Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells20
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory120.3 B

Variable types

Text4
Numeric7
Categorical2
DateTime1

Alerts

base_ymd has constant value ""Constant
city_do_cd is highly overall correlated with city_gn_gu_cd and 2 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
xpos_lo is highly overall correlated with area_nmHigh correlation
ypos_la is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 3 other fieldsHigh correlation
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 10:12:06.434858
Analysis finished2023-12-10 10:12:17.451734
Duration11.02 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
2023-12-10T19:12:17.722420image/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%
2023-12-10T19:12:18.426832image/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
2023-12-10T19:12:19.190580image/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%
2023-12-10T19:12:19.842117image/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
2023-12-10T19:12:20.051818image/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
2023-12-10T19:12:20.271789image/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
2023-12-10T19:12:20.525515image/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
2023-12-10T19:12:20.801587image/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%
Mean127.21259
Minimum126.40835
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:21.351922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40835
5-th percentile126.52188
Q1126.85918
median126.99063
Q3127.03664
95-th percentile129.15657
Maximum129.35736
Range2.9490161
Interquartile range (IQR)0.17745857

Descriptive statistics

Standard deviation0.79045565
Coefficient of variation (CV)0.0062136591
Kurtosis2.0653628
Mean127.21259
Median Absolute Deviation (MAD)0.07941935
Skewness1.871262
Sum12721.259
Variance0.62482014
MonotonicityNot monotonic
2023-12-10T19:12:21.611261image/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%
129.041933 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.727905
Minimum33.247674
Maximum37.655194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:21.951916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.247674
5-th percentile33.427692
Q136.866418
median37.488622
Q337.553208
95-th percentile37.574024
Maximum37.655194
Range4.4075202
Interquartile range (IQR)0.68679094

Descriptive statistics

Standard deviation1.401895
Coefficient of variation (CV)0.038169752
Kurtosis0.86190707
Mean36.727905
Median Absolute Deviation (MAD)0.079186815
Skewness-1.5318624
Sum3672.7905
Variance1.9653096
MonotonicityNot monotonic
2023-12-10T19:12:22.235472image/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
52 
경기
12 
인천
11 
부산
11 
제주
10 
Other values (4)
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 52
52.0%
경기 12
 
12.0%
인천 11
 
11.0%
부산 11
 
11.0%
제주 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

Length

2023-12-10T19:12:22.540246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:22.786339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 52
52.0%
경기 12
 
12.0%
인천 11
 
11.0%
부산 11
 
11.0%
제주 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

hotel_grad
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
40 
4
22 
2
20 
<NA>
8 
5
 
4
Other values (3)
6 

Length

Max length4
Median length1
Mean length1.29
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row3
2nd row특2급
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%
특2급 2
 
2.0%
1급 1
 
1.0%

Length

2023-12-10T19:12:23.055510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:23.291149image/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%
특2급 2
 
2.0%
1급 1
 
1.0%

tel_no
Text

MISSING 

Distinct94
Distinct (%)98.9%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T19:12:23.715716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.778947
Min length9

Characters and Unicode

Total characters1119
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 row02-2171-7000
3rd row02-6967-0999
4th row02-750-0999
5th row02-2276-3500
ValueCountFrequency (%)
051-409-8888 2
 
2.1%
051-741-3838 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 (84) 84
88.4%
2023-12-10T19:12:24.619477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 131
11.7%
1 90
 
8.0%
3 78
 
7.0%
5 74
 
6.6%
7 73
 
6.5%
6 66
 
5.9%
8 60
 
5.4%
4 51
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 930
83.1%
Dash Punctuation 189
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
28.6%
2 131
14.1%
1 90
 
9.7%
3 78
 
8.4%
5 74
 
8.0%
7 73
 
7.8%
6 66
 
7.1%
8 60
 
6.5%
4 51
 
5.5%
9 41
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 131
11.7%
1 90
 
8.0%
3 78
 
7.0%
5 74
 
6.6%
7 73
 
6.5%
6 66
 
5.9%
8 60
 
5.4%
4 51
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
23.8%
- 189
16.9%
2 131
11.7%
1 90
 
8.0%
3 78
 
7.0%
5 74
 
6.6%
7 73
 
6.5%
6 66
 
5.9%
8 60
 
5.4%
4 51
 
4.6%

homepage_url
Text

MISSING 

Distinct81
Distinct (%)95.3%
Missing15
Missing (%)15.0%
Memory size932.0 B
2023-12-10T19:12:25.133968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length41
Mean length33
Min length19

