Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells8
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory111.3 B

Variable types

Text4
Numeric6
Categorical3

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
klang_stsfdg_rt has 2 (2.0%) missing valuesMissing
jalng_stsfdg_rt has 2 (2.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique
xpos_lo has unique valuesUnique
ypos_la has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:12:57.488943
Analysis finished2023-12-10 10:13:05.883526
Duration8.39 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
2023-12-10T19:13:06.291796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length9.5
Min length4

Characters and Unicode

Total characters950
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%
2023-12-10T19:13:07.043162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
 
18.7%
호 88
 
9.3%
텔 88
 
9.3%
스 37
 
3.9%
리 24
 
2.5%
트 22
 
2.3%
라 18
 
1.9%
마 17
 
1.8%
아 15
 
1.6%
다 13
 
1.4%
Other values (171) 450
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 759
79.9%
Space Separator 178
 
18.7%
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 37
 
4.9%
리 24
 
3.2%
트 22
 
2.9%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 11
 
1.4%
Other values (160) 426
56.1%
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 759
79.9%
Common 179
 
18.8%
Latin 12
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 88
 
11.6%
텔 88
 
11.6%
스 37
 
4.9%
리 24
 
3.2%
트 22
 
2.9%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 11
 
1.4%
Other values (160) 426
56.1%
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 759
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 37
 
4.9%
리 24
 
3.2%
트 22
 
2.9%
라 18
 
2.4%
마 17
 
2.2%
아 15
 
2.0%
다 13
 
1.7%
드 11
 
1.4%
Other values (160) 426
56.1%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:13:07.582273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length26.5
Mean length21.19
Min length14

Characters and Unicode

Total characters2119
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

Unique100 ?
Unique (%)100.0%

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
 
3.3%
경기도 13
 
2.9%
부산광역시 13
 
2.9%
제주특별자치도 12
 
2.7%
강남구 8
 
1.8%
해운대구 7
 
1.6%
인천광역시 6
 
1.3%
제주시 6
 
1.3%
서귀포시 6
 
1.3%
Other values (267) 318
71.0%
2023-12-10T19:13:08.494323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
348
 
16.4%
시 96
 
4.5%
로 87
 
4.1%
구 76
 
3.6%
1 74
 
3.5%
서 63
 
3.0%
특 56
 
2.6%
별 56
 
2.6%
2 51
 
2.4%
울 49
 
2.3%
Other values (187) 1163
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1384
65.3%
Space Separator 348
 
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
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 63
 
4.6%
특 56
 
4.0%
별 56
 
4.0%
울 49
 
3.5%
길 43
 
3.1%
도 37
 
2.7%
대 31
 
2.2%
Other values (163) 790
57.1%
Uppercase Letter
ValueCountFrequency (%)
G 3
13.6%
I 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%
B 1
 
4.5%
R 1
 
4.5%
Other values (2) 2
9.1%
Decimal Number
ValueCountFrequency (%)
1 74
21.7%
2 51
15.0%
3 42
12.3%
4 31
9.1%
5 29
 
8.5%
6 28
 
8.2%
0 24
 
7.0%
7 22
 
6.5%
9 21
 
6.2%
8 19
 
5.6%
Space Separator
ValueCountFrequency (%)
348
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1384
65.3%
Common 713
33.6%
Latin 22
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 63
 
4.6%
특 56
 
4.0%
별 56
 
4.0%
울 49
 
3.5%
길 43
 
3.1%
도 37
 
2.7%
대 31
 
2.2%
Other values (163) 790
57.1%
Common
ValueCountFrequency (%)
348
48.8%
1 74
 
10.4%
2 51
 
7.2%
3 42
 
5.9%
4 31
 
4.3%
5 29
 
4.1%
6 28
 
3.9%
0 24
 
3.4%
- 24
 
3.4%
7 22
 
3.1%
Other values (2) 40
 
5.6%
Latin
ValueCountFrequency (%)
G 3
13.6%
I 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%
B 1
 
