Overview

Dataset statistics

Number of variables4
Number of observations53
Missing cells17
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory34.5 B

Variable types

Unsupported1
Text3

Dataset

Description서울특별시 중구 여행업 등록 현황(23.03.) : 국내여행업, 국내외여행업, 종합여행업 (상호명, 도로명주소, 사무실 전화번호)
URLhttps://www.data.go.kr/data/15113042/fileData.do

Alerts

국내여행업 등록 현황(23.3.기준) has 1 (1.9%) missing valuesMissing
Unnamed: 1 has 1 (1.9%) missing valuesMissing
Unnamed: 2 has 1 (1.9%) missing valuesMissing
Unnamed: 3 has 14 (26.4%) missing valuesMissing
국내여행업 등록 현황(23.3.기준) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 16:50:19.627339
Analysis finished2023-12-12 16:50:20.277875
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국내여행업 등록 현황(23.3.기준)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.9%
Memory size556.0 B

Unnamed: 1
Text

MISSING 

Distinct52
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Memory size556.0 B
2023-12-13T01:50:20.474720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.8846154
Min length2

Characters and Unicode

Total characters410
Distinct characters131
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st row상호
2nd row(주)광도관광여행사
3rd row(주)동성훼미리관광
4th row(주)야곱여행사
5th row우리테마투어
ValueCountFrequency (%)
주식회사 5
 
8.5%
상호 1
 
1.7%
호텔로드(주 1
 
1.7%
주)바네스투어 1
 
1.7%
주)동산항공여행사 1
 
1.7%
주)나라투어 1
 
1.7%
패스파인더 1
 
1.7%
봄봄여행사 1
 
1.7%
주)티라티에스 1
 
1.7%
노블스토리 1
 
1.7%
Other values (45) 45
76.3%
2023-12-13T01:50:20.976433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 41
 
10.0%
( 35
 
8.5%
) 35
 
8.5%
사 16
 
3.9%
스 15
 
3.7%
여 15
 
3.7%
행 15
 
3.7%
어 9
 
2.2%
리 9
 
2.2%
투 7
 
1.7%
Other values (121) 213
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 330
80.5%
Open Punctuation 35
 
8.5%
Close Punctuation 35
 
8.5%
Space Separator 7
 
1.7%
Uppercase Letter 2
 
0.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 41
 
12.4%
사 16
 
4.8%
스 15
 
4.5%
여 15
 
4.5%
행 15
 
4.5%
어 9
 
2.7%
리 9
 
2.7%
투 7
 
2.1%
블 7
 
2.1%
이 6
 
1.8%
Other values (115) 190
57.6%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
D 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 330
80.5%
Common 78
 
19.0%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 41
 
12.4%
사 16
 
4.8%
스 15
 
4.5%
여 15
 
4.5%
행 15
 
4.5%
어 9
 
2.7%
리 9
 
2.7%
투 7
 
2.1%
블 7
 
2.1%
이 6
 
1.8%
Other values (115) 190
57.6%
Common
ValueCountFrequency (%)
( 35
44.9%
) 35
44.9%
7
 
9.0%
& 1
 
1.3%
Latin
ValueCountFrequency (%)
H 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 330
80.5%
ASCII 80
 
19.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 41
 
12.4%
사 16
 
4.8%
스 15
 
4.5%
여 15
 
4.5%
행 15
 
4.5%
어 9
 
2.7%
리 9
 
2.7%
투 7
 
2.1%
블 7
 
2.1%
이 6
 
1.8%
Other values (115) 190
57.6%
ASCII
ValueCountFrequency (%)
( 35
43.8%
) 35
43.8%
7
 
8.8%
H 1
 
1.2%
D 1
 
1.2%
& 1
 
1.2%

Unnamed: 2
Text

MISSING 

Distinct52
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Memory size556.0 B
2023-12-13T01:50:21.361617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length40
Mean length35.903846
Min length8

Characters and Unicode

Total characters1867
Distinct characters154
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st row소재지(도로명)
2nd row서울특별시 중구 삼일대로 363, 1층 1호 (장교동, 장교빌딩)
3rd row서울특별시 중구 을지로1길 15, 서광빌딩 903호 (을지로1가)
4th row서울특별시 중구 명동길 80 (명동2가,카톨릭회관209호)
5th row서울특별시 중구 을지로1길 15 (을지로1가,서광빌딩703호)
ValueCountFrequency (%)
중구 51
 
