Overview

Dataset statistics

Number of variables23
Number of observations100
Missing cells441
Missing cells (%)19.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 KiB
Average record size in memory193.3 B

Variable types

Categorical9
Text5
Numeric4
Unsupported4
DateTime1

Alerts

BASE_YMD has constant value ""Constant
ctprvn_klang_nm is highly imbalanced (65.1%)Imbalance
ctprvn_engl_nm is highly imbalanced (65.1%)Imbalance
ctprvn_chnlng_nm is highly imbalanced (65.1%)Imbalance
ctprvn_jlang_nm is highly imbalanced (65.1%)Imbalance
tursm_goods_nm has 35 (35.0%) missing valuesMissing
bizcnd_nm has 100 (100.0%) missing valuesMissing
main_menu_nm has 100 (100.0%) missing valuesMissing
reqer_time has 100 (100.0%) missing valuesMissing
dstne has 100 (100.0%) missing valuesMissing
hmpg_url has 5 (5.0%) missing valuesMissing
bizcnd_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
main_menu_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
reqer_time is an unsupported type, check if it needs cleaning or further analysisUnsupported
dstne is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:43:09.393015
Analysis finished2023-12-10 09:43:10.488783
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)
65 
한국관광품질 인증 받은 쇼핑센터
32 
관광명품 매장
 
3

Length

Max length43
Median length43
Mean length33.6
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한국관광품질 인증 받은 쇼핑센터
2nd row한국관광품질 인증 받은 쇼핑센터
3rd row한국관광품질 인증 받은 쇼핑센터
4th row한국관광품질 인증 받은 쇼핑센터
5th row한국관광품질 인증 받은 쇼핑센터

Common Values

ValueCountFrequency (%)
KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020) 65
65.0%
한국관광품질 인증 받은 쇼핑센터 32
32.0%
관광명품 매장 3
 
3.0%

Length

2023-12-10T18:43:10.644856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:10.885780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kata에서 65
11.0%
우수 65
11.0%
여행상품 65
11.0%
인증받은 65
11.0%
국내여행상품 65
11.0%
매장정보 65
11.0%
2019-2020 65
11.0%
한국관광품질 32
5.4%
인증 32
5.4%
받은 32
5.4%
Other values (3) 38
6.5%

entrp_nm
Text

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:11.365549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length8.04
Min length2

Characters and Unicode

Total characters804
Distinct characters173
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)65.0%

Sample

1st row주식회사 청운에스피
2nd row플랫폼 플레이스 한남점
3rd row갈라 프리스비 명동점
4th row한국다원호간보
5th row세란안경
ValueCountFrequency (%)
하나투어 5
 
3.6%
롯데제이티비 4
 
2.9%
주식회사 4
 
2.9%
판문점트레블센타 3
 
2.2%
㈜정호여행사 3
 
2.2%
삼호투어앤트래블 3
 
2.2%
주)하나투어아이티씨 3
 
2.2%
서울 3
 
2.2%
롯데쇼핑 2
 
1.5%
롯데백화점 2
 
1.5%
Other values (92) 105
76.6%
2023-12-10T18:43:12.463133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
4.6%
주 32
 
4.0%
어 29
 
3.6%
투 28
 
3.5%
( 24
 
3.0%
㈜ 24
 
3.0%
사 24
 
3.0%
) 24
 
3.0%
이 21
 
2.6%
점 21
 
2.6%
Other values (163) 540
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 686
85.3%
Space Separator 37
 
4.6%
Open Punctuation 24
 
3.0%
Other Symbol 24
 
3.0%
Close Punctuation 24
 
3.0%
Uppercase Letter 8
 
1.0%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 32
 
4.7%
어 29
 
4.2%
투 28
 
4.1%
사 24
 
3.5%
이 21
 
3.1%
점 21
 
3.1%
행 20
 
2.9%
여 20
 
2.9%
스 19
 
2.8%
아 14
 
2.0%
Other values (152) 458
66.8%
Uppercase Letter
ValueCountFrequency (%)
Y 2
25.0%
M 2
25.0%
C 1
12.5%
I 1
12.5%
K 1
12.5%
H 1
12.5%
Space Separator
ValueCountFrequency (%)
37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 710
88.3%
Common 86
 
10.7%
Latin 8
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 32
 
4.5%
어 29
 
4.1%
투 28
 
3.9%
㈜ 24
 
3.4%
사 24
 
3.4%
이 21
 
3.0%
점 21
 
3.0%
행 20
 
2.8%
여 20
 
2.8%
스 19
 
2.7%
Other values (153) 472
66.5%
Latin
ValueCountFrequency (%)
Y 2
25.0%
M 2
25.0%
C 1
12.5%
I 1
12.5%
K 1
12.5%
H 1
12.5%
Common
ValueCountFrequency (%)
37
43.0%
( 24
27.9%
) 24
27.9%
. 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 686
85.3%
ASCII 94
 
11.7%
None 24
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
39.4%
( 24
25.5%
) 24
25.5%
Y 2
 
2.1%
M 2
 
2.1%
C 1
 
1.1%
I 1
 
1.1%
K 1
 
1.1%
. 1
 
1.1%
H 1
 
1.1%
Hangul
ValueCountFrequency (%)
주 32
 
4.7%
어 29
 
4.2%
투 28
 
4.1%
사 24
 
3.5%
이 21
 
3.1%
점 21
 
3.1%
행 20
 
2.9%
여 20
 
2.9%
스 19
 
2.8%
아 14
 
2.0%
Other values (152) 458
66.8%
None
ValueCountFrequency (%)
㈜ 24
100.0%

rn_adres
Text

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:12.969131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length33
Mean length25.11
Min length15

