Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells20
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory130.3 B

Variable types

Text4
Numeric8
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
tel_no has 5 (5.0%) missing valuesMissing
homepage_url has 15 (15.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:10:58.545522
Analysis finished2023-12-10 10:11:13.606576
Duration15.06 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
2023-12-10T19:11:13.869716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14.5
Mean length9.29
Min length3

Characters and Unicode

Total characters929
Distinct characters167
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 rowE호텔
2nd row호텔스카이파크킹스타운동대문
3rd row나인트리프리미어호텔명동2
4th row나인트리호텔명동
5th row라마다서울동대문
ValueCountFrequency (%)
호텔 54
24.2%
서울 7
 
3.1%
프리미어 7
 
3.1%
베스트웨스턴 5
 
2.2%
동대문 5
 
2.2%
강남 4
 
1.8%
송도 3
 
1.3%
명동 3
 
1.3%
베니키아 3
 
1.3%
스타즈 3
 
1.3%
Other values (119) 129
57.8%
2023-12-10T19:11:14.530994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
13.2%
86
 
9.3%
83
 
8.9%
55
 
5.9%
21
 
2.3%
21
 
2.3%
19
 
2.0%
16
 
1.7%
15
 
1.6%
14
 
1.5%
Other values (157) 476
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
85.7%
Space Separator 123
 
13.2%
Decimal Number 5
 
0.5%
Uppercase Letter 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
86
 
10.8%
83
 
10.4%
55
 
6.9%
21
 
2.6%
21
 
2.6%
19
 
2.4%
16
 
2.0%
15
 
1.9%
14
 
1.8%
14
 
1.8%
Other values (148) 452
56.8%
Uppercase Letter
ValueCountFrequency (%)
I 1
20.0%
T 1
20.0%
M 1
20.0%
S 1
20.0%
E 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
7 1
 
20.0%
1 1
 
20.0%
Space Separator
ValueCountFrequency (%)
123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
85.7%
Common 128
 
13.8%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
86
 
10.8%
83
 
10.4%
55
 
6.9%
21
 
2.6%
21
 
2.6%
19
 
2.4%
16
 
2.0%
15
 
1.9%
14
 
1.8%
14
 
1.8%
Other values (148) 452
56.8%
Latin
ValueCountFrequency (%)
I 1
20.0%
T 1
20.0%
M 1
20.0%
S 1
20.0%
E 1
20.0%
Common
ValueCountFrequency (%)
123
96.1%
2 3
 
2.3%
7 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
85.7%
ASCII 133
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
92.5%
2 3
 
2.3%
I 1
 
0.8%
T 1
 
0.8%
M 1
 
0.8%
S 1
 
0.8%
E 1
 
0.8%
7 1
 
0.8%
1 1
 
0.8%
Hangul
ValueCountFrequency (%)
86
 
10.8%
83
 
10.4%
55
 
6.9%
21
 
2.6%
21
 
2.6%
19
 
2.4%
16
 
2.0%
15
 
1.9%
14
 
1.8%
14
 
1.8%
Other values (148) 452
56.8%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:15.077777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length30.5
Mean length21.4
Min length12

Characters and Unicode

Total characters2140
Distinct characters198
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row서울특별시 서초구 반포대로18길 40 E 호텔
2nd row서울특별시 중구 장충단로13길 20 14층
3rd row서울특별시 중구 마른내로 28
4th row서울특별시 종로구 인사동길 49
5th row서울특별시 중구 동호로 354
ValueCountFrequency (%)
서울특별시 52
 
11.5%
중구 21
 
4.6%
경기도 13
 
2.9%
인천광역시 11
 
2.4%
부산광역시 10
 
2.2%
제주특별자치도 10
 
2.2%
서귀포시 9
 
2.0%
강남구 8
 
1.8%
강서구 6
 
1.3%
종로구 5
 
1.1%
Other values (255) 307
67.9%
2023-12-10T19:11:15.861943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
352
 
