Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells5
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory133.3 B

Variable types

Text4
Numeric4
Categorical7
DateTime1

Alerts

base_ymd has constant value ""Constant
parking_at is highly overall correlated with family_rum_ty and 3 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
cvnstr_ennc_at is highly overall correlated with city_do_cd and 9 other fieldsHigh correlation
exchg_at is highly overall correlated with city_do_cd and 9 other fieldsHigh correlation
sports_center_ennc_at is highly overall correlated with city_do_cd and 9 other fieldsHigh correlation
family_rum_ty is highly overall correlated with city_do_cd and 9 other fieldsHigh correlation
hotel_grad is highly overall correlated with family_rum_ty and 3 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 6 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
xpos_lo is highly overall correlated with area_nm and 4 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
area_nm is highly imbalanced (51.3%)Imbalance
homepage_url has 5 (5.0%) missing valuesMissing
entrp_nm has unique valuesUnique
tel_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:10:33.736915
Analysis finished2023-12-10 10:10:39.330021
Duration5.59 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
2023-12-10T19:10:39.705416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length9.65
Min length4

Characters and Unicode

Total characters965
Distinct characters168
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 row울릉도 라페루즈 리조트
2nd row서우봉 비치 호텔
3rd row엠에스 호텔
4th row호텔 컬리넌 개포
5th row호텔 데님
ValueCountFrequency (%)
호텔 70
26.6%
더 8
 
3.0%
디자이너스 7
 
2.7%
서울 6
 
2.3%
인천 5
 
1.9%
컬리넌 4
 
1.5%
프리미어 4
 
1.5%
관광 4
 
1.5%
제주 4
 
1.5%
송도 4
 
1.5%
Other values (127) 147
55.9%
2023-12-10T19:10:40.453463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
 
16.9%
호 91
 
9.4%
텔 86
 
8.9%
스 50
 
5.2%
이 29
 
3.0%
리 22
 
2.3%
트 19
 
2.0%
서 16
 
1.7%
동 14
 
1.5%
라 12
 
1.2%
Other values (158) 463
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 792
82.1%
Space Separator 163
 
16.9%
Decimal Number 7
 
0.7%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 91
 
11.5%
텔 86
 
10.9%
스 50
 
6.3%
이 29
 
3.7%
리 22
 
2.8%
트 19
 
2.4%
서 16
 
2.0%
동 14
 
1.8%
라 12
 
1.5%
파 12
 
1.5%
Other values (151) 441
55.7%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
3 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
163
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 792
82.1%
Common 171
 
17.7%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 91
 
11.5%
텔 86
 
10.9%
스 50
 
6.3%
이 29
 
3.7%
리 22
 
2.8%
트 19
 
2.4%
서 16
 
2.0%
동 14
 
1.8%
라 12
 
1.5%
파 12
 
1.5%
Other values (151) 441
55.7%
Common
ValueCountFrequency (%)
163
95.3%
1 3
 
1.8%
2 3
 
1.8%
3 1
 
0.6%
& 1
 
0.6%
Latin
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 792
82.1%
ASCII 173
 
17.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
94.2%
1 3
 
1.7%
2 3
 
1.7%
G 1
 
0.6%
S 1
 
0.6%
3 1
 
0.6%
& 1
 
0.6%
Hangul
ValueCountFrequency (%)
호 91
 
11.5%
텔 86
 
10.9%
스 50
 
6.3%
이 29
 
3.7%
리 22
 
2.8%
트 19
 
2.4%
서 16
 
2.0%
동 14
 
1.8%
라 12
 
1.5%
파 12
 
1.5%
Other values (151) 441
55.7%

load_addr
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:40.927212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27.5
Mean length21.66
Min length14

Characters and Unicode

Total characters2166
Distinct characters185
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st row경상북도 울릉군 울릉읍 중령길 129-128
2nd row제주특별자치도 제주시 조천읍 함덕 6길 10 (함덕리 2915)
3rd row부산광역시 해운대구 중동 해운대해변로 271
4th row서울특별시 강남구 개포4동 1229-8
5th row서울특별시 강남구 개포4동 논현로 66
ValueCountFrequency (%)
서울특별시 69
 
