Hi Experts,
I am doing the NLP course of python in power bi which is great source to enhance the skill. since in my work i have lot of data related to survey.
I just wanted to ask how to avoid the blank space in around the word cloud.
i tried to adjust the height and width and still blank spaces comes.
Can someone please assist.
attached pic for reference.
and below code of Python is running in power bi:
The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script:
dataset = pandas.DataFrame(country, description, designation, points, price, province, region_1, region_2, taster_name, taster_twitter_handle, title, variety, winery)
dataset = dataset.drop_duplicates()
Paste or type your script code here:
#Load the essential Libraries
import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk import ngrams
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
from wordcloud import WordCloud
from PIL import Image
Create a data cleaning Function to remove stop words, Punctuation and Lemmatize
def big_clean_text(sentence):
sentence = " ".join(sentence)
new_tokens = word_tokenize(sentence)
new_tokens = [t.lower() for t in new_tokens]
new_tokens = [t for t in new_tokens if t not in stopwords.words(‘english’)]
#new_tokens = [t for t in new_tokens if t not in bad_words]
new_tokens = [t for t in new_tokens if t.isalpha()]
lemmatizer = WordNetLemmatizer()
new_tokens = [lemmatizer.lemmatize(t) for t in new_tokens]
counted = Counter(new_tokens)
counted_2 = Counter(ngrams(new_tokens,2))
counted_3 = Counter(ngrams(new_tokens,3))
word_freq = pd.DataFrame(counted.items(),columns = [‘word’,‘frequency’])
bi_gram_freq = pd.DataFrame(counted_2.items(),columns = [‘bi-gram’,‘frequency’])
tri_gram_freq = pd.DataFrame(counted_3.items(),columns = [‘Tri-gram’,‘frequency’])
return new_tokens
Run the function
tokens = big_clean_text(dataset[‘description’])
joining all the words
bag_of_words = " ".join(tokens)
Creating Wordcloud
plt.figure(figsize=(16,9))
plt.axis(‘off’)
word_cloud = WordCloud(width=800, height=400, max_words=500, stopwords=[‘wine’,‘syrah’,‘flavor’]).generate(bag_of_words)
plt.imshow(word_cloud)
plt.show()