16.4%
103
 
4.8%
99
 
4.6%
86
 
4.0%
1 73
 
3.4%
72
 
3.4%
63
 
2.9%
63
 
2.9%
54
 
2.5%
43
 
2.0%
Other values (188) 1132
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1424
66.5%
Space Separator 352
 
16.4%
Decimal Number 336
 
15.7%
Dash Punctuation 16
 
0.7%
Uppercase Letter 8
 
0.4%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
103
 
7.2%
99
 
7.0%
86
 
6.0%
72
 
5.1%
63
 
4.4%
63
 
4.4%
54
 
3.8%
43
 
3.0%
37
 
2.6%
35
 
2.5%
Other values (167) 769
54.0%
Decimal Number
ValueCountFrequency (%)
1 73
21.7%
2 40
11.9%
3 35
10.4%
6 31
9.2%
9 29
 
8.6%
8 28
 
8.3%
5 27
 
8.0%
4 27
 
8.0%
7 24
 
7.1%
0 22
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
A 2
25.0%
C 2
25.0%
E 2
25.0%
P 1
12.5%
H 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
o 1
25.0%
l 1
25.0%
t 1
25.0%
Space Separator
ValueCountFrequency (%)
352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1424
66.5%
Common 704
32.9%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
103
 
7.2%
99
 
7.0%
86
 
6.0%
72
 
5.1%
63
 
4.4%
63
 
4.4%
54
 
3.8%
43
 
3.0%
37
 
2.6%
35
 
2.5%
Other values (167) 769
54.0%
Common
ValueCountFrequency (%)
352
50.0%
1 73
 
10.4%
2 40
 
5.7%
3 35
 
5.0%
6 31
 
4.4%
9 29
 
4.1%
8 28
 
4.0%
5 27
 
3.8%
4 27
 
3.8%
7 24
 
3.4%
Other values (2) 38
 
5.4%
Latin
ValueCountFrequency (%)
A 2
16.7%
C 2
16.7%
E 2
16.7%
P 1
8.3%
e 1
8.3%
H 1
8.3%
o 1
8.3%
l 1
8.3%
t 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1424
66.5%
ASCII 716
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352
49.2%
1 73
 
10.2%
2 40
 
5.6%
3 35
 
4.9%
6 31
 
4.3%
9 29
 
4.1%
8 28
 
3.9%
5 27
 
3.8%
4 27
 
3.8%
7 24
 
3.4%
Other values (11) 50
 
7.0%
Hangul
ValueCountFrequency (%)
103
 
7.2%
99
 
7.0%
86
 
6.0%
72
 
5.1%
63
 
4.4%
63
 
4.4%
54
 
3.8%
43
 
3.0%
37
 
2.6%
35
 
2.5%
Other values (167) 769
54.0%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.45
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:16.073850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.60896
Coefficient of variation (CV)0.62298338
Kurtosis-1.1164254
Mean23.45
Median Absolute Deviation (MAD)0
Skewness0.65465421
Sum2345
Variance213.42172
MonotonicityNot monotonic
2023-12-10T19:11:16.301739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 52
52.0%
41 13
 
13.0%
28 11
 
11.0%
50 10
 
10.0%
26 10
 
10.0%
31 1
 
1.0%
46 1
 
1.0%
48 1
 
1.0%
47 1
 
1.0%
ValueCountFrequency (%)
11 52
52.0%
26 10
 
10.0%
28 11
 
11.0%
31 1
 
1.0%
41 13
 
13.0%
46 1
 
1.0%
47 1
 
1.0%
48 1
 
1.0%
50 10
 
10.0%
ValueCountFrequency (%)
50 10
 
10.0%
48 1
 
1.0%
47 1
 
1.0%
46 1
 
1.0%
41 13
 
13.0%
31 1
 
1.0%
28 11
 
11.0%
26 10
 
10.0%
11 52
52.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23752.61
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:16.548944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11138.5
Q111425
median11695
Q341113
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)29688

Descriptive statistics

Standard deviation14522.343
Coefficient of variation (CV)0.61139988
Kurtosis-1.114113
Mean23752.61
Median Absolute Deviation (MAD)585
Skewness0.65719989
Sum2375261
Variance2.1089844 × 108
MonotonicityNot monotonic
2023-12-10T19:11:16.897673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
11140 15
15.0%
50130 9
 
