Overview

Dataset statistics

Number of variables81
Number of observations10000
Missing cells89072
Missing cells (%)11.0%
Duplicate rows728
Duplicate rows (%)7.3%
Total size in memory6.3 MiB
Average record size in memory657.0 B

Variable types

Unsupported3
Text20
Categorical55
DateTime2
Numeric1

Alerts

Dataset has 728 (7.3%) duplicate rowsDuplicates
opnsvcid is highly imbalanced (61.1%)Imbalance
updategbn is highly imbalanced (78.8%)Imbalance
clgstdt is highly imbalanced (93.5%)Imbalance
clgenddt is highly imbalanced (93.3%)Imbalance
ropnymd is highly imbalanced (84.9%)Imbalance
trdstatenm is highly imbalanced (50.3%)Imbalance
dtlstatenm is highly imbalanced (54.3%)Imbalance
bdngownsenm is highly imbalanced (52.3%)Imbalance
bdngsrvnm is highly imbalanced (80.4%)Imbalance
bdngunderflrcnt is highly imbalanced (55.1%)Imbalance
svnsr is highly imbalanced (84.9%)Imbalance
plninsurstdt is highly imbalanced (84.9%)Imbalance
plninsurenddt is highly imbalanced (84.9%)Imbalance
maneipcnt is highly imbalanced (75.6%)Imbalance
playutscntdtl is highly imbalanced (84.9%)Imbalance
playfacilcnt is highly imbalanced (80.9%)Imbalance
multusnupsoyn is highly imbalanced (87.9%)Imbalance
stagear is highly imbalanced (84.9%)Imbalance
culwrkrsenm is highly imbalanced (84.9%)Imbalance
culphyedcobnm is highly imbalanced (60.8%)Imbalance
geicpfacilen is highly imbalanced (84.9%)Imbalance
balhansilyn is highly imbalanced (88.6%)Imbalance
bcfacilen is highly imbalanced (84.9%)Imbalance
insurstdt is highly imbalanced (84.9%)Imbalance
insurenddt is highly imbalanced (84.9%)Imbalance
afc is highly imbalanced (84.9%)Imbalance
useunderendflr is highly imbalanced (61.7%)Imbalance
useunderstflr is highly imbalanced (58.4%)Imbalance
shpinfo is highly imbalanced (84.9%)Imbalance
shpcnt is highly imbalanced (84.9%)Imbalance
shptottons is highly imbalanced (87.8%)Imbalance
infoben is highly imbalanced (84.9%)Imbalance
wmeipcnt is highly imbalanced (74.5%)Imbalance
yoksilcnt is highly imbalanced (75.4%)Imbalance
dispenen is highly imbalanced (84.9%)Imbalance
mnfactreartclcn is highly imbalanced (84.9%)Imbalance
cndpermstymd is highly imbalanced (92.9%)Imbalance
cndpermntwhy is highly imbalanced (91.8%)Imbalance
cndpermendymd is highly imbalanced (92.9%)Imbalance
chaircnt is highly imbalanced (60.7%)Imbalance
nearenvnm is highly imbalanced (80.7%)Imbalance
jisgnumlay is highly imbalanced (83.4%)Imbalance
regnsenm is highly imbalanced (77.5%)Imbalance
undernumlay is highly imbalanced (85.5%)Imbalance
totnumlay is highly imbalanced (82.8%)Imbalance
abedcnt is highly imbalanced (51.5%)Imbalance
meetsamtimesygstf is highly imbalanced (84.9%)Imbalance
sitepostno has 1925 (19.2%) missing valuesMissing
rdnwhladdr has 2089 (20.9%) missing valuesMissing
dcbymd has 6605 (66.0%) missing valuesMissing
x has 466 (4.7%) missing valuesMissing
y has 470 (4.7%) missing valuesMissing
sitetel has 415 (4.2%) missing valuesMissing
stroomcnt has 9245 (92.5%) missing valuesMissing
cnstyarea has 9484 (94.8%) missing valuesMissing
insurorgnm has 9329 (93.3%) missing valuesMissing
facilscp has 8119 (81.2%) missing valuesMissing
facilar has 8119 (81.2%) missing valuesMissing
yangsilcnt has 2400 (24.0%) missing valuesMissing
engstntrnmnm has 9151 (91.5%) missing valuesMissing
engstntrnmaddr has 9154 (91.5%) missing valuesMissing
capt has 9177 (91.8%) missing valuesMissing
hanshilcnt has 2867 (28.7%) missing valuesMissing
skey is an unsupported type, check if it needs cleaning or further analysisUnsupported
opnsfteamcode is an unsupported type, check if it needs cleaning or further analysisUnsupported
rdnpostno is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 16:37:15.085623
Analysis finished2024-04-16 16:37:18.795325
Duration3.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

opnsfteamcode
Unsupported

REJECTED  UNSUPPORTED 

Missing4
Missing (%)< 0.1%
Memory size156.2 KiB

mgtno
Text

Distinct5546
Distinct (%)55.5%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-17T01:37:18.918618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.570228
Min length20

Characters and Unicode

Total characters215616
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2630 ?
Unique (%)26.3%

Sample

1st row3280000-201-1988-00695
2nd rowCDFI2262212019000043
3rd row3350000-201-1972-01077
4th row3380000-201-2004-00001
5th rowCDFI2260032019000002
ValueCountFrequency (%)
cdfi2262132019000001 68
 
0.7%
cdfi2262212020000001 65
 
0.7%
cdfi2262132020000001 65
 
0.7%
cdfi2262212019000001 64
 
0.6%
cdfi2260032019000001 58
 
0.6%
cdfi2260032020000001 57
 
0.6%
cdfi3261132019000001 41
 
0.4%
cdfi2262132019000002 40
 
0.4%
cdfi2262142020000001 38
 
0.4%
cdfi2262212019000003 36
 
0.4%
Other values (5536) 9464
94.7%
2024-04-17T01:37:19.232748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82909
38.5%
2 28505
 
13.2%
- 23544
 
10.9%
1 22933
 
10.6%
3 16445
 
7.6%
9 10019
 
4.6%
6 5347
 
2.5%
4 5012
 
2.3%
8 4698
 
2.2%
7 4219
 
2.0%
Other values (5) 11985
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183480
85.1%
Dash Punctuation 23544
 
10.9%
Uppercase Letter 8592
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 82909
45.2%
2 28505
 
15.5%
1 22933
 
12.5%
3 16445
 
9.0%
9 10019
 
5.5%
6 5347
 
2.9%
4 5012
 
2.7%
8 4698
 
2.6%
7 4219
 
2.3%
5 3393
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
C 2148
25.0%
D 2148
25.0%
F 2148
25.0%
I 2148
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 23544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 207024
96.0%
Latin 8592
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 82909
40.0%
2 28505
 
13.8%
- 23544
 
11.4%
1 22933
 
11.1%
3 16445
 
7.9%
9 10019
 
4.8%
6 5347
 
2.6%
4 5012
 
2.4%
8 4698
 
2.3%
7 4219
 
2.0%
Latin
ValueCountFrequency (%)
C 2148
25.0%
D 2148
25.0%
F 2148
25.0%
I 2148
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82909
38.5%
2 28505
 
13.2%
- 23544
 
10.9%
1 22933
 
10.6%
3 16445
 
7.6%
9 10019
 
4.6%
6 5347
 
2.5%
4 5012
 
2.3%
8 4698
 
2.2%
7 4219
 
2.0%
Other values (5) 11985
 
5.6%

opnsvcid
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
03_11_03_P
7847 
03_11_04_P
935 
03_11_07_P
 
390
03_11_01_P
 
343
03_11_06_P
 
233
Other values (4)
 
252

Length

Max length10
Median length10
Mean length9.9974
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row03_11_03_P
2nd row03_11_04_P
3rd row03_11_03_P
4th row03_11_03_P
5th row03_11_01_P

Common Values

ValueCountFrequency (%)
03_11_03_P 7847
78.5%
03_11_04_P 935
 
9.3%
03_11_07_P 390
 
3.9%
03_11_01_P 343
 
3.4%
03_11_06_P 233
 
2.3%
03_11_02_P 144
 
1.4%
03_11_05_P 103
 
1.0%
<NA> 4
 
< 0.1%
opnSvcId 1
 
< 0.1%

Length

2024-04-17T01:37:19.361013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:19.458504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
03_11_03_p 7847
78.5%
03_11_04_p 935
 
9.3%
03_11_07_p 390
 
3.9%
03_11_01_p 343
 
3.4%
03_11_06_p 233
 
2.3%
03_11_02_p 144
 
1.4%
03_11_05_p 103
 
1.0%
na 4
 
< 0.1%
opnsvcid 1
 
< 0.1%

updategbn
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
8942 
U
1053 
<NA>
 
2
180000000
 
2
u
 
1

Length

Max length9
Median length1
Mean length1.0022
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
I 8942
89.4%
U 1053
 
10.5%
<NA> 2
 
< 0.1%
180000000 2
 
< 0.1%
u 1
 
< 0.1%

Length

2024-04-17T01:37:19.571826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:19.666986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 8942
89.4%
u 1054
 
10.5%
na 2
 
< 0.1%
180000000 2
 
< 0.1%
Distinct905
Distinct (%)9.1%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-31 02:40:00
2024-04-17T01:37:19.775830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:37:19.890552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6339 
숙박업
1666 
외국인관광도시민박업
791 
일반야영장업
 
390
관광숙박업
 
343
Other values (4)
 
471

Length

Max length10
Median length4
Mean length4.4867
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6339
63.4%
숙박업 1666
 
16.7%
외국인관광도시민박업 791
 
7.9%
일반야영장업 390
 
3.9%
관광숙박업 343
 
3.4%
한옥체험업 233
 
2.3%
관광펜션업 142
 
1.4%
자동차야영장업 95
 
0.9%
opnSvcNm 1
 
< 0.1%

Length

2024-04-17T01:37:20.007356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:20.104553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6339
63.4%
숙박업 1666
 
16.7%
외국인관광도시민박업 791
 
7.9%
일반야영장업 390
 
3.9%
관광숙박업 343
 
3.4%
한옥체험업 233
 
2.3%
관광펜션업 142
 
1.4%
자동차야영장업 95
 
0.9%
opnsvcnm 1
 
< 0.1%

bplcnm
Text

Distinct5604
Distinct (%)56.1%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-17T01:37:20.539932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length5.8762505
Min length1

Characters and Unicode

Total characters58739
Distinct characters834
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2971 ?
Unique (%)29.7%

Sample

1st row부일장
2nd row코빈하우스
3rd row수정여관
4th row캐슬비치관광호텔
5th row삼화
ValueCountFrequency (%)
호텔 250
 
1.9%
게스트하우스 168
 
1.3%
house 151
 
1.2%
모텔 147
 
1.1%
하우스 73
 
0.6%
펜션 65
 
0.5%
호스텔 64
 
0.5%
여관 63
 
0.5%
야영장 61
 
0.5%
56
 
0.4%
Other values (5952) 11728
91.4%
2024-04-17T01:37:20.965371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2949
 
5.0%
2844
 
4.8%
2251
 
3.8%
1958
 
3.3%
1601
 
2.7%
1579
 
2.7%
1472
 
2.5%
1076
 
1.8%
1010
 
1.7%
983
 
1.7%
Other values (824) 41016
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47260
80.5%
Uppercase Letter 3214
 
5.5%
Lowercase Letter 2899
 
4.9%
Space Separator 2844
 
4.8%
Decimal Number 942
 
1.6%
Close Punctuation 658
 
1.1%
Open Punctuation 658
 
1.1%
Other Punctuation 175
 
0.3%
Dash Punctuation 58
 
0.1%
Letter Number 16
 
< 0.1%
Other values (5) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2949
 
6.2%
2251
 
4.8%
1958
 
4.1%
1601
 
3.4%
1579
 
3.3%
1472
 
3.1%
1076
 
2.3%
1010
 
2.1%
983
 
2.1%
904
 
1.9%
Other values (737) 31477
66.6%
Lowercase Letter
ValueCountFrequency (%)
e 421
14.5%
o 355
12.2%
s 319
11.0%
a 241
8.3%
u 237
8.2%
n 183
 
6.3%
t 167
 
5.8%
h 145
 
5.0%
l 134
 
4.6%
i 129
 
4.4%
Other values (16) 568
19.6%
Uppercase Letter
ValueCountFrequency (%)
H 331
 
10.3%
E 295
 
9.2%
O 284
 
8.8%
S 238
 
7.4%
T 215
 
6.7%
A 200
 
6.2%
U 147
 
4.6%
N 142
 
4.4%
L 137
 
4.3%
B 132
 
4.1%
Other values (16) 1093
34.0%
Decimal Number
ValueCountFrequency (%)
2 199
21.1%
1 195
20.7%
3 105
11.1%
0 89
9.4%
5 82
8.7%
9 77
 
8.2%
7 74
 
7.9%
6 47
 
5.0%
4 40
 
4.2%
8 34
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 61
34.9%
& 48
27.4%
' 48
27.4%
, 8
 
4.6%
: 5
 
2.9%
; 2
 
1.1%
1
 
0.6%
· 1
 
0.6%
# 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 656
99.7%
] 1
 
0.2%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 656
99.7%
[ 1
 
0.2%
1
 
0.2%
Letter Number
ValueCountFrequency (%)
12
75.0%
4
 
25.0%
Math Symbol
ValueCountFrequency (%)
+ 3
75.0%
1
 
25.0%
Space Separator
ValueCountFrequency (%)
2844
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Other Symbol
ValueCountFrequency (%)
5
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47244
80.4%
Latin 6129
 
10.4%
Common 5345
 
9.1%
Han 21
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2949
 
6.2%
2251
 
4.8%
1958
 
4.1%
1601
 
3.4%
1579
 
3.3%
1472
 
3.1%
1076
 
2.3%
1010
 
2.1%
983
 
2.1%
904
 
1.9%
Other values (723) 31461
66.6%
Latin
ValueCountFrequency (%)
e 421
 
6.9%
o 355
 
5.8%
H 331
 
5.4%
s 319
 
5.2%
E 295
 
4.8%
O 284
 
4.6%
a 241
 
3.9%
S 238
 
3.9%
u 237
 
3.9%
T 215
 
3.5%
Other values (44) 3193
52.1%
Common
ValueCountFrequency (%)
2844
53.2%
) 656
 
12.3%
( 656
 
12.3%
2 199
 
3.7%
1 195
 
3.6%
3 105
 
2.0%
0 89
 
1.7%
5 82
 
1.5%
9 77
 
1.4%
7 74
 
1.4%
Other values (22) 368
 
6.9%
Han
ValueCountFrequency (%)
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (5) 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47239
80.4%
ASCII 11452
 
19.5%
CJK 21
 
< 0.1%
Number Forms 16
 
< 0.1%
None 10
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2949
 
6.2%
2251
 
4.8%
1958
 
4.1%
1601
 
3.4%
1579
 
3.3%
1472
 
3.1%
1076
 
2.3%
1010
 
2.1%
983
 
2.1%
904
 
1.9%
Other values (722) 31456
66.6%
ASCII
ValueCountFrequency (%)
2844
24.8%
) 656
 
5.7%
( 656
 
5.7%
e 421
 
3.7%
o 355
 
3.1%
H 331
 
2.9%
s 319
 
2.8%
E 295
 
2.6%
O 284
 
2.5%
a 241
 
2.1%
Other values (68) 5050
44.1%
Number Forms
ValueCountFrequency (%)
12
75.0%
4
 
25.0%
None
ValueCountFrequency (%)
5
50.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
· 1
 
10.0%
1
 
10.0%
CJK
ValueCountFrequency (%)
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
2
 
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (5) 5
23.8%
Punctuation
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct1188
Distinct (%)14.7%
Missing1925
Missing (%)19.2%
Memory size156.2 KiB
2024-04-17T01:37:21.254403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.0004954
Min length6

