When running the line X_train = vectorizer.fit_transform(twenty_train_small.data)

I get a ValueError thrown. Is this a general issue in this notebook or is it just me. I have attached the stack trace as well.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-3cf347c25e00> in <module>()
     17 # Turn the text documents into vectors of word frequencies
     18 vectorizer = TfidfVectorizer(min_df=2)
---> 19 X_train = vectorizer.fit_transform(twenty_train_small.data)
     20 y_train = twenty_train_small.target
     21 

/Users/adamwalz/.virtualenvs/scikit_learn/lib/python2.7/site-packages/scikit_learn-0.15_git-py2.7-macosx-10.9-intel.egg/sklearn/feature_extraction/text.pyc in fit_transform(self, raw_documents, y)
   1236         # X is already a transformed view of raw_documents so
   1237         # we set copy to False
-> 1238         return self._tfidf.transform(X, copy=False)
   1239 
   1240     def transform(self, raw_documents, copy=True):

/Users/adamwalz/.virtualenvs/scikit_learn/lib/python2.7/site-packages/scikit_learn-0.15_git-py2.7-macosx-10.9-intel.egg/sklearn/feature_extraction/text.pyc in transform(self, X, copy)
   1008 
   1009         if self.norm:
-> 1010             X = normalize(X, norm=self.norm, copy=False)
   1011 
   1012         return X

/Users/adamwalz/.virtualenvs/scikit_learn/lib/python2.7/site-packages/scikit_learn-0.15_git-py2.7-macosx-10.9-intel.egg/sklearn/preprocessing/data.pyc in normalize(X, norm, axis, copy)
    540             inplace_csr_row_normalize_l1(X)
    541         elif norm == 'l2':
--> 542             inplace_csr_row_normalize_l2(X)
    543     else:
    544         if norm == 'l1':

/Users/adamwalz/.virtualenvs/scikit_learn/lib/python2.7/site-packages/scikit_learn-0.15_git-py2.7-macosx-10.9-intel.egg/sklearn/utils/sparsefuncs.so in sklearn.utils.sparsefuncs.inplace_csr_row_normalize_l2 (sklearn/utils/sparsefuncs.c:2714)()

ValueError: Buffer dtype mismatch, expected 'int' but got 'long'

Hi, I was going through your tutorial at Pycon 2015 and noticed couple of minor typos. :)

Thanks for the talk by the way, really helpful to get up to speed with scikit learn as well as pandas :-)

Thanks for making this available!!

Going through "01 - An Introduction to Predictive Modeling in Python L..."

Below, "Let us not forget to imput the median age for passengers without age information:", I found a typo.

rich_features_final = features.fillna(features.dropna().median())

should be

rich_features_final = rich_features.fillna(rich_features.dropna().median())

rich_features_final.head(5)

This adds the input field to the JSON/ipynb.

To fix these, I simply opened these in IPython notebook and re-saved them.

I'm currently going through to close issues on nbviewer, many of which are about specific notebooks. This one specifically comes from https://github.com/ipython/nbviewer/issues/54.

Hi Olivier,

In the 6th ipython notebook tutorial, the plot was invisible. pylab module was replaced by matplotlib.pyplot in model_selection.py, and IPython.parallel was replaced by ipyparallel.

And in my mac, !ipcluster did not work well in the notebook. I activate and deactivate it in terminal instead. I don't know why it happens though.

Thank you again for good tutorials. Namshik

Hi Olivier, I found your Github during studying ipyparallel. Thank you for the nice tutorials. I tried something on your code, and hope you don't mind it. Thanks, Namshik

Code:

import IPython.parallel

Warning:

C:\anaconda\lib\site-packages\IPython\parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated. You should import from ipyparallel instead.

"You should import from ipyparallel instead.", ShimWarning)

Code:

# Import the example plot from the figures directory
from figures import plot_sgd_separator
plot_sgd_separator()

Gives the warning:

C:\anaconda\lib\site-packages\sklearn\utils\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.

DeprecationWarning)

The error is repeated many times. The figure does load properly despite these warnings.