sankroh

#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

sankroh

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/

sankroh

// grab your file object from a file input
$('#fileInput').change(function () {
  sendFile(this.files[0]);
});

// can also be from a drag-from-desktop drop
$('dropZone')[0].ondrop = function (e) {
  e.preventDefault();
  sendFile(e.dataTransfer.files[0]);
};

sankroh

'''
Run this script either by doing:

  python gmaildomain.py
  
And you will be prompted for details, or you can save time by doing:

  python gmaildomain.py WEBFACTION_USERNAME_HERE DOMAIN_TO_USE_GMAIL_FOR_HERE_WITHOUT_WWW WEBMAIL_DOMAIN

And you will only be prompted for your password.