Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells20
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory102.3 B

Variable types

Text4
Numeric5
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
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:10:27.168768
Analysis finished2023-12-10 10:10:35.037281
Duration7.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:35.266021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length9.15
Min length3

Characters and Unicode

Total characters915
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

Unique98 ?
Unique (%)98.0%

Sample

1st rowE호텔
2nd row코리아나호텔
3rd row나인트리프리미어호텔명동2
4th row나인트리호텔명동
5th row라마다서울동대문
ValueCountFrequency (%)
호텔 53
24.2%
프리미어 7
 
3.2%
서울 7
 
3.2%
베스트웨스턴 5
 
2.3%
강남 4
 
1.8%
동대문 4
 
1.8%
베니키아 3
 
1.4%
송도 3
 
1.4%
스타즈 3
 
1.4%
명동 3
 
1.4%
Other values (116) 127
58.0%
2023-12-10T19:10:35.878417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
119
 
13.0%
호 86
 
9.4%
텔 83
 
9.1%
스 54
 
5.9%
트 21
 
2.3%
이 19
 
2.1%
리 19
 
2.1%
아 16
 
1.7%
라 16
 
1.7%
서 15
 
1.6%
Other values (155) 467
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
85.9%
Space Separator 119
 
13.0%
Decimal Number 5
 
0.5%
Uppercase Letter 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 86
 
10.9%
텔 83
 
10.6%
스 54
 
6.9%
트 21
 
2.7%
이 19
 
2.4%
리 19
 
2.4%
아 16
 
2.0%
라 16
 
2.0%
서 15
 
1.9%
어 13
 
1.7%
Other values (146) 444
56.5%
Uppercase Letter
ValueCountFrequency (%)
M 1
20.0%
T 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 (%)
119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
85.9%
Common 124
 
13.6%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 86
 
10.9%
텔 83
 
10.6%
스 54
 
6.9%
트 21
 
2.7%
이 19
 
2.4%
리 19
 
2.4%
아 16
 
2.0%
라 16
 
2.0%
서 15
 
1.9%
어 13
 
1.7%
Other values (146) 444
56.5%
Latin
ValueCountFrequency (%)
M 1
20.0%
T 1
20.0%
I 1
20.0%
S 1
20.0%
E 1
20.0%
Common
ValueCountFrequency (%)
119
96.0%
2 3
 
2.4%
7 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
85.9%
ASCII 129
 
14.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
119
92.2%
2 3
 
2.3%
M 1
 
0.8%
T 1
 
0.8%
I 1
 
0.8%
S 1
 
0.8%
7 1
 
0.8%
E 1
 
0.8%
1 1
 
0.8%
Hangul
ValueCountFrequency (%)
호 86
 
10.9%
텔 83
 
10.6%
스 54
 
6.9%
트 21
 
2.7%
이 19
 
2.4%
리 19
 
2.4%
아 16
 
2.0%
라 16
 
2.0%
서 15
 
1.9%
어 13
 
1.7%
Other values (146) 444
56.5%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:36.404352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length31
Mean length21.54
Min length12

Characters and Unicode

Total characters2154
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 태평로1가 세종대로 135
3rd row서울특별시 중구 마른내로 28
4th row서울특별시 종로구 인사동길 49
5th row서울특별시 중구 동호로 354
ValueCountFrequency (%)
서울특별시 52
 
11.5%
중구 20
 
4.4%
경기도 12
 
2.6%
인천광역시 11
 
2.4%
부산광역시 11
 
2.4%
제주특별자치도 10
 
2.2%
강남구 9
 
2.0%
서귀포시 9
 
2.0%
강서구 6
 
1.3%
종로구 5
 
1.1%
Other values (256) 309
68.1%
2023-12-10T19:10:37.051540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
354
 
16.4%
시 103
 
4.8%
로 100
 
4.6%
구 87
 
4.0%
서 72
 
3.3%
1 72
 
3.3%
특 63
 
2.9%
별 63
 
2.9%
울 54
 
2.5%
길 43
 
2.0%
Other values (188) 1143
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1433
66.5%
Space Separator 354
 
