Overview

Dataset statistics

Number of variables61
Number of observations500
Missing cells6332
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory258.4 KiB
Average record size in memory529.3 B

Variable types

Numeric30
Categorical16
Text11
Unsupported4

Dataset

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

Alerts

모범_음식점_여부(PRGN_RSTRT_AT) has constant value ""Constant
영업장_무도장_면적(BUZPLC_DCHL_AR) has constant value ""Constant
감사실_면적(ISP_AR) has constant value ""Constant
기준년월(STDR_YM) has constant value ""Constant
지하_사용_층_부터_수(UNDGRND_USE_FLOOR_FROM_CO) is highly imbalanced (79.8%)Imbalance
지하_사용_층_까지_수(UNDGRND_USE_FLOOR_TO_CO) is highly imbalanced (74.6%)Imbalance
최대급식인원수(MAX_MLSV_NMPR_CO) is highly imbalanced (97.4%)Imbalance
운영_형태_구분(OPER_STLE_SE) is highly imbalanced (94.4%)Imbalance
창고_면적(WRHOUS_AR) is highly imbalanced (97.4%)Imbalance
내국인_외국인_구분(NATIVE_FRGNR_SE) is highly imbalanced (89.4%)Imbalance
국적(NLTY) is highly imbalanced (57.6%)Imbalance
신_주소(OLD_ADRES) has 253 (50.6%) missing valuesMissing
업소_소재지_전화번호(BSSH_LOCPLC_TELNO) has 86 (17.2%) missing valuesMissing
법인명(CPR_NM) has 400 (80.0%) missing valuesMissing
법인번호(CPR_NO) has 412 (82.4%) missing valuesMissing
행정동명(ADSTRD_NM) has 22 (4.4%) missing valuesMissing
폐업_일자(BIZQIT_DE) has 123 (24.6%) missing valuesMissing
폐업_사유(BIZQIT_SE_RESN) has 207 (41.4%) missing valuesMissing
교육_수료_일(EDC_COMPL_DE) has 396 (79.2%) missing valuesMissing
급식소_종류_코드(DINRM_KND) has 497 (99.4%) missing valuesMissing
참고_사항(REFER_MATTER) has 500 (100.0%) missing valuesMissing
조건부_시작일(CNDL_BGNDE) has 480 (96.0%) missing valuesMissing
조건부_종료일(CNDL_ENDDE) has 484 (96.8%) missing valuesMissing
조건부_허가_신고_사유(CNDL_PRMISN_STTEMNT_RESN) has 477 (95.4%) missing valuesMissing
허가_신고_조건(PRMISN_PRMISN_CND) has 492 (98.4%) missing valuesMissing
업종업태(SNITAT_BIZCND_CD) has 500 (100.0%) missing valuesMissing
업종소분류코드(INDUTY_SCLAS_CD) has 500 (100.0%) missing valuesMissing
도로코드(ROAD_CD) has 500 (100.0%) missing valuesMissing
평균급식인원수(AVRG_MLSV_NMPR_CO) is highly skewed (γ1 = 21.09577064)Skewed
업소_명(BSSH_NM) has unique valuesUnique
허가_번호(PRMISN_NO) has unique valuesUnique
참고_사항(REFER_MATTER) is an unsupported type, check if it needs cleaning or further analysisUnsupported
업종업태(SNITAT_BIZCND_CD) 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
도로코드(ROAD_CD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업장_면적(BUZPLC_AR) has 347 (69.4%) zerosZeros
지상_사용_층_부터_수(GROUND_USE_FLOOR_FROM_CO) has 358 (71.6%) zerosZeros
지상_사용_층_까지_수(GROUND_USE_FLOOR_TO_CO) has 359 (71.8%) zerosZeros
총_층_수(TOT_FLOOR_CO) has 413 (82.6%) zerosZeros
남성_종업원_수(ML_EMPLY_CO) has 445 (89.0%) zerosZeros
여성_종업원_수(FML_EMPLY_CO) has 422 (84.4%) zerosZeros
평균급식인원수(AVRG_MLSV_NMPR_CO) has 493 (98.6%) zerosZeros
1일급식인원수(DAIL_MLSV_NMPR_CO) has 494 (98.8%) zerosZeros
인당_평균_급식_비(PSNBY_AVRG_MLSV_CT) has 494 (98.8%) zerosZeros
영업장_조리장_면적(BUZPLC_JORIJANG_AR) has 274 (54.8%) zerosZeros
영업장_객실_면적(BUZPLC_RUM_AR) has 469 (93.8%) zerosZeros
영업장_기타_면적(BUZPLC_ETC_AR) has 397 (79.4%) zerosZeros

Reproduction

Analysis started2023-12-10 15:00:37.288550
Analysis finished2023-12-10 15:00:40.658911
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3171300
Minimum3100000
Maximum3230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:40.922542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3100000
5-th percentile3110000
Q13140000
median3180000
Q33210000
95-th percentile3220000
Maximum3230000
Range130000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation38588.705
Coefficient of variation (CV)0.012168103
Kurtosis-1.1682572
Mean3171300
Median Absolute Deviation (MAD)30000
Skewness-0.34819391
Sum1.58565 × 109
Variance1.4890882 × 109
MonotonicityNot monotonic
2023-12-11T00:00:41.823441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3210000 75
15.0%
3220000 58
11.6%
3110000 45
9.0%
3200000 45
9.0%
3150000 43
8.6%
3180000 42
8.4%
3120000 40
8.0%
3170000 38
7.6%
3160000 37
7.4%
3190000 28
 
5.6%
Other values (4) 49
9.8%
ValueCountFrequency (%)
3100000 17
 
3.4%
3110000 45
9.0%
3120000 40
8.0%
3130000 13
 
2.6%
3140000 12
 
2.4%
3150000 43
8.6%
3160000 37
7.4%
3170000 38
7.6%
3180000 42
8.4%
3190000 28
5.6%
ValueCountFrequency (%)
3230000 7
 
1.4%
3220000 58
11.6%
3210000 75
15.0%
3200000 45
9.0%
3190000 28
 
5.6%
3180000 42
8.4%
3170000 38
7.6%
3160000 37
7.4%
3150000 43
8.6%
3140000 12
 
2.4%
Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.388
Minimum101
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:42.123539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median104
Q3110
95-th percentile134
Maximum134
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.7607377
Coefficient of variation (CV)0.090892257
Kurtosis2.6851145
Mean107.388
Median Absolute Deviation (MAD)3
Skewness1.9429147
Sum53694
Variance95.272
MonotonicityNot monotonic
2023-12-11T00:00:42.411179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
101 217
43.4%
104 74
 
14.8%
107 53
 
10.6%
134 48
 
9.6%
110 32
 
6.4%
112 31
 
6.2%
109 9
 
1.8%
113 7
 
1.4%
103 6
 
1.2%
105 6
 
1.2%
Other values (7) 17
 
3.4%
ValueCountFrequency (%)
101 217
43.4%
102 1
 
0.2%
103 6
 
1.2%
104 74
 
14.8%
105 6
 
1.2%
106 5
 
1.0%
107 53
 
10.6%
109 9
 
1.8%
110 32
 
6.4%
112 31
 
6.2%
ValueCountFrequency (%)
134 48
9.6%
133 2
 
0.4%
121 3
 
0.6%
120 3
 
0.6%
117 1
 
0.2%
114 2
 
0.4%
113 7
 
1.4%
112 31
6.2%
110 32
6.4%
109 9
 
1.8%

년도(YEAR)
Real number (ℝ)

Distinct46
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.678
Minimum1923
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:42.777485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1923
5-th percentile1986.95
Q11997
median2006
Q32014
95-th percentile2018
Maximum2020
Range97
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.080246
Coefficient of variation (CV)0.0055271951
Kurtosis4.9131087
Mean2004.678
Median Absolute Deviation (MAD)8
Skewness-1.2045575
Sum1002339
Variance122.77186
MonotonicityNot monotonic
2023-12-11T00:00:43.107220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2011 44
 
8.8%
2018 28
 
5.6%
2017 23
 
4.6%
2016 23
 
4.6%
2019 23
 
4.6%
2004 21
 
4.2%
2013 21
 
4.2%
2000 18
 
3.6%
1993 17
 
3.4%
2012 17
 
3.4%
Other values (36) 265
53.0%
ValueCountFrequency (%)
1923 1
 
0.2%
1972 1
 
0.2%
1975 1
 
0.2%
1976 3
0.6%
1977 1
 
0.2%
1980 2
0.4%
1981 1
 
0.2%
1982 1
 
0.2%
1983 3
0.6%
1984 3
0.6%
ValueCountFrequency (%)
2020 1
 
0.2%
2019 23
4.6%
2018 28
5.6%
2017 23
4.6%
2016 23
4.6%
2015 12
 
2.4%
2014 16
 
3.2%
2013 21
4.2%
2012 17
 
3.4%
2011 44
8.8%
Distinct400
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2179.528
Minimum1
Maximum16586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:43.522012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q183.75
median299.5
Q33088.5
95-th percentile10677.5
Maximum16586
Range16585
Interquartile range (IQR)3004.75

Descriptive statistics

Standard deviation3528.2219
Coefficient of variation (CV)1.6188009
Kurtosis3.002078
Mean2179.528
Median Absolute Deviation (MAD)275.5
Skewness1.9154857
Sum1089764
Variance12448350
MonotonicityNot monotonic
2023-12-11T00:00:43.872281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 6
 
1.2%
4 5
 
1.0%
24 4
 
0.8%
34 4
 
0.8%
19 3
 
0.6%
37 3
 
0.6%
108 3
 
0.6%
58 3
 
0.6%
15 3
 
0.6%
130 3
 
0.6%
Other values (390) 463
92.6%
ValueCountFrequency (%)
1 1
 
0.2%
2 1
 
0.2%
3 2
 
0.4%
4 5
1.0%
5 1
 
0.2%
6 2
 
0.4%
7 2
 
0.4%
8 2
 
0.4%
9 6
1.2%
10 2
 
0.4%
ValueCountFrequency (%)
16586 1
0.2%
15841 1
0.2%
15416 1
0.2%
14530 1
0.2%
14289 1
0.2%
14193 1
0.2%
14113 1
0.2%
13895 1
0.2%
13684 1
0.2%
13628 1
0.2%
Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
일반음식점
227 
휴게음식점
58 
건강기능식품일반판매업
49 
즉석판매제조가공업
46 
식품등 수입판매업
40 
Other values (12)
80 

Length

Max length13
Median length5
Mean length6.686
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row즉석판매제조가공업
2nd row식품등 수입판매업
3rd row일반음식점
4th row일반음식점
5th row식품등 수입판매업

Common Values

ValueCountFrequency (%)
일반음식점 227
45.4%
휴게음식점 58
 
11.6%
건강기능식품일반판매업 49
 
9.8%
즉석판매제조가공업 46
 
9.2%
식품등 수입판매업 40
 
8.0%
식품자동판매기영업 38
 
7.6%
집단급식소 9
 
1.8%
유통전문판매업 7
 
1.4%
식품소분업 4
 
0.8%
단란주점 4
 
0.8%
Other values (7) 18
 
3.6%

Length

2023-12-11T00:00:44.260227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 227
42.0%
휴게음식점 58
 
10.7%
건강기능식품일반판매업 49
 
9.1%
즉석판매제조가공업 46
 
8.5%
식품등 40
 
7.4%
수입판매업 40
 
7.4%
식품자동판매기영업 38
 
7.0%
집단급식소 9
 
1.7%
유통전문판매업 7
 
1.3%
식품제조가공업 4
 
0.7%
Other values (8) 22
 
4.1%
Distinct483
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20045122
Minimum19680925
Maximum20191230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:44.575574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19680925
5-th percentile19850529
Q119970519
median20060823
Q320130420
95-th percentile20190219
Maximum20191230
Range510305
Interquartile range (IQR)159901