Characters and Unicode

Total characters48454
Distinct characters22
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

Unique482 ?
Unique (%)6.0%

Sample

1st row606061
2nd row609834
3rd row613827
4th row750804
5th row409892
ValueCountFrequency (%)
612821 248
 
3.1%
지번우편번호 212
 
2.6%
616801 204
 
2.5%
612040 149
 
1.8%
612847 132
 
1.6%
607833 126
 
1.6%
601829 111
 
1.4%
607831 101
 
1.3%
617807 100
 
1.2%
613828 97
 
1.2%
Other values (1178) 6595
81.7%
2024-04-17T01:37:21.647871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 8313
17.2%
0 7504
15.5%
8 7329
15.1%
1 7183
14.8%
2 4328
8.9%
4 3605
7.4%
7 2729
 
5.6%
3 2674
 
5.5%
9 1788
 
3.7%
5 1719
 
3.5%
Other values (12) 1282
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47172
97.4%
Other Letter 1272
 
2.6%
Lowercase Letter 8
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 8313
17.6%
0 7504
15.9%
8 7329
15.5%
1 7183
15.2%
2 4328
9.2%
4 3605
7.6%
7 2729
 
5.8%
3 2674
 
5.7%
9 1788
 
3.8%
5 1719
 
3.6%
Other Letter
ValueCountFrequency (%)
424
33.3%
212
16.7%
212
16.7%
212
16.7%
212
16.7%
Lowercase Letter
ValueCountFrequency (%)
s 2
25.0%
t 2
25.0%
o 2
25.0%
i 1
12.5%
e 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
N 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47172
97.4%
Hangul 1272
 
2.6%
Latin 10
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 8313
17.6%
0 7504
15.9%
8 7329
15.5%
1 7183
15.2%
2 4328
9.2%
4 3605
7.6%
7 2729
 
5.8%
3 2674
 
5.7%
9 1788
 
3.8%
5 1719
 
3.6%
Latin
ValueCountFrequency (%)
s 2
20.0%
t 2
20.0%
o 2
20.0%
i 1
10.0%
e 1
10.0%
P 1
10.0%
N 1
10.0%
Hangul
ValueCountFrequency (%)
424
33.3%
212
16.7%
212
16.7%
212
16.7%
212
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47182
97.4%
Hangul 1272
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 8313
17.6%
0 7504
15.9%
8 7329
15.5%
1 7183
15.2%
2 4328
9.2%
4 3605
7.6%
7 2729
 
5.8%
3 2674
 
5.7%
9 1788
 
3.8%
5 1719
 
3.6%
Other values (7) 10
 
< 0.1%
Hangul
ValueCountFrequency (%)
424
33.3%
212
16.7%
212
16.7%
212
16.7%
212
16.7%
Distinct6266
Distinct (%)62.7%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-17T01:37:21.947473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length70
Mean length24.042321
Min length11

Characters and Unicode

Total characters240303
Distinct characters538
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3270 ?
Unique (%)32.7%

Sample

1st row부산광역시 영도구 봉래동1가 50-2번지
2nd row서울특별시 용산구 후암동 116-13번지 목화연립
3rd row부산광역시 금정구 서동 495-6번지
4th row부산광역시 수영구 민락동 110-60번지
5th row서울특별시 관악구 신림동 1432-62번지
ValueCountFrequency (%)
부산광역시 6549
 
14.6%
해운대구 853
 
1.9%
부산진구 831
 
1.8%
서울특별시 727
 
1.6%
동래구 688
 
1.5%
t통b반 667
 
1.5%
중구 548
 
1.2%
동구 541
 
1.2%
북구 528
 
1.2%
사상구 525
 
1.2%
Other values (8459) 32465
72.3%
2024-04-17T01:37:22.390326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44496
 
18.5%
10083
 
4.2%
1 9986
 
4.2%
9303
 
3.9%
9163
 
3.8%
8943
 
3.7%
- 8934
 
3.7%
8516
 
3.5%
8197
 
3.4%
8174
 
3.4%
Other values (528) 114508
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137671
57.3%
Decimal Number 46546
 
19.4%
Space Separator 44496
 
18.5%
Dash Punctuation 8934
 
3.7%
Uppercase Letter 1510
 
0.6%
Other Punctuation 495
 
0.2%
Math Symbol 223
 
0.1%
Close Punctuation 160
 
0.1%
Open Punctuation 160
 
0.1%
Lowercase Letter 105
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10083
 
7.3%
9303
 
6.8%
9163
 
6.7%
8943
 
6.5%
8516
 
6.2%
8197
 
6.0%
8174
 
5.9%
7319
 
5.3%
6933
 
5.0%
2857
 
2.1%
Other values (466) 58183
42.3%
Uppercase Letter
ValueCountFrequency (%)
B 695
46.0%
T 674
44.6%
A 24
 
1.6%
C 19
 
1.3%
S 12
 
0.8%
O 10
 
0.7%
E 9
 
0.6%
K 8
 
0.5%
L 8
 
0.5%
I 7
 
0.5%
Other values (11) 44
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
e 15
14.3%
n 12
11.4%
i 10
9.5%
l 9
8.6%
h 9
8.6%
d 7
 
6.7%
o 7
 
6.7%
t 6
 
5.7%
u 6
 
5.7%
a 6
 
5.7%
Other values (8) 18
17.1%
Decimal Number
ValueCountFrequency (%)
1 9986
21.5%
2 6244
13.4%
3 5118
11.0%
4 4624
9.9%
5 4309
9.3%
6 3579
 
7.7%
0 3531
 
7.6%
7 3341
 
7.2%
8 3015
 
6.5%
9 2799
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 405
81.8%
75
 
15.2%
. 9
 
1.8%
& 6
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 159
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 159
99.4%
[ 1
 
0.6%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
44496
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8934
100.0%
Math Symbol
ValueCountFrequency (%)
~ 223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 137671
57.3%
Common 101014
42.0%
Latin 1618
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10083
 
7.3%
9303
 
6.8%
9163
 
6.7%
8943
 
6.5%
8516
 
6.2%
8197
 
6.0%
8174
 
5.9%
7319
 
5.3%
6933
 
5.0%
2857
 
2.1%
Other values (466) 58183
42.3%
Latin
ValueCountFrequency (%)
B 695
43.0%
T 674
41.7%
A 24
 
1.5%
C 19
 
1.2%
e 15
 
0.9%
n 12
 
0.7%
S 12
 
0.7%
O 10
 
0.6%
i 10
 
0.6%
l 9
 
0.6%
Other values (31) 138
 
8.5%
Common
ValueCountFrequency (%)
44496
44.0%
1 9986
 
9.9%
- 8934
 
8.8%
2 6244
 
6.2%
3 5118
 
5.1%
4 4624
 
4.6%
5 4309
 
4.3%
6 3579
 
3.5%
0 3531
 
3.5%
7 3341
 
3.3%
Other values (11) 6852
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 137671
57.3%
ASCII 102554
42.7%
None 75
 
< 0.1%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44496
43.4%
1 9986
 
9.7%
- 8934
 
8.7%
2 6244
 
6.1%
3 5118
 
5.0%
4 4624
 
4.5%
5 4309
 
4.2%
6 3579
 
3.5%
0 3531
 
3.4%
7 3341
 
3.3%
Other values (49) 8392
 
8.2%
Hangul
ValueCountFrequency (%)
10083
 
7.3%
9303
 
6.8%
9163
 
6.7%
8943
 
6.5%
8516
 
6.2%
8197
 
6.0%
8174
 
5.9%
7319
 
5.3%
6933
 
5.0%
2857
 
2.1%
Other values (466) 58183
42.3%
None
ValueCountFrequency (%)
75
100.0%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%

rdnpostno
Unsupported

REJECTED  UNSUPPORTED 

Missing24
Missing (%)0.2%
Memory size156.2 KiB

rdnwhladdr
Text

MISSING 

Distinct5256
Distinct (%)66.4%
Missing2089
Missing (%)20.9%
Memory size156.2 KiB
2024-04-17T01:37:22.655547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length75
Mean length28.089622
Min length1

Characters and Unicode

Total characters222217
Distinct characters618
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3010 ?
Unique (%)38.0%

Sample

1st row서울특별시 용산구 후암로35길 48, 1층 103호 (후암동, 목화연립)
2nd row부산광역시 수영구 민락수변로 141 (민락동)
3rd row서울특별시 관악구 신림로65길 10-33, 삼화 (신림동)
4th row경상북도 영주시 풍기읍 죽령로 1398-36
5th row서울특별시 광진구 자양로44나길 17, 지층 (구의동, 솔라빌리지)
ValueCountFrequency (%)
부산광역시 4572
 
10.6%
서울특별시 727
 
1.7%
해운대구 682
 
1.6%
부산진구 565
 
1.3%
경기도 493
 
1.1%
중구 481
 
1.1%
동래구 460
 
1.1%
동구 410
 
0.9%
사상구 398
 
0.9%
강원도 377
 
0.9%
Other values (6966) 34066
78.8%
2024-04-17T01:37:23.055338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35322
 
15.9%
1 8656
 
3.9%
8633
 
3.9%
7256
 
3.3%
6820
 
3.1%
) 6407
 
2.9%
( 6407
 
2.9%
6324
 
2.8%
6306
 
2.8%
2 6022
 
2.7%
Other values (608) 124064
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129823
58.4%
Decimal Number 37240
 
16.8%
Space Separator 35322
 
15.9%
Close Punctuation 6412
 
2.9%
Open Punctuation 6412
 
2.9%
Other Punctuation 3245
 
1.5%
Dash Punctuation 2890
 
1.3%
Math Symbol 469
 
0.2%
Uppercase Letter 287
 
0.1%
Lowercase Letter 113
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8633
 
6.6%
7256
 
5.6%
6820
 
5.3%
6324
 
4.9%
6306
 
4.9%
5869
 
4.5%
5616
 
4.3%
5282
 
4.1%
4959
 
3.8%
3461
 
2.7%
Other values (541) 69297
53.4%
Uppercase Letter
ValueCountFrequency (%)
A 62
21.6%
B 59
20.6%
C 30
10.5%
S 15
 
5.2%
E 14
 
4.9%
D 12
 
4.2%
O 12
 
4.2%
K 11
 
3.8%
G 8
 
2.8%
L 8
 
2.8%
Other values (12) 56
19.5%
Lowercase Letter
ValueCountFrequency (%)
e 17
15.0%
n 13
11.5%
h 9
 
8.0%
l 9
 
8.0%
i 9
 
8.0%
d 8
 
7.1%
u 7
 
6.2%
o 7
 
6.2%
a 6
 
5.3%
c 5
 
4.4%
Other values (9) 23
20.4%
Decimal Number
ValueCountFrequency (%)
1 8656
23.2%
2 6022
16.2%
3 4186
11.2%
4 3248
 
8.7%
5 2830
 
7.6%
0 2814
 
7.6%
6 2606
 
7.0%
7 2533
 
6.8%
9 2213
 
5.9%
8 2132
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 3100
95.5%
96
 
3.0%
: 22
 
0.7%
. 16
 
0.5%
& 6
 
0.2%
* 4
 
0.1%
/ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 6407
99.9%
] 5
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 6407
99.9%
[ 5
 
0.1%
Letter Number
ValueCountFrequency (%)
2
50.0%
2
50.0%
Space Separator
ValueCountFrequency (%)
35322
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2890
100.0%
Math Symbol
ValueCountFrequency (%)
~ 469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129823
58.4%
Common 91990
41.4%
Latin 404
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8633
 
6.6%
7256
 
5.6%
6820
 
5.3%
6324
 
4.9%
6306
 
4.9%
5869
 
4.5%
5616
 
4.3%
5282
 
4.1%
4959
 
3.8%
3461
 
2.7%
Other values (541) 69297
53.4%
Latin
ValueCountFrequency (%)
A 62
 
15.3%
B 59
 
14.6%
C 30
 
7.4%
e 17
 
4.2%
S 15
 
3.7%
E 14
 
3.5%
n 13
 
3.2%
D 12
 
3.0%
O 12
 
3.0%
K 11
 
2.7%
Other values (33) 159
39.4%
Common
ValueCountFrequency (%)
35322
38.4%
1 8656
 
9.4%
) 6407
 
7.0%
( 6407
 
7.0%
2 6022
 
6.5%
3 4186
 
4.6%
4 3248
 
3.5%
, 3100
 
3.4%
- 2890
 
3.1%
5 2830
 
3.1%
Other values (14) 12922
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129823
58.4%
ASCII 92294
41.5%
None 96
 
< 0.1%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35322
38.3%
1 8656
 
9.4%
) 6407
 
6.9%
( 6407
 
6.9%
2 6022
 
6.5%
3 4186
 
4.5%
4 3248
 
3.5%
, 3100
 
3.4%
- 2890
 
3.1%
5 2830
 
3.1%
Other values (54) 13226
 
14.3%
Hangul
ValueCountFrequency (%)
8633
 
6.6%
7256
 
5.6%
6820
 
5.3%
6324
 
4.9%
6306
 
4.9%
5869
 
4.5%
5616
 
4.3%
5282
 
4.1%
4959
 
3.8%
3461
 
2.7%
Other values (541) 69297
53.4%
None
ValueCountFrequency (%)
96
100.0%
Number Forms
ValueCountFrequency (%)
2
50.0%
2
50.0%
Distinct3630
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T01:37:23.367256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.999
Min length4

Characters and Unicode

Total characters79990
Distinct characters28
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

Unique971 ?
Unique (%)9.7%

Sample

1st row19880222
2nd row20190802
3rd row19720831
4th row20040727
5th row20191015
ValueCountFrequency (%)
20190705 29
 
0.3%
20191227 27
 
0.3%
20200515 26
 
0.3%
20190322 25
 
0.2%
20200103 24
 
0.2%
20181017 24
 
0.2%
20181026 24
 
0.2%
20190719 24
 
0.2%
20190614 24
 
0.2%
20200717 24
 
0.2%
Other values (3620) 9749
97.5%
2024-04-17T01:37:23.758202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20528
25.7%
1 17554
21.9%
2 13870
17.3%
9 9078
11.3%
8 4572
 
5.7%
7 3950
 
4.9%
3 3049
 
3.8%
6 2654
 
3.3%
4 2413
 
3.0%
5 2292
 
2.9%
Other values (18) 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79960
> 99.9%
Other Letter 20
 
< 0.1%
Lowercase Letter 8
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20528
25.7%
1 17554
22.0%
2 13870
17.3%
9 9078
11.4%
8 4572
 
5.7%
7 3950
 
4.9%
3 3049
 
3.8%
6 2654
 
3.3%
4 2413
 
3.0%
5 2292
 
2.9%
Other Letter
ValueCountFrequency (%)
4
20.0%
4
20.0%
3
15.0%
3
15.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
25.0%
a 1
12.5%
p 1
12.5%
v 1
12.5%
e 1
12.5%
r 1
12.5%
d 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
Y 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79960
> 99.9%
Hangul 20
 
< 0.1%
Latin 10
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20528
25.7%
1 17554
22.0%
2 13870
17.3%
9 9078
11.4%
8 4572
 
5.7%
7 3950
 
4.9%
3 3049
 
3.8%
6 2654
 
3.3%
4 2413
 
3.0%
5 2292
 
2.9%
Hangul
ValueCountFrequency (%)
4
20.0%
4
20.0%
3
15.0%
3
15.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Latin
ValueCountFrequency (%)
m 2
20.0%
a 1
10.0%
p 1
10.0%
v 1
10.0%
P 1
10.0%
e 1
10.0%
r 1
10.0%
Y 1
10.0%
d 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79970
> 99.9%
Hangul 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20528
25.7%
1 17554
22.0%
2 13870
17.3%
9 9078
11.4%
8 4572
 
