Overview

Dataset statistics

Number of variables5
Number of observations51
Missing cells12
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory42.6 B

Variable types

Categorical1
Text4

Dataset

Description인천광역시 동구에 위치하고 있는 숙박업(여인숙, 여관, 모텔)에 대한 데이터로, 업종명, 업소명, 영업소주소, 전화번호 등 항목을 제공합니다.
Author인천광역시 동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15027851&srcSe=7661IVAWM27C61E190

Alerts

업종명 is highly imbalanced (86.1%)Imbalance
소재지전화 has 12 (23.5%) missing valuesMissing
영업소주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-28 10:52:53.402528
Analysis finished2024-01-28 10:52:53.792977
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
숙박업(일반)
50 
숙박업(생활)
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 50
98.0%
숙박업(생활) 1
 
2.0%

Length

2024-01-28T19:52:53.850718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:52:53.937265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 50
98.0%
숙박업(생활 1
 
2.0%

업소명
Text

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-01-28T19:52:54.117228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length5
Mean length5.1764706
Min length3

Characters and Unicode

Total characters264
Distinct characters78
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)92.2%

Sample

1st row서산여인숙
2nd row명수여인숙
3rd row황주여인숙
4th row명신여인숙
5th row명성여인숙
ValueCountFrequency (%)
삼화여인숙 2
 
3.6%
모텔 2
 
3.6%
만복여인숙 2
 
3.6%
유진여인숙 1
 
1.8%
스타여인숙 1
 
1.8%
팔레스여관 1
 
1.8%
그린우드 1
 
1.8%
광주여인숙 1
 
1.8%
서산여인숙 1
 
1.8%
스카이모텔 1
 
1.8%
Other values (42) 42
76.4%
2024-01-28T19:52:54.419738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
여 39
 
14.8%
인 39
 
14.8%
숙 37
 
14.0%
텔 8
 
3.0%
화 6
 
2.3%
스 6
 
2.3%
신 5
 
1.9%
모 5
 
1.9%
4
 
1.5%
이 4
 
1.5%
Other values (68) 111
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
93.2%
Uppercase Letter 12
 
4.5%
Space Separator 4
 
1.5%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
여 39
15.9%
인 39
15.9%
숙 37
 
15.0%
텔 8
 
3.3%
화 6
 
2.4%
스 6
 
2.4%
신 5
 
2.0%
모 5
 
2.0%
이 4
 
1.6%
아 3
 
1.2%
Other values (57) 94
38.2%
Uppercase Letter
ValueCountFrequency (%)
I 3
25.0%
M 2
16.7%
T 2
16.7%
R 1
 
8.3%
S 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%
L 1
 
8.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
93.2%
Latin 12
 
4.5%
Common 6
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
여 39
15.9%
인 39
15.9%
숙 37
 
15.0%
텔 8
 
3.3%
화 6
 
2.4%
스 6
 
2.4%
신 5
 
2.0%
모 5
 
2.0%
이 4
 
1.6%
아 3
 
1.2%
Other values (57) 94
38.2%
Latin
ValueCountFrequency (%)
I 3
25.0%
M 2
16.7%
T 2
16.7%
R 1
 
8.3%
S 1
 
8.3%
O 1
 
8.3%
E 1
 
8.3%
L 1
 
8.3%
Common
ValueCountFrequency (%)
4
66.7%
( 1
 
16.7%
) 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
93.2%
ASCII 18
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
여 39
15.9%
인 39
15.9%
숙 37
 
15.0%
텔 8
 
3.3%
화 6
 
2.4%
스 6
 
2.4%
신 5
 
2.0%
모 5
 
2.0%
이 4
 
1.6%
아 3
 
1.2%
Other values (57) 94
38.2%
ASCII
ValueCountFrequency (%)
4
22.2%
I 3
16.7%
M 2
11.1%
T 2
11.1%
( 1
 
5.6%
R 1
 
5.6%
S 1
 
5.6%
O 1
 
5.6%
E 1
 
5.6%
L 1
 
5.6%

영업소주소(도로명)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-01-28T19:52:54.632134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length32
Mean length25.823529
Min length20

