Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells19
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory42.3 B

Variable types

Text3
Numeric1
DateTime1

Dataset

Description경상남도 밀양시에서 제공하는 숙박업소 현황입니다. 숙박업소 상호명, 주소, 객실수, 허가일자, 전화번호를 확인할 수 있습니다.
Author경상남도 밀양시
URLhttps://www.data.go.kr/data/15029116/fileData.do

Alerts

전화번호 has 19 (19.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:36:47.475571
Analysis finished2023-12-12 08:36:48.708034
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T17:36:48.959161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length5.31
Min length2

Characters and Unicode

Total characters531
Distinct characters171
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

Unique95 ?
Unique (%)95.0%

Sample

1st row평화장
2nd row한일여인숙
3rd row남산여인숙
4th row시민여인숙
5th row제일여인숙
ValueCountFrequency (%)
에비앙 3
 
2.7%
펜션 2
 
1.8%
궁모텔 2
 
1.8%
아이스밸리 1
 
0.9%
브이원 1
 
0.9%
한솔모텔 1
 
0.9%
르네상스 1
 
0.9%
평화장 1
 
0.9%
리버모텔 1
 
0.9%
라임모텔 1
 
0.9%
Other values (97) 97
87.4%
2023-12-12T17:36:49.418467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 39
 
7.3%
모 36
 
6.8%
여 30
 
5.6%
관 26
 
4.9%
장 17
 
3.2%
13
 
2.4%
인 13
 
2.4%
스 13
 
2.4%
이 10
 
1.9%
션 10
 
1.9%
Other values (161) 324
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 495
93.2%
Uppercase Letter 14
 
2.6%
Space Separator 13
 
2.4%
Decimal Number 3
 
0.6%
Other Punctuation 2
 
0.4%
Lowercase Letter 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 39
 
7.9%
모 36
 
7.3%
여 30
 
6.1%
관 26
 
5.3%
장 17
 
3.4%
인 13
 
2.6%
스 13
 
2.6%
이 10
 
2.0%
션 10
 
2.0%
펜 9
 
1.8%
Other values (140) 292
59.0%
Uppercase Letter
ValueCountFrequency (%)
W 2
14.3%
A 2
14.3%
M 2
14.3%
V 1
7.1%
G 1
7.1%
Q 1
7.1%
K 1
7.1%
S 1
7.1%
O 1
7.1%
R 1
7.1%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
0 1
33.3%
5 1
33.3%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
? 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 495
93.2%
Common 20
 
3.8%
Latin 16
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 39
 
7.9%
모 36
 
7.3%
여 30
 
6.1%
관 26
 
5.3%
장 17
 
3.4%
인 13
 
2.6%
스 13
 
2.6%
이 10
 
2.0%
션 10
 
2.0%
펜 9
 
1.8%
Other values (140) 292
59.0%
Latin
ValueCountFrequency (%)
W 2
12.5%
A 2
12.5%
M 2
12.5%
V 1
 
6.2%
G 1
 
6.2%
Q 1
 
6.2%
K 1
 
6.2%
S 1
 
6.2%
O 1
 
6.2%
i 1
 
6.2%
Other values (3) 3
18.8%
Common
ValueCountFrequency (%)
13
65.0%
( 1
 
5.0%
) 1
 
5.0%
& 1
 
5.0%
? 1
 
5.0%
1 1
 
5.0%
0 1
 
5.0%
5 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 495
93.2%
ASCII 36
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 39
 
7.9%
모 36
 
7.3%
여 30
 
6.1%
관 26
 
5.3%
장 17
 
3.4%
인 13
 
2.6%
스 13
 
2.6%
이 10
 
2.0%
션 10
 
2.0%
펜 9
 
1.8%
Other values (140) 292
59.0%
ASCII
ValueCountFrequency (%)
13
36.1%
W 2
 
5.6%
A 2
 
5.6%
M 2
 
5.6%
( 1
 
2.8%
V 1
 
2.8%
G 1
 
2.8%
Q 1
 
2.8%
) 1
 
2.8%
& 1
 
2.8%
Other values (11) 11
30.6%

주소
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T17:36:49.855754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length22.45
Min length18

Characters and Unicode

Total characters2245
Distinct characters111
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row경상남도 밀양시 소전4길 5 (삼문동)
2nd row경상남도 밀양시 약산로 49-1 (내이동)
3rd row경상남도 밀양시 소전2길 20 (삼문동)
4th row경상남도 밀양시 노상하4길 8 (내이동)
5th row경상남도 밀양시 석정로 42-9 (내일동)
ValueCountFrequency (%)
경상남도 100
19.8%
밀양시 100
19.8%
단장면 15
 
