Overview

Dataset statistics

Number of variables36
Number of observations500
Missing cells4013
Missing cells (%)22.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.5 KiB
Average record size in memory310.3 B

Variable types

Numeric16
Categorical6
Text8
Boolean1
Unsupported4
DateTime1

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=23

Alerts

발한실_여부(BALHANSIL_AT) is highly imbalanced (75.4%)Imbalance
세탁기_수(WASHMC_CO) is highly imbalanced (90.4%)Imbalance
내국인_외국인_구분(NATIVE_FRGNR_SE) is highly imbalanced (94.7%)Imbalance
국적(NLTY) is highly imbalanced (94.7%)Imbalance
업소_소재지_전화번호(BSSH_LOCPLC_TELNO) has 51 (10.2%) missing valuesMissing
법인명(CPR_NM) has 470 (94.0%) missing valuesMissing
법인번호(CPR_NO) has 473 (94.6%) missing valuesMissing
폐업_일자(BIZQIT_DE) has 196 (39.2%) missing valuesMissing
폐업_사유(BIZQIT_SE_RESN) has 221 (44.2%) missing valuesMissing
교육_수료_일(EDC_COMPL_DE) has 339 (67.8%) missing valuesMissing
발한실_여부(BALHANSIL_AT) has 11 (2.2%) missing valuesMissing
참고_사항(REFER_MATTER) has 500 (100.0%) missing valuesMissing
신_주소(OLD_ADRES) has 248 (49.6%) missing valuesMissing
업종소분류코드(INDUTY_SCLAS_CD) has 500 (100.0%) missing valuesMissing
업종업태코드(SNITAT_BIZCND_CD) has 500 (100.0%) missing valuesMissing
도로코드(ROAD_CD) has 500 (100.0%) missing valuesMissing
참고_사항(REFER_MATTER) is an unsupported type, check if it needs cleaning or further analysisUnsupported
업종소분류코드(INDUTY_SCLAS_CD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
업종업태코드(SNITAT_BIZCND_CD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
도로코드(ROAD_CD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업장_면적(BUZPLC_AR) has 47 (9.4%) zerosZeros
객실_수(RUM_CO) has 482 (96.4%) zerosZeros
한실_수(HANSHIL_CO) has 482 (96.4%) zerosZeros
양실_수(YANGSIL_CO) has 485 (97.0%) zerosZeros
의자_수(CHAIR_CO) has 203 (40.6%) zerosZeros
욕실_수(BTR_CO) has 488 (97.6%) zerosZeros

Reproduction

Analysis started2023-12-10 15:00:02.381086
Analysis finished2023-12-10 15:00:04.450420
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3133740
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:04.723759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010000
Q13080000
median3140000
Q33190000
95-th percentile3230000
Maximum3240000
Range240000
Interquartile range (IQR)110000

Descriptive statistics

Standard deviation69130.097
Coefficient of variation (CV)0.022059934
Kurtosis-1.0146314
Mean3133740
Median Absolute Deviation (MAD)60000
Skewness-0.26929816
Sum1.56687 × 109
Variance4.7789703 × 109
MonotonicityNot monotonic
2023-12-11T00:00:05.148785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3220000 34
 
6.8%
3190000 31
 
6.2%
3200000 29
 
5.8%
3070000 29
 
5.8%
3130000 28
 
5.6%
3170000 25
 
5.0%
3160000 25
 
5.0%
3080000 24
 
4.8%
3120000 23
 
4.6%
3100000 22
 
4.4%
Other values (15) 230
46.0%
ValueCountFrequency (%)
3000000 18
3.6%
3010000 13
2.6%
3020000 15
3.0%
3030000 9
 
1.8%
3040000 11
 
2.2%
3050000 11
 
2.2%
3060000 10
 
2.0%
3070000 29
5.8%
3080000 24
4.8%
3090000 16
3.2%
ValueCountFrequency (%)
3240000 20
4.0%
3230000 20
4.0%
3220000 34
6.8%
3210000 17
3.4%
3200000 29
5.8%
3190000 31
6.2%
3180000 20
4.0%
3170000 25
5.0%
3160000 25
5.0%
3150000 17
3.4%
Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.962
Minimum201
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:05.377039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile201
Q1204
median205
Q3211
95-th percentile213
Maximum222
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1717338
Coefficient of variation (CV)0.020157004
Kurtosis-0.46027983
Mean206.962
Median Absolute Deviation (MAD)2
Skewness0.58977058
Sum103481
Variance17.403363
MonotonicityNot monotonic
2023-12-11T00:00:05.729382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
204 122
24.4%
211 106
21.2%
203 56
11.2%
205 54
10.8%
210 39
 
7.8%
201 30
 
6.0%
206 25
 
5.0%
212 24
 
4.8%
215 13
 
2.6%
202 10
 
2.0%
Other values (7) 21
 
4.2%
ValueCountFrequency (%)
201 30
 
6.0%
202 10
 
2.0%
203 56
11.2%
204 122
24.4%
205 54
10.8%
206 25
 
5.0%
208 1
 
0.2%
209 1
 
0.2%
210 39
 
7.8%
211 106
21.2%
ValueCountFrequency (%)
222 1
 
0.2%
220 3
 
0.6%
219 1
 
0.2%
217 4
 
0.8%
215 13
 
2.6%
213 10
 
2.0%
212 24
 
4.8%
211 106
21.2%
210 39
 
7.8%
209 1
 
0.2%

년도(YEAR)
Real number (ℝ)

Distinct55
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.046
Minimum1960
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:06.153040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1977
Q11992
median2003
Q32011
95-th percentile2018
Maximum2020
Range60
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.574264
Coefficient of variation (CV)0.0062838454
Kurtosis-0.17746968
Mean2001.046
Median Absolute Deviation (MAD)10
Skewness-0.65955161
Sum1000523
Variance158.11211
MonotonicityNot monotonic
2023-12-11T00:00:06.610640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2003 26
 
5.2%
2014 26
 
5.2%
2007 26
 
5.2%
2010 19
 
3.8%
1987 18
 
3.6%
2016 17
 
3.4%
1993 17
 
3.4%
1992 17
 
3.4%
2009 17
 
3.4%
2002 15
 
3.0%
Other values (45) 302
60.4%
ValueCountFrequency (%)
1960 1
 
0.2%
1966 1
 
0.2%
1967 1
 
0.2%
1968 1
 
0.2%
1969 4
0.8%
1970 5
1.0%
1971 1
 
0.2%
1972 3
0.6%
1973 3
0.6%
1974 2
 
0.4%
ValueCountFrequency (%)
2020 2
 
0.4%
2019 13
2.6%
2018 15
3.0%
2017 11
2.2%
2016 17
3.4%
2015 10
 
2.0%
2014 26
5.2%
2013 14
2.8%
2012 9
 
1.8%
2011 14
2.8%
Distinct283
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.582
Minimum1
Maximum3157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:06.968735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median64
Q3868
95-th percentile2074.25
Maximum3157
Range3156
Interquartile range (IQR)856

Descriptive statistics

Standard deviation747.18516
Coefficient of variation (CV)1.4956207
Kurtosis1.3415538
Mean499.582
Median Absolute Deviation (MAD)61
Skewness1.5226517
Sum249791
Variance558285.66
MonotonicityNot monotonic
2023-12-11T00:00:07.400461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
3.8%
2 17
 
3.4%
4 17
 
3.4%
3 15
 
3.0%
5 12
 
2.4%
7 12
 
2.4%
6 11
 
2.2%
10 9
 
1.8%
20 8
 
1.6%
17 7
 
1.4%
Other values (273) 373
74.6%
ValueCountFrequency (%)
1 19
3.8%
2 17
3.4%
3 15
3.0%
4 17
3.4%
5 12
2.4%
6 11
2.2%
7 12
2.4%
8 5
 
1.0%
9 4
 
0.8%
10 9
1.8%
ValueCountFrequency (%)
3157 1
0.2%
3131 1
0.2%
3012 1
0.2%
2988 1
0.2%
2879 1
0.2%
2861 1
0.2%
2837 1
0.2%
2819 1
0.2%
2774 1
0.2%
2652 1
0.2%
Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
미용업
128 
미용업(일반)
88 
이용업
63 
세탁업
59 
공중이용시설
35 
Other values (13)
127 