9.0%
11680 8
 
8.0%
11500 6
 
6.0%
11110 5
 
5.0%
28110 5
 
5.0%
26350 4
 
4.0%
28185 4
 
4.0%
11560 4
 
4.0%
11650 3
 
3.0%
Other values (31) 37
37.0%
ValueCountFrequency (%)
11110 5
 
5.0%
11140 15
15.0%
11170 1
 
1.0%
11215 1
 
1.0%
11290 1
 
1.0%
11350 1
 
1.0%
11380 1
 
1.0%
11440 1
 
1.0%
11500 6
 
6.0%
11530 1
 
1.0%
ValueCountFrequency (%)
50130 9
9.0%
50110 1
 
1.0%
48310 1
 
1.0%
47113 1
 
1.0%
46130 1
 
1.0%
41590 1
 
1.0%
41500 1
 
1.0%
41480 1
 
1.0%
41390 1
 
1.0%
41370 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.1898
Minimum126.40835
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:17.215784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40835
5-th percentile126.52188
Q1126.84463
median126.99063
Q3127.03575
95-th percentile129.15657
Maximum129.35736
Range2.9490161
Interquartile range (IQR)0.19112643

Descriptive statistics

Standard deviation0.76978336
Coefficient of variation (CV)0.0060522411
Kurtosis2.5393217
Mean127.1898
Median Absolute Deviation (MAD)0.07941935
Skewness1.9762485
Sum12718.98
Variance0.59256643
MonotonicityNot monotonic
2023-12-10T19:11:17.454381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0366394 2
 
2.0%
127.0126078 1
 
1.0%
126.980923 1
 
1.0%
127.0315241 1
 
1.0%
127.0087236 1
 
1.0%
126.5575562 1
 
1.0%
126.58147 1
 
1.0%
126.5977517 1
 
1.0%
126.7402653 1
 
1.0%
128.6034968 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.4083476 1
1.0%
126.4131379 1
1.0%
126.4305938 1
1.0%
126.4557688 1
1.0%
126.519265 1
1.0%
126.522019 1
1.0%
126.5575562 1
1.0%
126.578421 1
1.0%
126.58147 1
1.0%
126.5977517 1
1.0%
ValueCountFrequency (%)
129.3573637 1
1.0%
129.3473935 1
1.0%
129.165879 1
1.0%
129.1611261 1
1.0%
129.1569244 1
1.0%
129.1565486 1
1.0%
129.1179799 1
1.0%
129.058122 1
1.0%
129.057318 1
1.0%
129.0418332 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.754446
Minimum33.247674
Maximum37.715623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:17.709051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.247674
5-th percentile33.427692
Q137.200041
median37.490991
Q337.560967
95-th percentile37.574556
Maximum37.715623
Range4.4679489
Interquartile range (IQR)0.36092585

Descriptive statistics

Standard deviation1.3960997
Coefficient of variation (CV)0.037984513
Kurtosis1.0354692
Mean36.754446
Median Absolute Deviation (MAD)0.076817705
Skewness-1.5893287
Sum3675.4446
Variance1.9490945
MonotonicityNot monotonic
2023-12-10T19:11:17.935062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.52738988 2
 
2.0%
37.48697203 1
 
1.0%
37.56764227 1
 
1.0%
37.25904497 1
 
1.0%
37.56412429 1
 
1.0%
33.24770083 1
 
1.0%
37.49027215 1
 
1.0%
37.47399441 1
 
1.0%
37.39046414 1
 
1.0%
34.88593306 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.24767393 1
1.0%
33.24770083 1
1.0%
33.24905001 1
1.0%
33.25447711 1
1.0%
33.25743821 1
1.0%
33.4366529 1
1.0%
33.45210482 1
1.0%
33.46235117 1
1.0%
33.46598674 1
1.0%
33.512769 1
1.0%
ValueCountFrequency (%)
37.7156228 1
1.0%
37.65519408 1
1.0%
37.59989609 1
1.0%
37.59361073 1
1.0%
37.57625021 1
1.0%
37.57446681 1
1.0%
37.57400036 1
1.0%
37.57382681 1
1.0%
37.57332038 1
1.0%
37.56873587 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
52 
경기
13 
인천
11 
제주
10 
부산
10 
Other values (4)
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 52
52.0%
경기 13
 