5.7%
7 3950
 
4.9%
3 3049
 
3.8%
6 2654
 
3.3%
4 2413
 
3.0%
5 2292
 
2.9%
Other values (9) 10
 
< 0.1%
Hangul
ValueCountFrequency (%)
4
20.0%
4
20.0%
3
15.0%
3
15.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%

dcbymd
Text

MISSING 

Distinct1311
Distinct (%)38.6%
Missing6605
Missing (%)66.0%
Memory size156.2 KiB
2024-04-17T01:37:24.005189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.539028
Min length1

Characters and Unicode

Total characters25595
Distinct characters19
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

Unique435 ?
Unique (%)12.8%

Sample

1st row20040115
2nd row20020530
3rd row20060612
4th row20101028
5th row20180116
ValueCountFrequency (%)
폐업일자 389
 
11.5%
20041022 140
 
4.1%
20030122 53
 
1.6%
20120711 45
 
1.3%
20021024 30
 
0.9%
20030101 22
 
0.6%
20030305 22
 
0.6%
20030227 18
 
0.5%
20051117 17
 
0.5%
20030901 16
 
0.5%
Other values (1301) 2643
77.8%
2024-04-17T01:37:24.368819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8123
31.7%
2 5097
19.9%
1 4352
17.0%
3 1128
 
4.4%
9 1113
 
4.3%
7 942
 
3.7%
4 863
 
3.4%
5 861
 
3.4%
6 847
 
3.3%
8 707
 
2.8%
Other values (9) 1562
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24033
93.9%
Other Letter 1556
 
6.1%
Lowercase Letter 5
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8123
33.8%
2 5097
21.2%
1 4352
18.1%
3 1128
 
4.7%
9 1113
 
4.6%
7 942
 
3.9%
4 863
 
3.6%
5 861
 
3.6%
6 847
 
3.5%
8 707
 
2.9%
Other Letter
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%
Lowercase Letter
ValueCountFrequency (%)
d 2
40.0%
c 1
20.0%
b 1
20.0%
m 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
Y 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24033
93.9%
Hangul 1556
 
6.1%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8123
33.8%
2 5097
21.2%
1 4352
18.1%
3 1128
 
4.7%
9 1113
 
4.6%
7 942
 
3.9%
4 863
 
3.6%
5 861
 
3.6%
6 847
 
3.5%
8 707
 
2.9%
Latin
ValueCountFrequency (%)
d 2
33.3%
c 1
16.7%
b 1
16.7%
Y 1
16.7%
m 1
16.7%
Hangul
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24039
93.9%
Hangul 1556
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8123
33.8%
2 5097
21.2%
1 4352
18.1%
3 1128
 
4.7%
9 1113
 
4.6%
7 942
 
3.9%
4 863
 
3.6%
5 861
 
3.6%
6 847
 
3.5%
8 707
 
2.9%
Other values (5) 6
 
< 0.1%
Hangul
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9596 
휴업시작일자
 
390
20160608
 
1
1
 
1
20200901
 
1
Other values (11)
 
11

Length

Max length8
Median length4
Mean length4.0821
Min length1

Unique

Unique14 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9596
96.0%
휴업시작일자 390
 
3.9%
20160608 1
 
< 0.1%
1 1
 
< 0.1%
20200901 1
 
< 0.1%
20180719 1
 
< 0.1%
20201001 1
 
< 0.1%
20200301 1
 
< 0.1%
20191101 1
 
< 0.1%
20170413 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-17T01:37:24.515011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9596
96.0%
휴업시작일자 390
 
3.9%
20160608 1
 
< 0.1%
1 1
 
< 0.1%
20200901 1
 
< 0.1%
20180719 1
 
< 0.1%
20201001 1
 
< 0.1%
20200301 1
 
< 0.1%
20191101 1
 
< 0.1%
20170413 1
 
< 0.1%
Other values (6) 6
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9598 
휴업종료일자
 
390
20170607
 
1
20201231
 
1
20181231
 
1
Other values (9)
 
9

Length

Max length8
Median length4
Mean length4.0828
Min length4

Unique

Unique12 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9598
96.0%
휴업종료일자 390
 
3.9%
20170607 1
 
< 0.1%
20201231 1
 
< 0.1%
20181231 1
 
< 0.1%
20211001 1
 
< 0.1%
20210228 1
 
< 0.1%
20201101 1
 
< 0.1%
20190501 1
 
< 0.1%
clgEnddt 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Length

2024-04-17T01:37:24.634805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9598
96.0%
휴업종료일자 390
 
3.9%
20170607 1
 
< 0.1%
20201231 1
 
< 0.1%
20181231 1
 
< 0.1%
20211001 1
 
< 0.1%
20210228 1
 
< 0.1%
20201101 1
 
< 0.1%
20190501 1
 
< 0.1%
clgenddt 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
재개업일자
 
390
ropnYmd
 
1

Length

Max length7
Median length4
Mean length4.0393
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
재개업일자 390
 
3.9%
ropnYmd 1
 
< 0.1%

Length

2024-04-17T01:37:24.750886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:24.843410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
재개업일자 390
 
3.9%
ropnymd 1
 
< 0.1%

trdstatenm
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업/정상
3533 
01
3294 
02
2891 
13
 
107
폐업
 
75
Other values (7)
 
100

Length

Max length14
Median length2
Mean length3.0723
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row02
2nd row영업/정상
3rd row02
4th row01
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 3533
35.3%
01 3294
32.9%
02 2891
28.9%
13 107
 
1.1%
폐업 75
 
0.8%
<NA> 43
 
0.4%
03 42
 
0.4%
휴업 8
 
0.1%
영업상태 3
 
< 0.1%
취소/말소/만료/정지/중지 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-17T01:37:24.946678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업/정상 3533
35.3%
01 3294
32.9%
02 2891
28.9%
13 107
 
1.1%
폐업 75
 
0.8%
na 43
 
0.4%
03 42
 
0.4%
휴업 8
 
0.1%
영업상태 3
 
< 0.1%
취소/말소/만료/정지/중지 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

dtlstatenm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업
4926 
폐업
3004 
영업중
2047 
휴업
 
12
<NA>
 
4
Other values (5)
 
7

Length

Max length10
Median length2
Mean length2.2081
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
영업 4926
49.3%
폐업 3004
30.0%
영업중 2047
20.5%
휴업 12
 
0.1%
<NA> 4
 
< 0.1%
?????? 3
 
< 0.1%
직권말소 1
 
< 0.1%
지정취소 1
 
< 0.1%
등록취소 1
 
< 0.1%
dtlStateNm 1
 
< 0.1%

Length

2024-04-17T01:37:25.055864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:25.156107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 4926
49.3%
폐업 3004
30.0%
영업중 2047
20.5%
휴업 12
 
0.1%
na 4
 
< 0.1%
?????? 3
 
< 0.1%
직권말소 1
 
< 0.1%
지정취소 1
 
< 0.1%
등록취소 1
 
< 0.1%
dtlstatenm 1
 
< 0.1%

x
Text

MISSING 

Distinct5908
Distinct (%)62.0%
Missing466
Missing (%)4.7%
Memory size156.2 KiB
2024-04-17T01:37:25.386028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.958045
Min length1

Characters and Unicode

Total characters190280
Distinct characters22
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

Unique2990 ?
Unique (%)31.4%

Sample

1st row386005.990004
2nd row197801.284875486
3rd row391232.542570
4th row394509.50605400000
5th row193621.587472621
ValueCountFrequency (%)
좌표정보(x 29
 
0.3%
393521.998603867 13
 
0.1%
397447.054868 11
 
0.1%
388014.370428964 9
 
0.1%
393594.19935900000 8
 
0.1%
205271.275273064 8
 
0.1%
397816.16252117 8
 
0.1%
198535.364933014 8
 
0.1%
400102.463000748 7
 
0.1%
396916.80429800000 7
 
0.1%
Other values (5899) 9430
98.9%
2024-04-17T01:37:25.749646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37996
20.0%
0 31474
16.5%
3 16951
8.9%
8 14709
 
7.7%
9 12916
 
6.8%
1 11741
 
6.2%
2 11146
 
5.9%
6 11114
 
5.8%
7 10982
 
5.8%
4 10967
 
5.8%
Other values (12) 20284
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142582
74.9%
Space Separator 37996
 
20.0%
Other Punctuation 9486
 
5.0%
Other Letter 116
 
0.1%
Close Punctuation 29
 
< 0.1%
Uppercase Letter 29
 
< 0.1%
Open Punctuation 29
 
< 0.1%
Dash Punctuation 12
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31474
22.1%
3 16951
11.9%
8 14709
10.3%
9 12916
9.1%
1 11741
 
8.2%
2 11146
 
7.8%
6 11114
 
7.8%
7 10982
 
7.7%
4 10967
 
7.7%
5 10582
 
7.4%
Other Letter
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%
Other Punctuation
ValueCountFrequency (%)
. 9478
99.9%
: 8
 
0.1%
Space Separator
ValueCountFrequency (%)
37996
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190134
99.9%
Hangul 116
 
0.1%
Latin 30
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
37996
20.0%
0 31474
16.6%
3 16951
8.9%
8 14709
 
7.7%
9 12916
 
6.8%
1 11741
 
6.2%
2 11146
 
5.9%
6 11114
 
5.8%
7 10982
 
5.8%
4 10967
 
5.8%
Other values (6) 20138
10.6%
Hangul
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%
Latin
ValueCountFrequency (%)
X 29
96.7%
x 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190164
99.9%
Hangul 116
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37996
20.0%
0 31474
16.6%
3 16951
8.9%
8 14709
 
7.7%
9 12916
 
6.8%
1 11741
 
6.2%
2 11146
 
5.9%
6 11114
 
5.8%
7 10982
 
5.8%
4 10967
 
5.8%
Other values (8) 20168
10.6%
Hangul
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%

y
Text

MISSING 

Distinct5907
Distinct (%)62.0%
Missing470
Missing (%)4.7%
Memory size156.2 KiB
2024-04-17T01:37:25.966521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.958447
Min length1

Characters and Unicode

Total characters190204
Distinct characters22
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

Unique2990 ?
Unique (%)31.4%

Sample

1st row179413.445756
2nd row449680.257052731
3rd row193227.848629
4th row186541.35079900000
5th row442638.239212304
ValueCountFrequency (%)
좌표정보(y 29
 
0.3%
185933.100965604 13
 
0.1%
187092.852201 11
 
0.1%
176595.034934652 9
 
0.1%
450672.39149739 8
 
0.1%
259272.14964063 8
 
0.1%
445852.638814088 8
 
0.1%
186243.80665500000 8
 
0.1%
306334.983198896 7
 
0.1%
186965.39968300000 7
 
0.1%
Other values (5897) 9422
98.9%
2024-04-17T01:37:26.285095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37824
19.9%
0 31265
16.4%
1 17291
9.1%
8 14188
 
7.5%
9 12374
 
6.5%
4 12231
 
6.4%
7 11454
 
6.0%
2 11070
 
5.8%
3 10993
 
5.8%
6 10905
 
5.7%
Other values (12) 20609
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142579
75.0%
Space Separator 37824
 
19.9%
Other Punctuation 9478
 
5.0%
Other Letter 116
 
0.1%
Dash Punctuation 104
 
0.1%
Close Punctuation 44
 
< 0.1%
Uppercase Letter 29
 
< 0.1%
Open Punctuation 29
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31265
21.9%
1 17291
12.1%
8 14188
10.0%
9 12374
 
8.7%
4 12231
 
8.6%
7 11454
 
8.0%
2 11070
 
7.8%
3 10993
 
7.7%
6 10905
 
7.6%
5 10808
 
7.6%
Other Letter
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%
Close Punctuation
ValueCountFrequency (%)
) 29
65.9%
] 15
34.1%
Space Separator
ValueCountFrequency (%)
37824
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9478
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190058
99.9%
Hangul 116
 
0.1%
Latin 30
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
37824
19.9%
0 31265
16.5%
1 17291
9.1%
8 14188
 
7.5%
9 12374
 
6.5%
4 12231
 
6.4%
7 11454
 
6.0%
2 11070
 
5.8%
3 10993
 
5.8%
6 10905
 
5.7%
Other values (6) 20463
10.8%
Hangul
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%
Latin
ValueCountFrequency (%)
Y 29
96.7%
y 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190088
99.9%
Hangul 116
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37824
19.9%
0 31265
16.4%
1 17291
9.1%
8 14188
 
7.5%
9 12374
 
6.5%
4 12231
 
6.4%
7 11454
 
6.0%
2 11070
 
5.8%
3 10993
 
5.8%
6 10905
 
5.7%
Other values (8) 20493
10.8%
Hangul
ValueCountFrequency (%)
29
25.0%
29
25.0%
29
25.0%
29
25.0%

lastmodts
Real number (ℝ)

Distinct6076
Distinct (%)60.8%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.0152374 × 1013
Minimum1.9990211 × 1013
Maximum2.0210129 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T01:37:26.414633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990211 × 1013
5-th percentile2.0030106 × 1013
Q12.0141208 × 1013
median2.0180501 × 1013
Q32.0190703 × 1013
95-th percentile2.020103 × 1013
Maximum2.0210129 × 1013
Range2.1991818 × 1011
Interquartile range (IQR)4.949504 × 1010

Descriptive statistics

Standard deviation6.1713626 × 1010
Coefficient of variation (CV)0.0030623502
Kurtosis0.40979232
Mean2.0152374 × 1013
Median Absolute Deviation (MAD)1.0370027 × 1010
Skewness-1.3912618
Sum2.0144313 × 1017
Variance3.8085716 × 1021
MonotonicityNot monotonic
2024-04-17T01:37:26.535301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19990428000000 50
 
0.5%
20040902000000 49
 
0.5%
19990920000000 49
 
0.5%
20030122000000 48
 
0.5%
20040203000000 41
 
0.4%
20030414000000 29
 
0.3%
20070531000000 29
 
0.3%
20020515000000 27
 
0.3%
20030329000000 25
 
0.2%
20040427000000 24
 
0.2%
Other values (6066) 9625
96.2%
ValueCountFrequency (%)
19990211000000 2
 
< 0.1%
19990218000000 16
0.2%
19990223000000 1
 
< 0.1%
19990225000000 3
 
< 0.1%
19990302000000 2
 
< 0.1%
19990303000000 15
0.1%
19990308000000 23
0.2%
19990309000000 5
 
0.1%
19990310000000 2
 
< 0.1%
19990315000000 1
 
< 0.1%
ValueCountFrequency (%)
20210129175832 1
< 0.1%
20210129173035 1
< 0.1%
20210129153255 1
< 0.1%
20210129132440 1
< 0.1%
20210129132401 1
< 0.1%
20210129101951 2
< 0.1%
20210129090403 2
< 0.1%
20210128171800 1
< 0.1%
20210128162804 1
< 0.1%
20210128143958 1
< 0.1%

uptaenm
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여관업
4309 
<NA>
1943 
숙박업(생활)
1217 
여인숙업
865 
숙박업 기타
622 
Other values (5)
1044 

Length

Max length8
Median length7
Mean length4.0856
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 4309
43.1%
<NA> 1943
19.4%
숙박업(생활) 1217
 
12.2%
여인숙업 865
 
8.6%
숙박업 기타 622
 
6.2%
일반호텔 510
 
5.1%
관광호텔 308
 
3.1%
업태구분명 211
 
2.1%
휴양콘도미니엄업 14
 
0.1%
uptaeNm 1
 
< 0.1%

Length

2024-04-17T01:37:26.675653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:26.796201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 4309
40.6%
na 1943
18.3%
숙박업(생활 1217
 
11.5%
여인숙업 865
 
8.1%
숙박업 622
 
5.9%
기타 622
 
5.9%
일반호텔 510
 
4.8%
관광호텔 308
 
2.9%
업태구분명 211
 
2.0%
휴양콘도미니엄업 14
 
0.1%

sitetel
Text

MISSING 

Distinct153
Distinct (%)1.6%
Missing415
Missing (%)4.2%
Memory size156.2 KiB
2024-04-17T01:37:26.967875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.954408
Min length4