Descriptive statistics

Standard deviation103061.4
Coefficient of variation (CV)0.0051414706
Kurtosis-0.32229441
Mean20045122
Median Absolute Deviation (MAD)79665
Skewness-0.54037603
Sum1.0022561 × 1010
Variance1.0621653 × 1010
MonotonicityNot monotonic
2023-12-11T00:00:44.947014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19971101 5
 
1.0%
19940819 2
 
0.4%
20100429 2
 
0.4%
20030618 2
 
0.4%
20090707 2
 
0.4%
19961021 2
 
0.4%
20020831 2
 
0.4%
19891218 2
 
0.4%
20110621 2
 
0.4%
20100728 2
 
0.4%
Other values (473) 477
95.4%
ValueCountFrequency (%)
19680925 1
0.2%
19700514 1
0.2%
19760303 1
0.2%
19780828 1
0.2%
19781221 1
0.2%
19791217 1
0.2%
19820119 1
0.2%
19820406 1
0.2%
19820907 1
0.2%
19821209 1
0.2%
ValueCountFrequency (%)
20191230 1
0.2%
20191223 1
0.2%
20191211 1
0.2%
20191209 1
0.2%
20191203 1
0.2%
20191112 1
0.2%
20191111 1
0.2%
20191025 1
0.2%
20191016 1
0.2%
20191014 1
0.2%

업소_명(BSSH_NM)
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:45.721595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length18
Mean length6.11
Min length1

Characters and Unicode

Total characters3055
Distinct characters556
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row네스카페 마포KPX점
2nd row가락촌
3rd row솔루아(SOLUA)
4th row어류겐
5th row미스포테이토
ValueCountFrequency (%)
주식회사 7
 
1.2%
gs25 3
 
0.5%
2
 
0.3%
포장마차 2
 
0.3%
네스카페 1
 
0.2%
진명민속촌 1
 
0.2%
대성식당 1
 
0.2%
갯벌 1
 
0.2%
낙지촌 1
 
0.2%
티와이글로벌(주 1
 
0.2%
Other values (559) 559
96.5%
2023-12-11T00:00:47.063050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 81
 
2.7%
) 81
 
2.7%
79
 
2.6%
66
 
2.2%
64
 
2.1%
60
 
2.0%
53
 
1.7%
47
 
1.5%
45
 
1.5%
34
 
1.1%
Other values (546) 2445
80.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2620
85.8%
Open Punctuation 81
 
2.7%
Close Punctuation 81
 
2.7%
Space Separator 79
 
2.6%
Uppercase Letter 65
 
2.1%
Lowercase Letter 55
 
1.8%
Decimal Number 54
 
1.8%
Other Punctuation 15
 
0.5%
Dash Punctuation 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66
 
2.5%
64
 
2.4%
60
 
2.3%
53
 
2.0%
47
 
1.8%
45
 
1.7%
34
 
1.3%
34
 
1.3%
31
 
1.2%
29
 
1.1%
Other values (484) 2157
82.3%
Uppercase Letter
ValueCountFrequency (%)
S 9
13.8%
C 6
 
9.2%
G 5
 
7.7%
B 4
 
6.2%
P 4
 
6.2%
R 4
 
6.2%
A 4
 
6.2%
T 4
 
6.2%
U 3
 
4.6%
L 3
 
4.6%
Other values (12) 19
29.2%
Lowercase Letter
ValueCountFrequency (%)
e 10
18.2%
f 7
12.7%
a 5
9.1%
o 4
 
7.3%
l 4
 
7.3%
r 3
 
5.5%
h 3
 
5.5%
i 3
 
5.5%
y 3
 
5.5%
t 2
 
3.6%
Other values (7) 11
20.0%
Decimal Number
ValueCountFrequency (%)
2 16
29.6%
5 14
25.9%
1 10
18.5%
3 4
 
7.4%
4 3
 
5.6%
7 2
 
3.7%
6 2
 
3.7%
8 1
 
1.9%
9 1
 
1.9%
0 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 5
33.3%
& 3
20.0%
· 2
 
13.3%
! 1
 
6.7%
# 1
 
6.7%
' 1
 
6.7%
, 1
 
6.7%
% 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 81
100.0%
Close Punctuation
ValueCountFrequency (%)
) 81
100.0%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2620
85.8%
Common 315
 
10.3%
Latin 120
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66
 
2.5%
64
 
2.4%
60
 
2.3%
53
 
2.0%
47
 
1.8%
45
 
1.7%
34
 
1.3%
34
 
1.3%
31
 
1.2%
29
 
1.1%
Other values (484) 2157
82.3%
Latin
ValueCountFrequency (%)
e 10
 
8.3%
S 9
 
7.5%
f 7
 
5.8%
C 6
 
5.0%
a 5
 
4.2%
G 5
 
4.2%
B 4
 
3.3%
o 4
 
3.3%
l 4
 
3.3%
P 4
 
3.3%
Other values (29) 62
51.7%
Common
ValueCountFrequency (%)
( 81
25.7%
) 81
25.7%
79
25.1%
2 16
 
5.1%
5 14
 
4.4%
1 10
 
3.2%
. 5
 
1.6%
3 4
 
1.3%
- 4
 
1.3%
4 3
 
1.0%
Other values (13) 18
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2620
85.8%
ASCII 433
 
14.2%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 81
18.7%
) 81
18.7%
79
18.2%
2 16
 
3.7%
5 14
 
3.2%
1 10
 
2.3%
e 10
 
2.3%
S 9
 
2.1%
f 7
 
1.6%
C 6
 
1.4%
Other values (51) 120
27.7%
Hangul
ValueCountFrequency (%)
66
 
2.5%
64
 
2.4%
60
 
2.3%
53
 
2.0%
47
 
1.8%
45
 
1.7%
34
 
1.3%
34
 
1.3%
31
 
1.2%
29
 
1.1%
Other values (484) 2157
82.3%
None
ValueCountFrequency (%)
· 2
100.0%

신_주소(OLD_ADRES)
Text

MISSING 

Distinct246
Distinct (%)99.6%
Missing253
Missing (%)50.6%
Memory size4.0 KiB
2023-12-11T00:00:48.248905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length33.748988
Min length21

Characters and Unicode

Total characters8336
Distinct characters278
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)99.2%

Sample

1st row서울특별시 은평구 은평로10길 17, (응암동,1층)
2nd row서울특별시 강서구 강서로56길 17, 엔씨백화점 지하1층 (등촌동)
3rd row서울특별시 은평구 서오릉로 183, 지상1층 (구산동)
4th row서울특별시 금천구 독산로 353, 지상1층 (독산동)
5th row서울특별시 구로구 고척로 141, (고척동,,216)
ValueCountFrequency (%)
서울특별시 247
 
15.9%
1층 64
 
4.1%
영등포구 33
 
2.1%
서초구 32
 
2.1%
강남구 27
 
1.7%
강서구 26
 
1.7%
지하1층 23
 
1.5%
구로구 18
 
1.2%
은평구 18
 
1.2%
금천구 17
 
1.1%
Other values (664) 1045
67.4%
2023-12-11T00:00:49.547927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1303
 
15.6%
1 375
 
4.5%
, 372
 
4.5%
353
 
4.2%
312
 
3.7%
287
 
3.4%
274
 
3.3%
262
 
3.1%
) 255
 
3.1%
( 255
 
3.1%
Other values (268) 4288
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4843
58.1%
Space Separator 1303
 
15.6%
Decimal Number 1248
 
15.0%
Other Punctuation 372
 
4.5%
Close Punctuation 256
 
3.1%
Open Punctuation 256
 
3.1%
Dash Punctuation 27
 
0.3%
Uppercase Letter 24
 
0.3%
Math Symbol 5
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
353
 
7.3%
312
 
6.4%
287
 
5.9%
274
 
5.7%
262
 
5.4%
251
 
5.2%
247
 
5.1%
247
 
5.1%
165
 
3.4%
140
 
2.9%
Other values (239) 2305
47.6%
Decimal Number
ValueCountFrequency (%)
1 375
30.0%
2 193
15.5%
3 118
 
9.5%
0 109
 
8.7%
5 102
 
8.2%
6 86
 
6.9%
4 85
 
6.8%
8 68
 
5.4%
7 60
 
4.8%
9 52
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
C 5
20.8%
A 4
16.7%
B 4
16.7%
K 3
12.5%
M 2
 
8.3%
D 2
 
8.3%
S 2
 
8.3%
G 1
 
4.2%
N 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 255
99.6%
] 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 255
99.6%
[ 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
g 1
50.0%
e 1
50.0%
Space Separator
ValueCountFrequency (%)
1303
100.0%
Other Punctuation
ValueCountFrequency (%)
, 372
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4843
58.1%
Common 3467
41.6%
Latin 26
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
353
 
7.3%
312
 
6.4%
287
 
5.9%
274
 
5.7%
262
 
5.4%
251
 
5.2%
247
 
5.1%
247
 
5.1%
165
 
3.4%
140
 
2.9%
Other values (239) 2305
47.6%
Common
ValueCountFrequency (%)
1303
37.6%
1 375
 
10.8%
, 372
 
10.7%
) 255
 
7.4%
( 255
 
7.4%
2 193
 
5.6%
3 118
 
3.4%
0 109
 
3.1%
5 102
 
2.9%
6 86
 
2.5%
Other values (8) 299
 
8.6%
Latin
ValueCountFrequency (%)
C 5
19.2%
A 4
15.4%
B 4
15.4%
K 3
11.5%
M 2
 
7.7%
D 2
 
7.7%
S 2
 
7.7%
G 1
 
3.8%
g 1
 
3.8%
e 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4842
58.1%
ASCII 3493
41.9%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1303
37.3%
1 375
 
10.7%
, 372
 
10.6%
) 255
 
7.3%
( 255
 
7.3%
2 193
 
5.5%
3 118
 
3.4%
0 109
 
3.1%
5 102
 
2.9%
6 86
 
2.5%
Other values (19) 325
 
9.3%
Hangul
ValueCountFrequency (%)
353
 
7.3%
312
 
6.4%
287
 
5.9%
274
 
5.7%
262
 
5.4%
251
 
5.2%
247
 
5.1%
247
 
5.1%
165
 
3.4%
140
 
2.9%
Other values (238) 2304
47.6%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct487
Distinct (%)98.0%
Missing3
Missing (%)0.6%
Memory size4.0 KiB
2023-12-11T00:00:50.365698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length46
Mean length28.183099
Min length9

Characters and Unicode

Total characters14007
Distinct characters290
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique481 ?
Unique (%)96.8%

Sample

1st row서울특별시 영등포구 영등포동4가 441번지 21호 경방필백화점 지하1층내
2nd row서울특별시 은평구 역촌동 78번지 32호 1층
3rd row서울특별시 양천구 목동 531번지 12호 1층
4th row서울특별시 마포구 서교동 346번지 29호 1층
5th row서울특별시 양천구 목동 917번지 6호 하나로클럽
ValueCountFrequency (%)
서울특별시 497
 
17.8%
서초구 69
 
2.5%
강남구 58
 
2.1%
강서구 52
 
1.9%
1층 51
 
1.8%
동작구 51
 
1.8%
1호 50
 
1.8%
영등포구 49
 
1.8%
은평구 37
 
1.3%
구로구 36
 
1.3%
Other values (825) 1837
65.9%
2023-12-11T00:00:51.561812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3042
21.7%
682
 
4.9%
1 616
 
4.4%
576
 
4.1%
559
 
4.0%
554
 
4.0%
510
 
3.6%
498
 
3.6%
497
 
3.5%
497
 
3.5%
Other values (280) 5976
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8161
58.3%
Space Separator 3042
 
21.7%
Decimal Number 2600
 
18.6%
Dash Punctuation 57
 
0.4%
Close Punctuation 39
 
0.3%
Open Punctuation 39
 
0.3%
Other Punctuation 32
 
0.2%
Uppercase Letter 31
 
0.2%
Lowercase Letter 4
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
682
 