Characters and Unicode

Total characters2511
Distinct characters177
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)56.0%

Sample

1st row서울특별시 서대문구 모래내로 235
2nd row서울특별시 용산구 이태원로 268
3rd row서울특별시 중구 명동8길 11
4th row서울특별시 마포구 방울내로 83
5th row서울특별시 중구 을지로 30
ValueCountFrequency (%)
서울특별시 83
 
15.4%
중구 31
 
5.8%
종로구 27
 
5.0%
용산구 11
 
2.0%
인사동 9
 
1.7%
2층 9
 
1.7%
서소문동 8
 
1.5%
703호 6
 
1.1%
5길 6
 
1.1%
71 5
 
0.9%
Other values (199) 343
63.8%
2023-12-10T18:43:14.192072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438
 
17.4%
로 108
 
4.3%
서 103
 
4.1%
구 100
 
4.0%
시 99
 
3.9%
1 91
 
3.6%
특 87
 
3.5%
별 87
 
3.5%
울 84
 
3.3%
2 77
 
3.1%
Other values (167) 1237
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1528
60.9%
Decimal Number 439
 
17.5%
Space Separator 438
 
17.4%
Uppercase Letter 37
 
1.5%
Open Punctuation 22
 
0.9%
Dash Punctuation 22
 
0.9%
Close Punctuation 21
 
0.8%
Other Punctuation 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 108
 
7.1%
서 103
 
6.7%
구 100
 
6.5%
시 99
 
6.5%
특 87
 
5.7%
별 87
 
5.7%
울 84
 
5.5%
동 53
 
3.5%
층 37
 
2.4%
빌 37
 
2.4%
Other values (140) 733
48.0%
Uppercase Letter
ValueCountFrequency (%)
K 6
16.2%
G 4
10.8%
D 3
8.1%
X 3
8.1%
T 3
8.1%
L 3
8.1%
I 3
8.1%
B 3
8.1%
A 3
8.1%
R 3
8.1%
Decimal Number
ValueCountFrequency (%)
1 91
20.7%
2 77
17.5%
0 44
10.0%
5 43
9.8%
4 41
9.3%
3 38
8.7%
8 34
 
7.7%
6 27
 
6.2%
7 24
 
5.5%
9 20
 
4.6%
Space Separator
ValueCountFrequency (%)
438
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1528
60.9%
Common 946
37.7%
Latin 37
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 108
 
7.1%
서 103
 
6.7%
구 100
 
6.5%
시 99
 
6.5%
특 87
 
5.7%
별 87
 
5.7%
울 84
 
5.5%
동 53
 
3.5%
층 37
 
2.4%
빌 37
 
2.4%
Other values (140) 733
48.0%
Common
ValueCountFrequency (%)
438
46.3%
1 91
 
9.6%
2 77
 
8.1%
0 44
 
4.7%
5 43
 
4.5%
4 41
 
4.3%
3 38
 
4.0%
8 34
 
3.6%
6 27
 
2.9%
7 24
 
2.5%
Other values (6) 89
 
9.4%
Latin
ValueCountFrequency (%)
K 6
16.2%
G 4
10.8%
D 3
8.1%
X 3
8.1%
T 3
8.1%
L 3
8.1%
I 3
8.1%
B 3
8.1%
A 3
8.1%
R 3
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1528
60.9%
ASCII 983
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
438
44.6%
1 91
 
9.3%
2 77
 
7.8%
0 44
 
4.5%
5 43
 
4.4%
4 41
 
4.2%
3 38
 
3.9%
8 34
 
3.5%
6 27
 
2.7%
7 24
 
2.4%
Other values (17) 126
 
12.8%
Hangul
ValueCountFrequency (%)
로 108
 
7.1%
서 103
 
6.7%
구 100
 
6.5%
시 99
 
6.5%
특 87
 
5.7%
별 87
 
5.7%
울 84
 
5.5%
동 53
 
3.5%
층 37
 
2.4%
빌 37
 
2.4%
Other values (140) 733
48.0%

ctprvn_klang_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
84 
부산광역시
 
4
제주특별자치도
 
4
인천광역시
 
3
대구광역시
 
2
Other values (3)
 
3

Length

Max length7
Median length5
Mean length5.05
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 84
84.0%
부산광역시 4
 
4.0%
제주특별자치도 4
 
4.0%
인천광역시 3
 
3.0%
대구광역시 2
 
2.0%
전라남도 1
 
1.0%
광주광역시 1
 
1.0%
경기도 1
 
1.0%

Length

2023-12-10T18:43:14.603870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:14.923993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 84
84.0%
부산광역시 4
 
4.0%
제주특별자치도 4
 
4.0%
인천광역시 3
 
3.0%
대구광역시 2
 
2.0%
전라남도 1
 
1.0%
광주광역시 1
 
1.0%
경기도 1
 
1.0%

signgn_klang_nm
Categorical

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중구
31 
종로구
27 
용산구
11 
강남구
4 
제주시
4 
Other values (15)
23 

Length

Max length4
Median length3
Mean length2.74
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row서대문구
2nd row용산구
3rd row중구
4th row마포구
5th row중구

Common Values

ValueCountFrequency (%)
중구 31
31.0%
종로구 27
27.0%
용산구 11
 
11.0%
강남구 4
 
4.0%
제주시 4
 
4.0%
마포구 3
 
3.0%
금천구 3
 
3.0%
영등포구 2
 
2.0%
부평구 2
 
2.0%
송파구 2
 
2.0%
Other values (10) 11
 
11.0%

Length

2023-12-10T18:43:15.180274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 31
31.0%
종로구 27
27.0%
용산구 11
 
