Overview

Dataset statistics

Number of variables5
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory43.2 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description대구 지역 외국인관광도시민박업 현황정보에 관한 공공데이터로 구군, 상호명, 소재지, 주택종류 등의 정보를 제공합니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15054190/fileData.do

Alerts

연번 is highly overall correlated with 구군High correlation
구군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
주택종류 is highly overall correlated with 구군High correlation
연번 has unique valuesUnique
상호명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:33:34.276663
Analysis finished2024-04-29 22:33:36.967099
Duration2.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.5
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-30T07:33:37.035041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q115.75
median30.5
Q345.25
95-th percentile57.05
Maximum60
Range59
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.464249
Coefficient of variation (CV)0.57259833
Kurtosis-1.2
Mean30.5
Median Absolute Deviation (MAD)15
Skewness0
Sum1830
Variance305
MonotonicityStrictly increasing
2024-04-30T07:33:37.169228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.7%
32 1
 
1.7%
34 1
 
1.7%
35 1
 
1.7%
36 1
 
1.7%
37 1
 
1.7%
38 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
41 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
1 1
1.7%
2 1
1.7%
3 1
1.7%
4 1
1.7%
5 1
1.7%
6 1
1.7%
7 1
1.7%
8 1
1.7%
9 1
1.7%
10 1
1.7%
ValueCountFrequency (%)
60 1
1.7%
59 1
1.7%
58 1
1.7%
57 1
1.7%
56 1
1.7%
55 1
1.7%
54 1
1.7%
53 1
1.7%
52 1
1.7%
51 1
1.7%

구군
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
중구
18 
남구
18 
동구
10 
수성구
4 
달서구
4 
Other values (2)
6 

Length

Max length3
Median length2
Mean length2.1333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 18
30.0%
남구 18
30.0%
동구 10
16.7%
수성구 4
 
6.7%
달서구 4
 
6.7%
서구 3
 
5.0%
북구 3
 
5.0%

Length

2024-04-30T07:33:37.297562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:33:37.402244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 18
30.0%
남구 18
30.0%
동구 10
16.7%
수성구 4
 
6.7%
달서구 4
 
6.7%
서구 3
 
5.0%
북구 3
 
5.0%

상호명
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:33:37.635007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length17.5
Mean length7.8666667
Min length2

Characters and Unicode

Total characters472
Distinct characters150
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row청라언덕 게스트하우스
2nd row캐주얼하우스 소노
3rd row어반덴
4th row청라게스트하우스
5th row게스트하우스 만나
ValueCountFrequency (%)
house 9
 
8.8%
게스트하우스 4
 
3.9%
스테이 2
 
2.0%
of 2
 
2.0%
gallery 2
 
2.0%
하우스 2
 
2.0%
청라언덕 1
 
1.0%
in 1
 
1.0%
안지랑 1
 
1.0%
인 1
 
1.0%
Other values (77) 77
75.5%
2024-04-30T07:33:38.038948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
8.9%
스 30
 
6.4%
o 17
 
3.6%
하 17
 
3.6%
우 17
 
3.6%
e 16
 
3.4%
u 11
 
2.3%
a 10
 
2.1%
s 9
 
1.9%
E 9
 
1.9%
Other values (140) 294
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 225
47.7%
Lowercase Letter 122
25.8%
Uppercase Letter 76
 
16.1%
Space Separator 42
 
8.9%
Other Punctuation 3
 
0.6%
Decimal Number 2
 
0.4%
Modifier Symbol 1
 
0.2%
Connector Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 30
 
13.3%
하 17
 
7.6%
우 17
 
7.6%
이 8
 
3.6%
트 8
 
3.6%
게 7
 
3.1%
테 5
 
2.2%
오 3
 
1.3%
레 3
 
1.3%
안 3
 
1.3%
Other values (93) 124
55.1%
Lowercase Letter
ValueCountFrequency (%)
o 17
13.9%
e 16
13.1%
u 11
9.0%
a 10
 
8.2%
s 9
 
7.4%
n 8
 
6.6%
l 7
 
5.7%
g 6
 
4.9%
i 6
 
4.9%
h 5
 
4.1%
Other values (11) 27
22.1%
Uppercase Letter
ValueCountFrequency (%)
E 9
11.8%
S 8
10.5%
H 7
 