13.0%
인천 11
 
11.0%
제주 10
 
10.0%
부산 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

Length

2023-12-10T19:11:18.152362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:18.446082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 52
52.0%
경기 13
 
13.0%
인천 11
 
11.0%
제주 10
 
10.0%
부산 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

hotel_grad
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
40 
4
24 
2
21 
<NA>
5
 
4

Length

Max length4
Median length1
Mean length1.24
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 40
40.0%
4 24
24.0%
2 21
21.0%
<NA> 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%

Length

2023-12-10T19:11:18.789670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:19.034484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 40
40.0%
4 24
24.0%
2 21
21.0%
na 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%

tel_no
Text

MISSING 

Distinct95
Distinct (%)100.0%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T19:11:19.523919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.789474
Min length9

Characters and Unicode

Total characters1120
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

Unique95 ?
Unique (%)100.0%

Sample

1st row02-521-6555
2nd row02-6952-8991
3rd row02-6967-0999
4th row02-750-0999
5th row02-2276-3500
ValueCountFrequency (%)
02-521-6555 1
 
1.1%
031-230-5000 1
 
1.1%
02-2660-7300 1
 
1.1%
02-2079-8888 1
 
1.1%
064-741-0000 1
 
1.1%
032-890-0000 1
 
1.1%
032-777-7272 1
 
1.1%
031-318-0743 1
 
1.1%
02-867-9345 1
 
1.1%
02-2277-1141 1
 
1.1%
Other values (85) 85
89.5%
2023-12-10T19:11:20.456626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 135
12.1%
1 89
 
7.9%
3 79
 
7.1%
5 75
 
6.7%
7 69
 
6.2%
6 68
 
6.1%
8 55
 
4.9%
4 51
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 931
83.1%
Dash Punctuation 189
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
28.6%
2 135
14.5%
1 89
 
9.6%
3 79
 
8.5%
5 75
 
8.1%
7 69
 
7.4%
6 68
 
7.3%
8 55
 
5.9%
4 51
 
5.5%
9 44
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 135
12.1%
1 89
 
7.9%
3 79
 
7.1%
5 75
 
6.7%
7 69
 
6.2%
6 68
 
6.1%
8 55
 
4.9%
4 51
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 135
12.1%
1 89
 
7.9%
3 79
 
7.1%
5 75
 
6.7%
7 69
 
6.2%
6 68
 
6.1%
8 55
 
4.9%
4 51
 
4.6%

homepage_url
Text

MISSING 

Distinct82
Distinct (%)96.5%
Missing15
Missing (%)15.0%
Memory size932.0 B
2023-12-10T19:11:20.986086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length41
Mean length33.352941
Min length19

Characters and Unicode

Total characters2835
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)95.3%

Sample

1st rowhttp://www.e-hotel.kr/
2nd rowhttps://www.skyparkhotel.com/html/main.asp
3rd rowhttp://www.ninetreehotels.com/nth2/
4th rowhttps://ninetreehotel.com/
5th rowhttp://www.ramadaddm.com/main/
ValueCountFrequency (%)
http://www.aroomi.co.kr/kor/index.do 4
 
4.7%
http://www.arbanhotel.com/html/main.asp 1
 
1.2%
http://www.mirandahotel.com/renewal/index.asp 1
 
1.2%
https://www.ambatel.com/ibis/suwon/ko/main.do 1
 
1.2%
https://www.ambatel.com/ibisbudget/dongdaemun/ko/main.do 1
 
1.2%
http://www.hotelwinstory.com 1
 
1.2%
http://www.westerngracehotel.net 1
 
1.2%
http://www.premiers.kr 1
 
1.2%
http://www.youngbinhotel.co.kr 1
 
1.2%
http://www.yeosuvenezia.com 1
 
1.2%
Other values (72) 72
84.7%
2023-12-10T19:11:21.722798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 291
 
