Overview

Dataset statistics

Number of variables7
Number of observations59
Missing cells4
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory58.2 B

Variable types

Categorical1
DateTime2
Text4

Dataset

Description전라북도 정읍시에 소재한 여행업등록현황 중( 구분, 등록일자, 상호, 소재지도로명주소, 소재지지번주소, 전화번호)등의 정보를 제공합니다.
Author전라북도 정읍시
URLhttps://www.data.go.kr/data/3079978/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 4 (6.8%) missing valuesMissing

Reproduction

Analysis started2023-12-16 15:40:48.802666
Analysis finished2023-12-16 15:40:51.826766
Duration3.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size604.0 B
국내여행업
29 
국외여행업
27 
일반여행업
3 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내여행업 29
49.2%
국외여행업 27
45.8%
일반여행업 3
 
5.1%

Length

2023-12-16T15:40:52.309540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:40:52.762513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내여행업 29
49.2%
국외여행업 27
45.8%
일반여행업 3
 
5.1%

등록일자
Date

Distinct43
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size604.0 B
Minimum1986-07-28 00:00:00
Maximum2019-09-09 00:00:00
2023-12-16T15:40:53.420940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:40:54.306397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

상호
Text

Distinct35
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-16T15:40:55.589368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length8.779661
Min length4

Characters and Unicode

Total characters518
Distinct characters83
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)18.6%

Sample

1st row(유)대원관광
2nd row유)고려관광여행사
3rd row(주)정읍신성해외관광여행사
4th row화원투어
5th row유한회사 두승산관광
ValueCountFrequency (%)
유한회사 22
25.3%
유로항공여행사 2
 
2.3%
정읍대부항공여행사 2
 
2.3%
유)고려관광여행사 2
 
2.3%
롯데투어 2
 
2.3%
우리여행사 2
 
2.3%
삼인해외여행사 2
 
2.3%
세계여행사 2
 
2.3%
미소관광 2
 
2.3%
뉴)월드투어 2
 
2.3%
Other values (29) 47
54.0%
2023-12-16T15:40:57.513154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 52
 
10.0%
유 36
 
6.9%
여 30
 
5.8%
행 30
 
5.8%
28
 
5.4%
한 25
 
4.8%
회 22
 
4.2%
관 22
 
4.2%
광 22
 
4.2%
) 15
 
2.9%
Other values (73) 236
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 462
89.2%
Space Separator 28
 
5.4%
Close Punctuation 15
 
2.9%
Open Punctuation 13
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 52
 
11.3%
유 36
 
7.8%
여 30
 
6.5%
행 30
 
6.5%
한 25
 
5.4%
회 22
 
4.8%
관 22
 
4.8%
광 22
 
4.8%
공 12
 
2.6%
항 12
 
2.6%
Other values (70) 199
43.1%
Space Separator
ValueCountFrequency (%)
28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 462
89.2%
Common 56
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 52
 
11.3%
유 36
 
7.8%
여 30
 
6.5%
행 30
 
6.5%
한 25
 
5.4%
회 22
 
4.8%
관 22
 
4.8%
광 22
 
4.8%
공 12
 
2.6%
항 12
 
2.6%
Other values (70) 199
43.1%
Common
ValueCountFrequency (%)
28
50.0%
) 15
26.8%
( 13
23.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 462
89.2%
ASCII 56
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 52
 
11.3%
유 36
 
7.8%
여 30
 
6.5%
행 30
 
6.5%
한 25
 
5.4%
회 22
 
4.8%
관 22
 
4.8%
광 22
 
4.8%
공 12
 
2.6%
항 12
 
2.6%
Other values (70) 199
43.1%
ASCII
ValueCountFrequency (%)
28
50.0%
) 15
26.8%
( 13
23.2%

소재지도로명주소
Text

Distinct34
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-16T15:40:58.682714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length35
Mean length24.610169
Min length20

Characters and Unicode

Total characters1452
Distinct characters90
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

Unique13 ?
Unique (%)22.0%

Sample

1st row전라북도 정읍시 구미길 1 (수성동)
2nd row전라북도 정읍시 정읍북로 50 (구룡동, 신흥주유소)
3rd row전라북도 정읍시 용북길 117 (구룡동)
4th row전라북도 정읍시 중앙1길 15 (수성동)
5th row전라북도 정읍시 정읍북로 50 (유)두승산관광 (구룡동)
ValueCountFrequency (%)
전라북도 59
18.4%
정읍시 59
18.4%
연지동 19
 
