Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells25
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory118.3 B

Variable types

Text4
Numeric4
Categorical6

Alerts

base_ymd has constant value ""Constant
trrsrt3_nm is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
trrsrt2_nm is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
trrsrt1_nm is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 5 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 5 other fieldsHigh correlation
xpos_lo is highly overall correlated with area_nm and 3 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 5 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
tel_no has 6 (6.0%) missing valuesMissing
homepage_url has 19 (19.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique
xpos_lo has unique valuesUnique
ypos_la has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:20:15.229593
Analysis finished2023-12-10 10:20:20.333201
Duration5.1 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
2023-12-10T19:20:20.675170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length8.85
Min length3

Characters and Unicode

Total characters885
Distinct characters167
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row호텔 팝
2nd row호텔스카이파크제주1호점
3rd row호텔 더메이
4th rowJS호텔분당
5th row타소스 호텔
ValueCountFrequency (%)
호텔 64
28.2%
프리미어 7
 
3.1%
서울 6
 
2.6%
강남 5
 
2.2%
더 5
 
2.2%
디자이너스 5
 
2.2%
컬리넌 5
 
2.2%
베스트웨스턴 3
 
1.3%
종로 3
 
1.3%
관광 3
 
1.3%
Other values (109) 121
53.3%
2023-12-10T19:20:21.355804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
14.4%
호 94
 
10.6%
텔 88
 
9.9%
스 42
 
4.7%
이 24
 
2.7%
리 21
 
2.4%
트 15
 
1.7%
아 15
 
1.7%
서 14
 
1.6%
동 13
 
1.5%
Other values (157) 432
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 744
84.1%
Space Separator 127
 
14.4%
Decimal Number 7
 
0.8%
Uppercase Letter 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 94
 
12.6%
텔 88
 
11.8%
스 42
 
5.6%
이 24
 
3.2%
리 21
 
2.8%
트 15
 
2.0%
아 15
 
2.0%
서 14
 
1.9%
동 13
 
1.7%
어 12
 
1.6%
Other values (147) 406
54.6%
Uppercase Letter
ValueCountFrequency (%)
S 2
28.6%
J 1
14.3%
E 1
14.3%
T 1
14.3%
I 1
14.3%
M 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
7 1
 
14.3%
Space Separator
ValueCountFrequency (%)
127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 744
84.1%
Common 134
 
15.1%
Latin 7
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 94
 
12.6%
텔 88
 
11.8%
스 42
 
5.6%
이 24
 
3.2%
리 21
 
2.8%
트 15
 
2.0%
아 15
 
2.0%
서 14
 
1.9%
동 13
 
1.7%
어 12
 
1.6%
Other values (147) 406
54.6%
Latin
ValueCountFrequency (%)
S 2
28.6%
J 1
14.3%
E 1
14.3%
T 1
14.3%
I 1
14.3%
M 1
14.3%
Common
ValueCountFrequency (%)
127
94.8%
1 3
 
2.2%
2 3
 
2.2%
7 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 744
84.1%
ASCII 141
 
15.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
90.1%
1 3
 
2.1%
2 3
 
2.1%
S 2
 
1.4%
J 1
 
0.7%
E 1
 
0.7%
T 1
 
0.7%
7 1
 
0.7%
I 1
 
0.7%
M 1
 
0.7%
Hangul
ValueCountFrequency (%)
호 94
 
12.6%
텔 88
 
11.8%
스 42
 
5.6%
이 24
 
3.2%
리 21
 
2.8%
트 15
 
2.0%
아 15
 
2.0%
서 14
 
1.9%
동 13
 
1.7%
어 12
 
1.6%
Other values (147) 406
54.6%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:20:21.804310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length29.5
Mean length20.91
Min length15

Characters and Unicode

Total characters2091
Distinct characters197
Distinct categories8 ?
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 row경기도 구리시 안골로57번길 10-6 뉴호스텔모텔
2nd row제주특별자치도 제주시 삼무로 48
3rd row경기도 남양주시 별내2로 70 호텔 더 메이
4th row경기도 성남시 분당구 황새울로311번길 36
5th row경기도 수원시 권선구 권선로 669번길 26
ValueCountFrequency (%)
서울특별시 68
 
