Use python to solve year2014.txt is Distribution of wage earners by level of net

Question

Use python to solve

year2014.txt is

Distribution of wage earners by level of net compensation
Net compensation interval   Number   Cumulative Number   Percent of total   Aggregate amount   Average amount
0.01 - 4,999.99   22,574,440   22,574,440   14.27075   46,647,919,125.68   2,066.40
5,000.00 - 9,999.99   13,848,841   36,423,281   23.02549   102,586,913,092.61   7,407.62
10,000.00 - 14,999.99   12,329,270   48,752,551   30.81961   153,566,802,438.45   12,455.47
15,000.00 - 19,999.99   11,505,776   60,258,327   38.09315   200,878,198,035.07   17,458.90
20,000.00 - 24,999.99   10,918,555   71,176,882   44.99547   245,317,570,246.88   22,467.95
25,000.00 - 29,999.99   10,192,863   81,369,745   51.43903   279,865,461,187.05   27,457.00
30,000.00 - 34,999.99   9,487,840   90,857,585   57.43690   307,828,947,411.16   32,444.58
35,000.00 - 39,999.99   8,578,215   99,435,800   62.85974   321,200,755,103.44   37,443.78
40,000.00 - 44,999.99   7,553,972   106,989,772   67.63509   320,563,569,965.15   42,436.43
45,000.00 - 49,999.99   6,542,882   113,532,654   71.77126   310,391,706,424.23   47,439.60
50,000.00 - 54,999.99   5,723,269   119,255,923   75.38931   300,016,377,448.51   52,420.46
55,000.00 - 59,999.99   4,846,517   124,102,440   78.45310   278,354,367,841.41   57,433.90
60,000.00 - 64,999.99   4,201,232   128,303,672   81.10897   262,203,932,128.68   62,411.20
65,000.00 - 69,999.99   3,573,471   131,877,143   83.36799   240,948,179,180.40   67,426.93
70,000.00 - 74,999.99   3,094,739   134,971,882   85.32437   224,145,278,103.36   72,427.85
75,000.00 - 79,999.99   2,684,481   137,656,363   87.02140   207,853,372,824.62   77,427.77
80,000.00 - 84,999.99   2,297,338   139,953,701   88.47370   189,370,862,869.17   82,430.56
85,000.00 - 89,999.99   1,975,400   141,929,101   89.72248   172,719,042,418.70   87,434.97
90,000.00 - 94,999.99   1,714,370   143,643,471   90.80624   158,442,931,588.44   92,420.50
95,000.00 - 99,999.99   1,486,636   145,130,107   91.74604   144,858,203,365.61   97,440.26
100,000.00 - 104,999.99   1,309,068   146,439,175   92.57358   134,083,282,259.67   102,426.52
105,000.00 - 109,999.99   1,117,128   147,556,303   93.27979   120,020,513,136.11   107,436.67
110,000.00 - 114,999.99   977,055   148,533,358   93.89745   109,855,105,705.14   112,434.93
115,000.00 - 119,999.99   865,889   149,399,247   94.44483   101,693,061,676.62   117,443.53
120,000.00 - 124,999.99   773,339   150,172,586   94.93371   94,660,281,091.31   122,404.64
125,000.00 - 129,999.99   673,971   150,846,557   95.35977   85,886,152,964.93   127,433.01
130,000.00 - 134,999.99   595,827   151,442,384   95.73643   78,899,843,713.01   132,420.73
135,000.00 - 139,999.99   527,341   151,969,725   96.06980   72,476,546,845.30   137,437.72
140,000.00 - 144,999.99   466,992   152,436,717   96.36501   66,519,743,635.12   142,443.00
145,000.00 - 149,999.99   419,003   152,855,720   96.62989   61,787,674,520.19   147,463.56
150,000.00 - 154,999.99   384,581   153,240,301   96.87301   58,607,775,121.57   152,393.84
155,000.00 - 159,999.99   335,391   153,575,692   97.08503   52,801,735,517.69   157,433.37
160,000.00 - 164,999.99   296,048   153,871,740   97.27218   48,087,213,596.86   162,430.46
165,000.00 - 169,999.99   265,309   154,137,049   97.43990   44,426,198,104.69   167,450.78
170,000.00 - 174,999.99   239,515   154,376,564   97.59131   41,304,379,348.95   172,450.07
175,000.00 - 179,999.99   216,255   154,592,819   97.72802   38,370,042,895.27   177,429.62
180,000.00 - 184,999.99   200,592   154,793,411   97.85483   36,588,064,085.78   182,400.42
185,000.00 - 189,999.99   179,005   154,972,416   97.96799   33,554,727,208.93   187,451.34
190,000.00 - 194,999.99   165,277   155,137,693   98.07247   31,807,897,759.84   192,452.05
195,000.00 - 199,999.99   154,070   155,291,763   98.16987   30,425,466,536.83   197,478.20
200,000.00 - 249,999.99   1,039,897   156,331,660   98.82726   230,863,458,226.21   222,006.08
250,000.00 - 299,999.99   565,105   156,896,765   99.18450   153,945,762,663.99   272,419.75
300,000.00 - 349,999.99   333,584   157,230,349   99.39537   107,708,119,615.81   322,881.55
350,000.00 - 399,999.99   219,923   157,450,272   99.53440   82,117,070,706.61   373,390.10
400,000.00 - 449,999.99   151,162   157,601,434   99.62996   63,997,346,472.50   423,369.28
450,000.00 - 499,999.99   108,881   157,710,315   99.69879   51,583,042,398.64   473,756.14
500,000.00 - 999,999.99   345,935   158,056,250   99.91748   230,331,407,862.96   665,822.79
1,000,000.00 - 1,499,999.99   65,548   158,121,798   99.95892   78,672,933,288.58   1,200,233.92
1,500,000.00 - 1,999,999.99   24,140   158,145,938   99.97418   41,431,838,733.52   1,716,314.78
2,000,000.00 - 2,499,999.99   12,137   158,158,075   99.98185   26,997,226,154.27   2,224,373.91
2,500,000.00 - 2,999,999.99   6,871   158,164,946   99.98619   18,747,446,313.27   2,728,488.77
3,000,000.00 - 3,499,999.99   4,799   158,169,745   99.98923   15,507,304,422.66   3,231,361.62
3,500,000.00 - 3,999,999.99   3,258   158,173,003   99.99129   12,166,741,762.34   3,734,420.43
4,000,000.00 - 4,499,999.99   2,353   158,175,356   99.99277   9,970,953,222.98   4,237,549.18
4,500,000.00 - 4,999,999.99   1,822   158,177,178   99.99393   8,633,941,395.34   4,738,716.46
5,000,000.00 - 9,999,999.99   6,468   158,183,646   99.99802   43,887,775,808.42   6,785,370.41
10,000,000.00 - 19,999,999.99   2,230   158,185,876   99.99942   30,065,006,121.19   13,482,065.53
20,000,000.00 - 49,999,999.99   776   158,186,652   99.99992   22,450,911,983.01   28,931,587.61
50,000,000.00 and over   134   158,186,786   100.00000   11,564,829,969.82   86,304,701.27