8.4%
576
 
7.1%
559
 
6.8%
554
 
6.8%
510
 
6.2%
498
 
6.1%
497
 
6.1%
497
 
6.1%
485
 
5.9%
470
 
5.8%
Other values (246) 2833
34.7%
Uppercase Letter
ValueCountFrequency (%)
B 8
25.8%
A 5
16.1%
K 3
 
9.7%
G 2
 
6.5%
S 2
 
6.5%
F 2
 
6.5%
N 2
 
6.5%
C 2
 
6.5%
W 1
 
3.2%
I 1
 
3.2%
Other values (3) 3
 
9.7%
Decimal Number
ValueCountFrequency (%)
1 616
23.7%
2 321
12.3%
3 273
10.5%
5 237
 
9.1%
4 236
 
9.1%
0 214
 
8.2%
6 200
 
7.7%
9 177
 
6.8%
7 176
 
6.8%
8 150
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
r 1
25.0%
w 1
25.0%
o 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 31
96.9%
? 1
 
3.1%
Space Separator
ValueCountFrequency (%)
3042
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8161
58.3%
Common 5811
41.5%
Latin 35
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
682
 
8.4%
576
 
7.1%
559
 
6.8%
554
 
6.8%
510
 
6.2%
498
 
6.1%
497
 
6.1%
497
 
6.1%
485
 
5.9%
470
 
5.8%
Other values (246) 2833
34.7%
Common
ValueCountFrequency (%)
3042
52.3%
1 616
 
10.6%
2 321
 
5.5%
3 273
 
4.7%
5 237
 
4.1%
4 236
 
4.1%
0 214
 
3.7%
6 200
 
3.4%
9 177
 
3.0%
7 176
 
3.0%
Other values (7) 319
 
5.5%
Latin
ValueCountFrequency (%)
B 8
22.9%
A 5
14.3%
K 3
 
8.6%
G 2
 
5.7%
S 2
 
5.7%
F 2
 
5.7%
N 2
 
5.7%
C 2
 
5.7%
W 1
 
2.9%
I 1
 
2.9%
Other values (7) 7
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8161
58.3%
ASCII 5846
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3042
52.0%
1 616
 
10.5%
2 321
 
5.5%
3 273
 
4.7%
5 237
 
4.1%
4 236
 
4.0%
0 214
 
3.7%
6 200
 
3.4%
9 177
 
3.0%
7 176
 
3.0%
Other values (24) 354
 
6.1%
Hangul
ValueCountFrequency (%)
682
 
8.4%
576
 
7.1%
559
 
6.8%
554
 
6.8%
510
 
6.2%
498
 
6.1%
497
 
6.1%
497
 
6.1%
485
 
5.9%
470
 
5.8%
Other values (246) 2833
34.7%

영업장_면적(BUZPLC_AR)
Real number (ℝ)

ZEROS 

Distinct146
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.04106
Minimum0
Maximum352.19
Zeros347
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:51.882340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323.785
95-th percentile112.8315
Maximum352.19
Range352.19
Interquartile range (IQR)23.785

Descriptive statistics

Standard deviation43.659479
Coefficient of variation (CV)2.2929122
Kurtosis16.470582
Mean19.04106
Median Absolute Deviation (MAD)0
Skewness3.6141778
Sum9520.53
Variance1906.1501
MonotonicityNot monotonic
2023-12-11T00:00:52.319919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 347
69.4%
26.0 3
 
0.6%
26.5 2
 
0.4%
45.0 2
 
0.4%
30.36 2
 
0.4%
26.4 2
 
0.4%
20.16 2
 
0.4%
20.14 2
 
0.4%
23.35 1
 
0.2%
23.76 1
 
0.2%
Other values (136) 136
 
27.2%
ValueCountFrequency (%)
0.0 347
69.4%
3.0 1
 
0.2%
5.51 1
 
0.2%
6.1 1
 
0.2%
9.0 1
 
0.2%
9.86 1
 
0.2%
12.51 1
 
0.2%
12.87 1
 
0.2%
15.0 1
 
0.2%
15.18 1
 
0.2%
ValueCountFrequency (%)
352.19 1
0.2%
314.36 1
0.2%
254.64 1
0.2%
241.81 1
0.2%
234.8 1
0.2%
216.9 1
0.2%
189.89 1
0.2%
168.98 1
0.2%
166.08 1
0.2%
165.0 1
0.2%
Distinct385
Distinct (%)93.0%
Missing86
Missing (%)17.2%
Memory size4.0 KiB
2023-12-11T00:00:52.942115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.062802
Min length2

Characters and Unicode

Total characters4166
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)92.0%

Sample

1st row0220685430
2nd row02
3rd row0200000000
4th row0234528838
5th row027034540
ValueCountFrequency (%)
02 191
28.3%
010 37
 
5.5%
0200000000 14
 
2.1%
0 4
 
0.6%
070 3
 
0.4%
011 3
 
0.4%
016 2
 
0.3%
813 2
 
0.3%
972 2
 
0.3%
313 2
 
0.3%
Other values (414) 415
61.5%
2023-12-11T00:00:53.940189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 870
20.9%
2 640
15.4%
1 334
 
8.0%
8 329
 
7.9%
5 322
 
7.7%
3 321
 
7.7%
312
 
7.5%
6 299
 
7.2%
9 250
 
6.0%
7 249
 
6.0%
Other values (2) 240
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3853
92.5%
Space Separator 312
 
7.5%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 870
22.6%
2 640
16.6%
1 334
 
8.7%
8 329
 
8.5%
5 322
 
8.4%
3 321
 
8.3%
6 299
 
7.8%
9 250
 
6.5%
7 249
 
6.5%
4 239
 
6.2%
Space Separator
ValueCountFrequency (%)
312
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 870
20.9%
2 640
15.4%
1 334
 
8.0%
8 329
 
7.9%
5 322
 
7.7%
3 321
 
7.7%
312
 
7.5%
6 299
 
7.2%
9 250
 
6.0%
7 249
 
6.0%
Other values (2) 240
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 870
20.9%
2 640
15.4%
1 334
 
8.0%
8 329
 
7.9%
5 322
 
7.7%
3 321
 
7.7%
312
 
7.5%
6 299
 
7.2%
9 250
 
6.0%
7 249
 
6.0%
Other values (2) 240
 
5.8%
Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20069223
Minimum19811120
Maximum20200226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:54.315982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19811120
5-th percentile19930692
Q119990416
median20080623
Q320150805
95-th percentile20190726
Maximum20200226
Range389106
Interquartile range (IQR)160389.5

Descriptive statistics

Standard deviation91234.198
Coefficient of variation (CV)0.0045459756
Kurtosis-0.88030792
Mean20069223
Median Absolute Deviation (MAD)80093.5
Skewness-0.32651677
Sum1.0034611 × 1010
Variance8.3236788 × 109
MonotonicityNot monotonic
2023-12-11T00:00:54.748235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19981219 8
 
1.6%
19971101 4
 
0.8%
20070816 2
 
0.4%
20170330 2
 
0.4%
20090907 2
 
0.4%
20180313 2
 
0.4%
20040618 2
 
0.4%
20181022 2
 
0.4%
20020523 2
 
0.4%
20180418 2
 
0.4%
Other values (464) 472
94.4%
ValueCountFrequency (%)
19811120 1
0.2%
19821208 1
0.2%
19821216 1
0.2%
19841004 1
0.2%
19850819 1
0.2%
19851002 1
0.2%
19851127 1
0.2%
19860621 1
0.2%
19871118 1
0.2%
19880524 1
0.2%
ValueCountFrequency (%)
20200226 1
0.2%
20200217 1
0.2%
20200214 1
0.2%
20200213 1
0.2%
20200131 1
0.2%
20191219 1
0.2%
20191211 1
0.2%
20191202 1
0.2%
20191128 1
0.2%
20191119 1
0.2%

법인명(CPR_NM)
Text

MISSING 

Distinct98
Distinct (%)98.0%
Missing400
Missing (%)80.0%
Memory size4.0 KiB
2023-12-11T00:00:55.273267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length8.51
Min length2

Characters and Unicode

Total characters851
Distinct characters198
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row(주)미소담은
2nd row주식회사솔본푸드
3rd row(주)아이제이네트웍스
4th row(주)행복생활건강
5th row(주)한국티이아이
ValueCountFrequency (%)
주식회사 21
 
16.9%
주)행복생활건강 2
 
1.6%
주)인네이처 2
 
1.6%
주)앤앤컴퍼니 1
 
0.8%
주)온마켓 1
 
0.8%
프렐커퍼레이션 1
 
0.8%
주)지알지아이앤티 1
 
0.8%
우리찬 1
 
0.8%
행림약품(주 1
 
0.8%
주)다온에프앤비 1
 
0.8%
Other values (92) 92
74.2%
2023-12-11T00:00:56.363694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
10.0%
( 62
 
7.3%
) 62
 
7.3%
29
 
3.4%
28
 
3.3%
27
 
3.2%
25
 
2.9%
24
 
2.8%
22
 
2.6%
19
 
2.2%
Other values (188) 468
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 702
82.5%
Open Punctuation 62
 
7.3%
Close Punctuation 62
 
7.3%
Space Separator 24
 
2.8%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
85
 
12.1%
29
 
4.1%
28
 
4.0%
27
 
3.8%
25
 
3.6%
22
 
3.1%
19
 
2.7%
17
 
2.4%
17
 
2.4%
14
 
2.0%
Other values (184) 419
59.7%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 703
82.6%
Common 148
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
85
 
12.1%
29
 
4.1%
28
 
4.0%
27
 
3.8%
25
 
3.6%
22
 
3.1%
19
 
2.7%
17
 
2.4%
17
 
2.4%
14
 
2.0%
Other values (185) 420
59.7%
Common
ValueCountFrequency (%)
( 62
41.9%
) 62
41.9%
24
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 702
82.5%
ASCII 148
 
17.4%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
85
 
12.1%
29
 
4.1%
28
 
4.0%
27
 
3.8%
25
 
3.6%
22
 
3.1%
19
 
2.7%
17
 
2.4%
17
 
2.4%
14
 
2.0%
Other values (184) 419
59.7%
ASCII
ValueCountFrequency (%)
( 62
41.9%
) 62
41.9%
24
 
16.2%
None
ValueCountFrequency (%)
1
100.0%

법인번호(CPR_NO)
Real number (ℝ)

MISSING 

Distinct84
Distinct (%)95.5%
Missing412
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean1.2967949 × 1012
Minimum1.10111 × 1012
Maximum5.106281 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:56.697558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.10111 × 1012
5-th percentile1.1011104 × 1012
Q11.1011122 × 1012
median1.1011144 × 1012
Q31.1011164 × 1012
95-th percentile2.0661101 × 1012
Maximum5.106281 × 1012
Range4.005171 × 1012
Interquartile range (IQR)4245567.5

Descriptive statistics

Standard deviation6.4067911 × 1011
Coefficient of variation (CV)0.49404812
Kurtosis25.293427
Mean1.2967949 × 1012
Median Absolute Deviation (MAD)2212716.5
Skewness4.8279373
Sum1.1411796 × 1014
Variance4.1046972 × 1023
MonotonicityNot monotonic
2023-12-11T00:00:57.066322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101115423796 2
 
0.4%
1101110305288 2
 
0.4%
1101111342130 2
 
0.4%
1101110899976 2
 
0.4%
1101116263034 1
 
0.2%
1146410001939 1
 
0.2%
1101111437171 1
 
0.2%
1101115991248 1
 
0.2%
1341110088018 1
 
0.2%
1101111663289 1
 
0.2%
Other values (74) 74
 
14.8%
(Missing) 412
82.4%
ValueCountFrequency (%)
1101110028848 1
0.2%
1101110271596 1
0.2%
1101110305288 2
0.4%
1101110350225 1
0.2%
1101110568414 1
0.2%
1101110899976 2
0.4%
1101110920672 1
0.2%
1101110988977 1
0.2%
1101111167257 1
0.2%
1101111213597 1
0.2%
ValueCountFrequency (%)
5106281026018 1
0.2%
4908161010816 1
0.2%
2850110418322 1
0.2%
2301110196228 1
0.2%
2101110032187 1
0.2%
2001110344815 1
0.2%
1944110001129 1
0.2%
1741360000400 1
0.2%
1615110036967 1
0.2%
1611110010737 1
0.2%
Distinct479
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20059053
Minimum19750316
Maximum20200108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:57.406415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19750316
5-th percentile19910809
Q119990277
median20051109
Q320140917
95-th percentile20190134
Maximum20200108
Range449792
Interquartile range (IQR)150639.5