5.9%
중앙로 12
 
3.8%
시기동 11
 
3.4%
수성동 10
 
3.1%
15 8
 
2.5%
서부산업도로 7
 
2.2%
구룡동 5
 
1.6%
1층 4
 
1.2%
Other values (65) 126
39.4%
2023-12-16T15:41:00.705729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
261
18.0%
정 73
 
5.0%
시 70
 
4.8%
북 69
 
4.8%
읍 68
 
4.7%
도 66
 
4.5%
전 62
 
4.3%
라 59
 
4.1%
동 56
 
3.9%
1 55
 
3.8%
Other values (80) 613
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 885
61.0%
Space Separator 261
 
18.0%
Decimal Number 171
 
11.8%
Open Punctuation 54
 
3.7%
Close Punctuation 54
 
3.7%
Other Punctuation 15
 
1.0%
Dash Punctuation 12
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 73
 
8.2%
시 70
 
7.9%
북 69
 
7.8%
읍 68
 
7.7%
도 66
 
7.5%
전 62
 
7.0%
라 59
 
6.7%
동 56
 
6.3%
로 48
 
5.4%
지 19
 
2.1%
Other values (65) 295
33.3%
Decimal Number
ValueCountFrequency (%)
1 55
32.2%
2 20
 
11.7%
5 19
 
11.1%
3 15
 
8.8%
4 15
 
8.8%
0 14
 
8.2%
7 9
 
5.3%
8 8
 
4.7%
9 8
 
4.7%
6 8
 
4.7%
Space Separator
ValueCountFrequency (%)
261
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 885
61.0%
Common 567
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 73
 
8.2%
시 70
 
7.9%
북 69
 
7.8%
읍 68
 
7.7%
도 66
 
7.5%
전 62
 
7.0%
라 59
 
6.7%
동 56
 
6.3%
로 48
 
5.4%
지 19
 
2.1%
Other values (65) 295
33.3%
Common
ValueCountFrequency (%)
261
46.0%
1 55
 
9.7%
( 54
 
9.5%
) 54
 
9.5%
2 20
 
3.5%
5 19
 
3.4%
, 15
 
2.6%
3 15
 
2.6%
4 15
 
2.6%
0 14
 
2.5%
Other values (5) 45
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 885
61.0%
ASCII 567
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
261
46.0%
1 55
 
9.7%
( 54
 
9.5%
) 54
 
9.5%
2 20
 
3.5%
5 19
 
3.4%
, 15
 
2.6%
3 15
 
2.6%
4 15
 
2.6%
0 14
 
2.5%
Other values (5) 45
 
7.9%
Hangul
ValueCountFrequency (%)
정 73
 
8.2%
시 70
 
7.9%
북 69
 
7.8%
읍 68
 
7.7%
도 66
 
7.5%
전 62
 
7.0%
라 59
 
6.7%
동 56
 
6.3%
로 48
 
5.4%
지 19
 
2.1%
Other values (65) 295
33.3%

소재지지번주소
Text

Distinct32
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-16T15:41:01.701289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length19.711864
Min length15

Characters and Unicode

Total characters1163
Distinct characters75
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)15.3%

Sample

1st row전라북도 정읍시 수성동 517-8
2nd row전라북도 정읍시 구룡동 672-9
3rd row전라북도 정읍시 구룡동 487-2
4th row전라북도 정읍시 수성동 525-9
5th row전라북도 정읍시 구룡동 672-9 (유)두승산관광
ValueCountFrequency (%)
전라북도 59
23.0%
정읍시 59
23.0%
연지동 19
 
7.4%
시기동 11
 
4.3%
수성동 10
 
3.9%
구룡동 5
 
2.0%
525-9 4
 
1.6%
315-14 4
 
1.6%
672-9 3
 
1.2%
산 3
 
1.2%
Other values (47) 79
30.9%
2023-12-16T15:41:03.454354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
197
16.9%
시 72
 
6.2%
전 61
 
5.2%
북 61
 
5.2%
정 60
 
5.2%
도 59
 
5.1%
읍 59
 
5.1%
라 59
 
5.1%
- 56
 
4.8%
동 53
 
4.6%
Other values (65) 426
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 659
56.7%
Decimal Number 249
 
21.4%
Space Separator 197
 
16.9%
Dash Punctuation 56
 
4.8%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 72
10.9%
전 61
9.3%
북 61
9.3%
정 60
9.1%
도 59
9.0%
읍 59
9.0%
라 59
9.0%
동 53
 
8.0%
연 19
 
2.9%
지 19
 
2.9%
Other values (51) 137
20.8%
Decimal Number
ValueCountFrequency (%)
2 44
17.7%
1 40
16.1%
5 32
12.9%
3 30
12.0%
4 23
9.2%
9 21
8.4%
8 19
7.6%
7 18
7.2%
6 16
 