3.0%
내이동 15
 
3.0%
부북면 13
 
2.6%
삼문동 11
 
2.2%
삼랑진읍 10
 
2.0%
천태로 7
 
1.4%
가곡동 7
 
1.4%
춘화로 6
 
1.2%
Other values (160) 220
43.7%
2023-12-12T17:36:50.336325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404
18.0%
시 113
 
5.0%
남 105
 
4.7%
양 105
 
4.7%
상 105
 
4.7%
밀 103
 
4.6%
도 101
 
4.5%
경 100
 
4.5%
1 71
 
3.2%
로 58
 
2.6%
Other values (101) 980
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1370
61.0%
Space Separator 404
 
18.0%
Decimal Number 327
 
14.6%
Dash Punctuation 50
 
2.2%
Open Punctuation 45
 
2.0%
Close Punctuation 45
 
2.0%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 113
 
8.2%
남 105
 
7.7%
양 105
 
7.7%
상 105
 
7.7%
밀 103
 
7.5%
도 101
 
7.4%
경 100
 
7.3%
로 58
 
4.2%
길 50
 
3.6%
동 47
 
3.4%
Other values (86) 483
35.3%
Decimal Number
ValueCountFrequency (%)
1 71
21.7%
2 57
17.4%
4 33
10.1%
7 30
9.2%
8 30
9.2%
3 29
8.9%
6 22
 
6.7%
5 21
 
6.4%
9 18
 
5.5%
0 16
 
4.9%
Space Separator
ValueCountFrequency (%)
404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1370
61.0%
Common 875
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 113
 
8.2%
남 105
 
7.7%
양 105
 
7.7%
상 105
 
7.7%
밀 103
 
7.5%
도 101
 
7.4%
경 100
 
7.3%
로 58
 
4.2%
길 50
 
3.6%
동 47
 
3.4%
Other values (86) 483
35.3%
Common
ValueCountFrequency (%)
404
46.2%
1 71
 
8.1%
2 57
 
6.5%
- 50
 
5.7%
( 45
 
5.1%
) 45
 
5.1%
4 33
 
3.8%
7 30
 
3.4%
8 30
 
3.4%
3 29
 
3.3%
Other values (5) 81
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1370
61.0%
ASCII 875
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404
46.2%
1 71
 
8.1%
2 57
 
6.5%
- 50
 
5.7%
( 45
 
5.1%
) 45
 
5.1%
4 33
 
3.8%
7 30
 
3.4%
8 30
 
3.4%
3 29
 
3.3%
Other values (5) 81
 
9.3%
Hangul
ValueCountFrequency (%)
시 113
 
8.2%
남 105
 
7.7%
양 105
 
7.7%
상 105
 
7.7%
밀 103
 
7.5%
도 101
 
7.4%
경 100
 
7.3%
로 58
 
4.2%
길 50
 
3.6%
동 47
 
3.4%
Other values (86) 483
35.3%

객실수
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.17
Minimum4
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T17:36:50.463981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.95
Q110.75
median17
Q319
95-th percentile33.05
Maximum45
Range41
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation8.53993
Coefficient of variation (CV)0.49737507
Kurtosis1.3664514
Mean17.17
Median Absolute Deviation (MAD)5
Skewness1.0747724
Sum1717
Variance72.930404
MonotonicityNot monotonic
2023-12-12T17:36:50.569577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 11
 
11.0%
19 11
 
11.0%
12 8
 
8.0%
17 8
 
8.0%
10 6
 
6.0%
16 6
 
6.0%
9 5
 
5.0%
7 5
 
5.0%
28 4
 
4.0%
8 4
 
4.0%
Other values (21) 32
32.0%
ValueCountFrequency (%)
4 1
 
1.0%
5 2
 
2.0%
6 2
 
2.0%
7 5
5.0%
8 4
4.0%
9 5
5.0%
10 6
6.0%
11 3
 
3.0%
12 8
8.0%
13 1
 
1.0%
ValueCountFrequency (%)
45 2
2.0%
41 1
 
1.0%
35 1
 
1.0%
34 1
 
1.0%
33 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
29 2
2.0%
28 4
4.0%
27 1
 
1.0%

허가일자
Date

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum1963-06-30 00:00:00
Maximum2017-07-25 00:00:00
2023-12-12T17:36:50.700843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:36:50.882836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct80
Distinct (%)98.8%
Missing19
Missing (%)19.0%
Memory size932.0 B
2023-12-12T17:36:51.159949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters972
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