Descriptive statistics

Standard deviation91698.333
Coefficient of variation (CV)0.0045714188
Kurtosis-0.68894903
Mean20059053
Median Absolute Deviation (MAD)79303
Skewness-0.30650312
Sum1.0029527 × 1010
Variance8.4085843 × 109
MonotonicityNot monotonic
2023-12-11T00:00:57.717052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20010221 3
 
0.6%
19970116 3
 
0.6%
20190605 2
 
0.4%
20151127 2
 
0.4%
20001219 2
 
0.4%
20010831 2
 
0.4%
20130312 2
 
0.4%
19981204 2
 
0.4%
19970131 2
 
0.4%
20100428 2
 
0.4%
Other values (469) 478
95.6%
ValueCountFrequency (%)
19750316 1
0.2%
19810507 1
0.2%
19830709 1
0.2%
19831012 1
0.2%
19831216 1
0.2%
19841031 1
0.2%
19841105 1
0.2%
19850119 1
0.2%
19850406 1
0.2%
19851023 1
0.2%
ValueCountFrequency (%)
20200108 1
0.2%
20191218 1
0.2%
20191216 1
0.2%
20191209 1
0.2%
20191128 1
0.2%
20191105 1
0.2%
20191031 1
0.2%
20191021 1
0.2%
20191011 1
0.2%
20190829 1
0.2%
Distinct182
Distinct (%)38.1%
Missing22
Missing (%)4.4%
Memory size4.0 KiB
2023-12-11T00:00:58.454373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.2531381
Min length3

Characters and Unicode

Total characters2033
Distinct characters125
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

Unique67 ?
Unique (%)14.0%

Sample

1st row진관동
2nd row상도제1동
3rd row서림동
4th row홍은제1동
5th row불광제1동
ValueCountFrequency (%)
독산제1동 11
 
2.3%
서초제3동 11
 
2.3%
영등포동 10
 
2.1%
역삼1동 10
 
2.1%
신촌동 9
 
1.9%
양재제2동 9
 
1.9%
양재제1동 9
 
1.9%
청룡동 8
 
1.7%
행운동 8
 
1.7%
여의동 7
 
1.5%
Other values (172) 386
80.8%
2023-12-11T00:00:59.532973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
478
23.5%
240
 
11.8%
1 139
 
6.8%
2 80
 
3.9%
3 44
 
2.2%
43
 
2.1%
38
 
1.9%
33
 
1.6%
31
 
1.5%
29
 
1.4%
Other values (115) 878
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1721
84.7%
Decimal Number 310
 
15.2%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
478
27.8%
240
 
13.9%
43
 
2.5%
38
 
2.2%
33
 
1.9%
31
 
1.8%
29
 
1.7%
26
 
1.5%
24
 
1.4%
23
 
1.3%
Other values (106) 756
43.9%
Decimal Number
ValueCountFrequency (%)
1 139
44.8%
2 80
25.8%
3 44
 
14.2%
4 25
 
8.1%
5 14
 
4.5%
7 3
 
1.0%
6 3
 
1.0%
8 2
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1721
84.7%
Common 312
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
478
27.8%
240
 
13.9%
43
 
2.5%
38
 
2.2%
33
 
1.9%
31
 
1.8%
29
 
1.7%
26
 
1.5%
24
 
1.4%
23
 
1.3%
Other values (106) 756
43.9%
Common
ValueCountFrequency (%)
1 139
44.6%
2 80
25.6%
3 44
 
14.1%
4 25
 
8.0%
5 14
 
4.5%
7 3
 
1.0%
6 3
 
1.0%
8 2
 
0.6%
. 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1721
84.7%
ASCII 312
 
15.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
478
27.8%
240
 
13.9%
43
 
2.5%
38
 
2.2%
33
 
1.9%
31
 
1.8%
29
 
1.7%
26
 
1.5%
24
 
1.4%
23
 
1.3%
Other values (106) 756
43.9%
ASCII
ValueCountFrequency (%)
1 139
44.6%
2 80
25.6%
3 44
 
14.1%
4 25
 
8.0%
5 14
 
4.5%
7 3
 
1.0%
6 3
 
1.0%
8 2
 
0.6%
. 2
 
0.6%

폐업_일자(BIZQIT_DE)
Real number (ℝ)

MISSING 

Distinct345
Distinct (%)91.5%
Missing123
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean20074233
Minimum19881112
Maximum20200217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:00.393714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19881112
5-th percentile19940507
Q120020418
median20071220
Q320150204
95-th percentile20190336
Maximum20200217
Range319105
Interquartile range (IQR)129786

Descriptive statistics

Standard deviation78712.232
Coefficient of variation (CV)0.0039210581
Kurtosis-0.91559444
Mean20074233
Median Absolute Deviation (MAD)61011
Skewness-0.24255402
Sum7.5679857 × 109
Variance6.1956155 × 109
MonotonicityNot monotonic
2023-12-11T00:01:00.789891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20041231 9
 
1.8%
20060721 9
 
1.8%
20040515 2
 
0.4%
19951110 2
 
0.4%
20110329 2
 
0.4%
20160217 2
 
0.4%
20020418 2
 
0.4%
20170927 2
 
0.4%
20170118 2
 
0.4%
20041220 2
 
0.4%
Other values (335) 343
68.6%
(Missing) 123
 
24.6%
ValueCountFrequency (%)
19881112 1
0.2%
19890421 1
0.2%
19890801 1
0.2%
19900625 1
0.2%
19900927 1
0.2%
19910614 1
0.2%
19910804 1
0.2%
19921126 1
0.2%
19921224 1
0.2%
19930206 1
0.2%
ValueCountFrequency (%)
20200217 1
0.2%
20191117 1
0.2%
20190917 1
0.2%
20190905 1
0.2%
20190904 1
0.2%
20190828 1
0.2%
20190822 1
0.2%
20190812 1
0.2%
20190730 1
0.2%
20190716 1
0.2%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
자진폐업
264 
<NA>
134 
행정처분
65 
기타
29 
전출
 
8

Length

Max length4
Median length4
Mean length3.852
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자진폐업
2nd row자진폐업
3rd row자진폐업
4th row자진폐업
5th row전출

Common Values

ValueCountFrequency (%)
자진폐업 264
52.8%
<NA> 134
26.8%
행정처분 65
 
13.0%
기타 29
 
5.8%
전출 8
 
1.6%

Length

2023-12-11T00:01:01.121330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:01.651726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자진폐업 264
52.8%
na 134
26.8%
행정처분 65
 
13.0%
기타 29
 
5.8%
전출 8
 
1.6%
Distinct109
Distinct (%)37.2%
Missing207
Missing (%)41.4%
Memory size4.0 KiB
2023-12-11T00:01:02.050011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length7.4027304
Min length1

Characters and Unicode

Total characters2169
Distinct characters172
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

Unique86 ?
Unique (%)29.4%

Sample

1st row경영부진
2nd row자진폐업
3rd row자진폐업
4th row개인사정
5th row영업부진
ValueCountFrequency (%)
영업부진 46
 
10.2%
자진폐업 45
 
10.0%
행정처분 27
 
6.0%
따른 19
 
4.2%
조건부기간 14
 
3.1%
완료에 14
 
3.1%
폐업처리(자동폐업 14
 
3.1%
영업종료 11
 
2.4%
지역명으로 9
 
2.0%
자료 9
 
2.0%
Other values (144) 243
53.9%
2023-12-11T00:01:02.823424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
201
 
9.3%
158
 
7.3%
104
 
4.8%
104
 
4.8%
90
 
4.1%
77
 
3.6%
75
 
3.5%
54
 
2.5%
49
 
2.3%
43
 
2.0%
Other values (162) 1214
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1835
84.6%
Space Separator 158
 
7.3%
Decimal Number 73
 
3.4%
Open Punctuation 26
 
1.2%
Close Punctuation 26
 
1.2%
Other Punctuation 20
 
0.9%
Dash Punctuation 16
 
0.7%
Uppercase Letter 14
 
0.6%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
201
 
11.0%
104
 
5.7%
104
 
5.7%
90
 
4.9%
77
 
4.2%
75
 
4.1%
54
 
2.9%
49
 
2.7%
43
 
2.3%
42
 
2.3%
Other values (137) 996
54.3%
Decimal Number
ValueCountFrequency (%)
1 20
27.4%
0 15
20.5%
2 11
15.1%
4 6
 
8.2%
6 6
 
8.2%
5 5
 
6.8%
3 4
 
5.5%
8 3
 
4.1%
9 2
 
2.7%
7 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 6
42.9%
D 3
21.4%
T 3
21.4%
O 1
 
7.1%
K 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 15
75.0%
, 4
 
20.0%
: 1
 
5.0%
Open Punctuation
ValueCountFrequency (%)
( 25
96.2%
[ 1
 
3.8%
Close Punctuation
ValueCountFrequency (%)
) 25
96.2%
] 1
 
3.8%
Space Separator
ValueCountFrequency (%)
158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1835
84.6%
Common 319
 
14.7%
Latin 15
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
201
 
11.0%
104
 
5.7%
104
 
5.7%
90
 
4.9%
77
 
4.2%
75
 
4.1%
54
 
2.9%
49
 
2.7%
43
 
2.3%
42
 
2.3%
Other values (137) 996
54.3%
Common
ValueCountFrequency (%)
158
49.5%
( 25
 
7.8%
) 25
 
7.8%
1 20
 
6.3%
- 16
 
5.0%
. 15
 
4.7%
0 15
 
4.7%
2 11
 
3.4%
4 6
 
1.9%
6 6
 
1.9%
Other values (9) 22
 
6.9%
Latin
ValueCountFrequency (%)
A 6
40.0%
D 3
20.0%
T 3
20.0%
O 1
 
6.7%
e 1
 
6.7%
K 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1835
84.6%
ASCII 334
 
15.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
201
 
11.0%
104
 
5.7%
104
 
5.7%
90
 
4.9%
77
 
4.2%
75
 
4.1%
54
 
2.9%
49
 
2.7%
43
 
2.3%
42
 
2.3%
Other values (137) 996
54.3%
ASCII
ValueCountFrequency (%)
158
47.3%
( 25
 
7.5%
) 25
 
7.5%
1 20
 
6.0%
- 16
 
4.8%
. 15
 
4.5%
0 15
 
4.5%
2 11
 
3.3%
4 6
 
1.8%
6 6
 
1.8%
Other values (15) 37
 
11.1%
Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
한식
93 
즉석판매제조가공업
52 
분식
42 
식품등 수입판매업
38 
식품자동판매기영업
33 
Other values (37)
242 

Length

Max length15
Median length12
Mean length5.116
Min length2

Unique

Unique7 ?
Unique (%)1.4%

Sample

1st row영업장판매
2nd row일반조리판매
3rd row한식
4th row커피숍
5th row식품등 수입판매업

Common Values

ValueCountFrequency (%)
한식 93
18.6%
즉석판매제조가공업 52
 
10.4%
분식 42
 
8.4%
식품등 수입판매업 38
 
7.6%
식품자동판매기영업 33
 
6.6%
경양식 25
 
5.0%
커피숍 20
 
4.0%
영업장판매 18
 
3.6%
일반조리판매 16
 
3.2%
호프/통닭 15
 
3.0%
Other values (32) 148
29.6%

Length

2023-12-11T00:01:03.111437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 93
17.0%
즉석판매제조가공업 52
 