14.8%
중구 26
 
5.6%
인천광역시 18
 
3.9%
강남구 14
 
3.0%
종로구 8
 
1.7%
역삼동 8
 
1.7%
제주특별자치도 7
 
1.5%
서귀포시 6
 
1.3%
강서구 6
 
1.3%
남동구 5
 
1.1%
Other values (245) 298
64.1%
2023-12-10T19:10:41.647292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
365
 
16.9%
시 99
 
4.6%
서 95
 
4.4%
동 91
 
4.2%
구 91
 
4.2%
로 81
 
3.7%
별 76
 
3.5%
특 76
 
3.5%
1 74
 
3.4%
울 71
 
3.3%
Other values (175) 1047
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1399
64.6%
Decimal Number 367
 
16.9%
Space Separator 365
 
16.9%
Dash Punctuation 25
 
1.2%
Other Punctuation 6
 
0.3%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 99
 
7.1%
서 95
 
6.8%
동 91
 
6.5%
구 91
 
6.5%
로 81
 
5.8%
별 76
 
5.4%
특 76
 
5.4%
울 71
 
5.1%
길 43
 
3.1%
중 32
 
2.3%
Other values (158) 644
46.0%
Decimal Number
ValueCountFrequency (%)
1 74
20.2%
2 59
16.1%
3 43
11.7%
5 36
9.8%
4 29
 
7.9%
9 26
 
7.1%
6 26
 
7.1%
0 25
 
6.8%
8 25
 
6.8%
7 24
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
? 2
33.3%
, 1
 
16.7%
Space Separator
ValueCountFrequency (%)
365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1399
64.6%
Common 767
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 99
 
7.1%
서 95
 
6.8%
동 91
 
6.5%
구 91
 
6.5%
로 81
 
5.8%
별 76
 
5.4%
특 76
 
5.4%
울 71
 
5.1%
길 43
 
3.1%
중 32
 
2.3%
Other values (158) 644
46.0%
Common
ValueCountFrequency (%)
365
47.6%
1 74
 
9.6%
2 59
 
7.7%
3 43
 
5.6%
5 36
 
4.7%
4 29
 
3.8%
9 26
 
3.4%
6 26
 
3.4%
0 25
 
3.3%
- 25
 
3.3%
Other values (7) 59
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1399
64.6%
ASCII 767
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
365
47.6%
1 74
 
9.6%
2 59
 
7.7%
3 43
 
5.6%
5 36
 
4.7%
4 29
 
3.8%
9 26
 
3.4%
6 26
 
3.4%
0 25
 
3.3%
- 25
 
3.3%
Other values (7) 59
 
7.7%
Hangul
ValueCountFrequency (%)
시 99
 
7.1%
서 95
 
6.8%
동 91
 
6.5%
구 91
 
6.5%
로 81
 
5.8%
별 76
 
5.4%
특 76
 
5.4%
울 71
 
5.1%
길 43
 
3.1%
중 32
 
2.3%
Other values (158) 644
46.0%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.65
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:41.874286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q328
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.743289
Coefficient of variation (CV)0.68328627
Kurtosis0.83493444
Mean18.65
Median Absolute Deviation (MAD)0
Skewness1.4644374
Sum1865
Variance162.39141
MonotonicityNot monotonic
2023-12-10T19:10:42.054836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11 69
69.0%
28 18
 
18.0%
50 7
 
7.0%
44 2
 
2.0%
47 1
 
1.0%
26 1
 
1.0%
46 1
 
1.0%
45 1
 
1.0%
ValueCountFrequency (%)
11 69
69.0%
26 1
 
1.0%
28 18
 
18.0%
44 2
 
2.0%
45 1
 
1.0%
46 1
 
1.0%
47 1
 
1.0%
50 7
 
7.0%
ValueCountFrequency (%)
50 7
 
7.0%
47 1
 
1.0%
46 1
 
1.0%
45 1
 
1.0%
44 2
 
2.0%
28 18
 
18.0%
26 1
 
1.0%
11 69
69.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18975.3
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:42.294323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111140
median11635
Q328110
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)16970

Descriptive statistics

Standard deviation12672.777
Coefficient of variation (CV)0.66785649
Kurtosis0.85244268
Mean18975.3
Median Absolute Deviation (MAD)495
Skewness1.4692705
Sum1897530
Variance1.6059928 × 108
MonotonicityNot monotonic
2023-12-10T19:10:42.538466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
11140 20
20.0%
11680 14
14.0%
11110 8
 