13.9%
서울특별시 51
 
13.9%
다동 10
 
2.7%
40 8
 
2.2%
청계천로 8
 
2.2%
한국관광공사 6
 
1.6%
서울센터 5
 
1.4%
15 4
 
1.1%
서소문로 4
 
1.1%
퇴계로 4
 
1.1%
Other values (170) 217
59.0%
2023-12-13T01:50:21.950978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316
 
16.9%
1 77
 
4.1%
서 68
 
3.6%
, 63
 
3.4%
로 61
 
3.3%
울 56
 
3.0%
( 53
 
2.8%
) 53
 
2.8%
시 52
 
2.8%
구 51
 
2.7%
Other values (144) 1017
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1042
55.8%
Decimal Number 320
 
17.1%
Space Separator 316
 
16.9%
Other Punctuation 63
 
3.4%
Open Punctuation 53
 
2.8%
Close Punctuation 53
 
2.8%
Lowercase Letter 9
 
0.5%
Dash Punctuation 7
 
0.4%
Uppercase Letter 3
 
0.2%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 68
 
6.5%
로 61
 
5.9%
울 56
 
5.4%
시 52
 
5.0%
구 51
 
4.9%
중 51
 
4.9%
특 51
 
4.9%
별 51
 
4.9%
동 49
 
4.7%
호 49
 
4.7%
Other values (118) 503
48.3%
Decimal Number
ValueCountFrequency (%)
1 77
24.1%
0 48
15.0%
2 41
12.8%
4 31
9.7%
3 28
 
8.8%
7 22
 
6.9%
5 21
 
6.6%
6 20
 
6.2%
8 16
 
5.0%
9 16
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
33.3%
n 1
 
11.1%
t 1
 
11.1%
r 1
 
11.1%
l 1
 
11.1%
a 1
 
11.1%
c 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
P 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
316
100.0%
Other Punctuation
ValueCountFrequency (%)
, 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1042
55.8%
Common 813
43.5%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 68
 
6.5%
로 61
 
5.9%
울 56
 
5.4%
시 52
 
5.0%
구 51
 
4.9%
중 51
 
4.9%
특 51
 
4.9%
별 51
 
4.9%
동 49
 
4.7%
호 49
 
4.7%
Other values (118) 503
48.3%
Common
ValueCountFrequency (%)
316
38.9%
1 77
 
9.5%
, 63
 
7.7%
( 53
 
6.5%
) 53
 
6.5%
0 48
 
5.9%
2 41
 
5.0%
4 31
 
3.8%
3 28
 
3.4%
7 22
 
2.7%
Other values (6) 81
 
10.0%
Latin
ValueCountFrequency (%)
e 3
25.0%
n 1
 
8.3%
C 1
 
8.3%
t 1
 
8.3%
r 1
 
8.3%
P 1
 
8.3%
l 1
 
8.3%
a 1
 
8.3%
c 1
 
8.3%
S 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1042
55.8%
ASCII 825
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
38.3%
1 77
 
9.3%
, 63
 
7.6%
( 53
 
6.4%
) 53
 
6.4%
0 48
 
5.8%
2 41
 
5.0%
4 31
 
3.8%
3 28
 
3.4%
7 22
 
2.7%
Other values (16) 93
 
11.3%
Hangul
ValueCountFrequency (%)
서 68
 
6.5%
로 61
 
5.9%
울 56
 
5.4%
시 52
 
5.0%
구 51
 
4.9%
중 51
 
4.9%
특 51
 
4.9%
별 51
 
4.9%
동 49
 
4.7%
호 49
 
4.7%
Other values (118) 503
48.3%

Unnamed: 3
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing14
Missing (%)26.4%
Memory size556.0 B
2023-12-13T01:50:22.266321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.051282
Min length4

Characters and Unicode

Total characters392
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row779-5000
3rd row027557100
4th row755-3009
5th row733-0882
ValueCountFrequency (%)
전화번호 1
 