11.0%
강남구 4
 
4.0%
제주시 4
 
4.0%
마포구 3
 
3.0%
금천구 3
 
3.0%
송파구 2
 
2.0%
서대문구 2
 
2.0%
부평구 2
 
2.0%
Other values (10) 11
 
11.0%

ctprvn_engl_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Seoul
84 
Busan
 
4
Jeju
 
4
Incheon
 
3
Daegu
 
2
Other values (3)
 
3

Length

Max length12
Median length5
Mean length5.17
Min length4

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
Seoul 84
84.0%
Busan 4
 
4.0%
Jeju 4
 
4.0%
Incheon 3
 
3.0%
Daegu 2
 
2.0%
Jeollanam-do 1
 
1.0%
Gwangju 1
 
1.0%
Gyeonggi-do 1
 
1.0%

Length

2023-12-10T18:43:15.460830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:15.718301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 84
84.0%
busan 4
 
4.0%
jeju 4
 
4.0%
incheon 3
 
3.0%
daegu 2
 
2.0%
jeollanam-do 1
 
1.0%
gwangju 1
 
1.0%
gyeonggi-do 1
 
1.0%

signgn_engl_nm
Categorical

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Jung-gu
31 
Jongno-gu
27 
Yongsan-gu
11 
Gangnam-gu
4 
Jeju-si
4 
Other values (15)
23 

Length

Max length15
Median length13
Mean length8.8
Min length6

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st rowSeodaemun-gu
2nd rowYongsan-gu
3rd rowJung-gu
4th rowMapo-gu
5th rowJung-gu

Common Values

ValueCountFrequency (%)
Jung-gu 31
31.0%
Jongno-gu 27
27.0%
Yongsan-gu 11
 
11.0%
Gangnam-gu 4
 
4.0%
Jeju-si 4
 
4.0%
Mapo-gu 3
 
3.0%
Geumcheon-gu 3
 
3.0%
Yeongdeungpo-gu 2
 
2.0%
Bupyeong-gu 2
 
2.0%
Songpa-gu 2
 
2.0%
Other values (10) 11
 
11.0%

Length

2023-12-10T18:43:16.322624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jung-gu 31
31.0%
jongno-gu 27
27.0%
yongsan-gu 11
 
11.0%
gangnam-gu 4
 
4.0%
jeju-si 4
 
4.0%
mapo-gu 3
 
3.0%
geumcheon-gu 3
 
3.0%
songpa-gu 2
 
2.0%
seodaemun-gu 2
 
2.0%
bupyeong-gu 2
 
2.0%
Other values (10) 11
 
11.0%

ctprvn_chnlng_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
首爾特別市
84 
釜山廣域市
 
4
濟州特別自治道
 
4
仁川廣域市
 
3
大邱廣域市
 
2
Other values (3)
 
3

Length

Max length7
Median length5
Mean length5.05
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row首爾特別市
2nd row首爾特別市
3rd row首爾特別市
4th row首爾特別市
5th row首爾特別市

Common Values

ValueCountFrequency (%)
首爾特別市 84
84.0%
釜山廣域市 4
 
4.0%
濟州特別自治道 4
 
4.0%
仁川廣域市 3
 
3.0%
大邱廣域市 2
 
2.0%
全羅南道 1
 
1.0%
光州廣域市 1
 
1.0%
京畿道 1
 
1.0%

Length

2023-12-10T18:43:16.607814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:16.850968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
首爾特別市 84
84.0%
釜山廣域市 4
 
4.0%
濟州特別自治道 4
 
4.0%
仁川廣域市 3
 
3.0%
大邱廣域市 2
 
2.0%
全羅南道 1
 
1.0%
光州廣域市 1
 
1.0%
京畿道 1
 
1.0%

signgn_chnlng_nm
Categorical

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
中區
31 
鍾路區
27 
龍山區
11 
江南區
4 
濟州市
4 
Other values (15)
23 

Length

Max length4
Median length3
Mean length2.74
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row西大門區
2nd row龍山區
3rd row中區
4th row麻浦區
5th row中區

Common Values

ValueCountFrequency (%)
中區 31
31.0%
鍾路區 27
27.0%
龍山區 11
 
11.0%
江南區 4
 
4.0%
濟州市 4
 
4.0%
麻浦區 3
 
3.0%
衿川區 3
 
3.0%
永登浦區 2
 
2.0%
富平區 2
 
2.0%
松坡區 2
 
2.0%
Other values (10) 11
 
11.0%

Length

2023-12-10T18:43:17.201104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
中區 31
31.0%
鍾路區 27
27.0%
龍山區 11
 
11.0%
江南區 4
 
4.0%
濟州市 4
 
4.0%
麻浦區 3
 
3.0%
衿川區 3
 
3.0%
松坡區 2
 
2.0%
西大門區 2
 
2.0%
富平區 2
 
2.0%
Other values (10) 11
 
11.0%

ctprvn_jlang_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ソウル特別市
84 
釜山（プサン）広域市
 
4
済州（チェジュ）特別自治道（トゥッピョルチャチド）
 
4
仁川（インチョン）広域市
 
3
大邱（テグ）広域市
 
2
Other values (3)
 
3

Length

Max length25
Median length6
Mean length7.26
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowソウル特別市
2nd rowソウル特別市
3rd rowソウル特別市
4th rowソウル特別市
5th rowソウル特別市