Length

Max length31
Median length25
Mean length5.168
Min length3

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row미용업(피부), 미용업(손톱ㆍ발톱)
2nd row이용업
3rd row세탁업
4th row미용업(일반)
5th row공중이용시설

Common Values

ValueCountFrequency (%)
미용업 128
25.6%
미용업(일반) 88
17.6%
이용업 63
12.6%
세탁업 59
11.8%
공중이용시설 35
 
7.0%
위생관리용역업 33
 
6.6%
숙박업(일반) 29
 
5.8%
미용업(피부) 25
 
5.0%
목욕장업 12
 
2.4%
미용업(종합) 9
 
1.8%
Other values (8) 19
 
3.8%

Length

2023-12-11T00:00:07.720047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 128
25.0%
미용업(일반 93
18.2%
이용업 63
12.3%
세탁업 59
11.5%
공중이용시설 35
 
6.8%
위생관리용역업 33
 
6.5%
숙박업(일반 29
 
5.7%
미용업(피부 29
 
5.7%
목욕장업 12
 
2.3%
미용업(손톱ㆍ발톱 12
 
2.3%
Other values (4) 18
 
3.5%
Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20009642
Minimum19600411
Maximum20200218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:08.164986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19600411
5-th percentile19801204
Q119921207
median20030226
Q320101048
95-th percentile20170527
Maximum20200218
Range599807
Interquartile range (IQR)179840.25

Descriptive statistics

Standard deviation119003.22
Coefficient of variation (CV)0.0059472937
Kurtosis-0.20162625
Mean20009642
Median Absolute Deviation (MAD)89563
Skewness-0.58442806
Sum1.0004821 × 1010
Variance1.4161766 × 1010
MonotonicityNot monotonic
2023-12-11T00:00:08.515052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030226 4
 
0.8%
20030227 4
 
0.8%
20070331 3
 
0.6%
20070228 3
 
0.6%
19991108 2
 
0.4%
20160801 2
 
0.4%
20060420 2
 
0.4%
20041108 2
 
0.4%
19930804 2
 
0.4%
19840412 2
 
0.4%
Other values (464) 474
94.8%
ValueCountFrequency (%)
19600411 1
0.2%
19650605 1
0.2%
19660721 1
0.2%
19690417 1
0.2%
19690830 1
0.2%
19700828 1
0.2%
19701114 1
0.2%
19710716 1
0.2%
19720110 1
0.2%
19730219 1
0.2%
ValueCountFrequency (%)
20200218 1
0.2%
20191125 1
0.2%
20191113 1
0.2%
20190517 1
0.2%
20190419 1
0.2%
20190215 1
0.2%
20181127 1
0.2%
20181101 1
0.2%
20181023 1
0.2%
20181004 1
0.2%
Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:09.253578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16
Mean length5.404
Min length1

Characters and Unicode

Total characters2702
Distinct characters445
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique471 ?
Unique (%)94.2%

Sample

1st row포스헤어
2nd row샤넬헤어클럽
3rd row주식회사 대한노인회유통사업단
4th row소피소마에스테틱
5th row가슴벅찬 마음 헤어살롱
ValueCountFrequency (%)
헤어 6
 
1.0%
주식회사 5
 
0.8%
4
 
0.7%
현대 4
 
0.7%
이용원 3
 
0.5%
미용실 3
 
0.5%
백양사 3
 
0.5%
hair 3
 
0.5%
동양 2
 
0.3%
그린 2
 
0.3%
Other values (542) 554
94.1%
2023-12-11T00:00:10.267988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125
 
4.6%
118
 
4.4%
90
 
3.3%
62
 
2.3%
58
 
2.1%
57
 
2.1%
55
 
2.0%
53
 
2.0%
48
 
1.8%
) 37
 
1.4%
Other values (435) 1999
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2389
88.4%
Space Separator 90
 
3.3%
Lowercase Letter 65
 
2.4%
Uppercase Letter 56
 
2.1%
Close Punctuation 37
 
1.4%
Open Punctuation 37
 
1.4%
Decimal Number 18
 
0.7%
Other Punctuation 9
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
125
 
5.2%
118
 
4.9%
62
 
2.6%
58
 
2.4%
57
 
2.4%
55
 
2.3%
53
 
2.2%
48
 
2.0%
36
 
1.5%
34
 
1.4%
Other values (379) 1743
73.0%
Lowercase Letter
ValueCountFrequency (%)
o 9
13.8%
a 7
10.8%
i 6
9.2%
r 5
 
7.7%
e 5
 
7.7%
s 5
 
7.7%
h 4
 
6.2%
y 4
 
6.2%
n 3
 
4.6%
m 3
 
4.6%
Other values (10) 14
21.5%
Uppercase Letter
ValueCountFrequency (%)
A 8
14.3%
I 6
10.7%
K 6
10.7%
S 5
8.9%
O 4
 
7.1%
J 4
 
7.1%
N 4
 
7.1%
M 3
 
5.4%
T 2
 
3.6%
B 2
 
3.6%
Other values (8) 12
21.4%
Decimal Number
ValueCountFrequency (%)
1 4
22.2%
3 4
22.2%
2 3
16.7%
5 2
11.1%
4 2
11.1%
6 1
 
5.6%
7 1
 
5.6%
0 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
& 3
33.3%
' 2
22.2%
1
 
11.1%
, 1
 
11.1%
. 1
 
11.1%
? 1
 
11.1%
Space Separator
ValueCountFrequency (%)
90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2389
88.4%
Common 192
 
7.1%
Latin 121
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
125
 
5.2%
118
 
4.9%
62
 
2.6%
58
 
2.4%
57
 
2.4%
55
 
2.3%
53
 
2.2%
48
 
2.0%
36
 
1.5%
34
 
1.4%
Other values (379) 1743
73.0%
Latin
ValueCountFrequency (%)
o 9
 
7.4%
A 8
 
6.6%
a 7
 
5.8%
I 6
 
5.0%
K 6
 
5.0%
i 6
 
5.0%
r 5
 
4.1%
e 5
 
4.1%
s 5
 
4.1%
S 5
 
4.1%
Other values (28) 59
48.8%
Common
ValueCountFrequency (%)
90
46.9%
) 37
19.3%
( 37
19.3%
1 4
 
2.1%
3 4
 
2.1%
& 3
 
1.6%
2 3
 
1.6%
' 2
 
1.0%
5 2
 
1.0%
4 2
 
1.0%
Other values (8) 8
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2388
88.4%
ASCII 312
 
11.5%
None 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
125
 
5.2%
118
 
4.9%
62
 
2.6%
58
 
2.4%
57
 
2.4%
55
 
2.3%
53
 
2.2%
48
 
2.0%
36
 
1.5%
34
 
1.4%
Other values (378) 1742
72.9%
ASCII
ValueCountFrequency (%)
90
28.8%
) 37
 
11.9%
( 37
 
11.9%
o 9
 
2.9%
A 8
 
2.6%
a 7
 
2.2%
I 6
 
1.9%
K 6
 
1.9%
i 6
 
1.9%
r 5
 
1.6%
Other values (45) 101
32.4%
None
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

영업장_면적(BUZPLC_AR)
Real number (ℝ)

ZEROS 

Distinct359
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.85994
Minimum0
Maximum54466.09
Zeros47
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:10.697379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.7375
median26.485
Q362.9775
95-th percentile2203.3475
Maximum54466.09
Range54466.09
Interquartile range (IQR)46.24

Descriptive statistics

Standard deviation2609.3132
Coefficient of variation (CV)7.0739945
Kurtosis372.52245
Mean368.85994
Median Absolute Deviation (MAD)14.105
Skewness18.212658
Sum184429.97
Variance6808515.3
MonotonicityNot monotonic
2023-12-11T00:00:11.042276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 47
 
