Overview

Dataset statistics

Number of variables34
Number of observations36
Missing cells331
Missing cells (%)27.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory296.7 B

Variable types

Categorical14
Text5
Numeric6
Unsupported8
DateTime1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),문화체육업종명,공사립구분명,보험가입여부코드,지도자수,건축물동수,건축물연면적,회원모집총인원,세부업종명,법인명
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-19697/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
도로명우편번호 is highly imbalanced (68.8%)Imbalance
데이터갱신일자 is highly imbalanced (58.1%)Imbalance
문화체육업종명 is highly imbalanced (69.0%)Imbalance
공사립구분명 is highly imbalanced (69.0%)Imbalance
보험가입여부코드 is highly imbalanced (57.0%)Imbalance
회원모집총인원 is highly imbalanced (81.7%)Imbalance
인허가취소일자 has 36 (100.0%) missing valuesMissing
폐업일자 has 4 (11.1%) missing valuesMissing
휴업시작일자 has 36 (100.0%) missing valuesMissing
휴업종료일자 has 36 (100.0%) missing valuesMissing
재개업일자 has 36 (100.0%) missing valuesMissing
전화번호 has 29 (80.6%) missing valuesMissing
소재지면적 has 36 (100.0%) missing valuesMissing
소재지우편번호 has 2 (5.6%) missing valuesMissing
업태구분명 has 36 (100.0%) missing valuesMissing
건축물연면적 has 8 (22.2%) missing valuesMissing
세부업종명 has 36 (100.0%) missing valuesMissing
법인명 has 36 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
지번주소 has unique valuesUnique
도로명주소 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
세부업종명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
법인명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건축물연면적 has 22 (61.1%) zerosZeros

Reproduction

Analysis started2024-04-06 13:10:58.722474
Analysis finished2024-04-06 13:10:59.333944
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
3200000
36 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 36
100.0%

Length

2024-04-06T22:10:59.476557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:00.141042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 36
100.0%

관리번호
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-04-06T22:11:00.409628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters720
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st rowCDFH3301111993000001
2nd rowCDFH3301111993000002
3rd rowCDFH3301111995000002
4th rowCDFH3301111995000003
5th rowCDFH3301111996000001
ValueCountFrequency (%)
cdfh3301111993000001 1
 
2.8%
cdfh3301111993000002 1
 
2.8%
cdfh3301112000000002 1
 
2.8%
cdfh3301111999000008 1
 
2.8%
cdfh3301111999000009 1
 
2.8%
cdfh3301111999000010 1
 
2.8%
cdfh3301111999000011 1
 
2.8%
cdfh3301111999000012 1
 
2.8%
cdfh3301112000000001 1
 
2.8%
cdfh3301112001000001 1
 
2.8%
Other values (26) 26
72.2%
2024-04-06T22:11:00.913663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 234
32.5%
1 158
21.9%
3 79
 
11.0%
9 64
 
8.9%
C 36
 
5.0%
D 36
 
5.0%
F 36
 
5.0%
H 36
 
5.0%
2 20
 
2.8%
8 6
 
0.8%
Other values (4) 15
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 576
80.0%
Uppercase Letter 144
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234
40.6%
1 158
27.4%
3 79
 
13.7%
9 64
 
11.1%
2 20
 
3.5%
8 6
 
1.0%
5 5
 
0.9%
6 4
 
0.7%
7 3
 
0.5%
4 3
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 36
25.0%
D 36
25.0%
F 36
25.0%
H 36
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
80.0%
Latin 144
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234
40.6%
1 158
27.4%
3 79
 
13.7%
9 64
 
11.1%
2 20
 
3.5%
8 6
 
1.0%
5 5
 
0.9%
6 4
 
0.7%
7 3
 
0.5%
4 3
 
0.5%
Latin
ValueCountFrequency (%)
C 36
25.0%
D 36
25.0%
F 36
25.0%
H 36
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234
32.5%
1 158
21.9%
3 79
 
11.0%
9 64
 
8.9%
C 36
 
5.0%
D 36
 
5.0%
F 36
 
5.0%
H 36
 
5.0%
2 20
 
2.8%
8 6
 
0.8%
Other values (4) 15
 
2.1%

인허가일자
Real number (ℝ)

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20005980
Minimum19930608
Maximum20210804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:01.180847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19930608
5-th percentile19946081
Q119980410
median19990370
Q320003297
95-th percentile20133602
Maximum20210804
Range280196
Interquartile range (IQR)22886.25

Descriptive statistics

Standard deviation60211.432
Coefficient of variation (CV)0.0030096716
Kurtosis3.4627338
Mean20005980
Median Absolute Deviation (MAD)10452
Skewness1.8437754
Sum7.202153 × 108
Variance3.6254165 × 109
MonotonicityIncreasing
2024-04-06T22:11:01.399347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
19961028 2
 