15.3%
경기도 17
 
3.8%
강남구 14
 
3.1%
종로구 11
 
2.5%
부산광역시 10
 
2.2%
중구 9
 
2.0%
강서구 7
 
1.6%
영등포구 7
 
1.6%
수원시 5
 
1.1%
서초구 5
 
1.1%
Other values (246) 292
65.6%
2023-12-10T19:20:22.500813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
345
 
16.5%
시 103
 
4.9%
로 102
 
4.9%
구 93
 
4.4%
서 83
 
4.0%
별 73
 
3.5%
특 72
 
3.4%
울 70
 
3.3%
1 69
 
3.3%
길 49
 
2.3%
Other values (187) 1032
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1377
65.9%
Space Separator 345
 
16.5%
Decimal Number 336
 
16.1%
Dash Punctuation 19
 
0.9%
Uppercase Letter 8
 
0.4%
Lowercase Letter 4
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 103
 
7.5%
로 102
 
7.4%
구 93
 
6.8%
서 83
 
6.0%
별 73
 
5.3%
특 72
 
5.2%
울 70
 
5.1%
길 49
 
3.6%
강 26
 
1.9%
대 26
 
1.9%
Other values (164) 680
49.4%
Decimal Number
ValueCountFrequency (%)
1 69
20.5%
2 46
13.7%
3 30
8.9%
7 30
8.9%
8 30
8.9%
5 29
8.6%
9 28
8.3%
6 28
8.3%
4 26
 
7.7%
0 20
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
25.0%
A 2
25.0%
E 2
25.0%
H 1
12.5%
P 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
l 1
25.0%
t 1
25.0%
o 1
25.0%
e 1
25.0%
Space Separator
ValueCountFrequency (%)
345
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1377
65.9%
Common 702
33.6%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 103
 
7.5%
로 102
 
7.4%
구 93
 
6.8%
서 83
 
6.0%
별 73
 
5.3%
특 72
 
5.2%
울 70
 
5.1%
길 49
 
3.6%
강 26
 
1.9%
대 26
 
1.9%
Other values (164) 680
49.4%
Common
ValueCountFrequency (%)
345
49.1%
1 69
 
9.8%
2 46
 
6.6%
3 30
 
4.3%
7 30
 
4.3%
8 30
 
4.3%
5 29
 
4.1%
9 28
 
4.0%
6 28
 
4.0%
4 26
 
3.7%
Other values (4) 41
 
5.8%
Latin
ValueCountFrequency (%)
C 2
16.7%
A 2
16.7%
E 2
16.7%
l 1
8.3%
t 1
8.3%
o 1
8.3%
H 1
8.3%
P 1
8.3%
e 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1377
65.9%
ASCII 714
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
345
48.3%
1 69
 
9.7%
2 46
 
6.4%
3 30
 
4.2%
7 30
 
4.2%
8 30
 
4.2%
5 29
 
4.1%
9 28
 
3.9%
6 28
 
3.9%
4 26
 
3.6%
Other values (13) 53
 
7.4%
Hangul
ValueCountFrequency (%)
시 103
 
7.5%
로 102
 
7.4%
구 93
 
6.8%
서 83
 
6.0%
별 73
 
5.3%
특 72
 
5.2%
울 70
 
5.1%
길 49
 
3.6%
강 26
 
1.9%
대 26
 
1.9%
Other values (164) 680
49.4%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:22.738448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q326
95-th percentile41.3
Maximum50
Range39
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.315518
Coefficient of variation (CV)0.6828471
Kurtosis-0.4189417
Mean19.5
Median Absolute Deviation (MAD)0
Skewness1.1307526
Sum1950
Variance177.30303
MonotonicityNot monotonic
2023-12-10T19:20:22.935300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
11 68
68.0%
41 17
 
17.0%
26 10
 
10.0%
50 3
 
3.0%
47 1
 
1.0%
48 1
 
1.0%
ValueCountFrequency (%)
11 68
68.0%
26 10
 
10.0%
41 17
 
17.0%
47 1
 
1.0%
48 1
 
1.0%
50 3
 
3.0%
ValueCountFrequency (%)
50 3
 
3.0%
48 1
 
1.0%
47 1
 
1.0%
41 17
 
17.0%
26 10
 
10.0%
11 68
68.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19866.57
Minimum11110
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:23.191332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111275
median11650
Q326350
95-th percentile41866.15
Maximum50110
Range39000
Interquartile range (IQR)15075