9.4%
33.0 13
 
2.6%
19.8 8
 
1.6%
15.0 7
 
1.4%
26.4 6
 
1.2%
20.0 6
 
1.2%
23.1 6
 
1.2%
12.0 5
 
1.0%
49.5 4
 
0.8%
66.0 4
 
0.8%
Other values (349) 394
78.8%
ValueCountFrequency (%)
0.0 47
9.4%
4.0 1
 
0.2%
5.47 1
 
0.2%
6.6 1
 
0.2%
8.05 1
 
0.2%
8.25 1
 
0.2%
9.8 1
 
0.2%
9.81 1
 
0.2%
9.9 1
 
0.2%
9.92 1
 
0.2%
ValueCountFrequency (%)
54466.09 1
0.2%
10272.88 1
0.2%
8760.0 1
0.2%
6360.31 1
0.2%
4982.0 1
0.2%
4964.18 1
0.2%
4790.26 1
0.2%
4351.0 1
0.2%
4117.32 1
0.2%
3838.3 1
0.2%
Distinct415
Distinct (%)92.4%
Missing51
Missing (%)10.2%
Memory size4.0 KiB
2023-12-11T00:00:11.629191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters8980
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique408 ?
Unique (%)90.9%

Sample

1st row 02 9454022
2nd row
3rd row 0234910783
4th row 02 8863423
5th row 02 6934466
ValueCountFrequency (%)
02 237
31.9%
010 31
 
4.2%
00000 6
 
0.8%
0200000000 5
 
0.7%
070 3
 
0.4%
0 3
 
0.4%
403 2
 
0.3%
011 2
 
0.3%
9001 2
 
0.3%
546 2
 
0.3%
Other values (449) 450
60.6%
2023-12-11T00:00:12.607114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4927
54.9%
0 864
 
9.6%
2 694
 
7.7%
3 348
 
3.9%
8 330
 
3.7%
1 316
 
3.5%
6 308
 
3.4%
9 308
 
3.4%
4 307
 
3.4%
7 301
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 4927
54.9%
Decimal Number 4053
45.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 864
21.3%
2 694
17.1%
3 348
8.6%
8 330
 
8.1%
1 316
 
7.8%
6 308
 
7.6%
9 308
 
7.6%
4 307
 
7.6%
7 301
 
7.4%
5 277
 
6.8%
Space Separator
ValueCountFrequency (%)
4927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4927
54.9%
0 864
 
9.6%
2 694
 
7.7%
3 348
 
3.9%
8 330
 
3.7%
1 316
 
3.5%
6 308
 
3.4%
9 308
 
3.4%
4 307
 
3.4%
7 301
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4927
54.9%
0 864
 
9.6%
2 694
 
7.7%
3 348
 
3.9%
8 330
 
3.7%
1 316
 
3.5%
6 308
 
3.4%
9 308
 
3.4%
4 307
 
3.4%
7 301
 
3.4%
Distinct462
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20049543
Minimum19710810
Maximum20200227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:12.932402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19710810
5-th percentile19910314
Q119980529
median20050365
Q320121136
95-th percentile20181010
Maximum20200227
Range489417
Interquartile range (IQR)140606.5

Descriptive statistics

Standard deviation89360.433
Coefficient of variation (CV)0.0044569809
Kurtosis-0.30837161
Mean20049543
Median Absolute Deviation (MAD)70154
Skewness-0.28753925
Sum1.0024772 × 1010
Variance7.985287 × 109
MonotonicityNot monotonic
2023-12-11T00:00:13.259417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070413 3
 
0.6%
20080401 3
 
0.6%
20070416 3
 
0.6%
20171106 3
 
0.6%
19930730 2
 
0.4%
19971230 2
 
0.4%
20100202 2
 
0.4%
19951019 2
 
0.4%
19970715 2
 
0.4%
20191211 2
 
0.4%
Other values (452) 476
95.2%
ValueCountFrequency (%)
19710810 1
0.2%
19750314 1
0.2%
19800718 1
0.2%
19810304 1
0.2%
19821228 1
0.2%
19840430 1
0.2%
19850601 1
0.2%
19850709 1
0.2%
19850914 1
0.2%
19860129 1
0.2%
ValueCountFrequency (%)
20200227 1
0.2%
20200204 1
0.2%
20191211 2
0.4%
20191209 1
0.2%
20191205 1
0.2%
20191014 1
0.2%
20191004 1
0.2%
20190812 1
0.2%
20190729 1
0.2%
20190725 1
0.2%

법인명(CPR_NM)
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing470
Missing (%)94.0%
Memory size4.0 KiB
2023-12-11T00:00:13.656574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length8.9666667
Min length3

Characters and Unicode

Total characters269
Distinct characters105
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

Unique30 ?
Unique (%)100.0%

Sample

1st row한국생활환경연구소
2nd row주식회사 에스비종합관리
3rd row케이에프앤에스(주)
4th row사단법인 장애인복지일자리지원협회
5th row(주)호텔롯데
ValueCountFrequency (%)
주식회사 8
 
20.0%
박창선 1
 
2.5%
공상유공자회 1
 
2.5%
임호빌딩 1
 
2.5%
주)비오종합관리 1
 
2.5%
주)성도미화 1
 
2.5%
주)선진티엔알 1
 
2.5%
명서비스 1
 
2.5%
한국생활환경연구소 1
 
2.5%
사)대한민국공무원 1
 
2.5%
Other values (23) 23
57.5%
2023-12-11T00:00:14.340864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
 
8.9%
) 15
 
5.6%
( 15
 
5.6%
12
 
4.5%
11
 
4.1%
10
 
3.7%
9
 
3.3%
9
 
3.3%
7
 
2.6%
6
 
2.2%
Other values (95) 151
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 229
85.1%
Close Punctuation 15
 
5.6%
Open Punctuation 15
 
5.6%
Space Separator 10
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
 
10.5%
12
 
5.2%
11
 
4.8%
9
 
3.9%
9
 
3.9%
7
 
3.1%
6
 
2.6%
6
 
2.6%
5
 
2.2%
4
 
1.7%
Other values (92) 136
59.4%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 229
85.1%
Common 40
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
 
10.5%
12
 
5.2%
11
 
4.8%
9
 
3.9%
9
 
3.9%
7
 
3.1%
6
 
2.6%
6
 
2.6%
5
 
2.2%
4
 
1.7%
Other values (92) 136
59.4%
Common
ValueCountFrequency (%)
) 15
37.5%
( 15
37.5%
10
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 229
85.1%
ASCII 40
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
24
 
10.5%
12
 
5.2%
11
 
4.8%
9
 
3.9%
9
 
3.9%
7
 
3.1%
6
 
2.6%
6
 
2.6%
5
 
2.2%
4
 
1.7%
Other values (92) 136
59.4%
ASCII
ValueCountFrequency (%)
) 15
37.5%
( 15
37.5%
10
25.0%

법인번호(CPR_NO)
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)100.0%
Missing473
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean1.2991402 × 1012
Minimum1.1011103 × 1012
Maximum6.4061316 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:14.571800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1011103 × 1012
5-th percentile1.1011118 × 1012
Q11.1011132 × 1012
median1.101114 × 1012
Q31.1011154 × 1012
95-th percentile1.13026 × 1012
Maximum6.4061316 × 1012
Range5.3050214 × 1012
Interquartile range (IQR)2260473.5

Descriptive statistics

Standard deviation1.0206739 × 1012
Coefficient of variation (CV)0.78565335
Kurtosis26.996284
Mean1.2991402 × 1012
Median Absolute Deviation (MAD)1083077
Skewness5.195638
Sum3.5076786 × 1013
Variance1.0417752 × 1024
MonotonicityNot monotonic
2023-12-11T00:00:14.781732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1101115446855 1
 
0.2%
1101113197111 1
 
0.2%
1101113823641 1
 
0.2%
1101112530677 1
 
0.2%
1101114029347 1
 
0.2%
1101115333656 1
 
0.2%
1101115757682 1
 
0.2%
1101113715515 1
 
0.2%
1101113631086 1
 
0.2%
1142710001842 1
 
0.2%
Other values (17) 17
 
3.4%
(Missing) 473
94.6%
ValueCountFrequency (%)
1101110256788 1
0.2%
1101111488546 1
0.2%
1101112530677 1
0.2%
1101112946270 1
0.2%
1101112992538 1
0.2%
1101113008318 1
0.2%
1101113121714 1
0.2%
1101113197111 1
0.2%
1101113322859 1
0.2%
1101113341974 1
0.2%
ValueCountFrequency (%)
6406131639614 1
0.2%
1142710001842 1
0.2%
1101210004558 1
0.2%
1101116678689 1
0.2%
1101116175792 1
0.2%
1101115757682 1
0.2%
1101115446855 1
0.2%
1101115392917 1
0.2%
1101115333656 1
0.2%
1101115218246 1
0.2%
Distinct463
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20045871
Minimum19811214
Maximum20200131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:15.029113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19811214
5-th percentile19930406
Q119980330
median20040570
Q320110915
95-th percentile20171664
Maximum20200131
Range388917
Interquartile range (IQR)130585.75

