travel_plans.txt, contains the summer travel plans for someone with some commentary. Find the total number of characters in the file and save to the variable num.emotion_words.txt that contains lines of words that describe emotions. Find the total number of words in the file and assign this value to the variable num_words.num_lines the number of lines in the file school_prompt.txt.SP500.txt which has monthly data for 2016 and 2017 about the S&P 500 closing prices as well as some other financial indicators, including the “Long Term Interest Rate”, which is interest rate paid on 10-year U.S. government bonds.mean_SP and max_interest.Data: travel_plans.txtThis summer I will be travelling. I will go to... Italy: Rome Greece: Athens England: London, Manchester France: Paris, Nice, Lyon Spain: Madrid, Barcelona, Granada Austria: Vienna I will probably not even want to come back! However, I wonder how I will get by with all the different languages. I only know English!
Data: school_prompt.txtWriting essays for school can be difficult but many students find that by researching their topic that they have more to say and are better informed. Here are the university we require many undergraduate students to take a first year writing requirement so that they can have a solid foundation for their writing skills. This comes in handy for many students. Different schools have different requirements, but everyone uses writing at some point in their academic career, be it essays, research papers, technical write ups, or scripts.
Data: emotion_words.txtSad upset blue down melancholy somber bitter troubled Angry mad enraged irate irritable wrathful outraged infuriated Happy cheerful content elated joyous delighted lively glad Confused disoriented puzzled perplexed dazed befuddled Excited eager thrilled delighted Scared afraid fearful panicked terrified petrified startled Nervous anxious jittery jumpy tense uneasy apprehensive
Data: SP500.txtDate,SP500,Dividend,Earnings,Consumer Price Index,Long Interest Rate,Real Price,Real Dividend,Real Earnings,PE10 1/1/2016,1918.6,43.55,86.5,236.92,2.09,2023.23,45.93,91.22,24.21 2/1/2016,1904.42,43.72,86.47,237.11,1.78,2006.62,46.06,91.11,24 3/1/2016,2021.95,43.88,86.44,238.13,1.89,2121.32,46.04,90.69,25.37 4/1/2016,2075.54,44.07,86.6,239.26,1.81,2167.27,46.02,90.43,25.92 5/1/2016,2065.55,44.27,86.76,240.23,1.81,2148.15,46.04,90.23,25.69 6/1/2016,2083.89,44.46,86.92,241.02,1.64,2160.13,46.09,90.1,25.84 7/1/2016,2148.9,44.65,87.64,240.63,1.5,2231.13,46.36,91,26.69 8/1/2016,2170.95,44.84,88.37,240.85,1.56,2251.95,46.51,91.66,26.95 9/1/2016,2157.69,45.03,89.09,241.43,1.63,2232.83,46.6,92.19,26.73 10/1/2016,2143.02,45.25,90.91,241.73,1.76,2214.89,46.77,93.96,26.53 11/1/2016,2164.99,45.48,92.73,241.35,2.14,2241.08,47.07,95.99,26.85 12/1/2016,2246.63,45.7,94.55,241.43,2.49,2324.83,47.29,97.84,27.87 1/1/2017,2275.12,45.93,96.46,242.84,2.43,2340.67,47.25,99.24,28.06 2/1/2017,2329.91,46.15,98.38,243.6,2.42,2389.52,47.33,100.89,28.66 3/1/2017,2366.82,46.38,100.29,243.8,2.48,2425.4,47.53,102.77,29.09 4/1/2017,2359.31,46.66,101.53,244.52,2.3,2410.56,47.67,103.74,28.9 5/1/2017,2395.35,46.94,102.78,244.73,2.3,2445.29,47.92,104.92,29.31 6/1/2017,2433.99,47.22,104.02,244.96,2.19,2482.48,48.16,106.09,29.75 7/1/2017,2454.1,47.54,105.04,244.79,2.32,2504.72,48.52,107.21,30 8/1/2017,2456.22,47.85,106.06,245.52,2.21,2499.4,48.69,107.92,29.91 9/1/2017,2492.84,48.17,107.08,246.82,2.2,2523.31,48.76,108.39,30.17 10/1/2017,2557,48.42,108.01,246.66,2.36,2589.89,49.05,109.4,30.92 11/1/2017,2593.61,48.68,108.95,246.67,2.35,2626.9,49.3,110.35,31.3 12/1/2017,2664.34,48.93,109.88,246.52,2.4,2700.13,49.59,111.36,32.09
