Overview

Dataset statistics

Number of variables38
Number of observations100
Missing cells57
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.6 KiB
Average record size in memory323.3 B

Variable types

Numeric10
Categorical20
Text5
DateTime1
Boolean2

Alerts

svc_id_nm has constant value ""Constant
last_updt_de has constant value ""Constant
ply_fclty_co has constant value ""Constant
pyrxia_room_at has constant value ""Constant
svc_id has constant value ""Constant
data_updt_se is highly imbalanced (91.9%)Imbalance
data_updt_de is highly imbalanced (79.7%)Imbalance
clsbiz_de is highly imbalanced (63.9%)Imbalance
use_end_undgrnd_floor is highly imbalanced (51.4%)Imbalance
use_begin_undgrnd_floor is highly imbalanced (58.8%)Imbalance
sfrnd_code is highly imbalanced (91.9%)Imbalance
sfrnd_code_nm is highly imbalanced (91.9%)Imbalance
buld_ground_floor_co has 12 (12.0%) missing valuesMissing
use_end_ground_floor has 13 (13.0%) missing valuesMissing
use_begin_ground_floor has 12 (12.0%) missing valuesMissing
occdnt_form_room_co has 4 (4.0%) missing valuesMissing
btr_co has 12 (12.0%) missing valuesMissing
korea_room_co has 4 (4.0%) missing valuesMissing
skey has unique valuesUnique
lnm_adres has unique valuesUnique
person_prmisn_de has unique valuesUnique
person_prmisn_no has unique valuesUnique
buld_ground_floor_co has 46 (46.0%) zerosZeros
use_end_ground_floor has 45 (45.0%) zerosZeros
use_begin_ground_floor has 43 (43.0%) zerosZeros
occdnt_form_room_co has 4 (4.0%) zerosZeros
btr_co has 81 (81.0%) zerosZeros
korea_room_co has 45 (45.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:07:45.416373
Analysis finished2023-12-10 10:07:46.204854
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:46.394159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T19:07:46.656321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

rtrvl_cnstr_co
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
86 
-
14 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row-
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
86.0%
- 14
 
14.0%

Length

2023-12-10T19:07:46.880028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:47.055384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
86.0%
14
 
14.0%

data_updt_se
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
I
99 
U
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 99
99.0%
U 1
 
1.0%

Length

2023-12-10T19:07:47.263837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:47.442147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 99
99.0%
u 1
 
1.0%

data_updt_de
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-08-31 PM 11:59:00
95 
2019-11-13 AM 10:27:22
 
4
2018-09-06 AM 11:42:00
 
1

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2018-08-31 PM 11:59:00
2nd row2018-08-31 PM 11:59:00
3rd row2018-08-31 PM 11:59:00
4th row2018-08-31 PM 11:59:00
5th row2018-08-31 PM 11:59:00

Common Values

ValueCountFrequency (%)
2018-08-31 PM 11:59:00 95
95.0%
2019-11-13 AM 10:27:22 4
 
4.0%
2018-09-06 AM 11:42:00 1
 
1.0%

Length

2023-12-10T19:07:47.686261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:48.012409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-31 95
31.7%
pm 95
31.7%
11:59:00 95
31.7%
am 5
 
1.7%
2019-11-13 4
 
1.3%
10:27:22 4
 
1.3%
2018-09-06 1
 
0.3%
11:42:00 1
 
0.3%

svc_id_nm
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T19:07:48.195658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:48.362923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

bplc_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:48.771623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.67
Min length1

Characters and Unicode

Total characters567
Distinct characters173
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

Unique98 ?
Unique (%)98.0%

Sample

1st row호텔아벤트리부산
2nd row케이 칠구(K79)
3rd row영하장
4th row누리게스트하우스 더셀프
5th row야(Ya)
ValueCountFrequency (%)
호텔 9
 
6.5%
모텔 6
 
4.3%
게스트하우스 5
 
3.6%
더 2
 
1.4%
주식회사 2
 
1.4%
현대여인숙 2
 
1.4%
부산 1
 
0.7%
제이모텔 1
 
0.7%
그레이 1
 
0.7%
코코캡슐 1
 
0.7%
Other values (109) 109
78.4%
2023-12-10T19:07:49.574717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 51
 
9.0%
39
 
6.9%
스 28
 
4.9%
모 27
 
4.8%
호 24
 
4.2%
장 18
 
3.2%
이 16
 
2.8%
하 14
 
2.5%
트 13
 
2.3%
우 11
 
1.9%
Other values (163) 326
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 483
85.2%
Space Separator 39
 
6.9%
Uppercase Letter 11
 
1.9%
Decimal Number 9
 
1.6%
Open Punctuation 8
 
1.4%
Close Punctuation 8
 
1.4%
Lowercase Letter 8
 
1.4%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 51
 
10.6%
스 28
 
5.8%
모 27
 
5.6%
호 24
 
5.0%
장 18
 
3.7%
이 16
 
3.3%
하 14
 
2.9%
트 13
 
2.7%
우 11
 
2.3%
아 8
 
1.7%
Other values (140) 273
56.5%
Uppercase Letter
ValueCountFrequency (%)
Y 2
18.2%
A 2
18.2%
Z 1
9.1%
T 1
9.1%
S 1
9.1%
E 1
9.1%
V 1
9.1%
W 1
9.1%
K 1
9.1%
Decimal Number
ValueCountFrequency (%)
2 3
33.3%
4 2
22.2%
7 1
 
11.1%
3 1
 
11.1%
9 1
 
11.1%
1 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
n 3
37.5%
e 3
37.5%
w 1
 
12.5%
a 1
 
12.5%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 483
85.2%
Common 65
 
11.5%
Latin 19
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 51
 
10.6%
스 28
 
5.8%
모 27
 
5.6%
호 24
 
5.0%
장 18
 
3.7%
이 16
 
3.3%
하 14
 
2.9%
트 13
 
2.7%
우 11
 
2.3%
아 8
 
1.7%
Other values (140) 273
56.5%
Latin
ValueCountFrequency (%)
n 3
15.8%
e 3
15.8%
Y 2
10.5%
A 2
10.5%
Z 1
 
