Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells72
Missing cells (%)4.8%
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
klang_chtt_stsfdg_rt has 13 (13.0%) missing valuesMissing
engl_chtt_stsfdg_rt has 13 (13.0%) missing valuesMissing
chnlng_chtt_stsfdg_rt has 13 (13.0%) missing valuesMissing
jalng_chtt_stsfdg_rt has 13 (13.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:09:02.429329
Analysis finished2023-12-10 10:09:17.713609
Duration15.28 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
2023-12-10T19:09:17.996168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length9.17
Min length3

Characters and Unicode

Total characters917
Distinct characters165
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 row호텔시에나
3rd row나인트리프리미어호텔명동2
4th row나인트리호텔명동
5th row라마다서울동대문
ValueCountFrequency (%)
호텔 53
23.9%
서울 7
 
3.2%
프리미어 7
 
3.2%
베스트웨스턴 5
 
2.3%
동대문 4
 
1.8%
강남 4
 
1.8%
송도 4
 
1.8%
인천 3
 
1.4%
명동 3
 
1.4%
관광 3
 
1.4%
Other values (118) 129
58.1%
2023-12-10T19:09:18.672646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
 
13.3%
호 85
 
9.3%
텔 82
 
8.9%
스 53
 
5.8%
이 21
 
2.3%
트 21
 
2.3%
리 19
 
2.1%
라 16
 
1.7%
서 15
 
1.6%
아 14
 
1.5%
Other values (155) 469
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 785
85.6%
Space Separator 122
 
13.3%
Decimal Number 5
 
0.5%
Uppercase Letter 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%
Uppercase Letter
ValueCountFrequency (%)
T 1
20.0%
M 1
20.0%
I 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 (%)
122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 785
85.6%
Common 127
 
13.8%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%
Latin
ValueCountFrequency (%)
T 1
20.0%
M 1
20.0%
I 1
20.0%
S 1
20.0%
E 1
20.0%
Common
ValueCountFrequency (%)
122
96.1%
2 3
 
2.4%
7 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 785
85.6%
ASCII 132
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
92.4%
2 3
 
2.3%
T 1
 
0.8%
M 1
 
0.8%
I 1
 
0.8%
S 1
 
0.8%
E 1
 
0.8%
7 1
 
0.8%
1 1
 
0.8%
Hangul
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:19.191214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length31
Mean length21.39
Min length12

Characters and Unicode

Total characters2139
Distinct characters199
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 row경기도 파주시 소리천로 31
3rd row서울특별시 중구 마른내로 28
4th row서울특별시 종로구 인사동길 49
5th row서울특별시 중구 동호로 354
ValueCountFrequency (%)
서울특별시 50
 
11.1%
중구 19
 
4.2%
경기도 14
 
3.1%
인천광역시 12
 
2.7%
부산광역시 10
 
2.2%
제주특별자치도 10
 
2.2%
서귀포시 9
 
2.0%
강남구 8
 
1.8%
강서구 6
 
1.3%
수원시 5
 
1.1%
Other values (255) 309
68.4%
2023-12-10T19:09:20.001574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
352
 
16.5%
시 104
 
4.9%
로 99
 
4.6%
구 86
 
4.0%
1 72
 
3.4%
서 70
 
3.3%
특 61
 
2.9%
별 61
 
2.9%
울 52
 
2.4%
길 42
 
2.0%
Other values (189) 1140
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1427
66.7%
Space Separator 352
 
16.5%
Decimal Number 332
 
15.5%
Dash Punctuation 16
 
0.7%
Uppercase Letter 8
 
0.4%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%
Decimal Number
ValueCountFrequency (%)
1 72
21.7%
2 40
12.0%
3 35
10.5%
6 30
9.0%
9 29
8.7%
8 28
 
8.4%
5 27
 
8.1%
4 26
 
7.8%
7 25
 
7.5%
0 20
 
6.0%
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 (%)
l 1
25.0%
e 1
25.0%
t 1
25.0%
o 1
25.0%
Space Separator
ValueCountFrequency (%)
352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1427
66.7%
Common 700
32.7%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%
Common
ValueCountFrequency (%)
352
50.3%
1 72
 