Descriptive statistics

Standard deviation13236.914
Coefficient of variation (CV)0.66629085
Kurtosis-0.42054612
Mean19866.57
Median Absolute Deviation (MAD)510
Skewness1.1304884
Sum1986657
Variance1.7521588 × 108
MonotonicityNot monotonic
2023-12-10T19:20:23.475313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
11680 14
 
14.0%
11110 11
 
11.0%
11140 8
 
8.0%
11500 7
 
7.0%
11560 7
 
7.0%
11650 5
 
5.0%
26350 4
 
4.0%
41115 3
 
3.0%
50110 3
 
3.0%
11170 3
 
3.0%
Other values (30) 35
35.0%
ValueCountFrequency (%)
11110 11
11.0%
11140 8
8.0%
11170 3
 
3.0%
11215 2
 
2.0%
11230 1
 
1.0%
11290 1
 
1.0%
11305 1
 
1.0%
11350 1
 
1.0%
11380 1
 
1.0%
11440 1
 
1.0%
ValueCountFrequency (%)
50110 3
3.0%
48310 1
 
1.0%
47113 1
 
1.0%
41590 1
 
1.0%
41500 1
 
1.0%
41480 1
 
1.0%
41390 1
 
1.0%
41370 1
 
1.0%
41360 1
 
1.0%
41310 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.2234
Minimum126.49176
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:23.801455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49176
5-th percentile126.79936
Q1126.93255
median127.01257
Q3127.04801
95-th percentile129.11991
Maximum129.35736
Range2.8656047
Interquartile range (IQR)0.11545882

Descriptive statistics

Standard deviation0.70126902
Coefficient of variation (CV)0.0055121074
Kurtosis3.5519786
Mean127.2234
Median Absolute Deviation (MAD)0.0516874
Skewness2.2619651
Sum12722.34
Variance0.49177824
MonotonicityNot monotonic
2023-12-10T19:20:24.022009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1374986 1
 
1.0%
127.0204833 1
 
1.0%
126.9216068 1
 
1.0%
126.8904958 1
 
1.0%
126.8987369 1
 
1.0%
126.9205137 1
 
1.0%
126.920129 1
 
1.0%
127.0975344 1
 
1.0%
127.0960855 1
 
1.0%
127.0166679 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.491759 1
1.0%
126.527765 1
1.0%
126.547803 1
1.0%
126.7402653 1
1.0%
126.7618722 1
1.0%
126.8013283 1
1.0%
126.8142917 1
1.0%
126.8184735 1
1.0%
126.8267215 1
1.0%
126.8357949 1
1.0%
ValueCountFrequency (%)
129.3573637 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%
129.0373247 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.106472
Minimum33.445747
Maximum37.715623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:24.265203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.445747
5-th percentile35.100873
Q137.388768
median37.511294
Q337.564216
95-th percentile37.597759
Maximum37.715623
Range4.2698758
Interquartile range (IQR)0.17544798

Descriptive statistics

Standard deviation0.99708362
Coefficient of variation (CV)0.026870882
Kurtosis4.4468765
Mean37.106472
Median Absolute Deviation (MAD)0.05651452
Skewness-2.333788
Sum3710.6472
Variance0.99417575
MonotonicityNot monotonic
2023-12-10T19:20:24.476371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.59764692 1
 
1.0%
37.49616883 1
 
1.0%
37.51385233 1
 
1.0%
37.54087607 1
 
1.0%
37.53464944 1
 
1.0%
37.53036423 1
 
1.0%
37.52824985 1
 
1.0%
37.50601088 1
 
1.0%
37.50219886 1
 
1.0%
37.59361073 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.445747 1
1.0%
33.489376 1
1.0%
33.517558 1
1.0%
34.88593306 1
1.0%
35.09382162 1
1.0%
35.10124405 1
1.0%
35.11615674 1
1.0%
35.151201 1
1.0%
35.15342586 1
1.0%
35.154691 1
1.0%
ValueCountFrequency (%)
37.7156228 1
1.0%
37.65519408 1
1.0%
37.6466594 1
1.0%
37.61088514 1
1.0%
37.59989609 1
1.0%
37.59764692 1
1.0%
37.59361073 1
1.0%
37.58171189 1
1.0%
37.57625021 1
1.0%
37.57446681 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
68 
경기
17 
부산
10 
제주
 
3
경북
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row경기
2nd row제주
3rd row경기
4th row경기
5th row경기

