Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells4
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory111.3 B

Variable types

Text4
Numeric6
Categorical2
DateTime1

Alerts

base_ymd has constant value ""Constant
city_do_cd is highly overall correlated with city_gn_gu_cd and 2 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
xpos_lo is highly overall correlated with area_nmHigh correlation
ypos_la is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 3 other fieldsHigh correlation
homepage_url has 4 (4.0%) missing valuesMissing
entrp_nm has unique valuesUnique
xpos_lo has unique valuesUnique
ypos_la has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:50:02.704239
Analysis finished2023-12-10 09:50:12.313891
Duration9.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:12.724176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length9.48
Min length4

Characters and Unicode

Total characters948
Distinct characters181
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowDH 네상스 호텔
2nd row코리아나호텔
3rd rowGV 레지던스
4th rowKY 헤리티지 호텔
5th rowMS 호텔
ValueCountFrequency (%)
호텔 74
26.6%
라마다 11
 
4.0%
제주 8
 
2.9%
서울 7
 
2.5%
베니키아 5
 
1.8%
더 5
 
1.8%
부산 5
 
1.8%
동대문 4
 
1.4%
레지던스 3
 
1.1%
프리미어 3
 
1.1%
Other values (131) 153
55.0%
2023-12-10T18:50:13.987218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
 
18.8%
호 88
 
9.3%
텔 88
 
9.3%
스 36
 
3.8%
리 24
 
2.5%
트 21
 
2.2%
라 18
 
1.9%
마 17
 
1.8%
아 15
 
1.6%
다 13
 
1.4%
Other values (171) 450
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 757
79.9%
Space Separator 178
 
18.8%
Uppercase Letter 12
 
1.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 88
 
11.6%
텔 88
 
11.6%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 13
 
1.7%
Other values (160) 424
56.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
25.0%
G 2
16.7%
D 1
 
8.3%
J 1
 
8.3%
M 1
 
8.3%
Y 1
 
8.3%
K 1
 
8.3%
V 1
 
8.3%
H 1
 
8.3%
Space Separator
ValueCountFrequency (%)
178
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 757
79.9%
Common 179
 
18.9%
Latin 12
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 88
 
11.6%
텔 88
 
11.6%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 13
 
1.7%
Other values (160) 424
56.0%
Latin
ValueCountFrequency (%)
S 3
25.0%
G 2
16.7%
D 1
 
8.3%
J 1
 
8.3%
M 1
 
8.3%
Y 1
 
8.3%
K 1
 
8.3%
V 1
 
8.3%
H 1
 
8.3%
Common
ValueCountFrequency (%)
178
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 757
79.9%
ASCII 191
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
178
93.2%
S 3
 
1.6%
G 2
 
1.0%
D 1
 
0.5%
J 1
 
0.5%
M 1
 
0.5%
Y 1
 
0.5%
K 1
 
0.5%
V 1
 
0.5%
1 1
 
0.5%
Hangul
ValueCountFrequency (%)
호 88
 
11.6%
텔 88
 
11.6%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 13
 
1.7%
Other values (160) 424
56.0%

load_addr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:14.674527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length27
Mean length21.13
Min length14

Characters and Unicode

Total characters2113
Distinct characters197
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

Unique98 ?
Unique (%)98.0%

Sample

1st row서울특별시 성북구 동소문로20나길 39 동선동 복합빌
2nd row서울특별시 중구 태평로1가 세종대로 135
3rd row서울특별시 용산구 이태원로15길 14-4
4th row서울특별시 중구 장충단로 226
5th row부산광역시 해운대구 해운대해변로 271
ValueCountFrequency (%)
서울특별시 44
 
9.8%
중구 15
 
3.4%
부산광역시 13
 
2.9%
경기도 13
 
2.9%
제주특별자치도 12
 
2.7%
강남구 8
 
1.8%
서귀포시 6
 
1.3%
인천광역시 6
 
1.3%
해운대구 6
 
1.3%
제주시 6
 
1.3%
Other values (265) 318
71.1%
2023-12-10T18:50:15.493197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
347
 