5.3%
T 1
 
5.3%
S 1
 
5.3%
E 1
 
5.3%
w 1
 
5.3%
V 1
 
5.3%
Other values (3) 3
15.8%
Common
ValueCountFrequency (%)
39
60.0%
( 8
 
12.3%
) 8
 
12.3%
2 3
 
4.6%
4 2
 
3.1%
7 1
 
1.5%
3 1
 
1.5%
9 1
 
1.5%
1 1
 
1.5%
- 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 483
85.2%
ASCII 84
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 51
 
10.6%
스 28
 
5.8%
모 27
 
5.6%
호 24
 
5.0%
장 18
 
3.7%
이 16
 
3.3%
하 14
 
2.9%
트 13
 
2.7%
우 11
 
2.3%
아 8
 
1.7%
Other values (140) 273
56.5%
ASCII
ValueCountFrequency (%)
39
46.4%
( 8
 
9.5%
) 8
 
9.5%
2 3
 
3.6%
n 3
 
3.6%
e 3
 
3.6%
Y 2
 
2.4%
A 2
 
2.4%
4 2
 
2.4%
Z 1
 
1.2%
Other values (13) 13
 
15.5%

lnm_zip
Real number (ℝ)

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600425.43
Minimum600011
Maximum616801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:49.837342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600011
5-th percentile600021.6
Q1600039
median600046
Q3600805
95-th percentile600811.15
Maximum616801
Range16790
Interquartile range (IQR)766

Descriptive statistics

Standard deviation1689.69
Coefficient of variation (CV)0.0028141547
Kurtosis91.587024
Mean600425.43
Median Absolute Deviation (MAD)24
Skewness9.3770932
Sum60042543
Variance2855052.4
MonotonicityNot monotonic
2023-12-10T19:07:50.095812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
600046 14
 
14.0%
600045 11
 
11.0%
600022 8
 
8.0%
600811 7
 
7.0%
600042 5
 
5.0%
600805 5
 
5.0%
600023 5
 
5.0%
600807 4
 
4.0%
600012 3
 
3.0%
600808 2
 
2.0%
Other values (25) 36
36.0%
ValueCountFrequency (%)
600011 1
 
1.0%
600012 3
 
3.0%
600014 1
 
1.0%
600022 8
8.0%
600023 5
5.0%
600024 1
 
1.0%
600025 2
 
2.0%
600031 2
 
2.0%
600033 2
 
2.0%
600041 1
 
1.0%
ValueCountFrequency (%)
616801 1
 
1.0%
600817 1
 
1.0%
600816 1
 
1.0%
600815 1
 
1.0%
600814 1
 
1.0%
600811 7
7.0%
600809 1
 
1.0%
600808 2
 
2.0%
600807 4
4.0%
600806 2
 
2.0%

lnm_adres
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:50.628305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length34
Mean length22.25
Min length19

Characters and Unicode

Total characters2225
Distinct characters49
Distinct categories9 ?
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 row부산광역시 중구 창선동1가 12-1번지
2nd row부산광역시 중구 대청동2가 23-3번지
3rd row부산광역시 중구 부평동2가 24-3번지
4th row부산광역시 중구 중앙동2가 52-2번지
5th row부산광역시 중구 보수동3가 5-21번지
ValueCountFrequency (%)
부산광역시 100
23.3%
중구 99
23.1%
남포동6가 14
 
3.3%
남포동5가 11
 
2.6%
부평동1가 9
 
2.1%
동광동2가 8
 
1.9%
영주동 8
 
1.9%
부평동2가 6
 
1.4%
외 5
 
1.2%
남포동2가 5
 
1.2%
Other values (133) 164
38.2%
2023-12-10T19:07:51.385013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
329
 
14.8%
부 120
 
5.4%
광 120
 
5.4%
동 116
 
5.2%
지 110
 
4.9%
중 108
 
4.9%
1 107
 
4.8%
번 102
 
4.6%
구 101
 
4.5%
역 100
 
4.5%
Other values (39) 912
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1328
59.7%
Decimal Number 447
 
20.1%
Space Separator 329
 
14.8%
Dash Punctuation 89
 
4.0%
Other Punctuation 10
 
0.4%
Close Punctuation 8
 
0.4%
Open Punctuation 8
 
0.4%
Math Symbol 4
 
0.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 120
9.0%
광 120
9.0%
동 116
8.7%
지 110
8.3%
중 108
8.1%
번 102
7.7%
구 101
7.6%
역 100
 
7.5%
시 100
 
7.5%
산 100
 
7.5%
Other values (21) 251
18.9%
Decimal Number
ValueCountFrequency (%)
1 107
23.9%
2 80
17.9%
5 50
11.2%
3 47
10.5%
4 40
 
8.9%
6 38
 
8.5%
0 24
 
5.4%
9 21
 
4.7%
8 20
 
4.5%
7 20
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
329
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 89
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1328
59.7%
Common 895
40.2%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 120
9.0%
광 120
9.0%
동 116
8.7%
지 110
8.3%
중 108
8.1%
번 102
7.7%
구 101
7.6%
역 100
 
7.5%
시 100
 
7.5%
산 100
 
7.5%
Other values (21) 251
18.9%
Common
ValueCountFrequency (%)
329
36.8%
1 107
 
12.0%
- 89
 
9.9%
2 80
 
8.9%
5 50
 
5.6%
3 47
 
5.3%
4 40
 
4.5%
6 38
 
4.2%
0 24
 
2.7%
9 21
 
2.3%
Other values (6) 70
 
7.8%
Latin
ValueCountFrequency (%)
T 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1328
59.7%
ASCII 897
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
329
36.7%
1 107
 