8.0%
50130 6
 
6.0%
11500 6
 
6.0%
28110 6
 
6.0%
28200 5
 
5.0%
11560 5
 
5.0%
28185 4
 
4.0%
11650 4
 
4.0%
Other values (18) 22
22.0%
ValueCountFrequency (%)
11110 8
 
8.0%
11140 20
20.0%
11170 2
 
2.0%
11215 2
 
2.0%
11230 1
 
1.0%
11305 1
 
1.0%
11380 2
 
2.0%
11500 6
 
6.0%
11530 1
 
1.0%
11545 1
 
1.0%
ValueCountFrequency (%)
50130 6
6.0%
50110 1
 
1.0%
47940 1
 
1.0%
46130 1
 
1.0%
45190 1
 
1.0%
44200 1
 
1.0%
44130 1
 
1.0%
28710 1
 
1.0%
28260 2
 
2.0%
28200 5
5.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96389
Minimum126.37151
Maximum130.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:42.784877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37151
5-th percentile126.51707
Q1126.81744
median126.98751
Q3127.02057
95-th percentile127.11097
Maximum130.8702
Range4.4986868
Interquartile range (IQR)0.2031282

Descriptive statistics

Standard deviation0.50036607
Coefficient of variation (CV)0.003941011
Kurtosis40.956942
Mean126.96389
Median Absolute Deviation (MAD)0.05963295
Skewness5.7000519
Sum12696.389
Variance0.25036621
MonotonicityNot monotonic
2023-12-10T19:10:43.033702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9852932 2
 
2.0%
126.519265 2
 
2.0%
126.4080885 2
 
2.0%
126.9794674 1
 
1.0%
126.9872963 1
 
1.0%
126.9962989 1
 
1.0%
126.9913216 1
 
1.0%
126.9837308 1
 
1.0%
127.0021055 1
 
1.0%
126.9986635 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
126.3715088 1
1.0%
126.4080885 2
2.0%
126.4557688 1
1.0%
126.475336 1
1.0%
126.519265 2
2.0%
126.5674246 1
1.0%
126.58147 1
1.0%
126.5977517 1
1.0%
126.598433 1
1.0%
126.6004455 1
1.0%
ValueCountFrequency (%)
130.8701956 1
1.0%
129.1611261 1
1.0%
127.7518523 1
1.0%
127.4142847 1
1.0%
127.1403331 1
1.0%
127.1094211 1
1.0%
127.0960588 1
1.0%
127.067748 1
1.0%
127.049108 1
1.0%
127.047634 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.141745
Minimum33.249384
Maximum37.735461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:43.294173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.249384
5-th percentile33.442223
Q137.474337
median37.515346
Q337.563718
95-th percentile37.574113
Maximum37.735461
Range4.4860767
Interquartile range (IQR)0.089380873

Descriptive statistics

Standard deviation1.1379699
Coefficient of variation (CV)0.030638568
Kurtosis6.8756872
Mean37.141745
Median Absolute Deviation (MAD)0.047981795
Skewness-2.887488
Sum3714.1745
Variance1.2949755
MonotonicityNot monotonic
2023-12-10T19:10:43.524729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.56403715 2
 
2.0%
33.25447711 2
 
2.0%
33.24986732 2
 
2.0%
37.5682445 1
 
1.0%
37.5613066 1
 
1.0%
37.56617992 1
 
1.0%
37.56701485 1
 
1.0%
37.5651364 1
 
1.0%
37.56419454 1
 
1.0%
37.56415744 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
33.24938412 1
1.0%
33.24986732 2
2.0%
33.25447711 2
2.0%
33.45210482 1
1.0%
33.542916 1
1.0%
34.74580272 1
1.0%
35.15982469 1
1.0%
35.40798938 1
1.0%
36.78355551 1
1.0%
36.8124141 1
1.0%
ValueCountFrequency (%)
37.73546084 1
1.0%
37.61058438 1
1.0%
37.59989609 1
1.0%
37.581984 1
1.0%
37.57625021 1
1.0%
37.57400036 1
1.0%
37.573468 1
1.0%
37.57188771 1
1.0%
37.56935534 1
1.0%
37.56912704 1
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
69 
인천
18 
제주
7 
충남
 
