Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells158
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory129.3 B

Variable types

Text4
Numeric8
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
xpos_lo has 5 (5.0%) missing valuesMissing
ypos_la has 5 (5.0%) missing valuesMissing
homepage_url has 4 (4.0%) missing valuesMissing
asia_hotel_stsfdg_rt has 28 (28.0%) missing valuesMissing
europe_hotel_stsfdg_rt has 37 (37.0%) missing valuesMissing
america_hotel_stsfdg_rt has 40 (40.0%) missing valuesMissing
middle_hotel_east_stsfdg_rt has 38 (38.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:09:10.906846
Analysis finished2023-12-10 10:09:25.824754
Duration14.92 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
2023-12-10T19:09:26.187318image/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%
2023-12-10T19:09:27.013711image/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
2023-12-10T19:09:27.505638image/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%
2023-12-10T19:09:28.254999image/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
2023-12-10T19:09:28.500260image/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
2023-12-10T19:09:28.711603image/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
2023-12-10T19:09:29.019629image/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
2023-12-10T19:09:29.305414image/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  MISSING 

Distinct95
Distinct (%)100.0%
Missing5
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean127.34529
Minimum126.37151
Maximum130.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:29.585781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37151
5-th percentile126.51657
Q1126.88479
median127.00776
Q3127.11527
95-th percentile129.16161
Maximum130.8702
Range4.4986868
Interquartile range (IQR)0.2304754

Descriptive statistics

Standard deviation0.91870996
Coefficient of variation (CV)0.0072143222
Kurtosis1.8680494
Mean127.34529
Median Absolute Deviation (MAD)0.1168026
Skewness1.6556337
Sum12097.803
Variance0.84402798
MonotonicityNot monotonic
2023-12-10T19:09:29.823084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9723221 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%
127.0301916 1
 
1.0%
126.5984422 1
 
1.0%
Other values (85) 85
85.0%
(Missing) 5
 
5.0%
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  MISSING 

Distinct95
Distinct (%)100.0%
Missing5
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean36.613941
Minimum33.249867
Maximum38.189922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:30.098093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.249867
5-th percentile33.461822
Q135.313758
median37.478352
Q337.552243
95-th percentile37.589914
Maximum38.189922
Range4.9400543
Interquartile range (IQR)2.2384844

Descriptive statistics

Standard deviation1.4773529
Coefficient of variation (CV)0.040349463
Kurtosis0.15720429
Mean36.613941
Median Absolute Deviation (MAD)0.09181211
Skewness-1.286282
Sum3478.3243
Variance2.1825714
MonotonicityNot monotonic
2023-12-10T19:09:30.349653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.54083968 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%
37.50647548 1
 
1.0%
37.47435003 1
 
1.0%
Other values (85) 85
85.0%
(Missing) 5
 
5.0%
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%
37.65025711 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%
37.56797554 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

2023-12-10T19:09:30.580665image/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
2023-12-10T19:09:31.077373image/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%
2023-12-10T19:09:31.884163image/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

2023-12-10T19:09:32.149288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:32.363280image/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

Distinct98
Distinct (%)99.0%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:09:32.729041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.767677
Min length9

Characters and Unicode

Total characters1165
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

Unique97 ?
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 (88) 88
88.9%
2023-12-10T19:09:34.293478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 281
24.1%
- 197
16.9%
2 131
11.2%
1 111
 
9.5%
3 93
 
8.0%
7 77
 
6.6%
5 69
 
5.9%
6 59
 
5.1%
4 53
 
4.5%
9 51
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 968
83.1%
Dash Punctuation 197
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 281
29.0%
2 131
13.5%
1 111
 
11.5%
3 93
 
9.6%
7 77
 
8.0%
5 69
 
7.1%
6 59
 
6.1%
4 53
 
5.5%
9 51
 
5.3%
8 43
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 281
24.1%
- 197
16.9%
2 131
11.2%
1 111
 
9.5%
3 93
 
8.0%
7 77
 
6.6%
5 69
 
5.9%
6 59
 
5.1%
4 53
 
4.5%
9 51
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 281
24.1%
- 197
16.9%
2 131
11.2%
1 111
 
9.5%
3 93
 
8.0%
7 77
 
6.6%
5 69
 
5.9%
6 59
 
5.1%
4 53
 
4.5%
9 51
 
4.4%

asia_hotel_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)45.8%
Missing28
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean3.2027778
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:34.686740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.455
Q12.2
median3.3
Q34.525
95-th percentile4.945
Maximum5
Range4
Interquartile range (IQR)2.325

Descriptive statistics

Standard deviation1.2195557
Coefficient of variation (CV)0.38078062
Kurtosis-1.2952577
Mean3.2027778
Median Absolute Deviation (MAD)1.15
Skewness-0.060156327
Sum230.6
Variance1.4873161
MonotonicityNot monotonic
2023-12-10T19:09:35.026464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4.6 8
 