Common Values

ValueCountFrequency (%)
서울 68
68.0%
경기 17
 
17.0%
부산 10
 
10.0%
제주 3
 
3.0%
경북 1
 
1.0%
경남 1
 
1.0%

Length

2023-12-10T19:20:24.694535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:20:24.869678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 68
68.0%
경기 17
 
17.0%
부산 10
 
10.0%
제주 3
 
3.0%
경북 1
 
1.0%
경남 1
 
1.0%

hotel_grad
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
48 
2
20 
4
15 
1
9 
<NA>
7 

Length

Max length4
Median length1
Mean length1.21
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row3
3rd row3
4th row3
5th row<NA>

Common Values

ValueCountFrequency (%)
3 48
48.0%
2 20
20.0%
4 15
 
15.0%
1 9
 
9.0%
<NA> 7
 
7.0%
5 1
 
1.0%

Length

2023-12-10T19:20:25.064684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:20:25.244405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 48
48.0%
2 20
20.0%
4 15
 
15.0%
1 9
 
9.0%
na 7
 
7.0%
5 1
 
1.0%

tel_no
Text

MISSING 

Distinct94
Distinct (%)100.0%
Missing6
Missing (%)6.0%
Memory size932.0 B
2023-12-10T19:20:25.696780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.638298
Min length9

Characters and Unicode

Total characters1094
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

Unique94 ?
Unique (%)100.0%

Sample

1st row064-797-0000
2nd row031-551-8700
3rd row1877-8006
4th row031-230-5000
5th row064-795-7000
ValueCountFrequency (%)
051-805-9901 1
 
1.1%
02-538-5177 1
 
1.1%
02-2014-1111 1
 
1.1%
02-2671-9995 1
 
1.1%
02-783-2233 1
 
1.1%
02-786-5511 1
 
1.1%
02-2143-3000 1
 
1.1%
02-425-1000 1
 
1.1%
02-925-7000 1
 
1.1%
02-3474-3399 1
 
1.1%
Other values (84) 84
89.4%
2023-12-10T19:20:26.409281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 261
23.9%
- 187
17.1%
2 146
13.3%
5 80
 
7.3%
1 79
 
7.2%
3 70
 
6.4%
7 66
 
6.0%
6 59
 
5.4%
8 54
 
4.9%
9 47
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 907
82.9%
Dash Punctuation 187
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 261
28.8%
2 146
16.1%
5 80
 
8.8%
1 79
 
8.7%
3 70
 
7.7%
7 66
 
7.3%
6 59
 
6.5%
8 54
 
6.0%
9 47
 
5.2%
4 45
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1094
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 261
23.9%
- 187
17.1%
2 146
13.3%
5 80
 
7.3%
1 79
 
7.2%
3 70
 
6.4%
7 66
 
6.0%
6 59
 
5.4%
8 54
 
4.9%
9 47
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 261
23.9%
- 187
17.1%
2 146
13.3%
5 80
 
7.3%
1 79
 
7.2%
3 70
 
6.4%
7 66
 
6.0%
6 59
 
5.4%
8 54
 
4.9%
9 47
 
4.3%

homepage_url
Text

MISSING 

Distinct77
Distinct (%)95.1%
Missing19
Missing (%)19.0%
Memory size932.0 B
2023-12-10T19:20:26.997438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length42
Mean length32.864198
Min length19

Characters and Unicode

Total characters2662
Distinct characters44
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

Unique76 ?
Unique (%)93.8%

Sample

1st rowhttps://www.skyparkhotel.com/html/main.asp
2nd rowhttp://www.jshotelbundang.com/
3rd rowhttps://www.ambatel.com/ibis/suwon/ko/main.do
4th rowhttp://www.whistlelark.co.kr/
5th rowhttps://hoteltate.modoo.at/
ValueCountFrequency (%)
https://www.skyparkhotel.com/html/main.asp 5
 
6.2%
http://hotelthedesigners.kr 1
 
1.2%
https://www.mayfield.co.kr/2017/kor/html/index/index.asp 1
 
1.2%
http://www.cshotelseoul.com 1
 
1.2%
http://www.hotel-loft.co.kr 1
 
1.2%
http://www.hotelbenhur.co.kr 1
 
1.2%
http://hotelthedesigners.com 1
 
1.2%
http://www.rosanahotel.co.kr 1
 
1.2%
http://www.hotelinfini.co.kr/default 1
 
1.2%
https://www.hotelahill.com 1
 
1.2%
Other values (67) 67
82.7%
2023-12-10T19:20:27.805939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 275
 