5.6%
19930608 1
 
2.8%
20020131 1
 
2.8%
19990918 1
 
2.8%
19991228 1
 
2.8%
19991231 1
 
2.8%
20000518 1
 
2.8%
20001025 1
 
2.8%
20010112 1
 
2.8%
20031004 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
19930608 1
2.8%
19930713 1
2.8%
19951204 1
2.8%
19951228 1
2.8%
19961028 2
5.6%
19970220 1
2.8%
19970521 1
2.8%
19980121 1
2.8%
19980507 1
2.8%
19980708 1
2.8%
ValueCountFrequency (%)
20210804 1
2.8%
20141029 1
2.8%
20131126 1
2.8%
20110725 1
2.8%
20090812 1
2.8%
20050817 1
2.8%
20031004 1
2.8%
20020131 1
2.8%
20010112 1
2.8%
20001025 1
2.8%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

영업상태코드
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
3
28 
4
4 
1
4 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 28
77.8%
4 4
 
11.1%
1 4
 
11.1%

Length

2024-04-06T22:11:01.719291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:01.898585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 28
77.8%
4 4
 
11.1%
1 4
 
11.1%

영업상태명
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
폐업
28 
취소/말소/만료/정지/중지
4 
영업/정상
4 

Length

Max length14
Median length2
Mean length3.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취소/말소/만료/정지/중지
2nd row취소/말소/만료/정지/중지
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 28
77.8%
취소/말소/만료/정지/중지 4
 
11.1%
영업/정상 4
 
11.1%

Length

2024-04-06T22:11:02.103098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:02.329789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 28
77.8%
취소/말소/만료/정지/중지 4
 
11.1%
영업/정상 4
 
11.1%

상세영업상태코드
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
3
28 
35
4 
13
4 

Length

Max length2
Median length1
Mean length1.2222222
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35
2nd row35
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 28
77.8%
35 4
 
11.1%
13 4
 
11.1%

Length

2024-04-06T22:11:02.579833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:02.768895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 28
77.8%
35 4
 
11.1%
13 4
 
11.1%

상세영업상태명
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
폐업
28 
직권말소
4 
영업중
4 

Length

Max length4
Median length2
Mean length2.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 28
77.8%
직권말소 4
 
11.1%
영업중 4
 
11.1%

Length

2024-04-06T22:11:02.996542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:03.181351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 28
77.8%
직권말소 4
 
11.1%
영업중 4
 
11.1%

폐업일자
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)93.8%
Missing4
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean20078278
Minimum19991122
Maximum20220805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:03.368621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19991122
5-th percentile20000934
Q120018114
median20070262
Q320133314
95-th percentile20209641
Maximum20220805
Range229683
Interquartile range (IQR)115200

Descriptive statistics

Standard deviation68392.144
Coefficient of variation (CV)0.0034062754
Kurtosis-0.72800395
Mean20078278
Median Absolute Deviation (MAD)59434
Skewness0.53995964
Sum6.425049 × 108
Variance4.6774853 × 109
MonotonicityNot monotonic
2024-04-06T22:11:03.599282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20141112 3
 
8.3%
20011210 1
 
2.8%
20080212 1
 
2.8%
20200821 1
 
2.8%
20220420 1
 
2.8%
20060313 1
 
2.8%
20130916 1
 
2.8%
20131017 1
 
2.8%
20110722 1
 
2.8%
20001120 1
 
2.8%
Other values (20) 20
55.6%
(Missing) 4
 
11.1%
ValueCountFrequency (%)
19991122 1
2.8%
20000731 1
2.8%
20001101 1
2.8%
20001120 1
2.8%
20001228 1
2.8%
20010529 1
2.8%
20011128 1
2.8%
20011210 1
2.8%
20020416 1
2.8%
20020607 1
2.8%
ValueCountFrequency (%)
20220805 1
 
2.8%
20220420 1
 
2.8%
20200821 1
 
2.8%
20141112 3
8.3%
20141010 1
 
2.8%
20140207 1
 
2.8%
20131017 1
 
2.8%
20130916 1
 
2.8%
20111206 1
 
2.8%
20110722 1
 
2.8%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

전화번호
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing29
Missing (%)80.6%
Memory size420.0 B
2024-04-06T22:11:03.860566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.8571429
Min length8

Characters and Unicode

Total characters69
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

Unique7 ?
Unique (%)100.0%

Sample

1st row877-8071
2nd row02-866-6040
3rd row3281-7979
4th row830-2711
5th row02-830-5232
ValueCountFrequency (%)
877-8071 1
14.3%
02-866-6040 1
14.3%
3281-7979 1
14.3%
830-2711 1
14.3%
02-830-5232 1
14.3%
02-858-0650 1
14.3%
02-882-2467 1
14.3%
2024-04-06T22:11:04.353619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 11
15.9%
0 11
15.9%
8 10
14.5%
2 10
14.5%
7 7
10.1%
6 5
7.2%
1 4
 
