AnOnYmOus001100

"""
	Author: AnOnYmOus001100
	Date: 31/08/2020

Censor Dispenser
Overview
This project is slightly different than others you have encountered thus far on Codecademy. Instead of a step-by-step tutorial, this project contains a series of open-ended requirements which describe the project you’ll be building. There are many possible ways to correctly fulfill all of these requirements, and you should expect to use the internet, Codecademy, and other resources when you encounter a problem that you cannot easily solve.

Project Goals
You’ve recently gotten a job working in the IT department at one of Silicon Valley’s hottest new startups, AirWeb. The company is developing a state-of-the-art artificial intelligence engine designed to help provide a new perspective on the world’s problems. Interestingly, very few people know the details of AirWeb ‘s work and the company is very secretive about its technology, even to its own investors.

AnOnYmOus001100

# main classes of the game

#player class
class WOFPlayer():
    def __init__(self, name):
        self.name = name
        self.prizeMoney = 0
        self.prizes = []
        
    def goBankrupt(self):

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8

# # course_4_assessment_2

#  1. The class, Pokemon, is provided below and describes a Pokemon and its leveling and evolving characteristics. An instance of the class is one pokemon that you create.
# 
# Grass_Pokemon is a subclass that inherits from Pokemon but changes some aspects, for instance, the boost values are different.
# 
# For the subclass Grass_Pokemon, add another method called action that returns the string "[name of pokemon] knows a lot of different moves!". Create an instance of this class with the name as "Belle". Assign this instance to the variable p1.

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8

# # Final Project - Word Cloud

# For this project, you'll create a "word cloud" from a text by writing a script.  This script needs to process the text, remove punctuation, ignore case and words that do not contain all alphabets, count the frequencies, and ignore uninteresting or irrelevant words.  A dictionary is the output of the `calculate_frequencies` function.  The `wordcloud` module will then generate the image from your dictionary.

# For the input text of your script, you will need to provide a file that contains text only.  For the text itself, you can copy and paste the contents of a website you like.  Or you can use a site like [Project Gutenberg](https://www.gutenberg.org/) to find books that are available online.  You could see what word clouds you can get from famous books, like a Shakespeare play or a novel by Jane Austen. Save this as a .txt file somewhere on your computer.
# <br><br>
# Now you will need to upload your input file here so that your script

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8


"""
1. Define a class called Bike that accepts a string and a float as input, and assigns those inputs respectively
to two instance variables, color and price. Assign to the variable testOne an instance of Bike whose color is 
blue and whose price is 89.99. Assign to the variable testTwo an instance of Bike whose color is purple and 
whose price is 25.0.
"""

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8

# ##  Description: Final Project for Course 3 - OMDB and TasteDive Mashup

# In[ ]:


import json
import requests_with_caching

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8

# # course_3_assessment_2
# # course3, week2


'''
1. Write code to assign to the variable map_testing all the elements in lst_check while adding the string
“Fruit: ” to the beginning of each element using mapping.

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8


#1. The variable nested contains a nested list. Assign ‘snake’ to the variable output using indexing.
nested = [['dog', 'cat', 'horse'], ['frog', 'turtle', 'snake', 'gecko'], ['hamster', 'gerbil', 'rat', 'ferret']]
output = nested[1][2]
print (output)

AnOnYmOus001100

'''
Setinment Classifier:
We have provided some synthetic (fake, semi-randomly generated) twitter data in a csv file named project_twitter_data.csv which has the text of a tweet, the number of retweets of that tweet, and the number of replies to that tweet. We have also words that express positive sentiment and negative sentiment, in the files positive_words.txt and negative_words.txt.

Your task is to build a sentiment classifier, which will detect how positive or negative each tweet is. You will create a csv file, which contains columns for the Number of Retweets, Number of Replies, Positive Score (which is how many happy words are in the tweet), Negative Score (which is how many angry words are in the tweet), and the Net Score for each tweet. At the end, you upload the csv file to Excel or Google Sheets, and produce a graph of the Net Score vs Number of Retweets.

'''
punctuation_chars = ["'", '"', ",", ".", "!", ":", ";", '#', '@']
# lists of words to use
positive_words = []

AnOnYmOus001100

#!/usr/bin/env python
# coding: utf-8


#1. Sort the following string alphabetically, from z to a, and assign it to the variable sorted_letters.
letters = "alwnfiwaksuezlaeiajsdl"
sorted_letters = sorted(letters,reverse=True)
print (sorted_letters)