Characters and Unicode

Total characters114583
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)1.3%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row051-123-1234
ValueCountFrequency (%)
051-123-1234 9353
95.5%
전화번호 46
 
0.5%
051 17
 
0.2%
061 16
 
0.2%
054 14
 
0.1%
033 11
 
0.1%
031 10
 
0.1%
041 9
 
0.1%
052 7
 
0.1%
032 7
 
0.1%
Other values (218) 307
 
3.1%
2024-04-17T01:37:27.216636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28240
24.6%
3 18922
16.5%
2 18867
16.5%
- 18803
16.4%
0 9795
 
8.5%
5 9545
 
8.3%
4 9481
 
8.3%
214
 
0.2%
6 164
 
0.1%
7 129
 
0.1%
Other values (6) 423
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95382
83.2%
Dash Punctuation 18803
 
16.4%
Space Separator 214
 
0.2%
Other Letter 184
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28240
29.6%
3 18922
19.8%
2 18867
19.8%
0 9795
 
10.3%
5 9545
 
10.0%
4 9481
 
9.9%
6 164
 
0.2%
7 129
 
0.1%
8 124
 
0.1%
9 115
 
0.1%
Other Letter
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 18803
100.0%
Space Separator
ValueCountFrequency (%)
214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114399
99.8%
Hangul 184
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28240
24.7%
3 18922
16.5%
2 18867
16.5%
- 18803
16.4%
0 9795
 
8.6%
5 9545
 
8.3%
4 9481
 
8.3%
214
 
0.2%
6 164
 
0.1%
7 129
 
0.1%
Other values (2) 239
 
0.2%
Hangul
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114399
99.8%
Hangul 184
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28240
24.7%
3 18922
16.5%
2 18867
16.5%
- 18803
16.4%
0 9795
 
8.6%
5 9545
 
8.3%
4 9481
 
8.3%
214
 
0.2%
6 164
 
0.1%
7 129
 
0.1%
Other values (2) 239
 
0.2%
Hangul
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%

stroomcnt
Text

MISSING 

Distinct64
Distinct (%)8.5%
Missing9245
Missing (%)92.5%
Memory size156.2 KiB
2024-04-17T01:37:27.402977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length2.1112583
Min length1

Characters and Unicode

Total characters1594
Distinct characters20
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

Unique31 ?
Unique (%)4.1%

Sample

1st row49
2nd row2
3rd row객실수
4th row1
5th row객실수
ValueCountFrequency (%)
객실수 356
47.2%
1 122
 
16.2%
2 99
 
13.1%
3 33
 
4.4%
5 13
 
1.7%
4 12
 
1.6%
8 8
 
1.1%
7 8
 
1.1%
20 6
 
0.8%
11 5
 
0.7%
Other values (54) 93
 
12.3%
2024-04-17T01:37:27.720975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
356
22.3%
356
22.3%
356
22.3%
1 163
10.2%
2 127
 
8.0%
3 65
 
4.1%
4 35
 
2.2%
0 30
 
1.9%
5 30
 
1.9%
9 20
 
1.3%
Other values (10) 56
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1068
67.0%
Decimal Number 517
32.4%
Lowercase Letter 8
 
0.5%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 163
31.5%
2 127
24.6%
3 65
 
12.6%
4 35
 
6.8%
0 30
 
5.8%
5 30
 
5.8%
9 20
 
3.9%
7 19
 
3.7%
8 17
 
3.3%
6 11
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
t 2
25.0%
o 2
25.0%
s 1
12.5%
r 1
12.5%
m 1
12.5%
n 1
12.5%
Other Letter
ValueCountFrequency (%)
356
33.3%
356
33.3%
356
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1068
67.0%
Common 517
32.4%
Latin 9
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 163
31.5%
2 127
24.6%
3 65
 
12.6%
4 35
 
6.8%
0 30
 
5.8%
5 30
 
5.8%
9 20
 
3.9%
7 19
 
3.7%
8 17
 
3.3%
6 11
 
2.1%
Latin
ValueCountFrequency (%)
t 2
22.2%
o 2
22.2%
s 1
11.1%
r 1
11.1%
m 1
11.1%
C 1
11.1%
n 1
11.1%
Hangul
ValueCountFrequency (%)
356
33.3%
356
33.3%
356
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1068
67.0%
ASCII 526
33.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
356
33.3%
356
33.3%
356
33.3%
ASCII
ValueCountFrequency (%)
1 163
31.0%
2 127
24.1%
3 65
 
12.4%
4 35
 
6.7%
0 30
 
5.7%
5 30
 
5.7%
9 20
 
3.8%
7 19
 
3.6%
8 17
 
3.2%
6 11
 
2.1%
Other values (7) 9
 
1.7%

bdngownsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7667 
자가
1306 
임대
 
703
건물소유구분명
 
323
bdngOwnSeNm
 
1

Length

Max length11
Median length4
Mean length3.6958
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7667
76.7%
자가 1306
 
13.1%
임대 703
 
7.0%
건물소유구분명 323
 
3.2%
bdngOwnSeNm 1
 
< 0.1%

Length

2024-04-17T01:37:27.838049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:27.923347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7667
76.7%
자가 1306
 
13.1%
임대 703
 
7.0%
건물소유구분명 323
 
3.2%
bdngownsenm 1
 
< 0.1%

bdngsrvnm
Categorical

IMBALANCE 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8832 
단독주택
 
305
건물용도명
 
297
숙박시설
 
202
기타
 
74
Other values (16)
 
290

Length

Max length15
Median length4
Mean length4.087
Min length2

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8832
88.3%
단독주택 305
 
3.0%
건물용도명 297
 
3.0%
숙박시설 202
 
2.0%
기타 74
 
0.7%
다가구용 주택(공동주택적용) 69
 
0.7%
아파트 67
 
0.7%
호텔 52
 
0.5%
근린생활시설 36
 
0.4%
다세대주택 27
 
0.3%
Other values (11) 39
 
0.4%

Length

2024-04-17T01:37:28.033354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8832
87.7%
단독주택 305
 
3.0%
건물용도명 297
 
2.9%
숙박시설 202
 
2.0%
기타 74
 
0.7%
다가구용 69
 
0.7%
주택(공동주택적용 69
 
0.7%
아파트 67
 
0.7%
호텔 52
 
0.5%
근린생활시설 36
 
0.4%
Other values (12) 66
 
0.7%

bdngjisgflrcnt
Categorical

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
2999 
0
2551 
3
816 
4
779 
2
590 
Other values (34)
2265 

Length

Max length10
Median length1
Mean length2.0427
Min length1

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row10
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 2999
30.0%
0 2551
25.5%
3 816
 
8.2%
4 779
 
7.8%
2 590
 
5.9%
5 519
 
5.2%
6 280
 
2.8%
8 272
 
2.7%
7 247
 
2.5%
건물지상층수 215
 
2.1%
Other values (29) 732
 
7.3%

Length

2024-04-17T01:37:28.131054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2999
30.0%
0 2551
25.5%
3 816
 
8.2%
4 779
 
7.8%
2 590
 
5.9%
5 519
 
5.2%
6 280
 
2.8%
8 272
 
2.7%
7 247
 
2.5%
건물지상층수 215
 
2.1%
Other values (29) 732
 
7.3%

bdngunderflrcnt
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
4640 
<NA>
3436 
1
1361 
건물지하층수
 
215
2
 
200
Other values (11)
 
148

Length

Max length10
Median length1
Mean length2.1403
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 4640
46.4%
<NA> 3436
34.4%
1 1361
 
13.6%
건물지하층수 215
 
2.1%
2 200
 
2.0%
3 52
 
0.5%
4 44
 
0.4%
5 21
 
0.2%
10 7
 
0.1%
8 6
 
0.1%
Other values (6) 18
 
0.2%

Length

2024-04-17T01:37:28.227137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 4640
46.4%
na 3436
34.4%
1 1362
 
13.6%
건물지하층수 215
 
2.1%
2 200
 
2.0%
3 52
 
0.5%
4 44
 
0.4%
5 21
 
0.2%
10 7
 
0.1%
8 6
 
0.1%
Other values (5) 17
 
0.2%

cnstyarea
Text

MISSING 

Distinct107
Distinct (%)20.7%
Missing9484
Missing (%)94.8%
Memory size156.2 KiB
2024-04-17T01:37:28.431847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.503876
Min length2

Characters and Unicode

Total characters2324
Distinct characters23
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

Unique81 ?
Unique (%)15.7%

Sample

1st row1178
2nd row건축연면적
3rd row337
4th row건축연면적
5th row319
ValueCountFrequency (%)
건축연면적 374
72.5%
75 5
 
1.0%
83 4
 
0.8%
160 3
 
0.6%
248 3
 
0.6%
61 3
 
0.6%
1400 3
 
0.6%
319 3
 
0.6%
84 3
 
0.6%
3101 2
 
0.4%
Other values (97) 113
 
21.9%
2024-04-17T01:37:28.757337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
374
16.1%
374
16.1%
374
16.1%
374
16.1%
374
16.1%
2 69
 
3.0%
1 68
 
2.9%
4 48
 
2.1%
0 46
 
2.0%
3 41
 
1.8%
Other values (13) 182
7.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1870
80.5%
Decimal Number 445
 
19.1%
Lowercase Letter 8
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69
15.5%
1 68
15.3%
4 48
10.8%
0 46
10.3%
3 41
9.2%
8 38
8.5%
7 37
8.3%
5 34
7.6%
9 33
7.4%
6 31
7.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
25.0%
c 1
12.5%
n 1
12.5%
s 1
12.5%
t 1
12.5%
r 1
12.5%
e 1
12.5%
Other Letter
ValueCountFrequency (%)
374
20.0%
374
20.0%
374
20.0%
374
20.0%
374
20.0%
Uppercase Letter
ValueCountFrequency (%)
Y 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1870
80.5%
Common 445
 
19.1%
Latin 9
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 69
15.5%
1 68
15.3%
4 48
10.8%
0 46
10.3%
3 41
9.2%
8 38
8.5%
7 37
8.3%
5 34
7.6%
9 33
7.4%
6 31
7.0%
Latin
ValueCountFrequency (%)
a 2
22.2%
c 1
11.1%
n 1
11.1%
s 1
11.1%
t 1
11.1%
Y 1
11.1%
r 1
11.1%
e 1
11.1%
Hangul
ValueCountFrequency (%)
374
20.0%
374
20.0%
374
20.0%
374
20.0%
374
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1870
80.5%
ASCII 454
 
19.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
374
20.0%
374
20.0%
374
20.0%
374
20.0%
374
20.0%
ASCII
ValueCountFrequency (%)
2 69
15.2%
1 68
15.0%
4 48
10.6%
0 46
10.1%
3 41
9.0%
8 38
8.4%
7 37
8.1%
5 34
7.5%
9 33
7.3%
6 31
6.8%
Other values (8) 9
 
2.0%

svnsr
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
기념품종류
 
390
svnSr
 
1

Length

Max length5
Median length4
Mean length4.0391
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
기념품종류 390
 
3.9%
svnSr 1
 
< 0.1%

Length

2024-04-17T01:37:28.863578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:28.943490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
기념품종류 390
 
3.9%
svnsr 1
 
< 0.1%

plninsurstdt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
기획여행보험시작일자
 
390
plnInsurStdt
 
1

Length

Max length12
Median length4
Mean length4.2348
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
기획여행보험시작일자 390
 
3.9%
plnInsurStdt 1
 
< 0.1%

Length

2024-04-17T01:37:29.056254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:29.144629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
기획여행보험시작일자 390
 
3.9%
plninsurstdt 1
 
< 0.1%

plninsurenddt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
기획여행보험종료일자
 
390
plnInsurEnddt
 
1

Length

Max length13
Median length4
Mean length4.2349
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
기획여행보험종료일자 390
 
3.9%
plnInsurEnddt 1
 
< 0.1%

Length

2024-04-17T01:37:29.536181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:29.609641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
기획여행보험종료일자 390
 
3.9%
plninsurenddt 1
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7989 
0
1633 
남성종사자수
 
216
1
 
114
2
 
20
Other values (9)
 
28

Length

Max length9
Median length4
Mean length3.506
Min length1

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7989
79.9%
0 1633
 
16.3%
남성종사자수 216
 
2.2%
1 114
 
1.1%
2 20
 
0.2%
5 7
 
0.1%
3 7
 
0.1%
4 6
 
0.1%
10 3
 
< 0.1%
manEipCnt 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Length

2024-04-17T01:37:29.704939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7989
79.9%
0 1633
 
16.3%
남성종사자수 216
 
2.2%
1 114
 
1.1%
2 20
 
0.2%
5 7
 
0.1%
3 7
 
0.1%
4 6
 
0.1%
10 3
 
< 0.1%
maneipcnt 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

playutscntdtl
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
놀이기구수내역
 
390
playUtsCntDtl
 
1

Length

Max length13
Median length4
Mean length4.1179
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
놀이기구수내역 390
 
3.9%
playUtsCntDtl 1
 
< 0.1%

Length

2024-04-17T01:37:29.802274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:29.877963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
놀이기구수내역 390
 
3.9%
playutscntdtl 1
 
< 0.1%

playfacilcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9350 
<NA>
 
576
놀이시설수
 
71
Y
 
3

Length

Max length5
Median length1
Mean length1.2012
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9350
93.5%
<NA> 576
 
5.8%
놀이시설수 71
 
0.7%
Y 3
 
< 0.1%

Length

2024-04-17T01:37:29.973972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:30.083901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9350
93.5%
na 576
 
5.8%
놀이시설수 71
 
0.7%
y 3
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9659 
<NA>
 
284
Y
 
35
 
22

Length

Max length4
Median length1
Mean length1.0852
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9659
96.6%
<NA> 284
 
2.8%
Y 35
 
0.4%
22
 
0.2%

Length

2024-04-17T01:37:30.215066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:30.312117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9659
96.6%
na 284
 
2.8%
y 35
 
0.4%
22
 
0.2%

stagear
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
무대면적
 
390
stageAr
 
1

Length

Max length7
Median length4
Mean length4.0003
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
무대면적 390
 
3.9%
stageAr 1
 
< 0.1%

Length

2024-04-17T01:37:30.401284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:30.483061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
무대면적 390
 
3.9%
stagear 1
 
< 0.1%

culwrkrsenm
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
문화사업자구분명
 
390
culWrkrSeNm
 
1

Length

Max length11
Median length4
Mean length4.1567
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
문화사업자구분명 390
 
3.9%
culWrkrSeNm 1
 
< 0.1%

Length

2024-04-17T01:37:30.573307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:30.660698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
문화사업자구분명 390
 
3.9%
culwrkrsenm 1
 
< 0.1%

culphyedcobnm
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7676 
외국인관광 도시민박업
929 
일반야영장업
 
390
관광숙박업
 
343
한옥체험업
 
225
Other values (6)
 
437

Length

Max length14
Median length4
Mean length4.8908
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row외국인관광 도시민박업
3rd row<NA>
4th row<NA>
5th row관광숙박업

Common Values

ValueCountFrequency (%)
<NA> 7676
76.8%
외국인관광 도시민박업 929
 
9.3%
일반야영장업 390
 
3.9%
관광숙박업 343
 
3.4%
한옥체험업 225
 
2.2%
문화체육업종명 179
 
1.8%
관광펜션업 144
 
1.4%
자동차야영장업 103
 
1.0%
한옥체험업(구) 7
 
0.1%
?????? ???uι?? 3
 
< 0.1%

Length

2024-04-17T01:37:30.761547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7676
70.2%
외국인관광 929
 
