Overview

Dataset statistics

Number of variables34
Number of observations32
Missing cells302
Missing cells (%)27.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory297.1 B

Variable types

Categorical13
Text5
Numeric5
Unsupported9
DateTime2

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
Author동대문구
URLhttps://data.seoul.go.kr/dataList/OA-19682/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
영업상태코드 is highly imbalanced (55.1%)Imbalance
영업상태명 is highly imbalanced (55.1%)Imbalance
상세영업상태코드 is highly imbalanced (55.1%)Imbalance
상세영업상태명 is highly imbalanced (55.1%)Imbalance
도로명우편번호 is highly imbalanced (56.7%)Imbalance
데이터갱신구분 is highly imbalanced (66.3%)Imbalance
문화체육업종명 is highly imbalanced (79.9%)Imbalance
공사립구분명 is highly imbalanced (79.9%)Imbalance
지도자수 is highly imbalanced (79.9%)Imbalance
인허가취소일자 has 32 (100.0%) missing valuesMissing
폐업일자 has 3 (9.4%) missing valuesMissing
휴업시작일자 has 32 (100.0%) missing valuesMissing
휴업종료일자 has 32 (100.0%) missing valuesMissing
재개업일자 has 32 (100.0%) missing valuesMissing
전화번호 has 6 (18.8%) missing valuesMissing
소재지면적 has 32 (100.0%) missing valuesMissing
소재지우편번호 has 3 (9.4%) missing valuesMissing
도로명주소 has 2 (6.2%) missing valuesMissing
업태구분명 has 32 (100.0%) missing valuesMissing
회원모집총인원 has 32 (100.0%) missing valuesMissing
세부업종명 has 32 (100.0%) missing valuesMissing
법인명 has 32 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
최종수정일자 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
회원모집총인원 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

Reproduction

Analysis started2024-05-11 05:31:31.883293
Analysis finished2024-05-11 05:31:32.531596
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
3050000
32 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3050000
2nd row3050000
3rd row3050000
4th row3050000
5th row3050000

Common Values

ValueCountFrequency (%)
3050000 32
100.0%

Length

2024-05-11T14:31:32.651282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:32.814319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3050000 32
100.0%

관리번호
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-05-11T14:31:33.084799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters640
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

Unique32 ?
Unique (%)100.0%

Sample

1st rowCDFH3301111998000001
2nd rowCDFH3301111999000001
3rd rowCDFH3301111999000002
4th rowCDFH3301111999000003
5th rowCDFH3301111999000004
ValueCountFrequency (%)
cdfh3301111998000001 1
 
3.1%
cdfh3301111999000001 1
 
3.1%
cdfh3301112017000001 1
 
3.1%
cdfh3301112016000001 1
 
3.1%
cdfh3301112015000001 1
 
3.1%
cdfh3301112014000001 1
 
3.1%
cdfh3301112010000003 1
 
3.1%
cdfh3301112010000002 1
 
3.1%
cdfh3301112010000001 1
 
3.1%
cdfh3301112009000001 1
 
3.1%
Other values (22) 22
68.8%
2024-05-11T14:31:33.678043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 238
37.2%
1 128
20.0%
3 70
 
10.9%
2 38
 
5.9%
C 32
 
5.0%
D 32
 
5.0%
F 32
 
5.0%
H 32
 
5.0%
9 24
 
3.8%
8 3
 
0.5%
Other values (4) 11
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 512
80.0%
Uppercase Letter 128
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238
46.5%
1 128
25.0%
3 70
 
13.7%
2 38
 
7.4%
9 24
 
4.7%
8 3
 
0.6%
4 3
 
0.6%
5 3
 
0.6%
7 3
 
0.6%
6 2
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
C 32
25.0%
D 32
25.0%
F 32
25.0%
H 32
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 512
80.0%
Latin 128
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238
46.5%
1 128
25.0%
3 70
 
13.7%
2 38
 
7.4%
9 24
 
4.7%
8 3
 
0.6%
4 3
 
0.6%
5 3
 
0.6%
7 3
 
0.6%
6 2
 
0.4%
Latin
ValueCountFrequency (%)
C 32
25.0%
D 32
25.0%
F 32
25.0%
H 32
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238
37.2%
1 128
20.0%
3 70
 
10.9%
2 38
 
5.9%
C 32
 
5.0%
D 32
 
5.0%
F 32
 
5.0%
H 32
 
5.0%
9 24
 
3.8%
8 3
 
0.5%
Other values (4) 11
 
1.7%

인허가일자
Real number (ℝ)

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20049101
Minimum19980408
Maximum20220517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-11T14:31:33.937741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980408
5-th percentile19990224
Q119998042
median20020674
Q320093296
95-th percentile20165124
Maximum20220517
Range240109
Interquartile range (IQR)95254.25