16.4%
시 96
 
4.5%
로 86
 
4.1%
구 76
 
3.6%
1 75
 
3.5%
서 63
 
3.0%
별 56
 
2.7%
특 56
 
2.7%
2 49
 
2.3%
울 49
 
2.3%
Other values (187) 1160
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1379
65.3%
Space Separator 347
 
16.4%
Decimal Number 341
 
16.1%
Dash Punctuation 24
 
1.1%
Uppercase Letter 22
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
7.0%
로 86
 
6.2%
구 76
 
5.5%
서 63
 
4.6%
별 56
 
4.1%
특 56
 
4.1%
울 49
 
3.6%
길 43
 
3.1%
도 38
 
2.8%
대 30
 
2.2%
Other values (163) 786
57.0%
Uppercase Letter
ValueCountFrequency (%)
I 3
13.6%
G 3
13.6%
H 2
9.1%
T 2
9.1%
E 2
9.1%
L 2
9.1%
N 2
9.1%
D 2
9.1%
S 1
 
4.5%
O 1
 
4.5%
Other values (2) 2
9.1%
Decimal Number
ValueCountFrequency (%)
1 75
22.0%
2 49
14.4%
3 41
12.0%
4 32
9.4%
5 29
 
8.5%
6 28
 
8.2%
0 25
 
7.3%
7 22
 
6.5%
9 21
 
6.2%
8 19
 
5.6%
Space Separator
ValueCountFrequency (%)
347
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1379
65.3%
Common 712
33.7%
Latin 22
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
7.0%
로 86
 
6.2%
구 76
 
5.5%
서 63
 
4.6%
별 56
 
4.1%
특 56
 
4.1%
울 49
 
3.6%
길 43
 
3.1%
도 38
 
2.8%
대 30
 
2.2%
Other values (163) 786
57.0%
Common
ValueCountFrequency (%)
347
48.7%
1 75
 
10.5%
2 49
 
6.9%
3 41
 
5.8%
4 32
 
4.5%
5 29
 
4.1%
6 28
 
3.9%
0 25
 
3.5%
- 24
 
3.4%
7 22
 
3.1%
Other values (2) 40
 
5.6%
Latin
ValueCountFrequency (%)
I 3
13.6%
G 3
13.6%
H 2
9.1%
T 2
9.1%
E 2
9.1%
L 2
9.1%
N 2
9.1%
D 2
9.1%
S 1
 
4.5%
O 1
 
4.5%
Other values (2) 2
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1379
65.3%
ASCII 734
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
347
47.3%
1 75
 
10.2%
2 49
 
6.7%
3 41
 
5.6%
4 32
 
4.4%
5 29
 
4.0%
6 28
 
3.8%
0 25
 
3.4%
- 24
 
3.3%
7 22
 
3.0%
Other values (14) 62
 
8.4%
Hangul
ValueCountFrequency (%)
시 96
 
7.0%
로 86
 
6.2%
구 76
 
5.5%
서 63
 
4.6%
별 56
 
4.1%
특 56
 
4.1%
울 49
 
3.6%
길 43
 
3.1%
도 38
 
2.8%
대 30
 
2.2%
Other values (163) 786
57.0%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.39
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:15.746788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median26
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.382592
Coefficient of variation (CV)0.58289474
Kurtosis-1.5638944
Mean26.39
Median Absolute Deviation (MAD)15
Skewness0.28938722
Sum2639
Variance236.62414
MonotonicityNot monotonic
2023-12-10T18:50:15.965945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11 44
44.0%
26 13
 
13.0%
41 13
 
13.0%
50 12
 
12.0%
28 6
 
6.0%
42 5
 
5.0%
47 2
 
2.0%
44 2
 
2.0%
48 1
 
1.0%
45 1
 
1.0%
ValueCountFrequency (%)
11 44
44.0%
26 13
 
13.0%
28 6
 
6.0%
31 1
 
1.0%
41 13
 
13.0%
42 5
 
5.0%
44 2
 
2.0%
45 1
 
1.0%
47 2
 
2.0%
48 1
 
1.0%
ValueCountFrequency (%)
50 12
12.0%
48 1
 
1.0%
47 2
 
2.0%
45 1
 
1.0%
44 2
 
2.0%
42 5
 
5.0%
41 13
13.0%
31 1
 
1.0%
28 6
6.0%
26 13
13.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26695.71
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:16.262173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111500
median26245
Q341507.5
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)30007.5