10.3%
2 40
 
5.7%
3 35
 
5.0%
6 30
 
4.3%
9 29
 
4.1%
8 28
 
4.0%
5 27
 
3.9%
4 26
 
3.7%
7 25
 
3.6%
Other values (2) 36
 
5.1%
Latin
ValueCountFrequency (%)
A 2
16.7%
C 2
16.7%
E 2
16.7%
P 1
8.3%
l 1
8.3%
e 1
8.3%
t 1
8.3%
o 1
8.3%
H 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1427
66.7%
ASCII 712
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352
49.4%
1 72
 
10.1%
2 40
 
5.6%
3 35
 
4.9%
6 30
 
4.2%
9 29
 
4.1%
8 28
 
3.9%
5 27
 
3.8%
4 26
 
3.7%
7 25
 
3.5%
Other values (11) 48
 
6.7%
Hangul
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.92
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:20.224209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median18.5
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.607788
Coefficient of variation (CV)0.61069348
Kurtosis-1.1899489
Mean23.92
Median Absolute Deviation (MAD)7.5
Skewness0.58723114
Sum2392
Variance213.38747
MonotonicityNot monotonic
2023-12-10T19:09:20.434399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 50
50.0%
41 14
 
14.0%
28 12
 
12.0%
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 50
50.0%
26 10
 
10.0%
28 12
 
12.0%
31 1
 
1.0%
41 14
 
14.0%
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 14
 
14.0%
31 1
 
1.0%
28 12
 
12.0%
26 10
 
10.0%
11 50
50.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24223.37
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:20.683845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11138.5
Q111500
median18910
Q341113.5
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)29613.5

Descriptive statistics

Standard deviation14516.335
Coefficient of variation (CV)0.59926985
Kurtosis-1.1871659
Mean24223.37
Median Absolute Deviation (MAD)7770
Skewness0.5903281
Sum2422337
Variance2.1072399 × 108
MonotonicityNot monotonic
2023-12-10T19:09:21.063285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
11140 13
 
13.0%
50130 9
 
9.0%
11680 8
 
8.0%
11500 6
 
6.0%
11110 5
 
5.0%
28110 5
 
5.0%
28185 5
 
5.0%
26350 4
 
4.0%
11560 4
 
4.0%
11650 3
 
3.0%
Other values (31) 38
38.0%
ValueCountFrequency (%)
11110 5
 
5.0%
11140 13
13.0%
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%
Mean127.18539
Minimum126.40835
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:21.312038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40835
5-th percentile126.52188
Q1126.8394
median126.98795
Q3127.03575
95-th percentile129.15657
Maximum129.35736
Range2.9490161
Interquartile range (IQR)0.19635875

Descriptive statistics

Standard deviation0.77175323
Coefficient of variation (CV)0.0060679394
Kurtosis2.5317503
Mean127.18539
Median Absolute Deviation (MAD)0.08262305
Skewness1.9748162
Sum12718.539
Variance0.59560305
MonotonicityNot monotonic
2023-12-10T19:09:21.558095image/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%
126.8142917 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%
127.0055188 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%
Mean36.750864
Minimum33.247674
Maximum37.715623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:21.904689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.247674
5-th percentile33.427692
Q137.200041
median37.486884
Q337.553208
95-th percentile37.574556
Maximum37.715623
Range4.4679489
Interquartile range (IQR)0.35316722

Descriptive statistics

Standard deviation1.394222
Coefficient of variation (CV)0.037937121
Kurtosis1.0391406
Mean36.750864
Median Absolute Deviation (MAD)0.08351027
Skewness-1.5900356
Sum3675.0864
Variance1.9438549
MonotonicityNot monotonic
2023-12-10T19:09:22.143014image/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%
37.56157776 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%
37.56299991 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%
37.56797554 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
50 
경기
14 
인천
12 
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 row경기
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 50
50.0%
경기 14
 
14.0%
인천 12
 
12.0%
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

2023-12-10T19:09:22.360652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:22.893031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 50
50.0%
경기 14
 
14.0%
인천 12
 
12.0%
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
42 
4
23 
2
20 
<NA>
8 
5
 
