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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.

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

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

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