radovankavicky

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

radovankavicky

library(idbr) # devtools::install_github('walkerke/idbr')
library(ggplot2)
library(animation)
library(dplyr)
library(ggthemes)

idb_api_key("Your Census API key goes here")

male <- idb1('JA', 2010:2050, sex = 'male') %>%
  mutate(POP = POP * -1,

radovankavicky

2017 PyData Berlin

Data Science & Data Visualization in Python. How to harness power of Python for social good?

• Radovan Kavický
• President & Principal Data Scientist, GapData Institute
• @radovankavicky

Watch the talk on YouTube here:

radovankavicky

import os
from subprocess import check_call

def post_save(model, os_path, contents_manager):
    """post-save hook for converting notebooks to .py and .html files."""
    if model['type'] != 'notebook':
        return # only do this for notebooks
    d, fname = os.path.split(os_path)
    check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d)
    check_call(['jupyter', 'nbconvert', '--to', 'html', fname], cwd=d)

radovankavicky

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

radovankavicky

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

radovankavicky

library(rvest)
library(magrittr)
library(dplyr)
library(purrr)
library(lubridate)
library(tidyr)
library(ggplot2)
library(scales)

setwd("...working directory...")

radovankavicky

#SCRIPT_REAL is a function in Tableau which returns a result from an external service script. It's in this function we pass the python code.

SCRIPT_REAL("from nltk.sentiment import SentimentIntensityAnalyzer

text = _arg1 #you have to use _arg1 to reference the data column you're analyzing, in this case [Word]. It gets word further down after the ,
scores = [] #this is a python list where the scores will get stored
sid = SentimentIntensityAnalyzer() #this is a class from the nltk (Natural Language Toolkit) library. We'll pass our words through this to return the score

for word in text: # this loops through each row in the column you pass via _arg1; in this case [Word]
    ss = sid.polarity_scores(word) #passes the word through the sentiment analyzer to get the score

radovankavicky

############################################################################### 
# Batch script for setting up fun Python libraries for Computational Methods in the Civic Sphere 2017
#
# Should be run after installing Anaconda 4.2+/Python 3.5+ via https://www.continuum.io/downloads
#
# Doesn't include libraries that are installed as dependencies (e.g. numpy via pandas) 
##############################################################################

##############################################################################
###### Yes

radovankavicky

import pandas as pd
import time
from nltk.sentiment import SentimentIntensityAnalyzer

t0 = time.time()

top_100 = pd.read_csv('/Users/brit.cava/Desktop/TabPy/top100.csv')

text = top_100['Word']
sid = SentimentIntensityAnalyzer()