8.0%
1.7 5
 
5.0%
5.0 4
 
4.0%
2.2 4
 
4.0%
3.4 4
 
4.0%
2.5 3
 
3.0%
3.7 3
 
3.0%
3.3 3
 
3.0%
1.5 3
 
3.0%
2.1 3
 
3.0%
Other values (23) 32
32.0%
(Missing) 28
28.0%
ValueCountFrequency (%)
1.0 1
 
1.0%
1.1 2
 
2.0%
1.4 1
 
1.0%
1.5 3
3.0%
1.6 1
 
1.0%
1.7 5
5.0%
1.8 1
 
1.0%
2.1 3
3.0%
2.2 4
4.0%
2.3 1
 
1.0%
ValueCountFrequency (%)
5.0 4
4.0%
4.9 2
 
2.0%
4.8 2
 
2.0%
4.7 2
 
2.0%
4.6 8
8.0%
4.5 1
 
1.0%
4.4 1
 
1.0%
4.2 1
 
1.0%
4.1 2
 
2.0%
3.9 2
 
2.0%

europe_hotel_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)50.8%
Missing37
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean2.9095238
Minimum1.1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:35.360336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.3
Q11.95
median2.9
Q33.55
95-th percentile4.79
Maximum5
Range3.9
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0629712
Coefficient of variation (CV)0.36534199
Kurtosis-0.78270456
Mean2.9095238
Median Absolute Deviation (MAD)0.8
Skewness0.1928442
Sum183.3
Variance1.1299078
MonotonicityNot monotonic
2023-12-10T19:09:35.607736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.4 5
 
5.0%
2.9 5
 
5.0%
2.5 4
 
4.0%
1.8 4
 
4.0%
2.3 3
 
3.0%
1.7 3
 
3.0%
3.2 2
 
2.0%
3.5 2
 
2.0%
4.7 2
 
2.0%
4.8 2
 
2.0%
Other values (22) 31
31.0%
(Missing) 37
37.0%
ValueCountFrequency (%)
1.1 1
 
1.0%
1.2 2
2.0%
1.3 2
2.0%
1.5 1
 
1.0%
1.6 2
2.0%
1.7 3
3.0%
1.8 4
4.0%
1.9 1
 
1.0%
2.0 1
 
1.0%
2.3 3
3.0%
ValueCountFrequency (%)
5.0 2
2.0%
4.8 2
2.0%
4.7 2
2.0%
4.6 1
1.0%
4.3 1
1.0%
4.1 1
1.0%
4.0 2
2.0%
3.9 1
1.0%
3.8 1
1.0%
3.7 2
2.0%

america_hotel_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)51.7%
Missing40
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean2.8033333
Minimum1.1
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:35.838289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.2
Q11.875
median2.7
Q33.7
95-th percentile4.805
Maximum4.9
Range3.8
Interquartile range (IQR)1.825

Descriptive statistics

Standard deviation1.1540594
Coefficient of variation (CV)0.41167398
Kurtosis-1.0154929
Mean2.8033333
Median Absolute Deviation (MAD)0.9
Skewness0.34984454
Sum168.2
Variance1.3318531
MonotonicityNot monotonic
2023-12-10T19:09:36.094275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.7 4
 
4.0%
4.1 4
 
4.0%
2.1 3
 
3.0%
4.6 3
 
3.0%
4.9 3
 
3.0%
1.8 3
 
3.0%
1.5 3
 
3.0%
3.0 3
 
3.0%
1.3 2
 
2.0%
1.9 2
 
2.0%
Other values (21) 30
30.0%
(Missing) 40
40.0%
ValueCountFrequency (%)
1.1 2
2.0%
1.2 2
2.0%
1.3 2
2.0%
1.4 2
2.0%
1.5 3
3.0%
1.6 1
 
1.0%
1.8 3
3.0%
1.9 2
2.0%
2.0 1
 
1.0%
2.1 3
3.0%
ValueCountFrequency (%)
4.9 3
3.0%
4.8 1
 
1.0%
4.6 3
3.0%
4.5 2
2.0%
4.1 4
4.0%
4.0 2
2.0%
3.6 1
 
1.0%
3.5 1
 
1.0%
3.4 1
 
1.0%
3.3 1
 
1.0%

middle_hotel_east_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)50.0%
Missing38
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean3.0306452
Minimum1.2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:36.364293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.3
Q12.025
median3.1
Q34.1
95-th percentile4.8
Maximum5
Range3.8
Interquartile range (IQR)2.075