skeleton

import pylab

def do_plot(x_vals,y_vals,year):
'''Plot x_vals vs. y_vals where each is a list of numbers of the same length.'''
pylab.xlabel('Income')
pylab.ylabel('Cumulative Percent')
pylab.title("Cumulative Percent for Income in "+str(year))
pylab.plot(x_vals,y_vals)
pylab.show()
  
def open_file():
'''You fill in the doc string'''
year_str = input("Enter a year where 1990 <= year <= 2015: ")
pass # replace this line with your code
  
def read_file(fp):
'''You fill in the doc string'''
pass # replace this line with your code
  
def find_average(data_lst):
'''You fill in the doc string'''
pass # replace this line with your code
  
def find_median(data_lst):
'''You fill in the doc string'''
pass # replace this line with your code
  
def get_range(data_lst, percent):
'''You fill in the doc string'''
pass # replace this line with your code

def get_percent(data_lst,salary):
'''You fill in the doc string'''
pass # replace this line with your code
  

def main():
# Insert code here to determine year, average, and median
  
response = input("Do you want to plot values (yes/no)? ")
if response.lower() == 'yes':
pass # replace this line
# determine x_vals, a list of floats -- use the lowest 40 income ranges
# determine y_vales, a list of floats of the same length as x_vals
# do_plot(x_vals,y_vals,year)
  
choice = input("Enter a choice to get (r)ange, (p)ercent, or nothing to stop: ")
  
while choice:
# Insert code here to handle choice
choice = input("Enter a choice to get (r)ange, (p)ercent, or nothing to stop: ")

if __name__ == "__main__":
main()

Summer 2017 CSE 231 Computer Project #4 (6/7/2017 Fixed TestA median) Assignment overview This assignment focuses on the implementation of Python programs to read files and process data by using lists and functions It is worth 85 points (8.5% of course grade) and must be completed no later than 11:59 PM on Monday, June 12 Assignment Deliverable The deliverable for this assignment is the following file proj04.py the source code for your Python program Be sure to use the specified file name and to submit it for grading via the Mirmir system before the project deadline Assignment Background One commonly hears reference to "the one percent" referring to the people whose income is in the top 1% of incomes. What is the data behind that number and where do others fall? Using the National Average Wage Index (AWI), an index used by the Social Security Administration to gauge a individual's earnings for the purpose of calculating their retirement benefit, we can answer such questions In this project, you will process AWI data. Example data for 2014 and 2015 is provided in the files year 2014. txt and year 2015. txt (2015 is the most recent year of complete data-the 2016 data isn't available until October) The data is a table with the first row as the title and the second row defining the data fields, remaining rows are data. Note that the 2014 data is nicely formatted in columns, but the 2015 data is not. The URL for the data is: https://www.ssa.gov/cgi- bin/netcom p.cgi ear-2015 Here is the second line of data from the file followed by descriptions of the data. Notice that some data are ints and some are floats: 5,000.00 9,999.99 13,948,841 36,4 281 23.025 49 102, 586, 913,092. 61 7, 407.62 423, Column 0 is bottom of this income range. Column 1 is the hyphen separating the bottom of the range from the top Column 2 is the top of this income range. Column 3 is the number of individuals in the income range. Column 4 is the cumulative number of individuals in this income range and all lower ranges Column 5 is the Column 4 value represented as a cumulative percentage of all individuals 26

Explanation / Answer

main.py

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