Descriptive statistics

Standard deviation64848.946
Coefficient of variation (CV)0.0032345064
Kurtosis0.14979855
Mean20049101
Median Absolute Deviation (MAD)29609.5
Skewness1.0452774
Sum6.4157124 × 108
Variance4.2053859 × 109
MonotonicityNot monotonic
2024-05-11T14:31:34.180753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
19991122 2
 
6.2%
20080408 2
 
6.2%
19980408 1
 
3.1%
20020723 1
 
3.1%
20220517 1
 
3.1%
20171107 1
 
3.1%
20160229 1
 
3.1%
20150316 1
 
3.1%
20140331 1
 
3.1%
20100525 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
19980408 1
3.1%
19990111 1
3.1%
19990317 1
3.1%
19991019 1
3.1%
19991109 1
3.1%
19991122 2
6.2%
19991230 1
3.1%
20000313 1
3.1%
20001122 1
3.1%
20001127 1
3.1%
ValueCountFrequency (%)
20220517 1
3.1%
20171107 1
3.1%
20160229 1
3.1%
20150316 1
3.1%
20140331 1
3.1%
20100525 1
3.1%
20100201 1
3.1%
20100126 1
3.1%
20091020 1
3.1%
20080408 2
6.2%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
3
29 
1
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 29
90.6%
1 3
 
9.4%

Length

2024-05-11T14:31:34.421819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:34.590671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 29
90.6%
1 3
 
9.4%

영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
폐업
29 
영업/정상
3 

Length

Max length5
Median length2
Mean length2.28125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 29
90.6%
영업/정상 3
 
9.4%

Length

2024-05-11T14:31:34.756551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:34.949300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 29
90.6%
영업/정상 3
 
9.4%

상세영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
3
29 
13
3 

Length

Max length2
Median length1
Mean length1.09375
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 29
90.6%
13 3
 
9.4%

Length

2024-05-11T14:31:35.127948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:35.294388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 29
90.6%
13 3
 
9.4%

상세영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
폐업
29 
영업중
3 

Length

Max length3
Median length2
Mean length2.09375
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 29
90.6%
영업중 3
 
9.4%

Length

2024-05-11T14:31:35.497692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:35.677653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 29
90.6%
영업중 3
 
9.4%

폐업일자
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)75.9%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean20120028
Minimum20050328
Maximum20200303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-11T14:31:35.857316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050328
5-th percentile20050820
Q120071004
median20110906
Q320171129
95-th percentile20180202
Maximum20200303
Range149975
Interquartile range (IQR)100125

Descriptive statistics

Standard deviation49067.05
Coefficient of variation (CV)0.0024387168
Kurtosis-1.477403
Mean20120028
Median Absolute Deviation (MAD)40404
Skewness0.093512359
Sum5.8348081 × 108
Variance2.4075754 × 109
MonotonicityNot monotonic
2024-05-11T14:31:36.092611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20180202 6
18.8%
20101230 3
 
9.4%
20101231 1
 
3.1%
20070905 1
 
3.1%
20200303 1
 
3.1%
20171129 1
 
3.1%
20121210 1
 
3.1%
20110906 1
 
3.1%
20160509 1
 
3.1%
20160819 1
 
3.1%
Other values (12) 12
37.5%
(Missing) 3
 
9.4%
ValueCountFrequency (%)
20050328 1
3.1%
20050623 1
3.1%
20051116 1
3.1%
20060608 1
3.1%
20070502 1
3.1%
20070628 1
3.1%
20070905 1
3.1%
20071004 1
3.1%
20071024 1
3.1%
20101214 1
3.1%
ValueCountFrequency (%)
20200303 1
 
3.1%
20180202 6
18.8%
20171129 1
 
3.1%
20160819 1
 
3.1%
20160509 1
 
3.1%
20150511 1
 
3.1%
20140127 1
 
3.1%
20121210 1
 
3.1%
20111212 1
 
3.1%
20110906 1
 
3.1%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

전화번호
Text

MISSING 

Distinct24
Distinct (%)92.3%
Missing6
Missing (%)18.8%
Memory size388.0 B
2024-05-11T14:31:36.429974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.8461538
Min length8

Characters and Unicode

Total characters230
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

Unique22 ?
Unique (%)84.6%

Sample

1st row2247-0923
2nd row2244-2502
3rd row2244-4948
4th row967-8475
5th row957-8477
ValueCountFrequency (%)
2215-4339 2
 
7.7%
2244-4948 2
 
7.7%
2247-0923 1
 
3.8%
929-1233 1
 
3.8%
02-3394-5888 1
 
3.8%
960-7944 1
 
3.8%
2212-8169 1
 
3.8%
969-4034 1
 
3.8%
2215-8858 1
 
3.8%
969-8388 1
 
3.8%
Other values (14) 14
53.8%
2024-05-11T14:31:36.971749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 45
19.6%
4 34
14.8%
- 28
12.2%
9 26
11.3%
8 20
8.7%
3 17
 