Common Values

ValueCountFrequency (%)
ソウル特別市 84
84.0%
釜山（プサン）広域市 4
 
4.0%
済州（チェジュ）特別自治道（トゥッピョルチャチド） 4
 
4.0%
仁川（インチョン）広域市 3
 
3.0%
大邱（テグ）広域市 2
 
2.0%
全羅南道（チョルラナムド） 1
 
1.0%
光州（クァンジュ）広域市 1
 
1.0%
京畿道 1
 
1.0%

Length

2023-12-10T18:43:17.461298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:17.743300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ソウル特別市 84
84.0%
釜山（プサン）広域市 4
 
4.0%
済州（チェジュ）特別自治道（トゥッピョルチャチド） 4
 
4.0%
仁川（インチョン）広域市 3
 
3.0%
大邱（テグ）広域市 2
 
2.0%
全羅南道（チョルラナムド） 1
 
1.0%
光州（クァンジュ）広域市 1
 
1.0%
京畿道 1
 
1.0%

signgn_jlang_nm
Categorical

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
中区（チュング）
31 
鍾路区（チョンノグ）
27 
龍山区（ヨンサング）
11 
江南区（カンナムグ）
4 
済州市（チェジュシ）
4 
Other values (15)
23 

Length

Max length12
Median length11.5
Mean length9.36
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row西大門区（ソデムング）
2nd row龍山区（ヨンサング）
3rd row中区（チュング）
4th row麻浦区（マポグ）
5th row中区（チュング）

Common Values

ValueCountFrequency (%)
中区（チュング） 31
31.0%
鍾路区（チョンノグ） 27
27.0%
龍山区（ヨンサング） 11
 
11.0%
江南区（カンナムグ） 4
 
4.0%
済州市（チェジュシ） 4
 
4.0%
麻浦区（マポグ） 3
 
3.0%
衿川区（クムチョング） 3
 
3.0%
永登浦区（ヨンドンポク） 2
 
2.0%
富平区（プピョング） 2
 
2.0%
松坡区（ソンパグ） 2
 
2.0%
Other values (10) 11
 
11.0%

Length

2023-12-10T18:43:18.000615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
中区（チュング） 31
31.0%
鍾路区（チョンノグ） 27
27.0%
龍山区（ヨンサング） 11
 
11.0%
江南区（カンナムグ） 4
 
4.0%
済州市（チェジュシ） 4
 
4.0%
麻浦区（マポグ） 3
 
3.0%
衿川区（クムチョング） 3
 
3.0%
松坡区（ソンパグ） 2
 
2.0%
西大門区（ソデムング） 2
 
2.0%
富平区（プピョング） 2
 
2.0%
Other values (10) 11
 
11.0%

city_do_cd
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.68
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:18.182466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q311
95-th percentile31.25
Maximum39
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.8797463
Coefficient of variation (CV)0.50290543
Kurtosis6.6876034
Mean13.68
Median Absolute Deviation (MAD)0
Skewness2.7183251
Sum1368
Variance47.330909
MonotonicityNot monotonic
2023-12-10T18:43:18.467212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11 84
84.0%
21 4
 
4.0%
39 4
 
4.0%
23 3
 
3.0%
22 2
 
2.0%
36 1
 
1.0%
24 1
 
1.0%
31 1
 
1.0%
ValueCountFrequency (%)
11 84
84.0%
21 4
 
4.0%
22 2
 
2.0%
23 3
 
3.0%
24 1
 
1.0%
31 1
 
1.0%
36 1
 
1.0%
39 4
 
4.0%
ValueCountFrequency (%)
39 4
 
4.0%
36 1
 
1.0%
31 1
 
1.0%
24 1
 
1.0%
23 3
 
3.0%
22 2
 
2.0%
21 4
 
4.0%
11 84
84.0%

city_gn_gu_cd
Real number (ℝ)

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13737.4
Minimum11010
Maximum39010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:18.745442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11010
Q111010
median11020
Q311182.5
95-th percentile31317
Maximum39010
Range28000
Interquartile range (IQR)172.5

Descriptive statistics

Standard deviation6888.4995
Coefficient of variation (CV)0.50144129
Kurtosis6.6544065
Mean13737.4
Median Absolute Deviation (MAD)10
Skewness2.7136673
Sum1373740
Variance47451425
MonotonicityNot monotonic
2023-12-10T18:43:18.968691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
11020 28
28.0%
11010 27
27.0%
11030 11
 
11.0%
11230 4
 
4.0%
39010 4
 
4.0%
11140 3
 
3.0%
11180 3
 
3.0%
11130 2
 
2.0%
22010 2
 
2.0%
11240 2
 
2.0%
Other values (12) 14
14.0%
ValueCountFrequency (%)
11010 27
27.0%
11020 28
28.0%
11030 11
 
11.0%
11060 1
 
1.0%
11130 2
 
2.0%
11140 3
 
3.0%
11180 3
 
3.0%
11190 2
 
2.0%
11220 1
 
1.0%
11230 4
 
4.0%
ValueCountFrequency (%)
39010 4
4.0%
36390 1
 
1.0%
31050 1
 
1.0%
24020 1
 
1.0%
23320 1
 
1.0%
23060 2
2.0%
22010 2
2.0%
21130 1
 
1.0%
21110 1
 
1.0%
21090 1
 
1.0%

lo
Real number (ℝ)

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.06509
Minimum124.72122
Maximum129.14645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:19.254743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.72122
5-th percentile126.70077
Q1126.96852
median126.98142
Q3126.99836
95-th percentile128.60941
Maximum129.14645
Range4.4252325
Interquartile range (IQR)0.029841075

Descriptive statistics

Standard deviation0.57520184
Coefficient of variation (CV)0.0045268282
Kurtosis8.9955435
Mean127.06509
Median Absolute Deviation (MAD)0.01559615
Skewness1.8393427
Sum12706.509
Variance0.33085716
MonotonicityNot monotonic
2023-12-10T18:43:19.560818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9841571 5
 
