hugobowne

import pandas as pd
import pandas_profiling

# Create small df
data = {"name": ["Hugo", "Ville"], "city": ["Sydney", "SF"]}
df = pd.DataFrame(data)

# Create report and save to file
profile = pandas_profiling.ProfileReport(df)
profile.to_file("df_report.html")

hugobowne

# scheme.py
#
# Challenge:  Can you implement a mini-scheme interpreter (program that's running another program) capable of
# executing the following code (now at bottom of file):

def seval(sexp, env):
    if isinstance(sexp, (int, float)):
        return sexp
    elif isinstance(sexp, str):  #Symbols
        return env[sexp]  #Evaluate symbol names in the 'env'

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

hugobowne

These are the materials for my workshop on creating interactive data visualizations with D3! We will be using the following two tools to works through these exercises:

• bl.ocks.org: D3.js Gist renderer and online community
• Building Blocks: In-browser interactive D3.js bl.ock editor

And please do not hesitate to reach out to me directly via email at jondinu@gmail.com or over twitter @clearspandex

Throughout this workshop, you will learn how to make an interactive map of AirBnB listings in SF to better understand the companies impact on the city.