Characters and Unicode

Total characters1317
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row인천광역시 동구 화도진로68번길 3-6 (화평동)
2nd row인천광역시 동구 송화로2번길 1-8 (화평동)
3rd row인천광역시 동구 화도진로52번길 9-3 (송현동)
4th row인천광역시 동구 금곡로11번길 8-7 (금곡동)
5th row인천광역시 동구 화도진로56번길 6-3 (송현동)
ValueCountFrequency (%)
인천광역시 51
19.0%
동구 51
19.0%
송림동 40
14.9%
금곡로81번길 13
 
4.9%
샛골로 7
 
2.6%
화평동 6
 
2.2%
송현동 4
 
1.5%
5 3
 
1.1%
송화로 3
 
1.1%
솔빛로91번길 2
 
0.7%
Other values (79) 88
32.8%
2024-01-28T19:52:54.949109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
217
 
16.5%
동 105
 
8.0%
1 61
 
4.6%
인 56
 
4.3%
송 55
 
4.2%
( 51
 
3.9%
광 51
 
3.9%
역 51
 
3.9%
시 51
 
3.9%
구 51
 
3.9%
Other values (48) 568
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 777
59.0%
Space Separator 217
 
16.5%
Decimal Number 189
 
14.4%
Open Punctuation 51
 
3.9%
Close Punctuation 51
 
3.9%
Dash Punctuation 19
 
1.4%
Other Punctuation 10
 
0.8%
Math Symbol 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 105
13.5%
인 56
 
7.2%
송 55
 
7.1%
광 51
 
6.6%
역 51
 
6.6%
시 51
 
6.6%
구 51
 
6.6%
천 51
 
6.6%
로 51
 
6.6%
림 43
 
5.5%
Other values (32) 212
27.3%
Decimal Number
ValueCountFrequency (%)
1 61
32.3%
8 27
14.3%
3 20
 
10.6%
5 17
 
9.0%
6 17
 
9.0%
2 14
 
7.4%
9 11
 
5.8%
4 10
 
5.3%
7 9
 
4.8%
0 3
 
1.6%
Space Separator
ValueCountFrequency (%)
217
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 777
59.0%
Common 540
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 105
13.5%
인 56
 
7.2%
송 55
 
7.1%
광 51
 
6.6%
역 51
 
6.6%
시 51
 
6.6%
구 51
 
6.6%
천 51
 
6.6%
로 51
 
6.6%
림 43
 
5.5%
Other values (32) 212
27.3%
Common
ValueCountFrequency (%)
217
40.2%
1 61
 
11.3%
( 51
 
9.4%
) 51
 
9.4%
8 27
 
5.0%
3 20
 
3.7%
- 19
 
3.5%
5 17
 
3.1%
6 17
 
3.1%
2 14
 
2.6%
Other values (6) 46
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 777
59.0%
ASCII 540
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217
40.2%
1 61
 
11.3%
( 51
 
9.4%
) 51
 
9.4%
8 27
 
5.0%
3 20
 
3.7%
- 19
 
3.5%
5 17
 
3.1%
6 17
 
3.1%
2 14
 
2.6%
Other values (6) 46
 
8.5%
Hangul
ValueCountFrequency (%)
동 105
13.5%
인 56
 
7.2%
송 55
 
7.1%
광 51
 
6.6%
역 51
 
6.6%
시 51
 
6.6%
구 51
 
6.6%
천 51
 
6.6%
로 51
 
6.6%
림 43
 
5.5%
Other values (32) 212
27.3%

영업소주소(지번)
Text

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-01-28T19:52:55.138070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length19.078431
Min length16

Characters and Unicode

Total characters973
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)92.2%

Sample

1st row인천광역시 동구 화평동 72-3
2nd row인천광역시 동구 화평동 527-16
3rd row인천광역시 동구 송현동 72-177
4th row인천광역시 동구 금곡동 35-10
5th row인천광역시 동구 송현동 72-173
ValueCountFrequency (%)
인천광역시 51
23.7%
동구 51
23.7%
송림동 40
18.6%
화평동 6
 
2.8%
송현동 4
 
1.9%
55-5 2
 
0.9%
50-26 2
 
0.9%
56-17 1
 
0.5%
67-4 1
 
0.5%
67-24 1
 
0.5%
Other values (56) 56
26.0%
2024-01-28T19:52:55.420845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164
16.9%
동 102
 
