This exercise allows a user to load one of two CSV files and then perform histogram analysis and plots for select variables on the datasets.     

house.csv

AGE	BEDRMS	BUILT	NUNITS	ROOMS	WEIGHT	UTILITY
82	2	2006	1	6	3117.394239	169
50	4	1980	1	6	2150.725544	245.3333333
53	4	1985	1	7	2213.789404	159
67	3	1985	1	6	2364.585097	179
26	2	1980	100	4	2314.524902	146
56	1	1985	32	3	2482.655916	94.75
50	3	1985	1	6	4084.310118	236
26	2	1980	8	5	2823.39599	81
60	3	1985	1	7	2552.762241	184.0833333
26	1	1985	24	3	2845.454432	0
59	3	1985	1	5	2150.592362	172
54	5	2005	1	9	2791.995108	467.5833333
25	2	2009	12	4	1934.565082	135
53	3	2007	1	7	2840.95499	152.1666667
52	4	2009	1	9	2840.95499	364
67	5	1985	1	9	2428.354692	469.8333333
22	2	2005	8	4	2524.164373	50.08333333
55	3	1980	1	6	2918.327087	332.8333333
57	3	1980	1	9	2534.642076	245.6666667
84	2	1980	1	5	2226.80362	145.1666667
55	2	2004	1	6	2880.506791	65
74	2	2003	1	4	3152.342745	109
67	2	1990	1	6	3304.581422	141.8333333
70	3	1990	1	7	3522.801866	282.5
59	3	2005	1	7	2703.395803	288.25
62	2	1990	1	6	4007.388181	315
32	4	2004	1	7	2807.2076	310.8333333
-9	2	1980	24	5	1648.50752	0
54	4	2007	1	10	2080.747516	317
25	3	2012	1	6	4194.143162	184.8333333
41	3	1985	1	7	2023.995536	308
50	3	1990	1	10	3570.773875	207
15	2	2004	1	7	3736.447401	148.6666667
54	3	1980	1	7	2261.445448	163
24	3	2007	200	6	5081.703183	0
28	1	1990	60	3	3023.669904	52
24	2	2007	36	4	5756.60996	250.6666667
40	5	2007	1	9	2488.405077	301.8333333
26	3	2009	1	6	3266.401361	96
50	3	2008	1	6	1983.373313	174
86	1	1980	105	3	2045.933536	0
60	4	1990	1	8	3321.532218	241.1666667
39	3	2006	1	5	2660.859635	176.1666667
34	2	1985	1	4	3096.004644	33
64	3	1990	1	6	3996.478152	234.8333333
52	3	1990	1	7	4410.105187	258
71	3	1990	1	7	4946.868585	666.75
34	1	1985	1	3	1366.171372	0
72	4	1985	1	8	2483.823695	154

 

 

 