7.4%
7 13
 
5.7%
1 12
 
5.2%
5 12
 
5.2%
6 12
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202
87.8%
Dash Punctuation 28
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 45
22.3%
4 34
16.8%
9 26
12.9%
8 20
9.9%
3 17
 
8.4%
7 13
 
6.4%
1 12
 
5.9%
5 12
 
5.9%
6 12
 
5.9%
0 11
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 230
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 45
19.6%
4 34
14.8%
- 28
12.2%
9 26
11.3%
8 20
8.7%
3 17
 
7.4%
7 13
 
5.7%
1 12
 
5.2%
5 12
 
5.2%
6 12
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 45
19.6%
4 34
14.8%
- 28
12.2%
9 26
11.3%
8 20
8.7%
3 17
 
7.4%
7 13
 
5.7%
1 12
 
5.2%
5 12
 
5.2%
6 12
 
5.2%

소재지면적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

소재지우편번호
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)48.3%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean130800.97
Minimum130030
Maximum130870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-11T14:31:37.188828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130030
5-th percentile130764.2
Q1130805
median130816
Q3130846
95-th percentile130870
Maximum130870
Range840
Interquartile range (IQR)41

Descriptive statistics

Standard deviation151.3507
Coefficient of variation (CV)0.0011571069
Kurtosis26.464464
Mean130800.97
Median Absolute Deviation (MAD)26
Skewness-5.0424717
Sum3793228
Variance22907.034
MonotonicityNot monotonic
2024-05-11T14:31:37.461344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
130805 7
21.9%
130809 3
9.4%
130870 3
9.4%
130867 3
9.4%
130840 2
 
6.2%
130842 2
 
6.2%
130846 2
 
6.2%
130816 1
 
3.1%
130811 1
 
3.1%
130844 1
 
3.1%
Other values (4) 4
12.5%
(Missing) 3
9.4%
ValueCountFrequency (%)
130030 1
 
3.1%
130753 1
 
3.1%
130781 1
 
3.1%
130805 7
21.9%
130809 3
9.4%
130811 1
 
3.1%
130816 1
 
3.1%
130840 2
 
6.2%
130842 2
 
6.2%
130844 1
 
3.1%
ValueCountFrequency (%)
130870 3
9.4%
130867 3
9.4%
130864 1
 
3.1%
130846 2
6.2%
130844 1
 
3.1%
130842 2
6.2%
130840 2
6.2%
130816 1
 
3.1%
130811 1
 
3.1%
130809 3
9.4%

지번주소
Text

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-05-11T14:31:37.816762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34.5
Mean length27.78125
Min length20

Characters and Unicode

Total characters889
Distinct characters58
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

Unique29 ?
Unique (%)90.6%

Sample

1st row서울특별시 동대문구 답십리동 492-3번지 5층
2nd row서울특별시 동대문구 답십리동 961-9번지
3rd row서울특별시 동대문구 장안동 337-8번지
4th row서울특별시 동대문구 용두동 9-2번지 2층
5th row서울특별시 동대문구 청량리동 741-0번지 지상2층
ValueCountFrequency (%)
서울특별시 32
20.3%
동대문구 32
20.3%
답십리동 13
 
8.2%
청량리동 8
 
5.1%
장안동 8
 
5.1%
지하 5
 
3.2%
961-9번지 3
 
1.9%
235-4번지 3
 
1.9%
지하1층 3
 
1.9%
493-4번지 2
 
1.3%
Other values (45) 49
31.0%
2024-05-11T14:31:38.582395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
155
 
17.4%
동 69
 
7.8%
지 42
 
4.7%
서 33
 
3.7%
- 33
 
3.7%
울 33
 
3.7%
특 32
 
3.6%
별 32
 
3.6%
시 32
 
3.6%
대 32
 
3.6%
Other values (48) 396
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 534
60.1%
Decimal Number 158
 
17.8%
Space Separator 155
 
17.4%
Dash Punctuation 33
 
3.7%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 69
12.9%
지 42
 
7.9%
서 33
 
6.2%
울 33
 
6.2%
특 32
 
6.0%
별 32
 
6.0%
시 32
 
6.0%
대 32
 
6.0%
문 32
 
6.0%
구 32
 
6.0%
Other values (31) 165
30.9%
Decimal Number
ValueCountFrequency (%)
2 23
14.6%
1 23
14.6%
6 19
12.0%
3 18
11.4%
5 18
11.4%
4 17
10.8%
9 16
10.1%
0 13
8.2%
7 8
 