16.4%
Decimal Number 338
 
15.7%
Dash Punctuation 17
 
0.8%
Uppercase Letter 8
 
0.4%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 103
 
7.2%
로 100
 
7.0%
구 87
 
6.1%
서 72
 
5.0%
특 63
 
4.4%
별 63
 
4.4%
울 54
 
3.8%
길 43
 
3.0%
중 37
 
2.6%
광 35
 
2.4%
Other values (167) 776
54.2%
Decimal Number
ValueCountFrequency (%)
1 72
21.3%
2 40
11.8%
3 37
10.9%
6 31
9.2%
9 29
8.6%
8 29
8.6%
4 27
 
8.0%
5 27
 
8.0%
7 26
 
7.7%
0 20
 
5.9%
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 (%)
354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1433
66.5%
Common 709
32.9%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 103
 
7.2%
로 100
 
7.0%
구 87
 
6.1%
서 72
 
5.0%
특 63
 
4.4%
별 63
 
4.4%
울 54
 
3.8%
길 43
 
3.0%
중 37
 
2.6%
광 35
 
2.4%
Other values (167) 776
54.2%
Common
ValueCountFrequency (%)
354
49.9%
1 72
 
10.2%
2 40
 
5.6%
3 37
 
5.2%
6 31
 
4.4%
9 29
 
4.1%
8 29
 
4.1%
4 27
 
3.8%
5 27
 
3.8%
7 26
 
3.7%
Other values (2) 37
 
5.2%
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 1433
66.5%
ASCII 721
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354
49.1%
1 72
 
10.0%
2 40
 
5.5%
3 37
 
5.1%
6 31
 
4.3%
9 29
 
4.0%
8 29
 
4.0%
4 27
 
3.7%
5 27
 
3.7%
7 26
 
3.6%
Other values (11) 49
 
6.8%
Hangul
ValueCountFrequency (%)
시 103
 
7.2%
로 100
 
7.0%
구 87
 
6.1%
서 72
 
5.0%
특 63
 
4.4%
별 63
 
4.4%
울 54
 
3.8%
길 43
 
3.0%
중 37
 
2.6%
광 35
 
2.4%
Other values (167) 776
54.2%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.3
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:37.308425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q333.5
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation14.50357
Coefficient of variation (CV)0.6224708
Kurtosis-1.0526246
Mean23.3
Median Absolute Deviation (MAD)0
Skewness0.68248466
Sum2330
Variance210.35354
MonotonicityNot monotonic
2023-12-10T19:10:37.591278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 52
52.0%
41 12
 
12.0%
28 11
 
11.0%
26 11
 
11.0%
50 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 11
 
11.0%
28 11
 
11.0%
31 1
 
1.0%
41 12
 
12.0%
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 12
 
12.0%
31 1
 
1.0%
28 11
 
11.0%
26 11
 
11.0%
11 52
52.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23604.91
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:37.852339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11138.5
Q111485
median11695
Q333633.25
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)22148.25

Descriptive statistics

Standard deviation14409.248
Coefficient of variation (CV)0.61043437
Kurtosis-1.0482254
Mean23604.91
Median Absolute Deviation (MAD)585
Skewness0.68588177
Sum2360491
Variance2.0762644 × 108
MonotonicityNot monotonic
2023-12-10T19:10:38.165861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
11140 14
 
14.0%
50130 9
 
9.0%
11680 9
 
9.0%
11500 6
 
6.0%
11110 5
 
5.0%
28110 5
 
5.0%
11560 4
 
4.0%
28185 4
 
4.0%
26350 4
 
4.0%
41115 3
 
3.0%
Other values (30) 37
37.0%
ValueCountFrequency (%)
11110 5
 
5.0%
11140 14
14.0%
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%
41390 1
 
1.0%
41370 1
 
1.0%
41273 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.19203
Minimum126.40835
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:38.530111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40835
5-th percentile126.52188
Q1126.85918
median126.98942
Q3127.03575
95-th percentile129.15657
Maximum129.35736
Range2.9490161
Interquartile range (IQR)0.1765738

Descriptive statistics

Standard deviation0.76883682
Coefficient of variation (CV)0.0060446935
Kurtosis2.5421891
Mean127.19203
Median Absolute Deviation (MAD)0.07573105
Skewness1.9763799
Sum12719.203
Variance0.59111005
MonotonicityNot monotonic
2023-12-10T19:10:38.788074image/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%
Mean36.752385
Minimum33.247674
Maximum37.655194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:39.193000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.247674
5-th percentile33.427692
Q137.200041
median37.490991
Q337.559993
95-th percentile37.574024
Maximum37.655194
Range4.4075202
Interquartile range (IQR)0.35995175