Descriptive statistics

Standard deviation81056.191
Coefficient of variation (CV)0.0040435356
Kurtosis-0.84725518
Mean20045871
Median Absolute Deviation (MAD)69462.5
Skewness0.00274074
Sum1.0022935 × 1010
Variance6.5701061 × 109
MonotonicityNot monotonic
2023-12-11T00:00:15.838047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030226 5
 
1.0%
19970212 4
 
0.8%
19951030 3
 
0.6%
20070611 3
 
0.6%
19970426 3
 
0.6%
20030224 3
 
0.6%
19980413 2
 
0.4%
19930406 2
 
0.4%
19980522 2
 
0.4%
20011210 2
 
0.4%
Other values (453) 471
94.2%
ValueCountFrequency (%)
19811214 1
0.2%
19821228 1
0.2%
19830729 1
0.2%
19870618 1
0.2%
19870706 1
0.2%
19871005 1
0.2%
19871211 1
0.2%
19880425 1
0.2%
19880921 1
0.2%
19890502 1
0.2%
ValueCountFrequency (%)
20200131 1
0.2%
20191128 1
0.2%
20190722 1
0.2%
20190522 1
0.2%
20190429 1
0.2%
20190422 1
0.2%
20190416 1
0.2%
20190326 1
0.2%
20190315 1
0.2%
20181227 1
0.2%
Distinct256
Distinct (%)51.5%
Missing3
Missing (%)0.6%
Memory size4.0 KiB
2023-12-11T00:00:16.651181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.2092555
Min length2

Characters and Unicode

Total characters2092
Distinct characters161
Distinct categories3 ?
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 (%)25.2%

Sample

1st row교남동
2nd row개포4동
3rd row교남동
4th row천호제3동
5th row중계1동
ValueCountFrequency (%)
논현1동 7
 
1.4%
명동 7
 
1.4%
길동 6
 
1.2%
서교동 6
 
1.2%
회현동 5
 
1.0%
신대방제1동 5
 
1.0%
성내제1동 5
 
1.0%
신당동 5
 
1.0%
수유제2동 4
 
0.8%
공덕동 4
 
0.8%
Other values (246) 443
89.1%
2023-12-11T00:00:17.558063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
499
23.9%
219
 
10.5%
1 129
 
6.2%
2 95
 
4.5%
52
 
2.5%
3 48
 
2.3%
4 35
 
1.7%
29
 
1.4%
25
 
1.2%
25
 
1.2%
Other values (151) 936
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1752
83.7%
Decimal Number 326
 
15.6%
Other Punctuation 14
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
499
28.5%
219
 
12.5%
52
 
3.0%
29
 
1.7%
25
 
1.4%
25
 
1.4%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (142) 819
46.7%
Decimal Number
ValueCountFrequency (%)
1 129
39.6%
2 95
29.1%
3 48
 
14.7%
4 35
 
10.7%
5 8
 
2.5%
6 5
 
1.5%
7 5
 
1.5%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1752
83.7%
Common 340
 
16.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
499
28.5%
219
 
12.5%
52
 
3.0%
29
 
1.7%
25
 
1.4%
25
 
1.4%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (142) 819
46.7%
Common
ValueCountFrequency (%)
1 129
37.9%
2 95
27.9%
3 48
 
14.1%
4 35
 
10.3%
. 14
 
4.1%
5 8
 
2.4%
6 5
 
1.5%
7 5
 
1.5%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1752
83.7%
ASCII 340
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
499
28.5%
219
 
12.5%
52
 
3.0%
29
 
1.7%
25
 
1.4%
25
 
1.4%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (142) 819
46.7%
ASCII
ValueCountFrequency (%)
1 129
37.9%
2 95
27.9%
3 48
 
14.1%
4 35
 
10.3%
. 14
 
4.1%
5 8
 
2.4%
6 5
 
1.5%
7 5
 
1.5%
8 1
 
0.3%

폐업_일자(BIZQIT_DE)
Real number (ℝ)

MISSING 

Distinct290
Distinct (%)95.4%
Missing196
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean20079542
Minimum19910627
Maximum20200204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:17.934064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19910627
5-th percentile19952542
Q120030122
median20080910
Q320140909
95-th percentile20190401
Maximum20200204
Range289577
Interquartile range (IQR)110787.5

Descriptive statistics

Standard deviation73731.749
Coefficient of variation (CV)0.0036719836
Kurtosis-1.0469989
Mean20079542
Median Absolute Deviation (MAD)59706
Skewness-0.16730471
Sum6.1041808 × 109
Variance5.4363708 × 109
MonotonicityNot monotonic
2023-12-11T00:00:18.302946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030220 3
 
0.6%
20030226 3
 
0.6%
19980526 2
 
0.4%
19940711 2
 
0.4%
20030225 2
 
0.4%
20101110 2
 
0.4%
20180328 2
 
0.4%
20050930 2
 
0.4%
19990618 2
 
0.4%
20030218 2
 
0.4%
Other values (280) 282
56.4%
(Missing) 196
39.2%
ValueCountFrequency (%)
19910627 1
0.2%
19940311 1
0.2%
19940328 1
0.2%
19940512 1
0.2%
19940704 1
0.2%
19940711 2
0.4%
19940906 1
0.2%
19941220 1
0.2%
19941230 1
0.2%
19950601 1
0.2%
ValueCountFrequency (%)
20200204 1
0.2%
20200123 1
0.2%
20200109 2
0.4%
20191209 1
0.2%
20191128 1
0.2%
20191029 1
0.2%
20191022 1
0.2%
20191017 1
0.2%
20191011 1
0.2%
20190904 1
0.2%
Distinct114
Distinct (%)40.9%
Missing221
Missing (%)44.2%
Memory size4.0 KiB
2023-12-11T00:00:18.851921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length4
Mean length5.3225806
Min length2

Characters and Unicode

Total characters1485
Distinct characters159
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)32.6%

Sample

1st row직권폐업
2nd row영업종료
3rd row개인사정
4th row난곡로도로확장 건물철거
5th row사업부진
ValueCountFrequency (%)
영업부진 47
 
13.7%
자진폐업 24
 
7.0%
개인사정 22
 
6.4%
폐업 15
 
4.4%
사업부진 13
 
3.8%
전출처리됨 11
 
3.2%
직권폐업 10
 
2.9%
서울특별시 9
 
2.6%
행정처분 8
 
2.3%
이사 8
 
2.3%
Other values (127) 177
51.5%
2023-12-11T00:00:19.741017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
167
 
11.2%
89
 
6.0%
79
 
5.3%
72
 
4.8%
67
 
4.5%
66
 
4.4%
65
 
4.4%
37
 
2.5%
34
 
2.3%
32
 
2.2%
Other values (149) 777
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1344
90.5%
Space Separator 65
 
4.4%
Decimal Number 40
 
2.7%
Open Punctuation 13
 
0.9%
Close Punctuation 13
 
0.9%
Other Punctuation 5
 
0.3%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
167
 
12.4%
89
 
6.6%
79
 
5.9%
72
 
5.4%
67
 
5.0%
66
 
4.9%
37
 
2.8%
34
 
2.5%
32
 
2.4%
26
 
1.9%
Other values (135) 675
50.2%
Decimal Number
ValueCountFrequency (%)
0 12
30.0%
5 7
17.5%
1 6
15.0%
4 4
 
10.0%
2 4
 
10.0%
6 3
 
7.5%
7 2
 
5.0%
3 1
 
2.5%
9 1
 
2.5%
Space Separator
ValueCountFrequency (%)
65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1344
90.5%
Common 141
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
167
 
