word-cloud in pyhton source code-

!pip install matplotlib !pip install pandas !pip install wordcloud !pip install fileupload !pip install ipywidgets !jupyter nbextension install --py --user fileupload !jupyter nbextension enable --py fileupload

import wordcloud import numpy as np from matplotlib import pyplot as plt from IPython.display import display import fileupload import io import sys

def _upload():

_upload_widget = fileupload.FileUploadWidget()

def _cb(change):
    global file_contents
    decoded = io.StringIO(change['owner'].data.decode('utf-8'))
    filename = change['owner'].filename
    print('Uploaded `{}` ({:.2f} kB)'.format(
        filename, len(decoded.read()) / 2 **10))
    file_contents = decoded.getvalue()

_upload_widget.observe(_cb, names='data')
display(_upload_widget)

_upload()

#upload a .txt file contains any text/paragraph

def calculate_frequencies(file_contents): # Here is a list of punctuations and uninteresting words you can use to process your text punctuations = '''!()-[]{};:'",<>./?@#$%^&*_~''' uninteresting_words = ["the", "a", "to", "if", "is", "it", "of", "and", "or", "an", "as", "i", "me", "my",
"we", "our", "ours", "you", "your", "yours", "he", "she", "him", "his", "her", "hers", "its", "they", "them",
"their", "what", "which", "who", "whom", "this", "that", "am", "are", "was", "were", "be", "been", "being",
"have", "has", "had", "do", "does", "did", "but", "at", "by", "with", "from", "here", "when", "where", "how",
"all", "any", "both", "each", "few", "more", "some", "such", "no", "nor", "too", "very", "can", "will", "just"]

# LEARNER CODE START HERE
non_punctuation_text=""
for char in file_contents:
    if char not in punctuations:
        non_punctuation_text=non_punctuation_text+char
words=non_punctuation_text.split()
clean_words=[]
frequencies={}
for word in words:
    if word.isalpha():
        if word not in uninteresting_words:
            clean_words.append(word)
for alpha_word in clean_words:
    if alpha_word not in frequencies:
        frequencies[alpha_word]=1
    else:
        frequencies[alpha_word]+=1
#wordcloud
cloud = wordcloud.WordCloud()
cloud.generate_from_frequencies(frequencies)
return cloud.to_array()

Display your wordcloud image

myimage = calculate_frequencies(file_contents) plt.imshow(myimage, interpolation = 'nearest') plt.axis('off') plt.show()