IT Automation with python

Final Project - Word Cloud

# 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()

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","in", \

    "we", "our", "ours", "you", "your", "yours", "he", "she", "him", "his", "her", "hers", "its", "they", "them","for", \

    "their", "what", "which", "who", "whom", "this", "that", "am", "are", "was", "were", "be", "been", "being","the", \

    "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","on"]

    

    # LEARNER CODE START HERE

    word_count = {}

    final_text = []

    

    for word in file_contents.split():

        text = ""

        for letter in word.lower():

            if letter not in punctuations and letter.isalpha():

                text += letter

            

        if word not in uninteresting_words:

            final_text.append(text)

            

    for word in final_text:

        if word not in word_count:

            word_count[word] = 0

        word_count[word] += 1

    

    #wordcloud

    cloud = wordcloud.WordCloud()

    cloud.generate_from_frequencies(word_count)

    return cloud.to_array()

# Display your wordcloud image

myimage = calculate_frequencies(file_contents)

plt.imshow(myimage, interpolation = 'nearest')

plt.axis('off')

plt.show()