Unique79 ?
Unique (%)97.5%

Sample

1st row055-354-3551
2nd row055-354-2621
3rd row055-354-4195
4th row055-354-2174
5th row055-352-8249
ValueCountFrequency (%)
055-356-5553 2
 
2.5%
055-353-5440 1
 
1.2%
055-354-3551 1
 
1.2%
055-356-3718 1
 
1.2%
055-355-1285 1
 
1.2%
055-356-9810 1
 
1.2%
055-351-0334 1
 
1.2%
055-351-0745 1
 
1.2%
055-356-1392 1
 
1.2%
055-352-2211 1
 
1.2%
Other values (70) 70
86.4%
2023-12-12T17:36:51.581533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 297
30.6%
- 162
16.7%
0 126
13.0%
3 113
 
11.6%
4 53
 
5.5%
2 52
 
5.3%
1 51
 
5.2%
6 37
 
3.8%
8 28
 
2.9%
9 28
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 810
83.3%
Dash Punctuation 162
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 297
36.7%
0 126
15.6%
3 113
 
14.0%
4 53
 
6.5%
2 52
 
6.4%
1 51
 
6.3%
6 37
 
4.6%
8 28
 
3.5%
9 28
 
3.5%
7 25
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 297
30.6%
- 162
16.7%
0 126
13.0%
3 113
 
11.6%
4 53
 
5.5%
2 52
 
5.3%
1 51
 
5.2%
6 37
 
3.8%
8 28
 
2.9%
9 28
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 297
30.6%
- 162
16.7%
0 126
13.0%
3 113
 
11.6%
4 53
 
5.5%
2 52
 
5.3%
1 51
 
5.2%
6 37
 
3.8%
8 28
 
2.9%
9 28
 
2.9%

Interactions

2023-12-12T17:36:48.464428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:36:51.705704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호주소객실수허가일자전화번호
상호1.0000.9990.9460.9990.999
주소0.9991.0001.0001.0001.000
객실수0.9461.0001.0000.9891.000
허가일자0.9991.0000.9891.0001.000
전화번호0.9991.0001.0001.0001.000

Missing values

2023-12-12T17:36:48.559972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:36:48.660290image/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

상호주소객실수허가일자전화번호
0평화장경상남도 밀양시 소전4길 5 (삼문동)101963-06-30055-354-3551
1한일여인숙경상남도 밀양시 약산로 49-1 (내이동)71969-01-14055-354-2621
2남산여인숙경상남도 밀양시 소전2길 20 (삼문동)51969-01-14055-354-4195
3시민여인숙경상남도 밀양시 노상하4길 8 (내이동)101969-09-15<NA>
4제일여인숙경상남도 밀양시 석정로 42-9 (내일동)71969-01-14<NA>
5남일여관경상남도 밀양시 삼문중앙로6길 32-7 (삼문동)121970-12-27055-354-2174
6금호여관경상남도 밀양시 상동면 금산7길 7-181970-03-06055-352-8249
7일성여인숙경상남도 밀양시 석정로 70-8 (내일동)61970-12-19055-354-3410
8서울여인숙경상남도 밀양시 백민로2길 17-33 (내이동)61970-09-21055-352-3676
9삼호장여인숙경상남도 밀양시 삼문중앙로3길 26 (삼문동)51973-09-12055-354-3914
상호주소객실수허가일자전화번호
90밀양관광?션 물안개피는마을&들꽃향기경상남도 밀양시 단장면 고례2길 41122004-07-09055-352-4300
91밀양펜션경상남도 밀양시 단장면 구천2길 447 (,2201)102006-07-28055-352-3999
92밀양관광펜션 아름드리경상남도 밀양시 단장면 표충로 826-1482007-07-27055-351-0082
93밀양 내마음애풍경경상남도 밀양시 단장면 고례2길 7882008-06-24055-351-5500
94얼음골까투리펜션경상남도 밀양시 산내면 얼음골로 134-22112012-10-08055-356-8887
95포레스트 펜션경상남도 밀양시 단장면 시전중앙길 582014-03-25055-352-2545
96하늘정원펜션경상남도 밀양시 단장면 고례3길 10-78232014-07-10<NA>
97별빛마을펜션하우스경상남도 밀양시 단장면 도래재로 578-1292015-11-20<NA>
98위양지 관광농원경상남도 밀양시 부북면 위양2길 114162016-07-12055-355-8887
99마이웨이관광농원경상남도 밀양시 산외면 엄광길 103-1442017-07-25<NA>