5.8%
3 4
 
5.8%
5 3
 
4.3%
4 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
84.1%
Dash Punctuation 11
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11
19.0%
8 10
17.2%
2 10
17.2%
7 7
12.1%
6 5
8.6%
1 4
 
6.9%
3 4
 
6.9%
5 3
 
5.2%
4 2
 
3.4%
9 2
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 11
15.9%
0 11
15.9%
8 10
14.5%
2 10
14.5%
7 7
10.1%
6 5
7.2%
1 4
 
5.8%
3 4
 
5.8%
5 3
 
4.3%
4 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 11
15.9%
0 11
15.9%
8 10
14.5%
2 10
14.5%
7 7
10.1%
6 5
7.2%
1 4
 
5.8%
3 4
 
5.8%
5 3
 
4.3%
4 2
 
2.9%

소재지면적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

소재지우편번호
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)47.1%
Missing2
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean151629.76
Minimum151015
Maximum151930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:04.590155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum151015
5-th percentile151015
Q1151029
median151891
Q3151902
95-th percentile151930
Maximum151930
Range915
Interquartile range (IQR)873

Descriptive statistics

Standard deviation401.48701
Coefficient of variation (CV)0.0026478114
Kurtosis-1.1868486
Mean151629.76
Median Absolute Deviation (MAD)39
Skewness-0.9234593
Sum5155412
Variance161191.82
MonotonicityNot monotonic
2024-04-06T22:11:04.774178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
151015 8
22.2%
151892 4
11.1%
151930 4
11.1%
151902 3
 
8.3%
151903 3
 
8.3%
151891 2
 
5.6%
151894 1
 
2.8%
151811 1
 
2.8%
151809 1
 
2.8%
151899 1
 
2.8%
Other values (6) 6
16.7%
(Missing) 2
 
5.6%
ValueCountFrequency (%)
151015 8
22.2%
151022 1
 
2.8%
151050 1
 
2.8%
151800 1
 
2.8%
151809 1
 
2.8%
151810 1
 
2.8%
151811 1
 
2.8%
151836 1
 
2.8%
151876 1
 
2.8%
151891 2
 
5.6%
ValueCountFrequency (%)
151930 4
11.1%
151903 3
8.3%
151902 3
8.3%
151899 1
 
2.8%
151894 1
 
2.8%
151892 4
11.1%
151891 2
5.6%
151876 1
 
2.8%
151836 1
 
2.8%
151811 1
 
2.8%

지번주소
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-04-06T22:11:05.100886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length24.333333
Min length20

Characters and Unicode

Total characters876
Distinct characters36
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

Unique36 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 신림동 1433-45번지
2nd row서울특별시 관악구 신림동 1431-7번지
3rd row서울특별시 관악구 신림동 1431-1번지
4th row서울특별시 관악구 신림동 1422-4번지
5th row서울특별시 관악구 신림동 1423-4번지
ValueCountFrequency (%)
서울특별시 36
22.8%
관악구 36
22.8%
신림동 30
19.0%
봉천동 5
 
3.2%
3층 2
 
1.3%
1655-26번지 2
 
1.3%
16 2
 
1.3%
지층 2
 
1.3%
지하1층 2
 
1.3%
1423-4번지 2
 
1.3%
Other values (39) 39
24.7%
2024-04-06T22:11:05.719711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
17.8%
1 57
 
6.5%
지 37
 
4.2%
악 36
 
4.1%
울 36
 
4.1%
동 36
 
4.1%
구 36
 
4.1%
서 36
 
4.1%
관 36
 
4.1%
시 36
 
4.1%
Other values (26) 374
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 481
54.9%
Decimal Number 205
23.4%
Space Separator 156
 
17.8%
Dash Punctuation 34
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 37
 
7.7%
악 36
 
7.5%
울 36
 
7.5%
동 36
 
7.5%
구 36
 
7.5%
서 36
 
7.5%
관 36
 
7.5%
시 36
 
7.5%
별 36
 
7.5%
특 36
 
7.5%
Other values (14) 120
24.9%
Decimal Number
ValueCountFrequency (%)
1 57
27.8%
4 30
14.6%
2 28
13.7%
3 28
13.7%
6 23
11.2%
5 17
 
8.3%
0 7
 
3.4%
7 6
 
2.9%
9 5
 
2.4%
8 4
 
2.0%
Space Separator
ValueCountFrequency (%)
156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 481
54.9%
Common 395
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 37
 
7.7%
악 36
 
7.5%
울 36
 
7.5%
동 36
 
7.5%
구 36
 
7.5%
서 36
 
7.5%
관 36
 
7.5%
시 36
 
7.5%
별 36
 
7.5%
특 36
 
7.5%
Other values (14) 120
24.9%
Common
ValueCountFrequency (%)
156
39.5%
1 57
 
14.4%
- 34
 
8.6%
4 30
 
7.6%
2 28
 
7.1%
3 28
 
7.1%
6 23
 
5.8%
5 17
 
4.3%
0 7
 
1.8%
7 6
 
1.5%
Other values (2) 9
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 481
54.9%
ASCII 395
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
39.5%
1 57
 