pop change csv

Id	Geography	Target Geo Id	Target Geo Id2	Pop Apr 1	Pop Jul 1	Change Pop
0100000US	United States	310M400US10100	10100	40603	43191	2588
0100000US	United States	310M400US10140	10140	72798	73901	1103
0100000US	United States	310M400US10220	10220	37490	38247	757
0100000US	United States	310M400US10300	10300	99892	98266	-1626
0100000US	United States	310M400US10460	10460	63832	66781	2949
0100000US	United States	310M400US10620	10620	60586	62075	1489
0100000US	United States	310M400US10660	10660	31254	30444	-810
0100000US	United States	310M400US10700	10700	93019	96109	3090
0100000US	United States	310M400US10760	10760	41618	40497	-1121
0100000US	United States	310M400US10820	10820	36009	37964	1955
0100000US	United States	310M400US10860	10860	40836	40822	-14
0100000US	United States	310M400US10940	10940	42476	40599	-1877
0100000US	United States	310M400US10980	10980	29601	28360	-1241
0100000US	United States	310M400US11060	11060	26446	24949	-1497
0100000US	United States	310M400US11140	11140	37827	34969	-2858
0100000US	United States	310M400US11220	11220	50258	49455	-803
0100000US	United States	310M400US11380	11380	14786	18128	3342
0100000US	United States	310M400US11420	11420	34173	34586	413
0100000US	United States	310M400US11580	11580	34862	37489	2627
0100000US	United States	310M400US11620	11620	47733	48177	444
0100000US	United States	310M400US11660	11660	22993	22061	-932
0100000US	United States	310M400US11680	11680	36309	35218	-1091
0100000US	United States	310M400US11740	11740	53140	53745	605
0100000US	United States	310M400US11780	11780	101490	97493	-3997
0100000US	United States	310M400US11820	11820	37026	39764	2738
0100000US	United States	310M400US11860	11860	16921	16193	-728
0100000US	United States	310M400US11900	11900	64764	65818	1054
0100000US	United States	310M400US11940	11940	52279	53285	1006
0100000US	United States	310M400US11980	11980	78534	82299	3765
0100000US	United States	310M400US12120	12120	38320	36748	-1572
0100000US	United States	310M400US12140	12140	42229	43226	997
0100000US	United States	310M400US12180	12180	80017	77145	-2872
0100000US	United States	310M400US12300	12300	122154	122083	-71
0100000US	United States	310M400US12380	12380	39163	40011	848
0100000US	United States	310M400US12460	12460	27842	26575	-1267
0100000US	United States	310M400US12660	12660	61965	64249	2284
0100000US	United States	310M400US12680	12680	43443	45851	2408
0100000US	United States	310M400US12740	12740	59522	58140	-1382
0100000US	United States	310M400US12780	12780	50977	51843	866
0100000US	United States	310M400US12820	12820	27979	25398	-2581
0100000US	United States	310M400US12860	12860	59943	57511	-2432
0100000US	United States	310M400US12900	12900	36641	37678	1037
0100000US	United States	310M400US13060	13060	36702	36552	-150
0100000US	United States	310M400US13100	13100	22311	21493	-818
0100000US	United States	310M400US13180	13180	88759	87847	-912
0100000US	United States	310M400US13260	13260	46129	45668	-461
0100000US	United States	310M400US13300	13300	31861	32587	726
0100000US	United States	310M400US13340	13340	45851	45358	-493
0100000US	United States	310M400US13420	13420	44442	46847	2405

Transcribed Image Text:

This exercise (80 points) allows a user to load one of two CSV files and then perform histogram analysis and plots for select variables on the datasets. The first dataset represents the population change for specific dates for U.S. regions. The second dataset represents Housing data over an extended period of time describing home age, number of bedrooms and other variables. The first row provides a column name for each dataset. The following columns should be used to perform analysis: ● Pop Apr 1 Pop Jul 1 Change Pop ● PopChange.csv: Housing.csv: AGE BEDRMS ● BUILT ● ROOMS ● UTILITY Notice for the Housing CSV file, there are more columns in the file than are required to be analyzed. You can and should still load each column. Specific statistics should include: Count Mean ● Standard Deviation ● Min Max Histogram ● This exercise (80 points) allows a user to load one of two CSV files and then perform histogram analysis and plots for select variables on the datasets. The first dataset represents the population change for specific dates for U.S. regions. The second dataset represents Housing data over an extended period of time describing home age, number of bedrooms and other variables. The first row provides a column name for each dataset. The following columns should be used to perform analysis: ● Pop Apr 1 Pop Jul 1 Change Pop ● PopChange.csv: Housing.csv: AGE BEDRMS ● BUILT ● ROOMS ● UTILITY Notice for the Housing CSV file, there are more columns in the file than are required to be analyzed. You can and should still load each column. Specific statistics should include: Count Mean ● Standard Deviation ● Min Max Histogram ●

Answer rating: 100% (QA)

The full source code of the program is as follows Notice that 1 The pr... View the full answer