Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells4
Missing cells (%)0.3%
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

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
base_ymd is highly imbalanced (85.9%)Imbalance
homepage_url has 4 (4.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:55:35.222949
Analysis finished2023-12-10 09:55:44.158907
Duration8.94 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
2023-12-10T18:55:44.495643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15.5
Mean length9.65
Min length4

Characters and Unicode

Total characters965
Distinct characters189
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

Unique98 ?
Unique (%)98.0%

Sample

1st rowDH 네상스 호텔
2nd rowENA Suite 호텔
3rd rowGV 레지던스
4th rowKY 헤리티지 호텔
5th rowMS 호텔
ValueCountFrequency (%)
호텔 77
26.7%
라마다 11
 
3.8%
제주 9
 
3.1%
서울 7
 
2.4%
더 5
 
1.7%
부산 5
 
1.7%
베니키아 5
 
1.7%
동대문 4
 
1.4%
프리미어 3
 
1.0%
골든 3
 
1.0%
Other values (133) 159
55.2%
2023-12-10T18:55:45.355499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188
19.5%
호 87
 
9.0%
텔 87
 
9.0%
스 36
 
3.7%
리 24
 
2.5%
트 21
 
2.2%
라 18
 
1.9%
마 17
 
1.8%
아 15
 
1.6%
다 13
 
1.3%
Other values (179) 459
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 754
78.1%
Space Separator 188
 
19.5%
Uppercase Letter 18
 
1.9%
Lowercase Letter 4
 
0.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 87
 
11.5%
텔 87
 
11.5%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.3%
아 15
 
2.0%
다 13
 
1.7%
이 12
 
1.6%
Other values (161) 424
56.2%
Uppercase Letter
ValueCountFrequency (%)
S 4
22.2%
H 2
11.1%
D 2
11.1%
G 2
11.1%
J 1
 
5.6%
A 1
 
5.6%
N 1
 
5.6%
E 1
 
5.6%
M 1
 
5.6%
Y 1
 
5.6%
Other values (2) 2
11.1%
Lowercase Letter
ValueCountFrequency (%)
i 1
25.0%
u 1
25.0%
t 1
25.0%
e 1
25.0%
Space Separator
ValueCountFrequency (%)
188
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 754
78.1%
Common 189
 
19.6%
Latin 22
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 87
 
11.5%
텔 87
 
11.5%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.3%
아 15
 
2.0%
다 13
 
1.7%
이 12
 
1.6%
Other values (161) 424
56.2%
Latin
ValueCountFrequency (%)
S 4
18.2%
H 2
 
9.1%
D 2
 
9.1%
G 2
 
9.1%
J 1
 
4.5%
i 1
 
4.5%
u 1
 
4.5%
A 1
 
4.5%
N 1
 
4.5%
E 1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
188
99.5%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 754
78.1%
ASCII 211
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
89.1%
S 4
 
1.9%
H 2
 
0.9%
D 2
 
0.9%
G 2
 
0.9%
J 1
 
0.5%
i 1
 
0.5%
u 1
 
0.5%
A 1
 
0.5%
N 1
 
0.5%
Other values (8) 8
 
3.8%
Hangul
ValueCountFrequency (%)
호 87
 
11.5%
텔 87
 
11.5%
스 36
 
4.8%
리 24
 
3.2%
트 21
 
2.8%
라 18
 
2.4%
마 17
 
2.3%
아 15
 
2.0%
다 13
 
1.7%
이 12
 
1.6%
Other values (161) 424
56.2%

load_addr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:55:45.925774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length27
Mean length21.18
Min length14

Characters and Unicode

Total characters2118
Distinct characters195
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

Unique98 ?
Unique (%)98.0%

Sample

1st row서울특별시 성북구 동소문로20나길 39 동선동 복합빌
2nd row서울특별시 중구 세종대로11길 36
3rd row서울특별시 용산구 이태원로15길 14-4
4th row서울특별시 중구 장충단로 226
5th row부산광역시 해운대구 해운대해변로 271
ValueCountFrequency (%)
서울특별시 46
 