12.4%
89
 
6.6%
79
 
5.9%
72
 
5.4%
67
 
5.0%
66
 
4.9%
37
 
2.8%
34
 
2.5%
32
 
2.4%
26
 
1.9%
Other values (135) 675
50.2%
Common
ValueCountFrequency (%)
65
46.1%
( 13
 
9.2%
) 13
 
9.2%
0 12
 
8.5%
5 7
 
5.0%
1 6
 
4.3%
. 5
 
3.5%
- 5
 
3.5%
4 4
 
2.8%
2 4
 
2.8%
Other values (4) 7
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1344
90.5%
ASCII 141
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
167
 
12.4%
89
 
6.6%
79
 
5.9%
72
 
5.4%
67
 
5.0%
66
 
4.9%
37
 
2.8%
34
 
2.5%
32
 
2.4%
26
 
1.9%
Other values (135) 675
50.2%
ASCII
ValueCountFrequency (%)
65
46.1%
( 13
 
9.2%
) 13
 
9.2%
0 12
 
8.5%
5 7
 
5.0%
1 6
 
4.3%
. 5
 
3.5%
- 5
 
3.5%
4 4
 
2.8%
2 4
 
2.8%
Other values (4) 7
 
5.0%
Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
일반미용업
189 
일반세탁업
66 
일반이용업
62 
피부미용업
43 
위생관리용역업
30 
Other values (20)
110 

Length

Max length14
Median length5
Mean length5.078
Min length2

Unique

Unique9 ?
Unique (%)1.8%

Sample

1st row일반세탁업
2nd row일반미용업
3rd row여관업
4th row일반미용업
5th row위생관리용역업

Common Values

ValueCountFrequency (%)
일반미용업 189
37.8%
일반세탁업 66
 
13.2%
일반이용업 62
 
12.4%
피부미용업 43
 
8.6%
위생관리용역업 30
 
6.0%
네일아트업 29
 
5.8%
복합건축물 23
 
4.6%
여관업 15
 
3.0%
공동탕업 14
 
2.8%
여인숙업 5
 
1.0%
Other values (15) 24
 
4.8%

Length

2023-12-11T00:00:20.205439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 189
37.3%
일반세탁업 66
 
13.0%
일반이용업 62
 
12.2%
피부미용업 43
 
8.5%
위생관리용역업 31
 
6.1%
네일아트업 29
 
5.7%
복합건축물 23
 
4.5%
여관업 15
 
3.0%
공동탕업 14
 
2.8%
기타 9
 
1.8%
Other values (14) 26
 
5.1%

교육_수료_일(EDC_COMPL_DE)
Real number (ℝ)

MISSING 

Distinct135
Distinct (%)83.9%
Missing339
Missing (%)67.8%
Infinite0
Infinite (%)0.0%
Mean19958051
Minimum10101
Maximum20191108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:20.521991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile20030326
Q120050608
median20080508
Q320100706
95-th percentile20170802
Maximum20191108
Range20181007
Interquartile range (IQR)50098

Descriptive statistics

Standard deviation1582495.1
Coefficient of variation (CV)0.079291063
Kurtosis160.77181
Mean19958051
Median Absolute Deviation (MAD)29400
Skewness-12.675157
Sum3.2132462 × 109
Variance2.5042907 × 1012
MonotonicityNot monotonic
2023-12-11T00:00:20.863868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070817 3
 
0.6%
20090924 3
 
0.6%
20050530 3
 
0.6%
20090507 3
 
0.6%
20080711 2
 
0.4%
20050608 2
 
0.4%
20070605 2
 
0.4%
20090406 2
 
0.4%
20070628 2
 
0.4%
20100518 2
 
0.4%
Other values (125) 137
27.4%
(Missing) 339
67.8%
ValueCountFrequency (%)
10101 1
0.2%
20000905 1
0.2%
20001208 1
0.2%
20010522 1
0.2%
20010823 1
0.2%
20020130 1
0.2%
20020329 1
0.2%
20020628 1
0.2%
20030326 1
0.2%
20030528 2
0.4%
ValueCountFrequency (%)
20191108 1
0.2%
20191003 1
0.2%
20181210 1
0.2%
20181205 1
0.2%
20181120 1
0.2%
20180820 1
0.2%
20180409 1
0.2%
20180226 1
0.2%
20170802 1
0.2%
20170303 1
0.2%

객실_수(RUM_CO)
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9
Minimum0
Maximum120
Zeros482
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:21.244390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum120
Range120
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0992111
Coefficient of variation (CV)7.8880123
Kurtosis190.72726
Mean0.9
Median Absolute Deviation (MAD)0
Skewness12.806024
Sum450
Variance50.398798
MonotonicityNot monotonic
2023-12-11T00:00:21.512208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 482
96.4%
10 3
 
0.6%
19 3
 
0.6%
20 2
 
0.4%
80 1
 
0.2%
18 1
 
0.2%
6 1
 
0.2%
120 1
 
0.2%
14 1
 
0.2%
8 1
 
0.2%
Other values (4) 4
 
0.8%
ValueCountFrequency (%)
0 482
96.4%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
10 3
 
0.6%
11 1
 
0.2%
14 1
 
0.2%
18 1
 
0.2%
19 3
 
0.6%
20 2
 
0.4%
ValueCountFrequency (%)
120 1
 
0.2%
80 1
 
0.2%
34 1
 
0.2%
25 1
 
0.2%
20 2
0.4%
19 3
0.6%
18 1
 
0.2%
14 1
 
0.2%
11 1
 
0.2%
10 3
0.6%

한실_수(HANSHIL_CO)
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.258
Minimum0
Maximum13
Zeros482
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:21.826387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5046474
Coefficient of variation (CV)5.8319668
Kurtosis42.682053
Mean0.258
Median Absolute Deviation (MAD)0
Skewness6.4228185
Sum129
Variance2.2639639
MonotonicityNot monotonic
2023-12-11T00:00:22.231380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 482
96.4%
9 3
 
0.6%
4 2
 
0.4%
7 2
 
0.4%
3 2
 
0.4%
8 2
 
0.4%
13 2
 
0.4%
1 1
 
0.2%
11 1
 
0.2%
6 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
0 482
96.4%
1 1
 
0.2%
2 1
 
0.2%
3 2
 
0.4%
4 2
 
0.4%
6 1
 
0.2%
7 2
 
0.4%
8 2
 
0.4%
9 3
 
0.6%
11 1
 
0.2%
ValueCountFrequency (%)
13 2
0.4%
12 1
 
0.2%
11 1
 
0.2%
9 3
0.6%
8 2
0.4%
7 2
0.4%
6 1
 
0.2%
4 2
0.4%
3 2
0.4%
2 1
 
0.2%

양실_수(YANGSIL_CO)
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.564
Minimum0
Maximum34
Zeros485
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:22.700064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.522886
Coefficient of variation (CV)6.2462517
Kurtosis50.051541
Mean0.564
Median Absolute Deviation (MAD)0
Skewness6.9163938
Sum282
Variance12.410725
MonotonicityNot monotonic
2023-12-11T00:00:23.161464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 485
97.0%
8 2
 
0.4%
24 2
 
0.4%
30 2
 
0.4%
20 2
 
0.4%
11 1
 
0.2%
9 1
 
0.2%
17 1
 
0.2%
10 1
 
0.2%
23 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
0 485
97.0%
8 2
 
0.4%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
14 1
 
0.2%
17 1
 
0.2%
20 2
 
0.4%
23 1
 
0.2%
24 2
 
0.4%
ValueCountFrequency (%)
34 1
0.2%
30 2
0.4%
24 2
0.4%
23 1
0.2%
20 2
0.4%
17 1
0.2%
14 1
0.2%
11 1
0.2%
10 1
0.2%
9 1
0.2%

의자_수(CHAIR_CO)
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)3.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.4268537
Minimum0
Maximum28
Zeros203
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:24.029626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile8
Maximum28
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9677247
Coefficient of variation (CV)1.2228692
Kurtosis12.942498
Mean2.4268537
Median Absolute Deviation (MAD)2
Skewness2.5351944
Sum1211
Variance8.8073899
MonotonicityNot monotonic
2023-12-11T00:00:24.692075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 203
40.6%
3 109
21.8%
2 54
 