4

Length

Max length4
Median length1
Mean length1.24
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 42
42.0%
4 23
23.0%
2 20
20.0%
<NA> 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%

Length

2023-12-10T19:09:23.161297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:23.323383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 42
42.0%
4 23
23.0%
2 20
20.0%
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
2023-12-10T19:09:23.758254image/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 row031-943-7260
3rd row02-6967-0999
4th row02-750-0999
5th row02-2276-3500
ValueCountFrequency (%)
02-521-6555 1
 
1.1%
02-2079-8888 1
 
1.1%
064-763-2299 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%
061-664-0001 1
 
1.1%
Other values (85) 85
89.5%
2023-12-10T19:09:24.687826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270
29.0%
2 132
14.2%
1 88
 
9.5%
3 81
 
8.7%
5 73
 
7.8%
7 72
 
7.7%
6 67
 
7.2%
8 54
 
5.8%
4 53
 
5.7%
9 41
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

homepage_url
Text

MISSING 

Distinct82
Distinct (%)96.5%
Missing15
Missing (%)15.0%
Memory size932.0 B
2023-12-10T19:09:25.305653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length41
Mean length33.070588
Min length19

Characters and Unicode

Total characters2811
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 rowhttp://www.hotelsienna.com/
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.cshotelseoul.com 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%
http://mshotel.alltheway.kr 1
 
1.2%
http://www.afirsthotelgroup.com/ko 1
 
1.2%
Other values (72) 72
84.7%
2023-12-10T19:09:26.009955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 289
 
10.3%
t 269
 
9.6%
w 221
 
7.9%
o 221
 
7.9%
. 207
 
7.4%
h 164
 
5.8%
e 159
 
5.7%
p 124
 
4.4%
r 109
 
3.9%
a 107
 
3.8%
Other values (35) 941
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2178
77.5%
Other Punctuation 590
 
21.0%
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 269
12.4%
w 221
 
10.1%
o 221
 
10.1%
h 164
 
7.5%
e 159
 
7.3%
p 124
 
5.7%
r 109
 
5.0%
a 107
 
4.9%
c 103
 
4.7%
m 99
 
4.5%
Other values (15) 602
27.6%
Other Punctuation
ValueCountFrequency (%)
/ 289
49.0%
. 207
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 2189
77.9%
Common 622
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 269
12.3%
w 221
 
10.1%
o 221
 
10.1%
h 164
 
7.5%
e 159
 
7.3%
p 124
 
5.7%
r 109
 
5.0%
a 107
 
4.9%
c 103
 
4.7%
m 99
 
4.5%
Other values (20) 613
28.0%
Common
ValueCountFrequency (%)
/ 289
46.5%
. 207
33.3%
: 87
 
14.0%
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 2811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 289
 
10.3%
t 269
 
9.6%
w 221
 
7.9%
o 221
 
7.9%
. 207
 
7.4%
h 164
 
5.8%
e 159
 
5.7%
p 124
 
4.4%
r 109
 
3.9%
a 107
 
3.8%
Other values (35) 941
33.5%

klang_chtt_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct35
Distinct (%)40.2%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.983908
Minimum1.2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:26.238986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.43
Q12.2
median2.8
Q33.85
95-th percentile4.7
Maximum5
Range3.8
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation1.034169
Coefficient of variation (CV)0.34658205
Kurtosis-1.0583427
Mean2.983908
Median Absolute Deviation (MAD)0.8
Skewness0.26139804
Sum259.6
Variance1.0695055
MonotonicityNot monotonic
2023-12-10T19:09:26.495154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2.3 10
 
10.0%
2.0 6
 
6.0%
2.2 4
 
4.0%
4.7 4
 
4.0%
3.6 3
 
3.0%
2.4 3
 
3.0%
4.6 3
 
3.0%
2.7 3
 
3.0%
3.4 3
 
3.0%
4.1 3
 
3.0%
Other values (25) 45
45.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1.2 1
 
1.0%
1.3 2
 
2.0%
1.4 2
 
2.0%
1.5 1
 
1.0%
1.6 1
 
1.0%
1.7 2
 
2.0%
1.8 2
 
2.0%
1.9 2
 
2.0%
2.0 6
6.0%
2.2 4
4.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.9 1
 