10.3%
중구 16
 
3.6%
제주특별자치도 13
 
2.9%
경기도 12
 
2.7%
부산광역시 11
 
2.5%
강남구 7
 
1.6%
서귀포시 7
 
1.6%
제주시 6
 
1.3%
인천광역시 6
 
1.3%
해운대구 6
 
1.3%
Other values (259) 317
70.9%
2023-12-10T18:55:46.733506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
347
 
16.4%
시 96
 
4.5%
로 87
 
4.1%
구 76
 
3.6%
1 74
 
3.5%
서 68
 
3.2%
특 59
 
2.8%
별 59
 
2.8%
울 51
 
2.4%
2 49
 
2.3%
Other values (185) 1152
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1386
65.4%
Space Separator 347
 
16.4%
Decimal Number 340
 
16.1%
Dash Punctuation 23
 
1.1%
Uppercase Letter 22
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 68
 
4.9%
특 59
 
4.3%
별 59
 
4.3%
울 51
 
3.7%
길 45
 
3.2%
도 37
 
2.7%
대 30
 
2.2%
Other values (161) 778
56.1%
Uppercase Letter
ValueCountFrequency (%)
G 3
13.6%
I 3
13.6%
H 2
9.1%
L 2
9.1%
E 2
9.1%
T 2
9.1%
D 2
9.1%
N 2
9.1%
S 1
 
4.5%
R 1
 
4.5%
Other values (2) 2
9.1%
Decimal Number
ValueCountFrequency (%)
1 74
21.8%
2 49
14.4%
3 40
11.8%
4 32
9.4%
5 29
 
8.5%
6 28
 
8.2%
0 25
 
7.4%
9 23
 
6.8%
7 22
 
6.5%
8 18
 
5.3%
Space Separator
ValueCountFrequency (%)
347
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1386
65.4%
Common 710
33.5%
Latin 22
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 68
 
4.9%
특 59
 
4.3%
별 59
 
4.3%
울 51
 
3.7%
길 45
 
3.2%
도 37
 
2.7%
대 30
 
2.2%
Other values (161) 778
56.1%
Common
ValueCountFrequency (%)
347
48.9%
1 74
 
10.4%
2 49
 
6.9%
3 40
 
5.6%
4 32
 
4.5%
5 29
 
4.1%
6 28
 
3.9%
0 25
 
3.5%
- 23
 
3.2%
9 23
 
3.2%
Other values (2) 40
 
5.6%
Latin
ValueCountFrequency (%)
G 3
13.6%
I 3
13.6%
H 2
9.1%
L 2
9.1%
E 2
9.1%
T 2
9.1%
D 2
9.1%
N 2
9.1%
S 1
 
4.5%
R 1
 
4.5%
Other values (2) 2
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1386
65.4%
ASCII 732
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
347
47.4%
1 74
 
10.1%
2 49
 
6.7%
3 40
 
5.5%
4 32
 
4.4%
5 29
 
4.0%
6 28
 
3.8%
0 25
 
3.4%
- 23
 
3.1%
9 23
 
3.1%
Other values (14) 62
 
8.5%
Hangul
ValueCountFrequency (%)
시 96
 
6.9%
로 87
 
6.3%
구 76
 
5.5%
서 68
 
4.9%
특 59
 
4.3%
별 59
 
4.3%
울 51
 
3.7%
길 45
 
3.2%
도 37
 
2.7%
대 30
 
2.2%
Other values (161) 778
56.1%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.18
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:55:46.990349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median26
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.647235
Coefficient of variation (CV)0.59767895
Kurtosis-1.5802603
Mean26.18
Median Absolute Deviation (MAD)15
Skewness0.32344257
Sum2618
Variance244.83596
MonotonicityNot monotonic
2023-12-10T18:55:47.237293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11 46
46.0%
50 13
 