10.8%
4 52
 
10.4%
5 22
 
4.4%
6 15
 
3.0%
8 10
 
2.0%
1 9
 
1.8%
12 5
 
1.0%
10 5
 
1.0%
Other values (6) 15
 
3.0%
ValueCountFrequency (%)
0 203
40.6%
1 9
 
1.8%
2 54
 
10.8%
3 109
21.8%
4 52
 
10.4%
5 22
 
4.4%
6 15
 
3.0%
7 5
 
1.0%
8 10
 
2.0%
9 4
 
0.8%
ValueCountFrequency (%)
28 1
 
0.2%
15 2
 
0.4%
14 1
 
0.2%
13 2
 
0.4%
12 5
 
1.0%
10 5
 
1.0%
9 4
 
0.8%
8 10
2.0%
7 5
 
1.0%
6 15
3.0%

욕실_수(BTR_CO)
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.616
Minimum0
Maximum50
Zeros488
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:25.120292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4650215
Coefficient of variation (CV)7.2484115
Kurtosis69.441205
Mean0.616
Median Absolute Deviation (MAD)0
Skewness8.0800187
Sum308
Variance19.936417
MonotonicityNot monotonic
2023-12-11T00:00:25.409602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 488
97.6%
26 1
 
0.2%
45 1
 
0.2%
20 1
 
0.2%
27 1
 
0.2%
7 1
 
0.2%
33 1
 
0.2%
50 1
 
0.2%
18 1
 
0.2%
2 1
 
0.2%
Other values (3) 3
 
0.6%
ValueCountFrequency (%)
0 488
97.6%
2 1
 
0.2%
7 1
 
0.2%
15 1
 
0.2%
18 1
 
0.2%
20 1
 
0.2%
26 1
 
0.2%
27 1
 
0.2%
31 1
 
0.2%
33 1
 
0.2%
ValueCountFrequency (%)
50 1
0.2%
45 1
0.2%
34 1
0.2%
33 1
0.2%
31 1
0.2%
27 1
0.2%
26 1
0.2%
20 1
0.2%
18 1
0.2%
15 1
0.2%

발한실_여부(BALHANSIL_AT)
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing11
Missing (%)2.2%
Memory size1.1 KiB
False
469 
True
 
20
(Missing)
 
11
ValueCountFrequency (%)
False 469
93.8%
True 20
 
4.0%
(Missing) 11
 
2.2%
2023-12-11T00:00:25.639503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

세탁기_수(WASHMC_CO)
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
487 
1
 
5
3
 
3
2
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 487
97.4%
1 5
 
1.0%
3 3
 
0.6%
2 3
 
0.6%
4 2
 
0.4%

Length

2023-12-11T00:00:25.870622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:26.084591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 487
97.4%
1 5
 
1.0%
3 3
 
0.6%
2 3
 
0.6%
4 2
 
0.4%
Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:26.543951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters11000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)99.6%

Sample

1st row3160000-205-2016-00007
2nd row3010000-202-1987-00310
3rd row3180000-225-2016-00003
4th row3050000-206-2003-00018
5th row3220000-204-1989-01223
ValueCountFrequency (%)
3140000-211-2004-00019 2
 
0.4%
3170000-204-1994-00557 1
 
0.2%
3140000-210-2001-00046 1
 
0.2%
3090000-204-1996-00727 1
 
0.2%
3070000-204-1998-01937 1
 
0.2%
3150000-206-2009-00003 1
 
0.2%
3010000-204-2010-00001 1
 
0.2%
3230000-204-1988-01408 1
 
0.2%
3190000-212-2009-00012 1
 
0.2%
3220000-204-1988-01629 1
 
0.2%
Other values (489) 489
97.8%
2023-12-11T00:00:27.319484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4430
40.3%
- 1500
 
13.6%
2 1294
 
11.8%
1 1160
 
10.5%
3 822
 
7.5%
9 486
 
4.4%
4 355
 
3.2%
6 245
 
2.2%
5 244
 
2.2%
8 241
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9500
86.4%
Dash Punctuation 1500
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4430
46.6%
2 1294
 
13.6%
1 1160
 
12.2%
3 822
 
8.7%
9 486
 
5.1%
4 355
 
3.7%
6 245
 
2.6%
5 244
 
2.6%
8 241
 
2.5%
7 223
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4430
40.3%
- 1500
 
13.6%
2 1294
 
11.8%
1 1160
 
10.5%
3 822
 
7.5%
9 486
 
4.4%
4 355
 
3.2%
6 245
 
2.2%
5 244
 
2.2%
8 241
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4430
40.3%
- 1500
 
13.6%
2 1294
 
11.8%
1 1160
 
10.5%
3 822
 
7.5%
9 486
 
4.4%
4 355
 
3.2%
6 245
 
2.2%
5 244
 
2.2%
8 241
 
2.2%

참고_사항(REFER_MATTER)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
내국인
497 
외국인
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
내국인 497
99.4%
외국인 3
 
0.6%

Length

2023-12-11T00:00:27.655696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:27.922666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내국인 497
99.4%
외국인 3
 
0.6%

국적(NLTY)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
497 
중국
 
3

Length

Max length4
Median length4
Mean length3.988
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 497
99.4%
중국 3
 
0.6%

Length

2023-12-11T00:00:28.172446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:28.427370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 497
99.4%
중국 3
 
0.6%

신_주소(OLD_ADRES)
Text

MISSING 

Distinct251
Distinct (%)99.6%
Missing248
Missing (%)49.6%
Memory size4.0 KiB
2023-12-11T00:00:29.171620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length45
Mean length31.873016
Min length22

Characters and Unicode

Total characters8032
Distinct characters285
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

Unique250 ?
Unique (%)99.2%

Sample

1st row서울특별시 구로구 신도림로 56-13, 2층 202호 (신도림동)
2nd row서울특별시 송파구 법원로 90, 906호 (문정동, 파트너스2)
3rd row서울특별시 은평구 응암로29길 12, 지상1층 101호 (응암동)
4th row서울특별시 은평구 연서로4길 5-13, 1층 102호 (역촌동)
5th row서울특별시 송파구 동남로23길 40-27, (오금동)
ValueCountFrequency (%)
서울특별시 252
 
16.5%
1층 46
 
3.0%
관악구 20
 
1.3%
2층 19
 
1.2%
은평구 18
 
1.2%
동대문구 17
 
1.1%
송파구 15
 
1.0%
강동구 14
 
0.9%
강서구 14
 
0.9%
구로구 14
 
0.9%
Other values (688) 1096
71.9%
2023-12-11T00:00:30.320127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1273
 
15.8%
1 343
 
4.3%
, 341
 
4.2%
334
 
4.2%
306
 
3.8%
281
 
3.5%
272
 
3.4%
( 262
 
3.3%
) 262
 
3.3%
259
 
3.2%
Other values (275) 4099
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4546
56.6%
Decimal Number 1290
 
16.1%
Space Separator 1273
 
15.8%
Other Punctuation 341
 
4.2%
Open Punctuation 262
 
3.3%
Close Punctuation 262
 
3.3%
Dash Punctuation 42
 
0.5%
Uppercase Letter 15
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
334
 
7.3%
306
 
6.7%
281
 
6.2%
272
 
6.0%
259
 
5.7%
253
 
5.6%
252
 
5.5%
252
 
5.5%
161
 
3.5%
124
 
2.7%
Other values (255) 2052
45.1%
Decimal Number
ValueCountFrequency (%)
1 343
26.6%
2 212
16.4%
3 128
 
9.9%
0 125
 
9.7%
5 109
 
8.4%
6 92
 
7.1%
4 86
 
6.7%
8 75
 
5.8%
7 70
 
5.4%
9 50
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 10
66.7%
C 3
 
20.0%
M 1
 
6.7%
A 1
 
6.7%
Space Separator
ValueCountFrequency (%)
1273
100.0%
Other Punctuation
ValueCountFrequency (%)
, 341
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Close Punctuation
ValueCountFrequency (%)
) 262
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4546
56.6%
Common 3470
43.2%
Latin 16
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
334
 
