tiaplagata

#Define a list of stop words
stopwords = ['private', 'tour', 'transfer', 'guide', 'skip', 'line',
             'skiptheline', 'vice', 'versa']

#A function to generate the word cloud from text
def generate_basic_wordcloud(data, title):
    cloud = WordCloud(width=400,
                      height=330,
                      max_words=150,
                      colormap='tab20c',

tiaplagata

# Store our document term matrix data for Rome 
data = dtm.transpose()['Rome, Italy'].sort_values(ascending=False)

# Generate the word cloud from frequencies
wc = WordCloud().generate_from_frequencies(data)

plt.imshow(wc)
plt.axis('off')
plt.show()

tiaplagata

#Give our Rome corpus a variable name
rome_corpus = df.lemmatized[10]

#Instantiate wordcloud object and use method to feed it our corpus
wc = WordCloud().generate_from_text(rome_corpus)

#Use matplotlib.pyplot to display the fitted wordcloud
#Turn axis off to get rid of axis numbers
plt.imshow(wc)
plt.axis('off')

tiaplagata

from wordcloud import WordCloud, ImageColorGenerator
from PIL import Image
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np

xtian

!!! 5
html(class='no-js')
  head
    meta(charset='utf-8')
    meta(http-equiv='X-UA-Compatible', content='IE=edge')

    title

    meta(name='description', content='')
    meta(name='viewport', content='width=device-width, initial-scale=1')