14.4%
- 34
 
8.6%
4 30
 
7.6%
2 28
 
7.1%
3 28
 
7.1%
6 23
 
5.8%
5 17
 
4.3%
0 7
 
1.8%
7 6
 
1.5%
Other values (2) 9
 
2.3%
Hangul
ValueCountFrequency (%)
지 37
 
7.7%
악 36
 
7.5%
울 36
 
7.5%
동 36
 
7.5%
구 36
 
7.5%
서 36
 
7.5%
관 36
 
7.5%
시 36
 
7.5%
별 36
 
7.5%
특 36
 
7.5%
Other values (14) 120
24.9%

도로명주소
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-04-06T22:11:06.120954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30.5
Mean length27.138889
Min length23

Characters and Unicode

Total characters977
Distinct characters49
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

Unique36 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 남부순환로 1603 (신림동)
2nd row서울특별시 관악구 신림로 365 (신림동)
3rd row서울특별시 관악구 신림로 373 (신림동)
4th row서울특별시 관악구 남부순환로 1635 (신림동)
5th row서울특별시 관악구 봉천로12길 49 (신림동)
ValueCountFrequency (%)
서울특별시 36
19.4%
관악구 36
19.4%
신림동 25
13.4%
남부순환로 8
 
4.3%
신림로 6
 
3.2%
봉천동 4
 
2.2%
시흥대로 4
 
2.2%
봉천로12길 4
 
2.2%
조원로2길 3
 
1.6%
42 3
 
1.6%
Other values (47) 57
30.6%
2024-04-06T22:11:06.671890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174
 
17.8%
시 40
 
4.1%
관 40
 
4.1%
악 40
 
4.1%
림 38
 
3.9%
신 38
 
3.9%
서 36
 
3.7%
( 36
 
3.7%
특 36
 
3.7%
별 36
 
3.7%
Other values (39) 463
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
57.6%
Space Separator 174
 
17.8%
Decimal Number 148
 
15.1%
Open Punctuation 36
 
3.7%
Close Punctuation 36
 
3.7%
Other Punctuation 12
 
1.2%
Dash Punctuation 8
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40
 
7.1%
관 40
 
7.1%
악 40
 
7.1%
림 38
 
6.7%
신 38
 
6.7%
서 36
 
6.4%
특 36
 
6.4%
별 36
 
6.4%
구 36
 
6.4%
울 36
 
6.4%
Other values (24) 187
33.2%
Decimal Number
ValueCountFrequency (%)
2 31
20.9%
1 27
18.2%
3 16
10.8%
6 16
10.8%
5 15
10.1%
4 11
 
7.4%
9 9
 
6.1%
8 8
 
5.4%
0 8
 
5.4%
7 7
 
4.7%
Space Separator
ValueCountFrequency (%)
174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
57.6%
Common 414
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40
 
7.1%
관 40
 
7.1%
악 40
 
7.1%
림 38
 
6.7%
신 38
 
6.7%
서 36
 
6.4%
특 36
 
6.4%
별 36
 
6.4%
구 36
 
6.4%
울 36
 
6.4%
Other values (24) 187
33.2%
Common
ValueCountFrequency (%)
174
42.0%
( 36
 
8.7%
) 36
 
8.7%
2 31
 
7.5%
1 27
 
6.5%
3 16
 
3.9%
6 16
 
3.9%
5 15
 
3.6%
, 12
 
2.9%
4 11
 
2.7%
Other values (5) 40
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
57.6%
ASCII 414
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
174
42.0%
( 36
 
8.7%
) 36
 
8.7%
2 31
 
7.5%
1 27
 
6.5%
3 16
 
3.9%
6 16
 
3.9%
5 15
 
3.6%
, 12
 
2.9%
4 11
 
2.7%
Other values (5) 40
 
9.7%
Hangul
ValueCountFrequency (%)
시 40
 
7.1%
관 40
 
7.1%
악 40
 
7.1%
림 38
 
6.7%
신 38
 
6.7%
서 36
 
6.4%
특 36
 
6.4%
별 36
 
6.4%
구 36
 
6.4%
울 36
 
6.4%
Other values (24) 187
33.2%

도로명우편번호
Categorical

IMBALANCE 

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
32 
8753
 
1
8768
 
1
8744
 
1
8754
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique4 ?
Unique (%)11.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 32
88.9%
8753 1
 
2.8%
8768 1
 
2.8%
8744 1
 
2.8%
8754 1
 
2.8%

Length

2024-04-06T22:11:06.904381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:07.101448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
88.9%
8753 1
 
2.8%
8768 1
 
2.8%
8744 1
 
2.8%
8754 1
 
2.8%

사업장명
Text

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-04-06T22:11:07.428313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.3055556
Min length2

Characters and Unicode

Total characters191
Distinct characters73
Distinct categories2 ?
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 (%)83.3%