5.1%
8 3
 
1.9%
Space Separator
ValueCountFrequency (%)
155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 534
60.1%
Common 354
39.8%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 69
12.9%
지 42
 
7.9%
서 33
 
6.2%
울 33
 
6.2%
특 32
 
6.0%
별 32
 
6.0%
시 32
 
6.0%
대 32
 
6.0%
문 32
 
6.0%
구 32
 
6.0%
Other values (31) 165
30.9%
Common
ValueCountFrequency (%)
155
43.8%
- 33
 
9.3%
2 23
 
6.5%
1 23
 
6.5%
6 19
 
5.4%
3 18
 
5.1%
5 18
 
5.1%
4 17
 
4.8%
9 16
 
4.5%
0 13
 
3.7%
Other values (6) 19
 
5.4%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 534
60.1%
ASCII 355
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
43.7%
- 33
 
9.3%
2 23
 
6.5%
1 23
 
6.5%
6 19
 
5.4%
3 18
 
5.1%
5 18
 
5.1%
4 17
 
4.8%
9 16
 
4.5%
0 13
 
3.7%
Other values (7) 20
 
5.6%
Hangul
ValueCountFrequency (%)
동 69
12.9%
지 42
 
7.9%
서 33
 
6.2%
울 33
 
6.2%
특 32
 
6.0%
별 32
 
6.0%
시 32
 
6.0%
대 32
 
6.0%
문 32
 
6.0%
구 32
 
6.0%
Other values (31) 165
30.9%

도로명주소
Text

MISSING 

Distinct28
Distinct (%)93.3%
Missing2
Missing (%)6.2%
Memory size388.0 B
2024-05-11T14:31:38.953442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length30.2
Min length24

Characters and Unicode

Total characters906
Distinct characters64
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

Unique26 ?
Unique (%)86.7%

Sample

1st row서울특별시 동대문구 천호대로 317 (답십리동,5층)
2nd row서울특별시 동대문구 고미술로 99 (답십리동)
3rd row서울특별시 동대문구 답십리로72길 40 (장안동)
4th row서울특별시 동대문구 답십리로 10 (용두동,2층)
5th row서울특별시 동대문구 전농로 5 (답십리동)
ValueCountFrequency (%)
서울특별시 30
18.3%
동대문구 30
18.3%
답십리동 8
 
4.9%
왕산로 7
 
4.3%
천호대로 6
 
3.7%
장안동 5
 
3.0%
고미술로 4
 
2.4%
99 3
 
1.8%
전농로 3
 
1.8%
5 3
 
1.8%
Other values (53) 65
39.6%
2024-05-11T14:31:39.693651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150
 
16.6%
동 65
 
7.2%
대 36
 
4.0%
) 33
 
3.6%
( 33
 
3.6%
울 31
 
3.4%
서 31
 
3.4%
시 30
 
3.3%
로 30
 
3.3%
특 30
 
3.3%
Other values (54) 437
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 552
60.9%
Space Separator 150
 
16.6%
Decimal Number 109
 
12.0%
Close Punctuation 33
 
3.6%
Open Punctuation 33
 
3.6%
Other Punctuation 22
 
2.4%
Dash Punctuation 5
 
0.6%
Math Symbol 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 65
 
11.8%
대 36
 
6.5%
울 31
 
5.6%
서 31
 
5.6%
시 30
 
5.4%
로 30
 
5.4%
특 30
 
5.4%
별 30
 
5.4%
구 30
 
5.4%
문 30
 
5.4%
Other values (37) 209
37.9%
Decimal Number
ValueCountFrequency (%)
2 24
22.0%
1 20
18.3%
3 15
13.8%
0 11
10.1%
5 11
10.1%
6 10
9.2%
9 8
 
7.3%
7 4
 
3.7%
4 3
 
2.8%
8 3
 
2.8%
Space Separator
ValueCountFrequency (%)
150
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 552
60.9%
Common 353
39.0%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 65
 
11.8%
대 36
 
6.5%
울 31
 
5.6%
서 31
 
5.6%
시 30
 
5.4%
로 30
 
5.4%
특 30
 
5.4%
별 30
 
5.4%
구 30
 
5.4%
문 30
 
5.4%
Other values (37) 209
37.9%
Common
ValueCountFrequency (%)
150
42.5%
) 33
 
9.3%
( 33
 
9.3%
2 24
 
6.8%
, 22
 
6.2%
1 20
 
5.7%
3 15
 
4.2%
0 11
 
3.1%
5 11
 
3.1%
6 10
 
2.8%
Other values (6) 24
 
6.8%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 552
60.9%
ASCII 354
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
42.4%
) 33
 