11.9%
- 89
 
9.9%
2 80
 
8.9%
5 50
 
5.6%
3 47
 
5.2%
4 40
 
4.5%
6 38
 
4.2%
0 24
 
2.7%
9 21
 
2.3%
Other values (8) 72
 
8.0%
Hangul
ValueCountFrequency (%)
부 120
9.0%
광 120
9.0%
동 116
8.7%
지 110
8.3%
중 108
8.1%
번 102
7.7%
구 101
7.6%
역 100
 
7.5%
시 100
 
7.5%
산 100
 
7.5%
Other values (21) 251
18.9%

rn_zip
Categorical

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
16 
48951
10 
48980
8 
48981
8 
48983
7 
Other values (29)
51 

Length

Max length5
Median length5
Mean length4.36
Min length1

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row48947
2nd row48948
3rd row48977
4th row48956
5th row-

Common Values

ValueCountFrequency (%)
- 16
16.0%
48951 10
 
10.0%
48980 8
 
8.0%
48981 8
 
8.0%
48983 7
 
7.0%
48982 5
 
5.0%
48954 4
 
4.0%
48953 3
 
3.0%
48956 3
 
3.0%
48911 3
 
3.0%
Other values (24) 33
33.0%

Length

2023-12-10T19:07:51.637697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16
16.0%
48951 10
 
10.0%
48980 8
 
8.0%
48981 8
 
8.0%
48983 7
 
7.0%
48982 5
 
5.0%
48954 4
 
4.0%
48953 3
 
3.0%
48956 3
 
3.0%
48911 3
 
3.0%
Other values (24) 33
33.0%

rdnmadr
Text

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:52.171110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length24.46
Min length1

Characters and Unicode

Total characters2446
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)89.0%

Sample

1st row부산광역시 중구 광복로39번길 6 (창선동1가)
2nd row부산광역시 중구 광복로49번길 38 (대청동2가)
3rd row부산광역시 중구 중구로23번길 34 (부평동2가)
4th row부산광역시 중구 중앙대로49번길 13 (중앙동2가)
5th row부산광역시 중구 보수대로106번길 5 (보수동3가)
ValueCountFrequency (%)
부산광역시 89
 
18.9%
중구 89
 
18.9%
남포동6가 13
 
2.8%
11
 
2.3%
대청로126번길 7
 
1.5%
남포동5가 7
 
1.5%
영주동 7
 
1.5%
부평동1가 7
 
1.5%
동광동2가 7
 
1.5%
4 6
 
1.3%
Other values (149) 227
48.3%
2023-12-10T19:07:53.132778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
370
 
15.1%
광 128
 
5.2%
중 116
 
4.7%
부 107
 
4.4%
구 106
 
4.3%
동 106
 
4.3%
1 95
 
3.9%
산 91
 
3.7%
( 89
 
3.6%
역 89
 
3.6%
Other values (60) 1149
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1398
57.2%
Decimal Number 428
 
17.5%
Space Separator 370
 
15.1%
Open Punctuation 89
 
3.6%
Close Punctuation 89
 
3.6%
Dash Punctuation 51
 
2.1%
Other Punctuation 19
 
0.8%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 128
 
9.2%
중 116
 
8.3%
부 107
 
7.7%
구 106
 
7.6%
동 106
 
7.6%
산 91
 
6.5%
역 89
 
6.4%
시 89
 
6.4%
가 81
 
5.8%
로 81
 
5.8%
Other values (44) 404
28.9%
Decimal Number
ValueCountFrequency (%)
1 95
22.2%
2 82
19.2%
3 50
11.7%
5 44
10.3%
4 43
10.0%
6 37
 
8.6%
8 23
 
5.4%
7 23
 
5.4%
9 16
 
3.7%
0 15
 
3.5%
Space Separator
ValueCountFrequency (%)
370
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1398
57.2%
Common 1048
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 128
 
9.2%
중 116
 
8.3%
부 107
 
7.7%
구 106
 
7.6%
동 106
 
7.6%
산 91
 
6.5%
역 89
 
6.4%
시 89
 
6.4%
가 81
 
5.8%
로 81
 
5.8%
Other values (44) 404
28.9%
Common
ValueCountFrequency (%)
370
35.3%
1 95
 
9.1%
( 89
 
8.5%
) 89
 
8.5%
2 82
 
7.8%
- 51
 
4.9%
3 50
 
4.8%
5 44
 
4.2%
4 43
 
4.1%
6 37
 
3.5%
Other values (6) 98
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1398
57.2%
ASCII 1048
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
370
35.3%
1 95
 
9.1%
( 89
 
8.5%
) 89
 
8.5%
2 82
 
7.8%
- 51
 
4.9%
3 50
 
4.8%
5 44
 
4.2%
4 43
 
4.1%
6 37
 
3.5%
Other values (6) 98
 
9.4%
Hangul
ValueCountFrequency (%)
광 128
 
9.2%
중 116
 
8.3%
부 107
 
7.7%
구 106
 
7.6%
동 106
 
7.6%
산 91
 
6.5%
역 89
 
6.4%
시 89
 
6.4%
가 81
 
5.8%
로 81
 
5.8%
Other values (44) 404
28.9%

person_prmisn_de
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum1960-11-21 00:00:00
Maximum2018-08-30 00:00:00
2023-12-10T19:07:53.476163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:53.780257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

clsbiz_de
Categorical

IMBALANCE 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
80 
2014-03-10
 
1
2012-12-31
 
1
2017-11-20
 
1
2017-11-07
 
1
Other values (16)
16 

Length

Max length10
Median length1
Mean length2.8
Min length1

Unique

Unique20 ?
Unique (%)20.0%

Sample

1st row-
2nd row-
3rd row2014-03-10
4th row2012-12-31
5th row2017-11-20

Common Values

ValueCountFrequency (%)
- 80
80.0%
2014-03-10 1
 
1.0%
2012-12-31 1
 
1.0%
2017-11-20 1
 
1.0%
2017-11-07 1
 
1.0%
2012-05-14 1
 
1.0%
2014-04-01 1
 
1.0%
2007-02-21 1
 
1.0%
2012-04-10 1
 
1.0%
2018-04-04 1
 
1.0%
Other values (11) 11
 
11.0%

Length

2023-12-10T19:07:54.081789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80
80.0%
2014-12-23 1
 