Descriptive statistics

Standard deviation1.3948255
Coefficient of variation (CV)0.037951973
Kurtosis1.0392886
Mean36.752385
Median Absolute Deviation (MAD)0.07633736
Skewness-1.5908848
Sum3675.2385
Variance1.9455382
MonotonicityNot monotonic
2023-12-10T19:10:39.542183image/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 (%)
37.65519408 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%
37.568224 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
53 
경기
12 
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 53
53.0%
경기 12
 
12.0%
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

2023-12-10T19:10:39.859345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:40.059114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 53
53.0%
경기 12
 
12.0%
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

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
40 
4
22 
2
20 
<NA>
8 
5
 
4
Other values (4)
6 

Length

Max length7
Median length1
Mean length1.41
Min length1

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 40
40.0%
4 22
22.0%
2 20
20.0%
<NA> 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%
코리아나호텔 1
 
1.0%
파티오세븐호텔 1
 
1.0%
아스티호텔부산 1
 
1.0%

Length

2023-12-10T19:10:40.313434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:40.535324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 40
40.0%
4 22
22.0%
2 20
20.0%
na 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%
코리아나호텔 1
 
1.0%
파티오세븐호텔 1
 
1.0%
아스티호텔부산 1
 
1.0%

tel_no
Text

MISSING 

Distinct94
Distinct (%)98.9%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T19:10:40.923747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.768421
Min length9

Characters and Unicode

Total characters1118
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

Unique93 ?
Unique (%)97.9%

Sample

1st row02-521-6555
2nd row02-771-4500
3rd row02-6967-0999
4th row02-750-0999
5th row02-2276-3500
ValueCountFrequency (%)
02-771-4500 2
 
2.1%
02-768-8777 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%
051-741-3838 1
 
1.1%
Other values (84) 84
88.4%
2023-12-10T19:10:41.602327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 267
23.9%
- 189
16.9%
2 132
11.8%
1 90
 
8.1%
3 78
 
7.0%
7 75
 
6.7%
5 74
 
6.6%
6 66
 
5.9%
8 56
 
5.0%
4 51
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 929
83.1%
Dash Punctuation 189
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 267
28.7%
2 132
14.2%
1 90
 
9.7%
3 78
 
8.4%
7 75
 
8.1%
5 74
 
8.0%
6 66
 
7.1%
8 56
 
6.0%
4 51
 
5.5%
9 40
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 267
23.9%
- 189
16.9%
2 132
11.8%
1 90
 
8.1%
3 78
 
7.0%
7 75
 
6.7%
5 74
 
6.6%
6 66
 
5.9%
8 56
 
5.0%
4 51
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 267
23.9%
- 189
16.9%
2 132
11.8%
1 90
 
8.1%
3 78
 
7.0%
7 75
 
6.7%
5 74
 
6.6%
6 66
 
5.9%
8 56
 
5.0%
4 51
 
4.6%

homepage_url
Text

MISSING 

Distinct82
Distinct (%)96.5%
Missing15
Missing (%)15.0%
Memory size932.0 B
2023-12-10T19:10:42.018981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length41
Mean length33.164706
Min length19

Characters and Unicode

Total characters2819
Distinct characters46
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.royal.co.kr/2014_renew/ko/
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%
2023-12-10T19:10:42.688823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 290
 
10.3%
t 270
 
9.6%
w 225
 
8.0%
o 220
 
7.8%
. 211
 
7.5%
h 162
 
5.7%
e 159
 
5.6%
p 125
 
4.4%
r 112
 
4.0%
a 108
 
3.8%
Other values (36) 937
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2176
77.2%
Other Punctuation 596
 
21.1%
Decimal Number 28
 
1.0%
Uppercase Letter 11
 
0.4%
Dash Punctuation 5
 
0.2%
Connector Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 270
12.4%
w 225
 