9.3%
( 33
 
9.3%
2 24
 
6.8%
, 22
 
6.2%
1 20
 
5.6%
3 15
 
4.2%
0 11
 
3.1%
5 11
 
3.1%
6 10
 
2.8%
Other values (7) 25
 
7.1%
Hangul
ValueCountFrequency (%)
동 65
 
11.8%
대 36
 
6.5%
울 31
 
5.6%
서 31
 
5.6%
시 30
 
5.4%
로 30
 
5.4%
특 30
 
5.4%
별 30
 
5.4%
구 30
 
5.4%
문 30
 
5.4%
Other values (37) 209
37.9%

도로명우편번호
Categorical

IMBALANCE 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
26 
2645
 
2
2603
 
1
130030
 
1
2489
 
1

Length

Max length6
Median length4
Mean length4.0625
Min length4

Unique

Unique4 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 26
81.2%
2645 2
 
6.2%
2603 1
 
3.1%
130030 1
 
3.1%
2489 1
 
3.1%
2622 1
 
3.1%

Length

2024-05-11T14:31:39.971942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:40.240377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
81.2%
2645 2
 
6.2%
2603 1
 
3.1%
130030 1
 
3.1%
2489 1
 
3.1%
2622 1
 
3.1%

사업장명
Text

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-05-11T14:31:40.581622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.125
Min length2

Characters and Unicode

Total characters196
Distinct characters69
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

Unique28 ?
Unique (%)87.5%

Sample

1st row국제무도학원
2nd row국도무도학원
3rd row한국무도학원
4th row법인현대스포츠
5th row동성무도학원
ValueCountFrequency (%)
한국무도학원 2
 
5.1%
댄스 2
 
5.1%
진주무도학원 2
 
5.1%
아카데미 1
 
2.6%
중앙댄스 1
 
2.6%
국제무도학원 1
 
2.6%
아가페 1
 
2.6%
두봉무도학원 1
 
2.6%
굿댄스라이프 1
 
2.6%
경동 1
 
2.6%
Other values (26) 26
66.7%
2024-05-11T14:31:41.312823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 17
 
8.7%
원 16
 
8.2%
학 15
 
7.7%
도 13
 
6.6%
무 12
 
6.1%
댄 10
 
5.1%
7
 
3.6%
츠 6
 
3.1%
포 6
 
3.1%
미 5
 
2.6%
Other values (59) 89
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
96.4%
Space Separator 7
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 17
 
9.0%
원 16
 
8.5%
학 15
 
7.9%
도 13
 
6.9%
무 12
 
6.3%
댄 10
 
5.3%
츠 6
 
3.2%
포 6
 
3.2%
미 5
 
2.6%
아 5
 
2.6%
Other values (58) 84
44.4%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
96.4%
Common 7
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 17
 
9.0%
원 16
 
8.5%
학 15
 
7.9%
도 13
 
6.9%
무 12
 
6.3%
댄 10
 
5.3%
츠 6
 
3.2%
포 6
 
3.2%
미 5
 
2.6%
아 5
 
2.6%
Other values (58) 84
44.4%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
96.4%
ASCII 7
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 17
 
9.0%
원 16
 
8.5%
학 15
 
7.9%
도 13
 
6.9%
무 12
 
6.3%
댄 10
 
5.3%
츠 6
 
3.2%
포 6
 
3.2%
미 5
 
2.6%
아 5
 
2.6%
Other values (58) 84
44.4%
ASCII
ValueCountFrequency (%)
7
100.0%

최종수정일자
Date

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2005-04-01 15:40:13
Maximum2022-05-17 10:49:09
2024-05-11T14:31:41.573144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:31:41.844394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

데이터갱신구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
I
30 
U
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 30
93.8%
U 2
 
6.2%

Length

2024-05-11T14:31:42.074746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:42.265646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 30
93.8%
u 2
 
6.2%

데이터갱신일자
Date

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2018-08-31 23:59:59
Maximum2021-12-04 23:09:00
2024-05-11T14:31:42.414003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:31:42.647991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

업태구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

좌표정보(X)
Real number (ℝ)

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204753.35
Minimum202179.31
Maximum206429.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-11T14:31:42.873339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202179.31
5-th percentile203530.28
Q1204089.82
median204796.71
Q3205299.52
95-th percentile206147.79
Maximum206429.14
Range4249.8222
Interquartile range (IQR)1209.7068

Descriptive statistics

Standard deviation934.16135
Coefficient of variation (CV)0.0045623739
Kurtosis0.52275625
Mean204753.35
Median Absolute Deviation (MAD)656.74606
Skewness-0.37631313
Sum6552107.3
Variance872657.43
MonotonicityNot monotonic
2024-05-11T14:31:43.405582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
204750.498366411 3
 