9.2%
O 7
 
9.2%
L 6
 
7.9%
U 5
 
6.6%
G 4
 
5.3%
A 4
 
5.3%
C 4
 
5.3%
B 4
 
5.3%
Other values (9) 18
23.7%
Other Punctuation
ValueCountFrequency (%)
: 1
33.3%
' 1
33.3%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
42
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
47.5%
Latin 198
41.9%
Common 49
 
10.4%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 30
 
13.4%
하 17
 
7.6%
우 17
 
7.6%
이 8
 
3.6%
트 8
 
3.6%
게 7
 
3.1%
테 5
 
2.2%
오 3
 
1.3%
레 3
 
1.3%
안 3
 
1.3%
Other values (92) 123
54.9%
Latin
ValueCountFrequency (%)
o 17
 
8.6%
e 16
 
8.1%
u 11
 
5.6%
a 10
 
5.1%
s 9
 
4.5%
E 9
 
4.5%
n 8
 
4.0%
S 8
 
4.0%
H 7
 
3.5%
O 7
 
3.5%
Other values (30) 96
48.5%
Common
ValueCountFrequency (%)
42
85.7%
2 2
 
4.1%
: 1
 
2.0%
' 1
 
2.0%
` 1
 
2.0%
_ 1
 
2.0%
& 1
 
2.0%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247
52.3%
Hangul 224
47.5%
CJK 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42
 
17.0%
o 17
 
6.9%
e 16
 
6.5%
u 11
 
4.5%
a 10
 
4.0%
s 9
 
3.6%
E 9
 
3.6%
n 8
 
3.2%
S 8
 
3.2%
H 7
 
2.8%
Other values (37) 110
44.5%
Hangul
ValueCountFrequency (%)
스 30
 
13.4%
하 17
 
7.6%
우 17
 
7.6%
이 8
 
3.6%
트 8
 
3.6%
게 7
 
3.1%
테 5
 
2.2%
오 3
 
1.3%
레 3
 
1.3%
안 3
 
1.3%
Other values (92) 123
54.9%
CJK
ValueCountFrequency (%)
休 1
100.0%

소재지
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-30T07:33:38.324392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length37
Mean length29.633333
Min length16

Characters and Unicode

Total characters1778
Distinct characters129
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row대구광역시 중구 달구벌대로401길 22(동산동)
2nd row대구광역시 중구 명륜로 86, 2층(남산동)
3rd row대구광역시 중구 동덕로8길 40-16, 3층(대봉동)
4th row대구광역시 중구 달구벌대로 401길 16
5th row대구광역시 중구 명륜로 121-37, 3층(봉산동)
ValueCountFrequency (%)
대구광역시 60
 
18.6%
중구 18
 
5.6%
남구 18
 
5.6%
동구 10
 
3.1%
3층 5
 
1.5%
달서구 4
 
1.2%
대명동 4
 
1.2%
501호 4
 
1.2%
수성구 4
 
1.2%
달구벌대로 3
 
0.9%
Other values (175) 193
59.8%
2024-04-30T07:33:38.765984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
264
 
14.8%
구 126
 
7.1%
1 91
 
5.1%
대 89
 
5.0%
동 83
 
4.7%
광 61
 
3.4%
역 61
 
3.4%
시 61
 
3.4%
3 59
 
3.3%
) 54
 
3.0%
Other values (119) 829
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 951
53.5%
Decimal Number 376
 
21.1%
Space Separator 264
 
14.8%
Close Punctuation 54
 
3.0%
Open Punctuation 54
 
3.0%
Other Punctuation 46
 
2.6%
Dash Punctuation 32
 
1.8%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 126
 
13.2%
대 89
 
9.4%
동 83
 
8.7%
광 61
 
6.4%
역 61
 
6.4%
시 61
 
6.4%
로 52
 
5.5%
길 48
 
5.0%
중 24
 
2.5%
남 22
 
2.3%
Other values (103) 324
34.1%
Decimal Number
ValueCountFrequency (%)
1 91
24.2%
3 59
15.7%
2 53
14.1%
0 42
11.2%
4 33
 
8.8%
6 28
 
7.4%
5 25
 
6.6%
9 21
 
5.6%
8 12
 
3.2%
7 12
 
3.2%
Space Separator
ValueCountFrequency (%)
264
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 951
53.5%
Common 826
46.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 126
 