1.0%
4.7 4
4.0%
4.6 3
3.0%
4.5 2
2.0%
4.4 2
2.0%
4.3 1
 
1.0%
4.2 1
 
1.0%
4.1 3
3.0%
4.0 2
2.0%

engl_chtt_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)44.8%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.9712644
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:26.773345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.43
Q12.2
median2.9
Q33.8
95-th percentile4.77
Maximum5
Range4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.073346
Coefficient of variation (CV)0.36124217
Kurtosis-0.95761204
Mean2.9712644
Median Absolute Deviation (MAD)0.9
Skewness0.13534386
Sum258.5
Variance1.1520716
MonotonicityNot monotonic
2023-12-10T19:09:27.064559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2.4 6
 
6.0%
1.9 4
 
4.0%
3.8 4
 
4.0%
2.2 4
 
4.0%
1.6 4
 
4.0%
2.9 4
 
4.0%
2.6 4
 
4.0%
3.2 3
 
3.0%
3.1 3
 
3.0%
1.5 3
 
3.0%
Other values (29) 48
48.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1.0 2
2.0%
1.1 1
 
1.0%
1.2 1
 
1.0%
1.4 1
 
1.0%
1.5 3
3.0%
1.6 4
4.0%
1.7 1
 
1.0%
1.9 4
4.0%
2.0 2
2.0%
2.1 2
2.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.9 2
2.0%
4.8 2
2.0%
4.7 1
 
1.0%
4.6 3
3.0%
4.5 1
 
1.0%
4.4 2
2.0%
4.3 1
 
1.0%
4.2 3
3.0%
4.1 1
 
1.0%

chnlng_chtt_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)41.4%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.8321839
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:27.338259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.6
median2.9
Q33.9
95-th percentile4.87
Maximum5
Range4
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.2608132
Coefficient of variation (CV)0.44517348
Kurtosis-1.3613969
Mean2.8321839
Median Absolute Deviation (MAD)1.2
Skewness0.14426998
Sum246.4
Variance1.5896498
MonotonicityNot monotonic
2023-12-10T19:09:27.587931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1.6 6
 
6.0%
1.5 6
 
6.0%
4.0 4
 
4.0%
2.0 4
 
4.0%
4.9 4
 
4.0%
3.9 4
 
4.0%
1.4 4
 
4.0%
2.9 4
 
4.0%
1.0 3
 
3.0%
4.2 3
 
3.0%
Other values (26) 45
45.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1.0 3
3.0%
1.1 3
3.0%
1.2 1
 
1.0%
1.3 3
3.0%
1.4 4
4.0%
1.5 6
6.0%
1.6 6
6.0%
1.7 3
3.0%
1.9 1
 
1.0%
2.0 4
4.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.9 4
4.0%
4.8 3
3.0%
4.7 1
 
1.0%
4.6 1
 
1.0%
4.5 2
2.0%
4.3 1
 
1.0%
4.2 3
3.0%
4.1 1
 
1.0%
4.0 4
4.0%

jalng_chtt_stsfdg_rt
Real number (ℝ)

MISSING 

Distinct35
Distinct (%)40.2%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.8896552
Minimum1.1
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:27.839918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.2
Q11.8
median2.8
Q34.05
95-th percentile4.9
Maximum4.9
Range3.8
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.2091737
Coefficient of variation (CV)0.41844913
Kurtosis-1.2756563
Mean2.8896552
Median Absolute Deviation (MAD)1.1
Skewness0.16931594
Sum251.4
Variance1.462101
MonotonicityNot monotonic
2023-12-10T19:09:28.113342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4.9 6
 
6.0%
4.2 6
 
6.0%
1.6 6
 
6.0%
2.8 4
 
4.0%
2.5 4
 
4.0%
2.7 4
 
4.0%
1.2 4
 
4.0%
1.1 3
 
3.0%
2.3 3
 
3.0%
1.9 3
 
3.0%
Other values (25) 44
44.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1.1 3
3.0%
1.2 4
4.0%
1.3 3
3.0%
1.4 3
3.0%
1.6 6
6.0%
1.7 2
 