1.0%
2010-04-21 1
 
1.0%
2007-02-12 1
 
1.0%
2013-10-31 1
 
1.0%
2004-06-21 1
 
1.0%
2012-02-21 1
 
1.0%
2017-12-22 1
 
1.0%
2005-03-29 1
 
1.0%
2004-10-22 1
 
1.0%
Other values (11) 11
 
11.0%

engl_sttus
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
80 
2
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 80
80.0%
2 20
 
20.0%

Length

2023-12-10T19:07:54.344904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:54.661661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 80
80.0%
2 20
 
20.0%

detail_engl_sttus
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
영업
80 
폐업
20 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row영업
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
영업 80
80.0%
폐업 20
 
20.0%

Length

2023-12-10T19:07:54.921056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:55.098966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 80
80.0%
폐업 20
 
20.0%

xcnts
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369737.74
Minimum1
Maximum385850.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:55.294449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile384438.78
Q1384781.95
median385096.63
Q3385509.28
95-th percentile385678.84
Maximum385850.07
Range385849.07
Interquartile range (IQR)727.33313

Descriptive statistics

Standard deviation75853.364
Coefficient of variation (CV)0.20515451
Kurtosis21.142343
Mean369737.74
Median Absolute Deviation (MAD)365.6301
Skewness-4.7664599
Sum36973774
Variance5.7537329 × 109
MonotonicityNot monotonic
2023-12-10T19:07:55.555075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 4
 
4.0%
385314.9581 2
 
2.0%
385065.8515 1
 
1.0%
385023.9301 1
 
1.0%
385117.2807 1
 
1.0%
385438.69 1
 
1.0%
385622.0 1
 
1.0%
384830.0684 1
 
1.0%
384718.8623 1
 
1.0%
385518.1787 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
1.0 4
4.0%
384329.0808 1
 
1.0%
384444.557 1
 
1.0%
384449.5283 1
 
1.0%
384451.3466 1
 
1.0%
384579.5473 1
 
1.0%
384582.6077 1
 
1.0%
384622.4507 1
 
1.0%
384626.9649 1
 
1.0%
384667.7483 1
 
1.0%
ValueCountFrequency (%)
385850.0683 1
1.0%
385779.6045 1
1.0%
385737.9865 1
1.0%
385721.4297 1
1.0%
385715.4836 1
1.0%
385676.9076 1
1.0%
385667.8867 1
1.0%
385654.5855 1
1.0%
385652.8404 1
1.0%
385622.0 1
1.0%

ydnts
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172972.21
Minimum1
Maximum181508.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:55.899866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile179493.57
Q1179850.61
median180047.39
Q3180330.9
95-th percentile181214.28
Maximum181508.16
Range181507.16
Interquartile range (IQR)480.28575

Descriptive statistics

Standard deviation35488.329
Coefficient of variation (CV)0.20516781
Kurtosis21.135798
Mean172972.21
Median Absolute Deviation (MAD)231.0357
Skewness-4.7654018
Sum17297221
Variance1.2594215 × 109
MonotonicityNot monotonic
2023-12-10T19:07:56.249438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 4
 
4.0%
179888.7647 2
 
2.0%
180046.7914 1
 
1.0%
179788.3284 1
 
1.0%
179929.355 1
 
1.0%
180137.4291 1
 
1.0%
180635.0 1
 
1.0%
179875.9591 1
 
1.0%
179943.9337 1
 
1.0%
180851.2458 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
1.0 4
4.0%
179484.7144 1
 
1.0%
179494.0364 1
 
1.0%
179542.3092 1
 
1.0%
179548.2668 1
 
1.0%
179690.7938 1
 
1.0%
179696.7122 1
 
1.0%
179701.533 1
 
1.0%
179706.6404 1
 
1.0%
179716.1187 1
 
1.0%
ValueCountFrequency (%)
181508.1645 1
1.0%
181485.588 1
1.0%
181464.9632 1
1.0%
181262.4334 1
1.0%
181253.1844 1
1.0%
181212.2291 1
1.0%
181180.6717 1
1.0%
181106.4787 1
1.0%
181038.9412 1
1.0%
180865.7317 1
1.0%

last_updt_de
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200401
2nd row20200401
3rd row20200401
4th row20200401
5th row20200401

Common Values

ValueCountFrequency (%)
20200401 100
100.0%

Length

2023-12-10T19:07:56.564981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:56.854945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200401 100
100.0%

bizcnd_se_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
여관업
60 
일반호텔
18 
숙박업(생활)
 
6
숙박업 기타
 
6
여인숙업
 
6

Length

Max length7
Median length3
Mean length3.7
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반호텔
2nd row숙박업(생활)
3rd row여관업
4th row숙박업 기타
5th row여관업

Common Values

ValueCountFrequency (%)
여관업 60
60.0%
일반호텔 18
 
18.0%
숙박업(생활) 6
 
6.0%
숙박업 기타 6
 
6.0%
여인숙업 6
 
6.0%
관광호텔 4
 
4.0%

Length

2023-12-10T19:07:57.171743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:57.499267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 60
56.6%
일반호텔 18
 
17.0%
숙박업(생활 6
 
5.7%
숙박업 6
 
5.7%
기타 6
 
5.7%
여인숙업 6
 
5.7%
관광호텔 4
 
3.8%

telno
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:58.010751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.99
Min length1

Characters and Unicode

Total characters999
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)90.0%

Sample

1st row051 260 5007
2nd row-
3rd row051245 5555
4th row-
5th row051 2542612
ValueCountFrequency (%)
051 72
35.1%
10
 
4.9%
231 5
 
2.4%
051245 4
 
2.0%
243 4
 
2.0%
051248 2
 
1.0%
051247 2
 
1.0%
3123 2
 
1.0%
5555 2
 
1.0%
051246 2
 
1.0%
Other values (100) 100
48.8%
2023-12-10T19:07:58.889393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 158
15.8%
5 142
14.2%
0 133
13.3%
106
10.6%
4 106
10.6%
2 103
10.3%
6 76
7.6%
3 52
 