Sample

1st row한국
2nd row현대
3rd row서울
4th row신세계
5th row보라매
ValueCountFrequency (%)
댄스 3
 
6.8%
한국 2
 
4.5%
신세계 2
 
4.5%
공단무도학원 2
 
4.5%
무학무도학원 2
 
4.5%
이계창댄스스포츠 1
 
2.3%
무지개무도학원 1
 
2.3%
신 1
 
2.3%
댄스스포츠 1
 
2.3%
태양 1
 
2.3%
Other values (28) 28
63.6%
2024-04-06T22:11:07.985164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 20
 
10.5%
무 16
 
8.4%
학 16
 
8.4%
원 13
 
6.8%
도 12
 
6.3%
댄 10
 
5.2%
8
 
4.2%
포 6
 
3.1%
츠 6
 
3.1%
신 4
 
2.1%
Other values (63) 80
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
95.8%
Space Separator 8
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 20
 
10.9%
무 16
 
8.7%
학 16
 
8.7%
원 13
 
7.1%
도 12
 
6.6%
댄 10
 
5.5%
포 6
 
3.3%
츠 6
 
3.3%
신 4
 
2.2%
계 3
 
1.6%
Other values (62) 77
42.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
95.8%
Common 8
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 20
 
10.9%
무 16
 
8.7%
학 16
 
8.7%
원 13
 
7.1%
도 12
 
6.6%
댄 10
 
5.5%
포 6
 
3.3%
츠 6
 
3.3%
신 4
 
2.2%
계 3
 
1.6%
Other values (62) 77
42.1%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
95.8%
ASCII 8
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 20
 
10.9%
무 16
 
8.7%
학 16
 
8.7%
원 13
 
7.1%
도 12
 
6.6%
댄 10
 
5.5%
포 6
 
3.3%
츠 6
 
3.3%
신 4
 
2.2%
계 3
 
1.6%
Other values (62) 77
42.1%
ASCII
ValueCountFrequency (%)
8
100.0%

최종수정일자
Date

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2003-02-06 10:45:13
Maximum2022-08-05 14:44:25
2024-04-06T22:11:08.232270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:11:08.460187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

데이터갱신구분
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
I
29 
U
7 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 29
80.6%
U 7
 
19.4%

Length

2024-04-06T22:11:08.722944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:08.960374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 29
80.6%
u 7
 
19.4%

데이터갱신일자
Categorical

IMBALANCE 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
2018-08-31 23:59:59.0
29 
2021-12-08 00:07:00.0
 
1
2020-06-14 02:40:00.0
 
1
2021-11-24 02:40:00.0
 
1
2021-12-03 22:02:00.0
 
1
Other values (3)
3 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique7 ?
Unique (%)19.4%

Sample

1st row2018-08-31 23:59:59.0
2nd row2018-08-31 23:59:59.0
3rd row2018-08-31 23:59:59.0
4th row2018-08-31 23:59:59.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 29
80.6%
2021-12-08 00:07:00.0 1
 
2.8%
2020-06-14 02:40:00.0 1
 
2.8%
2021-11-24 02:40:00.0 1
 
2.8%
2021-12-03 22:02:00.0 1
 
2.8%
2020-04-09 02:40:00.0 1
 
2.8%
2020-08-22 02:40:00.0 1
 
2.8%
2021-10-31 02:40:00.0 1
 
2.8%

Length

2024-04-06T22:11:09.165465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:09.388987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-31 29
40.3%
23:59:59.0 29
40.3%
02:40:00.0 5
 
6.9%
2021-12-08 1
 
1.4%
00:07:00.0 1
 
1.4%
2020-06-14 1
 
1.4%
2021-11-24 1
 
1.4%
2021-12-03 1
 
1.4%
22:02:00.0 1
 
1.4%
2020-04-09 1
 
1.4%
Other values (2) 2
 
2.8%

업태구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

좌표정보(X)
Real number (ℝ)

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193527.88
Minimum191215.88
Maximum198266.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:09.626327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191215.88
5-th percentile191221.29
Q1192388.39
median193692.22
Q3193859.54
95-th percentile195951.23
Maximum198266.77
Range7050.8859
Interquartile range (IQR)1471.1509

Descriptive statistics

Standard deviation1616.7858
Coefficient of variation (CV)0.0083542782
Kurtosis0.91503247
Mean193527.88
Median Absolute Deviation (MAD)229.88108
Skewness0.62221439
Sum6967003.7
Variance2613996.2
MonotonicityNot monotonic
2024-04-06T22:11:09.852927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
193755.14368469 3
 
8.3%
191376.602183978 3
 
8.3%
191222.709779684 2
 
5.6%
193859.537537582 2
 
5.6%
195931.943928008 2
 
5.6%
193639.035785327 1
 
2.8%
193735.762391997 1
 
2.8%
191217.026445816 1
 
2.8%
193688.002534384 1
 
2.8%
195999.282149107 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
191215.879312952 1
 