5.0%
126.9733797 4
 
4.0%
126.9889103 4
 
4.0%
126.9811419 4
 
4.0%
126.9853831 3
 
3.0%
126.9694115 3
 
3.0%
126.9765031 3
 
3.0%
126.7099395 2
 
2.0%
126.9658264 2
 
2.0%
126.9983563 2
 
2.0%
Other values (60) 68
68.0%
ValueCountFrequency (%)
124.7212187 1
1.0%
126.4652862 1
1.0%
126.5233103 2
2.0%
126.5266298 1
1.0%
126.7099395 2
2.0%
126.7527789 1
1.0%
126.8012561 1
1.0%
126.883294 2
2.0%
126.8871266 1
1.0%
126.8872538 1
1.0%
ValueCountFrequency (%)
129.1464512 1
1.0%
129.0879411 1
1.0%
129.0728464 1
1.0%
129.037238 1
1.0%
129.0350539 1
1.0%
128.5870122 2
2.0%
127.1026097 1
1.0%
127.0980274 1
1.0%
127.0444024 1
1.0%
127.0328014 1
1.0%

la
Real number (ℝ)

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.188558
Minimum33.500041
Maximum37.970253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:19.856210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.500041
5-th percentile35.073677
Q137.527445
median37.562536
Q337.571814
95-th percentile37.578342
Maximum37.970253
Range4.4702115
Interquartile range (IQR)0.044368825

Descriptive statistics

Standard deviation1.0149989
Coefficient of variation (CV)0.027293312
Kurtosis6.023185
Mean37.188558
Median Absolute Deviation (MAD)0.0100387
Skewness-2.6608722
Sum3718.8558
Variance1.0302228
MonotonicityNot monotonic
2023-12-10T18:43:20.221076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5718136 5
 
5.0%
37.5630992 4
 
4.0%
37.5767677 4
 
4.0%
37.5725168 4
 
4.0%
37.5720602 3
 
3.0%
37.5528154 3
 
3.0%
37.5681809 3
 
3.0%
37.4840349 2
 
2.0%
37.5524394 2
 
2.0%
37.5724511 2
 
2.0%
Other values (60) 68
68.0%
ValueCountFrequency (%)
33.5000413 2
2.0%
33.5018336 1
1.0%
33.5020951 1
1.0%
34.5066833 1
1.0%
35.103519 1
1.0%
35.1090506 1
1.0%
35.1584328 1
1.0%
35.1608822 1
1.0%
35.1886688 1
1.0%
35.2303805 1
1.0%
ValueCountFrequency (%)
37.9702528 1
 
1.0%
37.5928788 1
 
1.0%
37.5905069 1
 
1.0%
37.5872135 1
 
1.0%
37.5821726 1
 
1.0%
37.5781401 1
 
1.0%
37.5767677 4
4.0%
37.5758466 1
 
1.0%
37.5750807 1
 
1.0%
37.574546 1
 
1.0%

tursm_goods_nm
Text

MISSING 

Distinct65
Distinct (%)100.0%
Missing35
Missing (%)35.0%
Memory size932.0 B
2023-12-10T18:43:20.689171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length36
Mean length26.769231
Min length9

Characters and Unicode

Total characters1740
Distinct characters322
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)100.0%

Sample

1st row최고급 럭셔리 패키지
2nd row[★신라스테이★리무진버스 힐링 치유여행 2박3일]
3rd rowDMZ 관광 고성여행(1박2일)
4th row전남맛기행! 장흥강진맛기행
5th row[경북시티투어] 능선따라~골목따라 고령/대구 2일
ValueCountFrequency (%)
19
 
5.7%
korea 9
 
2.7%
tour 8
 
2.4%
당일 4
 
1.2%
2박3일 4
 
1.2%
여행 4
 
1.2%
투어 4
 
1.2%
부산 3
 
0.9%
명품여행◈「2박3일」 3
 
0.9%
하는 3
 
0.9%
Other values (243) 271
81.6%
2023-12-10T18:43:21.580564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
267
 
15.3%
일 46
 
2.6%
박 28
 
1.6%
o 27
 
1.6%
) 24
 
1.4%
( 24
 
1.4%
a 22
 
1.3%
행 21
 
1.2%
r 20
 
1.1%
e 19
 
1.1%
Other values (312) 1242
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 875
50.3%
Space Separator 267
 
15.3%
Lowercase Letter 185
 
10.6%
Uppercase Letter 183
 
10.5%
Decimal Number 72
 
4.1%
Close Punctuation 45
 
2.6%
Open Punctuation 45
 
2.6%
Other Punctuation 23
 
1.3%
Math Symbol 21
 
1.2%
Dash Punctuation 13
 
0.7%
Other values (4) 11
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 46
 
5.3%
박 28
 
3.2%
행 21
 
2.4%
여 18
 
2.1%
도 16
 
1.8%
해 16
 
1.8%
산 15
 
1.7%
이 14
 
1.6%
는 14
 
1.6%
어 13
 
1.5%
Other values (238) 674
77.0%
Lowercase Letter
ValueCountFrequency (%)
o 27
14.6%
a 22
11.9%
r 20
10.8%
e 19
10.3%
u 15
8.1%
n 14
7.6%
i 12
 
6.5%
s 8
 
4.3%
m 7
 
3.8%
l 7
 
3.8%
Other values (12) 34
18.4%
Uppercase Letter
ValueCountFrequency (%)
T 16
 
8.7%
K 15
 
8.2%
O 15
 
8.2%
D 12
 
6.6%
N 12
 
6.6%
E 11
 
6.0%
R 11
 
6.0%
A 11
 
6.0%
U 10
 
5.5%
P 10
 
5.5%
Other values (12) 60
32.8%
Decimal Number
ValueCountFrequency (%)
2 19
26.4%
3 15
20.8%
1 13
18.1%
4 5
 