10.3%
o 220
 
10.1%
h 162
 
7.4%
e 159
 
7.3%
p 125
 
5.7%
r 112
 
5.1%
a 108
 
5.0%
c 102
 
4.7%
m 98
 
4.5%
Other values (15) 595
27.3%
Decimal Number
ValueCountFrequency (%)
2 8
28.6%
1 6
21.4%
0 5
17.9%
5 3
 
10.7%
3 3
 
10.7%
7 2
 
7.1%
4 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 290
48.7%
. 211
35.4%
: 87
 
14.6%
% 4
 
0.7%
? 3
 
0.5%
# 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
F 3
27.3%
A 3
27.3%
R 2
18.2%
M 2
18.2%
J 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2187
77.6%
Common 632
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 270
12.3%
w 225
 
10.3%
o 220
 
10.1%
h 162
 
7.4%
e 159
 
7.3%
p 125
 
5.7%
r 112
 
5.1%
a 108
 
4.9%
c 102
 
4.7%
m 98
 
4.5%
Other values (20) 606
27.7%
Common
ValueCountFrequency (%)
/ 290
45.9%
. 211
33.4%
: 87
 
13.8%
2 8
 
1.3%
1 6
 
0.9%
0 5
 
0.8%
- 5
 
0.8%
% 4
 
0.6%
5 3
 
0.5%
3 3
 
0.5%
Other values (6) 10
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 290
 
10.3%
t 270
 
9.6%
w 225
 
8.0%
o 220
 
7.8%
. 211
 
7.5%
h 162
 
5.7%
e 159
 
5.6%
p 125
 
4.4%
r 112
 
4.0%
a 108
 
3.8%
Other values (36) 937
33.2%

prcuse_ix
Real number (ℝ)

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.967
Minimum1.1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:42.941405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.2
Q11.975
median2.95
Q33.925
95-th percentile4.7
Maximum5
Range3.9
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation1.1446013
Coefficient of variation (CV)0.38577731
Kurtosis-1.1961022
Mean2.967
Median Absolute Deviation (MAD)1
Skewness0.052052805
Sum296.7
Variance1.3101121
MonotonicityNot monotonic
2023-12-10T19:10:43.212407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4.7 6
 
6.0%
2.4 6
 
6.0%
1.7 5
 
5.0%
3.0 5
 
5.0%
3.6 5
 
5.0%
1.8 5
 
5.0%
2.9 4
 
4.0%
4.1 4
 
4.0%
1.2 4
 
4.0%
1.1 3
 
3.0%
Other values (28) 53
53.0%
ValueCountFrequency (%)
1.1 3
3.0%
1.2 4
4.0%
1.3 3
3.0%
1.5 2
 
2.0%
1.6 2
 
2.0%
1.7 5
5.0%
1.8 5
5.0%
1.9 1
 
1.0%
2.0 2
 
2.0%
2.1 2
 
2.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
4.8 3
3.0%
4.7 6
6.0%
4.6 2
 
2.0%
4.5 2
 
2.0%
4.4 2
 
2.0%
4.3 1
 
1.0%
4.2 3
3.0%
4.1 4
4.0%
4.0 1
 
1.0%

base_ymd
Categorical

CONSTANT 

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

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T19:10:43.472076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:43.634990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 100
100.0%