2.8%
191217.026445816 1
 
2.8%
191222.709779684 2
5.6%
191376.602183978 3
8.3%
192292.366819576 1
 
2.8%
192341.964494839 1
 
2.8%
192403.860741324 1
 
2.8%
192511.06027527 1
 
2.8%
193497.894787693 1
 
2.8%
193551.235689789 1
 
2.8%
ValueCountFrequency (%)
198266.765199796 1
 
2.8%
195999.282149107 1
 
2.8%
195935.219212734 1
 
2.8%
195931.943928008 2
5.6%
195566.237122474 1
 
2.8%
193945.509928744 1
 
2.8%
193898.694557768 1
 
2.8%
193859.537537582 2
5.6%
193776.075917276 1
 
2.8%
193755.14368469 3
8.3%

좌표정보(Y)
Real number (ℝ)

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442418.12
Minimum441410.61
Maximum442833.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:10.086285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441410.61
5-th percentile442099.93
Q1442267.24
median442460.27
Q3442538.66
95-th percentile442756.56
Maximum442833.23
Range1422.6175
Interquartile range (IQR)271.42482

Descriptive statistics

Standard deviation270.24893
Coefficient of variation (CV)0.00061084506
Kurtosis4.1977521
Mean442418.12
Median Absolute Deviation (MAD)170.42328
Skewness-1.3390838
Sum15927052
Variance73034.482
MonotonicityNot monotonic
2024-04-06T22:11:10.349741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
442698.285576063 3
 
8.3%
442456.748277117 3
 
8.3%
442193.584387981 2
 
5.6%
442511.026089094 2
 
5.6%
442485.329600106 2
 
5.6%
442470.548427476 1
 
2.8%
442833.230656408 1
 
2.8%
442233.874417452 1
 
2.8%
442387.531613201 1
 
2.8%
442463.795943253 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
441410.613140173 1
2.8%
442095.332266396 1
2.8%
442101.456388316 1
2.8%
442114.145209749 1
2.8%
442183.150167516 1
2.8%
442188.620474149 1
2.8%
442193.584387981 2
5.6%
442233.874417452 1
2.8%
442278.360687964 1
2.8%
442301.336968562 1
2.8%
ValueCountFrequency (%)
442833.230656408 1
 
2.8%
442802.92355539 1
 
2.8%
442741.110638087 1
 
2.8%
442736.890384652 1
 
2.8%
442733.564216119 1
 
2.8%
442698.285576063 3
8.3%
442597.46066594 1
 
2.8%
442519.065029911 1
 
2.8%
442511.026089094 2
5.6%
442510.166080003 1
 
2.8%

문화체육업종명
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
무도학원업
34 
<NA>
 
2

Length

Max length5
Median length5
Mean length4.9444444
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무도학원업 34
94.4%
<NA> 2
 
5.6%

Length

2024-04-06T22:11:10.575323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:10.713260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무도학원업 34
94.4%
na 2
 
5.6%

공사립구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
사립
34 
<NA>
 
2

Length

Max length4
Median length2
Mean length2.1111111
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사립 34
94.4%
<NA> 2
 
5.6%

Length

2024-04-06T22:11:10.899336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:11.059786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 34
94.4%
na 2
 
5.6%

보험가입여부코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
31 
0
4 
Y
 
1

Length

Max length4
Median length4
Mean length3.5833333
Min length1

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
86.1%
0 4
 
11.1%
Y 1
 
2.8%

Length

2024-04-06T22:11:11.248609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:11.450770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
86.1%
0 4
 
11.1%
y 1
 
2.8%

지도자수
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
29 
0
7 

Length

Max length4
Median length4
Mean length3.4166667
Min length1

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> 29
80.6%
0 7
 
19.4%

Length

2024-04-06T22:11:11.665385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:11.845829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
80.6%
0 7
 
19.4%

건축물동수
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
23 
<NA>
11 
1
 
2

Length

Max length4
Median length1
Mean length1.9166667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 23
63.9%
<NA> 11
30.6%
1 2
 
5.6%

Length

2024-04-06T22:11:12.037876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:12.284338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
63.9%
na 11
30.6%
1 2
 
5.6%

건축물연면적
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)25.0%
Missing8
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean852.69107
Minimum0
Maximum10469.08
Zeros22
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T22:11:12.473011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7015.6435
Maximum10469.08
Range10469.08
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2690.8794
Coefficient of variation (CV)3.1557495
Kurtosis10.841834
Mean852.69107
Median Absolute Deviation (MAD)0
Skewness3.4480996
Sum23875.35
Variance7240832.2
MonotonicityNot monotonic
2024-04-06T22:11:12.649972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 22
61.1%
680.28 1
 