6.9%
5 4
 
5.6%
6 4
 
5.6%
0 4
 
5.6%
8 4
 
5.6%
9 2
 
2.8%
7 2
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/ 15
65.2%
& 4
 
17.4%
! 2
 
8.7%
* 2
 
8.7%
Close Punctuation
ValueCountFrequency (%)
) 24
53.3%
] 18
40.0%
」 3
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 24
53.3%
[ 18
40.0%
「 3
 
6.7%
Math Symbol
ValueCountFrequency (%)
+ 16
76.2%
~ 4
 
19.0%
| 1
 
4.8%
Other Symbol
ValueCountFrequency (%)
◈ 6
75.0%
★ 2
 
25.0%
Space Separator
ValueCountFrequency (%)
267
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 874
50.2%
Common 496
28.5%
Latin 369
21.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 46
 
5.3%
박 28
 
3.2%
행 21
 
2.4%
여 18
 
2.1%
도 16
 
1.8%
해 16
 
1.8%
산 15
 
1.7%
이 14
 
1.6%
는 14
 
1.6%
어 13
 
1.5%
Other values (237) 673
77.0%
Latin
ValueCountFrequency (%)
o 27
 
7.3%
a 22
 
6.0%
r 20
 
5.4%
e 19
 
5.1%
T 16
 
4.3%
K 15
 
4.1%
u 15
 
4.1%
O 15
 
4.1%
n 14
 
3.8%
D 12
 
3.3%
Other values (35) 194
52.6%
Common
ValueCountFrequency (%)
267
53.8%
) 24
 
4.8%
( 24
 
4.8%
2 19
 
3.8%
] 18
 
3.6%
[ 18
 
3.6%
+ 16
 
3.2%
/ 15
 
3.0%
3 15
 
3.0%
1 13
 
2.6%
Other values (19) 67
 
13.5%
Han
ValueCountFrequency (%)
路 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 874
50.2%
ASCII 848
48.7%
Geometric Shapes 6
 
0.3%
None 6
 
0.3%
Misc Symbols 2
 
0.1%
Punctuation 2
 
0.1%
CJK 1
 
0.1%
Number Forms 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
267
31.5%
o 27
 
3.2%
) 24
 
2.8%
( 24
 
2.8%
a 22
 
2.6%
r 20
 
2.4%
e 19
 
2.2%
2 19
 
2.2%
] 18
 
2.1%
[ 18
 
2.1%
Other values (57) 390
46.0%
Hangul
ValueCountFrequency (%)
일 46
 
5.3%
박 28
 
3.2%
행 21
 
2.4%
여 18
 
2.1%
도 16
 
1.8%
해 16
 
1.8%
산 15
 
1.7%
이 14
 
1.6%
는 14
 
1.6%
어 13
 
1.5%
Other values (237) 673
77.0%
Geometric Shapes
ValueCountFrequency (%)
◈ 6
100.0%
None
ValueCountFrequency (%)
「 3
50.0%
」 3
50.0%
Misc Symbols
ValueCountFrequency (%)
★ 2
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
50.0%
‘ 1
50.0%
CJK
ValueCountFrequency (%)
路 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

bizcnd_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

main_menu_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

reqer_time
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

dstne
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

hmpg_url
Text

MISSING 

Distinct65
Distinct (%)68.4%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T18:43:22.048737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length35
Mean length26.494737
Min length16

Characters and Unicode

Total characters2517
Distinct characters42
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

Unique47 ?
Unique (%)49.5%

Sample

1st rowhttp://www.chunsp.com
2nd rowhttp://www.platformshop.co.kr
3rd rowhttp://www.frisbeekorea.com
4th rowhttp://dawonkorea.co.kr/
5th rowhttp://blog.naver.com/saeran777
ValueCountFrequency (%)
http://www.hanatour.com 5
 
5.3%
http://www.lottejtb.com 4
 
4.2%
https://www.usajutour.com 4
 
4.2%
http://www.hanatouritc.com 4
 
4.2%
http://dawonkorea.co.kr 3
 
3.2%
http://www.samhotour.com 3
 
3.2%
http://www.korailtravel.com 3
 
3.2%
http://www.koreadmztour.com 3
 
3.2%
http://www.theoneyiwatravel.com 2
 
2.1%
http://www.saint-james.co.kr 2
 
2.1%
Other values (54) 62
65.3%
2023-12-10T18:43:22.857618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 291
11.6%
/ 261
 
10.4%
w 253
 
10.1%
. 222
 
8.8%
o 208
 
8.3%
r 138
 
5.5%
h 128
 
5.1%
p 112
 
4.4%
c 106
 
4.2%
: 94
 
3.7%
Other values (32) 704
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1896
75.3%
Other Punctuation 577
 
22.9%
Decimal Number 38
 
1.5%
Dash Punctuation 2
 
0.1%
Connector Punctuation 2
 
0.1%
Other Letter 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 291
15.3%
w 253
13.3%
o 208
11.0%
r 138
 
7.3%
h 128
 
6.8%
p 112
 
5.9%
c 106
 
5.6%
a 86
 
4.5%
m 84
 
4.4%
e 81
 
4.3%
Other values (15) 409
21.6%
Decimal Number
ValueCountFrequency (%)
0 7
18.4%
8 5
13.2%
1 5
13.2%
3 5
13.2%
7 5
13.2%
2 4
10.5%
6 3
7.9%
4 2
 
