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# Question 3 | |
# Import the libraries | |
import numpy as np | |
import pandas as pd | |
# Recreate the dataset function | |
def recreate_dataset(set_size, conversion_ratios): | |
import random | |
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# Text Analyzer | |
# Import text & break up into words | |
raw_text = open('test.txt').read().lower() | |
# Clean up special words: replacing false sentence stop from exception words | |
def cleanup_special_words(text): | |
# Since sentences ussually end with a [.?!], we need to create a dictionary of | |
# exception words that contain these characters | |
exceptions = {'u.s.':'u*s*', |
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# Choose Ticker Universe (example: Run russell3000.py) | |
exec(open("russell3000(v.1.0).py").read()) | |
# Import Libraries | |
import datetime as dt | |
import pandas_datareader as web | |
import multiprocessing as multi | |
import numpy as np | |
import math |
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# Build Russell 3000 List | |
# Import libraries | |
import urllib.request | |
import datetime | |
# Download Russell 3000 to local repository | |
f_path = "/Russell3000/Membership Lists/" | |
f_name = f_path + "Russell3000 " + datetime.date.today().strftime("(%b %d, %Y)") + ".pdf" | |
def download_file(url): |