8.5%
도시민박업 929
 
8.5%
일반야영장업 390
 
3.6%
관광숙박업 343
 
3.1%
한옥체험업 225
 
2.1%
문화체육업종명 179
 
1.6%
관광펜션업 144
 
1.3%
자동차야영장업 103
 
0.9%
한옥체험업(구 7
 
0.1%
Other values (3) 7
 
0.1%

geicpfacilen
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
 
390
g
 
1

Length

Max length4
Median length4
Mean length3.8827
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
390
 
3.9%
g 1
 
< 0.1%

Length

2024-04-17T01:37:30.860830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:30.943640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
390
 
3.9%
g 1
 
< 0.1%

balhansilyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9675 
<NA>
 
284
 
22
Y
 
19

Length

Max length4
Median length1
Mean length1.0852
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9675
96.8%
<NA> 284
 
2.8%
22
 
0.2%
Y 19
 
0.2%

Length

2024-04-17T01:37:31.032686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:31.129103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9675
96.8%
na 284
 
2.8%
22
 
0.2%
y 19
 
0.2%

bcfacilen
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
 
390
b
 
1

Length

Max length4
Median length4
Mean length3.8827
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
390
 
3.9%
b 1
 
< 0.1%

Length

2024-04-17T01:37:31.237803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:31.346227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
390
 
3.9%
b 1
 
< 0.1%

insurorgnm
Text

MISSING 

Distinct89
Distinct (%)13.3%
Missing9329
Missing (%)93.3%
Memory size156.2 KiB
2024-04-17T01:37:31.500211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length5
Mean length5.8450075
Min length2

Characters and Unicode

Total characters3922
Distinct characters106
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)7.2%

Sample

1st row삼성화재
2nd row1실(4명)
3rd row보험기관명
4th row3실(10명)
5th row2실(4명)
ValueCountFrequency (%)
보험기관명 372
53.4%
1실(4명 44
 
6.3%
1실(2명 22
 
3.2%
1실(3명 16
 
2.3%
2실(6명 16
 
2.3%
2실(5명 13
 
1.9%
2실(4명 12
 
1.7%
2실(8명 11
 
1.6%
농협손해보험주식회사 9
 
1.3%
1실(6명 8
 
1.1%
Other values (86) 174
25.0%
2024-04-17T01:37:31.794632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
590
15.0%
418
10.7%
413
10.5%
372
9.5%
372
9.5%
240
 
6.1%
) 221
 
5.6%
( 220
 
5.6%
1 153
 
3.9%
2 132
 
3.4%
Other values (96) 791
20.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2855
72.8%
Decimal Number 530
 
13.5%
Close Punctuation 221
 
5.6%
Open Punctuation 220
 
5.6%
Other Punctuation 47
 
1.2%
Space Separator 26
 
0.7%
Uppercase Letter 12
 
0.3%
Lowercase Letter 8
 
0.2%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
590
20.7%
418
14.6%
413
14.5%
372
13.0%
372
13.0%
240
8.4%
36
 
1.3%
30
 
1.1%
26
 
0.9%
25
 
0.9%
Other values (66) 333
11.7%
Decimal Number
ValueCountFrequency (%)
1 153
28.9%
2 132
24.9%
4 75
14.2%
3 43
 
8.1%
6 42
 
7.9%
5 33
 
6.2%
8 23
 
4.3%
0 16
 
3.0%
7 9
 
1.7%
9 4
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
r 2
25.0%
s 1
12.5%
i 1
12.5%
n 1
12.5%
u 1
12.5%
g 1
12.5%
m 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
B 5
41.7%
K 4
33.3%
O 1
 
8.3%
N 1
 
8.3%
D 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 23
48.9%
12
25.5%
: 11
23.4%
, 1
 
2.1%
Close Punctuation
ValueCountFrequency (%)
) 221
100.0%
Open Punctuation
ValueCountFrequency (%)
( 220
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2855
72.8%
Common 1047
 
26.7%
Latin 20
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
590
20.7%
418
14.6%
413
14.5%
372
13.0%
372
13.0%
240
8.4%
36
 
1.3%
30
 
1.1%
26
 
0.9%
25
 
0.9%
Other values (66) 333
11.7%
Common
ValueCountFrequency (%)
) 221
21.1%
( 220
21.0%
1 153
14.6%
2 132
12.6%
4 75
 
7.2%
3 43
 
4.1%
6 42
 
4.0%
5 33
 
3.2%
26
 
2.5%
8 23
 
2.2%
Other values (8) 79
 
7.5%
Latin
ValueCountFrequency (%)
B 5
25.0%
K 4
20.0%
r 2
 
10.0%
s 1
 
5.0%
i 1
 
5.0%
n 1
 
5.0%
u 1
 
5.0%
O 1
 
5.0%
g 1
 
5.0%
N 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2855
72.8%
ASCII 1055
 
26.9%
None 12
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
590
20.7%
418
14.6%
413
14.5%
372
13.0%
372
13.0%
240
8.4%
36
 
1.3%
30
 
1.1%
26
 
0.9%
25
 
0.9%
Other values (66) 333
11.7%
ASCII
ValueCountFrequency (%)
) 221
20.9%
( 220
20.9%
1 153
14.5%
2 132
12.5%
4 75
 
7.1%
3 43
 
4.1%
6 42
 
4.0%
5 33
 
3.1%
26
 
2.5%
8 23
 
2.2%
Other values (19) 87
 
8.2%
None
ValueCountFrequency (%)
12
100.0%

insurstdt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
보험시작일자
 
390
insurStdt
 
1

Length

Max length9
Median length4
Mean length4.0785
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
보험시작일자 390
 
3.9%
insurStdt 1
 
< 0.1%

Length

2024-04-17T01:37:31.901335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:31.978521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
보험시작일자 390
 
3.9%
insurstdt 1
 
< 0.1%

insurenddt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
보험종료일자
 
390
insurEnddt
 
1

Length

Max length10
Median length4
Mean length4.0786
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
보험종료일자 390
 
3.9%
insurEnddt 1
 
< 0.1%

Length

2024-04-17T01:37:32.064793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:32.153644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
보험종료일자 390
 
3.9%
insurenddt 1
 
< 0.1%

afc
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
부대시설내역
 
390
afc
 
1

Length

Max length6
Median length4
Mean length4.0779
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
부대시설내역 390
 
3.9%
afc 1
 
< 0.1%

Length

2024-04-17T01:37:32.239684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:32.321031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
부대시설내역 390
 
3.9%
afc 1
 
< 0.1%

usejisgendflr
Categorical

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4019 
0
1734 
3
760 
4
720 
2
586 
Other values (32)
2181 

Length

Max length10
Median length1
Mean length2.3708
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row9
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4019
40.2%
0 1734
17.3%
3 760
 
7.6%
4 720
 
7.2%
2 586
 
5.9%
5 427
 
4.3%
6 350
 
3.5%
사용끝지상층 255
 
2.5%
7 230
 
2.3%
8 222
 
2.2%
Other values (27) 697
 
7.0%

Length

2024-04-17T01:37:32.415688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4019
40.2%
0 1734
17.3%
3 760
 
7.6%
4 720
 
7.2%
2 586
 
5.9%
5 427
 
4.3%
6 350
 
3.5%
사용끝지상층 255
 
2.5%
7 230
 
2.3%
8 222
 
2.2%
Other values (27) 697
 
7.0%

useunderendflr
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5304 
0
4035 
사용끝지하층
 
364
1
 
233
2
 
37
Other values (7)
 
27

Length

Max length10
Median length4
Mean length2.7744
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5304
53.0%
0 4035
40.4%
사용끝지하층 364
 
3.6%
1 233
 
2.3%
2 37
 
0.4%
3 12
 
0.1%
4 5
 
0.1%
6 4
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-17T01:37:32.513427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5304
53.0%
0 4035
40.4%
사용끝지하층 364
 
3.6%
1 233
 
2.3%
2 37
 
0.4%
3 12
 
0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

usejisgstflr
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3318 
1
2194 
0
2081 
2
977 
3
496 
Other values (14)
934 

Length

Max length10
Median length1
Mean length2.1492
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row5
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3318
33.2%
1 2194
21.9%
0 2081
20.8%
2 977
 
9.8%
3 496
 
5.0%
4 290
 
2.9%
사용시작지상층 246
 
2.5%
5 155
 
1.6%
6 75
 
0.8%
7 58
 
0.6%
Other values (9) 110
 
1.1%

Length

2024-04-17T01:37:32.609933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3318
33.2%
1 2194
21.9%
0 2081
20.8%
2 977
 
9.8%
3 496
 
5.0%
4 290
 
2.9%
사용시작지상층 246
 
2.5%
5 155
 
1.6%
6 75
 
0.8%
7 58
 
0.6%
Other values (9) 110
 
1.1%

useunderstflr
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
4758 
<NA>
4546 
사용시작지하층
 
357
1
 
317
4
 
6
Other values (5)
 
16

Length

Max length10
Median length1
Mean length2.5789
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 4758
47.6%
<NA> 4546
45.5%
사용시작지하층 357
 
3.6%
1 317
 
3.2%
4 6
 
0.1%
3 5
 
0.1%
2 5
 
0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
useUnderSt 1
 
< 0.1%

Length

2024-04-17T01:37:32.704198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:32.795899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4758
47.6%
na 4546
45.5%
사용시작지하층 357
 
3.6%
1 317
 
3.2%
4 6
 
0.1%
3 5
 
< 0.1%
2 5
 
< 0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
useunderst 1
 
< 0.1%

shpinfo
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
선박제원
 
390
shpInfo
 
1

Length

Max length7
Median length4
Mean length4.0003
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
선박제원 390
 
3.9%
shpInfo 1
 
< 0.1%

Length

2024-04-17T01:37:32.930504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:33.025908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
선박제원 390
 
3.9%
shpinfo 1
 
< 0.1%

shpcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
선박척수
 
390
shpCnt
 
1

Length

Max length6
Median length4
Mean length4.0002
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
선박척수 390
 
3.9%
shpCnt 1
 
< 0.1%

Length

2024-04-17T01:37:33.125190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:33.213279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
선박척수 390
 
3.9%
shpcnt 1
 
< 0.1%

shptottons
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9606 
선박총톤수
 
390
1
 
3
shpTotTons
 
1

Length

Max length10
Median length4
Mean length4.0387
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9606
96.1%
선박총톤수 390
 
3.9%
1 3
 
< 0.1%
shpTotTons 1
 
< 0.1%

Length

2024-04-17T01:37:33.299345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:33.376932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9606
96.1%
선박총톤수 390
 
3.9%
1 3
 
< 0.1%
shptottons 1
 
< 0.1%

washmccnt
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5356 
<NA>
4428 
세탁기수
 
215
washmcCnt
 
1

Length

Max length9
Median length1
Mean length2.3937
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 5356
53.6%
<NA> 4428
44.3%
세탁기수 215
 
2.1%
washmcCnt 1
 
< 0.1%

Length

2024-04-17T01:37:33.471820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:33.591423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5356
53.6%
na 4428
44.3%
세탁기수 215
 
2.1%
washmccnt 1
 
< 0.1%

facilscp
Text

MISSING 

Distinct601
Distinct (%)32.0%
Missing8119
Missing (%)81.2%
Memory size156.2 KiB
2024-04-17T01:37:33.895596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length2.9744817
Min length1

Characters and Unicode

Total characters5595
Distinct characters21
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

Unique319 ?
Unique (%)17.0%

Sample

1st row48
2nd row1078
3rd row83
4th row4524
5th row47
ValueCountFrequency (%)
시설규모 230
 
12.2%
85 35
 
1.9%
83 23
 
1.2%
60 23
 
1.2%
46 18
 
1.0%
53 18
 
1.0%
59 17
 
0.9%
63 17
 
0.9%
61 17
 
0.9%
49 17
 
0.9%
Other values (591) 1466
77.9%
2024-04-17T01:37:34.335788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 743
13.3%
4 511
9.1%
5 486
8.7%
2 478
8.5%
3 450
8.0%
6 448
8.0%
9 425
7.6%
8 389
7.0%
0 381
6.8%
7 356
 
6.4%
Other values (11) 928
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4667
83.4%
Other Letter 920
 
16.4%
Lowercase Letter 7
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 743
15.9%
4 511
10.9%
5 486
10.4%
2 478
10.2%
3 450
9.6%
6 448
9.6%
9 425
9.1%
8 389
8.3%
0 381
8.2%
7 356
7.6%
Lowercase Letter
ValueCountFrequency (%)
c 2
28.6%
f 1
14.3%
a 1
14.3%
i 1
14.3%
l 1
14.3%
p 1
14.3%
Other Letter
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4667
83.4%
Hangul 920
 
16.4%
Latin 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 743
15.9%
4 511
10.9%
5 486
10.4%
2 478
10.2%
3 450
9.6%
6 448
9.6%
9 425
9.1%
8 389
8.3%
0 381
8.2%
7 356
7.6%
Latin
ValueCountFrequency (%)
c 2
25.0%
f 1
12.5%
a 1
12.5%
i 1
12.5%
l 1
12.5%
S 1
12.5%
p 1
12.5%
Hangul
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4675
83.6%
Hangul 920
 
16.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 743
15.9%
4 511
10.9%
5 486
10.4%
2 478
10.2%
3 450
9.6%
6 448
9.6%
9 425
9.1%
8 389
8.3%
0 381
8.1%
7 356
7.6%
Other values (7) 8
 
0.2%
Hangul
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%

facilar
Text

MISSING 

Distinct1155
Distinct (%)61.4%
Missing8119
Missing (%)81.2%
Memory size156.2 KiB
2024-04-17T01:37:34.615857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.8442318
Min length1

Characters and Unicode

Total characters9112
Distinct characters22
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

Unique844 ?
Unique (%)44.9%

Sample

1st row48.26
2nd row1077.84
3rd row83.14
4th row4523.56
5th row47.11
ValueCountFrequency (%)
시설면적 230
 
12.2%
45.5 6
 
0.3%
36.65 6
 
0.3%
598.73 6
 
0.3%
59.5 6
 
0.3%
1544.48 6
 
0.3%
74.74 5
 
0.3%
70 5
 
0.3%
64.79 5
 
0.3%
45 5
 
0.3%
Other values (1145) 1601
85.1%
2024-04-17T01:37:35.040550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1245
13.7%
1 921
10.1%
4 805
8.8%
5 751
8.2%
2 742
8.1%
6 707
7.8%
8 679
7.5%
9 662
7.3%
3 650
7.1%
7 558
6.1%
Other values (12) 1392
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6940
76.2%
Other Punctuation 1245
 
13.7%
Other Letter 920
 
10.1%
Lowercase Letter 6
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 921
13.3%
4 805
11.6%
5 751
10.8%
2 742
10.7%
6 707
10.2%
8 679
9.8%
9 662
9.5%
3 650
9.4%
7 558
8.0%
0 465
6.7%
Lowercase Letter
ValueCountFrequency (%)
f 1
16.7%
a 1
16.7%
c 1
16.7%
i 1
16.7%
l 1
16.7%
r 1
16.7%
Other Letter
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1245
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8185
89.8%
Hangul 920
 
10.1%
Latin 7
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1245
15.2%
1 921
11.3%
4 805
9.8%
5 751
9.2%
2 742
9.1%
6 707
8.6%
8 679
8.3%
9 662
8.1%
3 650
7.9%
7 558
6.8%
Latin
ValueCountFrequency (%)
f 1
14.3%
a 1
14.3%
c 1
14.3%
i 1
14.3%
l 1
14.3%
A 1
14.3%
r 1
14.3%
Hangul
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8192
89.9%
Hangul 920
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1245
15.2%
1 921
11.2%
4 805
9.8%
5 751
9.2%
2 742
9.1%
6 707
8.6%
8 679
8.3%
9 662
8.1%
3 650
7.9%
7 558
6.8%
Other values (8) 472
 
5.8%
Hangul
ValueCountFrequency (%)
230
25.0%
230
25.0%
230
25.0%
230
25.0%

infoben
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
 
390
i
 
1

Length

Max length4
Median length4
Mean length3.8827
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
390
 