9.5%
분식 42
 
7.7%
식품등 38
 
7.0%
수입판매업 38
 
7.0%
식품자동판매기영업 33
 
6.0%
경양식 25
 
4.6%
커피숍 20
 
3.7%
영업장판매 18
 
3.3%
기타 16
 
2.9%
Other values (33) 171
31.3%
Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.366
Minimum0
Maximum10
Zeros358
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:03.361275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85410948
Coefficient of variation (CV)2.3336325
Kurtosis67.203152
Mean0.366
Median Absolute Deviation (MAD)0
Skewness6.6806062
Sum183
Variance0.72950301
MonotonicityNot monotonic
2023-12-11T00:01:03.613308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 358
71.6%
1 122
 
24.4%
2 16
 
3.2%
10 2
 
0.4%
3 1
 
0.2%
6 1
 
0.2%
ValueCountFrequency (%)
0 358
71.6%
1 122
 
24.4%
2 16
 
3.2%
3 1
 
0.2%
6 1
 
0.2%
10 2
 
0.4%
ValueCountFrequency (%)
10 2
 
0.4%
6 1
 
0.2%
3 1
 
0.2%
2 16
 
3.2%
1 122
 
24.4%
0 358
71.6%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.438
Minimum0
Maximum20
Zeros359
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:03.965134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2301148
Coefficient of variation (CV)2.8084812
Kurtosis133.83759
Mean0.438
Median Absolute Deviation (MAD)0
Skewness9.5218346
Sum219
Variance1.5131824
MonotonicityNot monotonic
2023-12-11T00:01:04.308683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 359
71.8%
1 108
 
21.6%
2 22
 
4.4%
3 4
 
0.8%
4 2
 
0.4%
7 2
 
0.4%
20 1
 
0.2%
8 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 359
71.8%
1 108
 
21.6%
2 22
 
4.4%
3 4
 
0.8%
4 2
 
0.4%
5 1
 
0.2%
7 2
 
0.4%
8 1
 
0.2%
20 1
 
0.2%
ValueCountFrequency (%)
20 1
 
0.2%
8 1
 
0.2%
7 2
 
0.4%
5 1
 
0.2%
4 2
 
0.4%
3 4
 
0.8%
2 22
 
4.4%
1 108
 
21.6%
0 359
71.8%
Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
467 
1
 
27
2
 
5
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 467
93.4%
1 27
 
5.4%
2 5
 
1.0%
3 1
 
0.2%

Length

2023-12-11T00:01:04.660141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:04.939216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 467
93.4%
1 27
 
5.4%
2 5
 
1.0%
3 1
 
0.2%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
464 
1
 
33
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 464
92.8%
1 33
 
6.6%
2 3
 
0.6%

Length

2023-12-11T00:01:05.156395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:05.471967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 464
92.8%
1 33
 
6.6%
2 3
 
0.6%

총_층_수(TOT_FLOOR_CO)
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.07
Minimum0
Maximum38
Zeros413
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:05.702850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6463257
Coefficient of variation (CV)3.4077811
Kurtosis55.606979
Mean1.07
Median Absolute Deviation (MAD)0
Skewness6.621449
Sum535
Variance13.295691
MonotonicityNot monotonic
2023-12-11T00:01:05.982614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 413
82.6%
3 19
 
3.8%
5 18
 
3.6%
4 14
 
2.8%
2 9
 
1.8%
6 7
 
1.4%
1 3
 
0.6%
9 3
 
0.6%
7 2
 
0.4%
8 2
 
0.4%
Other values (8) 10
 
2.0%
ValueCountFrequency (%)
0 413
82.6%
1 3
 
0.6%
2 9
 
1.8%
3 19
 
3.8%
4 14
 
2.8%
5 18
 
3.6%
6 7
 
1.4%
7 2
 
0.4%
8 2
 
0.4%
9 3
 
0.6%
ValueCountFrequency (%)
38 2
0.4%
32 1
 
0.2%
23 1
 
0.2%
19 1
 
0.2%
17 1
 
0.2%
13 1
 
0.2%
11 2
0.4%
10 1
 
0.2%
9 3
0.6%
8 2
0.4%

교육_수료_일(EDC_COMPL_DE)
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)94.2%
Missing396
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean20064300
Minimum20000407
Maximum20110928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:06.359301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000407
5-th percentile20020918
Q120040998
median20060912
Q320090239
95-th percentile20110198
Maximum20110928
Range110521
Interquartile range (IQR)49241

Descriptive statistics

Standard deviation27591.284
Coefficient of variation (CV)0.0013751431
Kurtosis-0.99393516
Mean20064300
Median Absolute Deviation (MAD)20106
Skewness0.021432908
Sum2.0866872 × 109
Variance7.6127893 × 108
MonotonicityNot monotonic
2023-12-11T00:01:06.692263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20050419 2
 
0.4%
20110107 2
 
0.4%
20060913 2
 
0.4%
20020823 2
 
0.4%
20060712 2
 
0.4%
20030723 2
 
0.4%
20040331 1
 
0.2%
20090109 1
 
0.2%
20070320 1
 
0.2%
20110214 1
 
0.2%
Other values (88) 88
 
17.6%
(Missing) 396
79.2%
ValueCountFrequency (%)
20000407 1
0.2%
20020206 1
0.2%
20020403 1
0.2%
20020823 2
0.4%
20020902 1
0.2%
20021007 1
0.2%
20021211 1
0.2%
20030116 1
0.2%
20030203 1
0.2%
20030205 1
0.2%
ValueCountFrequency (%)
20110928 1
0.2%
20110720 1
0.2%
20110613 1
0.2%
20110420 1
0.2%
20110314 1
0.2%
20110214 1
0.2%
20110107 2
0.4%
20101228 1
0.2%
20101210 1
0.2%
20100913 1
0.2%
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
비대상
500 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비대상 500
100.0%

Length

2023-12-11T00:01:06.978851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:07.167146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비대상 500
100.0%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
289 
상수도전용
210 
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length4
Mean length4.446
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 289
57.8%
상수도전용 210
42.0%
상수도(음용)지하수(주방용)겸용 1
 
0.2%

Length

2023-12-11T00:01:07.412450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:07.688570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 289
57.8%
상수도전용 210
42.0%
상수도(음용)지하수(주방용)겸용 1
 
0.2%
Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
지상
234 
<NA>
193 
지하
55 
지상2층이상
 
18

Length

Max length6
Median length2
Mean length2.916
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지상 234
46.8%
<NA> 193
38.6%
지하 55
 
11.0%
지상2층이상 18
 
3.6%

Length

2023-12-11T00:01:08.072350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:08.402705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 234
46.8%
na 193
38.6%
지하 55
 
11.0%
지상2층이상 18
 
3.6%

남성_종업원_수(ML_EMPLY_CO)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.186
Minimum0
Maximum10
Zeros445
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:08.689890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.71303635
Coefficient of variation (CV)3.8335288
Kurtosis79.573984
Mean0.186
Median Absolute Deviation (MAD)0
Skewness7.3385083
Sum93
Variance0.50842084
MonotonicityNot monotonic
2023-12-11T00:01:08.913048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 445
89.0%
1 36
 
7.2%
2 10
 
2.0%
3 6
 
1.2%
4 1
 
0.2%
10 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 445
89.0%
1 36
 
7.2%
2 10
 
2.0%
3 6
 
1.2%
4 1
 
0.2%
5 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 6
 
1.2%
2 10
 
2.0%
1 36
 
7.2%
0 445
89.0%

여성_종업원_수(FML_EMPLY_CO)
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.256
Minimum0
Maximum8
Zeros422
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:09.291921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7533918
Coefficient of variation (CV)2.9429367
Kurtosis33.771353
Mean0.256
Median Absolute Deviation (MAD)0
Skewness4.8467687
Sum128
Variance0.5675992
MonotonicityNot monotonic
2023-12-11T00:01:09.620011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 422
84.4%
1 47
 
9.4%
2 23
 
4.6%
3 4
 
0.8%
4 1
 
0.2%
6 1
 
0.2%
8 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 422
84.4%
1 47
 
9.4%
2 23
 
4.6%
3 4
 
0.8%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 4
 
0.8%
2 23
 
4.6%
1 47
 
9.4%
0 422
84.4%
Distinct3
Distinct (%)100.0%
Missing497
Missing (%)99.4%
Memory size4.0 KiB
2023-12-11T00:01:09.910854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row사업체
2nd row기타
3rd row병원
ValueCountFrequency (%)
사업체 1
33.3%
기타 1
33.3%
병원 1
33.3%
2023-12-11T00:01:10.681429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

평균급식인원수(AVRG_MLSV_NMPR_CO)
Real number (ℝ)

SKEWED  ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.136
Minimum0
Maximum2100
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:10.996483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2100
Range2100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation95.797745
Coefficient of variation (CV)15.61241
Kurtosis460.09923
Mean6.136
Median Absolute Deviation (MAD)0
Skewness21.095771
Sum3068
Variance9177.2079
MonotonicityNot monotonic
2023-12-11T00:01:11.223200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
177 1
 
0.2%
200 1
 
0.2%
283 1
 
0.2%
100 1
 
0.2%
155 1
 
0.2%
2100 1
 
0.2%
53 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
53 1
 
0.2%
100 1
 
0.2%
155 1
 
0.2%
177 1
 
0.2%
200 1
 
0.2%
283 1
 
0.2%
2100 1
 
0.2%
ValueCountFrequency (%)
2100 1
 
0.2%
283 1
 
0.2%
200 1
 
0.2%
177 1
 
0.2%
155 1
 
0.2%
100 1
 
0.2%
53 1
 
0.2%
0 493
98.6%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
498 
300
 
1
600
 
1

Length

Max length3
Median length1
Mean length1.008
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 498
99.6%
300 1
 
0.2%
600 1
 
0.2%

Length

2023-12-11T00:01:11.528410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:11.799311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 498
99.6%
300 1
 
0.2%
600 1
 
0.2%
Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.228
Minimum0
Maximum1800
Zeros494
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:11.982375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1800
Range1800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation87.693239
Coefficient of variation (CV)14.080482
Kurtosis359.78457
Mean6.228
Median Absolute Deviation (MAD)0
Skewness18.276337
Sum3114
Variance7690.1042
MonotonicityNot monotonic
2023-12-11T00:01:12.211312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 494
98.8%
100 2
 
0.4%
350 1
 
0.2%
80 1
 
0.2%
1800 1
 
0.2%
684 1
 
0.2%
ValueCountFrequency (%)
0 494
98.8%
80 1
 
0.2%
100 2
 
0.4%
350 1
 
0.2%
684 1
 
0.2%
1800 1
 
0.2%
ValueCountFrequency (%)
1800 1
 
0.2%
684 1
 
0.2%
350 1
 
0.2%
100 2
 
0.4%
80 1
 
0.2%
0 494
98.8%
Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.95
Minimum0
Maximum2000
Zeros494
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:12.446816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation184.40678
Coefficient of variation (CV)9.2434474
Kurtosis87.673097
Mean19.95
Median Absolute Deviation (MAD)0
Skewness9.374347
Sum9975
Variance34005.859
MonotonicityNot monotonic
2023-12-11T00:01:12.691951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 494
98.8%
1000 1
 
0.2%
1745 1
 
0.2%
2000 1
 
0.2%
1700 1
 
0.2%
1800 1
 
0.2%
1730 1
 
0.2%
ValueCountFrequency (%)
0 494
98.8%
1000 1
 
0.2%
1700 1
 
0.2%
1730 1
 
0.2%
1745 1
 
0.2%
1800 1
 
0.2%
2000 1
 
0.2%
ValueCountFrequency (%)
2000 1
 
0.2%
1800 1
 
0.2%
1745 1
 
0.2%
1730 1
 
0.2%
1700 1
 
0.2%
1000 1
 
0.2%
0 494
98.8%

운영_형태_구분(OPER_STLE_SE)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
495 
직영
 
4
(조합)위탁
 
1

Length

Max length6
Median length4
Mean length3.988
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 495
99.0%
직영 4
 
0.8%
(조합)위탁 1
 
0.2%

Length

2023-12-11T00:01:13.061390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:13.282319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 495
99.0%
직영 4
 