10.5%
인 57
 
5.9%
구 51
 
5.2%
천 51
 
5.2%
시 51
 
5.2%
역 51
 
5.2%
광 51
 
5.2%
- 50
 
5.1%
5 45
 
4.6%
Other values (37) 300
30.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 548
56.3%
Decimal Number 203
 
20.9%
Space Separator 164
 
16.9%
Dash Punctuation 50
 
5.1%
Other Punctuation 8
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 102
18.6%
인 57
10.4%
구 51
9.3%
천 51
9.3%
시 51
9.3%
역 51
9.3%
광 51
9.3%
송 44
8.0%
림 40
 
7.3%
화 7
 
1.3%
Other values (24) 43
7.8%
Decimal Number
ValueCountFrequency (%)
5 45
22.2%
1 28
13.8%
6 28
13.8%
7 27
13.3%
2 19
9.4%
9 15
 
7.4%
8 13
 
6.4%
0 11
 
5.4%
3 9
 
4.4%
4 8
 
3.9%
Space Separator
ValueCountFrequency (%)
164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 548
56.3%
Common 425
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 102
18.6%
인 57
10.4%
구 51
9.3%
천 51
9.3%
시 51
9.3%
역 51
9.3%
광 51
9.3%
송 44
8.0%
림 40
 
7.3%
화 7
 
1.3%
Other values (24) 43
7.8%
Common
ValueCountFrequency (%)
164
38.6%
- 50
 
11.8%
5 45
 
10.6%
1 28
 
6.6%
6 28
 
6.6%
7 27
 
6.4%
2 19
 
4.5%
9 15
 
3.5%
8 13
 
3.1%
0 11
 
2.6%
Other values (3) 25
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 548
56.3%
ASCII 425
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
38.6%
- 50
 
11.8%
5 45
 
10.6%
1 28
 
6.6%
6 28
 
6.6%
7 27
 
6.4%
2 19
 
4.5%
9 15
 
3.5%
8 13
 
3.1%
0 11
 
2.6%
Other values (3) 25
 
5.9%
Hangul
ValueCountFrequency (%)
동 102
18.6%
인 57
10.4%
구 51
9.3%
천 51
9.3%
시 51
9.3%
역 51
9.3%
광 51
9.3%
송 44
8.0%
림 40
 
7.3%
화 7
 
1.3%
Other values (24) 43
7.8%

소재지전화
Text

MISSING 

Distinct37
Distinct (%)94.9%
Missing12
Missing (%)23.5%
Memory size540.0 B
2024-01-28T19:52:55.598170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
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

Unique35 ?
Unique (%)89.7%

Sample

1st row032-772-6621
2nd row032-765-2770
3rd row032-773-0978
4th row032-773-6723
5th row032-765-2611
ValueCountFrequency (%)
032-773-0978 2
 
5.1%
032-762-3594 2
 
5.1%
032-766-7311 1
 
2.6%
032-777-0243 1
 
2.6%
032-762-9767 1
 
2.6%
032-765-2276 1
 
2.6%
032-777-3294 1
 
2.6%
032-766-1141 1
 
2.6%
032-764-6583 1
 
2.6%
032-762-8322 1
 
2.6%
Other values (27) 27
69.2%
2024-01-28T19:52:55.855211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 78
16.7%
7 77
16.5%
2 73
15.6%
3 67
14.3%
0 51
10.9%
6 45
9.6%
5 20
 
4.3%
1 19
 
4.1%
9 18
 
3.8%
8 10
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 77
19.7%
2 73
18.7%
3 67
17.2%
0 51
13.1%
6 45
11.5%
5 20
 
5.1%
1 19
 
4.9%
9 18
 
4.6%
8 10
 
2.6%
4 10
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 78
16.7%
7 77
16.5%
2 73
15.6%
3 67
14.3%
0 51
10.9%
6 45
9.6%
5 20
 
4.3%
1 19
 
4.1%
9 18
 
3.8%
8 10
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 78
16.7%
7 77
16.5%
2 73
15.6%
3 67
14.3%
0 51
10.9%
6 45
9.6%
5 20
 