Interactions

2023-12-10T19:10:33.066287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:29.181531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:30.318589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:31.291702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.309992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:33.241197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:29.407951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:30.512325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:31.504041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.439502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:33.420387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:29.678750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:30.708986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:31.801055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.605769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:33.921241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:29.891292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:30.938353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:31.994275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.770683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:34.069729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:30.075044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:31.088067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.129527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:32.905531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:10:43.758191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlprcuse_ix
entrp_nm1.0001.0001.0001.0000.7650.9450.9760.0000.9990.9960.951
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0001.0000.8350.9710.9910.0001.0001.0000.000
city_gn_gu_cd1.0001.0001.0001.0000.8330.9710.9910.0001.0001.0000.000
xpos_lo0.7651.0000.8350.8331.0000.7960.8970.0001.0000.9670.227
ypos_la0.9451.0000.9710.9710.7961.0000.9600.0001.0001.0000.000
area_nm0.9761.0000.9910.9910.8970.9601.0000.0001.0001.0000.000
hotel_grad0.0001.0000.0000.0000.0000.0000.0001.0000.0000.9900.062
tel_no0.9991.0001.0001.0001.0001.0001.0000.0001.0000.9960.914
homepage_url0.9961.0001.0001.0000.9671.0001.0000.9900.9961.0000.914
prcuse_ix0.9511.0000.0000.0000.2270.0000.0000.0620.9140.9141.000
2023-12-10T19:10:43.986055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nm
hotel_grad1.0000.000
area_nm0.0001.000
2023-12-10T19:10:44.167809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laprcuse_ixarea_nmhotel_grad
city_do_cd1.0000.926-0.169-0.852-0.1700.9790.000
city_gn_gu_cd0.9261.000-0.114-0.901-0.1590.9790.000
xpos_lo-0.169-0.1141.000-0.0380.1380.7180.000
ypos_la-0.852-0.901-0.0381.0000.1230.9020.000
prcuse_ix-0.170-0.1590.1380.1231.0000.0000.000
area_nm0.9790.9790.7180.9020.0001.0000.000
hotel_grad0.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T19:10:34.304560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:10:34.744563image/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.
2023-12-10T19:10:34.931814image/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_urlprcuse_ixbase_ymd
0E호텔서울특별시 서초구 반포대로18길 40 E 호텔1111650127.01260837.486972서울302-521-6555http://www.e-hotel.kr/1.72020-12-31
1코리아나호텔서울특별시 중구 태평로1가 세종대로 1351111140126.9856937.564329서울코리아나호텔02-771-4500https://www.royal.co.kr/2014_renew/ko/3.72020-12-31
2나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/4.32020-12-31
3나인트리호텔명동서울특별시 종로구 인사동길 491111110126.98355337.574467서울302-750-0999https://ninetreehotel.com/2.32020-12-31
4라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/3.42020-12-31
5라마다앙코르서울마곡서울특별시 강서구 마곡중앙로 161-11 힐스테이트에코마곡나루역라마다앙코르서울특별시1111500126.82672237.567976서울302-2161-9000http://www.ramadaencore-seoulmagok.com/4.72020-12-31
6라마다앙코르제주서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주4064-735-2000https://www.ramadaencorejejuseogwipo.com/1.32020-12-31
7파티오세븐호텔서울특별시 강남구 논현동 논현로 7361111680126.97654837.568224서울파티오세븐호텔02-2171-7000https://www.koreanahotel.com/index.htm?2.22020-12-31
8라비타호텔서울특별시 강남구 영동대로 7121111680127.05688437.521414서울202-545-0015<NA>4.12020-12-31
9레지던스 강남서울특별시 강남구 학동로4길 151111680127.02353637.510515서울<NA>02-3485-3100<NA>1.22020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlprcuse_ixbase_ymd
90프린세스 호텔서울특별시 강남구 압구정로46길 171111680127.03663937.52739서울202-544-0366http://www.princesshotel.co.kr/xe/page_nRij554.42020-12-31
91필로스 호텔경북 포항시 북구 죽파로 64747113129.35736436.029035경북4054-250-2000http://www.philoshotel.kr/4.72020-12-31
92한강 호텔서울특별시 광진구 광장동 188-21111215127.03663937.52739서울4<NA><NA>1.52020-12-31
93해담채 가산서울특별시 금천구 벚꽃로56길 1901111545126.88872737.4795서울302-853-7111https://www.hotelhaedamchae.com/2.12020-12-31
94해담채 스테이서울특별시 강서구 곰달래로 70-11111500126.84059637.530015서울1070-4632-3500http://www.haedamchaestay.com/3.62020-12-31
95해운대 비지니스호텔S부산광역시 해운대구 구남로8번길 492626350129.15692435.1616부산3051-741-5009http://www.businesshotelshaeundae.com/4.82020-12-31
96해운대골든튤립부산광역시 해운대구 해운대해변로 3222626350129.16587935.162277부산4051-795-7000http://www.goldentulip-haeundae.com/3.32020-12-31
97호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA>1.62020-12-31
98호메르스 호텔부산광역시 수영구 광안해변로 217 호메르스관광호텔2626500129.1179835.153426부산3051-750-8000http://www.homershotel.com/4.62020-12-31
99호텔 노블레스 서울서울특별시 노원구 노해로77길 22 노블레스관광호텔1111350127.0593537.655194서울102-558-1202http://www.noblesse.com/home/main.php#1.52020-12-31