7.3%
306
 
6.7%
281
 
6.2%
272
 
6.0%
259
 
5.7%
253
 
5.6%
252
 
5.5%
252
 
5.5%
161
 
3.5%
124
 
2.7%
Other values (255) 2052
45.1%
Common
ValueCountFrequency (%)
1273
36.7%
1 343
 
9.9%
, 341
 
9.8%
( 262
 
7.6%
) 262
 
7.6%
2 212
 
6.1%
3 128
 
3.7%
0 125
 
3.6%
5 109
 
3.1%
6 92
 
2.7%
Other values (5) 323
 
9.3%
Latin
ValueCountFrequency (%)
B 10
62.5%
C 3
 
18.8%
M 1
 
6.2%
A 1
 
6.2%
e 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4546
56.6%
ASCII 3486
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1273
36.5%
1 343
 
9.8%
, 341
 
9.8%
( 262
 
7.5%
) 262
 
7.5%
2 212
 
6.1%
3 128
 
3.7%
0 125
 
3.6%
5 109
 
3.1%
6 92
 
2.6%
Other values (10) 339
 
9.7%
Hangul
ValueCountFrequency (%)
334
 
7.3%
306
 
6.7%
281
 
6.2%
272
 
6.0%
259
 
5.7%
253
 
5.6%
252
 
5.5%
252
 
5.5%
161
 
3.5%
124
 
2.7%
Other values (255) 2052
45.1%
Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:31.085042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length44
Mean length28.05
Min length21

Characters and Unicode

Total characters14025
Distinct characters262
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

Unique498 ?
Unique (%)99.6%

Sample

1st row서울특별시 마포구 동교동 182번지 9호
2nd row서울특별시 광진구 군자동 48번지 59호
3rd row서울특별시 영등포구 대림동 733번지 6호
4th row서울특별시 강남구 신사동 653번지 2호 정립빌딩 지상2층
5th row서울특별시 금천구 가산동 143번지 18호
ValueCountFrequency (%)
서울특별시 500
 
18.3%
강남구 48
 
1.8%
1호 47
 
1.7%
1층 38
 
1.4%
성북구 32
 
1.2%
관악구 30
 
1.1%
송파구 29
 
1.1%
마포구 26
 
1.0%
양천구 24
 
0.9%
구로구 24
 
0.9%
Other values (830) 1932
70.8%
2023-12-11T00:00:32.076943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3533
25.2%
1 608
 
4.3%
580
 
4.1%
569
 
4.1%
560
 
4.0%
540
 
3.9%
508
 
3.6%
502
 
3.6%
502
 
3.6%
500
 
3.6%
Other values (252) 5623
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7797
55.6%
Space Separator 3533
25.2%
Decimal Number 2574
 
18.4%
Dash Punctuation 39
 
0.3%
Open Punctuation 30
 
0.2%
Close Punctuation 30
 
0.2%
Uppercase Letter 12
 
0.1%
Other Punctuation 9
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
580
 
7.4%
569
 
7.3%
560
 
7.2%
540
 
6.9%
508
 
6.5%
502
 
6.4%
502
 
6.4%
500
 
6.4%
500
 
6.4%
499
 
6.4%
Other values (227) 2537
32.5%
Decimal Number
ValueCountFrequency (%)
1 608
23.6%
2 361
14.0%
3 274
10.6%
0 257
10.0%
4 225
 
8.7%
5 206
 
8.0%
6 181
 
7.0%
7 159
 
6.2%
9 152
 
5.9%
8 151
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
A 4
33.3%
B 3
25.0%
T 1
 
8.3%
P 1
 
8.3%
C 1
 
8.3%
K 1
 
8.3%
S 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 7
77.8%
@ 1
 
11.1%
/ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
3533
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7797
55.6%
Common 6216
44.3%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
580
 
7.4%
569
 
7.3%
560
 
7.2%
540
 
6.9%
508
 
6.5%
502
 
6.4%
502
 
6.4%
500
 
6.4%
500
 
6.4%
499
 
6.4%
Other values (227) 2537
32.5%
Common
ValueCountFrequency (%)
3533
56.8%
1 608
 
9.8%
2 361
 
5.8%
3 274
 
4.4%
0 257
 
4.1%
4 225
 
3.6%
5 206
 
3.3%
6 181
 
2.9%
7 159
 
2.6%
9 152
 
2.4%
Other values (8) 260
 
4.2%
Latin
ValueCountFrequency (%)
A 4
33.3%
B 3
25.0%
T 1
 
8.3%
P 1
 
8.3%
C 1
 
8.3%
K 1
 
8.3%
S 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7797
55.6%
ASCII 6228
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3533
56.7%
1 608
 
9.8%
2 361
 
5.8%
3 274
 
4.4%
0 257
 
4.1%
4 225
 
3.6%
5 206
 
3.3%
6 181
 
2.9%
7 159
 
2.6%
9 152
 
2.4%
Other values (15) 272
 
4.4%
Hangul
ValueCountFrequency (%)
580
 
7.4%
569
 
7.3%
560
 
7.2%
540
 
6.9%
508
 
6.5%
502
 
6.4%
502
 
6.4%
500
 
6.4%
500
 
6.4%
499
 
6.4%
Other values (227) 2537
32.5%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2019-10-31 00:00:00
Maximum2020-03-17 00:00:00
2023-12-11T00:00:32.325773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:32.542662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
201911
146 
201910
141 
201909
108 
201912
96 
201908
 
9

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201908
2nd row201912
3rd row201909
4th row201911
5th row201912

Common Values

ValueCountFrequency (%)
201911 146
29.2%
201910 141
28.2%
201909 108
21.6%
201912 96
19.2%
201908 9
 
1.8%

Length

2023-12-11T00:00:32.853305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:33.082801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201911 146
29.2%
201910 141
28.2%
201909 108
21.6%
201912 96
19.2%
201908 9
 