10.3%
t 272
 
9.6%
w 221
 
7.8%
o 220
 
7.8%
. 208
 
7.3%
h 166
 
5.9%
e 163
 
5.7%
p 125
 
4.4%
a 109
 
3.8%
r 109
 
3.8%
Other values (35) 951
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2199
77.6%
Other Punctuation 593
 
20.9%
Decimal Number 25
 
0.9%
Uppercase Letter 11
 
0.4%
Dash Punctuation 5
 
0.2%
Math Symbol 1
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 272
12.4%
w 221
 
10.1%
o 220
 
10.0%
h 166
 
7.5%
e 163
 
7.4%
p 125
 
5.7%
a 109
 
5.0%
r 109
 
5.0%
m 102
 
4.6%
c 102
 
4.6%
Other values (15) 610
27.7%
Other Punctuation
ValueCountFrequency (%)
/ 291
49.1%
. 208
35.1%
: 87
 
14.7%
% 4
 
0.7%
? 2
 
0.3%
# 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 7
28.0%
1 6
24.0%
0 5
20.0%
3 3
12.0%
5 3
12.0%
7 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
27.3%
F 3
27.3%
M 2
18.2%
R 2
18.2%
J 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2210
78.0%
Common 625
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 272
12.3%
w 221
 
10.0%
o 220
 
10.0%
h 166
 
7.5%
e 163
 
7.4%
p 125
 
5.7%
a 109
 
4.9%
r 109
 
4.9%
m 102
 
4.6%
c 102
 
4.6%
Other values (20) 621
28.1%
Common
ValueCountFrequency (%)
/ 291
46.6%
. 208
33.3%
: 87
 
13.9%
2 7
 
1.1%
1 6
 
1.0%
- 5
 
0.8%
0 5
 
0.8%
% 4
 
0.6%
3 3
 
0.5%
5 3
 
0.5%
Other values (5) 6
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 291
 
10.3%
t 272
 
9.6%
w 221
 
7.8%
o 220
 
7.8%
. 208
 
7.3%
h 166
 
5.9%
e 163
 
5.7%
p 125
 
4.4%
a 109
 
3.8%
r 109
 
3.8%
Other values (35) 951
33.5%

klang_trrsrt_stsfdg_rt
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.598
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:21.962439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.295
Q12.9
median3.55
Q34.4
95-th percentile4.9
Maximum5
Range3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.86234119
Coefficient of variation (CV)0.23967237
Kurtosis-1.1787762
Mean3.598
Median Absolute Deviation (MAD)0.7
Skewness0.014782784
Sum359.8
Variance0.74363232
MonotonicityNot monotonic
2023-12-10T19:11:22.189695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.0 7
 
7.0%
4.9 7
 
7.0%
4.5 6
 
6.0%
2.9 6
 
6.0%
3.5 5
 
5.0%
3.8 5
 
5.0%
2.8 4
 
4.0%
4.8 4
 
4.0%
2.3 4
 
4.0%
3.4 4
 
4.0%
Other values (21) 48
48.0%
ValueCountFrequency (%)
2.0 1
 
1.0%
2.1 1
 
1.0%
2.2 3
3.0%
2.3 4
4.0%
2.4 2
 
2.0%
2.5 3
3.0%
2.6 2
 
2.0%
2.7 1
 
1.0%
2.8 4
4.0%
2.9 6
6.0%
ValueCountFrequency (%)
5.0 2
 
2.0%
4.9 7
7.0%
4.8 4
4.0%
4.7 3
3.0%
4.6 2
 
2.0%
4.5 6
6.0%
4.4 3
3.0%
4.3 2
 
2.0%
4.2 3
3.0%
4.1 1
 
1.0%

engl_trrsrt_stsfdg_rt
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.499
Minimum2
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:22.398409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.6
median3.5
Q34.5
95-th percentile4.9
Maximum4.9
Range2.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation0.96791925
Coefficient of variation (CV)0.27662739
Kurtosis-1.4633911
Mean3.499
Median Absolute Deviation (MAD)0.95
Skewness-0.0016225499
Sum349.9
Variance0.93686768
MonotonicityNot monotonic
2023-12-10T19:11:22.614943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.5 7
 
