archatas

from tqdm import tqdm
from myapp.models import MyModel

progress_bar = tqdm(desc="Processing", total=MyModel.objects.count())
for obj in MyModel.objects.all():
    obj.process()  # do something time consuming
    progress_bar.update(1)
progress_bar.close()

dogrocker

Oh my zsh.

Install with curl

sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Enabling Plugins (zsh-autosuggestions & zsh-syntax-highlighting)

• Download zsh-autosuggestions by

alvations

Getting Stanford NLP and MaltParser to work in NLTK for Windows Users

Firstly, I strongly think that if you're working with NLP/ML/AI related tools, getting things to work on Linux and Mac OS is much easier and save you quite a lot of time.

Disclaimer: I am not affiliated with Continuum (conda), Git, Java, Windows OS or Stanford NLP or MaltParser group. And the steps presented below is how I, IMHO, would setup a Windows computer if I own one.

Please please please understand the solution don't just copy and paste!!! We're not monkeys typing Shakespeare ;P

---

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

karambir

Many-to-many relation with a ‘through’

If only one link is required, this can be simply performed with a RelatedFactory. If more links are needed, simply add more RelatedFactory declarations:

# models.py

class User(models.Model):
    name = models.CharField()

elleryq

#!/usr/bin/expect

set timeout -1;
spawn {{django_dir}}/venv/bin/python manage.py changepassword {{admin_user}};
expect {
    "Password:" { exp_send "{{admin_pass}}\r" ; exp_continue }
    "Password (again):" { exp_send "{{admin_pass}}\r" ; exp_continue }
    eof
}

alvations

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

duhaime

from __future__ import division
from sklearn.cluster import KMeans
from numbers import Number
from pandas import DataFrame
import sys, codecs, numpy

class autovivify_list(dict):
        '''Pickleable class to replicate the functionality of collections.defaultdict'''
        def __missing__(self, key):
                value = self[key] = []

Kmaschta

# How to display only interesting fields for a Django Rest Framework API
# /?fields=field1,field2,field3

from rest_framework import serializers, pagination

class DynamicFieldsModelSerializer(serializers.ModelSerializer):
    """
    A ModelSerializer that takes an additional `fields` argument that
    controls which fields should be displayed.

tgarc

I often like to start my ipython session from where I last left off - similar to saving a firefox browsing session. IPython already automatically saves your input history so that you can look up commands in your history, but it doesn't save your variables. Here are the steps to save the state of your variables on exit and have them loaded on startup:

1. Add the save_user_variables.py script below to your ipython folder (by default $HOME/.ipython). This script takes care of saving user variables on exit.

2. Add this line to your profile's ipython startup script (e.g., $HOME/.ipython/profile_default/startup/startup.py):
   get_ipython().ex("import save_user_variables;del save_user_variables")

3. In your ipython profile config file (by default $HOME/.ipython/profile_default/ipython_config.py) find the following line:
   # c.StoreMagics.autorestore = False
   Uncomment it and set it to true. This automatically reloads stored variables on startup. Alternatively you can use reload the last session manually us