Descriptive statistics

Standard deviation15316.828
Coefficient of variation (CV)0.57375616
Kurtosis-1.5669629
Mean26695.71
Median Absolute Deviation (MAD)14933
Skewness0.29052847
Sum2669571
Variance2.3460522 × 108
MonotonicityNot monotonic
2023-12-10T18:50:16.600855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
11140 11
 
11.0%
11680 8
 
8.0%
50110 6
 
6.0%
26350 6
 
6.0%
50130 6
 
6.0%
11500 5
 
5.0%
41115 4
 
4.0%
28110 3
 
3.0%
11170 3
 
3.0%
11230 3
 
3.0%
Other values (36) 45
45.0%
ValueCountFrequency (%)
11110 2
 
2.0%
11140 11
11.0%
11170 3
 
3.0%
11230 3
 
3.0%
11290 2
 
2.0%
11305 1
 
1.0%
11440 2
 
2.0%
11500 5
5.0%
11545 2
 
2.0%
11560 2
 
2.0%
ValueCountFrequency (%)
50130 6
6.0%
50110 6
6.0%
48170 1
 
1.0%
47940 1
 
1.0%
47130 1
 
1.0%
45190 1
 
1.0%
44200 1
 
1.0%
44130 1
 
1.0%
42830 1
 
1.0%
42760 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.3665
Minimum126.37151
Maximum130.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:17.146168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37151
5-th percentile126.51882
Q1126.88788
median127.01167
Q3127.12487
95-th percentile129.16143
Maximum130.8702
Range4.4986868
Interquartile range (IQR)0.23699065

Descriptive statistics

Standard deviation0.92992498
Coefficient of variation (CV)0.0073011742
Kurtosis1.4710727
Mean127.3665
Median Absolute Deviation (MAD)0.1224129
Skewness1.5654687
Sum12736.65
Variance0.86476047
MonotonicityNot monotonic
2023-12-10T18:50:17.396947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0975344 1
 
1.0%
129.1545873 1
 
1.0%
126.6602847 1
 
1.0%
126.9145249 1
 
1.0%
127.1212276 1
 
1.0%
129.1613998 1
 
1.0%
129.0373247 1
 
1.0%
127.032221 1
 
1.0%
129.0818704 1
 
1.0%
126.6569041 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.3715088 1
1.0%
126.4080885 1
1.0%
126.4837714 1
1.0%
126.5033589 1
1.0%
126.5102947 1
1.0%
126.519265 1
1.0%
126.519934 1
1.0%
126.5218499 1
1.0%
126.578421 1
1.0%
126.5984422 1
1.0%
ValueCountFrequency (%)
130.8701956 1
1.0%
129.3473935 1
1.0%
129.2774041 1
1.0%
129.1646681 1
1.0%
129.1620947 1
1.0%
129.1613998 1
1.0%
129.1611261 1
1.0%
129.1545873 1
1.0%
129.1328184 1
1.0%
129.0818704 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.613362
Minimum33.249867
Maximum38.189922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:17.695729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.249867
5-th percentile33.465293
Q135.209047
median37.478614
Q337.555421
95-th percentile37.59546
Maximum38.189922
Range4.9400543
Interquartile range (IQR)2.346374

Descriptive statistics

Standard deviation1.4656214
Coefficient of variation (CV)0.040029685
Kurtosis0.11670799
Mean36.613362
Median Absolute Deviation (MAD)0.09396371
Skewness-1.2611856
Sum3661.3362
Variance2.148046
MonotonicityNot monotonic
2023-12-10T18:50:17.977335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.50601088 1
 