Characters and Unicode

Total characters2805
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

Unique79 ?
Unique (%)92.9%

Sample

1st rowhttp://www.e-hotel.kr/
2nd rowhttps://www.koreanahotel.com/index.htm?
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%
https://astihotel.co.kr 2
 
2.4%
http://www.intercityseoul.kr 1
 
1.2%
http://www.mirandahotel.com/renewal/index.asp 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%
https://www.ambatel.com/ibis/suwon/ko/main.do 1
 
1.2%
Other values (71) 71
83.5%
2023-12-10T19:12:25.921172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 288
 
10.3%
t 272
 
9.7%
w 221
 
7.9%
o 219
 
7.8%
. 210
 
7.5%
h 163
 
5.8%
e 158
 
5.6%
p 125
 
4.5%
r 110
 
3.9%
a 108
 
3.9%
Other values (35) 931
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2170
77.4%
Other Punctuation 593
 
21.1%
Decimal Number 24
 
0.9%
Uppercase Letter 11
 
0.4%
Dash Punctuation 5
 
0.2%
Math Symbol 1
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 272
12.5%
w 221
 
10.2%
o 219
 
10.1%
h 163
 
7.5%
e 158
 
7.3%
p 125
 
5.8%
r 110
 
5.1%
a 108
 
5.0%
c 102
 
4.7%
m 98
 
4.5%
Other values (15) 594
27.4%
Other Punctuation
ValueCountFrequency (%)
/ 288
48.6%
. 210
35.4%
: 87
 
14.7%
% 4
 
0.7%
? 3
 
0.5%
# 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 7
29.2%
1 5
20.8%
0 4
16.7%
5 3
12.5%
3 3
12.5%
7 2
 
8.3%
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 2181
77.8%
Common 624
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 272
12.5%
w 221
 
10.1%
o 219
 
10.0%
h 163
 
7.5%
e 158
 
7.2%
p 125
 
5.7%
r 110
 
5.0%
a 108
 
5.0%
c 102
 
4.7%
m 98
 
4.5%
Other values (20) 605
27.7%
Common
ValueCountFrequency (%)
/ 288
46.2%
. 210
33.7%
: 87
 
13.9%
2 7
 
1.1%
1 5
 
0.8%
- 5
 
0.8%
0 4
 
0.6%
% 4
 
0.6%
5 3
 
0.5%
3 3
 
0.5%
Other values (5) 8
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 288
 
10.3%
t 272
 
9.7%
w 221
 
7.9%
o 219
 
7.8%
. 210
 
7.5%
h 163
 
5.8%
e 158
 
5.6%
p 125
 
4.5%
r 110
 
3.9%
a 108
 
3.9%
Other values (35) 931
33.2%

klang_stsfdg_rt
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.546
Minimum1.7
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:26.224291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile2.1
Q12.675
median3.6
Q34.4
95-th percentile4.8
Maximum4.9
Range3.2
Interquartile range (IQR)1.725

Descriptive statistics

Standard deviation0.90759088
Coefficient of variation (CV)0.2559478
Kurtosis-1.3036734
Mean3.546
Median Absolute Deviation (MAD)0.9
Skewness-0.18509556
Sum354.6
Variance0.82372121
MonotonicityNot monotonic
2023-12-10T19:12:26.498180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 7
 
7.0%
4.8 6
 
6.0%
4.5 6
 
6.0%
2.6 5
 
5.0%
4.1 5
 
5.0%
4.3 5
 
5.0%
3.6 4
 
4.0%
4.6 4
 
4.0%
4.7 4
 
4.0%
3.0 4
 
4.0%
Other values (20) 50
50.0%
ValueCountFrequency (%)
1.7 1
 
1.0%
2.0 2
 
2.0%
2.1 3
3.0%
2.2 3
3.0%
2.3 2
 
2.0%
2.4 2
 
2.0%
2.5 7
7.0%
2.6 5
5.0%
2.7 4
4.0%
2.8 1
 
1.0%
ValueCountFrequency (%)
4.9 2
 
2.0%
4.8 6
6.0%
4.7 4
4.0%
4.6 4
4.0%
4.5 6
6.0%
4.4 4
4.0%
4.3 5
5.0%
4.2 4
4.0%
4.1 5
5.0%
4.0 1
 
1.0%

chnlng_stsfdg_rt
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.358
Minimum1.5
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:26.736855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.1
Q12.6
median3.3
Q34
95-th percentile4.9
Maximum4.9
Range3.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.88319667
Coefficient of variation (CV)0.26301271
Kurtosis-1.0102516
Mean3.358
Median Absolute Deviation (MAD)0.7
Skewness0.25385541
Sum335.8
Variance0.78003636
MonotonicityNot monotonic
2023-12-10T19:12:26.993597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.6 10
 
