Project: Python Project It's Python and Jupyter notebook Grading | Submitting | Rules In this project, you will create a TextAnalyzer class. The methods of the class are described below.

You will do your work in the Analyzing Text Jupyter notebook included in the project files. Be sure to comment your code well. Read through the entire project instructions before beginning the project. Be Sure to Download the Latest Project Files Create a TextAnalyzer class with the following methods: _init__() TextAnalyzer objects are instantiated by passing in one of the following to the src parameter: A valid URL beginning with "http" A path to a text file ending with the file extension "txt" A string of text The __init__() method also includes a src_type parameter, which is used to specify the type of the src argument. Options are: discover (default) – You must write code to discover the type of src. If the src begins with "http", it is a url. If the src ends in "txt", it is a path. Otherwise, it is text. url path text You should set self._src_type, self._content, and self._orig_content in the _init__() method. set_content_to_tag(self, tag, tag_id=None) Changes_content to the text within a specific element of an HTML document. Keyword arguments: tag (str)- Tag to read tag_id (str)- ID of tag to read It's possible the HTML does not contain the tag being searched. You should use exception handling to catch any errors. reset_content(self) Resets _content to full text that was originally loaded. Useful after a call to set_content_to_tag(). _words(self, casesensitive=False) Returns words in _content as list. Keyword arguments: casesensitive (bool) - If False makes all words uppercase. Hints After splitting the text into words using the split() method, strip any leading and trailing punctuation using: [word.strip(string.punctuation) for word in words] common_words(self, minlen=1, maxlen=100, count=10, casesensitive=False) Returns a list of 2-element tuples of the structure (word, num), where num is the number of times word shows up in _content. Keyword arguments: minlen (int) – Minimum length of words to include. maxlen (int) – Maximum length of words to include. count (int) - Number of words to include. casesensitive (bool) – If False makes all words uppercase./li> char_distribution(self, casesensitive=False, letters_only=False) Returns a list of 2-element tuples of the format (char, num), where num is the number of times char shows up in _content. The list should be sorted by num in descending order. Keyword arguments: casesensitive (bool) – Consider case? letters_only (bool) - Exclude non-letters? plot_common_words(self, minlen=1, maxlen=100, count=10, casesensitive=False) Plots most common words. Keyword arguments: minlen (int) - Minimum length of words to include. maxlen (int) - Maximum length of words to include. count (int) - Number of words to include. casesensitive (bool) - If False makes all words uppercase. plot_char_distribution(self, casesensitive=False, letters_only=False) Plots character distribution. Keyword arguments: casesensitive (bool) - If False makes all words uppercase. letters_only (bool) - Exclude non-letters? Properties In addition, the class must include these properties: avg_word_length(self) The average word length in _content rounded to the 100th place (e.g, 3.82). word_count(self) The number of words in _content. distinct_word_count(self) The number of distinct words in _content. This should not be case sensitive: "You” and “you” should be considered the same word. words(self) A list of all words used in _content, including repeats, in all uppercase letters. positivity (self) A positivity score calculated as follows: Create local tally variable with initial value of 0. Increment tally by 1 for every word in self.words found in positive.txt (in same directory) Decrement tally by 1 for every word in self.words found in negative.txt (in same directory) Calculate score as follows: round( tally / self.word_count * 1000) Testing Your Class When you have finished, you should run the tests included in the Analyzing Text Jupyter notebook. If you get errors, you should do your very best to fix those errors before submitting the project. If you submit your project while still getting errors, you should explain that in your project submission email. The very first thing we will do to grade your project is run it through these tests. If it fails any of the tests, and you have not indicated that you are aware of specific test failures, we will stop grading and ask you to resubmit. You should also run the last cell in the Analyzing Text Jupyter notebook to make sure your plot methods work. They should produce plots that look like the following images: Common Words Character Distribution Finally, you should run help(TextAnalyzer) to make sure the help documentation for the class is useful and thorough.