13.0%
41 12
 
12.0%
26 11
 
11.0%
28 6
 
6.0%
42 5
 
5.0%
47 2
 
2.0%
44 2
 
2.0%
48 1
 
1.0%
45 1
 
1.0%
ValueCountFrequency (%)
11 46
46.0%
26 11
 
11.0%
28 6
 
6.0%
31 1
 
1.0%
41 12
 
12.0%
42 5
 
5.0%
44 2
 
2.0%
45 1
 
1.0%
47 2
 
2.0%
48 1
 
1.0%
ValueCountFrequency (%)
50 13
13.0%
48 1
 
1.0%
47 2
 
2.0%
45 1
 
1.0%
44 2
 
2.0%
42 5
 
5.0%
41 12
12.0%
31 1
 
1.0%
28 6
6.0%
26 11
11.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26482.51
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:55:47.508975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111440
median26245
Q341590
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)30150

Descriptive statistics

Standard deviation15580.245
Coefficient of variation (CV)0.58832205
Kurtosis-1.583092
Mean26482.51
Median Absolute Deviation (MAD)14955
Skewness0.32392091
Sum2648251
Variance2.4274402 × 108
MonotonicityNot monotonic
2023-12-10T18:55:47.860503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
11140 12
 
12.0%
50130 7
 
7.0%
11680 7
 
7.0%
26350 6
 
6.0%
50110 6
 
6.0%
11500 5
 
5.0%
41115 4
 
4.0%
11230 3
 
3.0%
28110 3
 
3.0%
11290 3
 
3.0%
Other values (35) 44
44.0%
ValueCountFrequency (%)
11110 2
 
2.0%
11140 12
12.0%
11170 3
 
3.0%
11230 3
 
3.0%
11290 3
 
3.0%
11305 1
 
1.0%
11440 2
 
2.0%
11500 5
5.0%
11545 2
 
2.0%
11560 2
 
2.0%
ValueCountFrequency (%)
50130 7
7.0%
50110 6
6.0%
48170 1
 
1.0%
47940 1
 
1.0%
47130 1
 
1.0%
45190 1
 
1.0%
44200 1
 
1.0%
44130 1
 
1.0%
42830 1
 
1.0%
42760 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.32441
Minimum126.37151
Maximum130.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:55:48.180945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37151
5-th percentile126.51882
Q1126.88788
median127.00528
Q3127.10048
95-th percentile129.16143
Maximum130.8702
Range4.4986868
Interquartile range (IQR)0.2126022

Descriptive statistics

Standard deviation0.90083568
Coefficient of variation (CV)0.0070751216
Kurtosis2.1616287
Mean127.32441
Median Absolute Deviation (MAD)0.11311705
Skewness1.727896
Sum12732.441
Variance0.81150492
MonotonicityNot monotonic
2023-12-10T18:55:48.470485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0975344 2
 
2.0%
126.9852027 1
 
1.0%
126.9145249 1
 
1.0%
127.1212276 1
 
1.0%
129.1613998 1
 
1.0%
129.0373247 1
 
1.0%
127.032221 1
 
1.0%
129.0818704 1
 
1.0%
126.6569041 1
 
1.0%
127.0301916 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.3715088 1
1.0%
126.4080885 1
1.0%
126.4837714 1
1.0%
126.5033589 1
1.0%
126.5102947 1
1.0%
126.519265 1
1.0%
126.519934 1
1.0%
126.5218499 1
1.0%
126.5674246 1
1.0%
126.578421 1
1.0%
ValueCountFrequency (%)
130.8701956 1
1.0%
129.3473935 1
1.0%
129.2774041 1
1.0%
129.1646681 1
1.0%
129.1620947 1
1.0%
129.1613998 1
1.0%
129.1611261 1
1.0%
129.1545873 1
1.0%
129.1328184 1
1.0%
129.0818704 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.618098
Minimum33.249384
Maximum38.189922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:55:48.779160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.249384
5-th percentile33.442225
Q135.360906
median37.485334
Q337.560419
95-th percentile37.592776
Maximum38.189922
Range4.9405375
Interquartile range (IQR)2.1995134