7.0%
2.6 6
 
6.0%
4.8 6
 
6.0%
2.2 6
 
6.0%
4.7 6
 
6.0%
4.9 6
 
6.0%
3.2 5
 
5.0%
4.0 5
 
5.0%
2.4 5
 
5.0%
2.1 5
 
5.0%
Other values (18) 43
43.0%
ValueCountFrequency (%)
2.0 2
 
2.0%
2.1 5
5.0%
2.2 6
6.0%
2.3 2
 
2.0%
2.4 5
5.0%
2.5 3
3.0%
2.6 6
6.0%
2.7 3
3.0%
2.8 3
3.0%
2.9 3
3.0%
ValueCountFrequency (%)
4.9 6
6.0%
4.8 6
6.0%
4.7 6
6.0%
4.6 2
 
2.0%
4.5 7
7.0%
4.4 3
3.0%
4.3 2
 
2.0%
4.2 1
 
1.0%
4.0 5
5.0%
3.9 3
3.0%

chnlng_trrsrt_stsfdg_rt
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.504
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:22.835547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2
Q12.775
median3.45
Q34.3
95-th percentile4.8
Maximum5
Range3
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation0.8540953
Coefficient of variation (CV)0.24374866
Kurtosis-1.2194811
Mean3.504
Median Absolute Deviation (MAD)0.75
Skewness0.035954558
Sum350.4
Variance0.72947879
MonotonicityNot monotonic
2023-12-10T19:11:23.387211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.3 8
 
8.0%
2.2 6
 
6.0%
2.9 6
 
6.0%
2.4 6
 
6.0%
3.9 5
 
5.0%
2.7 5
 
5.0%
2.5 5
 
5.0%
4.8 5
 
5.0%
4.1 4
 
4.0%
3.3 4
 
4.0%
Other values (20) 46
46.0%
ValueCountFrequency (%)
2.0 1
 
1.0%
2.1 1
 
1.0%
2.2 6
6.0%
2.4 6
6.0%
2.5 5
5.0%
2.6 1
 
1.0%
2.7 5
5.0%
2.8 1
 
1.0%
2.9 6
6.0%
3.0 4
4.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.9 3
 
3.0%
4.8 5
5.0%
4.7 4
4.0%
4.6 2
 
2.0%
4.5 1
 
1.0%
4.4 2
 
2.0%
4.3 8
8.0%
4.2 2
 
2.0%
4.1 4
4.0%

jalng_trrsrt_stsfdg_rt
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.38
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:23.614690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2
Q12.6
median3.25
Q34.225
95-th percentile4.805
Maximum5
Range3
Interquartile range (IQR)1.625

Descriptive statistics

Standard deviation0.89476593
Coefficient of variation (CV)0.26472365
Kurtosis-1.2013661
Mean3.38
Median Absolute Deviation (MAD)0.75
Skewness0.31453467
Sum338
Variance0.80060606
MonotonicityNot monotonic
2023-12-10T19:11:23.846705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.4 8
 
8.0%
3.1 6
 
6.0%
2.2 6
 
6.0%
4.4 6
 
6.0%
3.4 5
 
5.0%
2.3 5
 
5.0%
2.6 4
 
4.0%
3.9 4
 
4.0%
2.7 4
 
4.0%
2.8 4
 
4.0%
Other values (21) 48
48.0%
ValueCountFrequency (%)
2.0 1
 
1.0%
2.1 1
 
1.0%
2.2 6
6.0%
2.3 5
5.0%
2.4 8
8.0%
2.5 3
 
3.0%
2.6 4
4.0%
2.7 4
4.0%
2.8 4
4.0%
2.9 1
 
1.0%
ValueCountFrequency (%)
5.0 3
3.0%
4.9 2
 
2.0%
4.8 4
4.0%
4.7 3
3.0%
4.6 3
3.0%
4.5 2
 
2.0%
4.4 6
6.0%
4.3 2
 
2.0%
4.2 2
 
2.0%
4.1 1
 
1.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-12-09
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-09
2nd row2019-12-09
3rd row2019-12-09
4th row2019-12-09
5th row2019-12-09