5.2%
7 43
 
4.3%
8 40
 
4.0%
Other values (2) 40
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 883
88.4%
Space Separator 106
 
10.6%
Dash Punctuation 10
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 158
17.9%
5 142
16.1%
0 133
15.1%
4 106
12.0%
2 103
11.7%
6 76
8.6%
3 52
 
5.9%
7 43
 
4.9%
8 40
 
4.5%
9 30
 
3.4%
Space Separator
ValueCountFrequency (%)
106
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 158
15.8%
5 142
14.2%
0 133
13.3%
106
10.6%
4 106
10.6%
2 103
10.3%
6 76
7.6%
3 52
 
5.2%
7 43
 
4.3%
8 40
 
4.0%
Other values (2) 40
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 158
15.8%
5 142
14.2%
0 133
13.3%
106
10.6%
4 106
10.6%
2 103
10.3%
6 76
7.6%
3 52
 
5.2%
7 43
 
4.3%
8 40
 
4.0%
Other values (2) 40
 
4.0%

buld_posesn_se_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
임대
48 
자가
29 
<NA>
23 

Length

Max length4
Median length2
Mean length2.46
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대 48
48.0%
자가 29
29.0%
<NA> 23
23.0%

Length

2023-12-10T19:07:59.295666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:59.554034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 48
48.0%
자가 29
29.0%
na 23
23.0%

buld_ground_floor_co
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)14.8%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean3.2727273
Minimum0
Maximum27
Zeros46
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:59.787208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile10
Maximum27
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5276349
Coefficient of variation (CV)1.383444
Kurtosis7.6103064
Mean3.2727273
Median Absolute Deviation (MAD)0
Skewness2.1467944
Sum288
Variance20.499478
MonotonicityNot monotonic
2023-12-10T19:08:00.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 46
46.0%
5 10
 
10.0%
8 5
 
5.0%
10 5
 
5.0%
3 5
 
5.0%
4 5
 
5.0%
6 4
 
4.0%
9 2
 
2.0%
7 2
 
2.0%
15 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
0 46
46.0%
2 1
 
1.0%
3 5
 
5.0%
4 5
 
5.0%
5 10
 
10.0%
6 4
 
4.0%
7 2
 
2.0%
8 5
 
5.0%
9 2
 
2.0%
10 5
 
5.0%
ValueCountFrequency (%)
27 1
 
1.0%
15 1
 
1.0%
13 1
 
1.0%
10 5
5.0%
9 2
 
2.0%
8 5
5.0%
7 2
 
2.0%
6 4
 
4.0%
5 10
10.0%
4 5
5.0%

buld_undgrnd_floor_co
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
63 
1
19 
<NA>
12 
4
 
3
2
 
2

Length

Max length4
Median length1
Mean length1.36
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row4
2nd row1
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63
63.0%
1 19
 
19.0%
<NA> 12
 
12.0%
4 3
 
3.0%
2 2
 
2.0%
3 1
 
1.0%

Length

2023-12-10T19:08:00.697577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:00.896636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
63.0%
1 19
 
19.0%
na 12
 
12.0%
4 3
 
3.0%
2 2
 
2.0%
3 1
 
1.0%

ply_fclty_co
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T19:08:01.050086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

pyrxia_room_at
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T19:08:01.201512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

use_end_ground_floor
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)14.9%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean3.091954
Minimum0
Maximum27
Zeros45
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:01.369883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile10
Maximum27
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3123192
Coefficient of variation (CV)1.3946906
Kurtosis9.6790279
Mean3.091954
Median Absolute Deviation (MAD)0
Skewness2.3622384
Sum269
Variance18.596097
MonotonicityNot monotonic
2023-12-10T19:08:01.585372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 45
45.0%
5 7
 
7.0%
4 7
 
7.0%
8 6
 
6.0%
3 6
 
6.0%
6 4
 
4.0%
10 3
 
3.0%
2 2
 
2.0%
9 2
 
2.0%
7 2
 
2.0%
Other values (3) 3
 
3.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
0 45
45.0%
2 2
 
2.0%
3 6
 
6.0%
4 7
 
7.0%
5 7
 
7.0%
6 4
 
4.0%
7 2
 
2.0%
8 6
 
6.0%
9 2
 
2.0%
10 3
 
3.0%
ValueCountFrequency (%)
27 1
 
1.0%
12 1
 
1.0%
11 1
 
1.0%
10 3
3.0%
9 2
 
2.0%
8 6
6.0%
7 2
 
2.0%
6 4
4.0%
5 7
7.0%
4 7
7.0%

use_end_undgrnd_floor
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
82 
<NA>
16 
1
 
2

Length

Max length4
Median length1
Mean length1.48
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 82
82.0%
<NA> 16
 
16.0%
1 2
 
2.0%

Length

2023-12-10T19:08:01.819243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:02.024883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 82
82.0%
na 16
 
16.0%
1 2
 
2.0%

use_begin_ground_floor
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)13.6%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1.625
Minimum0
Maximum13
Zeros43
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:02.183073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7.65
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7008407
Coefficient of variation (CV)1.6620558
Kurtosis5.7292399
Mean1.625
Median Absolute Deviation (MAD)1
Skewness2.3793652
Sum143
Variance7.2945402
MonotonicityNot monotonic
2023-12-10T19:08:02.360990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 43
43.0%
1 19
19.0%
2 7
 
7.0%
3 7
 
7.0%
6 2
 
2.0%
4 2
 
2.0%
5 2
 
2.0%
10 2
 
2.0%
8 1
 
1.0%
11 1
 
1.0%
Other values (2) 2
 
2.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
0 43
43.0%
1 19
19.0%
2 7
 