10.0%
3.4 7
 
7.0%
4.9 7
 
7.0%
2.9 6
 
6.0%
4.0 5
 
5.0%
2.5 5
 
5.0%
2.4 4
 
4.0%
3.3 4
 
4.0%
3.1 4
 
4.0%
3.9 4
 
4.0%
Other values (20) 44
44.0%
ValueCountFrequency (%)
1.5 1
 
1.0%
2.0 2
 
2.0%
2.1 4
 
4.0%
2.2 1
 
1.0%
2.3 3
 
3.0%
2.4 4
 
4.0%
2.5 5
5.0%
2.6 10
10.0%
2.7 2
 
2.0%
2.8 2
 
2.0%
ValueCountFrequency (%)
4.9 7
7.0%
4.8 2
 
2.0%
4.7 3
3.0%
4.6 3
3.0%
4.5 2
 
2.0%
4.3 2
 
2.0%
4.2 3
3.0%
4.1 2
 
2.0%
4.0 5
5.0%
3.9 4
4.0%

jalng_stsfdg_rt
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.563
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:27.223045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.2
Q12.9
median3.6
Q34.3
95-th percentile4.9
Maximum5
Range3.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.83226052
Coefficient of variation (CV)0.2335842
Kurtosis-0.81159112
Mean3.563
Median Absolute Deviation (MAD)0.7
Skewness-0.128704
Sum356.3
Variance0.69265758
MonotonicityNot monotonic
2023-12-10T19:12:27.472716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.7 7
 
7.0%
4.0 6
 
6.0%
4.4 6
 
6.0%
3.6 5
 
5.0%
2.6 5
 
5.0%
3.5 5
 
5.0%
3.4 5
 
5.0%
2.5 5
 
5.0%
2.9 4
 
4.0%
3.0 4
 
4.0%
Other values (21) 48
48.0%
ValueCountFrequency (%)
1.5 1
 
1.0%
2.0 2
 
2.0%
2.1 1
 
1.0%
2.2 2
 
2.0%
2.3 1
 
1.0%
2.4 1
 
1.0%
2.5 5
5.0%
2.6 5
5.0%
2.7 1
 
1.0%
2.8 4
4.0%
ValueCountFrequency (%)
5.0 2
 
2.0%
4.9 4
4.0%
4.8 3
3.0%
4.7 2
 
2.0%
4.6 3
3.0%
4.5 3
3.0%
4.4 6
6.0%
4.3 4
4.0%
4.2 2
 
2.0%
4.0 6
6.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:12:27.704881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:27.880717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:12:15.414417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:08.074256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:09.711869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.868925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.977152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.028254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.222939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:15.585736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:08.279642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:09.902622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.015614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.124342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.215276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.402640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:15.792608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:08.489867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.085207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.195126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.271858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.386378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.588576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:15.967465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:08.690300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.225698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.344341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.410184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.557868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.748956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:16.138110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:09.219147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.352519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.557780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.516240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.712672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.891335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:16.286719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:09.373708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.507410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.703107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.687867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.882267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:15.054551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:16.463064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:09.543297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:10.676050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:11.847344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.866527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:14.044707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:15.231614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:12:28.040622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_stsfdg_rtchnlng_stsfdg_rtjalng_stsfdg_rt
entrp_nm1.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.9511.0000.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0001.0000.8430.9741.0000.0001.0001.0000.0000.0000.000
city_gn_gu_cd1.0001.0001.0001.0000.8420.9741.0000.0001.0001.0000.0000.0000.000
xpos_lo1.0001.0000.8430.8421.0000.7980.8980.0001.0000.9780.0770.0000.254
ypos_la1.0001.0000.9740.9740.7981.0000.9600.0001.0001.0000.0000.0000.159
area_nm1.0001.0001.0001.0000.8980.9601.0000.0001.0001.0000.0000.0000.000
hotel_grad0.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.2630.6320.505
tel_no1.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.9391.0000.000
homepage_url1.0001.0001.0001.0000.9781.0001.0000.0001.0001.0000.8220.9720.000
klang_stsfdg_rt0.9511.0000.0000.0000.0770.0000.0000.2630.9390.8221.0000.6080.319
chnlng_stsfdg_rt1.0001.0000.0000.0000.0000.0000.0000.6321.0000.9720.6081.0000.292
jalng_stsfdg_rt0.0001.0000.0000.0000.2540.1590.0000.5050.0000.0000.3190.2921.000
2023-12-10T19:12:28.359346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmhotel_grad
area_nm1.0000.000
hotel_grad0.0001.000
2023-12-10T19:12:28.544040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laklang_stsfdg_rtchnlng_stsfdg_rtjalng_stsfdg_rtarea_nmhotel_grad
city_do_cd1.0000.926-0.169-0.853-0.0210.1370.0100.9890.000
city_gn_gu_cd0.9261.000-0.107-0.905-0.0150.1180.0290.9890.000
xpos_lo-0.169-0.1071.000-0.056-0.048-0.140-0.1090.7190.000
ypos_la-0.853-0.905-0.0561.0000.0110.001-0.0150.9020.000
klang_stsfdg_rt-0.021-0.015-0.0480.0111.000-0.0100.0160.0000.125
chnlng_stsfdg_rt0.1370.118-0.1400.001-0.0101.000-0.0070.0000.370
jalng_stsfdg_rt0.0100.029-0.109-0.0150.016-0.0071.0000.0900.241
area_nm0.9890.9890.7190.9020.0000.0000.0901.0000.000
hotel_grad0.0000.0000.0000.0000.1250.3700.2410.0001.000

