Overview

Dataset statistics

Number of variables8
Number of observations38
Missing cells4
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory67.5 B

Variable types

Categorical4
Text4

Dataset

Description감포해양관광단지, 안동문화관광단지, 보문관광단지의 투자유치 물건 현황
Author경상북도관광공사
URLhttps://www.data.go.kr/data/15045131/fileData.do

Alerts

관광단지명 is highly overall correlated with 시설지구 and 2 other fieldsHigh correlation
시설지구 is highly overall correlated with 관광단지명 and 2 other fieldsHigh correlation
층수 is highly overall correlated with 관광단지명 and 2 other fieldsHigh correlation
건폐 is highly overall correlated with 관광단지명 and 2 other fieldsHigh correlation
물 건 명 has 1 (2.6%) missing valuesMissing
부지(㎡) has 1 (2.6%) missing valuesMissing
건축(㎡) has 1 (2.6%) missing valuesMissing
용적 has 1 (2.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:23:08.049279
Analysis finished2023-12-12 19:23:09.050007
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관광단지명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
감포관광단지
29 
안동관광단지
7 
보문관광단지
 
1
<NA>
 
1

Length

Max length6
Median length6
Mean length5.9473684
Min length4

Unique

Unique2 ?
Unique (%)5.3%

Sample

1st row감포관광단지
2nd row감포관광단지
3rd row감포관광단지
4th row감포관광단지
5th row감포관광단지

Common Values

ValueCountFrequency (%)
감포관광단지 29
76.3%
안동관광단지 7
 
18.4%
보문관광단지 1
 
2.6%
<NA> 1
 
2.6%

Length

2023-12-13T04:23:09.138939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:23:09.274540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포관광단지 29
76.3%
안동관광단지 7
 
18.4%
보문관광단지 1
 
2.6%
na 1
 
2.6%

시설지구
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
상가시설
15 
숙박시설
12 
운동오락시설
4 
휴양문화시설
2 
공공시설
2 
Other values (3)
3 

Length

Max length8
Median length4
Mean length4.4210526
Min length4

Unique

Unique3 ?
Unique (%)7.9%

Sample

1st row숙박시설
2nd row숙박시설
3rd row숙박시설
4th row숙박시설
5th row숙박시설

Common Values

ValueCountFrequency (%)
상가시설 15
39.5%
숙박시설 12
31.6%
운동오락시설 4
 
10.5%
휴양문화시설 2
 
5.3%
공공시설 2
 
5.3%
휴양문화 1
 
2.6%
상가시설(1개) 1
 
2.6%
<NA> 1
 
2.6%

Length

2023-12-13T04:23:09.435054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:23:09.617736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상가시설 15
39.5%
숙박시설 12
31.6%
운동오락시설 4
 
10.5%
휴양문화시설 2
 
5.3%
공공시설 2
 
5.3%
휴양문화 1
 
2.6%
상가시설(1개 1
 
2.6%
na 1
 
2.6%

물 건 명
Text

MISSING 

Distinct37
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-13T04:23:09.929899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.5135135
Min length3

Characters and Unicode

Total characters204
Distinct characters72
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row호텔1
2nd row호텔2
3rd row호텔4
4th row호텔5
5th row빌라형콘도
ValueCountFrequency (%)
호텔1 1
 
2.5%
ocean 1
 
2.5%
복합상가8 1
 
2.5%
복합상가9 1
 
2.5%
보문상가 1
 
2.5%
중심상가1 1
 
2.5%
중심상가2-1 1
 
2.5%
중심상가2-2 1
 
2.5%
중심상가2-3 1
 
2.5%
중심상가2-4 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T04:23:10.405660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
상 16
 
7.8%
가 16
 
7.8%
2 11
 
5.4%
합 10
 
4.9%
복 9
 
4.4%
1 7
 
3.4%
호 6
 
2.9%
콘 6
 
2.9%
심 6
 
2.9%
중 6
 
2.9%
Other values (62) 111
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140
68.6%
Decimal Number 31
 
