Sending Emails using Templates

Here is a simple Python module to send emails using HTML/TEXT templates. Feel free to use the code.

https://github.com/ludmal/pylib

Step 1:

Create a TEXT/HTML file with the Keys as below

Hi [username],

Thank you for your registration.

Best regards,
[from]
[url]

Step 2:

Create a template passing the values to replace with the keys in template

values = {}
values['username'] = 'Ludmal de silva!'
values['from'] = 'The Team'
values['url'] = 'http://www.ludmal.com'
temp = EmailTemplate(template_name='welcome.txt', values=values)

Step 3:

Create a Mail Server

server = MailServer(server_name='smtp.gmail.com', username='', password='', port=0,   require_starttls=True)

Step 4:

Create a mail message and send the email

msg = MailMessage(from_email='ludmal@gmail.com', to_emails=['ludmal@gmail.com'], subject='Welcome')
send(mail_msg=msg, mail_server=server, template=temp)

Follow me on Twitter @ludmal

Python File Archiver

I always wanted to quickly cleanup my harddrive without manually searching files and moving them. So here is the solution, a simple File Arhicver python script which I wrote to clean up my hard drive. It will search for files older than a given time period & size (parameterized) and then move to a Arhicve folder. I'll try to improve the code when I get some free time and here is the script file, download and feel free to use it.

https://github.com/ludmal/pylib/blob/master/file_archiver

tf-idf, Term Frequency–Inverse Document Frequency

tf-idf is stands for Term Frequency - Inverse document frequency and it is one of the effective algorithms to extract the keywords from a given document. It is often used in NLP and IR. The extraction is performed in a statistical measure by calculating the weight of a word in a document list or a corpus. Many search engines using the benefits of tf-idf to extract keywords, frequently with combinations of other algorithms. Word's weight or importance is measured by the word frequency in a document and offset by the same word's frequency in the given corpus.

tf-idf = term frequency x inverse document frequency

For example, if the word 'peace' appears 6 times in a document with 100 words the tf is 6/100 = 0.06. And if the corpus or document list contains 1000 documents and if 'peace' appears in 200 documents in the corpus then the idf is 1000/200 = 5. Hence the tf-idf for the word is 0.06 x 5 = 0.3. The weight or tf-idf is directly related to the importance word, i.e. if the tf-idf is higher then the importance of the word is high.

I've written a python module to extract the keywords from a given corpus. This is useful if you want to extract the keywords from a given website links and categorized them according to the keywords. You can use the code freely by downloading from the following Github location.

https://github.com/ludmal/pylib

Complete sample of the usage can be found here: https://github.com/ludmal/pylib/blob/master/sample.py