10.3%
t 273
 
10.3%
w 197
 
7.4%
o 191
 
7.2%
. 190
 
7.1%
h 178
 
6.7%
e 157
 
5.9%
p 116
 
4.4%
a 110
 
4.1%
l 105
 
3.9%
Other values (34) 870
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2071
77.8%
Other Punctuation 555
 
20.8%
Decimal Number 23
 
0.9%
Uppercase Letter 6
 
0.2%
Dash Punctuation 4
 
0.2%
Math Symbol 2
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 273
13.2%
w 197
 
9.5%
o 191
 
9.2%
h 178
 
8.6%
e 157
 
7.6%
p 116
 
5.6%
a 110
 
5.3%
l 105
 
5.1%
m 101
 
4.9%
c 95
 
4.6%
Other values (15) 548
26.5%
Decimal Number
ValueCountFrequency (%)
2 6
26.1%
1 4
17.4%
0 4
17.4%
3 3
13.0%
5 3
13.0%
6 2
 
8.7%
7 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/ 275
49.5%
. 190
34.2%
: 83
 
15.0%
% 4
 
0.7%
? 2
 
0.4%
# 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
F 3
50.0%
R 2
33.3%
A 1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
= 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2077
78.0%
Common 585
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 273
13.1%
w 197
 
9.5%
o 191
 
9.2%
h 178
 
8.6%
e 157
 
7.6%
p 116
 
5.6%
a 110
 
5.3%
l 105
 
5.1%
m 101
 
4.9%
c 95
 
4.6%
Other values (18) 554
26.7%
Common
ValueCountFrequency (%)
/ 275
47.0%
. 190
32.5%
: 83
 
14.2%
2 6
 
1.0%
- 4
 
0.7%
1 4
 
0.7%
% 4
 
0.7%
0 4
 
0.7%
3 3
 
0.5%
5 3
 
0.5%
Other values (6) 9
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 275
 
10.3%
t 273
 
10.3%
w 197
 
7.4%
o 191
 
7.2%
. 190
 
7.1%
h 178
 
6.7%
e 157
 
5.9%
p 116
 
4.4%
a 110
 
4.1%
l 105
 
3.9%
Other values (34) 870
32.7%

trrsrt1_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
명동 거리
20 
코엑스
16 
경복궁
9 
수원화성
6 
예술의전당
5 
Other values (25)
44 

Length

Max length8
Median length7
Mean length4.51
Min length3

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row제부도
2nd row함덕해수욕장
3rd row물향기수목원
4th row바람의언덕
5th row안양예술공원

Common Values

ValueCountFrequency (%)
명동 거리 20
20.0%
코엑스 16
16.0%
경복궁 9
 
9.0%
수원화성 6
 
6.0%
예술의전당 5
 
5.0%
서울식물원 5
 
5.0%
여의도 공원 5
 
5.0%
동백섬 4
 
4.0%
안양예술공원 4
 
4.0%
함덕해수욕장 3
 
3.0%
Other values (20) 23
23.0%

Length

2023-12-10T19:20:28.058316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
거리 23
17.8%
명동 20
15.5%
코엑스 16
12.4%
경복궁 9
 
7.0%
수원화성 6
 
4.7%
예술의전당 5
 
3.9%
서울식물원 5
 
3.9%
여의도 5
 
3.9%
공원 5
 
3.9%
동백섬 4
 
3.1%
Other values (23) 31
24.0%

trrsrt2_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N서울타워
20 
가로수길
16 
인사동
9 
화성행궁
6 
세빛섬
5 
Other values (25)
44 

Length

Max length13
Median length8.5
Mean length4.88
Min length2

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row궁평항
2nd row우도
3rd row유엔군 초전기념관
4th row외도
5th row안양중앙공원

Common Values

ValueCountFrequency (%)
N서울타워 20
20.0%
가로수길 16
16.0%
인사동 9
 
9.0%
화성행궁 6
 
6.0%
세빛섬 5
 
5.0%
우장산 5
 
5.0%
63빌딩 5
 
5.0%
SEALIFE 아쿠아리움 4
 
4.0%
안양중앙공원 4
 
4.0%
우도 3
 
3.0%
Other values (20) 23
23.0%

Length

2023-12-10T19:20:28.275699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n서울타워 20
18.7%
가로수길 16
15.0%
인사동 9
 