2
경북
 
1
Other values (3)
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 69
69.0%
인천 18
 
18.0%
제주 7
 
7.0%
충남 2
 
2.0%
경북 1
 
1.0%
부산 1
 
1.0%
전남 1
 
1.0%
전북 1
 
1.0%

Length

2023-12-10T19:10:43.738609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:44.293848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 69
69.0%
인천 18
 
18.0%
제주 7
 
7.0%
충남 2
 
2.0%
경북 1
 
1.0%
부산 1
 
1.0%
전남 1
 
1.0%
전북 1
 
1.0%

hotel_grad
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1급
60 
특2급
19 
2급
16 
3급
 
2
특1급
 
2

Length

Max length4
Median length2
Mean length2.23
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1급
2nd row1급
3rd row1급
4th row2급
5th row1급

Common Values

ValueCountFrequency (%)
1급 60
60.0%
특2급 19
 
19.0%
2급 16
 
16.0%
3급 2
 
2.0%
특1급 2
 
2.0%
<NA> 1
 
1.0%

Length

2023-12-10T19:10:44.631400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:44.936200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1급 60
60.0%
특2급 19
 
19.0%
2급 16
 
16.0%
3급 2
 
2.0%
특1급 2
 
2.0%
na 1
 
1.0%

tel_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:45.379242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.71
Min length11

Characters and Unicode

Total characters1171
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

Unique100 ?
Unique (%)100.0%

Sample

1st row054-791-0114
2nd row064-731-5000
3rd row051-741-3838
4th row02-2058-1558
5th row02-2058-1178
ValueCountFrequency (%)
054-791-0114 1
 
1.0%
02-6900-9351 1
 
1.0%
02-752-3191 1
 
1.0%
02-773-6000 1
 
1.0%
02-756-9700 1
 
1.0%
02-6466-1234 1
 
1.0%
02-2020-4000 1
 
1.0%
02-752-1112 1
 
1.0%
02-2264-2200 1
 
1.0%
02-3782-9800 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:10:46.191158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 284
24.3%
- 200
17.1%
2 177
15.1%
7 81
 
6.9%
1 78
 
6.7%
3 71
 
6.1%
6 68
 
5.8%
5 67
 
5.7%
8 54
 
4.6%
4 47
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 971
82.9%
Dash Punctuation 200
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284
29.2%
2 177
18.2%
7 81
 
8.3%
1 78
 
8.0%
3 71
 
7.3%
6 68
 
7.0%
5 67
 
6.9%
8 54
 
5.6%
4 47
 
4.8%
9 44
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284
24.3%
- 200
17.1%
2 177
15.1%
7 81
 
6.9%
1 78
 
6.7%
3 71
 
6.1%
6 68
 
5.8%
5 67
 
5.7%
8 54
 
4.6%
4 47
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284
24.3%
- 200
17.1%
2 177
15.1%
7 81
 
6.9%
1 78
 
6.7%
3 71
 
6.1%
6 68
 
5.8%
5 67
 
5.7%
8 54
 
4.6%
4 47
 
4.0%

homepage_url
Text

MISSING 

Distinct93
Distinct (%)97.9%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T19:10:46.680094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length187
Median length49
Mean length40.052632
Min length19

Characters and Unicode

Total characters3805
Distinct characters58
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

Unique91 ?
Unique (%)95.8%

Sample

1st rowhttp://www.laperouse.co.kr/
2nd rowhttp://www.seoubongbeachhotel.com/
3rd rowhttp://mshotel.alltheway.kr/
4th rowhttp://hotelcullinangaepo.co.kr/
5th rowhttp://hoteldenim.com/
ValueCountFrequency (%)
https://www.skyparkhotel.com/html/main.asp 2
 
2.1%
https://www.ramadaencorejejuseogwipo.com 2
 
2.1%
http://hotelatrium.co.kr 1
 
1.1%
http://www.orienshotel.com 1
 
1.1%
https://www.skyparkhotel.com/html/accommdation/accom3_tab1_01.asp 1
 
1.1%
http://www.hotelkukdo.com 1
 
1.1%
http://stazhotelmyeongdong1.com 1
 
1.1%
http://www.metrohotel.co.kr 1
 
1.1%
http://www.skyparkhotel.com 1
 
1.1%
http://www.chisunhotelmd.com 1
 
1.1%
Other values (83) 83
87.4%
2023-12-10T19:10:47.541406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 369
 