Common Values

ValueCountFrequency (%)
2019-12-09 100
100.0%

Length

2023-12-10T19:11:24.113037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:24.278265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 100
100.0%

Interactions

2023-12-10T19:11:10.626777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.839061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.353885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.849942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:04.345596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.678917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.340462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.674397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:10.853213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.157703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.523332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.037675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:04.642297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.992654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.522847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.858812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.021133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.366265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.698837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.206833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:04.795354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:06.315860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.693072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:09.065887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.268291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.587014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.905474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.406102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:04.942467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:06.490872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.890774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:09.228267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.497550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.714873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.066109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.551863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.057207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:06.646807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.040534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:09.726787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.825966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.874734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.249188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.716898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.195028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:06.832223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.185987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:09.881403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.164184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.057286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.439378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:03.883399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.343659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.005561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.358078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:10.214117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.326809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.207108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.663202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:04.079294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:05.503458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:07.172254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:08.504538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:10.454928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:11:24.403454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_trrsrt_stsfdg_rtengl_trrsrt_stsfdg_rtchnlng_trrsrt_stsfdg_rtjalng_trrsrt_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_cd1.0001.0001.0001.0000.8420.9821.0000.1081.0001.0000.1450.0000.0000.000
city_gn_gu_cd1.0001.0001.0001.0000.8400.9821.0000.0981.0001.0000.1460.0000.0000.000
xpos_lo1.0001.0000.8420.8401.0000.8070.8970.0001.0000.9670.0000.0000.0000.000
ypos_la1.0001.0000.9820.9820.8071.0000.9730.0001.0001.0000.1760.0000.0000.141
area_nm1.0001.0001.0001.0000.8970.9731.0000.0001.0001.0000.0510.0000.0000.000
hotel_grad1.0001.0000.1080.0980.0000.0000.0001.0001.0000.9580.0000.2950.4450.000
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0001.0001.0001.0000.9671.0001.0000.9581.0001.0000.6230.9110.0000.000
klang_trrsrt_stsfdg_rt1.0001.0000.1450.1460.0000.1760.0510.0001.0000.6231.0000.0000.4050.514
engl_trrsrt_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.2951.0000.9110.0001.0000.2900.000
chnlng_trrsrt_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.4451.0000.0000.4050.2901.0000.491
jalng_trrsrt_stsfdg_rt1.0001.0000.0000.0000.0000.1410.0000.0001.0000.0000.5140.0000.4911.000
2023-12-10T19:11:24.663401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nm
hotel_grad1.0000.000
area_nm0.0001.000
2023-12-10T19:11:24.811768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laklang_trrsrt_stsfdg_rtengl_trrsrt_stsfdg_rtchnlng_trrsrt_stsfdg_rtjalng_trrsrt_stsfdg_rtarea_nmhotel_grad
city_do_cd1.0000.927-0.190-0.8180.012-0.0550.002-0.0780.9890.062
city_gn_gu_cd0.9271.000-0.136-0.8690.0540.0180.021-0.1490.9890.062
xpos_lo-0.190-0.1361.000-0.048-0.0270.1650.120-0.1070.7170.000
ypos_la-0.818-0.869-0.0481.0000.023-0.013-0.0790.1200.9350.000
klang_trrsrt_stsfdg_rt0.0120.054-0.0270.0231.000-0.1340.0780.0080.0840.176
engl_trrsrt_stsfdg_rt-0.0550.0180.165-0.013-0.1341.000-0.046-0.0640.0000.118
chnlng_trrsrt_stsfdg_rt0.0020.0210.120-0.0790.078-0.0461.000-0.0920.0000.114
jalng_trrsrt_stsfdg_rt-0.078-0.149-0.1070.1200.008-0.064-0.0921.0000.0000.106
area_nm0.9890.9890.7170.9350.0840.0000.0000.0001.0000.000
hotel_grad0.0620.0620.0000.0000.1760.1180.1140.1060.0001.000