1.0%
35.15964137 1
 
1.0%
33.54282848 1
 
1.0%
33.45210482 1
 
1.0%
37.38653939 1
 
1.0%
35.16012514 1
 
1.0%
35.09382162 1
 
1.0%
37.2637485 1
 
1.0%
35.21946372 1
 
1.0%
33.54420799 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.24986732 1
1.0%
33.25142402 1
1.0%
33.25447711 1
1.0%
33.254517 1
1.0%
33.45210482 1
1.0%
33.46598674 1
1.0%
33.4857485 1
1.0%
33.50067983 1
1.0%
33.51287223 1
1.0%
33.54282848 1
1.0%
ValueCountFrequency (%)
38.18992163 1
1.0%
38.11488063 1
1.0%
37.715627 1
1.0%
37.65025711 1
1.0%
37.64757519 1
1.0%
37.59271709 1
1.0%
37.58871323 1
1.0%
37.57617521 1
1.0%
37.57446681 1
1.0%
37.5696537 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
44 
부산
13 
경기
13 
제주
12 
인천
6 
Other values (7)
12 

Length

Max length3
Median length2
Mean length2.01
Min length2

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row서울
2nd row서울
3rd row서울
4th row서울
5th row부산

Common Values

ValueCountFrequency (%)
서울 44
44.0%
부산 13
 
13.0%
경기 13
 
13.0%
제주 12
 
12.0%
인천 6
 
6.0%
강원 5
 
5.0%
충남 2
 
2.0%
경남 1
 
1.0%
경북 1
 
1.0%
전북 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:50:18.206574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 44
44.0%
부산 13
 
13.0%
경기 13
 
13.0%
제주 12
 
12.0%
인천 6
 
6.0%
강원 5
 
5.0%
충남 2
 
2.0%
경남 1
 
1.0%
경북 1
 
1.0%
전북 1
 
1.0%
Other values (2) 2
 
2.0%

homepage_url
Text

MISSING 

Distinct94
Distinct (%)97.9%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T18:50:18.696022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length32
Mean length25.270833
Min length9

Characters and Unicode

Total characters2426
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st rowwww.dhnaissance.com
2nd rowhttps://www.koreanahotel.com/index.htm?
3rd rowgv-residence.com
4th rowhttp://seouldongdaemun.splaisir.com
5th rowmshotel.alltheway.kr
ValueCountFrequency (%)
www.valuehotelbusan.com 2
 
2.1%
https://www.ramadaencorejejuseogwipo.com 2
 
2.1%
www.hotelthem.com 1
 
1.0%
www.riverpark.co.kr 1
 
1.0%
www.dhnaissance.com 1
 
1.0%
www.hotelacacia.co.kr 1
 
1.0%
www.jshotelbundang.com 1
 
1.0%
www.busanbusinesshotel.com 1
 
1.0%
www.vellasuitehotel.co.kr 1
 
1.0%
http://www.bestincityhotel.co.kr 1
 
1.0%
Other values (84) 84
87.5%
2023-12-10T18:50:19.764431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 246
 
10.1%
w 228
 
9.4%
. 213
 
8.8%
t 193
 
8.0%
e 180
 
7.4%
a 135
 
5.6%
h 123
 
5.1%
c 113
 
4.7%
/ 105
 
4.3%
l 102
 
4.2%
Other values (32) 788
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2053
84.6%
Other Punctuation 354
 
14.6%
Dash Punctuation 7
 
0.3%
Uppercase Letter 6
 
0.2%
Decimal Number 6
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 246
12.0%
w 228
11.1%
t 193
 
9.4%
e 180
 
8.8%
a 135
 
6.6%
h 123
 
6.0%
c 113
 
5.5%
l 102
 
5.0%
m 96
 
4.7%
n 95
 
4.6%
Other values (16) 542
26.4%
Decimal Number
ValueCountFrequency (%)
0 1
16.7%
5 1
16.7%
3 1
16.7%
9 1
16.7%
7 1
16.7%
2 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
G 1
16.7%
M 1
16.7%
R 1
16.7%
K 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 213
60.2%
/ 105
29.7%
: 35
 
9.9%
? 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2059
84.9%
Common 367
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 246
11.9%
w 228
11.1%
t 193
 
9.4%
e 180
 
8.7%
a 135
 
6.6%
h 123
 
6.0%
c 113
 
5.5%
l 102
 
5.0%
m 96
 
4.7%
n 95
 
4.6%
Other values (21) 548
26.6%
Common
ValueCountFrequency (%)
. 213
58.0%
/ 105
28.6%
: 35
 