9.7%
/ 322
 
8.5%
o 310
 
8.1%
e 248
 
6.5%
h 234
 
6.1%
. 208
 
5.5%
w 196
 
5.2%
a 163
 
4.3%
s 156
 
4.1%
c 153
 
4.0%
Other values (48) 1446
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2938
77.2%
Other Punctuation 662
 
17.4%
Decimal Number 76
 
2.0%
Uppercase Letter 45
 
1.2%
Dash Punctuation 41
 
1.1%
Connector Punctuation 24
 
0.6%
Math Symbol 19
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 369
12.6%
o 310
 
10.6%
e 248
 
8.4%
h 234
 
8.0%
w 196
 
6.7%
a 163
 
5.5%
s 156
 
5.3%
c 153
 
5.2%
l 142
 
4.8%
m 142
 
4.8%
Other values (15) 825
28.1%
Uppercase Letter
ValueCountFrequency (%)
R 7
15.6%
I 6
13.3%
G 5
11.1%
C 5
11.1%
L 4
8.9%
M 3
6.7%
K 3
6.7%
N 3
6.7%
D 2
 
4.4%
A 2
 
4.4%
Other values (4) 5
11.1%
Decimal Number
ValueCountFrequency (%)
0 17
22.4%
1 16
21.1%
9 12
15.8%
2 8
10.5%
4 6
 
7.9%
6 5
 
6.6%
5 4
 
5.3%
3 3
 
3.9%
7 3
 
3.9%
8 2
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 322
48.6%
. 208
31.4%
: 107
 
16.2%
& 14
 
2.1%
% 6
 
0.9%
? 5
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 24
100.0%
Math Symbol
ValueCountFrequency (%)
= 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2983
78.4%
Common 822
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 369
12.4%
o 310
 
10.4%
e 248
 
8.3%
h 234
 
7.8%
w 196
 
6.6%
a 163
 
5.5%
s 156
 
5.2%
c 153
 
5.1%
l 142
 
4.8%
m 142
 
4.8%
Other values (29) 870
29.2%
Common
ValueCountFrequency (%)
/ 322
39.2%
. 208
25.3%
: 107
 
13.0%
- 41
 
5.0%
_ 24
 
2.9%
= 19
 
2.3%
0 17
 
2.1%
1 16
 
1.9%
& 14
 
1.7%
9 12
 
1.5%
Other values (9) 42
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 369
 
9.7%
/ 322
 
8.5%
o 310
 
8.1%
e 248
 
6.5%
h 234
 
6.1%
. 208
 
5.5%
w 196
 
5.2%
a 163
 
4.3%
s 156
 
4.1%
c 153
 
4.0%
Other values (48) 1446
38.0%

parking_at
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가능(무료)
72 
<NA>
22 
가능(유료)
 
4
불가능
 
2

Length

Max length6
Median length6
Mean length5.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row가능(무료)
3rd row<NA>
4th row가능(무료)
5th row가능(무료)

Common Values

ValueCountFrequency (%)
가능(무료) 72
72.0%
<NA> 22
 
22.0%
가능(유료) 4
 
4.0%
불가능 2
 
2.0%

Length

2023-12-10T19:10:47.886295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:48.167166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가능(무료 72
72.0%
na 22
 
22.0%
가능(유료 4
 
4.0%
불가능 2
 
2.0%

family_rum_ty
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가능
61 
<NA>
39 

Length

Max length4
Median length2
Mean length2.78
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가능 61
61.0%
<NA> 39
39.0%

Length

2023-12-10T19:10:48.423568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:48.686231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가능 61
61.0%
na 39
39.0%

sports_center_ennc_at
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
69 
가능
31 

Length

Max length4
Median length4
Mean length3.38
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row가능
3rd row<NA>
4th row<NA>
5th row가능

Common Values

ValueCountFrequency (%)
<NA> 69
69.0%
가능 31
31.0%

Length

2023-12-10T19:10:48.910237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:49.106030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 69
69.0%
가능 31
31.0%

exchg_at
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
60 
가능
40 

Length

Max length4
Median length4
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row가능
3rd row<NA>
4th row가능
5th row가능