3.9%
i 1
 
< 0.1%

Length

2024-04-17T01:37:35.160853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:35.246426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
390
 
3.9%
i 1
 
< 0.1%

yangsilcnt
Text

MISSING 

Distinct206
Distinct (%)2.7%
Missing2400
Missing (%)24.0%
Memory size156.2 KiB
2024-04-17T01:37:35.412932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length1.7338158
Min length1

Characters and Unicode

Total characters13177
Distinct characters22
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

Unique67 ?
Unique (%)0.9%

Sample

1st row10
2nd row51
3rd row20
4th row16
5th row35
ValueCountFrequency (%)
0 985
 
13.0%
10 398
 
5.2%
18 303
 
4.0%
12 286
 
3.8%
14 274
 
3.6%
15 254
 
3.3%
8 243
 
3.2%
양실수 215
 
2.8%
13 215
 
2.8%
19 206
 
2.7%
Other values (196) 4221
55.5%
2024-04-17T01:37:35.731294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3114
23.6%
0 1835
13.9%
2 1817
13.8%
3 1293
9.8%
4 1002
 
7.6%
5 812
 
6.2%
8 795
 
6.0%
6 628
 
4.8%
7 620
 
4.7%
9 606
 
4.6%
Other values (12) 655
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12522
95.0%
Other Letter 645
 
4.9%
Lowercase Letter 9
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3114
24.9%
0 1835
14.7%
2 1817
14.5%
3 1293
10.3%
4 1002
 
8.0%
5 812
 
6.5%
8 795
 
6.3%
6 628
 
5.0%
7 620
 
5.0%
9 606
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
n 2
22.2%
y 1
11.1%
a 1
11.1%
g 1
11.1%
s 1
11.1%
i 1
11.1%
l 1
11.1%
t 1
11.1%
Other Letter
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12522
95.0%
Hangul 645
 
4.9%
Latin 10
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3114
24.9%
0 1835
14.7%
2 1817
14.5%
3 1293
10.3%
4 1002
 
8.0%
5 812
 
6.5%
8 795
 
6.3%
6 628
 
5.0%
7 620
 
5.0%
9 606
 
4.8%
Latin
ValueCountFrequency (%)
n 2
20.0%
y 1
10.0%
a 1
10.0%
g 1
10.0%
s 1
10.0%
i 1
10.0%
l 1
10.0%
C 1
10.0%
t 1
10.0%
Hangul
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12532
95.1%
Hangul 645
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3114
24.8%
0 1835
14.6%
2 1817
14.5%
3 1293
10.3%
4 1002
 
8.0%
5 812
 
6.5%
8 795
 
6.3%
6 628
 
5.0%
7 620
 
4.9%
9 606
 
4.8%
Other values (9) 10
 
0.1%
Hangul
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%

wmeipcnt
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7989 
0
1667 
여성종사자수
 
216
1
 
79
2
 
21
Other values (7)
 
28

Length

Max length8
Median length4
Mean length3.5057
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7989
79.9%
0 1667
 
16.7%
여성종사자수 216
 
2.2%
1 79
 
0.8%
2 21
 
0.2%
3 11
 
0.1%
7 5
 
0.1%
4 5
 
0.1%
5 3
 
< 0.1%
15 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-17T01:37:35.874654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7989
79.9%
0 1667
 
16.7%
여성종사자수 216
 
2.2%
1 79
 
0.8%
2 21
 
0.2%
3 11
 
0.1%
7 5
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
15 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

engstntrnmnm
Text

MISSING 

Distinct380
Distinct (%)44.8%
Missing9151
Missing (%)91.5%
Memory size156.2 KiB
2024-04-17T01:37:36.198033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length49
Mean length10.025913
Min length2

Characters and Unicode

Total characters8512
Distinct characters80
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)35.1%

Sample

1st rowSamhwa
2nd rowLily
3rd row영문상호명
4th rowYEONGDOARE
5th rowHarry tage
ValueCountFrequency (%)
영문상호명 350
24.2%
house 148
 
10.2%
guesthouse 57
 
3.9%
hotel 22
 
1.5%
guest 20
 
1.4%
stay 16
 
1.1%
pension 11
 
0.8%
hanok 11
 
0.8%
ocean 9
 
0.6%
seoul 9
 
0.6%
Other values (494) 796
54.9%
2024-04-17T01:37:36.654818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
600
 
7.0%
e 572
 
6.7%
o 490
 
5.8%
s 413
 
4.9%
u 355
 
4.2%
350
 
4.1%
350
 
4.1%
350
 
4.1%
350
 
4.1%
350
 
4.1%
Other values (70) 4332
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3917
46.0%
Uppercase Letter 2081
24.4%
Other Letter 1751
20.6%
Space Separator 600
 
7.0%
Decimal Number 87
 
1.0%
Other Punctuation 55
 
0.6%
Dash Punctuation 10
 
0.1%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 572
14.6%
o 490
12.5%
s 413
10.5%
u 355
9.1%
a 310
7.9%
n 295
 
7.5%
t 197
 
5.0%
h 194
 
5.0%
i 187
 
4.8%
r 157
 
4.0%
Other values (15) 747
19.1%
Uppercase Letter
ValueCountFrequency (%)
H 235
 
11.3%
S 190
 
9.1%
O 189
 
9.1%
E 180
 
8.6%
A 134
 
6.4%
N 115
 
5.5%
U 113
 
5.4%
G 104
 
5.0%
T 93
 
4.5%
M 84
 
4.0%
Other values (15) 644
30.9%
Decimal Number
ValueCountFrequency (%)
2 21
24.1%
3 16
18.4%
1 16
18.4%
0 8
 
9.2%
4 7
 
8.0%
5 6
 
6.9%
9 5
 
5.7%
6 4
 
4.6%
7 3
 
3.4%
8 1
 
1.1%
Other Letter
ValueCountFrequency (%)
350
20.0%
350
20.0%
350
20.0%
350
20.0%
350
20.0%
1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
' 33
60.0%
. 11
 
20.0%
& 5
 
9.1%
, 4
 
7.3%
: 2
 
3.6%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6000
70.5%
Hangul 1750
 
20.6%
Common 761
 
8.9%
Han 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 572
 
9.5%
o 490
 
8.2%
s 413
 
6.9%
u 355
 
5.9%
a 310
 
5.2%
n 295
 
4.9%
H 235
 
3.9%
t 197
 
3.3%
h 194
 
3.2%
S 190
 
3.2%
Other values (42) 2749
45.8%
Common
ValueCountFrequency (%)
600
78.8%
' 33
 
4.3%
2 21
 
2.8%
3 16
 
2.1%
1 16
 
2.1%
. 11
 
1.4%
- 10
 
1.3%
0 8
 
1.1%
4 7
 
0.9%
5 6
 
0.8%
Other values (12) 33
 
4.3%
Hangul
ValueCountFrequency (%)
350
20.0%
350
20.0%
350
20.0%
350
20.0%
350
20.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6758
79.4%
Hangul 1750
 
20.6%
Number Forms 2
 
< 0.1%
Punctuation 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600
 
8.9%
e 572
 
8.5%
o 490
 
7.3%
s 413
 
6.1%
u 355
 
5.3%
a 310
 
4.6%
n 295
 
4.4%
H 235
 
3.5%
t 197
 
2.9%
h 194
 
2.9%
Other values (61) 3097
45.8%
Hangul
ValueCountFrequency (%)
350
20.0%
350
20.0%
350
20.0%
350
20.0%
350
20.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
CJK
ValueCountFrequency (%)
1
100.0%

engstntrnmaddr
Text

MISSING 

Distinct72
Distinct (%)8.5%
Missing9154
Missing (%)91.5%
Memory size156.2 KiB
2024-04-17T01:37:36.855215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length55
Mean length22.514184
Min length5

Characters and Unicode

Total characters19047
Distinct characters54
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)4.1%

Sample

1st rowThe tourist accommodation business(A hostel)
2nd rowUrban Home Visit System for Foreign Tourist
3rd rowURBAN HOMESTAY FOR FOREIGN TOURISTS
4th row영문상호주소
5th rowGuesthouse for Foreign Tourists
ValueCountFrequency (%)
영문상호주소 351
13.2%
for 330
12.4%
foreign 311
11.7%
urban 304
11.4%
tourists 302
11.3%
homestay 237
8.9%
business 110
 
4.1%
tourist 65
 
2.4%
private 45
 
1.7%
room 45
 
1.7%
Other values (64) 568
21.3%
2024-04-17T01:37:37.189956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1822
 
9.6%
O 1003
 
5.3%
R 957
 
5.0%
T 886
 
4.7%
o 780
 
4.1%
s 768
 
4.0%
S 759
 
4.0%
r 756
 
4.0%
e 706
 
3.7%
i 666
 
3.5%
Other values (44) 9944
52.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8210
43.1%
Lowercase Letter 6847
35.9%
Other Letter 2106
 
11.1%
Space Separator 1822
 
9.6%
Dash Punctuation 24
 
0.1%
Open Punctuation 19
 
0.1%
Close Punctuation 19
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1003
12.2%
R 957
11.7%
T 886
10.8%
S 759
9.2%
U 571
 
7.0%
F 529
 
6.4%
I 518
 
6.3%
E 510
 
6.2%
A 501
 
6.1%
N 496
 
6.0%
Other values (12) 1480
18.0%
Lowercase Letter
ValueCountFrequency (%)
o 780
11.4%
s 768
11.2%
r 756
11.0%
e 706
10.3%
i 666
9.7%
n 623
9.1%
t 493
7.2%
u 387
 
5.7%
a 366
 
5.3%
m 188
 
2.7%
Other values (12) 1114
16.3%
Other Letter
ValueCountFrequency (%)
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%
Space Separator
ValueCountFrequency (%)
1822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15057
79.1%
Hangul 2106
 
11.1%
Common 1884
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1003
 
6.7%
R 957
 
6.4%
T 886
 
5.9%
o 780
 
5.2%
s 768
 
5.1%
S 759
 
5.0%
r 756
 
5.0%
e 706
 
4.7%
i 666
 
4.4%
n 623
 
4.1%
Other values (34) 7153
47.5%
Hangul
ValueCountFrequency (%)
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%
Common
ValueCountFrequency (%)
1822
96.7%
- 24
 
1.3%
( 19
 
1.0%
) 19
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16941
88.9%
Hangul 2106
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1822
 
10.8%
O 1003
 
5.9%
R 957
 
5.6%
T 886
 
5.2%
o 780
 
4.6%
s 768
 
4.5%
S 759
 
4.5%
r 756
 
4.5%
e 706
 
4.2%
i 666
 
3.9%
Other values (38) 7838
46.3%
Hangul
ValueCountFrequency (%)
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%
351
16.7%

yoksilcnt
Categorical

IMBALANCE 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6018 
<NA>
3616 
욕실수
 
215
15
 
12
10
 
11
Other values (29)
 
128

Length

Max length9
Median length1
Mean length2.1415
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 6018
60.2%
<NA> 3616
36.2%
욕실수 215
 
2.1%
15 12
 
0.1%
10 11
 
0.1%
9 10
 
0.1%
12 10
 
0.1%
8 9
 
0.1%
14 9
 
0.1%
18 9
 
0.1%
Other values (24) 81
 
0.8%

Length

2024-04-17T01:37:37.313999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 6018
60.2%
na 3616
36.2%
욕실수 215
 
2.1%
15 12
 
0.1%
10 11
 
0.1%
9 10
 
0.1%
12 10
 
0.1%
18 9
 
0.1%
14 9
 
0.1%
8 9
 
0.1%
Other values (24) 81
 
0.8%

sntuptaenm
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여관업
4309 
<NA>
1944 
숙박업(생활)
1216 
여인숙업
865 
숙박업 기타
622 
Other values (5)
1044 

Length

Max length10
Median length8
Mean length4.0856
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 4309
43.1%
<NA> 1944
19.4%
숙박업(생활) 1216
 
12.2%
여인숙업 865
 
8.6%
숙박업 기타 622
 
6.2%
일반호텔 510
 
5.1%
관광호텔 308
 
3.1%
위생업태명 211
 
2.1%
휴양콘도미니엄업 14
 
0.1%
sntUptaeNm 1
 
< 0.1%

Length

2024-04-17T01:37:37.422282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:37.522378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 4309
40.6%
na 1944
18.3%
숙박업(생활 1216
 
11.4%
여인숙업 865
 
8.1%
숙박업 622
 
5.9%
기타 622
 
5.9%
일반호텔 510
 
4.8%
관광호텔 308
 
2.9%
위생업태명 211
 
2.0%
휴양콘도미니엄업 14
 
0.1%

dispenen
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
 
390
d
 
1

Length

Max length4
Median length4
Mean length3.8827
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
390
 
3.9%
d 1
 
< 0.1%

Length

2024-04-17T01:37:37.641539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:37.730443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
390
 
3.9%
d 1
 
< 0.1%

capt
Text

MISSING 

Distinct121
Distinct (%)14.7%
Missing9177
Missing (%)91.8%
Memory size156.2 KiB
2024-04-17T01:37:37.878050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.2976914
Min length1

Characters and Unicode

Total characters5183
Distinct characters17
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)8.3%

Sample

1st row30000000
2nd row5860000000
3rd row자본금
4th row자본금
5th row자본금
ValueCountFrequency (%)
자본금 326
39.6%
10000000 45
 
5.5%
50000000 45
 
5.5%
30000000 38
 
4.6%
100000000 37
 
4.5%
20000000 36
 
4.4%
5000000 22
 
2.7%
200000000 20
 
2.4%
300000000 12
 
1.5%
15000000 12
 
1.5%
Other values (111) 230
27.9%
2024-04-17T01:37:38.140662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3448
66.5%
326
 
6.3%
326
 
6.3%
326
 
6.3%
1 174
 
3.4%
5 170
 
3.3%
2 138
 
2.7%
3 104
 
2.0%
6 41
 
0.8%
8 37
 
0.7%
Other values (7) 93
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4201
81.1%
Other Letter 978
 
18.9%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3448
82.1%
1 174
 
4.1%
5 170
 
4.0%
2 138
 
3.3%
3 104
 
2.5%
6 41
 
1.0%
8 37
 
0.9%
4 34
 
0.8%
9 30
 
0.7%
7 25
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
c 1
25.0%
a 1
25.0%
p 1
25.0%
t 1
25.0%
Other Letter
ValueCountFrequency (%)
326
33.3%
326
33.3%
326
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4201
81.1%
Hangul 978
 
18.9%
Latin 4
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3448
82.1%
1 174
 
4.1%
5 170
 
4.0%
2 138
 
3.3%
3 104
 
2.5%
6 41
 
1.0%
8 37
 
0.9%
4 34
 
0.8%
9 30
 
0.7%
7 25
 
0.6%
Latin
ValueCountFrequency (%)
c 1
25.0%
a 1
25.0%
p 1
25.0%
t 1
25.0%
Hangul
ValueCountFrequency (%)
326
33.3%
326
33.3%
326
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4205
81.1%
Hangul 978
 
18.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3448
82.0%
1 174
 
4.1%
5 170
 
4.0%
2 138
 
3.3%
3 104
 
2.5%
6 41
 
1.0%
8 37
 
0.9%
4 34
 
0.8%
9 30
 
0.7%
7 25
 
0.6%
Other values (4) 4
 
0.1%
Hangul
ValueCountFrequency (%)
326
33.3%
326
33.3%
326
33.3%

mnfactreartclcn
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
제작취급품목내용
 
390
mnfacTreArtclCn
 
1

Length

Max length15
Median length4
Mean length4.1571
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
제작취급품목내용 390
 
3.9%
mnfacTreArtclCn 1
 
< 0.1%

Length

2024-04-17T01:37:38.255416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:38.611812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
제작취급품목내용 390
 
3.9%
mnfactreartclcn 1
 
< 0.1%

cndpermstymd
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9598 
조건부허가시작일자
 