4.5%
R 1
 
4.5%
Other values (2) 2
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1384
65.3%
ASCII 735
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
348
47.3%
1 74
 
10.1%
2 51
 
6.9%
3 42
 
5.7%
4 31
 
4.2%
5 29
 
3.9%
6 28
 
3.8%
0 24
 
3.3%
- 24
 
3.3%
7 22
 
3.0%
Other values (14) 62
 
8.4%
Hangul
ValueCountFrequency (%)
시 96
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 63
 
4.6%
특 56
 
4.0%
별 56
 
4.0%
울 49
 
3.5%
길 43
 
3.1%
도 37
 
2.7%
대 31
 
2.2%
Other values (163) 790
57.1%

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
2023-12-10T19:13:08.763740image/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
2023-12-10T19:13:08.992943image/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%
Mean26697.21
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:09.305545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111500
median26305
Q341507.5
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)30007.5

Descriptive statistics

Standard deviation15316.786
Coefficient of variation (CV)0.57372236
Kurtosis-1.5670648
Mean26697.21
Median Absolute Deviation (MAD)14890.5
Skewness0.29023129
Sum2669721
Variance2.3460394 × 108
MonotonicityNot monotonic
2023-12-10T19:13:09.757077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
11140 11
 
11.0%
11680 8
 
8.0%
26350 7
 
7.0%
50110 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) 44
44.0%
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%
Mean127.36778
Minimum126.37151
Maximum130.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:10.048450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37151
5-th percentile126.51882
Q1126.88788
median127.01167
Q3127.12487
95-th percentile129.16222
Maximum130.8702
Range4.4986868
Interquartile range (IQR)0.23699065

Descriptive statistics

Standard deviation0.93234402
Coefficient of variation (CV)0.0073200931
Kurtosis1.4531007
Mean127.36778
Median Absolute Deviation (MAD)0.1224129
Skewness1.5638679
Sum12736.778
Variance0.86926537
MonotonicityNot monotonic
2023-12-10T19:13:10.401354image/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%
129.16587 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%

ypos_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.614047
Minimum33.249867
Maximum38.189922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:10.670397image/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 deviation1.4649201
Coefficient of variation (CV)0.040009784
Kurtosis0.12322326
Mean36.614047
Median Absolute Deviation (MAD)0.09396371
Skewness-1.2629424
Sum3661.4047
Variance2.1459909
MonotonicityNot monotonic
2023-12-10T19:13:11.393647image/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

2023-12-10T19:13:11.648070image/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 

Distinct95
Distinct (%)99.0%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T19:13:12.241807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length32
Mean length25.322917
Min length9

Characters and Unicode

Total characters2431
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

Unique94 ?
Unique (%)97.9%

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 (%)
https://www.ramadaencorejejuseogwipo.com 2
 
2.1%
www.riverpark.co.kr 1
 
1.0%
www.dhnaissance.com 1
 
1.0%
www.hotelacacia.co.kr 1
 
1.0%
brownsuitesjeju.com 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%
www.hotel-bestone.com 1
 
1.0%
Other values (85) 85
88.5%
2023-12-10T19:13:13.191138image/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
 
7.9%
e 181
 
7.4%
a 135
 
5.6%
h 123
 
5.1%
c 113
 
4.6%
/ 105
 
4.3%
l 102
 
4.2%
Other values (32) 792
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2057
84.6%
Other Punctuation 354
 
14.6%
Dash Punctuation 8
 
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 181
 
8.8%
a 135
 
6.6%
h 123
 
6.0%
c 113
 
5.5%
l 102
 
5.0%
m 96
 
4.7%
n 96
 
4.7%
Other values (16) 544
26.4%
Decimal Number
ValueCountFrequency (%)
0 1
16.7%
5 1
16.7%
7 1
16.7%
3 1
16.7%
9 1
16.7%
2 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
M 1
16.7%
G 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 (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2063
84.9%
Common 368
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 246
11.9%
w 228
11.1%
t 193
 