Descriptive statistics

Standard deviation1.4909054
Coefficient of variation (CV)0.040714988
Kurtosis0.17783171
Mean36.618098
Median Absolute Deviation (MAD)0.087076225
Skewness-1.3034962
Sum3661.8098
Variance2.222799
MonotonicityNot monotonic
2023-12-10T18:55:49.064078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.50601088 2
 
2.0%
37.56463398 1
 
1.0%
33.45210482 1
 
1.0%
37.38653939 1
 
1.0%
35.16012514 1
 
1.0%
35.09382162 1
 
1.0%
37.2637485 1
 
1.0%
35.21946372 1
 
1.0%
33.54420799 1
 
1.0%
37.50647548 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.24938412 1
1.0%
33.24986732 1
1.0%
33.25142402 1
1.0%
33.25447711 1
1.0%
33.254517 1
1.0%
33.45210482 1
1.0%
33.46598674 1
1.0%
33.4857485 1
1.0%
33.50067983 1
1.0%
33.51287223 1
1.0%
ValueCountFrequency (%)
38.18992163 1
1.0%
38.11488063 1
1.0%
37.65025711 1
1.0%
37.64757519 1
1.0%
37.5938887 1
1.0%
37.59271709 1
1.0%
37.58871323 1
1.0%
37.57617521 1
1.0%
37.57446681 1
1.0%
37.5696537 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
46 
제주
13 
경기
12 
부산
11 
인천
6 
Other values (7)
12 

Length

Max length3
Median length2
Mean length2.01
Min length2

Unique

Unique5 ?
Unique (%)5.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 46
46.0%
제주 13
 
13.0%
경기 12
 
12.0%
부산 11
 
11.0%
인천 6
 
6.0%
강원 5
 
5.0%
충남 2
 
2.0%
경남 1
 
1.0%
경북 1
 
1.0%
전북 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:55:49.320370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 46
46.0%
제주 13
 
13.0%
경기 12
 
12.0%
부산 11
 
11.0%
인천 6
 
6.0%
강원 5
 
5.0%
충남 2
 
2.0%
경남 1
 
1.0%
경북 1
 
1.0%
전북 1
 
1.0%
Other values (2) 2
 
2.0%

homepage_url
Text

MISSING 

Distinct94
Distinct (%)97.9%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T18:55:49.873441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length32
Mean length25.208333
Min length9

Characters and Unicode

Total characters2420
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st rowwww.dhnaissance.com
2nd rowwww.enahotel.co.kr
3rd rowgv-residence.com
4th rowhttp://seouldongdaemun.splaisir.com
5th rowmshotel.alltheway.kr
ValueCountFrequency (%)
https://www.ramadaencorejejuseogwipo.com 2
 
2.1%
www.dhnaissance.com 2
 
2.1%
www.hotelthem.com 1
 
1.0%
www.riverpark.co.kr 1
 
1.0%
http://www.suwonhotel.co.kr/kor 1
 
1.0%
www.jshotelbundang.com 1
 
1.0%
www.busanbusinesshotel.com 1
 
1.0%
www.vellasuitehotel.co.kr 1
 
1.0%
http://www.bestincityhotel.co.kr 1
 
1.0%
www.hotel-bestone.com 1
 
1.0%
Other values (84) 84
87.5%
2023-12-10T18:55:50.877043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 246
 
10.2%
w 231
 
9.5%
. 213
 
8.8%
t 189
 
7.8%
e 181
 
7.5%
a 135
 
5.6%
h 123
 
5.1%
c 114
 
4.7%
/ 102
 
4.2%
l 101
 
4.2%
Other values (31) 785
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2052
84.8%
Other Punctuation 350
 
14.5%
Dash Punctuation 7
 
0.3%
Uppercase Letter 6
 
0.2%
Decimal Number 5
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 246
12.0%
w 231
11.3%
t 189
 