15.2%
Lowercase Letter 15
 
7.4%
Uppercase Letter 7
 
3.4%
Dash Punctuation 5
 
2.5%
Space Separator 3
 
1.5%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 16
 
11.4%
가 16
 
11.4%
합 10
 
7.1%
복 9
 
6.4%
호 6
 
4.3%
콘 6
 
4.3%
심 6
 
4.3%
중 6
 
4.3%
텔 6
 
4.3%
도 6
 
4.3%
Other values (33) 53
37.9%
Decimal Number
ValueCountFrequency (%)
2 11
35.5%
1 7
22.6%
4 3
 
9.7%
3 3
 
9.7%
5 3
 
9.7%
7 1
 
3.2%
8 1
 
3.2%
9 1
 
3.2%
6 1
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
a 4
26.7%
e 3
20.0%
n 2
13.3%
d 1
 
6.7%
c 1
 
6.7%
i 1
 
6.7%
f 1
 
6.7%
r 1
 
6.7%
k 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
L 2
28.6%
O 1
14.3%
C 1
14.3%
A 1
14.3%
S 1
14.3%
P 1
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140
68.6%
Common 42
 
20.6%
Latin 22
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 16
 
11.4%
가 16
 
11.4%
합 10
 
7.1%
복 9
 
6.4%
호 6
 
4.3%
콘 6
 
4.3%
심 6
 
4.3%
중 6
 
4.3%
텔 6
 
4.3%
도 6
 
4.3%
Other values (33) 53
37.9%
Latin
ValueCountFrequency (%)
a 4
18.2%
e 3
13.6%
L 2
 
9.1%
n 2
 
9.1%
d 1
 
4.5%
c 1
 
4.5%
O 1
 
4.5%
C 1
 
4.5%
A 1
 
4.5%
S 1
 
4.5%
Other values (5) 5
22.7%
Common
ValueCountFrequency (%)
2 11
26.2%
1 7
16.7%
- 5
11.9%
4 3
 
7.1%
3 3
 
7.1%
5 3
 
7.1%
3
 
7.1%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
Other values (4) 4
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140
68.6%
ASCII 64
31.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
상 16
 
11.4%
가 16
 
11.4%
합 10
 
7.1%
복 9
 
6.4%
호 6
 
4.3%
콘 6
 
4.3%
심 6
 
4.3%
중 6
 
4.3%
텔 6
 
4.3%
도 6
 
4.3%
Other values (33) 53
37.9%
ASCII
ValueCountFrequency (%)
2 11
17.2%
1 7
 
10.9%
- 5
 
7.8%
a 4
 
6.2%
4 3
 
4.7%
e 3
 
4.7%
3 3
 
4.7%
5 3
 
4.7%
3
 
4.7%
L 2
 
3.1%
Other values (19) 20
31.2%

부지(㎡)
Text

MISSING 

Distinct35
Distinct (%)94.6%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-13T04:23:10.631274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.6486486
Min length5

Characters and Unicode

Total characters246
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)91.9%

Sample

1st row27,869
2nd row38,354
3rd row14,548
4th row28,794
5th row36,479.40
ValueCountFrequency (%)
3,610 3
 
8.1%
7,131 1
 
2.7%
5,440 1
 
2.7%
38,354 1
 
2.7%
2,882.60 1
 
2.7%
7,023.70 1
 
2.7%
3,241.90 1
 
2.7%
3,859 1
 
2.7%
12,680 1
 
2.7%
2,160.60 1
 
2.7%
Other values (25) 25
67.6%
2023-12-13T04:23:11.047478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 38
15.4%
0 30
12.2%
8 24
9.8%
3 23
9.3%
6 20
8.1%
1 20
8.1%
2 20
8.1%
4 20
8.1%
5 16
6.5%
7 13
 
5.3%
Other values (2) 22
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 195
79.3%
Other Punctuation 51
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
15.4%
8 24
12.3%
3 23
11.8%
6 20
10.3%
1 20
10.3%
2 20
10.3%
4 20
10.3%
5 16
8.2%
7 13
6.7%
9 9
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 38
74.5%
. 13
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 246
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 38
15.4%
0 30
12.2%
8 24
9.8%
3 23
9.3%
6 20
8.1%
1 20
8.1%
2 20
8.1%
4 20
8.1%
5 16
6.5%
7 13
 