Missing values

2023-12-10T19:11:12.609184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:11:13.094095image/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.
2023-12-10T19:11:13.416440image/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_nmhotel_gradtel_nohomepage_urlklang_trrsrt_stsfdg_rtengl_trrsrt_stsfdg_rtchnlng_trrsrt_stsfdg_rtjalng_trrsrt_stsfdg_rtbase_ymd
0E호텔서울특별시 서초구 반포대로18길 40 E 호텔1111650127.01260837.486972서울302-521-6555http://www.e-hotel.kr/4.03.84.62.62019-12-09
1호텔스카이파크킹스타운동대문서울특별시 중구 장충단로13길 20 14층1111140127.00766737.568736서울402-6952-8991https://www.skyparkhotel.com/html/main.asp3.13.33.93.22019-12-09
2나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/3.03.22.53.92019-12-09
3나인트리호텔명동서울특별시 종로구 인사동길 491111110126.98355337.574467서울302-750-0999https://ninetreehotel.com/3.72.52.04.62019-12-09
4라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/4.32.23.54.52019-12-09
5라마다앙코르서울마곡서울특별시 강서구 마곡중앙로 161-11 힐스테이트에코마곡나루역라마다앙코르서울특별시1111500126.82672237.567976서울302-2161-9000http://www.ramadaencore-seoulmagok.com/4.94.03.43.92019-12-09
6라마다앙코르제주서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주4064-735-2000https://www.ramadaencorejejuseogwipo.com/3.04.42.23.12019-12-09
7호텔시에나경기도 파주시 소리천로 314141480126.76187237.715623경기3031-943-7260http://www.hotelsienna.com/4.52.83.82.82019-12-09
8라비타호텔서울특별시 강남구 영동대로 7121111680127.05688437.521414서울202-545-0015<NA>2.53.64.04.32019-12-09
9레지던스 강남서울특별시 강남구 학동로4길 151111680127.02353637.510515서울<NA>02-3485-3100<NA>3.42.82.14.22019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_trrsrt_stsfdg_rtengl_trrsrt_stsfdg_rtchnlng_trrsrt_stsfdg_rtjalng_trrsrt_stsfdg_rtbase_ymd
90필로스 호텔경북 포항시 북구 죽파로 64747113129.35736436.029035경북4054-250-2000http://www.philoshotel.kr/2.34.03.92.82019-12-09
91한강 호텔서울특별시 광진구 광장동 188-21111215127.03663937.52739서울4<NA><NA>3.82.14.03.72019-12-09
92해담채 가산서울특별시 금천구 벚꽃로56길 1901111545126.88872737.4795서울302-853-7111https://www.hotelhaedamchae.com/3.22.92.93.62019-12-09
93해담채 스테이서울특별시 강서구 곰달래로 70-11111500126.84059637.530015서울1070-4632-3500http://www.haedamchaestay.com/4.23.73.63.02019-12-09
94해운대 비지니스호텔S부산광역시 해운대구 구남로8번길 492626350129.15692435.1616부산3051-741-5009http://www.businesshotelshaeundae.com/4.22.64.32.42019-12-09
95해운대골든튤립부산광역시 해운대구 해운대해변로 3222626350129.16587935.162277부산4051-795-7000http://www.goldentulip-haeundae.com/4.84.74.92.42019-12-09
96호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA>4.92.22.73.42019-12-09
97호메르스 호텔부산광역시 수영구 광안해변로 217 호메르스관광호텔2626500129.1179835.153426부산3051-750-8000http://www.homershotel.com/4.33.34.42.42019-12-09
98호텔 노블레스 서울서울특별시 노원구 노해로77길 22 노블레스관광호텔1111350127.0593537.655194서울102-558-1202http://www.noblesse.com/home/main.php#4.44.84.13.52019-12-09
99호텔 더 디자이너스 동대문서울특별시 중구 퇴계로 3061111140127.0056637.563611서울202-2271-3501http://hotelthedesigners.com/dongdaemun/2.02.93.43.32019-12-09