9.4%
e 181
 
8.8%
a 135
 
6.5%
h 123
 
6.0%
c 113
 
5.5%
l 102
 
4.9%
m 96
 
4.7%
n 96
 
4.7%
Other values (21) 550
26.7%
Common
ValueCountFrequency (%)
. 213
57.9%
/ 105
28.5%
: 35
 
9.5%
- 8
 
2.2%
0 1
 
0.3%
5 1
 
0.3%
7 1
 
0.3%
? 1
 
0.3%
3 1
 
0.3%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 246
 
10.1%
w 228
 
9.4%
. 213
 
8.8%
t 193
 
7.9%
e 181
 
7.4%
a 135
 
5.6%
h 123
 
5.1%
c 113
 
4.6%
/ 105
 
4.3%
l 102
 
4.2%
Other values (32) 792
32.6%

hotel_grad
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3급
66 
특2급
19 
1급
8 
2급
 
3
특1급
 
2
Other values (2)
 
2

Length

Max length4
Median length2
Mean length2.23
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
3급 66
66.0%
특2급 19
 
19.0%
1급 8
 
8.0%
2급 3
 
3.0%
특1급 2
 
2.0%
<NA> 1
 
1.0%
4급 1
 
1.0%

Length

2023-12-10T19:13:13.476959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:13.744900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3급 66
66.0%
특2급 19
 
19.0%
1급 8
 
8.0%
2급 3
 
3.0%
특1급 2
 
2.0%
na 1
 
1.0%
4급 1
 
1.0%

tel_no
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:13:14.137956image/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%
2023-12-10T19:13:15.005941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 283
24.0%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 77
 
6.5%
5 71
 
6.0%
6 60
 
5.1%
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 (%)
0 283
28.9%
2 132
13.5%
1 114
11.6%
3 93
 
9.5%
7 77
 
7.9%
5 71
 
7.3%
6 60
 
6.1%
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 (%)
0 283
24.0%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 77
 
6.5%
5 71
 
6.0%
6 60
 
5.1%
4 55
 
4.7%
9 51
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283
24.0%
- 199
16.9%
2 132
11.2%
1 114
9.7%
3 93
 
7.9%
7 77
 
6.5%
5 71
 
6.0%
6 60
 
5.1%
4 55
 
4.7%
9 51
 
4.3%

klang_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)30.6%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.3061224
Minimum2
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:15.289691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.6
median3.1
Q34
95-th percentile4.715
Maximum4.9
Range2.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.83860664
Coefficient of variation (CV)0.25365263
Kurtosis-1.1228796
Mean3.3061224
Median Absolute Deviation (MAD)0.7
Skewness0.34177916
Sum324
Variance0.7032611
MonotonicityNot monotonic
2023-12-10T19:13:15.705209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.6 8
 
8.0%
3.9 8
 
8.0%
2.5 7
 
7.0%
2.9 6
 
6.0%
3.0 6
 
6.0%
2.4 5
 
5.0%
2.1 5
 
5.0%
4.9 4
 
4.0%
3.4 4
 
4.0%
4.1 4
 
4.0%
Other values (20) 41
41.0%
ValueCountFrequency (%)
2.0 1
 
1.0%
2.1 5
5.0%
2.2 2
 
2.0%
2.3 2
 
2.0%
2.4 5
5.0%
2.5 7
7.0%
2.6 8
8.0%
2.7 3
 
3.0%
2.8 3
 
3.0%
2.9 6
6.0%
ValueCountFrequency (%)
4.9 4
4.0%
4.8 1
 
1.0%
4.7 2
2.0%
4.6 3
3.0%
4.5 2
2.0%
4.4 2
2.0%
4.3 3
3.0%
4.2 2
2.0%
4.1 4
4.0%
4.0 3
3.0%

jalng_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)28.6%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.4397959
Minimum2
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:15.996460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.7
median3.45
Q34.2
95-th percentile4.8
Maximum4.9
Range2.9
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.86973628
Coefficient of variation (CV)0.25284531
Kurtosis-1.2192154
Mean3.4397959
Median Absolute Deviation (MAD)0.75
Skewness0.037187809
Sum337.1
Variance0.7564412
MonotonicityNot monotonic
2023-12-10T19:13:16.320579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2.5 8
 