8.4%
화성행궁 6
 
5.6%
세빛섬 5
 
4.7%
우장산 5
 
4.7%
63빌딩 5
 
4.7%
sealife 4
 
3.7%
아쿠아리움 4
 
3.7%
안양중앙공원 4
 
3.7%
Other values (24) 29
27.1%

trrsrt3_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
남산공원
20 
강남역거리
16 
북촌 한옥마을
9 
광교호수공원
6 
반포한강공원
5 
Other values (25)
44 

Length

Max length11
Median length9
Mean length5.46
Min length3

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row전곡항
2nd row만장굴
3rd row독산성 세마대지
4th row거제포로수용소유적공원
5th row안양일번가

Common Values

ValueCountFrequency (%)
남산공원 20
20.0%
강남역거리 16
16.0%
북촌 한옥마을 9
 
9.0%
광교호수공원 6
 
6.0%
반포한강공원 5
 
5.0%
개화산 5
 
5.0%
타임스퀘어 5
 
5.0%
스파랜드 센텀시티 4
 
4.0%
안양일번가 4
 
4.0%
만장굴 3
 
3.0%
Other values (20) 23
23.0%

Length

2023-12-10T19:20:28.486921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남산공원 20
17.4%
강남역거리 16
13.9%
북촌 9
 
7.8%
한옥마을 9
 
7.8%
광교호수공원 6
 
5.2%
반포한강공원 5
 
4.3%
개화산 5
 
4.3%
타임스퀘어 5
 
4.3%
스파랜드 4
 
3.5%
센텀시티 4
 
3.5%
Other values (24) 32
27.8%

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

2023-12-10T19:20:28.664988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:20:29.163929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 100
100.0%

Interactions

2023-12-10T19:20:18.991686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:16.931278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:17.706897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.417606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:19.170298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:17.128947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:17.917050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.587856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:19.310729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:17.362505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.095563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.753077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:19.446297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:17.564046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.269056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:18.879792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:20:29.284806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urltrrsrt1_nmtrrsrt2_nmtrrsrt3_nm
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0001.0000.8240.9421.0000.2001.0000.0000.9900.9900.990
city_gn_gu_cd1.0001.0001.0001.0000.8200.9401.0000.2091.0000.0000.9880.9880.988
xpos_lo1.0001.0000.8240.8201.0000.8000.8680.0001.0000.8520.9070.9070.907
ypos_la1.0001.0000.9420.9400.8001.0000.9870.0001.0000.6130.9790.9790.979
area_nm1.0001.0001.0001.0000.8680.9871.0000.2721.0000.0001.0001.0001.000
hotel_grad1.0001.0000.2000.2090.0000.0000.2721.0001.0000.9440.0000.0000.000
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0001.0000.0000.0000.8520.6130.0000.9441.0001.0000.9960.9960.996
trrsrt1_nm1.0001.0000.9900.9880.9070.9791.0000.0001.0000.9961.0001.0001.000
trrsrt2_nm1.0001.0000.9900.9880.9070.9791.0000.0001.0000.9961.0001.0001.000
trrsrt3_nm1.0001.0000.9900.9880.9070.9791.0000.0001.0000.9961.0001.0001.000
2023-12-10T19:20:29.528939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
trrsrt3_nmarea_nmhotel_gradtrrsrt2_nmtrrsrt1_nm
trrsrt3_nm1.0000.8380.0001.0001.000
area_nm0.8381.0000.1020.8380.838
hotel_grad0.0000.1021.0000.0000.000
trrsrt2_nm1.0000.8380.0001.0001.000
trrsrt1_nm1.0000.8380.0001.0001.000
2023-12-10T19:20:29.727684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtrrsrt1_nmtrrsrt2_nmtrrsrt3_nm
city_do_cd1.0000.8270.212-0.6350.9900.1620.8130.8130.813
city_gn_gu_cd0.8271.0000.294-0.7710.9900.1620.8130.8130.813
xpos_lo0.2120.2941.000-0.2950.7350.0000.5840.5840.584
ypos_la-0.635-0.771-0.2951.0000.8270.0000.7010.7010.701
area_nm0.9900.9900.7350.8271.0000.1020.8380.8380.838
hotel_grad0.1620.1620.0000.0000.1021.0000.0000.0000.000
trrsrt1_nm0.8130.8130.5840.7010.8380.0001.0001.0001.000
trrsrt2_nm0.8130.8130.5840.7010.8380.0001.0001.0001.000
trrsrt3_nm0.8130.8130.5840.7010.8380.0001.0001.0001.000