5.3%
5 1
 
2.6%
9 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 261
45.2%
. 222
38.5%
: 94
 
16.3%
Other Letter
ValueCountFrequency (%)
청 1
50.0%
자 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1896
75.3%
Common 619
 
24.6%
Hangul 2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 291
15.3%
w 253
13.3%
o 208
11.0%
r 138
 
7.3%
h 128
 
6.8%
p 112
 
5.9%
c 106
 
5.6%
a 86
 
4.5%
m 84
 
4.4%
e 81
 
4.3%
Other values (15) 409
21.6%
Common
ValueCountFrequency (%)
/ 261
42.2%
. 222
35.9%
: 94
 
15.2%
0 7
 
1.1%
8 5
 
0.8%
1 5
 
0.8%
3 5
 
0.8%
7 5
 
0.8%
2 4
 
0.6%
6 3
 
0.5%
Other values (5) 8
 
1.3%
Hangul
ValueCountFrequency (%)
청 1
50.0%
자 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2515
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 291
11.6%
/ 261
 
10.4%
w 253
 
10.1%
. 222
 
8.8%
o 208
 
8.3%
r 138
 
5.5%
h 128
 
5.1%
p 112
 
4.5%
c 106
 
4.2%
: 94
 
3.7%
Other values (30) 702
27.9%
Hangul
ValueCountFrequency (%)
청 1
50.0%
자 1
50.0%

tel_no
Text

Distinct71
Distinct (%)71.7%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:43:23.338956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length11
Min length9

Characters and Unicode

Total characters1089
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)55.6%

Sample

1st row02-373-8898
2nd row02-797-4628
3rd row02-318-7120
4th row02-325-3893
5th row02-777-6789
ValueCountFrequency (%)
1577-1233 5
 
5.0%
02-365-1500 4
 
4.0%
02-522-8686 4
 
4.0%
1577-6511 4
 
4.0%
02-771-5593 3
 
3.0%
02-722-3575 3
 
3.0%
1544-7755 3
 
3.0%
02-717-1002 2
 
2.0%
02-323-6850 2
 
2.0%
02-2260-2000 2
 
2.0%
Other values (62) 68
68.0%
2023-12-10T18:43:24.077629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 176
16.2%
0 160
14.7%
2 158
14.5%
7 112
10.3%
5 101
9.3%
1 92
8.4%
3 74
6.8%
6 70
 
6.4%
4 59
 
5.4%
8 56
 
5.1%
Other values (4) 31
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 909
83.5%
Dash Punctuation 176
 
16.2%
Other Punctuation 2
 
0.2%
Control 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
17.6%
2 158
17.4%
7 112
12.3%
5 101
11.1%
1 92
10.1%
3 74
8.1%
6 70
7.7%
4 59
 
6.5%
8 56
 
6.2%
9 27
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 176
16.2%
0 160
14.7%
2 158
14.5%
7 112
10.3%
5 101
9.3%
1 92
8.4%
3 74
6.8%
6 70
 
6.4%
4 59
 
5.4%
8 56
 
5.1%
Other values (4) 31
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 176
16.2%
0 160
14.7%
2 158
14.5%
7 112
10.3%
5 101
9.3%
1 92
8.4%
3 74
6.8%
6 70
 