7.0%
3 7
 
7.0%
4 2
 
2.0%
5 2
 
2.0%
6 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
10 2
 
2.0%
ValueCountFrequency (%)
13 1
 
1.0%
11 1
 
1.0%
10 2
 
2.0%
8 1
 
1.0%
7 1
 
1.0%
6 2
 
2.0%
5 2
 
2.0%
4 2
 
2.0%
3 7
7.0%
2 7
7.0%

use_begin_undgrnd_floor
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
82 
<NA>
15 
1
 
2
4
 
1

Length

Max length4
Median length1
Mean length1.45
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 82
82.0%
<NA> 15
 
15.0%
1 2
 
2.0%
4 1
 
1.0%

Length

2023-12-10T19:08:02.581495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:02.758460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 82
82.0%
na 15
 
15.0%
1 2
 
2.0%
4 1
 
1.0%

lndr_mchn_co
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
86 
<NA>
14 

Length

Max length4
Median length1
Mean length1.42
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
86.0%
<NA> 14
 
14.0%

Length

2023-12-10T19:08:02.948886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:03.122175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
86.0%
na 14
 
14.0%

occdnt_form_room_co
Real number (ℝ)

MISSING  ZEROS 

Distinct45
Distinct (%)46.9%
Missing4
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean27.65625
Minimum0
Maximum500
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:03.320066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.75
Q19
median15.5
Q326
95-th percentile53.75
Maximum500
Range500
Interquartile range (IQR)17

Descriptive statistics

Standard deviation59.099172
Coefficient of variation (CV)2.1369192
Kurtosis47.908362
Mean27.65625
Median Absolute Deviation (MAD)7.5
Skewness6.5766968
Sum2655
Variance3492.7122
MonotonicityNot monotonic
2023-12-10T19:08:03.600733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
13 8
 
8.0%
10 6
 
6.0%
0 4
 
4.0%
18 4
 
4.0%
16 4
 
4.0%
17 4
 
4.0%
15 3
 
3.0%
12 3
 
3.0%
6 3
 
3.0%
9 3
 
3.0%
Other values (35) 54
54.0%
(Missing) 4
 
4.0%
ValueCountFrequency (%)
0 4
4.0%
1 1
 
1.0%
2 2
2.0%
3 1
 
1.0%
4 2
2.0%
5 3
3.0%
6 3
3.0%
7 3
3.0%
8 3
3.0%
9 3
3.0%
ValueCountFrequency (%)
500 1
1.0%
302 1
1.0%
102 1
1.0%
81 1
1.0%
62 1
1.0%
51 2
2.0%
48 1
1.0%
46 1
1.0%
44 2
2.0%
41 2
2.0%

btr_co
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)9.1%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1.8181818
Minimum0
Maximum38
Zeros81
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:03.833115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.25
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0295688
Coefficient of variation (CV)3.8662629
Kurtosis16.715294
Mean1.8181818
Median Absolute Deviation (MAD)0
Skewness4.1275756
Sum160
Variance49.414838
MonotonicityNot monotonic
2023-12-10T19:08:04.035326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 81
81.0%
38 1
 
1.0%
21 1
 
1.0%
4 1
 
1.0%
36 1
 
1.0%
13 1
 
1.0%
30 1
 
1.0%
18 1
 
1.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
0 81
81.0%
4 1
 
1.0%
13 1
 
1.0%
18 1
 
1.0%
21 1
 
1.0%
30 1
 
1.0%
36 1
 
1.0%
38 1
 
1.0%
ValueCountFrequency (%)
38 1
 
1.0%
36 1
 
1.0%
30 1
 
1.0%
21 1
 
1.0%
18 1
 
1.0%
13 1
 
1.0%
4 1
 
1.0%
0 81
81.0%

snitat_biznd_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
여관업
60 
일반호텔
18 
숙박업(생활)
 
6
숙박업 기타
 
6
여인숙업
 
6

Length

Max length7
Median length3
Mean length3.7
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반호텔
2nd row숙박업(생활)
3rd row여관업
4th row숙박업 기타
5th row여관업

Common Values

ValueCountFrequency (%)
여관업 60
60.0%
일반호텔 18
 
18.0%
숙박업(생활) 6
 
6.0%
숙박업 기타 6
 
6.0%
여인숙업 6
 
6.0%
관광호텔 4
 
4.0%

Length

2023-12-10T19:08:04.325214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:04.689697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 60
56.6%
일반호텔 18
 
17.0%
숙박업(생활 6
 
5.7%
숙박업 6
 
5.7%
기타 6
 
5.7%
여인숙업 6
 
5.7%
관광호텔 4
 
3.8%

bedd_co
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
86 
<NA>
14 

Length

Max length4
Median length1
Mean length1.42
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86
86.0%
<NA> 14
 
14.0%

Length

2023-12-10T19:08:05.073278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:05.301708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 86
86.0%
na 14
 
14.0%

korea_room_co
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)14.6%
Missing4
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean2.9270833
Minimum0
Maximum16
Zeros45
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:05.490065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8668491
Coefficient of variation (CV)1.3210588
Kurtosis1.088061
Mean2.9270833
Median Absolute Deviation (MAD)1
Skewness1.3360459
Sum281
Variance14.952522
MonotonicityNot monotonic
2023-12-10T19:08:05.747411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 45
45.0%
2 9
 
9.0%
3 7
 
7.0%
10 7
 
7.0%
7 6
 
6.0%
1 5
 
5.0%
4 4
 
4.0%
8 3
 
3.0%
6 3
 
3.0%
5 3
 
3.0%
Other values (4) 4
 
4.0%
(Missing) 4
 
4.0%
ValueCountFrequency (%)
0 45
45.0%
1 5
 
5.0%
2 9
 
9.0%
3 7
 
7.0%
4 4
 
4.0%
5 3
 
3.0%
6 3
 
3.0%
7 6
 
6.0%
8 3
 
3.0%
9 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
15 1
 
1.0%
12 1
 
1.0%
10 7
7.0%
9 1
 
1.0%
8 3
3.0%
7 6
6.0%
6 3
3.0%
5 3
3.0%
4 4
4.0%

sfrnd_code
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3250000
99 
3320000
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row3250000
2nd row3250000
3rd row3250000
4th row3250000
5th row3250000