2.0%
1.8 3
3.0%
1.9 3
3.0%
2.0 1
 
1.0%
2.1 1
 
1.0%
ValueCountFrequency (%)
4.9 6
6.0%
4.7 1
 
1.0%
4.6 3
3.0%
4.5 2
 
2.0%
4.4 1
 
1.0%
4.3 2
 
2.0%
4.2 6
6.0%
4.1 1
 
1.0%
4.0 1
 
1.0%
3.9 3
3.0%

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

2023-12-10T19:09:28.350108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:28.513380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 100
100.0%

Interactions

2023-12-10T19:09:14.895864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:03.882235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:05.427954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.969177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:08.355186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.815146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:11.347965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.423011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.080314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:04.098283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:05.625593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.142750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:08.551991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.976252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:11.601750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.586532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.287503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:04.316921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:05.801172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.335501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:08.815895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:10.154490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:11.919061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.775493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.470282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:04.524144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.041113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.498800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.002524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:10.319284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.528354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.942466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.618472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:04.715489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.204932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.631542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.145259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:10.516438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.671128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.102142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.793411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:04.904479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.393753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.820741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.331998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:10.732305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:12.823838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.283000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.048043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:05.079514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.573582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:07.998112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.505540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:10.936342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.029616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.499062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.297044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:05.261905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:06.783773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:08.181752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:09.670429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:11.115411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:13.232801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:14.690352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:28.638516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_chtt_stsfdg_rtengl_chtt_stsfdg_rtchnlng_chtt_stsfdg_rtjalng_chtt_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_cd1.0001.0001.0001.0000.8420.9811.0000.0521.0001.0000.0000.0000.0000.277
city_gn_gu_cd1.0001.0001.0001.0000.8410.9811.0000.0291.0001.0000.0000.0000.0000.246
xpos_lo1.0001.0000.8420.8411.0000.8070.8970.0001.0000.9700.0000.0000.0360.000
ypos_la1.0001.0000.9810.9810.8071.0000.9710.0001.0001.0000.0000.0000.0000.355
area_nm1.0001.0001.0001.0000.8970.9711.0000.0001.0001.0000.0000.0000.0000.189
hotel_grad1.0001.0000.0520.0290.0000.0000.0001.0001.0000.9550.0720.3090.0000.215
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0001.0001.0001.0000.9701.0001.0000.9551.0001.0000.8100.8990.8000.904
klang_chtt_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.0721.0000.8101.0000.4270.3030.377
engl_chtt_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.3091.0000.8990.4271.0000.0000.150
chnlng_chtt_stsfdg_rt1.0001.0000.0000.0000.0360.0000.0000.0001.0000.8000.3030.0001.0000.281
jalng_chtt_stsfdg_rt1.0001.0000.2770.2460.0000.3550.1890.2151.0000.9040.3770.1500.2811.000
2023-12-10T19:09:28.911894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nm
hotel_grad1.0000.000
area_nm0.0001.000
2023-12-10T19:09:29.115607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laklang_chtt_stsfdg_rtengl_chtt_stsfdg_rtchnlng_chtt_stsfdg_rtjalng_chtt_stsfdg_rtarea_nmhotel_grad
city_do_cd1.0000.934-0.208-0.8180.1210.0340.110-0.0260.9890.017
city_gn_gu_cd0.9341.000-0.156-0.8630.117-0.0360.135-0.0460.9890.017
xpos_lo-0.208-0.1561.000-0.0430.089-0.0630.0330.2180.7170.000
ypos_la-0.818-0.863-0.0431.000-0.0890.008-0.1340.0220.9320.000
klang_chtt_stsfdg_rt0.1210.1170.089-0.0891.000-0.1090.1710.1030.0000.000
engl_chtt_stsfdg_rt0.034-0.036-0.0630.008-0.1091.0000.192-0.1870.1230.143
chnlng_chtt_stsfdg_rt0.1100.1350.033-0.1340.1710.1921.000-0.1810.0000.000
jalng_chtt_stsfdg_rt-0.026-0.0460.2180.0220.103-0.187-0.1811.0000.1030.091
area_nm0.9890.9890.7170.9320.0000.1230.0000.1031.0000.000
hotel_grad0.0170.0170.0000.0000.0000.1430.0000.0910.0001.000