9.4%
205018.506913318 3
 
9.4%
204089.817117361 3
 
9.4%
205851.797111459 2
 
6.2%
204190.10878253 2
 
6.2%
204802.884455346 2
 
6.2%
204848.092924206 1
 
3.1%
205401.574373249 1
 
3.1%
205265.507077949 1
 
3.1%
204447.698147581 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
202179.314168815 1
 
3.1%
203287.352380135 1
 
3.1%
203729.036122627 1
 
3.1%
203778.875777015 1
 
3.1%
203828.823156356 1
 
3.1%
203847.879299723 1
 
3.1%
204089.817117361 3
9.4%
204190.10878253 2
6.2%
204447.698147581 1
 
3.1%
204750.498366411 3
9.4%
ValueCountFrequency (%)
206429.136367187 1
 
3.1%
206162.986259812 1
 
3.1%
206135.358071906 1
 
3.1%
205998.37441487 1
 
3.1%
205851.797111459 2
6.2%
205711.263315761 1
 
3.1%
205401.574373249 1
 
3.1%
205265.507077949 1
 
3.1%
205018.506913318 3
9.4%
204999.401520352 1
 
3.1%

좌표정보(Y)
Real number (ℝ)

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452087.33
Minimum451057.53
Maximum453415.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-11T14:31:43.618014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum451057.53
5-th percentile451061.81
Q1451404.72
median451841.48
Q3452959.78
95-th percentile453359.88
Maximum453415.8
Range2358.2666
Interquartile range (IQR)1555.0612

Descriptive statistics

Standard deviation845.56231
Coefficient of variation (CV)0.0018703517
Kurtosis-1.4163051
Mean452087.33
Median Absolute Deviation (MAD)530.46479
Skewness0.45833904
Sum14466795
Variance714975.62
MonotonicityNot monotonic
2024-05-11T14:31:43.853908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
451520.024760224 3
 
9.4%
451331.654449855 3
 
9.4%
453314.135663101 3
 
9.4%
451057.528462082 2
 
6.2%
453415.795068163 2
 
6.2%
451543.494717439 2
 
6.2%
451429.070131005 1
 
3.1%
451108.047802152 1
 
3.1%
452126.144140822 1
 
3.1%
451803.558407339 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
451057.528462082 2
6.2%
451065.321633611 1
 
3.1%
451108.047802152 1
 
3.1%
451290.385714542 1
 
3.1%
451331.654449855 3
9.4%
451429.070131005 1
 
3.1%
451520.024760224 3
9.4%
451530.326923326 1
 
3.1%
451543.494717439 2
6.2%
451803.558407339 1
 
3.1%
ValueCountFrequency (%)
453415.795068163 2
6.2%
453314.135663101 3
9.4%
453290.633976694 1
 
3.1%
453156.130497033 1
 
3.1%
453071.86687043 1
 
3.1%
452922.414254167 1
 
3.1%
452887.352617116 1
 
3.1%
452637.252289507 1
 
3.1%
452126.144140822 1
 
3.1%
452088.753698115 1
 
3.1%

문화체육업종명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
무도학원업
31 
<NA>
 
1

Length

Max length5
Median length5
Mean length4.96875
Min length4

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
무도학원업 31
96.9%
<NA> 1
 
3.1%

Length

2024-05-11T14:31:44.075517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:44.356527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무도학원업 31
96.9%
na 1
 
3.1%

공사립구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
사립
31 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0625
Min length2

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
사립 31
96.9%
<NA> 1
 
3.1%

Length

2024-05-11T14:31:44.547111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:44.720572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 31
96.9%
na 1
 
3.1%

보험가입여부코드
Categorical

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
15 
0
10 
Y
5 
1
2 

Length

Max length4
Median length1
Mean length2.40625
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 15
46.9%
0 10
31.2%
Y 5
 
15.6%
1 2
 
6.2%

Length

2024-05-11T14:31:44.899270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:45.093228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
46.9%
0 10
31.2%
y 5
 
15.6%
1 2
 
6.2%

지도자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
31 
0
 
1

Length

Max length4
Median length4
Mean length3.90625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
96.9%
0 1
 
3.1%

Length

2024-05-11T14:31:45.313435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:45.524642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
96.9%
0 1
 
3.1%

건축물동수
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
26 
0
6 

Length

Max length4
Median length4
Mean length3.4375
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 26
81.2%
0 6
 
18.8%

Length

2024-05-11T14:31:45.731566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:45.909354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
81.2%
0 6
 
18.8%

건축물연면적
Categorical

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
25 
0.0
6 
7628.12
 
1

Length

Max length7
Median length4
Mean length3.90625
Min length3

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row<NA>
2nd row<NA>
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
<NA> 25
78.1%
0.0 6
 
18.8%
7628.12 1
 
3.1%

Length

2024-05-11T14:31:46.129133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:31:46.379592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
78.1%
0.0 6
 