8.0%
3.0 7
 
7.0%
3.9 6
 
6.0%
2.3 5
 
5.0%
3.7 5
 
5.0%
3.5 5
 
5.0%
2.9 5
 
5.0%
4.5 5
 
5.0%
4.0 4
 
4.0%
4.4 4
 
4.0%
Other values (18) 44
44.0%
ValueCountFrequency (%)
2.0 3
 
3.0%
2.1 3
 
3.0%
2.2 2
 
2.0%
2.3 5
5.0%
2.4 3
 
3.0%
2.5 8
8.0%
2.7 2
 
2.0%
2.8 1
 
1.0%
2.9 5
5.0%
3.0 7
7.0%
ValueCountFrequency (%)
4.9 3
3.0%
4.8 3
3.0%
4.7 3
3.0%
4.6 4
4.0%
4.5 5
5.0%
4.4 4
4.0%
4.3 2
 
2.0%
4.2 2
 
2.0%
4.0 4
4.0%
3.9 6
6.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-31
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 100
100.0%

Length

2023-12-10T19:13:16.573225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:16.753848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 100
100.0%

Interactions

2023-12-10T19:13:04.154146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:58.636463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:00.177145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.236345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.214787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.090677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:04.343587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:58.836840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:00.358378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.420464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.371907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.270710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:04.527786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:59.050302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:00.530102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.597916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.542144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.466003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:04.690530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:59.618023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:00.681555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.738099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.674598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.624861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:04.848465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:59.799107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:00.835732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.860034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.803732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.775441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:05.006622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:59.980924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:01.017523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.044497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:02.947651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:03.955723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:13:16.893416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noklang_stsfdg_rtjalng_stsfdg_rt
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0000.9990.7250.9491.0001.0000.3671.0000.0000.316
city_gn_gu_cd1.0001.0000.9991.0000.7480.9360.9871.0000.1481.0000.0000.217
xpos_lo1.0001.0000.7250.7481.0000.7240.9241.0000.4191.0000.0000.000
ypos_la1.0001.0000.9490.9360.7241.0000.9521.0000.4821.0000.0000.320
area_nm1.0001.0001.0000.9870.9240.9521.0001.0000.8381.0000.0000.376
homepage_url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8900.950
hotel_grad1.0001.0000.3670.1480.4190.4820.8381.0001.0001.0000.0000.357
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8940.949
klang_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.8900.0000.8941.0000.000
jalng_stsfdg_rt1.0001.0000.3160.2170.0000.3200.3760.9500.3570.9490.0001.000
2023-12-10T19:13:17.175966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nm
hotel_grad1.0000.472
area_nm0.4721.000
2023-12-10T19:13:17.373649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laklang_stsfdg_rtjalng_stsfdg_rtarea_nmhotel_grad
city_do_cd1.0000.954-0.051-0.660-0.027-0.0380.9730.226
city_gn_gu_cd0.9541.000-0.023-0.671-0.042-0.0240.9470.089
xpos_lo-0.051-0.0231.000-0.0440.1490.0720.6940.244
ypos_la-0.660-0.671-0.0441.000-0.076-0.0640.8420.310
klang_stsfdg_rt-0.027-0.0420.149-0.0761.0000.1240.0000.000
jalng_stsfdg_rt-0.038-0.0240.072-0.0640.1241.0000.1620.190
area_nm0.9730.9470.6940.8420.0000.1621.0000.472
hotel_grad0.2260.0890.2440.3100.0000.1900.4721.000

