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"]