Common Values

ValueCountFrequency (%)
<NA> 60
60.0%
가능 40
40.0%

Length

2023-12-10T19:10:49.315309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:49.522360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
60.0%
가능 40
40.0%

cvnstr_ennc_at
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
80 
가능
20 

Length

Max length4
Median length4
Mean length3.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row가능
3rd row<NA>
4th row<NA>
5th row가능

Common Values

ValueCountFrequency (%)
<NA> 80
80.0%
가능 20
 
20.0%

Length

2023-12-10T19:10:49.735323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:49.922513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 80
80.0%
가능 20
 
20.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2019-12-09 00:00:00
Maximum2019-12-09 00:00:00
2023-12-10T19:10:50.057194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:50.241798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:10:37.977507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:35.828691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:36.664472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.338410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:38.144820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:36.037694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:36.859076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.506485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:38.324530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:36.338443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.025499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.696121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:38.498063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:36.490300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.187495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:37.840408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:10:50.374303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlparking_at
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0000.9990.000
city_do_cd1.0001.0001.0001.0000.8540.9661.0000.0001.0001.0000.134
city_gn_gu_cd1.0001.0001.0001.0000.8530.9651.0000.0001.0001.0000.109
xpos_lo1.0001.0000.8540.8531.0000.8190.9990.0001.0001.0000.000
ypos_la1.0001.0000.9660.9650.8191.0001.0000.0001.0001.0000.000
area_nm1.0001.0001.0001.0000.9991.0001.0000.0001.0001.0000.000
hotel_grad1.0001.0000.0000.0000.0000.0000.0001.0001.0000.9760.000
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0000.9991.0001.0001.0001.0001.0000.9761.0001.0000.000
parking_at1.0000.0000.1340.1090.0000.0000.0000.0001.0000.0001.000
2023-12-10T19:10:50.596896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
parking_atarea_nmcvnstr_ennc_atexchg_atsports_center_ennc_atfamily_rum_tyhotel_grad
parking_at1.0000.0001.0001.0001.0001.0000.000
area_nm0.0001.0001.0001.0001.0001.0000.000
cvnstr_ennc_at1.0001.0001.0001.0001.0001.0001.000
exchg_at1.0001.0001.0001.0001.0001.0001.000
sports_center_ennc_at1.0001.0001.0001.0001.0001.0001.000
family_rum_ty1.0001.0001.0001.0001.0001.0001.000
hotel_grad0.0000.0001.0001.0001.0001.0001.000
2023-12-10T19:10:50.813539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradparking_atfamily_rum_tysports_center_ennc_atexchg_atcvnstr_ennc_at
city_do_cd1.0000.821-0.482-0.7260.9840.0000.1231.0001.0001.0001.000
city_gn_gu_cd0.8211.000-0.284-0.8760.9840.0000.1231.0001.0001.0001.000
xpos_lo-0.482-0.2841.0000.2340.9730.0000.0001.0001.0001.0001.000
ypos_la-0.726-0.8760.2341.0000.9840.0000.0001.0001.0001.0001.000