Missing values

2023-12-10T19:13:05.240990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:13:05.555844image/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.
2023-12-10T19:13:05.771614image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noklang_stsfdg_rtjalng_stsfdg_rtbase_ymd
0DH 네상스 호텔서울특별시 성북구 동소문로20나길 39 동선동 복합빌1111290127.09753437.506011서울www.dhnaissance.com3급02-921-20803.34.02020-12-31
1코리아나호텔서울특별시 중구 태평로1가 세종대로 1351111140126.97654837.568224서울https://www.koreanahotel.com/index.htm?특2급02-2171-7000<NA><NA>2020-12-31
2GV 레지던스서울특별시 용산구 이태원로15길 14-41111170126.97232237.54084서울gv-residence.com3급02-797-58002.94.62020-12-31
3KY 헤리티지 호텔서울특별시 중구 장충단로 2261111140127.0010537.550703서울http://seouldongdaemun.splaisir.com특2급02-2198-12123.02.52020-12-31
4MS 호텔부산광역시 해운대구 해운대해변로 2712626350129.16466835.161447부산mshotel.alltheway.kr3급051-741-38382.84.42020-12-31
5SG 관광 호텔인천광역시 서구 탁옥로51번길 13-9 SG관광호텔2828260126.6743437.545036인천sghotel.kr3급032-562-05122.34.42020-12-31
6가야 라트리 호텔서울특별시 용산구 한강대로 253 가야라트리 호텔1111170126.97214137.541439서울kayalatreehotel.com3급02-798-51012.64.62020-12-31
7파티오세븐호텔서울특별시 강남구 논현동 논현로 7361111680127.02939737.517824서울https://www.patio7.co.kr/1급02-517-88333.93.92020-12-31
8강남 패밀리 호텔서울특별시 강남구 봉은사로 143 운현오피스텔1111680127.04527937.510347서울www.gangnamfamilyhotel.com3급02-6474-15152.12.32020-12-31
9골드 리버 호텔서울특별시 금천구 서부샛길 5841111545127.02607337.576175서울goldriverhotel.co.kr3급02-6021-81004.14.92020-12-31
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90이 천안호텔충청남도 천안시 서북구 성정동 734-24444130127.14024136.81241충남http://www.cheonanhotel.kr/2급041-592-00003.04.32020-12-31
91제주 라마다 앙코르 이스트 호텔제주특별자치도 서귀포시 서호동5050130126.51993433.254517제주https://www.ramadaencorejejuseogwipo.com/3급064-735-20004.13.02020-12-31
92제주 하나 호텔제주특별자치도 서귀포시 중문관광로72번길 535050130126.40808933.249867제주http://www.hotelhana.co.kr/main/특2급064-738-70013.63.32020-12-31
93코업 시티 호텔 성산제주특별자치도 서귀포시 성산읍 성산등용로 285050130126.93192733.465987제주https://www.coopcityhotel-seongsan.co.kr/3급064-780-98002.72.12020-12-31
94호텔 노블레스 제주제주특별자치도 제주시 월성로4길 195050110126.50335933.50068제주hotelnoblessejeju.modoo.at3급064-748-71612.23.62020-12-31
95호텔 로베로제주특별자치도 제주시 관덕로 265050110126.5218533.512872제주stazhoteljejurobero.com/1급064-757-71113.72.12020-12-31
96호텔 위드 제주제주특별자치도 제주시 노연로 345050110126.48377133.485748제주www.hotelwithjeju.com/특2급02-522-58733.04.42020-12-31
97힐리언스선마을강원도 홍천군 서면 종자산길 1224242720127.63080237.650257강원https://www.healience.co.kr/3급033-434-27722.32.92020-12-31
98호텔시에나경기도 파주시 소리천로 314141480126.76188237.715627경기www.hotelsienna.com1급031-943-72602.54.22020-12-31
99골든튤립해운대호텔앤스위트부산광역시 해운대구 중동 해운대해변로 3222626350129.1658735.162292부산www.goldentulip-haeundae.com특2급051-795-70004.13.92020-12-31