Common Values

ValueCountFrequency (%)
3250000 99
99.0%
3320000 1
 
1.0%

Length

2023-12-10T19:08:06.005542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:06.197027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3250000 99
99.0%
3320000 1
 
1.0%

sfrnd_code_nm
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산 중구
99 
부산 북구
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산 중구 99
99.0%
부산 북구 1
 
1.0%

Length

2023-12-10T19:08:06.430789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:06.662584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 100
50.0%
중구 99
49.5%
북구 1
 
0.5%

person_prmisn_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:07.227179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2200
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row3250000-201-2014-00001
2nd row3250000-214-2017-00003
3rd row3250000-201-1971-00116
4th row3250000-201-2012-00005
5th row3250000-201-1988-00069
ValueCountFrequency (%)
3250000-201-2014-00001 1
 
1.0%
3250000-201-1971-00043 1
 
1.0%
3250000-201-1990-00009 1
 
1.0%
3250000-201-2015-00008 1
 
1.0%
3250000-201-1972-00123 1
 
1.0%
3250000-201-2014-00006 1
 
1.0%
3250000-201-1969-00034 1
 
1.0%
3250000-201-1970-00103 1
 
1.0%
3250000-201-2001-00002 1
 
1.0%
3250000-201-1974-00105 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:08:08.012828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 890
40.5%
- 300
 
13.6%
2 270
 
12.3%
1 248
 
11.3%
3 126
 
5.7%
5 119
 
5.4%
9 87
 
4.0%
7 48
 
2.2%
6 43
 
2.0%
8 41
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1900
86.4%
Dash Punctuation 300
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 890
46.8%
2 270
 
14.2%
1 248
 
13.1%
3 126
 
6.6%
5 119
 
6.3%
9 87
 
4.6%
7 48
 
2.5%
6 43
 
2.3%
8 41
 
2.2%
4 28
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 890
40.5%
- 300
 
13.6%
2 270
 
12.3%
1 248
 
11.3%
3 126
 
5.7%
5 119
 
5.4%
9 87
 
4.0%
7 48
 
2.2%
6 43
 
2.0%
8 41
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 890
40.5%
- 300
 
13.6%
2 270
 
12.3%
1 248
 
11.3%
3 126
 
5.7%
5 119
 
5.4%
9 87
 
4.0%
7 48
 
2.2%
6 43
 
2.0%
8 41
 
1.9%

svc_id
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
03_11_03_P
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03_11_03_P
2nd row03_11_03_P
3rd row03_11_03_P
4th row03_11_03_P
5th row03_11_03_P

Common Values

ValueCountFrequency (%)
03_11_03_P 100
100.0%

Length

2023-12-10T19:08:08.262556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:08.469981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
03_11_03_p 100
100.0%