18.8%
7628.12 1
 
3.1%

회원모집총인원
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

세부업종명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

법인명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)문화체육업종명공사립구분명보험가입여부코드지도자수건축물동수건축물연면적회원모집총인원세부업종명법인명
03050000CDFH330111199800000119980408<NA>3폐업3폐업20180202<NA><NA><NA>2247-0923<NA>130805서울특별시 동대문구 답십리동 492-3번지 5층서울특별시 동대문구 천호대로 317 (답십리동,5층)<NA>국제무도학원2018-02-02 16:51:12I2018-08-31 23:59:59.0<NA>204848.092924451429.070131무도학원업사립<NA><NA><NA><NA><NA><NA><NA>
13050000CDFH330111199900000119990111<NA>3폐업3폐업20050623<NA><NA><NA>2244-2502<NA>130809서울특별시 동대문구 답십리동 961-9번지서울특별시 동대문구 고미술로 99 (답십리동)<NA>국도무도학원2005-06-23 13:16:03I2018-08-31 23:59:59.0<NA>205018.506913451331.65445무도학원업사립0<NA><NA><NA><NA><NA><NA>
23050000CDFH330111199900000219991122<NA>3폐업3폐업20101230<NA><NA><NA>2244-4948<NA>130840서울특별시 동대문구 장안동 337-8번지서울특별시 동대문구 답십리로72길 40 (장안동)<NA>한국무도학원2018-11-07 10:27:38U2018-11-09 02:38:06.0<NA>206429.136367452019.423155무도학원업사립<NA>000.0<NA><NA><NA>
33050000CDFH330111199900000319991122<NA>3폐업3폐업20180202<NA><NA><NA>967-8475<NA>130816서울특별시 동대문구 용두동 9-2번지 2층서울특별시 동대문구 답십리로 10 (용두동,2층)<NA>법인현대스포츠2018-02-02 10:54:12I2018-08-31 23:59:59.0<NA>203729.036123452887.352617무도학원업사립<NA><NA>00.0<NA><NA><NA>
43050000CDFH330111199900000419991109<NA>3폐업3폐업20101231<NA><NA><NA>957-8477<NA>130870서울특별시 동대문구 청량리동 741-0번지 지상2층<NA><NA>동성무도학원2010-12-31 14:27:49I2018-08-31 23:59:59.0<NA>203847.8793453156.130497무도학원업사립<NA><NA>00.0<NA><NA><NA>
53050000CDFH330111199900000519990317<NA>3폐업3폐업20070905<NA><NA><NA>2214-0440<NA>130805서울특별시 동대문구 답십리동 532-15번지서울특별시 동대문구 전농로 5 (답십리동)<NA>김운제 댄스 스포츠학원2007-11-19 14:29:10I2018-08-31 23:59:59.0<NA>204790.53357451530.326923무도학원업사립0<NA><NA><NA><NA><NA><NA>
63050000CDFH330111199900000619991019<NA>3폐업3폐업20101230<NA><NA><NA>966-7283<NA>130867서울특별시 동대문구 청량리동 235-6번지 미주상가 B동지하1층서울특별시 동대문구 왕산로 239 (청량리동,미주상가 B동지하1층)<NA>진주무도학원2010-12-30 13:46:56I2018-08-31 23:59:59.0<NA>204190.108783453415.795068무도학원업사립<NA><NA><NA><NA><NA><NA><NA>
73050000CDFH330111199900000719991230<NA>3폐업3폐업20180202<NA><NA><NA>2215-1244<NA>130805서울특별시 동대문구 답십리동 493-4번지 지하1층서울특별시 동대문구 천호대로 305 (답십리동,지하1층)<NA>성원스포츠댄스학원2018-02-02 10:54:54I2018-08-31 23:59:59.0<NA>204750.498366451520.02476무도학원업사립<NA><NA>00.0<NA><NA><NA>
83050000CDFH330111200000000120001122<NA>3폐업3폐업20180202<NA><NA><NA>2244-7881<NA>130842서울특별시 동대문구 장안동 382-6번지서울특별시 동대문구 장한로21길 11-12 (장안동)<NA>한양2018-02-02 10:56:35I2018-08-31 23:59:59.0<NA>205998.374415451879.411331무도학원업사립<NA><NA>00.0<NA><NA><NA>
93050000CDFH330111200000000220000313<NA>3폐업3폐업20070502<NA><NA><NA>964-2021<NA>130870서울특별시 동대문구 청량리동 766-0번지서울특별시 동대문구 왕산로 183 (청량리동)<NA>성문2007-11-19 14:41:09I2018-08-31 23:59:59.0<NA>203778.875777453071.86687무도학원업사립0<NA><NA><NA><NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)문화체육업종명공사립구분명보험가입여부코드지도자수건축물동수건축물연면적회원모집총인원세부업종명법인명