1.8%

업종소분류코드(INDUTY_SCLAS_CD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

업종업태코드(SNITAT_BIZCND_CD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

도로코드(ROAD_CD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

Sample

자치구_코드(ATDRC_CD)위생_업종_코드(SNITAT_INDUTY_CD)년도(YEAR)업소_일련번호(INDUTY_SN)위생_업종_명(SNITAT_INDUTY_NM)허가_신고_일자(PRMISN_PRMISN_DE)업소_명(BSSH_NM)영업장_면적(BUZPLC_AR)업소_소재지_전화번호(BSSH_LOCPLC_TELNO)영업자시작일(BMAN_BGNDE)법인명(CPR_NM)법인번호(CPR_NO)소재지시작일(LOCPLC_BGNDE)행정동명(ADSTRD_NM)폐업_일자(BIZQIT_DE)폐업_사유(BIZQIT_SE_RESN)위생_업태_명(SNITAT_BIZCND_NM)교육_수료_일(EDC_COMPL_DE)객실_수(RUM_CO)한실_수(HANSHIL_CO)양실_수(YANGSIL_CO)의자_수(CHAIR_CO)욕실_수(BTR_CO)발한실_여부(BALHANSIL_AT)세탁기_수(WASHMC_CO)허가_번호(PRMISN_NO)참고_사항(REFER_MATTER)내국인_외국인_구분(NATIVE_FRGNR_SE)국적(NLTY)신_주소(OLD_ADRES)구_주소(NW_ADRES)DW_등록_일시(DW_REGIST_DT)기준년월(STDR_YM)업종소분류코드(INDUTY_SCLAS_CD)업종업태코드(SNITAT_BIZCND_CD)도로코드(ROAD_CD)
03020000213199428미용업(피부), 미용업(손톱ㆍ발톱)19910605포스헤어64.66<NA>20130716<NA><NA>20110415교남동<NA>직권폐업일반세탁업<NA>00000N03160000-205-2016-00007<NA>내국인<NA>서울특별시 구로구 신도림로 56-13, 2층 202호 (신도림동)서울특별시 마포구 동교동 182번지 9호2020-03-17201908<NA><NA><NA>
13050000206201218이용업19830607샤넬헤어클럽15.5802 945402219870721<NA><NA>20180306개포4동20090619<NA>일반미용업<NA>00000N03010000-202-1987-00310<NA>내국인<NA>서울특별시 송파구 법원로 90, 906호 (문정동, 파트너스2)서울특별시 광진구 군자동 48번지 59호2020-03-17201912<NA><NA><NA>
2323000020519951세탁업20160226주식회사 대한노인회유통사업단42.020020309한국생활환경연구소<NA>20010316교남동<NA><NA>여관업20100506000226N03180000-225-2016-00003<NA>내국인<NA>서울특별시 은평구 응암로29길 12, 지상1층 101호 (응암동)서울특별시 영등포구 대림동 733번지 6호2020-03-17201909<NA><NA><NA>
33240000206201453미용업(일반)20100323소피소마에스테틱40.0023491078320030116<NA><NA>20160721천호제3동20150811<NA>일반미용업<NA>00050N03050000-206-2003-00018<NA>내국인<NA><NA>서울특별시 강남구 신사동 653번지 2호 정립빌딩 지상2층2020-03-17201911<NA><NA><NA>
43190000211200884공중이용시설20080318가슴벅찬 마음 헤어살롱43.002 886342320080401<NA><NA>19920316중계1동20031030영업종료위생관리용역업2004102500020N03220000-204-1989-01223<NA>내국인<NA><NA>서울특별시 금천구 가산동 143번지 18호2020-03-17201912<NA><NA><NA>
5308000021520072미용업(피부), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)19600411백성사54466.0902 693446620050511<NA><NA>20140523사당제4동<NA>개인사정일반세탁업<NA>00050N03000000-203-1983-00647<NA>내국인<NA><NA>서울특별시 구로구 오류동 155번지 8호 2층2020-03-17201911<NA><NA><NA>
63080000205199037미용업20140919익수0.002 6408880120120207<NA><NA>20030226삼각산동20100609<NA>복합건축물2007052200080N03210000-203-1989-01459<NA>내국인<NA><NA>서울특별시 은평구 갈현동 472번지 3호 지층2020-03-17201910<NA><NA><NA>
7309000020619914미용업(종합)19961122서원장21.4502 855868920041224<NA><NA>20140929서초제3동20090825난곡로도로확장 건물철거피부미용업<NA>00040N03200000-204-1990-01201<NA>내국인<NA><NA>서울특별시 중랑구 중화동 286번지 25호 대신빌딩2020-03-17201910<NA><NA><NA>
83220000204199216미용업20070921모아모아197.0603857 540020030225<NA>110121000455819981008도화동<NA>사업부진일반미용업<NA>00000N03110000-215-2018-00018<NA>내국인<NA>서울특별시 은평구 연서로4길 5-13, 1층 102호 (역촌동)서울특별시 송파구 문정동 29번지 24호2020-03-17201912<NA><NA><NA>
931600002031990239미용업20050516상원여관50.170103004344120081103<NA><NA>20040910종로1.2.3.4가동20111005<NA>일반세탁업<NA>000045N03170000-204-1996-00462<NA>내국인<NA><NA>서울특별시 성동구 행당동 138번지 39호2020-03-17201910<NA><NA><NA>
자치구_코드(ATDRC_CD)위생_업종_코드(SNITAT_INDUTY_CD)년도(YEAR)업소_일련번호(INDUTY_SN)위생_업종_명(SNITAT_INDUTY_NM)허가_신고_일자(PRMISN_PRMISN_DE)업소_명(BSSH_NM)영업장_면적(BUZPLC_AR)업소_소재지_전화번호(BSSH_LOCPLC_TELNO)영업자시작일(BMAN_BGNDE)법인명(CPR_NM)법인번호(CPR_NO)소재지시작일(LOCPLC_BGNDE)행정동명(ADSTRD_NM)폐업_일자(BIZQIT_DE)폐업_사유(BIZQIT_SE_RESN)위생_업태_명(SNITAT_BIZCND_NM)교육_수료_일(EDC_COMPL_DE)객실_수(RUM_CO)한실_수(HANSHIL_CO)양실_수(YANGSIL_CO)의자_수(CHAIR_CO)욕실_수(BTR_CO)발한실_여부(BALHANSIL_AT)세탁기_수(WASHMC_CO)허가_번호(PRMISN_NO)참고_사항(REFER_MATTER)내국인_외국인_구분(NATIVE_FRGNR_SE)국적(NLTY)신_주소(OLD_ADRES)구_주소(NW_ADRES)DW_등록_일시(DW_REGIST_DT)기준년월(STDR_YM)업종소분류코드(INDUTY_SCLAS_CD)업종업태코드(SNITAT_BIZCND_CD)도로코드(ROAD_CD)
49030700002111991847미용업20180813수림헤어라인15.0419971213<NA><NA>20171218문정2동<NA><NA>복합건축물<NA>00030N03240000-211-2015-00028<NA>내국인<NA>서울특별시 용산구 보광로 57, (보광동,1층)서울특별시 양천구 목동 506번지 9호2019-12-16201911<NA><NA><NA>
491324000021120135미용업20060816실비아미영실22.802 792 99519961202<NA><NA>19970328성수2가제1동20030215부진일반미용업2009040600040N03080000-204-1996-00563<NA>내국인<NA><NA>서울특별시 강남구 신사동 549번지 4호 지상2층3층2019-12-16201909<NA><NA><NA>
492310000020319932644미용업20011126챠밍15.6819960227<NA><NA>20050613청담동<NA>업종전환피부미용업<NA>00060N03220000-204-2005-00178<NA>내국인<NA><NA>서울특별시 서초구 서초동 1550번지 2호 (지하1층)2020-03-17201909<NA><NA><NA>
4933000000205199290미용업20181023창성기업31.6010 6709979719951026<NA><NA>19930420방화제3동<NA><NA>일반미용업<NA>00020N03240000-203-1991-00334<NA>내국인<NA>서울특별시 관악구 난곡로24길 32, 1층 (신림동)서울특별시 구로구 오류동 6번지 222호2019-12-16201911<NA><NA><NA>
4943040000204201946이용업19990324주스킨(Joo'SKIN)41.2502 953301820130122<NA><NA>20110915창제2동<NA><NA>일반이용업2018120500050N03090000-211-2003-00005<NA>내국인<NA><NA>서울특별시 강남구 논현동 148번지 19호 -2022019-12-16201912<NA><NA><NA>
49531600002122012959미용업20150303디엔디 헤어클럽2348.940220020504<NA><NA>19960604구의제2동20030124<NA>공중이용시설 기타<NA>00030N03220000-213-2010-00133<NA>내국인<NA>서울특별시 영등포구 국회대로74길 9, (여의도동,지하1층 107호)서울특별시 동작구 사당동 276번지 4호2020-03-17201910<NA><NA><NA>
496319000020419871519이용업20020720힐링&뷰티4790.2602 2699662420050420<NA><NA>20150317남현동19941220영업부진일반미용업2009051100060N03060000-204-1990-00931<NA>내국인<NA><NA>서울특별시 영등포구 문래동3가 54번지 문래엘지빌리지상가 108호2019-12-16201910<NA><NA><NA>
4973040000206200613미용업(피부)20110101예원이용원0.002 945356620010619<NA><NA>20030729수서동20160726개인사정일반세탁업<NA>08040N03240000-203-2003-00060<NA>내국인<NA>서울특별시 은평구 불광로 20, 지상10층 A-45,46,49,50호 (대조동, 팜스퀘어)서울특별시 중랑구 상봉동 129번지 69호2020-03-17201912<NA><NA><NA>
4983230000211201114이용업19931028박헤어센스19.2602 232391919980831<NA><NA>19950828방이2동<NA>자진폐업복합건축물<NA>00000N03070000-204-1994-01111<NA>내국인<NA>서울특별시 마포구 월드컵북로12길 93, (성산동)서울특별시 양천구 목동 917번지 1호 2층2020-03-17201911<NA><NA><NA>
499321000021219956미용업20000908백조세탁소49.002 384810820100713<NA>110111319711120030516신촌동<NA>질병치료일반이용업2004102800040N03220000-212-2011-00061<NA>내국인<NA><NA>서울특별시 강남구 개포동 1258번지 지상1층 102호2019-12-16201912<NA><NA><NA>