13.2%
대 89
 
9.4%
동 83
 
8.7%
광 61
 
6.4%
역 61
 
6.4%
시 61
 
6.4%
로 52
 
5.5%
길 48
 
5.0%
중 24
 
2.5%
남 22
 
2.3%
Other values (103) 324
34.1%
Common
ValueCountFrequency (%)
264
32.0%
1 91
 
11.0%
3 59
 
7.1%
) 54
 
6.5%
( 54
 
6.5%
2 53
 
6.4%
, 46
 
5.6%
0 42
 
5.1%
4 33
 
4.0%
- 32
 
3.9%
Other values (5) 98
 
11.9%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 951
53.5%
ASCII 827
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264
31.9%
1 91
 
11.0%
3 59
 
7.1%
) 54
 
6.5%
( 54
 
6.5%
2 53
 
6.4%
, 46
 
5.6%
0 42
 
5.1%
4 33
 
4.0%
- 32
 
3.9%
Other values (6) 99
 
12.0%
Hangul
ValueCountFrequency (%)
구 126
 
13.2%
대 89
 
9.4%
동 83
 
8.7%
광 61
 
6.4%
역 61
 
6.4%
시 61
 
6.4%
로 52
 
5.5%
길 48
 
5.0%
중 24
 
2.5%
남 22
 
2.3%
Other values (103) 324
34.1%

주택종류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
단독
25 
다가구
16 
아파트
7 
단독주택
7 
단독(다가구)
3 

Length

Max length7
Median length4
Mean length2.9
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row다가구
2nd row다가구
3rd row다가구
4th row다가구
5th row단독

Common Values

ValueCountFrequency (%)
단독 25
41.7%
다가구 16
26.7%
아파트 7
 
11.7%
단독주택 7
 
11.7%
단독(다가구) 3
 
5.0%
다세대 2
 
3.3%

Length

2024-04-30T07:33:38.894664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:33:39.014449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독 25
41.7%
다가구 16
26.7%
아파트 7
 
11.7%
단독주택 7
 
11.7%
단독(다가구 3
 
5.0%
다세대 2
 
3.3%

Interactions

2024-04-30T07:33:36.664082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:33:39.087646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구군상호명소재지주택종류
연번1.0000.8861.0001.0000.602
구군0.8861.0001.0001.0000.754
상호명1.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.000
주택종류0.6020.7541.0001.0001.000
2024-04-30T07:33:39.188575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택종류구군
주택종류1.0000.569
구군0.5691.000
2024-04-30T07:33:39.265262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구군주택종류
연번1.0000.6990.354
구군0.6991.0000.569
주택종류0.3540.5691.000

Missing values

2024-04-30T07:33:36.833307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:33:36.921122image/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.

Sample

연번구군상호명소재지주택종류
01중구청라언덕 게스트하우스대구광역시 중구 달구벌대로401길 22(동산동)다가구
12중구캐주얼하우스 소노대구광역시 중구 명륜로 86, 2층(남산동)다가구
23중구어반덴대구광역시 중구 동덕로8길 40-16, 3층(대봉동)다가구
34중구청라게스트하우스대구광역시 중구 달구벌대로 401길 16다가구
45중구게스트하우스 만나대구광역시 중구 명륜로 121-37, 3층(봉산동)단독
56중구글렌즈게스트하우스대구광역시 중구 달구벌대로 445길 16-13(삼덕3가)단독
67중구수피네 게스트하우스대구광역시 중구 중앙대로58길 9, 3층 (남산동)단독
78중구이든대구광역시 중구 명륜로23길 38-2, 4층 501호 (봉산동)단독
89중구Guma대구광역시 중구 서성로16길 46-12 (북내동)단독
910중구포레스트 삼덕대구광역시 중구 달구벌대로447길 34-9, 501호 (삼덕동3가)단독
연번구군상호명소재지주택종류
5051북구옐로벙커대구광역시 북구 동북로26길 19-13 (산격동)단독주택
5152북구비움스테이대구광역시 북구 침산로 162-9, 501호 (침산동)단독주택
5253수성구Leo's house대구광역시 수성구 청수로31길 2-3,3층(황금동)단독(다가구)
5354수성구Memory대구광역시 수성구 무학로 187, 102동 903호(지산동, 녹원맨션)아파트
5455수성구SUSEONG LAKE GUEST HOUSE대구광역시 수성구 용학로30길 12, 3층 (지산동)단독(다가구)
5556수성구CHLOE BNB대구광역시 수성구 공경로 67-25 (만촌동)단독(다가구)
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