Missing values

2023-12-10T19:09:16.644404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:17.124126image/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.
2023-12-10T19:09:17.524634image/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_chtt_stsfdg_rtengl_chtt_stsfdg_rtchnlng_chtt_stsfdg_rtjalng_chtt_stsfdg_rtbase_ymd
0E호텔서울특별시 서초구 반포대로18길 40 E 호텔1111650127.01260837.486972서울302-521-6555http://www.e-hotel.kr/1.32.71.51.32019-12-09
1호텔시에나경기도 파주시 소리천로 314141480126.76187237.715623경기3031-943-7260http://www.hotelsienna.com/2.72.41.32.52019-12-09
2나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/4.01.91.62.82019-12-09
3나인트리호텔명동서울특별시 종로구 인사동길 491111110126.98355337.574467서울302-750-0999https://ninetreehotel.com/3.94.01.24.92019-12-09
4라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/2.04.11.04.22019-12-09
5라마다앙코르서울마곡서울특별시 강서구 마곡중앙로 161-11 힐스테이트에코마곡나루역라마다앙코르서울특별시1111500126.82672237.567976서울302-2161-9000http://www.ramadaencore-seoulmagok.com/2.32.34.91.42019-12-09
6라마다앙코르제주서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주4064-735-2000https://www.ramadaencorejejuseogwipo.com/3.12.11.63.82019-12-09
7호텔이루다경기도 안양시 만안구 만안로 714141171126.93293137.389511경기3031-443-2700http://www.hoteliruda.com/2.83.83.92.52019-12-09
8라비타호텔서울특별시 강남구 영동대로 7121111680127.05688437.521414서울202-545-0015<NA><NA><NA><NA><NA>2019-12-09
9레지던스 강남서울특별시 강남구 학동로4길 151111680127.02353637.510515서울<NA>02-3485-3100<NA><NA><NA><NA><NA>2019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_chtt_stsfdg_rtengl_chtt_stsfdg_rtchnlng_chtt_stsfdg_rtjalng_chtt_stsfdg_rtbase_ymd
90프린세스 호텔서울특별시 강남구 압구정로46길 171111680127.03663937.52739서울202-544-0366http://www.princesshotel.co.kr/xe/page_nRij553.04.73.73.12019-12-09
91필로스 호텔경북 포항시 북구 죽파로 64747113129.35736436.029035경북4054-250-2000http://www.philoshotel.kr/4.11.43.94.42019-12-09
92한강 호텔서울특별시 광진구 광장동 188-21111215127.03663937.52739서울4<NA><NA><NA><NA><NA><NA>2019-12-09
93해담채 가산서울특별시 금천구 벚꽃로56길 1901111545126.88872737.4795서울302-853-7111https://www.hotelhaedamchae.com/3.74.54.81.62019-12-09
94해담채 스테이서울특별시 강서구 곰달래로 70-11111500126.84059637.530015서울1070-4632-3500http://www.haedamchaestay.com/4.51.53.13.32019-12-09
95해운대 비지니스호텔S부산광역시 해운대구 구남로8번길 492626350129.15692435.1616부산3051-741-5009http://www.businesshotelshaeundae.com/2.83.44.71.92019-12-09
96해운대골든튤립부산광역시 해운대구 해운대해변로 3222626350129.16587935.162277부산4051-795-7000http://www.goldentulip-haeundae.com/4.32.41.54.92019-12-09
97호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA><NA><NA><NA><NA>2019-12-09
98호메르스 호텔부산광역시 수영구 광안해변로 217 호메르스관광호텔2626500129.1179835.153426부산3051-750-8000http://www.homershotel.com/2.63.81.54.22019-12-09
99호텔 노블레스 서울서울특별시 노원구 노해로77길 22 노블레스관광호텔1111350127.0593537.655194서울102-558-1202http://www.noblesse.com/home/main.php#2.34.21.32.52019-12-09