389
20181221
 
2
20180202
 
2
20201120
 
2
Other values (7)
 
7

Length

Max length12
Median length4
Mean length4.2001
Min length4

Unique

Unique7 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9598
96.0%
조건부허가시작일자 389
 
3.9%
20181221 2
 
< 0.1%
20180202 2
 
< 0.1%
20201120 2
 
< 0.1%
20200731 1
 
< 0.1%
20200818 1
 
< 0.1%
20191121 1
 
< 0.1%
cndPermStYmd 1
 
< 0.1%
20190628 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-17T01:37:38.696802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9598
96.0%
조건부허가시작일자 389
 
3.9%
20181221 2
 
< 0.1%
20180202 2
 
< 0.1%
20201120 2
 
< 0.1%
20200731 1
 
< 0.1%
20200818 1
 
< 0.1%
20191121 1
 
< 0.1%
cndpermstymd 1
 
< 0.1%
20190628 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

cndpermntwhy
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9605 
조건부허가신고사유
 
389
임대차 계약기간(2020.06.30~2022.06.30) 동안 운영
 
1
기간 내에 「관광진흥법」에 따라 '관광펜션업'지정받아야 하며, 관광펜션업으로 지정 받은 후 영업 가능
 
1
cndPermNtWhy
 
1
Other values (3)
 
3

Length

Max length56
Median length4
Mean length4.2072
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9605
96.0%
조건부허가신고사유 389
 
3.9%
임대차 계약기간(2020.06.30~2022.06.30) 동안 운영 1
 
< 0.1%
기간 내에 「관광진흥법」에 따라 '관광펜션업'지정받아야 하며, 관광펜션업으로 지정 받은 후 영업 가능 1
 
< 0.1%
cndPermNtWhy 1
 
< 0.1%
가평군 뮤직빌리지 사무 위.수탁 협약 (숙박업) 1
 
< 0.1%
임시사용승인기간 내 영업가능함 1
 
< 0.1%
위탁기간 1
 
< 0.1%

Length

2024-04-17T01:37:38.793166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:38.877418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9605
95.8%
조건부허가신고사유 389
 
3.9%
1
 
< 0.1%
영업가능함 1
 
< 0.1%
1
 
< 0.1%
임시사용승인기간 1
 
< 0.1%
숙박업 1
 
< 0.1%
협약 1
 
< 0.1%
위.수탁 1
 
< 0.1%
사무 1
 
< 0.1%
Other values (19) 19
 
0.2%

cndpermendymd
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9598 
조건부허가종료일자
 
389
20201220
 
2
20190202
 
2
20221031
 
2
Other values (7)
 
7

Length

Max length13
Median length4
Mean length4.2002
Min length4

Unique

Unique7 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9598
96.0%
조건부허가종료일자 389
 
3.9%
20201220 2
 
< 0.1%
20190202 2
 
< 0.1%
20221031 2
 
< 0.1%
20220807 1
 
< 0.1%
20220630 1
 
< 0.1%
20200120 1
 
< 0.1%
cndPermEndYmd 1
 
< 0.1%
20211230 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-17T01:37:38.986826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9598
96.0%
조건부허가종료일자 389
 
3.9%
20201220 2
 
< 0.1%
20190202 2
 
< 0.1%
20221031 2
 
< 0.1%
20220807 1
 
< 0.1%
20220630 1
 
< 0.1%
20200120 1
 
< 0.1%
cndpermendymd 1
 
< 0.1%
20211230 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

chaircnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5918 
0
3863 
좌석수
 
215
12
 
1
6
 
1
Other values (2)
 
2

Length

Max length8
Median length4
Mean length2.8192
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5918
59.2%
0 3863
38.6%
좌석수 215
 
2.1%
12 1
 
< 0.1%
6 1
 
< 0.1%
chairCnt 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-04-17T01:37:39.091723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:39.189606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5918
59.2%
0 3863
38.6%
좌석수 215
 
2.1%
12 1
 
< 0.1%
6 1
 
< 0.1%
chaircnt 1
 
< 0.1%
2 1
 
< 0.1%

nearenvnm
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9096 
기타
 
351
주변환경명
 
329
주택가주변
 
140
학교정화(상대)
 
46
Other values (4)
 
38

Length

Max length9
Median length4
Mean length4.0011
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9096
91.0%
기타 351
 
3.5%
주변환경명 329
 
3.3%
주택가주변 140
 
1.4%
학교정화(상대) 46
 
0.5%
아파트지역 31
 
0.3%
유흥업소밀집지역 4
 
< 0.1%
학교정화(절대) 2
 
< 0.1%
nearEnvNm 1
 
< 0.1%

Length

2024-04-17T01:37:39.294629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:39.399461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9096
91.0%
기타 351
 
3.5%
주변환경명 329
 
3.3%
주택가주변 140
 
1.4%
학교정화(상대 46
 
0.5%
아파트지역 31
 
0.3%
유흥업소밀집지역 4
 
< 0.1%
학교정화(절대 2
 
< 0.1%
nearenvnm 1
 
< 0.1%

jisgnumlay
Categorical

IMBALANCE 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8947 
지상층수
 
296
1
 
216
2
 
183
3
 
101
Other values (25)
 
257

Length

Max length10
Median length4
Mean length3.7816
Min length1

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row12

Common Values

ValueCountFrequency (%)
<NA> 8947
89.5%
지상층수 296
 
3.0%
1 216
 
2.2%
2 183
 
1.8%
3 101
 
1.0%
4 73
 
0.7%
5 35
 
0.4%
10 26
 
0.3%
6 21
 
0.2%
9 19
 
0.2%
Other values (20) 83
 
0.8%

Length

2024-04-17T01:37:39.508953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8947
89.5%
지상층수 296
 
3.0%
1 216
 
2.2%
2 183
 
1.8%
3 101
 
1.0%
4 73
 
0.7%
5 35
 
0.4%
10 26
 
0.3%
6 21
 
0.2%
9 19
 
0.2%
Other values (20) 83
 
0.8%

regnsenm
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8699 
일반주거지역
 
330
지역구분명
 
278
관리지역
 
248
자연녹지지역
 
133
Other values (13)
 
312

Length

Max length8
Median length4
Mean length4.1554
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8699
87.0%
일반주거지역 330
 
3.3%
지역구분명 278
 
2.8%
관리지역 248
 
2.5%
자연녹지지역 133
 
1.3%
일반상업지역 99
 
1.0%
주거지역 81
 
0.8%
준주거지역 41
 
0.4%
상업지역 20
 
0.2%
보전녹지지역 18
 
0.2%
Other values (8) 53
 
0.5%

Length

2024-04-17T01:37:39.610759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8699
87.0%
일반주거지역 330
 
3.3%
지역구분명 278
 
2.8%
관리지역 248
 
2.5%
자연녹지지역 133
 
1.3%
일반상업지역 99
 
1.0%
주거지역 81
 
0.8%
준주거지역 41
 
0.4%
상업지역 20
 
0.2%
보전녹지지역 18
 
0.2%
Other values (8) 53
 
0.5%

undernumlay
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9305 
지하층수
 
333
1
 
195
0
 
93
2
 
34
Other values (6)
 
40

Length

Max length10
Median length4
Mean length3.8923
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9305
93.0%
지하층수 333
 
3.3%
1 195
 
1.9%
0 93
 
0.9%
2 34
 
0.3%
3 20
 
0.2%
4 11
 
0.1%
5 5
 
0.1%
6 2
 
< 0.1%
underNumLa 1
 
< 0.1%

Length

2024-04-17T01:37:39.711542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9305
93.0%
지하층수 333
 
3.3%
1 195
 
1.9%
0 93
 
0.9%
2 34
 
0.3%
3 20
 
0.2%
4 11
 
0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
undernumla 1
 
< 0.1%

totnumlay
Categorical

IMBALANCE 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8881 
총층수
 
291
1
 
250
2
 
186
3
 
133
Other values (26)
 
259

Length

Max length9
Median length4
Mean length3.7314
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row12

Common Values

ValueCountFrequency (%)
<NA> 8881
88.8%
총층수 291
 
2.9%
1 250
 
2.5%
2 186
 
1.9%
3 133
 
1.3%
4 71
 
0.7%
5 44
 
0.4%
6 28
 
0.3%
10 17
 
0.2%
11 12
 
0.1%
Other values (21) 87
 
0.9%

Length

2024-04-17T01:37:39.822716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8881
88.8%
총층수 291
 
2.9%
1 250
 
2.5%
2 186
 
1.9%
3 133
 
1.3%
4 71
 
0.7%
5 44
 
0.4%
6 28
 
0.3%
10 17
 
0.2%
11 12
 
0.1%
Other values (21) 87
 
0.9%

abedcnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5319 
<NA>
4464 
침대수
 
215
abedCnt
 
1
41
 
1

Length

Max length7
Median length1
Mean length2.3829
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 5319
53.2%
<NA> 4464
44.6%
침대수 215
 
2.1%
abedCnt 1
 
< 0.1%
41 1
 
< 0.1%

Length

2024-04-17T01:37:39.936436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:40.023135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5319
53.2%
na 4464
44.6%
침대수 215
 
2.1%
abedcnt 1
 
< 0.1%
41 1
 
< 0.1%

hanshilcnt
Text

MISSING 

Distinct52
Distinct (%)0.7%
Missing2867
Missing (%)28.7%
Memory size156.2 KiB
2024-04-17T01:37:40.132905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.2112716
Min length1

Characters and Unicode

Total characters8640
Distinct characters21
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

Unique9 ?
Unique (%)0.1%

Sample

1st row3
2nd row4
3rd row16
4th row0
5th row1
ValueCountFrequency (%)
0 3979
55.8%
2 305
 
4.3%
1 266
 
3.7%
10 258
 
3.6%
3 257
 
3.6%
한실수 215
 
3.0%
4 196
 
2.7%
8 187
 
2.6%
5 185
 
2.6%
6 176
 
2.5%
Other values (42) 1109
 
15.5%
2024-04-17T01:37:40.356555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4279
49.5%
1 1298
 
15.0%
2 557
 
6.4%
3 382
 
4.4%
4 294
 
3.4%
5 271
 
3.1%
6 260
 
3.0%
8 240
 
2.8%
9 218
 
2.5%
215
 
2.5%
Other values (11) 626
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7985
92.4%
Other Letter 645
 
7.5%
Lowercase Letter 9
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4279
53.6%
1 1298
 
16.3%
2 557
 
7.0%
3 382
 
4.8%
4 294
 
3.7%
5 271
 
3.4%
6 260
 
3.3%
8 240
 
3.0%
9 218
 
2.7%
7 186
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
h 2
22.2%
n 2
22.2%
a 1
11.1%
s 1
11.1%
i 1
11.1%
l 1
11.1%
t 1
11.1%
Other Letter
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7985
92.4%
Hangul 645
 
7.5%
Latin 10
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4279
53.6%
1 1298
 
16.3%
2 557
 
7.0%
3 382
 
4.8%
4 294
 
3.7%
5 271
 
3.4%
6 260
 
3.3%
8 240
 
3.0%
9 218
 
2.7%
7 186
 
2.3%
Latin
ValueCountFrequency (%)
h 2
20.0%
n 2
20.0%
a 1
10.0%
s 1
10.0%
i 1
10.0%
l 1
10.0%
C 1
10.0%
t 1
10.0%
Hangul
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7995
92.5%
Hangul 645
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4279
53.5%
1 1298
 
16.2%
2 557
 
7.0%
3 382
 
4.8%
4 294
 
3.7%
5 271
 
3.4%
6 260
 
3.3%
8 240
 
3.0%
9 218
 
2.7%
7 186
 
2.3%
Other values (8) 10
 
0.1%
Hangul
ValueCountFrequency (%)
215
33.3%
215
33.3%
215
33.3%

rcvdryncnt
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5325 
<NA>
4459 
회수건조수
 
215
rcvDrynCnt
 
1

Length

Max length10
Median length1
Mean length2.4246
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 5325
53.2%
<NA> 4459
44.6%
회수건조수 215
 
2.1%
rcvDrynCnt 1
 
< 0.1%

Length

2024-04-17T01:37:40.481020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:40.565223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5325
53.2%
na 4459
44.6%
회수건조수 215
 
2.1%
rcvdryncnt 1
 
< 0.1%

meetsamtimesygstf
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9609 
회의실별동시수용인원
 
390
meetSamTim
 
1

Length

Max length10
Median length4
Mean length4.2346
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9609
96.1%
회의실별동시수용인원 390
 
3.9%
meetSamTim 1
 
< 0.1%

Length

2024-04-17T01:37:40.675482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:37:40.755281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9609
96.1%
회의실별동시수용인원 390
 