Missing values

2023-12-10T19:12:16.738825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:12:17.074597image/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.
2023-12-10T19:12:17.306789image/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_stsfdg_rtchnlng_stsfdg_rtjalng_stsfdg_rtbase_ymd
0E호텔서울특별시 서초구 반포대로18길 40 E 호텔1111650127.01260837.486972서울302-521-6555http://www.e-hotel.kr/2.62.92.92020-12-31
1코리아나호텔서울특별시 중구 태평로1가 세종대로 1351111140126.97654837.568224서울특2급02-2171-7000https://www.koreanahotel.com/index.htm?2.62.95.02020-12-31
2나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/3.52.02.82020-12-31
3나인트리호텔명동서울특별시 종로구 인사동길 491111110126.98355337.574467서울302-750-0999https://ninetreehotel.com/2.53.24.32020-12-31
4라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/3.64.04.92020-12-31
5라마다앙코르서울마곡서울특별시 강서구 마곡중앙로 161-11 힐스테이트에코마곡나루역라마다앙코르서울특별시1111500126.82672237.567976서울302-2161-9000http://www.ramadaencore-seoulmagok.com/2.04.54.92020-12-31
6라마다앙코르제주서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주4064-735-2000https://www.ramadaencorejejuseogwipo.com/3.33.43.12020-12-31
7파티오세븐호텔서울특별시 강남구 논현동 논현로 7361111680127.02939737.517824서울1급02-517-8833https://www.patio7.co.kr/1.71.54.02020-12-31
8라비타호텔서울특별시 강남구 영동대로 7121111680127.05688437.521414서울202-545-0015<NA>4.53.84.42020-12-31
9레지던스 강남서울특별시 강남구 학동로4길 151111680127.02353637.510515서울<NA>02-3485-3100<NA>3.13.03.32020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlklang_stsfdg_rtchnlng_stsfdg_rtjalng_stsfdg_rtbase_ymd
90프린세스 호텔서울특별시 강남구 압구정로46길 171111680127.03663937.52739서울202-544-0366http://www.princesshotel.co.kr/xe/page_nRij552.12.44.02020-12-31
91필로스 호텔경북 포항시 북구 죽파로 64747113129.35736436.029035경북4054-250-2000http://www.philoshotel.kr/4.84.92.82020-12-31
92한강 호텔서울특별시 광진구 광장동 188-21111215127.03663937.52739서울4<NA><NA>4.22.73.72020-12-31
93해담채 가산서울특별시 금천구 벚꽃로56길 1901111545126.88872737.4795서울302-853-7111https://www.hotelhaedamchae.com/4.52.33.82020-12-31
94해담채 스테이서울특별시 강서구 곰달래로 70-11111500126.84059637.530015서울1070-4632-3500http://www.haedamchaestay.com/2.72.53.72020-12-31
95해운대 비지니스호텔S부산광역시 해운대구 구남로8번길 492626350129.15692435.1616부산3051-741-5009http://www.businesshotelshaeundae.com/3.32.02.52020-12-31
96해운대골든튤립부산광역시 해운대구 해운대해변로 3222626350129.16587935.162277부산4051-795-7000http://www.goldentulip-haeundae.com/4.82.14.32020-12-31
97호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA>3.24.04.02020-12-31
98호메르스 호텔부산광역시 수영구 광안해변로 217 호메르스관광호텔2626500129.1179835.153426부산3051-750-8000http://www.homershotel.com/4.94.14.52020-12-31
99호텔 노블레스 서울서울특별시 노원구 노해로77길 22 노블레스관광호텔1111350127.0593537.655194서울102-558-1202http://www.noblesse.com/home/main.php#2.64.82.02020-12-31