6.4%
0 6
 
2.4%
Space Separator
ValueCountFrequency (%)
197
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 659
56.7%
Common 504
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 72
10.9%
전 61
9.3%
북 61
9.3%
정 60
9.1%
도 59
9.0%
읍 59
9.0%
라 59
9.0%
동 53
 
8.0%
연 19
 
2.9%
지 19
 
2.9%
Other values (51) 137
20.8%
Common
ValueCountFrequency (%)
197
39.1%
- 56
 
11.1%
2 44
 
8.7%
1 40
 
7.9%
5 32
 
6.3%
3 30
 
6.0%
4 23
 
4.6%
9 21
 
4.2%
8 19
 
3.8%
7 18
 
3.6%
Other values (4) 24
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 659
56.7%
ASCII 504
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
197
39.1%
- 56
 
11.1%
2 44
 
8.7%
1 40
 
7.9%
5 32
 
6.3%
3 30
 
6.0%
4 23
 
4.6%
9 21
 
4.2%
8 19
 
3.8%
7 18
 
3.6%
Other values (4) 24
 
4.8%
Hangul
ValueCountFrequency (%)
시 72
10.9%
전 61
9.3%
북 61
9.3%
정 60
9.1%
도 59
9.0%
읍 59
9.0%
라 59
9.0%
동 53
 
8.0%
연 19
 
2.9%
지 19
 
2.9%
Other values (51) 137
20.8%

전화번호
Text

MISSING 

Distinct31
Distinct (%)56.4%
Missing4
Missing (%)6.8%
Memory size604.0 B
2023-12-16T15:41:04.389294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters660
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

Unique10 ?
Unique (%)18.2%

Sample

1st row063-535-6666
2nd row063-533-3373
3rd row063-532-8888
4th row063-536-2600
5th row063-533-9522
ValueCountFrequency (%)
063-536-2600 5
 
9.1%
063-532-6045 2
 
3.6%
063-536-6600 2
 
3.6%
063-535-6666 2
 
3.6%
063-538-6363 2
 
3.6%
063-537-8840 2
 
3.6%
063-535-5114 2
 
3.6%
063-538-2600 2
 
3.6%
063-533-5252 2
 
3.6%
063-531-2200 2
 
3.6%
Other values (21) 32
58.2%
2023-12-16T15:41:05.582894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 132
20.0%
- 110
16.7%
6 103
15.6%
0 96
14.5%
5 77
11.7%
2 43
 
6.5%
8 38
 
5.8%
7 22
 
3.3%
4 16
 
2.4%
1 14
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 550
83.3%
Dash Punctuation 110
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 132
24.0%
6 103
18.7%
0 96
17.5%
5 77
14.0%
2 43
 
7.8%
8 38
 
6.9%
7 22
 
4.0%
4 16
 
2.9%
1 14
 
2.5%
9 9
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 132
20.0%
- 110
16.7%
6 103
15.6%
0 96
14.5%
5 77
11.7%
2 43
 
6.5%
8 38
 
5.8%
7 22
 
3.3%
4 16
 
2.4%
1 14
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 132
20.0%
- 110
16.7%
6 103
15.6%
0 96
14.5%
5 77
11.7%
2 43
 
6.5%
8 38
 
5.8%
7 22
 
3.3%
4 16
 
2.4%
1 14
 
2.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
Minimum2022-12-02 00:00:00
Maximum2022-12-02 00:00:00
2023-12-16T15:41:06.227981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:41:06.774133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-16T15:41:07.378325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분등록일자상호소재지도로명주소소재지지번주소전화번호
구분1.0000.1710.0000.4560.4300.571
등록일자0.1711.0000.9990.9960.9980.999
상호0.0000.9991.0000.9960.9990.993
소재지도로명주소0.4560.9960.9961.0000.9990.999
소재지지번주소0.4300.9980.9990.9991.0000.996
전화번호0.5710.9990.9930.9990.9961.000

Missing values

2023-12-16T15:40:50.978680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:40:51.545522image/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

구분등록일자상호소재지도로명주소소재지지번주소전화번호데이터기준일자
0국내여행업1986-07-28(유)대원관광전라북도 정읍시 구미길 1 (수성동)전라북도 정읍시 수성동 517-8063-535-66662022-12-02
1국내여행업1988-05-09유)고려관광여행사전라북도 정읍시 정읍북로 50 (구룡동, 신흥주유소)전라북도 정읍시 구룡동 672-9063-533-33732022-12-02
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