0.8%
조합)위탁 1
 
0.2%
Distinct191
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0464
Minimum0
Maximum164.94
Zeros274
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:13.536409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.835
95-th percentile23.781
Maximum164.94
Range164.94
Interquartile range (IQR)7.835

Descriptive statistics

Standard deviation13.14238
Coefficient of variation (CV)2.1735876
Kurtosis53.521284
Mean6.0464
Median Absolute Deviation (MAD)0
Skewness5.9706943
Sum3023.2
Variance172.72215
MonotonicityNot monotonic
2023-12-11T00:01:13.829871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 274
54.8%
6.0 4
 
0.8%
9.0 4
 
0.8%
20.0 3
 
0.6%
8.0 3
 
0.6%
4.5 3
 
0.6%
6.6 3
 
0.6%
7.82 3
 
0.6%
5.6 3
 
0.6%
10.8 2
 
0.4%
Other values (181) 198
39.6%
ValueCountFrequency (%)
0.0 274
54.8%
1.48 1
 
0.2%
1.5 1
 
0.2%
2.0 1
 
0.2%
2.1 1
 
0.2%
2.2 1
 
0.2%
2.5 1
 
0.2%
2.7 1
 
0.2%
2.72 1
 
0.2%
2.97 1
 
0.2%
ValueCountFrequency (%)
164.94 1
0.2%
99.0 1
0.2%
82.5 1
0.2%
80.0 1
0.2%
75.34 1
0.2%
68.09 1
0.2%
52.0 1
0.2%
45.0 1
0.2%
42.88 1
0.2%
41.36 1
0.2%
Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9627
Minimum0
Maximum99.85
Zeros469
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:14.085799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.083
Maximum99.85
Range99.85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.372779
Coefficient of variation (CV)4.7754517
Kurtosis42.688584
Mean1.9627
Median Absolute Deviation (MAD)0
Skewness6.0737699
Sum981.35
Variance87.848986
MonotonicityNot monotonic
2023-12-11T00:01:14.335622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 469
93.8%
17.55 1
 
0.2%
30.96 1
 
0.2%
13.92 1
 
0.2%
64.04 1
 
0.2%
58.0 1
 
0.2%
14.08 1
 
0.2%
9.46 1
 
0.2%
20.3 1
 
0.2%
39.68 1
 
0.2%
Other values (22) 22
 
4.4%
ValueCountFrequency (%)
0.0 469
93.8%
2.0 1
 
0.2%
8.12 1
 
0.2%
9.0 1
 
0.2%
9.46 1
 
0.2%
13.92 1
 
0.2%
14.08 1
 
0.2%
14.14 1
 
0.2%
14.43 1
 
0.2%
15.81 1
 
0.2%
ValueCountFrequency (%)
99.85 1
0.2%
68.0 1
0.2%
64.04 1
0.2%
59.64 1
0.2%
58.0 1
0.2%
57.15 1
0.2%
50.4 1
0.2%
45.7 1
0.2%
39.68 1
0.2%
39.42 1
0.2%
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

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 (%)
0 500
100.0%

Length

2023-12-11T00:01:14.573550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:14.763067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%
Distinct95
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.07238
Minimum0
Maximum577.82
Zeros397
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:15.005577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile59.0405
Maximum577.82
Range577.82
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.249788
Coefficient of variation (CV)3.9967088
Kurtosis62.415182
Mean12.07238
Median Absolute Deviation (MAD)0
Skewness7.1266
Sum6036.19
Variance2328.042
MonotonicityNot monotonic
2023-12-11T00:01:15.381854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 397
79.4%
33.0 3
 
0.6%
32.0 2
 
0.4%
6.6 2
 
0.4%
12.4 2
 
0.4%
3.0 2
 
0.4%
15.0 2
 
0.4%
30.0 2
 
0.4%
3.3 2
 
0.4%
180.0 1
 
0.2%
Other values (85) 85
 
17.0%
ValueCountFrequency (%)
0.0 397
79.4%
0.48 1
 
0.2%
1.0 1
 
0.2%
1.2 1
 
0.2%
1.22 1
 
0.2%
2.04 1
 
0.2%
2.25 1
 
0.2%
2.6 1
 
0.2%
2.64 1
 
0.2%
3.0 2
 
0.4%
ValueCountFrequency (%)
577.82 1
0.2%
413.28 1
0.2%
393.06 1
0.2%
371.33 1
0.2%
242.69 1
0.2%
201.13 1
0.2%
190.57 1
0.2%
185.82 1
0.2%
180.0 1
0.2%
171.41 1
0.2%
Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.958
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:15.772847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile22.05
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0710845
Coefficient of variation (CV)1.7865297
Kurtosis14.335039
Mean3.958
Median Absolute Deviation (MAD)0
Skewness3.8635431
Sum1979
Variance50.000236
MonotonicityNot monotonic
2023-12-11T00:01:16.222749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2 450
90.0%
24 3
 
0.6%
3 3
 
0.6%
7 3
 
0.6%
38 2
 
0.4%
29 2
 
0.4%
1 1
 
0.2%
20 1
 
0.2%
6 1
 
0.2%
26 1
 
0.2%
Other values (33) 33
 
6.6%
ValueCountFrequency (%)
1 1
 
0.2%
2 450
90.0%
3 3
 
0.6%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 3
 
0.6%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
43 1
0.2%
42 1
0.2%
41 1
0.2%
40 1
0.2%
39 1
0.2%
38 2
0.4%
37 1
0.2%
36 1
0.2%
35 1
0.2%
34 1
0.2%
Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.818
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:16.481141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33.25
95-th percentile44
Maximum61
Range60
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation15.433641
Coefficient of variation (CV)1.7502428
Kurtosis1.9726493
Mean8.818
Median Absolute Deviation (MAD)0
Skewness1.8262486
Sum4409
Variance238.19727
MonotonicityNot monotonic
2023-12-11T00:01:17.343778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 372
74.4%
44 17
 
3.4%
21 15
 
3.0%
37 15
 
3.0%
10 6
 
1.2%
24 6
 
1.2%
28 3
 
0.6%
48 3
 
0.6%
57 3
 
0.6%
34 2
 
0.4%
Other values (51) 58
 
11.6%
ValueCountFrequency (%)
1 372
74.4%
2 2
 
0.4%
3 1
 
0.2%
4 2
 
0.4%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 6
 
1.2%
ValueCountFrequency (%)
61 1
 
0.2%
60 1
 
0.2%
59 1
 
0.2%
58 1
 
0.2%
57 3
0.6%
56 1
 
0.2%
55 1
 
0.2%
54 1
 
0.2%
53 2
0.4%
52 1
 
0.2%
Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.45
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:17.694826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile14.05
Maximum38
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8381219
Coefficient of variation (CV)2.3829069
Kurtosis18.544255
Mean2.45
Median Absolute Deviation (MAD)0
Skewness4.3414127
Sum1225
Variance34.083667
MonotonicityNot monotonic
2023-12-11T00:01:18.005064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 462
92.4%
23 2
 
0.4%
38 1
 
0.2%
34 1
 
0.2%
4 1
 
0.2%
2 1
 
0.2%
15 1
 
0.2%
36 1
 
0.2%
3 1
 
0.2%
21 1
 
0.2%
Other values (28) 28
 
5.6%
ValueCountFrequency (%)
1 462
92.4%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
38 1
0.2%
37 1
0.2%
36 1
0.2%
35 1
0.2%
34 1
0.2%
33 1
0.2%
32 1
0.2%
31 1
0.2%
30 1
0.2%
29 1
0.2%
Distinct214
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.46
Minimum1
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:18.319252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q393.25
95-th percentile189.05
Maximum214
Range213
Interquartile range (IQR)92.25

Descriptive statistics

Standard deviation66.711267
Coefficient of variation (CV)1.3766254
Kurtosis-0.25095721
Mean48.46
Median Absolute Deviation (MAD)0
Skewness1.1023785
Sum24230
Variance4450.3932
MonotonicityNot monotonic
2023-12-11T00:01:18.590211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 276
55.2%
156 3
 
0.6%
59 2
 
0.4%
87 2
 
0.4%
129 2
 
0.4%
29 2
 
0.4%
76 2
 
0.4%
14 2
 
0.4%
66 2
 
0.4%
61 2
 
0.4%
Other values (204) 205
41.0%
ValueCountFrequency (%)
1 276
55.2%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
214 1
0.2%
213 1
0.2%
212 1
0.2%
211 1
0.2%
210 1
0.2%
209 1
0.2%
208 1
0.2%
207 1
0.2%
206 1
0.2%
205 1
0.2%
Distinct34
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.306
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:18.837696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile13.05
Maximum34
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.2960798
Coefficient of variation (CV)2.2966521
Kurtosis18.641102
Mean2.306
Median Absolute Deviation (MAD)0
Skewness4.3571606
Sum1153
Variance28.048461
MonotonicityNot monotonic
2023-12-11T00:01:19.122467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 463
92.6%
17 3
 
0.6%
33 2
 
0.4%
29 2
 
0.4%
14 1
 
0.2%
32 1
 
0.2%
19 1
 
0.2%
23 1
 
0.2%
11 1
 
0.2%
21 1
 
0.2%
Other values (24) 24
 
4.8%
ValueCountFrequency (%)
1 463
92.6%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
34 1
0.2%
33 2
0.4%
32 1
0.2%
31 1
0.2%
30 1
0.2%
29 2
0.4%
28 1
0.2%
27 1
0.2%
26 1
0.2%
25 1
0.2%

감사실_면적(ISP_AR)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2023-12-11T00:01:19.415101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:19.622146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%
Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.05
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:19.771953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46035187
Coefficient of variation (CV)0.43843036
Kurtosis106.79472
Mean1.05
Median Absolute Deviation (MAD)0
Skewness10.086624
Sum525
Variance0.21192385
MonotonicityNot monotonic
2023-12-11T00:01:19.976763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 493
98.6%
5 2
 
0.4%
7 1
 
0.2%
2 1
 
0.2%
4 1
 
0.2%
6 1
 
0.2%
3 1
 
0.2%
ValueCountFrequency (%)
1 493
98.6%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
 
0.4%
6 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 1
 
0.2%
5 2
 
0.4%
4 1
 
0.2%
3 1
 
0.2%
2 1
 
0.2%
1 493
98.6%

창고_면적(WRHOUS_AR)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
498 
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 498
99.6%
3 1
 
0.2%
2 1
 
0.2%

Length

2023-12-11T00:01:20.227005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:20.414144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 498
99.6%
3 1
 
0.2%
2 1
 
0.2%
Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:20.778181image/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

Unique500 ?
Unique (%)100.0%

Sample

1st row3160000-110-2011-00077
2nd row3130000-104-2016-00407
3rd row3150000-110-2015-00040
4th row3150000-104-2016-00194
5th row3150000-101-1991-06520
ValueCountFrequency (%)
3160000-110-2011-00077 1
 
0.2%
3220000-104-2014-00249 1
 
0.2%
3190000-104-2017-00108 1
 
0.2%
3170000-134-2009-00096 1
 
0.2%
3210000-134-2009-00253 1
 
0.2%
3150000-107-1996-00324 1
 
0.2%
3200000-101-1992-03313 1
 
0.2%
3220000-101-1983-11819 1
 
0.2%
3220000-104-2011-00079 1
 
0.2%
3200000-101-2016-00204 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:01:21.469040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3993
36.3%
1 1998
18.2%
- 1500
 
13.6%
2 835
 
7.6%
3 818
 
7.4%
9 535
 
4.9%
4 333
 
3.0%
7 261
 
2.4%
5 255
 
2.3%
8 251
 
2.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3993
42.0%
1 1998
21.0%
2 835
 
8.8%
3 818
 
8.6%
9 535
 
5.6%
4 333
 
3.5%
7 261
 
2.7%
5 255
 
2.7%
8 251
 
2.6%
6 221
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3993
36.3%
1 1998
18.2%
- 1500
 