9.5%
- 7
 
1.9%
0 1
 
0.3%
5 1
 
0.3%
3 1
 
0.3%
9 1
 
0.3%
? 1
 
0.3%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 246
 
10.1%
w 228
 
9.4%
. 213
 
8.8%
t 193
 
8.0%
e 180
 
7.4%
a 135
 
5.6%
h 123
 
5.1%
c 113
 
4.7%
/ 105
 
4.3%
l 102
 
4.2%
Other values (32) 788
32.5%

hotel_grad
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3급
66 
특2급
18 
1급
8 
2급
 
3
특1급
 
2
Other values (3)
 
3

Length

Max length4
Median length2
Mean length2.22
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row3급
2nd row특2급
3rd row3급
4th row특2급
5th row3급

Common Values

ValueCountFrequency (%)
3급 66
66.0%
특2급 18
 
18.0%
1급 8
 
8.0%
2급 3
 
3.0%
특1급 2
 
2.0%
<NA> 1
 
1.0%
4급 1
 
1.0%
4성 1
 
1.0%

Length

2023-12-10T18:50:20.180265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:20.434390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3급 66
66.0%
특2급 18
 
18.0%
1급 8
 
8.0%
2급 3
 
3.0%
특1급 2
 
2.0%
na 1
 
1.0%
4급 1
 
1.0%
4성 1
 
1.0%

tel_no
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:20.896899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.78
Min length9

Characters and Unicode

Total characters1178
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

Unique98 ?
Unique (%)98.0%

Sample

1st row02-921-2080
2nd row02-2171-7000
3rd row02-797-5800
4th row02-2198-1212
5th row051-741-3838
ValueCountFrequency (%)
064-735-2000 2
 
2.0%
064-731-5000 1
 
1.0%
031-236-7112 1
 
1.0%
1877-8006 1
 
1.0%
051-243-8001 1
 
1.0%
051-808-2000 1
 
1.0%
031-231-2121 1
 
1.0%
051-464-8883 1
 
1.0%
064-731-3700 1
 
1.0%
051-741-7711 1
 
1.0%
Other values (89) 89
89.0%
2023-12-10T18:50:21.840729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 281
23.9%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 75
 
6.4%
5 74
 
6.3%
6 61
 
5.2%
4 55
 
4.7%
9 51
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 979
83.1%
Dash Punctuation 199
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 281
28.7%
2 132
13.5%
1 114
11.6%
3 93
 
9.5%
7 75
 
7.7%
5 74
 
7.6%
6 61
 
6.2%
4 55
 
5.6%
9 51
 
5.2%
8 43
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 281
23.9%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 75
 
6.4%
5 74
 
6.3%
6 61
 
5.2%
4 55
 
4.7%
9 51
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 281
23.9%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 75
 
6.4%
5 74
 
6.3%
6 61
 
5.2%
4 55
 
4.7%
9 51
 
4.3%

trrsrt1_nm
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.494
Minimum1.4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:22.152003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.195
Q12.775
median3.4
Q34.325
95-th percentile4.9
Maximum5
Range3.6
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation0.90774668
Coefficient of variation (CV)0.25980157
Kurtosis-1.0525412
Mean3.494
Median Absolute Deviation (MAD)0.7
Skewness0.056117046
Sum349.4
Variance0.82400404
MonotonicityNot monotonic
2023-12-10T18:50:22.554546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.4 8
 
8.0%
2.9 8
 
8.0%
3.9 7
 
7.0%
2.5 6
 
6.0%
3.3 5
 
5.0%
4.9 5
 
5.0%
4.0 5
 
5.0%
4.7 4
 
4.0%
4.8 4
 
4.0%
2.4 4
 
4.0%
Other values (21) 44
44.0%
ValueCountFrequency (%)
1.4 1
 
1.0%
2.0 2
 
2.0%
2.1 2
 
2.0%
2.2 3
3.0%
2.3 1
 
1.0%
2.4 4
4.0%
2.5 6
6.0%
2.6 3
3.0%
2.7 3
3.0%
2.8 2
 
2.0%
ValueCountFrequency (%)
5.0 3
3.0%
4.9 5
5.0%
4.8 4
4.0%
4.7 4
4.0%
4.6 3
3.0%
4.5 2
 