9.2%
e 181
 
8.8%
a 135
 
6.6%
h 123
 
6.0%
c 114
 
5.6%
l 101
 
4.9%
m 97
 
4.7%
n 97
 
4.7%
Other values (16) 538
26.2%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
K 1
16.7%
R 1
16.7%
M 1
16.7%
G 1
16.7%
Decimal Number
ValueCountFrequency (%)
3 1
20.0%
9 1
20.0%
5 1
20.0%
0 1
20.0%
2 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 213
60.9%
/ 102
29.1%
: 34
 
9.7%
? 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2058
85.0%
Common 362
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 246
12.0%
w 231
11.2%
t 189
 
9.2%
e 181
 
8.8%
a 135
 
6.6%
h 123
 
6.0%
c 114
 
5.5%
l 101
 
4.9%
m 97
 
4.7%
n 97
 
4.7%
Other values (21) 544
26.4%
Common
ValueCountFrequency (%)
. 213
58.8%
/ 102
28.2%
: 34
 
9.4%
- 7
 
1.9%
3 1
 
0.3%
9 1
 
0.3%
5 1
 
0.3%
0 1
 
0.3%
2 1
 
0.3%
? 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 246
 
10.2%
w 231
 
9.5%
. 213
 
8.8%
t 189
 
7.8%
e 181
 
7.5%
a 135
 
5.6%
h 123
 
5.1%
c 114
 
4.7%
/ 102
 
4.2%
l 101
 
4.2%
Other values (31) 785
32.4%

hotel_grad
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3급
69 
특2급
18 
1급
 
6
2급
 
3
특1급
 
2
Other values (2)
 
2

Length

Max length4
Median length2
Mean length2.22
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row3급
2nd row특2급
3rd row3급
4th row특2급
5th row3급

Common Values

ValueCountFrequency (%)
3급 69
69.0%
특2급 18
 
18.0%
1급 6
 
6.0%
2급 3
 
3.0%
특1급 2
 
2.0%
<NA> 1
 
1.0%
4급 1
 
1.0%

Length

2023-12-10T18:55:51.217891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:51.587856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3급 69
69.0%
특2급 18
 
18.0%
1급 6
 
6.0%
2급 3
 
3.0%
특1급 2
 
2.0%
na 1
 
1.0%
4급 1
 
1.0%

tel_no
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:55:52.075982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.77
Min length9

Characters and Unicode

Total characters1177
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

Unique96 ?
Unique (%)96.0%

Sample

1st row02-921-2080
2nd row02-6020-7000
3rd row02-797-5800
4th row02-2198-1212
5th row051-741-3838
ValueCountFrequency (%)
02-921-2080 2
 
2.0%
064-735-2000 2
 
2.0%
051-720-9000 1
 
1.0%
02-2277-4917 1
 
1.0%
031-236-7112 1
 
1.0%
051-243-8001 1
 
1.0%
051-808-2000 1
 
1.0%
031-231-2121 1
 
1.0%
051-464-8883 1
 
1.0%
064-731-3700 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T18:55:52.941365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 291
24.7%
- 199
16.9%
2 137
11.6%
1 111
 
9.4%
3 89
 
7.6%
7 77
 
6.5%
5 69
 
5.9%
6 61
 
5.2%
4 54
 
4.6%
9 49
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 978
83.1%
Dash Punctuation 199
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291
29.8%
2 137
14.0%
1 111
 
11.3%
3 89
 
9.1%
7 77
 
7.9%
5 69
 
7.1%
6 61
 
6.2%
4 54
 
5.5%
9 49
 
5.0%
8 40
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291
24.7%
- 199
16.9%
2 137
11.6%
1 111
 
9.4%
3 89
 
7.6%
7 77
 
6.5%
5 69
 
5.9%
6 61
 
5.2%
4 54
 
4.6%
9 49
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291
24.7%
- 199
16.9%
2 137
11.6%
1 111
 
