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Home Forums Cody Bank Word Cloud with Python

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      Abhishek TyagiAbhishek Tyagi
      Keymaster

      https://github.com/AbhishekTyagi404/Word-Cloud-Python

      Word Cloud is a data visualization technique used for representing text data in which the size of each word indicates its frequency or importance. Significant textual data points can be highlighted using a word cloud. Word clouds are widely used for analyzing data from social network websites.

      # Here are all the installs and imports you will need for your word cloud script and uploader widget

      !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

      # This is the uploader widget
      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()

      #FileUploadWidget(label=’Browse’, _dom_classes=(‘widget_item’, ‘btn-group’))

      # 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
      newFile = “”
      for index, char in enumerate(file_contents):
      if (char.isalpha() == True or char.isspace()):
      newFile += char

      newFile = newFile.split()
      wordCloudFile = []

      for word in newFile:
      if ((word.lower() not in uninteresting_words) and (word.isalpha() == True)):
      wordCloudFile.append(word)

      frequencies = {}

      for word in wordCloudFile:
      if (word.lower() not in frequencies):
      frequencies[word.lower()] = 1
      else:
      frequencies[word.lower()] += 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()

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