from bokeh.models import ColumnDataSource, HoverTool import reverse_geocoder as rg import pandas as pd import numpy as np # Some global defaults max_words = 200 # Load most recent tweets from Hillary Clinton and Donald Trump # s = TweetLoader(filename='search.json', track_location=True) s = TweetLoader(filename='search_2016-07-13.json', track_location=True, path='data/backup/') s.load() # Calculate and grab model results mod = Analyzer(s.tweets['text'], max_words=max_words, load_pca=True, load_svm=True) predict = mod.load_full_model() # Hillary=0 Trump=1 s.tweets['predict'] = predict # Clean up missing coordinates df = s.tweets['geo.coordinates'] bad = df.apply(lambda x: x is None) df = df[~bad] s.tweets = s.tweets[~bad] lat = df.apply(lambda x: x[0]) lon = df.apply(lambda x: x[1]) # lat, lon = zip(*df) # Alternate # Remove Alaska and Hawaii del states["HI"] del states["AK"]
# Some global defaults
max_words = 200
# Load most recent tweets from Hillary Clinton and Donald Trump
# s = TweetLoader(filename='search.json', track_location=True)
s = TweetLoader(filename='search_2016-07-13.json',
track_location=True,
path='data/backup/')
s.load()
# Calculate and grab model results
mod = Analyzer(s.tweets['text'],
max_words=max_words,
load_pca=True,
load_svm=True)
predict = mod.load_full_model() # Hillary=0 Trump=1
s.tweets['predict'] = predict
# Clean up missing coordinates
df = s.tweets['geo.coordinates']
bad = df.apply(lambda x: x is None)
df = df[~bad]
s.tweets = s.tweets[~bad]
lat = df.apply(lambda x: x[0])
lon = df.apply(lambda x: x[1])
# lat, lon = zip(*df) # Alternate
# Remove Alaska and Hawaii
del states["HI"]
del states["AK"]