Descriptive statistics

Standard deviation1.1718822
Coefficient of variation (CV)0.38667745
Kurtosis-1.2983338
Mean3.0306452
Median Absolute Deviation (MAD)1
Skewness0.0095534765
Sum187.9
Variance1.3733078
MonotonicityNot monotonic
2023-12-10T19:09:36.616843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.3 4
 
4.0%
2.1 3
 
3.0%
4.1 3
 
3.0%
3.1 3
 
3.0%
4.8 3
 
3.0%
4.4 3
 
3.0%
2.2 3
 
3.0%
1.2 3
 
3.0%
3.8 3
 
3.0%
1.8 2
 
2.0%
Other values (21) 32
32.0%
(Missing) 38
38.0%
ValueCountFrequency (%)
1.2 3
3.0%
1.3 2
2.0%
1.4 1
 
1.0%
1.5 2
2.0%
1.6 2
2.0%
1.7 2
2.0%
1.8 2
2.0%
2.0 2
2.0%
2.1 3
3.0%
2.2 3
3.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.9 1
 
1.0%
4.8 3
3.0%
4.7 2
2.0%
4.4 3
3.0%
4.3 2
2.0%
4.2 2
2.0%
4.1 3
3.0%
4.0 1
 
1.0%
3.9 1
 
1.0%

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
2023-12-10T19:09:36.846814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.018160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:09:23.353815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.367241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.868144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.587460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.007744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:18.653903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:20.089078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:21.576658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.530870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.582239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.047840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.758523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.183250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:18.843391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:20.271479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:21.807119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.684782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.772666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.314769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.948353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.388332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.020427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:20.455949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:22.323867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.831360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.937631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.477323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.088775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.524274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.184704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:20.630069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:22.487661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.966426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.117925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.646837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.286995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.745715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.393447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:20.804611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:22.654809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:24.203132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.292823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.833077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.518895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.991396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.564943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:21.031957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:22.835791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:24.374889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.487142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.018647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.687618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:18.247444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.741487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:21.217910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.005822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:24.563262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.680951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.389829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.851659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:18.487869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:19.931296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:21.403660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:23.187576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:37.172596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noasia_hotel_stsfdg_rteurope_hotel_stsfdg_rtamerica_hotel_stsfdg_rtmiddle_hotel_east_stsfdg_rt
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0000.9990.7280.9461.0001.0000.3671.0000.0000.1420.0000.000
city_gn_gu_cd1.0001.0000.9991.0000.7510.9320.9871.0000.1481.0000.0000.0000.0000.000
xpos_lo1.0001.0000.7280.7511.0000.7410.9271.0000.4301.0000.0000.4480.0000.247
ypos_la1.0001.0000.9460.9320.7411.0000.9551.0000.4891.0000.0000.0690.0000.000
area_nm1.0001.0001.0000.9870.9270.9551.0001.0000.8381.0000.0000.1980.0000.367
homepage_url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9770.9391.0000.957
hotel_grad1.0001.0000.3670.1480.4300.4890.8381.0001.0001.0000.0000.6540.0000.169
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9770.9391.0000.957
asia_hotel_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.9770.0000.9771.0000.6590.3590.293
europe_hotel_stsfdg_rt1.0001.0000.1420.0000.4480.0690.1980.9390.6540.9390.6591.0000.4560.000
america_hotel_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0001.0000.0001.0000.3590.4561.0000.340
middle_hotel_east_stsfdg_rt1.0001.0000.0000.0000.2470.0000.3670.9570.1690.9570.2930.0000.3401.000
2023-12-10T19:09:37.452194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmhotel_grad
area_nm1.0000.472
hotel_grad0.4721.000
2023-12-10T19:09:37.611090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laasia_hotel_stsfdg_rteurope_hotel_stsfdg_rtamerica_hotel_stsfdg_rtmiddle_hotel_east_stsfdg_rtarea_nmhotel_grad
city_do_cd1.0000.954-0.047-0.6930.123-0.080-0.2930.0690.9730.226
city_gn_gu_cd0.9541.000-0.021-0.7040.139-0.094-0.2520.0500.9470.089
xpos_lo-0.047-0.0211.0000.0050.0160.095-0.118-0.1180.7010.251
ypos_la-0.693-0.7040.0051.000-0.1160.1940.142-0.0810.8500.315
asia_hotel_stsfdg_rt0.1230.1390.016-0.1161.000-0.065-0.0660.0970.0900.205
europe_hotel_stsfdg_rt-0.080-0.0940.0950.194-0.0651.0000.0020.0690.0640.310
america_hotel_stsfdg_rt-0.293-0.252-0.1180.142-0.0660.0021.0000.0290.0000.000
middle_hotel_east_stsfdg_rt0.0690.050-0.118-0.0810.0970.0690.0291.0000.1490.070
area_nm0.9730.9470.7010.8500.0900.0640.0000.1491.0000.472
hotel_grad0.2260.0890.2510.3150.2050.3100.0000.0700.4721.000