4.3%
1 19
 
4.1%
9 18
 
3.8%
8 10
 
2.1%

Correlations

2024-01-28T19:52:55.933631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소명영업소주소(도로명)영업소주소(지번)소재지전화
업종명1.0001.0001.0001.000NaN
업소명1.0001.0001.0000.9880.982
영업소주소(도로명)1.0001.0001.0001.0001.000
영업소주소(지번)1.0000.9881.0001.0001.000
소재지전화NaN0.9821.0001.0001.000

Missing values

2024-01-28T19:52:53.693979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T19:52:53.762899image/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

업종명업소명영업소주소(도로명)영업소주소(지번)소재지전화
0숙박업(일반)서산여인숙인천광역시 동구 화도진로68번길 3-6 (화평동)인천광역시 동구 화평동 72-3032-772-6621
1숙박업(일반)명수여인숙인천광역시 동구 송화로2번길 1-8 (화평동)인천광역시 동구 화평동 527-16032-765-2770
2숙박업(일반)황주여인숙인천광역시 동구 화도진로52번길 9-3 (송현동)인천광역시 동구 송현동 72-177032-773-0978
3숙박업(일반)명신여인숙인천광역시 동구 금곡로11번길 8-7 (금곡동)인천광역시 동구 금곡동 35-10032-773-6723
4숙박업(일반)명성여인숙인천광역시 동구 화도진로56번길 6-3 (송현동)인천광역시 동구 송현동 72-173032-765-2611
5숙박업(일반)서울여인숙인천광역시 동구 솔빛로91번길 26-6 (송림동)인천광역시 동구 송림동 55-6<NA>
6숙박업(일반)상신여인숙인천광역시 동구 화도진로56번길 6-4 (송현동)인천광역시 동구 송현동 72-181032-773-0978
7숙박업(일반)황해여인숙인천광역시 동구 솔빛로 77 (송림동)인천광역시 동구 송림동 151-2<NA>
8숙박업(일반)보경여인숙인천광역시 동구 금곡로81번길 29 (송림동)인천광역시 동구 송림동 59-11032-772-1774
9숙박업(일반)신진여인숙인천광역시 동구 송화로 16-6, 신진여인숙 (화평동)인천광역시 동구 화평동 72-4, 신진여인숙032-772-6392
업종명업소명영업소주소(도로명)영업소주소(지번)소재지전화
41숙박업(일반)수정여인숙인천광역시 동구 금곡로81번길 25, 수정여인숙 (송림동)인천광역시 동구 송림동 59-13, 수정여인숙032-765-7035
42숙박업(일반)대화여인숙인천광역시 동구 금곡로81번길 9, 2층 (송림동)인천광역시 동구 송림동 67-45<NA>
43숙박업(일반)아미파크인천광역시 동구 금곡로81번길 33, 아미파크 (송림동)인천광역시 동구 송림동 59-9, 아미파크032-766-7311
44숙박업(일반)탑모텔인천광역시 동구 금곡로81번길 17, 탑모텔 1-4층 (송림동)인천광역시 동구 송림동 67-13, 탑모텔 1-4층032-765-9555
45숙박업(일반)스타여인숙인천광역시 동구 금곡로81번길 31, 스타여인숙 1~3층 (송림동)인천광역시 동구 송림동 59-10, 스타여인숙<NA>
46숙박업(일반)유진여인숙인천광역시 동구 금곡로81번길 25-1, 유진여인숙 (송림동)인천광역시 동구 송림동 59-26, 유진여인숙032-762-9767
47숙박업(일반)연안여인숙인천광역시 동구 동산로87번길 8, 연안여인숙 (송림동)인천광역시 동구 송림동 59-86, 연안여인숙<NA>
48숙박업(일반)리더스탤인천광역시 동구 금곡로91번길 2, 2~4층 (송림동)인천광역시 동구 송림동 67-4032-762-6611
49숙박업(일반)그린우드인천광역시 동구 금곡로81번길 7, 1~4층 (송림동)인천광역시 동구 송림동 67-16, 문화여인숙<NA>
50숙박업(생활)인타운인천광역시 동구 금곡로 86 (송림동)인천광역시 동구 송림동 69-1<NA>