. This is what df.head() looks like: 0 This book is such a life saver. There are 18000 rows, none of which return isnan as True. I checked the CSV file and DataFrame for anything that's being read as NaN but I can't find anything. Super Vectorizer Pro pour Windows PC et Mac Captures d'cran Super Vectorizer Pro Caractristiques et description Caractristiques cls Dernire version: 2.1. ValueError: np.nan is an invalid document, expected byte or unicode string. Super Vectorizer 2 is a professional vector tracing software that automatically converts bitmap images like JPEG, GIF and PNG to clean, scalable vector graphic of Ai, SVG, DXF and PDF. Raise ValueError("np.nan is an invalid document, expected byte or " Tokenize(preprocess(code(doc))), stop_words)įile "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 118, in decode X = super(TfidfVectorizer, self).fit_transform(raw_documents)įile "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 817, in fit_transformįile "/home/b/work/local/lib/python2.7/site- packages/sklearn/feature_extraction/text.py", line 752, in _count_vocabįile "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 238, in This is the traceback for the error I get: Traceback (most recent call last):įile "/home/PycharmProjects/Review/src/feature_extraction.py", line 16, in įile "/home/b/hw1/local/lib/python2.7/site- packages/sklearn/feature_extraction/text.py", line 1305, in fit_transform V = TfidfVectorizer(decode_error='replace', encoding='utf-8') # print x.to_csv(path='FindNaN.csv', sep=',', na_rep = 'string', index=True) I pulled this data into a DataFrame so I can run the Vectorizer.įrom sklearn.feature_extraction.text import TfidfVectorizer I have a CSV file with a Score (can be +1 or -1) and a Review (text). ![]() I'm using TfidfVectorizer from scikit-learn to do some feature extraction from text data. ![]()
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