223050000CDFH330111200800000220080408<NA>3폐업3폐업20160819<NA><NA><NA>2212-8169<NA>130805서울특별시 동대문구 답십리동 530-15번지서울특별시 동대문구 전농로 5 (답십리동)<NA>텐 댄스크럽2016-08-19 10:34:54I2018-08-31 23:59:59.0<NA>204802.884455451543.494717무도학원업사립1<NA><NA><NA><NA><NA><NA>
233050000CDFH330111200900000120091020<NA>3폐업3폐업20160509<NA><NA><NA>960-7944<NA>130781서울특별시 동대문구 청량리동 235-4번지 미주아파트 상가 지하 6-2호서울특별시 동대문구 왕산로 225, 6-2호 (청량리동,미주아파트 상가 지하)<NA>미주학원2016-05-09 14:06:05I2018-08-31 23:59:59.0<NA>204089.817117453314.135663무도학원업사립<NA><NA><NA><NA><NA><NA><NA>
243050000CDFH330111201000000120100126<NA>3폐업3폐업20110906<NA><NA><NA><NA><NA>130809서울특별시 동대문구 답십리동 961-2번지 지하 1~2호서울특별시 동대문구 고미술로 100 (답십리동,지하 1~2호)<NA>하나댄스스포츠2011-09-06 15:41:51I2018-08-31 23:59:59.0<NA>204999.40152451290.385715무도학원업사립<NA><NA><NA>7628.12<NA><NA><NA>
253050000CDFH330111201000000220100201<NA>1영업/정상13영업중<NA><NA><NA><NA>2244-4948<NA>130805서울특별시 동대문구 답십리동 497-67번지 부룡빌딩 지하1층서울특별시 동대문구 천호대로 263 (답십리동)2603해바라기 댄스2018-03-08 13:35:56I2018-08-31 23:59:59.0<NA>204447.698148451803.558407무도학원업사립Y<NA><NA><NA><NA><NA><NA>
263050000CDFH330111201000000320100525<NA>3폐업3폐업20121210<NA><NA><NA><NA><NA>130753서울특별시 동대문구 답십리동 41번지 동서울한양아파트 2동 206호서울특별시 동대문구 답십리로 184, 2동 206호 (답십리동,동서울한양아파트)<NA>최정심아카데미2012-12-10 15:51:35I2018-08-31 23:59:59.0<NA>205265.507078452126.144141무도학원업사립<NA><NA><NA><NA><NA><NA><NA>
273050000CDFH330111201400000120140331<NA>1영업/정상13영업중<NA><NA><NA><NA>2215-4339<NA>130030서울특별시 동대문구 답십리동 305번지서울특별시 동대문구 천호대로 지하 305 (답십리동)130030굿댄스라이프2014-10-13 14:19:22I2018-08-31 23:59:59.0<NA>204750.498366451520.02476무도학원업사립Y<NA><NA><NA><NA><NA><NA>
283050000CDFH330111201500000120150316<NA>3폐업3폐업20180202<NA><NA><NA>02-3394-5888<NA>130846서울특별시 동대문구 장안동 465-2번지서울특별시 동대문구 장한로2길 36 (장안동)2645두봉무도학원2018-02-02 10:57:32I2018-08-31 23:59:59.0<NA>205851.797111451057.528462무도학원업사립Y<NA><NA><NA><NA><NA><NA>
293050000CDFH330111201600000120160229<NA>3폐업3폐업20171129<NA><NA><NA><NA><NA><NA>서울특별시 동대문구 청량리동 235-6번지서울특별시 동대문구 왕산로 239 (청량리동, 미주상가)2489진주무도학원2017-11-29 14:35:17I2018-08-31 23:59:59.0<NA>204190.108783453415.795068무도학원업사립Y<NA><NA><NA><NA><NA><NA>
303050000CDFH330111201700000120171107<NA>3폐업3폐업20200303<NA><NA><NA>02-2213-9950<NA><NA>서울특별시 동대문구 답십리동 961-9번지서울특별시 동대문구 고미술로 99 (답십리동)2622아가페2020-03-03 10:30:14U2020-03-05 02:40:00.0<NA>205018.506913451331.65445무도학원업사립Y<NA><NA><NA><NA><NA><NA>
313050000CDFH330111202200000120220517<NA>1영업/정상13영업중<NA><NA><NA><NA><NA><NA><NA>서울특별시 동대문구 장안동 465-2서울특별시 동대문구 장한로2길 36, 2,3층 (장안동)2645더오페라2022-05-17 10:49:09I2021-12-04 23:09:00.0<NA>205851.797111451057.528462<NA><NA><NA><NA><NA><NA><NA><NA><NA>