5.3%
Other values (2) 22
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 38
15.4%
0 30
12.2%
8 24
9.8%
3 23
9.3%
6 20
8.1%
1 20
8.1%
2 20
8.1%
4 20
8.1%
5 16
6.5%
7 13
 
5.3%
Other values (2) 22
8.9%

건축(㎡)
Text

MISSING 

Distinct31
Distinct (%)83.8%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-13T04:23:11.314872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4324324
Min length3

Characters and Unicode

Total characters201
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)70.3%

Sample

1st row22,000
2nd row29,000
3rd row11,500
4th row22,500
5th row36,000
ValueCountFrequency (%)
3,100 3
 
8.1%
2,500 2
 
5.4%
13,000 2
 
5.4%
2,300 2
 
5.4%
2,600 2
 
5.4%
2,800 1
 
2.7%
30,920 1
 
2.7%
14,790 1
 
2.7%
14,860 1
 
2.7%
18,540 1
 
2.7%
Other values (21) 21
56.8%
2023-12-13T04:23:11.716001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76
37.8%
, 36
17.9%
1 18
 
9.0%
3 16
 
8.0%
2 16
 
8.0%
5 12
 
6.0%
6 9
 
4.5%
4 6
 
3.0%
8 6
 
3.0%
9 4
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 165
82.1%
Other Punctuation 36
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
46.1%
1 18
 
10.9%
3 16
 
9.7%
2 16
 
9.7%
5 12
 
7.3%
6 9
 
5.5%
4 6
 
3.6%
8 6
 
3.6%
9 4
 
2.4%
7 2
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76
37.8%
, 36
17.9%
1 18
 
9.0%
3 16
 
8.0%
2 16
 
8.0%
5 12
 
6.0%
6 9
 
4.5%
4 6
 
3.0%
8 6
 
3.0%
9 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76
37.8%
, 36
17.9%
1 18
 
9.0%
3 16
 
8.0%
2 16
 
8.0%
5 12
 
6.0%
6 9
 
4.5%
4 6
 
3.0%
8 6
 
3.0%
9 4
 
2.0%

층수
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
3층
17 
7층
7 
6층
5 
2층
5 
5층
2 
Other values (2)
2 

Length

Max length4
Median length2
Mean length2.0526316
Min length2

Unique

Unique2 ?
Unique (%)5.3%

Sample

1st row6층
2nd row6층
3rd row6층
4th row6층
5th row5층

Common Values

ValueCountFrequency (%)
3층 17
44.7%
7층 7
18.4%
6층 5
 
13.2%
2층 5
 
13.2%
5층 2
 
5.3%
1층 1
 
2.6%
<NA> 1
 
2.6%

Length

2023-12-13T04:23:11.906207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:23:12.084991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3층 17
44.7%
7층 7
18.4%
6층 5
 
13.2%
2층 5
 
13.2%
5층 2
 
5.3%
1층 1
 
2.6%
na 1
 
2.6%

건폐
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size436.0 B
40%
24 
30%
9 
25%
 
2
10%
 
1
20%
 
1

Length

Max length4
Median length3
Mean length3.0263158
Min length3

Unique

Unique3 ?
Unique (%)7.9%

Sample

1st row40%
2nd row40%
3rd row40%
4th row40%
5th row40%

Common Values

ValueCountFrequency (%)
40% 24
63.2%
30% 9
 
23.7%
25% 2
 
5.3%
10% 1
 
2.6%
20% 1
 
2.6%
<NA> 1
 
2.6%

Length

2023-12-13T04:23:12.278001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:23:12.431761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 24
63.2%
30 9
 
23.7%
25 2
 
5.3%
10 1
 
2.6%
20 1
 
2.6%
na 1
 
2.6%

용적
Text

MISSING 

Distinct20
Distinct (%)54.1%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-13T04:23:12.634109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0810811
Min length2

Characters and Unicode

Total characters114
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)29.7%

Sample

1st row79%
2nd row76%
3rd row79%
4th row78%
5th row99%
ValueCountFrequency (%)
79 4
 
10.8%
88 4
 
10.8%
90 4
 
10.8%
86 4
 
10.8%
98 2
 
5.4%
100 2
 
5.4%
110 2
 
5.4%
80 2
 
5.4%
89 2
 
5.4%
120 1
 
2.7%
Other values (10) 10
27.0%
2023-12-13T04:23:13.027373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 37
32.5%
8 20
17.5%
9 14
 