13.6%
2 835
 
7.6%
3 818
 
7.4%
9 535
 
4.9%
4 333
 
3.0%
7 261
 
2.4%
5 255
 
2.3%
8 251
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3993
36.3%
1 1998
18.2%
- 1500
 
13.6%
2 835
 
7.6%
3 818
 
7.4%
9 535
 
4.9%
4 333
 
3.0%
7 261
 
2.4%
5 255
 
2.3%
8 251
 
2.3%

참고_사항(REFER_MATTER)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

조건부_시작일(CNDL_BGNDE)
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)100.0%
Missing480
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean20173725
Minimum20060925
Maximum20190905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:21.745972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060925
5-th percentile20155453
Q120171122
median20180568
Q320190260
95-th percentile20190726
Maximum20190905
Range129980
Interquartile range (IQR)19137.75

Descriptive statistics

Standard deviation28218.417
Coefficient of variation (CV)0.0013987707
Kurtosis14.969243
Mean20173725
Median Absolute Deviation (MAD)9545.5
Skewness-3.6664081
Sum4.0347451 × 108
Variance7.9627905 × 108
MonotonicityNot monotonic
2023-12-11T00:01:22.031058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20181115 1
 
0.2%
20180124 1
 
0.2%
20180516 1
 
0.2%
20181009 1
 
0.2%
20171116 1
 
0.2%
20181101 1
 
0.2%
20160428 1
 
0.2%
20190717 1
 
0.2%
20190207 1
 
0.2%
20180621 1
 
0.2%
Other values (10) 10
 
2.0%
(Missing) 480
96.0%
ValueCountFrequency (%)
20060925 1
0.2%
20160428 1
0.2%
20161121 1
0.2%
20170406 1
0.2%
20171116 1
0.2%
20171124 1
0.2%
20171228 1
0.2%
20180124 1
0.2%
20180409 1
0.2%
20180516 1
0.2%
ValueCountFrequency (%)
20190905 1
0.2%
20190717 1
0.2%
20190516 1
0.2%
20190502 1
0.2%
20190418 1
0.2%
20190207 1
0.2%
20181115 1
0.2%
20181101 1
0.2%
20181009 1
0.2%
20180621 1
0.2%

조건부_종료일(CNDL_ENDDE)
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)100.0%
Missing484
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean20183084
Minimum20161031
Maximum20200111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:22.459129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20161031
5-th percentile20167997
Q120180302
median20180830
Q320190518
95-th percentile20193431
Maximum20200111
Range39080
Interquartile range (IQR)10216.75

Descriptive statistics

Standard deviation9950.8967
Coefficient of variation (CV)0.00049303153
Kurtosis0.28750847
Mean20183084
Median Absolute Deviation (MAD)9585.5
Skewness-0.56802528
Sum3.2292934 × 108
Variance99020345
MonotonicityNot monotonic
2023-12-11T00:01:22.752458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20180329 1
 
0.2%
20190420 1
 
0.2%
20200111 1
 
0.2%
20180425 1
 
0.2%
20170319 1
 
0.2%
20190930 1
 
0.2%
20180731 1
 
0.2%
20190410 1
 
0.2%
20180220 1
 
0.2%
20161031 1
 
0.2%
Other values (6) 6
 
1.2%
(Missing) 484
96.8%
ValueCountFrequency (%)
20161031 1
0.2%
20170319 1
0.2%
20170428 1
0.2%
20180220 1
0.2%
20180329 1
0.2%
20180425 1
0.2%
20180711 1
0.2%
20180731 1
0.2%
20180928 1
0.2%
20190331 1
0.2%
ValueCountFrequency (%)
20200111 1
0.2%
20191204 1
0.2%
20190930 1
0.2%
20190814 1
0.2%
20190420 1
0.2%
20190410 1
0.2%
20190331 1
0.2%
20180928 1
0.2%
20180731 1
0.2%
20180711 1
0.2%
Distinct18
Distinct (%)78.3%
Missing477
Missing (%)95.4%
Memory size4.0 KiB
2023-12-11T00:01:23.166621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length21
Mean length19.478261
Min length10

Characters and Unicode

Total characters448
Distinct characters83
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)60.9%

Sample

1st row아래기간 내 한시적으로 영업승인
2nd row*위 기간동안만 한시적 영업가능*
3rd row즉석판매제조가공업 한시적 영업신고
4th row조건부 기간동안 한시적으로 영업 가능함
5th row위 기간동안만 한시적으로 영업가능함.
ValueCountFrequency (%)
영업 11
 
11.2%
한시적으로 10
 
10.2%
한시적 8
 
8.2%
7
 
7.1%
기간동안만 6
 
6.1%
가능 6
 
6.1%
기간 4
 
4.1%
조건부 3
 
3.1%
기간동안 3
 
3.1%
가능함 3
 
3.1%
Other values (25) 37
37.8%
2023-12-11T00:01:24.051156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
 
16.7%
23
 
5.1%
19
 
4.2%
19
 
4.2%
19
 
4.2%
19
 
4.2%
19
 
4.2%
18
 
4.0%
16
 
3.6%
15
 
3.3%
Other values (73) 206
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
74.3%
Space Separator 75
 
16.7%
Decimal Number 22
 
4.9%
Other Punctuation 11
 
2.5%
Dash Punctuation 3
 
0.7%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
 
6.9%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
18
 
5.4%
16
 
4.8%
15
 
4.5%
14
 
4.2%
Other values (57) 152
45.6%
Decimal Number
ValueCountFrequency (%)
0 7
31.8%
2 4
18.2%
3 4
18.2%
6 2
 
9.1%
1 2
 
9.1%
9 1
 
4.5%
8 1
 
4.5%
4 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
5
45.5%
* 2
 
18.2%
. 2
 
18.2%
/ 2
 
18.2%
Space Separator
ValueCountFrequency (%)
75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 333
74.3%
Common 115
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
 
6.9%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
18
 
5.4%
16
 
4.8%
15
 
4.5%
14
 
4.2%
Other values (57) 152
45.6%
Common
ValueCountFrequency (%)
75
65.2%
0 7
 
6.1%
5
 
4.3%
2 4
 
3.5%
3 4
 
3.5%
- 3
 
2.6%
* 2
 
1.7%
) 2
 
1.7%
( 2
 
1.7%
6 2
 
1.7%
Other values (6) 9
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 333
74.3%
ASCII 110
 
24.6%
Punctuation 5
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75
68.2%
0 7
 
6.4%
2 4
 
3.6%
3 4
 
3.6%
- 3
 
2.7%
* 2
 
1.8%
) 2
 
1.8%
( 2
 
1.8%
6 2
 
1.8%
. 2
 
1.8%
Other values (5) 7
 
6.4%
Hangul
ValueCountFrequency (%)
23
 
6.9%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
19
 
5.7%
18
 
5.4%
16
 
4.8%
15
 
4.5%
14
 
4.2%
Other values (57) 152
45.6%
Punctuation
ValueCountFrequency (%)
5
100.0%
Distinct6
Distinct (%)75.0%
Missing492
Missing (%)98.4%
Memory size4.0 KiB
2023-12-11T00:01:24.467369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length20
Mean length15.75
Min length7

Characters and Unicode

Total characters126
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)50.0%

Sample

1st row한시적영업신고
2nd row2017 쿡 페스타 5/3~5/7
3rd row한시적 영업신고
4th row식품소분.판매.운반영업자의 준수사항 이행
5th row식품소분.판매.운반영업자의 준수사항 이행
ValueCountFrequency (%)
한시적 2
 
8.7%
식품소분.판매.운반영업자의 2
 
8.7%
준수사항 2
 
8.7%
이행 2
 
8.7%
1월 2
 
8.7%
영업신고 2
 
8.7%
1
 
4.3%
22일 1
 
4.3%
19일~2017년 1
 
4.3%
2017년 1
 
4.3%
Other values (7) 7
30.4%
2023-12-11T00:01:25.205424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
11.9%
2 7
 
5.6%
6
 
4.8%
6
 
4.8%
1 6
 
4.8%
. 4
 
3.2%
7 4
 
3.2%
3
 
2.4%
0 3
 
2.4%
3
 
2.4%
Other values (39) 69
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
61.9%
Decimal Number 24
 
19.0%
Space Separator 15
 
11.9%
Other Punctuation 7
 
5.6%
Math Symbol 2
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
7.7%
6
 
7.7%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
2
 
2.6%
Other values (27) 43
55.1%
Decimal Number
ValueCountFrequency (%)
2 7
29.2%
1 6
25.0%
7 4
16.7%
0 3
12.5%
5 2
 
8.3%
9 1
 
4.2%
3 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
/ 2
28.6%
: 1
 
14.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
61.9%
Common 48
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
7.7%
6
 
7.7%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
2
 
2.6%
Other values (27) 43
55.1%
Common
ValueCountFrequency (%)
15
31.2%
2 7
14.6%
1 6
 
12.5%
. 4
 
8.3%
7 4
 
8.3%
0 3
 
6.2%
~ 2
 
4.2%
/ 2
 
4.2%
5 2
 
4.2%
9 1
 
2.1%
Other values (2) 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
61.9%
ASCII 48
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
31.2%
2 7
14.6%
1 6
 
12.5%
. 4
 
8.3%
7 4
 
8.3%
0 3
 
6.2%
~ 2
 
4.2%
/ 2
 
4.2%
5 2
 
4.2%
9 1
 
2.1%
Other values (2) 2
 
4.2%
Hangul
ValueCountFrequency (%)
6
 
7.7%
6
 
7.7%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
2
 
2.6%
Other values (27) 43
55.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
내국인
493 
외국인
 
7

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 493
98.6%
외국인 7
 
1.4%

Length

2023-12-11T00:01:25.495389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:25.727597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내국인 493
98.6%
외국인 7
 
1.4%

국적(NLTY)
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
대한민국
276 
<NA>
218 
미국
 
2
중국
 
2
캐나다
 
1

Length

Max length4
Median length4
Mean length3.98
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
대한민국 276
55.2%
<NA> 218
43.6%
미국 2
 
0.4%
중국 2
 
0.4%
캐나다 1
 
0.2%
베트남 1
 
0.2%

Length

2023-12-11T00:01:25.964661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:26.217422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대한민국 276
55.2%
na 218
43.6%
미국 2
 
0.4%
중국 2
 
0.4%
캐나다 1
 
0.2%
베트남 1
 
0.2%

기준년월(STDR_YM)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
201911
500 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201911
2nd row201911
3rd row201911
4th row201911
5th row201911

Common Values

ValueCountFrequency (%)
201911 500
100.0%

Length

2023-12-11T00:01:26.533926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:26.769442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201911 500
100.0%