Missing values

2023-12-10T19:20:19.664395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:20:20.015236image/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.
2023-12-10T19:20:20.228491image/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_urltrrsrt1_nmtrrsrt2_nmtrrsrt3_nmbase_ymd
0호텔 팝경기도 구리시 안골로57번길 10-6 뉴호스텔모텔4141310127.13749937.597647경기<NA><NA><NA>제부도궁평항전곡항2019-12-09
1호텔스카이파크제주1호점제주특별자치도 제주시 삼무로 485050110126.49175933.489376제주3064-797-0000https://www.skyparkhotel.com/html/main.asp함덕해수욕장우도만장굴2019-12-09
2호텔 더메이경기도 남양주시 별내2로 70 호텔 더 메이4141360127.12514137.646659경기3031-551-8700<NA>물향기수목원유엔군 초전기념관독산성 세마대지2019-12-09
3JS호텔분당경기도 성남시 분당구 황새울로311번길 364141135127.12122837.386539경기31877-8006http://www.jshotelbundang.com/바람의언덕외도거제포로수용소유적공원2019-12-09
4타소스 호텔경기도 수원시 권선구 권선로 669번길 264141113127.02526437.260968경기<NA><NA><NA>안양예술공원안양중앙공원안양일번가2019-12-09
5호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA>안양예술공원안양중앙공원안양일번가2019-12-09
6이비스 앰배서더 수원경기도 수원시 팔달구 권광로 1324141115127.03152437.259045경기3031-230-5000https://www.ambatel.com/ibis/suwon/ko/main.do대부도탄도항안산갈대습지공원2019-12-09
7호텔 휘슬락제주특별자치도 제주시 서부두2길 265050110126.52776533.517558제주4064-795-7000http://www.whistlelark.co.kr/함덕해수욕장우도만장굴2019-12-09
8호텔 테이트경기도 수원시 팔달구 권광로180번길 53-224141115127.03502537.263575경기3031-222-6100https://hoteltate.modoo.at/DMZ판문점임진각평화누리공원2019-12-09
9알렉스 72 호텔경기도 수원시 팔달구 효원로235번길 354141115127.02790337.264631경기3<NA><NA>수원화성화성행궁광교호수공원2019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urltrrsrt1_nmtrrsrt2_nmtrrsrt3_nmbase_ymd
90서울 로프트 아파트먼트서울특별시 종로구 창경궁로 1581111110126.99761137.57625서울3<NA><NA>서울식물원우장산개화산2019-12-09
91호텔 베뉴지서울특별시 종로구 청계천로 1171111110126.99074837.568607서울202-2223-6500http://www.hotelvenueg.com/명동 거리N서울타워남산공원2019-12-09
92에이퍼스트 호텔 명동서울특별시 중구 다동길 301111140126.98092337.567642서울302-768-8777http://www.afirsthotelgroup.com/ko/롯데월드서울스카이(제2롯데월드)올림픽공원2019-12-09
93호텔스카이파크동대문1호점서울특별시 중구 동호로 3351111140127.00210537.564195서울202-2264-2200https://www.skyparkhotel.com/html/main.asp명동 거리N서울타워남산공원2019-12-09
94라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/코엑스가로수길강남역거리2019-12-09
95나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/코엑스가로수길강남역거리2019-12-09
96호텔스카이파크명동1호점서울특별시 중구 명동8나길 151111140126.98529337.564037서울302-6900-9300https://www.skyparkhotel.com/html/main.asp명동 거리N서울타워남산공원2019-12-09
97메트로호텔서울특별시 중구 명동9가길 14 메트로호텔1111140126.98594637.563004서울302-752-1112https://www.metrohotel.co.kr:11031/main.asp코엑스가로수길강남역거리2019-12-09
98호텔스카이파크센트럴명동점서울특별시 중구 명동9길 161111140126.98527337.564282서울302-752-0022https://www.skyparkhotel.com/html/main.asp명동 거리N서울타워남산공원2019-12-09
99호텔스카이파크명동2호점서울특별시 중구 명동9길 221111140126.97946737.568244서울202-755-0091https://www.skyparkhotel.com/html/main.asp명동 거리N서울타워남산공원2019-12-09