2.0%
4.4 4
4.0%
4.3 1
 
1.0%
4.2 1
 
1.0%
4.0 5
5.0%

trrsrt2_nm
Real number (ℝ)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.553
Minimum1.4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:22.829078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2
Q12.8
median3.6
Q34.5
95-th percentile4.9
Maximum5
Range3.6
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation0.96456983
Coefficient of variation (CV)0.27148039
Kurtosis-1.1645243
Mean3.553
Median Absolute Deviation (MAD)0.8
Skewness-0.22131058
Sum355.3
Variance0.93039495
MonotonicityNot monotonic
2023-12-10T18:50:23.124855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4.7 9
 
9.0%
2.8 7
 
7.0%
4.1 6
 
6.0%
4.6 5
 
5.0%
4.0 4
 
4.0%
4.9 4
 
4.0%
3.5 4
 
4.0%
2.0 4
 
4.0%
2.2 4
 
4.0%
3.2 4
 
4.0%
Other values (23) 49
49.0%
ValueCountFrequency (%)
1.4 1
 
1.0%
1.7 1
 
1.0%
2.0 4
4.0%
2.1 3
3.0%
2.2 4
4.0%
2.3 2
2.0%
2.4 2
2.0%
2.5 2
2.0%
2.6 1
 
1.0%
2.7 3
3.0%
ValueCountFrequency (%)
5.0 2
 
2.0%
4.9 4
4.0%
4.8 3
 
3.0%
4.7 9
9.0%
4.6 5
5.0%
4.5 3
 
3.0%
4.4 1
 
1.0%
4.3 3
 
3.0%
4.2 3
 
3.0%
4.1 6
6.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T18:50:23.379120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:23.573700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:50:10.683409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:04.846413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.173279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.538223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.569186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.569133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:10.860807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.041982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.379465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.720731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.742780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.769122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.059097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.242401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.621537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.900209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.905898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.960168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.197075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.427788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.837544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.044388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.053385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:10.140425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.352209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.642134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.035726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.189060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.225021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:10.292539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.587642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.906918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.306196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.386507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.387500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:10.462192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:23.744523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_notrrsrt1_nmtrrsrt2_nm
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0000.0000.9990.0000.000
city_do_cd1.0001.0001.0000.9990.7240.9491.0001.0000.5361.0000.2900.196
city_gn_gu_cd1.0001.0000.9991.0000.7480.9360.9871.0000.2441.0000.2760.301
xpos_lo1.0001.0000.7240.7481.0000.7250.9231.0000.4221.0000.3610.228
ypos_la1.0001.0000.9490.9360.7251.0000.9521.0000.6661.0000.1830.343
area_nm1.0001.0001.0000.9870.9230.9521.0001.0000.7001.0000.1810.285
homepage_url1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.000
hotel_grad1.0000.0000.5360.2440.4220.6660.7000.0001.0001.0000.6420.527
tel_no1.0000.9991.0001.0001.0001.0001.0001.0001.0001.0000.9720.928
trrsrt1_nm1.0000.0000.2900.2760.3610.1830.1810.0000.6420.9721.0000.626
trrsrt2_nm1.0000.0000.1960.3010.2280.3430.2850.0000.5270.9280.6261.000
2023-12-10T18:50:24.008298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nm
hotel_grad1.0000.424
area_nm0.4241.000
2023-12-10T18:50:24.179150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_latrrsrt1_nmtrrsrt2_nmarea_nmhotel_grad
city_do_cd1.0000.954-0.050-0.659-0.215-0.0490.9730.210
city_gn_gu_cd0.9541.000-0.023-0.670-0.217-0.0290.9470.089
xpos_lo-0.050-0.0231.000-0.044-0.087-0.0640.6920.239
ypos_la-0.659-0.670-0.0441.0000.2390.0590.8420.285
trrsrt1_nm-0.215-0.217-0.0870.2391.0000.1150.0670.387
trrsrt2_nm-0.049-0.029-0.0640.0590.1151.0000.1170.294
area_nm0.9730.9470.6920.8420.0670.1171.0000.424
hotel_grad0.2100.0890.2390.2850.3870.2940.4241.000