9.4%
3 89
 
7.6%
7 77
 
6.5%
5 69
 
5.9%
6 61
 
5.2%
4 54
 
4.6%
9 49
 
4.2%

klang_prcuse_ix
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.525
Minimum2
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:55:53.312805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2
Q12.7
median3.6
Q34.225
95-th percentile4.9
Maximum4.9
Range2.9
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation0.8859595
Coefficient of variation (CV)0.25133603
Kurtosis-1.2499237
Mean3.525
Median Absolute Deviation (MAD)0.8
Skewness-0.097506203
Sum352.5
Variance0.78492424
MonotonicityNot monotonic
2023-12-10T18:55:53.628093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.7 7
 
7.0%
2.2 6
 
6.0%
4.2 6
 
6.0%
4.9 6
 
6.0%
3.6 5
 
5.0%
4.0 5
 
5.0%
2.4 5
 
5.0%
3.4 5
 
5.0%
2.7 4
 
4.0%
4.4 4
 
4.0%
Other values (20) 47
47.0%
ValueCountFrequency (%)
2.0 2
 
2.0%
2.1 1
 
1.0%
2.2 6
6.0%
2.3 4
4.0%
2.4 5
5.0%
2.5 3
3.0%
2.6 2
 
2.0%
2.7 4
4.0%
2.8 2
 
2.0%
2.9 2
 
2.0%
ValueCountFrequency (%)
4.9 6
6.0%
4.8 1
 
1.0%
4.7 7
7.0%
4.6 2
 
2.0%
4.5 3
3.0%
4.4 4
4.0%
4.3 2
 
2.0%
4.2 6
6.0%
4.1 1
 
1.0%
4.0 5
5.0%

base_ymd
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-12-09
98 
2020-12-31
 
2

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 98
98.0%
2020-12-31 2
 
2.0%

Length

2023-12-10T18:55:53.949196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:54.202516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 98
98.0%
2020-12-31 2
 