2.8%
10169.63 1
 
2.8%
10469.08 1
 
2.8%
866.52 1
 
2.8%
531.6 1
 
2.8%
1158.24 1
 
2.8%
(Missing) 8
 
22.2%
ValueCountFrequency (%)
0.0 22
61.1%
531.6 1
 
2.8%
680.28 1
 
2.8%
866.52 1
 
2.8%
1158.24 1
 
2.8%
10169.63 1
 
2.8%
10469.08 1
 
2.8%
ValueCountFrequency (%)
10469.08 1
 
2.8%
10169.63 1
 
2.8%
1158.24 1
 
2.8%
866.52 1
 
2.8%
680.28 1
 
2.8%
531.6 1
 
2.8%
0.0 22
61.1%

회원모집총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
35 
0
 
1

Length

Max length4
Median length4
Mean length3.9166667
Min length1

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 35
97.2%
0 1
 
2.8%

Length

2024-04-06T22:11:12.844164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:11:13.029509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
97.2%
0 1
 
2.8%

세부업종명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

법인명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)문화체육업종명공사립구분명보험가입여부코드지도자수건축물동수건축물연면적회원모집총인원세부업종명법인명
03200000CDFH330111199300000119930608<NA>4취소/말소/만료/정지/중지35직권말소20141112<NA><NA><NA><NA><NA>151892서울특별시 관악구 신림동 1433-45번지서울특별시 관악구 남부순환로 1603 (신림동)<NA>한국2014-11-25 09:12:42I2018-08-31 23:59:59.0<NA>193639.035785442470.548427무도학원업사립<NA><NA>00.0<NA><NA><NA>
13200000CDFH330111199300000219930713<NA>4취소/말소/만료/정지/중지35직권말소20141112<NA><NA><NA><NA><NA>151892서울특별시 관악구 신림동 1431-7번지서울특별시 관악구 신림로 365 (신림동)<NA>현대2014-11-25 09:09:13I2018-08-31 23:59:59.0<NA>193637.45133442741.110638무도학원업사립<NA><NA>00.0<NA><NA><NA>
23200000CDFH330111199500000219951204<NA>3폐업3폐업20090526<NA><NA><NA><NA><NA>151892서울특별시 관악구 신림동 1431-1번지서울특별시 관악구 신림로 373 (신림동)<NA>서울2009-05-26 09:46:47I2018-08-31 23:59:59.0<NA>193610.378561442802.923555무도학원업사립<NA><NA>00.0<NA><NA><NA>
33200000CDFH330111199500000319951228<NA>3폐업3폐업20011210<NA><NA><NA><NA><NA>151015서울특별시 관악구 신림동 1422-4번지서울특별시 관악구 남부순환로 1635 (신림동)<NA>신세계2010-04-29 17:21:49I2018-08-31 23:59:59.0<NA>193945.509929442519.06503무도학원업사립<NA><NA>00.0<NA><NA><NA>
43200000CDFH330111199600000119961028<NA>3폐업3폐업20020416<NA><NA><NA><NA><NA>151015서울특별시 관악구 신림동 1423-4번지서울특별시 관악구 봉천로12길 49 (신림동)<NA>보라매2010-04-29 17:25:07I2018-08-31 23:59:59.0<NA>193755.143685442698.285576무도학원업사립<NA><NA>00.0<NA><NA><NA>
53200000CDFH330111199600000219961028<NA>3폐업3폐업20111206<NA><NA><NA>877-8071<NA>151015서울특별시 관악구 신림동 1423-4번지 3층서울특별시 관악구 봉천로12길 49 (신림동,3층)<NA>무학무도학원2011-12-06 15:55:24I2018-08-31 23:59:59.0<NA>193755.143685442698.285576무도학원업사립0<NA><NA><NA><NA><NA><NA>
63200000CDFH330111199700000119970220<NA>3폐업3폐업20001228<NA><NA><NA><NA><NA>151836서울특별시 관악구 봉천동 869-6번지서울특별시 관악구 남부순환로218길 1 (봉천동)<NA>관악2003-02-06 10:45:13I2018-08-31 23:59:59.0<NA>195566.237122442101.456388무도학원업사립<NA>000.0<NA><NA><NA>
73200000CDFH330111199700000219970521<NA>3폐업3폐업20001101<NA><NA><NA><NA><NA>151892서울특별시 관악구 신림동 1433-104번지서울특별시 관악구 남부순환로 1591-6 (신림동)<NA>성심2003-02-06 10:45:13I2018-08-31 23:59:59.0<NA>193497.894788442467.452202무도학원업사립<NA>000.0<NA><NA><NA>
83200000CDFH330111199800000119980121<NA>3폐업3폐업20040510<NA><NA><NA><NA><NA>151930서울특별시 관악구 신림동 1641-10번지서울특별시 관악구 신림로 322-1 (신림동)<NA>신림2010-04-29 17:27:10I2018-08-31 23:59:59.0<NA>193748.074172442333.077344무도학원업사립<NA><NA>00.0<NA><NA><NA>