Missing values

2023-12-10T18:50:11.823641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:12.167330image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_notrrsrt1_nmtrrsrt2_nmbase_ymd
0DH 네상스 호텔서울특별시 성북구 동소문로20나길 39 동선동 복합빌1111290127.09753437.506011서울www.dhnaissance.com3급02-921-20803.92.12020-12-31
1코리아나호텔서울특별시 중구 태평로1가 세종대로 1351111140126.97654837.568224서울https://www.koreanahotel.com/index.htm?특2급02-2171-70003.44.62020-12-31
2GV 레지던스서울특별시 용산구 이태원로15길 14-41111170126.97232237.54084서울gv-residence.com3급02-797-58003.84.22020-12-31
3KY 헤리티지 호텔서울특별시 중구 장충단로 2261111140127.0010537.550703서울http://seouldongdaemun.splaisir.com특2급02-2198-12124.52.82020-12-31
4MS 호텔부산광역시 해운대구 해운대해변로 2712626350129.16466835.161447부산mshotel.alltheway.kr3급051-741-38382.24.62020-12-31
5SG 관광 호텔인천광역시 서구 탁옥로51번길 13-9 SG관광호텔2828260126.6743437.545036인천sghotel.kr3급032-562-05123.44.72020-12-31
6가야 라트리 호텔서울특별시 용산구 한강대로 253 가야라트리 호텔1111170126.97214137.541439서울kayalatreehotel.com3급02-798-51013.02.02020-12-31
7파티오세븐호텔서울특별시 강남구 논현동 논현로 7361111680127.02939737.517824서울https://www.patio7.co.kr/1급02-517-88332.33.72020-12-31
8강남 패밀리 호텔서울특별시 강남구 봉은사로 143 운현오피스텔1111680127.04527937.510347서울www.gangnamfamilyhotel.com3급02-6474-15153.94.22020-12-31
9골드 리버 호텔서울특별시 금천구 서부샛길 5841111545127.02607337.576175서울goldriverhotel.co.kr3급02-6021-81004.64.72020-12-31
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90이 천안호텔충청남도 천안시 서북구 성정동 734-24444130127.14024136.81241충남http://www.cheonanhotel.kr/2급041-592-00003.34.02020-12-31
91제주 라마다 앙코르 이스트 호텔제주특별자치도 서귀포시 서호동5050130126.51993433.254517제주https://www.ramadaencorejejuseogwipo.com/3급064-735-20003.53.02020-12-31
92제주 하나 호텔제주특별자치도 서귀포시 중문관광로72번길 535050130126.40808933.249867제주http://www.hotelhana.co.kr/main/특2급064-738-70012.62.82020-12-31
93코업 시티 호텔 성산제주특별자치도 서귀포시 성산읍 성산등용로 285050130126.93192733.465987제주https://www.coopcityhotel-seongsan.co.kr/3급064-780-98002.94.12020-12-31
94호텔 노블레스 제주제주특별자치도 제주시 월성로4길 195050110126.50335933.50068제주hotelnoblessejeju.modoo.at3급064-748-71612.54.72020-12-31
95호텔 로베로제주특별자치도 제주시 관덕로 265050110126.5218533.512872제주stazhoteljejurobero.com/1급064-757-71113.43.52020-12-31
96호텔 위드 제주제주특별자치도 제주시 노연로 345050110126.48377133.485748제주www.hotelwithjeju.com/특2급02-522-58735.03.52020-12-31
97힐리언스선마을강원도 홍천군 서면 종자산길 1224242720127.63080237.650257강원https://www.healience.co.kr/3급033-434-27722.22.22020-12-31
98호텔시에나경기도 파주시 소리천로 314141480126.76188237.715627경기www.hotelsienna.com1급031-943-72605.01.42020-12-31
99밸류호텔월드와이드부산부산광역시 영도구 대교동1가 402626200129.03730335.093819부산www.valuehotelbusan.com4성051-960-55551.41.72020-12-31