area_nm0.9840.9840.9730.9841.0000.0000.0001.0001.0001.0001.000
hotel_grad0.0000.0000.0000.0000.0001.0000.0001.0001.0001.0001.000
parking_at0.1230.1230.0000.0000.0000.0001.0001.0001.0001.0001.000
family_rum_ty1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
sports_center_ennc_at1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
exchg_at1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
cvnstr_ennc_at1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T19:10:38.731806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:10:39.170452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlparking_atfamily_rum_tysports_center_ennc_atexchg_atcvnstr_ennc_atbase_ymd
0울릉도 라페루즈 리조트경상북도 울릉군 울릉읍 중령길 129-1284747940130.87019637.465384경북1급054-791-0114http://www.laperouse.co.kr/<NA>가능<NA><NA><NA>2019-12-09
1서우봉 비치 호텔제주특별자치도 제주시 조천읍 함덕 6길 10 (함덕리 2915)5050110126.66046833.542916제주1급064-731-5000http://www.seoubongbeachhotel.com/가능(무료)가능가능가능가능2019-12-09
2엠에스 호텔부산광역시 해운대구 중동 해운대해변로 2712626350129.16112635.159825부산1급051-741-3838http://mshotel.alltheway.kr/<NA>가능<NA><NA><NA>2019-12-09
3호텔 컬리넌 개포서울특별시 강남구 개포4동 1229-81111680127.045937.47684서울2급02-2058-1558http://hotelcullinangaepo.co.kr/가능(무료)가능<NA>가능<NA>2019-12-09
4호텔 데님서울특별시 강남구 개포4동 논현로 661111680127.04665437.476196서울1급02-2058-1178http://hoteldenim.com/가능(무료)가능가능가능가능2019-12-09
5호텔 더 디자이너스 리즈 강남 프리미어서울특별시 강남구 논현동 201-111111680127.02583137.505198서울1급02-567-4000http://hotelthedesigners.com/lyjgangnampremier/가능(무료)<NA><NA><NA>가능2019-12-09
6호텔 포레힐서울특별시 강남구 논현동 54-41111680127.02360737.512091서울특2급02-511-8810http://www.foreheal.com/가능(무료)<NA><NA><NA><NA>2019-12-09
7온양 관광 호텔충청남도 아산시 온천대로 14594444200126.99992336.783556충남특2급041-540-1000http://www.onyanghotel.co.kr/가능(무료)<NA>가능<NA><NA>2019-12-09
8호텔뉴브서울특별시 강남구 선릉로85길 6(역삼동)?호텔뉴브1111680127.04910837.503025서울1급02-740-5000https://www.newvhotel.com/가능(무료)가능<NA><NA><NA>2019-12-09
9온 리버 스테이션서울특별시 강남구 신사동 380-2 한강공원잠원지구1111680127.01587237.525685서울<NA>02-3442-1582http://www.onriver.co.kr/가능(무료)가능가능가능<NA>2019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlparking_atfamily_rum_tysports_center_ennc_atexchg_atcvnstr_ennc_atbase_ymd
90더 프린스 호텔인천광역시 중구 용유동 을왕로58번길 52828110126.37150937.449413인천2급032-746-7007http://www.theprincehotel.co.kr/<NA>가능<NA><NA><NA>2019-12-09
91웨스턴 그레이스 호텔인천광역시 중구 은하수 로 29 번길 362828110126.5814737.490272인천1급02-730-1101http://www.westerngracehotel.net/가능(무료)<NA><NA><NA><NA>2019-12-09
92여수 베네치아 호텔전라남도 여수시 수정동 오동도로 61-134646130127.75185234.745803전남1급061-664-0001https://www.yeosuvenezia.com/가능(무료)가능가능가능<NA>2019-12-09
93스위트호텔남원전라북도 남원시 주천면 용담리 384545190127.41428535.407989전북1급063-630-7100http://namwon.suites.co.kr/가능(무료)<NA>가능<NA>가능2019-12-09
94제주 하나 호텔제주특별자치도 서귀포시 색달동 중문관광로72번길 535050130126.40808933.249867제주1급064-738-7001http://jejuresort.kr/가능(무료)가능가능가능가능2019-12-09
95코업 시티 호텔 성산제주특별자치도 서귀포시 색달동 중문관광로72번길 535050130126.40808933.249867제주1급064-780-9800http://www.coopcityhotel-seongsan.co.kr/가능(무료)가능가능가능<NA>2019-12-09
96데이즈 호텔 제주 서귀포 오션제주특별자치도 서귀포시 서귀동 동홍로 75050130126.56742533.249384제주1급064-802-7700http://www.dayshoteljejudo.com/<NA><NA><NA><NA><NA>2019-12-09
97라마다 앙코르 제주 서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주특2급064-735-2000https://www.ramadaencorejejuseogwipo.com/가능(무료)<NA>가능가능<NA>2019-12-09
98라마다제주서귀포호텔제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주특2급064-735-2050https://www.ramadaencorejejuseogwipo.com/불가능가능<NA><NA><NA>2019-12-09
99브라운스위트 제주제주특별자치도 서귀포시 성산읍 고성오조로 945050130126.91452533.452105제주1급064-786-6200http://www.brownsuitesjeju.com/<NA><NA><NA><NA><NA>2019-12-09