2.6%
02-738-3501 1
 
2.6%
02-2273-5800 1
 
2.6%
070-8679-2624 1
 
2.6%
027567373 1
 
2.6%
027577788 1
 
2.6%
15772151 1
 
2.6%
02-511-1746 1
 
2.6%
02-318-4410 1
 
2.6%
02-2272-6880 1
 
2.6%
Other values (29) 29
74.4%
2023-12-13T01:50:22.757406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 78
19.9%
7 53
13.5%
2 51
13.0%
3 37
9.4%
- 32
8.2%
5 28
 
7.1%
1 28
 
7.1%
8 25
 
6.4%
6 21
 
5.4%
4 21
 
5.4%
Other values (5) 18
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 356
90.8%
Dash Punctuation 32
 
8.2%
Other Letter 4
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 78
21.9%
7 53
14.9%
2 51
14.3%
3 37
10.4%
5 28
 
7.9%
1 28
 
7.9%
8 25
 
7.0%
6 21
 
5.9%
4 21
 
5.9%
9 14
 
3.9%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 388
99.0%
Hangul 4
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 78
20.1%
7 53
13.7%
2 51
13.1%
3 37
9.5%
- 32
8.2%
5 28
 
7.2%
1 28
 
7.2%
8 25
 
6.4%
6 21
 
5.4%
4 21
 
5.4%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388
99.0%
Hangul 4
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 78
20.1%
7 53
13.7%
2 51
13.1%
3 37
9.5%
- 32
8.2%
5 28
 
7.2%
1 28
 
7.2%
8 25
 
6.4%
6 21
 
5.4%
4 21
 
5.4%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Correlations

2023-12-13T01:50:22.903467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3
Unnamed: 11.0001.0001.000
Unnamed: 21.0001.0001.000
Unnamed: 31.0001.0001.000

Missing values

2023-12-13T01:50:19.969898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:50:20.087795image/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.
2023-12-13T01:50:20.198628image/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

국내여행업 등록 현황(23.3.기준)Unnamed: 1Unnamed: 2Unnamed: 3
0NaN<NA><NA><NA>
1순번상호소재지(도로명)전화번호
21(주)광도관광여행사서울특별시 중구 삼일대로 363, 1층 1호 (장교동, 장교빌딩)779-5000
32(주)동성훼미리관광서울특별시 중구 을지로1길 15, 서광빌딩 903호 (을지로1가)027557100
43(주)야곱여행사서울특별시 중구 명동길 80 (명동2가,카톨릭회관209호)755-3009
54우리테마투어서울특별시 중구 을지로1길 15 (을지로1가,서광빌딩703호)733-0882
65(주)해피시아 여행사서울특별시 중구 다동길 46 (다동,다동빌딩 1202호)732-7060
76(주)시온트래블서울특별시 중구 무교로 15, 남강건설회관빌딩 1001호 (무교동)027400731
87(주)주은항공서울특별시 중구 을지로3길 34, 601호 (다동, 산다빌딩)755-5900
98프레스티지투어&마케팅서울특별시 중구 퇴계로50가길 2 (묵정동,평화빌딩 301호)2264-0706
국내여행업 등록 현황(23.3.기준)Unnamed: 1Unnamed: 2Unnamed: 3
4342캠핑톡 주식회사서울특별시 중구 청계천로 40, 한국관광공사 서울센터 902호 (다동)07043361824
4443(주)쥬스컴퍼니서울특별시 중구 새문안로 26, 청양빌딩 10층 (충정로1가)<NA>
4544(주)팀즈코리아서울특별시 중구 서소문로 130, 대양빌딩 905호 (서소문동)025896333
4645워너비컴서울특별시 중구 서소문로 89, 순화빌딩 17층 S-1728호 (순화동)027577101
4746(주)프랜딧서울특별시 중구 청계천로 40, 한국관광공사 서울센터 712호 (다동)<NA>
4847(주)나라안여행서울특별시 중구 퇴계로27길 50, 진영빌딩 5층 (초동)07080641344
4948(주)필더필서울특별시 중구 청계천로 40, 한국관광공사 서울센터 12층 1207호 (다동)<NA>
5049(주)와이리서울특별시 중구 청계천로 40, 한국관광공사 서울센터 821호 (다동)<NA>
5150서울여행사서울특별시 중구 을지로44길 21, 501호 (광희동1가)02-2272-6880
5251주식회사 올어바웃서울특별시 중구 청계천로 40, 한국관광공사 서울센터 7층 8-에이호 (다동)07080984033