Answer Happy

EGR 125: Introduction to Engineering Methods (Summer 2022) Programming Assignment 5: Input and Output Files In this assignment, you will be reading data from two different input files and writing to a single output file. The two data files to be analyzed are "unformatted.txt" and "economy2021.txt". Start with "unformatted.txt", which as its name implies, is an unformatted list of numbers. When reading the file, ignore the first line, which contains the word "Start". Read all the values into a numerical list. Using the NumPy library, calculate the average and standard deviation of the values. Write all the values to an output file named "output.txt" in tabular format with 5 columns. Use formatting of 11 spaces and 4 decimal places per number. After the table, write a blank line, then the average and standard deviation. Finally, write at least two blank lines to separate this part from the next part of the assignment. In the second part, you will read the file "economy2021.txt", which contains actual monthly data from 2021 for job growth (in millions), gross domestic product (GDP, in trillions of dollars), and the consumer price index (CPI). All are common measures of the state of the country's economy and frequently discussed in the news. The first two lines in the file are to be ignored; they contain a description and column headings (Jobs, GDP, CPI). The first column should also be ignored; it contains the numerical value for months January to December. Extract the three relevant columns of data into three separate lists. Calculate the total number of jobs created in 2021 as well as the average for each of job growth, GDP, and CPI. Write these results to the same output file as above. There is no need to create a library for this assignment.
Start 132.1198 129.4821 105.9035 108.5087 183.7949 173.7206. 153.4881 139.9009 103.6191 152.0306 190.8820 117.3293 118.9752 163.5355 174.1806 181.4775 189.3184 146.2798 106.70951 191.5808 102.9513 141.5598 170.8473 186.4448 124.1375 156.8033 130.7476 119.9643 122.2347 152.6042 199.1312 108.3624 193.8078 188.3036 134.8279 199.2295 185.7849 120.5697 180.7950 111.6763 152.4750 168.8582 122.1835 165.7051 149.9106 149.6960 162.6624 132.4487 161.6143 142.4231 115.9945 146.3304 108.5593 189.6398 169.5469) 140.0213 120.9502 129.1194 150.7346 125.6491 179.0661 179.2642 121.9453 182.8950 175.9229 176.4343 101.8671 147.4801 173.7960 142.0714 178.0386 140.9149 163.2892 147.5336 134.6039 167.5593 171.4291 119.0752 152.2200 142.5561 196.3874 199.0050 182.4025 100.7588 141.5449 115.2733 118.7009 138.3344 149.6013 174.1734 271.7179 100.9917 178.4993 119.6603 130.2277 105.0005 183,82774 195.0847 131.8988 181.7452 103.9401 143.1376. 128.0994 158.5256 153.8229 125.9731 150.5944 176.7158 196.9286 120.4449 102.8688 143.8268 176.0335 131.9571 154.1028 135.5332 150.2369 163.4916 157.2905 145.6566 110.6050 110.6235 163.4256 131.4320 181.6006 105.3227 154 1220 158.6922 191.8433 122.0792 110.2072 171.5039 144.1476 104.5935 142.5145 133.5504 102.4799 181.4260 103.8302 195.60785 163.3170 198.2352 122.4480 194 0221 112.9431 167.5997 117.8665 154.5385 105.5564 314 4003 O
136.0937 194.7195 194.3806 165.6002 157.9038 142.0176 179.5765 179.9759 153.6326 145.6079 159.7244 142.2153 135.1972 125.4543 177.9566 146.7348 150.3808 183.4162 194.2271 196.9442 155.2331 187.3939 175.4080 164.1757. 157.4510 128.0148 141.8704 147.5575 188.0346 158.1598 121.0697 176.2791 114.6288 111.6394 140.2388 146.4168 151.2892 126.2473
2021 U.S. Monthly Job Growth, GDP, and Month Jobs GDP CPI 1 0.520 22.02 261.582 2 0.710 21.71 263.014. 0.704 22.39 264.877 0.263 22.60 267.054 0.447 22.72 269.195 0.557 22.90 271.696 0.689 22.95 273.003 0.517 23.26 273.567 0.424 23.40 274.310 0.677 23.96 276.589 0.647 23.96 277.948 0.588 24.09 278.802 234567BSHEE 12 8 9 10 11 12
import math Import statistics #Part 1: Unformatted data A- [1 with open("unformatted.txt', 'e') as infile: infile.readline() for line in infile: for x in line.split(): A.append(float(x)) avgA statistics.mean(A) stdeva statistics.stdev(A) outfile open('output.txt', '') nvals len(A) ncols 5 nrows math.cell(nvals/ncols) index e for row in range(nrous): for col in range(ncols): outfile.write("%11.4f %(A[index])) # or outfile.write(f' (ay_list[index:5.1f)") index 1 if index > nvals: break outfile.write('\n') outfile.write("\n") outfile.write('Average- %.4f \n (avga)) outfile.urite('St Dev- .4f \n (stdevA)] # Part 2: Economy data