6.4%
4 59
 
5.4%
8 56
 
5.1%
Other values (4) 31
 
2.8%

BASE_YMD
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-09 00:00:00
Maximum2020-12-09 00:00:00
2023-12-10T18:43:24.238990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:24.377416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolatursm_goods_nmbizcnd_nmmain_menu_nmreqer_timedstnehmpg_urltel_noBASE_YMD
0한국관광품질 인증 받은 쇼핑센터주식회사 청운에스피서울특별시 서대문구 모래내로 235서울특별시서대문구SeoulSeodaemun-gu首爾特別市西大門區ソウル特別市西大門区（ソデムング）1111130126.92483937.575081<NA><NA><NA><NA><NA>http://www.chunsp.com02-373-88982020-12-09
1한국관광품질 인증 받은 쇼핑센터플랫폼 플레이스 한남점서울특별시 용산구 이태원로 268서울특별시용산구SeoulYongsan-gu首爾特別市龍山區ソウル特別市龍山区（ヨンサング）1111030127.00181637.5383<NA><NA><NA><NA><NA>http://www.platformshop.co.kr02-797-46282020-12-09
2한국관광품질 인증 받은 쇼핑센터갈라 프리스비 명동점서울특별시 중구 명동8길 11서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020126.98483137.563251<NA><NA><NA><NA><NA>http://www.frisbeekorea.com02-318-71202020-12-09
3한국관광품질 인증 받은 쇼핑센터한국다원호간보서울특별시 마포구 방울내로 83서울특별시마포구SeoulMapo-gu首爾特別市麻浦區ソウル特別市麻浦区（マポグ）1111140126.90417337.561162<NA><NA><NA><NA><NA>http://dawonkorea.co.kr/02-325-38932020-12-09
4한국관광품질 인증 받은 쇼핑센터세란안경서울특별시 중구 을지로 30서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020126.98136937.56499<NA><NA><NA><NA><NA>http://blog.naver.com/saeran77702-777-67892020-12-09
5한국관광품질 인증 받은 쇼핑센터시리즈코너 이태원점서울특별시 용산구 이태원로 244서울특별시용산구SeoulYongsan-gu首爾特別市龍山區ソウル特別市龍山区（ヨンサング）1111030127.0004637.536418<NA><NA><NA><NA><NA>http://www.byseries.com02-797-07102020-12-09
6한국관광품질 인증 받은 쇼핑센터인사칠기서울특별시 종로구 인사동길 12서울특별시종로구SeoulJongno-gu首爾特別市鍾路區ソウル特別市鍾路区（チョンノグ）1111010126.98497937.574546<NA><NA><NA><NA><NA>http://www.insagallery.co.kr02-6261-27272020-12-09
7한국관광품질 인증 받은 쇼핑센터저집서울특별시 종로구 창의문로 142-1서울특별시종로구SeoulJongno-gu首爾特別市鍾路區ソウル特別市鍾路区（チョンノグ）1111010126.96486237.592879<NA><NA><NA><NA><NA>http://www.chopstickshouse.co.kr02-3417-01192020-12-09
8한국관광품질 인증 받은 쇼핑센터아트코리아서울특별시 종로구 자하문로 231서울특별시종로구SeoulJongno-gu首爾特別市鍾路區ソウル特別市鍾路区（チョンノグ）1111010126.96529937.590507<NA><NA><NA><NA><NA>http://www.artkoreagroup.com02-394-5551~22020-12-09
9한국관광품질 인증 받은 쇼핑센터세인트제임스 서울서울특별시 강남구 논현로175길 94서울특별시강남구SeoulGangnam-gu首爾特別市江南區ソウル特別市江南区（カンナムグ）1111230127.02162537.523159<NA><NA><NA><NA><NA>http://www.saint-james.co.kr02-543-46282020-12-09
se_nmentrp_nmrn_adresctprvn_klang_nmsigngn_klang_nmctprvn_engl_nmsigngn_engl_nmctprvn_chnlng_nmsigngn_chnlng_nmctprvn_jlang_nmsigngn_jlang_nmcity_do_cdcity_gn_gu_cdlolatursm_goods_nmbizcnd_nmmain_menu_nmreqer_timedstnehmpg_urltel_noBASE_YMD
90KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)코레일관광개발㈜서울특별시 용산구 청파로 378 (동자동 43-229) KORAIL KTX B/D 1층서울특별시용산구SeoulYongsan-gu首爾特別市龍山區ソウル特別市龍山区（ヨンサング）1111030126.96941237.552815도라산 평화관광 당일<NA><NA><NA><NA>http://www.korailtravel.com/1544-77552020-12-09
91KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)(주)코리아브릿지서울특별시 금천구 가산디지털1로 30 에이스하이엔드타워10 1911호서울특별시금천구SeoulGeumcheon-gu首爾特別市衿川區ソウル特別市衿川区（クムチョング）1111180126.88725437.4688812020년 대구 경북 관광의 해 특화상품<NA><NA><NA><NA><NA><NA>2020-12-09
92KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)(주)코앤씨서울특별시 서초구 서초대로51길 9 동성빌딩서울특별시서초구SeoulSeocho-gu首爾特別市瑞草區ソウル特別市瑞草区（ソチョグ)1111220127.01399837.494845You&Me(국제 학생 교류 상품)<NA><NA><NA><NA><NA>02-532-11142020-12-09
93KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)(주) 킴스여행사서울특별시 강남구 남부순환로 2728 유일빌딩서울특별시강남구SeoulGangnam-gu首爾特別市江南區ソウル特別市江南区（カンナムグ）1111230127.04440237.485647IN-DEPTH KOREA TOUR<NA><NA><NA><NA>http://www.kimstravel.com/02-570-35002020-12-09
94KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)파라다이스투어서울특별시 중구 동호로 268 (장충동2가 186-210) 파라다이스빌딩 2층서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020127.00420837.559773산들바람 자연과 동행하는 마음치유 경기도 투어<NA><NA><NA><NA>http://www.paradisetour.co.kr/02-2260-20002020-12-09
95KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)파라다이스투어서울특별시 중구 동호로 268 (장충동2가 186-210) 파라다이스빌딩 2층서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020127.00420837.559773소소하지만 꽉 차있는 이로움의 고장 인천과 이천 여행기<NA><NA><NA><NA>http://www.paradisetour.co.kr/02-2260-20002020-12-09
96KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)판문점트레블센타서울특별시 중구 세종대로 135 코리아나 호텔사무동빌딩803호서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020126.97650337.568181탈북자와 함께 하는 동시투어(일명 ‘컴바인드 투어’) (JSA/DMZ Combined Tour)<NA><NA><NA><NA>http://www.koreadmztour.com/02-771-55932020-12-09
97KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)판문점트레블센타서울특별시 중구 세종대로 135 코리아나 호텔사무동빌딩803호서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020126.97650337.568181탈북자와 함께 하는 프리미엄 판문점 투어(Premium Panmunjom Tour)<NA><NA><NA><NA>http://www.koreadmztour.com/02-771-55932020-12-09
98KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)판문점트레블센타서울특별시 중구 세종대로 135 코리아나 호텔사무동빌딩803호서울특별시중구SeoulJung-gu首爾特別市中區ソウル特別市中区（チュング）1111020126.97650337.568181한반도의 어제와 오늘 그리고 내일을 이야기 하는 원코리아 투어 (ONE KOREA Tour)<NA><NA><NA><NA>http://www.koreadmztour.com/02-771-55932020-12-09
99KATA에서 우수 여행상품 인증받은 국내여행상품 매장정보 (2019-2020)㈜하나투어아이티씨서울특별시 종로구 삼봉로 71 5층 (수송동 G타워)서울특별시종로구SeoulJongno-gu首爾特別市鍾路區ソウル特別市鍾路区（チョンノグ）1111010126.98114237.572517[Discover Korea] Eastern Korea 4days<NA><NA><NA><NA>http://www.hanatouritc.com/02-365-15002020-12-09