3.9%
meetsamtim 1
 
< 0.1%
Distinct5
Distinct (%)0.1%
Missing8
Missing (%)0.1%
Memory size156.2 KiB
Minimum2021-02-01 05:09:03
Maximum2021-02-01 05:09:07
2024-04-17T01:37:40.826535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:37:40.913841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelstroomcntbdngownsenmbdngsrvnmbdngjisgflrcntbdngunderflrcntcnstyareasvnsrplninsurstdtplninsurenddtmaneipcntplayutscntdtlplayfacilcntmultusnupsoynstagearculwrkrsenmculphyedcobnmgeicpfacilenbalhansilynbcfacileninsurorgnminsurstdtinsurenddtafcusejisgendflruseunderendflrusejisgstflruseunderstflrshpinfoshpcntshptottonswashmccntfacilscpfacilarinfobenyangsilcntwmeipcntengstntrnmnmengstntrnmaddryoksilcntsntuptaenmdispenencaptmnfactreartclcncndpermstymdcndpermntwhycndpermendymdchaircntnearenvnmjisgnumlayregnsenmundernumlaytotnumlayabedcnthanshilcntrcvdryncntmeetsamtimesygstflast_load_dttm
4907490332800003280000-201-1988-0069503_11_03_PI2018-08-31 23:59:59.0<NA>부일장606061부산광역시 영도구 봉래동1가 50-2번지48947<NA>1988022220040115<NA><NA><NA>02폐업386005.990004179413.44575620030428000000여관업051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>10<NA><NA><NA><NA>여관업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:09:04
10125101253020000.0CDFI226221201900004303_11_04_PI2019-08-04 02:21:53.0외국인관광도시민박업코빈하우스<NA>서울특별시 용산구 후암동 116-13번지 목화연립04333서울특별시 용산구 후암로35길 48, 1층 103호 (후암동, 목화연립)20190802<NA><NA><NA><NA>영업/정상영업중197801.284875486449680.25705273120190802154712<NA>051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA>외국인관광 도시민박업<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4848.26<NA><NA><NA><NA><NA><NA><NA><NA>30000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:09:06
2937293833500003350000-201-1972-0107703_11_03_PI2018-08-31 23:59:59.0<NA>수정여관609834부산광역시 금정구 서동 495-6번지48947<NA>1972083120020530<NA><NA><NA>02폐업391232.542570193227.84862920030527000000여관업051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>여관업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:09:04
7425742333800003380000-201-2004-0000103_11_03_PI2018-08-31 23:59:59.0<NA>캐슬비치관광호텔613827부산광역시 수영구 민락동 110-60번지48280부산광역시 수영구 민락수변로 141 (민락동)20040727<NA><NA><NA><NA>01영업394509.50605400000186541.3507990000020171107135815관광호텔051-123-1234<NA>자가<NA>101<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>9050<NA><NA><NA>0<NA><NA><NA>51<NA><NA><NA>0관광호텔<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>030<NA>2021-02-01 05:09:04
10338103383200000.0CDFI226003201900000203_11_01_PU2019-10-23 02:40:00.0관광숙박업삼화<NA>서울특별시 관악구 신림동 1432-62번지08760서울특별시 관악구 신림로65길 10-33, 삼화 (신림동)20191015<NA><NA><NA><NA>영업/정상영업중193621.587472621442638.23921230420191021132151<NA>051-123-123449<NA>호텔<NA><NA>1178<NA><NA><NA><NA><NA>NN<NA><NA>관광숙박업<NA>N<NA>삼성화재<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>10781077.84<NA><NA><NA>SamhwaThe tourist accommodation business(A hostel)<NA><NA><NA>5860000000<NA><NA><NA><NA><NA><NA>12일반상업지역112<NA><NA><NA><NA>2021-02-01 05:09:06
918191775090000.05090000-201-2019-0000103_11_03_PI2019-03-31 02:20:09.0숙박업소백산풍기온천리조트호텔동750804경상북도 영주시 풍기읍 창락리 434-1번지36020경상북도 영주시 풍기읍 죽령로 1398-3620190329<NA><NA><NA><NA>영업/정상영업331874.620606198377203.52818597120190329161338숙박업 기타051-123-1234<NA>자가<NA>30<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>3<NA>1<NA><NA><NA><NA>0<NA><NA><NA>200<NA><NA>0숙박업 기타<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>040<NA>2021-02-01 05:09:05
875687533040000.0CDFI226221201900000103_11_04_PU2019-03-02 02:40:00.0외국인관광도시민박업아늑공간<NA>서울특별시 광진구 구의동 45-19번지 솔라빌리지04960서울특별시 광진구 자양로44나길 17, 지층 (구의동, 솔라빌리지)20190121<NA><NA><NA><NA>영업/정상영업중208294.41877782449807.52080777420190228173013<NA>051-123-12342<NA>다세대주택<NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA>외국인관광 도시민박업<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8383.14<NA><NA><NA><NA>Urban Home Visit System for Foreign Tourist<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA>2021-02-01 05:09:05
10691106894980000.0CDFI226213201900000103_11_07_PU2020-12-09 02:40:00.0일반야영장업장성잔디로야영장<NA>전라남도 장성군 삼서면 수양리 666-157256전라남도 장성군 삼서면 수양로 100-3220191129<NA><NA><NA><NA>영업/정상영업중169174.219773193623.23877820201207133347<NA>061-393-8981<NA><NA>기타<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>일반야영장업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>45244523.56<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>기타<NA>관리지역<NA><NA><NA><NA><NA><NA>2021-02-01 05:09:06
906090593580000.03580000-214-2019-0000603_11_03_PI2019-03-14 02:21:50.0숙박업(주)골드스타 호텔409892인천광역시 옹진군 덕적면 서포리 163번지23131인천광역시 옹진군 덕적면 덕적남로606번안길 22, 2~4층20190312<NA><NA><NA><NA>영업/정상영업121764.513862414413700.79863100420190312174202숙박업(생활)051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>4<NA>2<NA><NA><NA><NA>0<NA><NA><NA>160<NA><NA>0숙박업(생활)<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>0160<NA>2021-02-01 05:09:05
3153315633600003360000-201-2014-0000203_11_03_PI2018-08-31 23:59:59.0<NA>썸모텔618290부산광역시 강서구 신호동 317-5번지46760부산광역시 강서구 신호산단1로72번길 41 (신호동)20141104<NA><NA><NA><NA>01영업370634.420939177891.56557120180821101138여관업051-123-1234<NA>임대<NA>10<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>7010<NA><NA><NA>0<NA><NA><NA>350<NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:04
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelstroomcntbdngownsenmbdngsrvnmbdngjisgflrcntbdngunderflrcntcnstyareasvnsrplninsurstdtplninsurenddtmaneipcntplayutscntdtlplayfacilcntmultusnupsoynstagearculwrkrsenmculphyedcobnmgeicpfacilenbalhansilynbcfacileninsurorgnminsurstdtinsurenddtafcusejisgendflruseunderendflrusejisgstflruseunderstflrshpinfoshpcntshptottonswashmccntfacilscpfacilarinfobenyangsilcntwmeipcntengstntrnmnmengstntrnmaddryoksilcntsntuptaenmdispenencaptmnfactreartclcncndpermstymdcndpermntwhycndpermendymdchaircntnearenvnmjisgnumlayregnsenmundernumlaytotnumlayabedcnthanshilcntrcvdryncntmeetsamtimesygstflast_load_dttm
1902189933100003310000-201-1997-0003903_11_03_PI2018-08-31 23:59:59.0<NA>케이(K)모텔608813부산광역시 남구 대연동 1746-4번지 T통B반48492부산광역시 남구 유엔평화로4번길 10 (대연동)19970509<NA><NA><NA><NA>01영업390613.81184700000184034.8678130000020180517150349숙박업 기타051-123-1234<NA><NA><NA>41<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>4000<NA><NA><NA>0<NA><NA><NA>32<NA><NA><NA>18숙박업 기타<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:03
877087693220000.0CDFI226221201900000703_11_04_PI2019-01-26 02:21:01.0외국인관광도시민박업광택하우스<NA>서울특별시 강남구 삼성동 107번지 삼성동 미켈란 10706083서울특별시 강남구 영동대로 602 (삼성동, 삼성동 미켈란 107)20190124<NA><NA><NA><NA>영업/정상영업중205271.275273064445852.63881408820190124172951<NA>051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA>외국인관광 도시민박업<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8383<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:09:05
22322232500003250000-201-1982-0012803_11_03_PI2018-08-31 23:59:59.0<NA>로즈텔600083부산광역시 중구 보수동3가 52-5번지 외 1필지48966부산광역시 중구 흑교로45번길 30-3 (보수동3가)19820109<NA><NA><NA><NA>01영업384343.99671300000180482.4279110000020180501172732여관업051-123-1234<NA>임대<NA>00<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>0000<NA><NA><NA>0<NA><NA><NA>9<NA><NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>020<NA>2021-02-01 05:09:03
3458346033800003380000-201-2000-0000503_11_03_PI2018-08-31 23:59:59.0<NA>호텔런더너613827부산광역시 수영구 민락동 121-8번지48289부산광역시 수영구 민락본동로 12-4 (민락동)2000080520170831<NA><NA><NA>02폐업393966.81494400000186660.0680970000020170831145547여관업051-123-1234<NA><NA><NA>91<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>9050<NA><NA><NA>0<NA><NA><NA>32<NA><NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>050<NA>2021-02-01 05:09:04
931593143020000.0CDFI226221201900001603_11_04_PI2019-04-21 02:20:27.0외국인관광도시민박업1937 HOUSE<NA>서울특별시 용산구 서계동 33-50번지04303서울특별시 용산구 청파로83길 28 (객실:1개) (서계동)20190419<NA><NA><NA><NA>영업/정상영업중197076.52734736449956.15257014120190419152155<NA>051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA>외국인관광 도시민박업<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>6363.34<NA><NA><NA><NA><NA><NA><NA><NA>5000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:09:05
2811281433400003340000-201-1997-0066503_11_03_PI2018-08-31 23:59:59.0<NA>H 모텔604852부산광역시 사하구 하단동 512-2번지49311부산광역시 사하구 하신번영로300번길 122 (하단동)19970604<NA><NA><NA><NA>01영업379212.52305700000180870.8660300000020180221100739여관업051-123-1234<NA>임대<NA>51<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>4010<NA><NA><NA>0<NA><NA><NA>23<NA><NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>030<NA>2021-02-01 05:09:03
12153121724160000.04160000-214-2020-0002803_11_03_PI2020-08-21 00:23:14.0숙박업(주)혜성 오하브477841경기도 가평군 북면 제령리 533-4 외 1필지(533-7), A,B,C,D,부속1동12405경기도 가평군 북면 꽃넘이길 37, A,B,C,D동, 꽃넘이길43,부속1동20200819<NA><NA><NA><NA>영업/정상영업246291.582126413488264.81826453420200819145127숙박업(생활)051-123-1234<NA><NA><NA>20<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>2<NA>1<NA><NA><NA><NA>0<NA><NA><NA>20<NA><NA>0숙박업(생활)<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>010<NA>2021-02-01 05:09:06
74474332800003280000-201-1986-0068103_11_03_PI2018-08-31 23:59:59.0<NA>태성파크606011부산광역시 영도구 대교동1가 112-2번지49045부산광역시 영도구 절영로36번길 2 (대교동1가)19860110<NA><NA><NA><NA>01영업385949.64299700000179143.1376880000020171124095819여관업051-123-1234<NA><NA><NA>00<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>0000<NA><NA><NA>0<NA><NA><NA>15<NA><NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:03
869886965010000.0CDFI326109201900000103_11_02_PI2019-01-11 02:20:45.0관광펜션업소풍펜션지번우편번호전라남도 신안군 압해읍 학교리 336-14번지58824전라남도 신안군 압해읍 학동길 44-420190109폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중137437.599765152418.65448720190109155008업태구분명051-123-1234객실수건물소유구분명숙박시설건물지상층수건물지하층수건축연면적기념품종류기획여행보험시작일자기획여행보험종료일자남성종사자수놀이기구수내역NN무대면적문화사업자구분명관광펜션업N보험기관명보험시작일자보험종료일자부대시설내역사용끝지상층사용끝지하층사용시작지상층사용시작지하층선박제원선박척수선박총톤수세탁기수357356.92양실수여성종사자수영문상호명영문상호주소욕실수위생업태명200000000제작취급품목내용조건부허가시작일자조건부허가신고사유조건부허가종료일자좌석수기타2관리지역02침대수한실수회수건조수회의실별동시수용인원2021-02-01 05:09:05
6275627833200003320000-201-1988-0008203_11_03_PI2018-08-31 23:59:59.0<NA>백야장616801부산광역시 북구 구포동 492번지 T통B반48947<NA>1988112619971203<NA><NA><NA>02폐업382186.326973192120.81319520050824000000여관업051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>여관업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>19<NA><NA>2021-02-01 05:09:04

Duplicate rows

Most frequently occurring

mgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelstroomcntbdngownsenmbdngsrvnmbdngjisgflrcntbdngunderflrcntcnstyareasvnsrplninsurstdtplninsurenddtmaneipcntplayutscntdtlplayfacilcntmultusnupsoynstagearculwrkrsenmculphyedcobnmgeicpfacilenbalhansilynbcfacileninsurorgnminsurstdtinsurenddtafcusejisgendflruseunderendflrusejisgstflruseunderstflrshpinfoshpcntshptottonswashmccntfacilscpfacilarinfobenyangsilcntwmeipcntengstntrnmnmengstntrnmaddryoksilcntsntuptaenmdispenencaptmnfactreartclcncndpermstymdcndpermntwhycndpermendymdchaircntnearenvnmjisgnumlayregnsenmundernumlaytotnumlayabedcnthanshilcntrcvdryncntmeetsamtimesygstflast_load_dttm# duplicates
422CDFI226003201800000503_11_01_PU2019-04-14 02:40:00.0관광숙박업일로이리조트<NA>부산광역시 해운대구 송정동 809번지부산광역시 해운대구 송정구덕포길 130 (송정동)20181017<NA><NA><NA><NA>영업/정상영업중400102.463000748187863.01536593920190412092534<NA>051-123-1234<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NN<NA><NA>관광숙박업<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>599598.73<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4주거지역<NA>4<NA><NA><NA><NA>2021-02-01 05:09:046
3324810000-214-2018-0004903_11_03_PU2018-10-21 02:36:21.0숙박업다올550833전라남도 여수시 돌산읍 우두리 1128-6번지전라남도 여수시 돌산읍 강남3길 73-2120181017<NA><NA><NA><NA><NA>영업269061.383127135486.23745220181019093755숙박업(생활)051-123-1234<NA>자가<NA>30<NA><NA><NA><NA><NA><NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>3010<NA><NA><NA>0<NA><NA><NA>3<NA><NA><NA>0숙박업(생활)<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>010<NA>2021-02-01 05:09:044
13010000-201-2019-0000403_11_03_PU2020-08-30 02:40:00.0숙박업stx 명동호텔100011서울특별시 중구 충무로1가 24-1 밀레오레 명동서울특별시 중구 퇴계로 115 (충무로1가)20190329<NA><NA><NA><NA>영업/정상영업198562.632745775450975.11640604820200828130852일반호텔051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>17030<NA><NA><NA>0<NA><NA><NA>1230<NA><NA>0일반호텔<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:053
93220000-201-2019-0000103_11_03_PI2019-02-03 02:21:09.0숙박업호텔 인 나인135871서울특별시 강남구 삼성동 91-28번지서울특별시 강남구 영동대로 618, 3층~18층 (삼성동)20190201<NA><NA><NA><NA>영업/정상영업205230.860541362445942.05099574120190201152358관광호텔051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0<NA><NA><NA>1510<NA><NA>0관광호텔<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:053
133270000-201-2019-0000303_11_03_PI2019-06-23 02:21:37.0숙박업대구여관601829부산광역시 동구 초량동 388-2번지 지하1층, 지상1~3층, 4층 일부부산광역시 동구 중앙대로221번길 14-5 (초량동)20190621<NA><NA><NA><NA>영업/정상영업385886.372041979181704.05848345720190621114502여관업051-123-1234<NA>자가<NA>41<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>4111<NA><NA><NA>0<NA><NA><NA>110<NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>060<NA>2021-02-01 05:09:053
163290000-201-2020-0000103_11_03_PU2020-12-15 02:40:00.0숙박업지지배614849부산광역시 부산진구 부전동 417-25부산광역시 부산진구 부전로 99-1, 3층 (부전동)20200221<NA><NA><NA><NA>영업/정상영업387128.557245587186361.92755692120201212135845여관업051 806 7779<NA>자가<NA>41<NA><NA><NA><NA>0<NA><NA>N<NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA>3<NA><NA><NA><NA>0<NA><NA><NA>60<NA><NA>0여관업<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:063
173330000-201-2018-0001103_11_03_PI2018-12-15 02:20:16.0숙박업베이몬드호텔612821부산광역시 해운대구 우동 648-8번지부산광역시 해운대구 해운대해변로209번가길 27 (우동)20181213<NA><NA><NA><NA>영업/정상영업396446.063994506186617.52221071320181214133722일반호텔051-123-1234<NA><NA><NA>122<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>12211<NA><NA><NA>0<NA><NA><NA>670<NA><NA>0일반호텔<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:053
183330000-201-2019-0000503_11_03_PU2019-10-10 02:40:00.0숙박업투헤븐모텔612821부산광역시 해운대구 우동 1366-1번지 호텔 투헤븐부산광역시 해운대구 구남로30번길 9-5, 호텔 투헤븐 (우동)20191004<NA><NA><NA><NA>영업/정상영업396760.841390586186766.4413622820191008104810숙박업 기타051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>5<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>8<NA>1<NA><NA><NA><NA>0<NA><NA><NA>420<NA><NA>0숙박업 기타<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:063
193330000-214-2020-0000203_11_03_PU2020-06-11 02:40:00.0숙박업유에이치스위트 렌드스케이프612846부산광역시 해운대구 중동 1417-2번지 씨스타부산광역시 해운대구 해운대해변로 271, 씨스타 3층 301~305호 (중동)20200424<NA><NA><NA><NA>영업/정상영업396841.843557054186655.37392305320200609083206숙박업(생활)051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>0000<NA><NA><NA>0<NA><NA><NA>53<NA><NA>0숙박업(생활)<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:063
203330000-214-2020-0000303_11_03_PU2020-08-09 02:40:00.0숙박업빌라 코트야드612040부산광역시 해운대구 송정동 158-3부산광역시 해운대구 송정중앙로6번길 54 (송정동)20200501<NA><NA><NA><NA>영업/정상영업400336.809488059189143.14786508120200807095216숙박업(생활)051-123-1234<NA><NA><NA>00<NA><NA><NA><NA>0<NA>NN<NA><NA><NA><NA>N<NA><NA><NA><NA><NA>6<NA>1<NA><NA><NA><NA>0<NA><NA><NA>50<NA><NA>0숙박업(생활)<NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>000<NA>2021-02-01 05:09:063