93200000CDFH330111199800000319980507<NA>3폐업3폐업19991122<NA><NA><NA><NA><NA>151809서울특별시 관악구 봉천동 62-19번지서울특별시 관악구 관악로 208-40 (봉천동)<NA>봉천무도학원2003-02-06 10:45:13I2018-08-31 23:59:59.0<NA>195935.219213442326.286165무도학원업사립<NA>000.0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)문화체육업종명공사립구분명보험가입여부코드지도자수건축물동수건축물연면적회원모집총인원세부업종명법인명
263200000CDFH330111200000000220001025<NA>3폐업3폐업20001120<NA><NA><NA><NA><NA>151930서울특별시 관악구 신림동 1640-13번지서울특별시 관악구 신림로 329 (신림동)<NA>대명무도학원2003-02-06 10:45:13I2018-08-31 23:59:59.0<NA>193688.002534442387.531613무도학원업사립<NA>000.0<NA><NA><NA>
273200000CDFH330111200100000120010112<NA>3폐업3폐업20110722<NA><NA><NA><NA><NA>151015서울특별시 관악구 신림동 1422-17번지서울특별시 관악구 남부순환로 1627 (신림동)<NA>이계창댄스스포츠2011-07-22 16:40:48I2018-08-31 23:59:59.0<NA>193859.537538442511.026089무도학원업사립<NA>000.0<NA><NA><NA>
283200000CDFH330111200200000120020131<NA>3폐업3폐업20131017<NA><NA><NA>3281-7979<NA>151903서울특별시 관악구 신림동 1655-26번지 지층서울특별시 관악구 조원로2길 42 (신림동,지층)<NA>신세계 스포츠댄스2013-10-17 17:35:48I2018-08-31 23:59:59.0<NA>191222.70978442193.584388무도학원업사립<NA><NA><NA><NA><NA><NA><NA>
293200000CDFH330111200300000120031004<NA>3폐업3폐업20130916<NA><NA><NA>830-2711<NA>151903서울특별시 관악구 신림동 1655-23번지 5층서울특별시 관악구 시흥대로 552-1 (신림동,5층)<NA>국제스포츠댄스학원2013-09-16 19:22:08I2018-08-31 23:59:59.0<NA>191217.026446442233.874417무도학원업사립0<NA><NA><NA><NA><NA><NA>
303200000CDFH330111200500000120050817<NA>3폐업3폐업20060313<NA><NA><NA>02-830-5232<NA>151902서울특별시 관악구 신림동 1643번지서울특별시 관악구 시흥대로 578 (신림동)<NA>김형섭 댄스2012-09-20 16:30:29I2018-08-31 23:59:59.0<NA>191376.602184442456.748277무도학원업사립<NA><NA>110169.63<NA><NA><NA>
313200000CDFH330111200900000120090812<NA>1영업/정상13영업중<NA><NA><NA><NA>02-858-0650<NA>151902서울특별시 관악구 신림동 1643 광안빌딩 지2층서울특별시 관악구 시흥대로 578 (신림동,광안빌딩 지2층)<NA>공단무도학원2021-11-22 16:17:39U2021-11-24 02:40:00.0<NA>191376.602184442456.748277무도학원업사립Y<NA><NA>10469.08<NA><NA><NA>
323200000CDFH330111201100000120110725<NA>3폐업3폐업20220420<NA><NA><NA>02-882-2467<NA>151015서울특별시 관악구 신림동 1422-17 403호서울특별시 관악구 남부순환로 1627, 403호 (신림동)<NA>백만불 댄스 무도학원2022-04-20 14:33:49U2021-12-03 22:02:00.0<NA>193859.537538442511.026089<NA><NA><NA><NA><NA><NA><NA><NA><NA>
333200000CDFH330111201300000120131126<NA>1영업/정상13영업중<NA><NA><NA><NA><NA><NA>151015서울특별시 관악구 신림동 1655-26번지 지하 1층서울특별시 관악구 조원로2길 42, 지하1층 (신림동)8768디앤스토리 댄스 스튜디오2020-04-07 13:55:07U2020-04-09 02:40:00.0<NA>191222.70978442193.584388무도학원업사립<NA><NA>1866.52<NA><NA><NA>
343200000CDFH330111201400000120141029<NA>4취소/말소/만료/정지/중지35직권말소20200821<NA><NA><NA><NA><NA>151050서울특별시 관악구 봉천동 32-21 지하1층서울특별시 관악구 관악로25길 5-9, 지하1층 (봉천동)8744은하댄스스포츠2020-08-20 19:20:48U2020-08-22 02:40:00.0<NA>195931.943928442485.3296무도학원업사립<NA><NA><NA>531.6<NA><NA><NA>
353200000CDFH330111202100000120210804<NA>1영업/정상13영업중<NA><NA><NA><NA><NA><NA><NA>서울특별시 관악구 신림동 1423-4 3층서울특별시 관악구 봉천로12길 49, 3층 (신림동)8754무학2021-10-29 15:43:33U2021-10-31 02:40:00.0<NA>193755.143685442698.285576무도학원업사립<NA>001158.240<NA><NA>