Missing values

2023-12-10T19:09:24.805239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:25.340595image/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.
2023-12-10T19:09:25.643544image/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_noasia_hotel_stsfdg_rteurope_hotel_stsfdg_rtamerica_hotel_stsfdg_rtmiddle_hotel_east_stsfdg_rtbase_ymd
0DH 네상스 호텔서울특별시 성북구 동소문로20나길 39 동선동 복합빌1111290127.09753437.506011서울www.dhnaissance.com3급02-921-20803.32.4<NA>2.52020-12-31
1코리아나호텔서울특별시 중구 태평로1가 세종대로 1351111140<NA><NA>서울https://www.koreanahotel.com/index.htm?특2급02-2171-7000<NA><NA><NA><NA>2020-12-31
2GV 레지던스서울특별시 용산구 이태원로15길 14-41111170126.97232237.54084서울gv-residence.com3급02-797-5800<NA><NA><NA><NA>2020-12-31
3KY 헤리티지 호텔서울특별시 중구 장충단로 2261111140127.0010537.550703서울http://seouldongdaemun.splaisir.com특2급02-2198-12125.03.31.14.22020-12-31
4MS 호텔부산광역시 해운대구 해운대해변로 2712626350129.16466835.161447부산mshotel.alltheway.kr3급051-741-3838<NA><NA><NA><NA>2020-12-31
5SG 관광 호텔인천광역시 서구 탁옥로51번길 13-9 SG관광호텔2828260126.6743437.545036인천sghotel.kr3급032-562-0512<NA><NA><NA><NA>2020-12-31
6가야 라트리 호텔서울특별시 용산구 한강대로 253 가야라트리 호텔1111170126.97214137.541439서울kayalatreehotel.com3급02-798-51012.14.0<NA>1.22020-12-31
7파티오세븐호텔서울특별시 강남구 논현동 논현로 7361111680<NA><NA>서울https://www.patio7.co.kr/1급02-517-8833<NA><NA><NA><NA>2020-12-31
8강남 패밀리 호텔서울특별시 강남구 봉은사로 143 운현오피스텔1111680127.04527937.510347서울www.gangnamfamilyhotel.com3급02-6474-15154.5<NA>3.04.92020-12-31
9골드 리버 호텔서울특별시 금천구 서부샛길 5841111545127.02607337.576175서울goldriverhotel.co.kr3급02-6021-81001.12.5<NA><NA>2020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noasia_hotel_stsfdg_rteurope_hotel_stsfdg_rtamerica_hotel_stsfdg_rtmiddle_hotel_east_stsfdg_rtbase_ymd
90이 천안호텔충청남도 천안시 서북구 성정동 734-24444130127.14024136.81241충남http://www.cheonanhotel.kr/2급041-592-00002.12.01.13.12020-12-31
91제주 라마다 앙코르 이스트 호텔제주특별자치도 서귀포시 서호동5050130126.51993433.254517제주https://www.ramadaencorejejuseogwipo.com/3급064-735-20002.52.5<NA>3.42020-12-31
92제주 하나 호텔제주특별자치도 서귀포시 중문관광로72번길 535050130126.40808933.249867제주http://www.hotelhana.co.kr/main/특2급064-738-70012.23.53.12.02020-12-31
93코업 시티 호텔 성산제주특별자치도 서귀포시 성산읍 성산등용로 285050130126.93192733.465987제주https://www.coopcityhotel-seongsan.co.kr/3급064-780-98004.71.61.32.82020-12-31
94호텔 노블레스 제주제주특별자치도 제주시 월성로4길 195050110126.50335933.50068제주hotelnoblessejeju.modoo.at3급064-748-7161<NA><NA><NA><NA>2020-12-31
95호텔 로베로제주특별자치도 제주시 관덕로 265050110126.5218533.512872제주stazhoteljejurobero.com/1급064-757-71112.3<NA>1.3<NA>2020-12-31
96호텔 위드 제주제주특별자치도 제주시 노연로 345050110126.48377133.485748제주www.hotelwithjeju.com/특2급02-522-58734.92.32.44.42020-12-31
97힐리언스선마을강원도 홍천군 서면 종자산길 1224242720127.63080237.650257강원https://www.healience.co.kr/3급033-434-27723.93.11.52.12020-12-31
98호텔시에나경기도 파주시 소리천로 314141480<NA><NA>경기www.hotelsienna.com1급031-943-7260<NA><NA><NA><NA>2020-12-31
99골든튤립해운대호텔앤스위트부산광역시 해운대구 중동 해운대해변로 3222626350<NA><NA>부산www.goldentulip-haeundae.com특2급051-795-7000<NA><NA><NA><NA>2020-12-31