업종업태(SNITAT_BIZCND_CD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

업종소분류코드(INDUTY_SCLAS_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)신_주소(OLD_ADRES)구_주소(NW_ADRES)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_구분(BIZQIT_SE)폐업_사유(BIZQIT_SE_RESN)위생_업태_명(SNITAT_BIZCND_NM)지상_사용_층_부터_수(GROUND_USE_FLOOR_FROM_CO)지상_사용_층_까지_수(GROUND_USE_FLOOR_TO_CO)지하_사용_층_부터_수(UNDGRND_USE_FLOOR_FROM_CO)지하_사용_층_까지_수(UNDGRND_USE_FLOOR_TO_CO)총_층_수(TOT_FLOOR_CO)교육_수료_일(EDC_COMPL_DE)모범_음식점_여부(PRGN_RSTRT_AT)급수_시설(WSP_FCLTY)업소위치(BSSH_LC)남성_종업원_수(ML_EMPLY_CO)여성_종업원_수(FML_EMPLY_CO)급식소_종류_코드(DINRM_KND)평균급식인원수(AVRG_MLSV_NMPR_CO)최대급식인원수(MAX_MLSV_NMPR_CO)1일급식인원수(DAIL_MLSV_NMPR_CO)인당_평균_급식_비(PSNBY_AVRG_MLSV_CT)운영_형태_구분(OPER_STLE_SE)영업장_조리장_면적(BUZPLC_JORIJANG_AR)영업장_객실_면적(BUZPLC_RUM_AR)영업장_무도장_면적(BUZPLC_DCHL_AR)영업장_기타_면적(BUZPLC_ETC_AR)업소_화장실_면적(BSSH_TOILET_AR)공동_화장실면적(COPERTN_TOILET_AR)영업장_탈의실_면적(BUZPLC_DSRM_AR)영업장_좌식_면적(BUZPLC_SEAT_AR)작업장_면적(WRKSHP_AR)감사실_면적(ISP_AR)진열대_면적(EXHBI_AR)창고_면적(WRHOUS_AR)허가_번호(PRMISN_NO)참고_사항(REFER_MATTER)조건부_시작일(CNDL_BGNDE)조건부_종료일(CNDL_ENDDE)조건부_허가_신고_사유(CNDL_PRMISN_STTEMNT_RESN)허가_신고_조건(PRMISN_PRMISN_CND)내국인_외국인_구분(NATIVE_FRGNR_SE)국적(NLTY)기준년월(STDR_YM)업종업태(SNITAT_BIZCND_CD)업종소분류코드(INDUTY_SCLAS_CD)도로코드(ROAD_CD)
03150000101199012즉석판매제조가공업20110527네스카페 마포KPX점서울특별시 은평구 은평로10길 17, (응암동,1층)서울특별시 영등포구 영등포동4가 441번지 21호 경방필백화점 지하1층내0.0022068543019910126<NA><NA>20170508진관동<NA>자진폐업경영부진영업장판매20018<NA>비대상<NA><NA>00<NA>0000<NA>0.00.000.0211111113160000-110-2011-00077<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
131100001072000669식품등 수입판매업20171102가락촌<NA>서울특별시 은평구 역촌동 78번지 32호 1층0.00220080715<NA><NA>20150526상도제1동<NA>자진폐업<NA>일반조리판매10000<NA>비대상<NA>지상00<NA>0000<NA>0.00.000.0211111113130000-104-2016-00407<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
23200000101200810098일반음식점20010621솔루아(SOLUA)<NA>서울특별시 양천구 목동 531번지 12호 1층0.0020000000019931106<NA><NA>19870404서림동20161024자진폐업자진폐업한식00100<NA>비대상상수도전용<NA>00<NA>0000<NA>0.00.000.0211111113150000-110-2015-00040<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
33150000107200584일반음식점20111116어류겐서울특별시 강서구 강서로56길 17, 엔씨백화점 지하1층 (등촌동)서울특별시 마포구 서교동 346번지 29호 1층0.0023452883820180309<NA>110111220800020010412홍은제1동20010912자진폐업자진폐업커피숍0000020090527비대상상수도전용지상00<NA>0000<NA>0.00.000.021114511113150000-104-2016-00194<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
432100001341994577식품등 수입판매업20081103미스포테이토<NA>서울특별시 양천구 목동 917번지 6호 하나로클럽0.002703454020190624<NA><NA>20160415불광제1동<NA>전출개인사정식품등 수입판매업00000<NA>비대상<NA><NA>00<NA>0000<NA>20.00.000.038381111113150000-101-1991-06520<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
531900001012009792즉석판매제조가공업19910910치킨또또서울특별시 은평구 서오릉로 183, 지상1층 (구산동)서울특별시 양천구 목동 905번지 22호 목동트윈빌 지상1층-117,1180.002 891993620071220<NA><NA>20121127대학동20060426행정처분영업부진산업체21000<NA>비대상상수도전용<NA>00<NA>0000<NA>3.750.000.0211111113230000-101-1996-04859<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
63170000112201811409건강기능식품일반판매업20020314국제무역<NA>서울특별시 강남구 역삼동 678번지 17호0.0<NA>19880524<NA><NA>20161214역촌동<NA>자진폐업<NA>한식00000<NA>비대상상수도전용<NA>00<NA>0000<NA>0.00.000.02443220711113110000-101-2001-08368<NA>20180621<NA><NA><NA>내국인대한민국201911<NA><NA><NA>
7318000010120112098휴게음식점20100820(주)피에이미트<NA>서울특별시 관악구 봉천동 731번지 1호0.03148558820150731<NA>161111001073720040706신사제1동20120430행정처분<NA>한식00000<NA>비대상<NA>지상2층이상00<NA>177000<NA>12.00.0040.02111811113150000-101-1996-04696<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
831200001011999222식품등 수입판매업19870916오혜숙생활단식 공항지사서울특별시 금천구 독산로 353, 지상1층 (독산동)서울특별시 서대문구 창천동 5번지 23호 -24호, -25호(지상1층)58.69022646726520031215(주)미소담은<NA>20181203서초제3동<NA>자진폐업<NA>커피숍01000<NA>비대상상수도전용<NA>00<NA>0000<NA>5.4645.700.0211111113130000-104-1999-06858<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
93120000106201613895일반음식점20100308소설서울특별시 구로구 고척로 141, (고척동,,216)서울특별시 금천구 시흥동 994번지 5호104.17010 6365411119971101<NA><NA>20090805역삼1동20110131<NA><NA>경양식01000<NA>비대상<NA>지상10<NA>0000<NA>5.550.000.0211111113220000-110-2012-00267<NA><NA><NA><NA><NA>내국인대한민국201911<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)신_주소(OLD_ADRES)구_주소(NW_ADRES)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_구분(BIZQIT_SE)폐업_사유(BIZQIT_SE_RESN)위생_업태_명(SNITAT_BIZCND_NM)지상_사용_층_부터_수(GROUND_USE_FLOOR_FROM_CO)지상_사용_층_까지_수(GROUND_USE_FLOOR_TO_CO)지하_사용_층_부터_수(UNDGRND_USE_FLOOR_FROM_CO)지하_사용_층_까지_수(UNDGRND_USE_FLOOR_TO_CO)총_층_수(TOT_FLOOR_CO)교육_수료_일(EDC_COMPL_DE)모범_음식점_여부(PRGN_RSTRT_AT)급수_시설(WSP_FCLTY)업소위치(BSSH_LC)남성_종업원_수(ML_EMPLY_CO)여성_종업원_수(FML_EMPLY_CO)급식소_종류_코드(DINRM_KND)평균급식인원수(AVRG_MLSV_NMPR_CO)최대급식인원수(MAX_MLSV_NMPR_CO)1일급식인원수(DAIL_MLSV_NMPR_CO)인당_평균_급식_비(PSNBY_AVRG_MLSV_CT)운영_형태_구분(OPER_STLE_SE)영업장_조리장_면적(BUZPLC_JORIJANG_AR)영업장_객실_면적(BUZPLC_RUM_AR)영업장_무도장_면적(BUZPLC_DCHL_AR)영업장_기타_면적(BUZPLC_ETC_AR)업소_화장실_면적(BSSH_TOILET_AR)공동_화장실면적(COPERTN_TOILET_AR)영업장_탈의실_면적(BUZPLC_DSRM_AR)영업장_좌식_면적(BUZPLC_SEAT_AR)작업장_면적(WRKSHP_AR)감사실_면적(ISP_AR)진열대_면적(EXHBI_AR)창고_면적(WRHOUS_AR)허가_번호(PRMISN_NO)참고_사항(REFER_MATTER)조건부_시작일(CNDL_BGNDE)조건부_종료일(CNDL_ENDDE)조건부_허가_신고_사유(CNDL_PRMISN_STTEMNT_RESN)허가_신고_조건(PRMISN_PRMISN_CND)내국인_외국인_구분(NATIVE_FRGNR_SE)국적(NLTY)기준년월(STDR_YM)업종업태(SNITAT_BIZCND_CD)업종소분류코드(INDUTY_SCLAS_CD)도로코드(ROAD_CD)
490320000010720199153건강기능식품일반판매업20040816마징가 Live & 노래장서울특별시 영등포구 도신로 218, (신길동, 지하1층)서울특별시 구로구 가리봉동 135번지 6호 3층0.0715522120000801<NA><NA>20040802중계1동20060721자진폐업사업부진경양식0100520060414비대상상수도전용지상00<NA>0000<NA>12.00.000.021112711113100000-101-2019-00242<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
49131000001102014111휴게음식점20040618한국암웨이서울특별시 동작구 노량진로23가길 23, (본동, 래미안트윈파크 관리동 1층)서울특별시 송파구 오금동 40번지 9호82.502589123819960703<NA><NA>20111108당산제2동20010227자진폐업영업부진호프/통닭00100<NA>비대상상수도전용<NA>00<NA>0000<NA>8.80.000.0211111113170000-107-2018-00127<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
492318000010120002462일반음식점20040831나뚜루아이스크림오목교점<NA>서울특별시 서초구 양재동 275번지 1호 A 삼호물산-B112-20.0<NA>20150401(주)앤앤컴퍼니<NA>19950821대조동20100802<NA>개인사정식품등 수입판매업01000<NA>비대상<NA>지상00<NA>0000<NA>0.00.005.6921114411113170000-101-2002-05468<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
4933170000104201441일반음식점19971101춘자 자판기<NA>서울특별시 관악구 봉천동 729번지 22호0.0022608107720070126(주)에이치제이에프<NA>19931216청룡동20090805전출행정처분커피숍1000320101228비대상<NA><NA>00<NA>0000<NA>0.00.000.0211111113210000-101-2018-00176<NA>20180124<NA><NA><NA>내국인대한민국201911<NA><NA><NA>
4943170000101201919일반음식점20050112첼로<NA>서울특별시 양천구 신정동 1031번지 2호 지하1층0.0671937119880617<NA><NA>20010911고척제1동20180302<NA>자진폐업중국식00004<NA>비대상<NA>지상00<NA>0000<NA>8.850.000.0221114011113160000-101-1994-04846<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
49531800001101996220건강기능식품일반판매업20070319토종한우한약돼지전문점<NA>서울특별시 서대문구 홍제동 367번지 24호 (지상1층)0.0<NA>20161229<NA><NA>20191128문래동20130429자진폐업자진폐업한식10000<NA>비대상<NA>지상00<NA>0000<NA>0.00.0012.962511111113120000-101-1985-01948<NA><NA><NA><NA><NA>내국인<NA>201911<NA><NA><NA>
496311000010120011383일반음식점20180604장원식품서울특별시 서초구 서초중앙로24길 55, 608호 (서초동, 서초프라자)서울특별시 서초구 양재동 327번지 18호 1층0.0020000000020190729(주)본야록<NA>20160321신길제3동20160729행정처분행사 종료분식10005<NA>비대상<NA>지하10<NA>0000직영20.650.000.025712911113190000-114-2016-00003<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
4973200000107201620식품등 수입판매업20060911대림 웅젓갈상회서울특별시 구로구 구로중앙로19길 13, (구로동,B1)서울특별시 은평구 갈현동 460번지 20호 1층0.00220090227<NA><NA>20191105서강동20171219자진폐업<NA>식육(숯불구이)0100220030205비대상상수도전용지상00<NA>0000<NA>0.00.000.023715511113230000-101-1994-10315<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
49832100001012010350일반음식점19940819혜인식당<NA>서울특별시 구로구 구로동 83번지 4호 광진빌딩-4070.0023481729720090729<NA><NA>19971115신촌동19981103자진폐업서울청담당자확인후 일괄폐업-허가번호 지역명으로 시작된 자료호프/통닭00005<NA>비대상상수도전용지하03<NA>0000<NA>0.00.0012.42371111113210000-101-1991-10395<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>
49931000001012014145휴게음식점2010072025김밥서울특별시 금천구 시흥대로141길 55-1, (독산동, 지상1층)서울특별시 강남구 역삼동 779번지 7호0.002 583639319950804<NA>110111213523719990616독산제3동<NA>자진폐업영업부진식품등 수입판매업00004<NA>비대상상수도전용<NA>31<NA>0000<NA>0.00.000.0211111113150000-112-2003-00003<NA><NA><NA><NA><NA>내국인대한민국201911<NA><NA><NA>