2.0%

Interactions

2023-12-10T18:55:42.152271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:37.805945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:39.049805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:40.162843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:41.214564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:42.372720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:38.042104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:39.268577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:40.401533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:41.439984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:42.582686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:38.303843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:39.510559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:40.657121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:41.652261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:42.760270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:38.599455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:39.713181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:40.815798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:41.803463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:42.940484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:38.834594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:39.943914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:40.996695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:41.969326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:55:54.432750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noklang_prcuse_ixbase_ymd
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
city_do_cd1.0001.0001.0000.9990.7320.9471.0001.0000.3691.0000.0000.000
city_gn_gu_cd1.0001.0000.9991.0000.7550.9340.9871.0000.1461.0000.0000.000
xpos_lo1.0001.0000.7320.7551.0000.7480.9281.0000.4411.0000.1570.000
ypos_la1.0001.0000.9470.9340.7481.0000.9561.0000.4971.0000.0000.000
area_nm1.0001.0001.0000.9870.9280.9561.0001.0000.8401.0000.0520.000
homepage_url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9700.000
hotel_grad1.0001.0000.3690.1460.4410.4970.8401.0001.0001.0000.0000.000
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9710.000
klang_prcuse_ix1.0001.0000.0000.0000.1570.0000.0520.9700.0000.9711.0000.000
base_ymd0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T18:55:54.778113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmbase_ymdhotel_grad
area_nm1.0000.0000.474
base_ymd0.0001.0000.000
hotel_grad0.4740.0001.000
2023-12-10T18:55:54.960625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laklang_prcuse_ixarea_nmhotel_gradbase_ymd
city_do_cd1.0000.949-0.066-0.706-0.1020.9730.2270.000
city_gn_gu_cd0.9491.000-0.036-0.714-0.0600.9460.0860.000
xpos_lo-0.066-0.0361.0000.027-0.0140.7050.2590.000
ypos_la-0.706-0.7140.0271.0000.0660.8520.3220.000
klang_prcuse_ix-0.102-0.060-0.0140.0661.0000.0000.0000.000
area_nm0.9730.9460.7050.8520.0001.0000.4740.000
hotel_grad0.2270.0860.2590.3220.0000.4741.0000.000
base_ymd0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T18:55:43.660221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:55:44.023851image/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_nmhomepage_urlhotel_gradtel_noklang_prcuse_ixbase_ymd
0DH 네상스 호텔서울특별시 성북구 동소문로20나길 39 동선동 복합빌1111290127.09753437.506011서울www.dhnaissance.com3급02-921-20804.72019-12-09
1ENA Suite 호텔서울특별시 중구 세종대로11길 361111140126.97796837.567946서울www.enahotel.co.kr특2급02-6020-70002.52019-12-09
2GV 레지던스서울특별시 용산구 이태원로15길 14-41111170126.97232237.54084서울gv-residence.com3급02-797-58002.82019-12-09
3KY 헤리티지 호텔서울특별시 중구 장충단로 2261111140127.0010537.550703서울http://seouldongdaemun.splaisir.com특2급02-2198-12122.32019-12-09
4MS 호텔부산광역시 해운대구 해운대해변로 2712626350129.16466835.161447부산mshotel.alltheway.kr3급051-741-38384.22019-12-09
5SG 관광 호텔인천광역시 서구 탁옥로51번길 13-9 SG관광호텔2828260126.6743437.545036인천sghotel.kr3급032-562-05124.62019-12-09
6가야 라트리 호텔서울특별시 용산구 한강대로 253 가야라트리 호텔1111170126.97214137.541439서울kayalatreehotel.com3급02-798-51012.42019-12-09
7강남 아르누보 씨티서울특별시 서초구 서초대로74길 491111650127.01878437.593889서울www.gnanhotel.com3급02-580-75004.72019-12-09
8강남 패밀리 호텔서울특별시 강남구 봉은사로 143 운현오피스텔1111680127.04527937.510347서울www.gangnamfamilyhotel.com3급02-6474-15154.92019-12-09
9골드 리버 호텔서울특별시 금천구 서부샛길 5841111545127.02607337.576175서울goldriverhotel.co.kr3급02-6021-81003.82019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urlhotel_gradtel_noklang_prcuse_ixbase_ymd
90이 천안호텔충청남도 천안시 서북구 성정동 734-24444130127.14024136.81241충남http://www.cheonanhotel.kr/2급041-592-00003.42019-12-09
91제주 라마다 앙코르 이스트 호텔제주특별자치도 서귀포시 서호동5050130126.51993433.254517제주https://www.ramadaencorejejuseogwipo.com/3급064-735-20003.42019-12-09
92제주 하나 호텔제주특별자치도 서귀포시 중문관광로72번길 535050130126.40808933.249867제주http://www.hotelhana.co.kr/main/특2급064-738-70013.32019-12-09
93코업 시티 호텔 성산제주특별자치도 서귀포시 성산읍 성산등용로 285050130126.93192733.465987제주https://www.coopcityhotel-seongsan.co.kr/3급064-780-98002.42019-12-09
94호텔 노블레스 제주제주특별자치도 제주시 월성로4길 195050110126.50335933.50068제주hotelnoblessejeju.modoo.at3급064-748-71612.92019-12-09
95호텔 로베로제주특별자치도 제주시 관덕로 265050110126.5218533.512872제주stazhoteljejurobero.com/1급064-757-71114.02019-12-09
96호텔 위드 제주제주특별자치도 제주시 노연로 345050110126.48377133.485748제주www.hotelwithjeju.com/특2급02-522-58733.82019-12-09
97힐리언스선마을강원도 홍천군 서면 종자산길 1224242720127.63080237.650257강원https://www.healience.co.kr/3급033-434-27723.62019-12-09
98DH 네상스 호텔서울특별시 성북구 동소문로20나길 39 동선동 복합빌1111290127.09753437.506011서울www.dhnaissance.com3급02-921-20804.72020-12-31
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