Sample

skeyrtrvl_cnstr_codata_updt_sedata_updt_desvc_id_nmbplc_nmlnm_ziplnm_adresrn_ziprdnmadrperson_prmisn_declsbiz_deengl_sttusdetail_engl_sttusxcntsydntslast_updt_debizcnd_se_nmtelnobuld_posesn_se_nmbuld_ground_floor_cobuld_undgrnd_floor_coply_fclty_copyrxia_room_atuse_end_ground_flooruse_end_undgrnd_flooruse_begin_ground_flooruse_begin_undgrnd_floorlndr_mchn_cooccdnt_form_room_cobtr_cosnitat_biznd_nmbedd_cokorea_room_cosfrnd_codesfrnd_code_nmperson_prmisn_nosvc_id
010I2018-08-31 PM 11:59:00-호텔아벤트리부산600051부산광역시 중구 창선동1가 12-1번지48947부산광역시 중구 광복로39번길 6 (창선동1가)2014-03-18-1영업385065.8515180046.791420200401일반호텔051 260 5007자가84NN80600810일반호텔003250000부산 중구3250000-201-2014-0000103_11_03_P
120I2018-08-31 PM 11:59:00-케이 칠구(K79)600092부산광역시 중구 대청동2가 23-3번지48948부산광역시 중구 광복로49번길 38 (대청동2가)2017-07-31-1영업385140.1574180362.446120200401숙박업(생활)-<NA>51NN30100160숙박업(생활)003250000부산 중구3250000-214-2017-0000303_11_03_P
23-I2018-08-31 PM 11:59:00-영하장600806부산광역시 중구 부평동2가 24-3번지48977부산광역시 중구 중구로23번길 34 (부평동2가)1971-08-072014-03-102폐업384736.7337180083.042520200401여관업051245 5555임대<NA><NA>NN<NA><NA><NA><NA><NA>7<NA>여관업<NA>33250000부산 중구3250000-201-1971-0011603_11_03_P
340I2018-08-31 PM 11:59:00-누리게스트하우스 더셀프600012부산광역시 중구 중앙동2가 52-2번지48956부산광역시 중구 중앙대로49번길 13 (중앙동2가)2012-11-272012-12-312폐업385546.889180289.006520200401숙박업 기타-임대50NN2<NA>2<NA>050숙박업 기타003250000부산 중구3250000-201-2012-0000503_11_03_P
450I2018-08-31 PM 11:59:00-야(Ya)600083부산광역시 중구 보수동3가 5-21번지-부산광역시 중구 보수대로106번길 5 (보수동3가)1988-01-262017-11-202폐업384329.0808180631.273720200401여관업051 2542612임대00NN00000100여관업023250000부산 중구3250000-201-1988-0006903_11_03_P
560I2018-08-31 PM 11:59:00-세피아600071부산광역시 중구 부평동1가 41-7번지 ,848980부산광역시 중구 광복로12번길 7-5 (부평동1가, 41-7,8)1970-08-28-1영업384788.8621179954.32520200401여관업051245 5277<NA>00NN00000160여관업023250000부산 중구3250000-201-1970-0011603_11_03_P
670I2018-08-31 PM 11:59:00-식스나인모텔600046부산광역시 중구 남포동6가 29번지 (8~11층)48982부산광역시 중구 자갈치로 21 (남포동6가)2007-07-12-1영업384804.9091179701.53320200401여관업051231 1178자가101NN110800250여관업003250000부산 중구3250000-201-2007-0000203_11_03_P
780I2018-08-31 PM 11:59:00-동산장모텔600033부산광역시 중구 광복동3가 5-20번지48949부산광역시 중구 광복로67번길 30-22 (광복동3가)1971-03-15-1영업385245.4176180087.314620200401여관업051245 3605<NA>30NN0000040여관업083250000부산 중구3250000-201-1971-0011703_11_03_P
890I2018-08-31 PM 11:59:00-주식회사 경원건설 호텔노아600045부산광역시 중구 남포동5가 81-1번지48983부산광역시 중구 자갈치로47번길 3-1 (남포동5가)1972-10-10-1영업385043.0882179794.610720200401관광호텔051 2464361자가91NN90100510관광호텔0<NA>3250000부산 중구3250000-201-1972-0000303_11_03_P
9100I2018-08-31 PM 11:59:00-더 하운드 호텔600023부산광역시 중구 동광동3가 31-1번지 외 1필지48951부산광역시 중구 대청로126번길 9 (동광동3가)2014-05-28-1영업385461.4608180305.95520200401일반호텔051 231 3123자가80NN80100370일반호텔003250000부산 중구3250000-201-2014-0000303_11_03_P
skeyrtrvl_cnstr_codata_updt_sedata_updt_desvc_id_nmbplc_nmlnm_ziplnm_adresrn_ziprdnmadrperson_prmisn_declsbiz_deengl_sttusdetail_engl_sttusxcntsydntslast_updt_debizcnd_se_nmtelnobuld_posesn_se_nmbuld_ground_floor_cobuld_undgrnd_floor_coply_fclty_copyrxia_room_atuse_end_ground_flooruse_end_undgrnd_flooruse_begin_ground_flooruse_begin_undgrnd_floorlndr_mchn_cooccdnt_form_room_cobtr_cosnitat_biznd_nmbedd_cokorea_room_cosfrnd_codesfrnd_code_nmperson_prmisn_nosvc_id
90910I2018-08-31 PM 11:59:00-소유호텔600022부산광역시 중구 동광동2가 16-5번지48951부산광역시 중구 대청로126번길 23 (동광동2가, 동남빌딩)2016-11-23-1영업385499.135180172.633620200401일반호텔-자가50NN50100440일반호텔003250000부산 중구3250000-201-2016-0000703_11_03_P
91920I2018-08-31 PM 11:59:00-보하모텔600083부산광역시 중구 보수동3가 72-2번지48965부산광역시 중구 대청로25번길 4 (보수동3가)1988-02-29-1영업384444.557180451.874520200401여관업051 2421130임대51NN4020080여관업063250000부산 중구3250000-201-1988-0007003_11_03_P
92930I2018-08-31 PM 11:59:00-반달호텔600815부산광역시 중구 중앙동4가 52-8번지48928부산광역시 중구 해관로 79-1 (중앙동4가)2017-01-03-1영업385563.3239180755.001620200401관광호텔051 441 7190자가60NN60100280관광호텔003250000부산 중구3250000-201-2017-0000103_11_03_P
93940I2018-08-31 PM 11:59:00-엠지엠 호텔600046부산광역시 중구 남포동6가 101-4번지48981부산광역시 중구 보수대로 8-7 (남포동6가)1981-11-11-1영업384752.2471179840.841820200401일반호텔051 231 8661자가100NN1001004430일반호텔003250000부산 중구3250000-201-1981-0001803_11_03_P
94950I2018-08-31 PM 11:59:00-펀스테이 게스트하우스600042부산광역시 중구 남포동2가 28-3번지48954부산광역시 중구 구덕로 30 (남포동2가)1972-02-09-1영업385348.5373179870.431920200401숙박업(생활)051 2461156임대00NN000001518숙박업(생활)033250000부산 중구3250000-214-1972-0000103_11_03_P
95960I2018-08-31 PM 11:59:00-투 헤븐 호텔600809부산광역시 중구 부평동3가 63-1번지48975부산광역시 중구 흑교로21번길 7 (부평동3가)1991-09-30-1영업384579.5473180213.87220200401일반호텔051256 3338자가00NN80100480일반호텔003250000부산 중구3250000-201-1991-0006903_11_03_P
96970I2018-08-31 PM 11:59:00-중앙여인숙600012부산광역시 중구 중앙동2가 22-6번지48956부산광역시 중구 대청로138번길 15-4 (중앙동2가)1972-04-112013-10-312폐업385585.5971180253.412920200401여인숙업051 2464748임대30NN0000000여인숙업083250000부산 중구3250000-201-1972-0012203_11_03_P
9798-I2018-08-31 PM 11:59:00-아카데미모텔600046부산광역시 중구 남포동6가 97-2번지--2002-11-132007-02-122폐업384740.9747179548.266820200401여관업051 2310116임대<NA><NA>NN<NA><NA><NA><NA><NA>33<NA>여관업<NA><NA>3250000부산 중구3250000-201-2002-0000703_11_03_P
9899-I2019-11-13 AM 10:27:22-산해600022부산광역시 중구 동광동2가 11번지--1966-11-122010-04-212폐업1.01.020200401여관업051 2450842<NA><NA><NA>NN<NA><NA><NA><NA><NA><NA><NA>여관업<NA>123250000부산 중구3250000-201-1966-0011203_11_03_P
99100-I2018-08-31 PM 11:59:00-동림장 여관600804부산광역시 중구 부평동1가 29-61번지--1974-01-222005-01-172폐업384781.3882179815.626320200401여관업2464903임대<NA><NA>NN<NA><NA><NA><NA><NA>13<NA>여관업<NA>23250000부산 중구3250000-201-1974-0008203_11_03_P