jkullick

# install dependecies
apt-get install qemu qemu-user-static binfmt-support

# download raspbian image
wget https://downloads.raspberrypi.org/raspbian_latest

# extract raspbian image
unzip raspbian_latest

hugobowne

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

pamelafox

/*
Earned Badges
This table contains badges earned by a user, including the most recent date achieved, the type, the name, the # of energy points earned, and the activity earned from.
Collected by: https://www.khanacademy.org/profile/chopsor/
*/

CREATE TABLE badges (
    date TEXT,
    badge_type TEXT,
    badge_name TEXT,

natelandau

#  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases
#
#  Sections:
#  1.   Environment Configuration
#  2.   Make Terminal Better (remapping defaults and adding functionality)
#  3.   File and Folder Management
#  4.   Searching
#  5.   Process Management

wildmichael

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