12.3%
0 14
 
12.3%
1 9
 
7.9%
7 7
 
6.1%
6 6
 
5.3%
3 2
 
1.8%
4 2
 
1.8%
2 2
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
67.5%
Other Punctuation 37
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 20
26.0%
9 14
18.2%
0 14
18.2%
1 9
11.7%
7 7
 
9.1%
6 6
 
7.8%
3 2
 
2.6%
4 2
 
2.6%
2 2
 
2.6%
5 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
% 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 37
32.5%
8 20
17.5%
9 14
 
12.3%
0 14
 
12.3%
1 9
 
7.9%
7 7
 
6.1%
6 6
 
5.3%
3 2
 
1.8%
4 2
 
1.8%
2 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 37
32.5%
8 20
17.5%
9 14
 
12.3%
0 14
 
12.3%
1 9
 
7.9%
7 7
 
6.1%
6 6
 
5.3%
3 2
 
1.8%
4 2
 
1.8%
2 2
 
1.8%

Correlations

2023-12-13T04:23:13.162244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관광단지명시설지구물 건 명부지(㎡)건축(㎡)층수건폐용적
관광단지명1.0000.8501.0001.0001.0000.8980.8461.000
시설지구0.8501.0001.0001.0000.8960.7780.8030.938
물 건 명1.0001.0001.0001.0001.0001.0001.0001.000
부지(㎡)1.0001.0001.0001.0001.0001.0001.0001.000
건축(㎡)1.0000.8961.0001.0001.0000.7770.7400.939
층수0.8980.7781.0001.0000.7771.0000.7190.947
건폐0.8460.8031.0001.0000.7400.7191.0001.000
용적1.0000.9381.0001.0000.9390.9471.0001.000
2023-12-13T04:23:13.300989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설지구관광단지명건폐층수
시설지구1.0000.7750.6620.593
관광단지명0.7751.0000.8560.588
건폐0.6620.8561.0000.575
층수0.5930.5880.5751.000
2023-12-13T04:23:13.760288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관광단지명시설지구층수건폐
관광단지명1.0000.7750.5880.856
시설지구0.7751.0000.5930.662
층수0.5880.5931.0000.575
건폐0.8560.6620.5751.000

Missing values

2023-12-13T04:23:08.546571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:23:08.753536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T04:23:08.924613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관광단지명시설지구물 건 명부지(㎡)건축(㎡)층수건폐용적
0감포관광단지숙박시설호텔127,86922,0006층40%79%
1감포관광단지숙박시설호텔238,35429,0006층40%76%
2감포관광단지숙박시설호텔414,54811,5006층40%79%
3감포관광단지숙박시설호텔528,79422,5006층40%78%
4감포관광단지숙박시설빌라형콘도36,479.4036,0005층40%99%
5감포관광단지숙박시설타워형콘도87,40486,0007층40%98%
6감포관광단지휴양문화시설수목원/산림동물원53,5492,5001층10%5%
7감포관광단지휴양문화시설연수및수련시설130,36213,0006층40%43%
8감포관광단지운동오락시설Ocean Land68,47525,0003층30%37%
9감포관광단지운동오락시설Sea Life Park50,288.8013,0002층30%26%
관광단지명시설지구물 건 명부지(㎡)건축(㎡)층수건폐용적
28감포관광단지공공시설공공시설25,4404,3002층40%79%
29안동관광단지숙박시설호텔C12,68010,1507층30%80%
30안동관광단지숙박시설휴양콘도(빌라형)14,64916,1105층30%110%
31안동관광단지숙박시설호텔A23,48525,8407층30%110%
32안동관광단지숙박시설콘도118,54018,5407층25%100%
33안동관광단지숙박시설콘도212,38014,8607층30%120%
34안동관광단지숙박시설콘도314,78614,7907층25%100%
35안동관광단지휴양문화종합휴양문화시설38,65030,9207층30%80%
36보문관광단지상가시설(1개)보문상가26,56310,5632층20%40%
37<NA><NA><NA><NA><NA><NA><NA><NA>