Overview

Dataset statistics

Number of variables26
Number of observations55
Missing cells232
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 KiB
Average record size in memory221.4 B

Variable types

Categorical11
Numeric5
DateTime4
Text6

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),점포구분명
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-18720/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
재개업일자 has constant value ""Constant
인허가취소일자 is highly imbalanced (86.9%)Imbalance
영업상태코드 is highly imbalanced (50.8%)Imbalance
영업상태명 is highly imbalanced (50.8%)Imbalance
상세영업상태코드 is highly imbalanced (50.8%)Imbalance
상세영업상태명 is highly imbalanced (50.8%)Imbalance
휴업시작일자 is highly imbalanced (83.5%)Imbalance
휴업종료일자 is highly imbalanced (83.5%)Imbalance
폐업일자 has 48 (87.3%) missing valuesMissing
재개업일자 has 54 (98.2%) missing valuesMissing
전화번호 has 2 (3.6%) missing valuesMissing
소재지면적 has 18 (32.7%) missing valuesMissing
소재지우편번호 has 41 (74.5%) missing valuesMissing
지번주소 has 3 (5.5%) missing valuesMissing
도로명주소 has 13 (23.6%) missing valuesMissing
도로명우편번호 has 25 (45.5%) missing valuesMissing
좌표정보(X) has 14 (25.5%) missing valuesMissing
좌표정보(Y) has 14 (25.5%) missing valuesMissing
관리번호 has unique valuesUnique
사업장명 has unique valuesUnique
소재지면적 has 2 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-11 05:02:13.212286
Analysis finished2024-05-11 05:02:14.249634
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
3180000
55 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 55
100.0%

Length

2024-05-11T05:02:14.449898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:14.794654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 55
100.0%

관리번호
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0065544 × 1018
Minimum1.982318 × 1018
Maximum2.023318 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-05-11T05:02:15.361989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.982318 × 1018
5-th percentile2.001318 × 1018
Q12.001318 × 1018
median2.003318 × 1018
Q32.011318 × 1018
95-th percentile2.021618 × 1018
Maximum2.023318 × 1018
Range4.1000014 × 1016
Interquartile range (IQR)1.0000009 × 1016

Descriptive statistics

Standard deviation8.0253435 × 1015
Coefficient of variation (CV)0.0039995644
Kurtosis0.80436859
Mean2.0065544 × 1018
Median Absolute Deviation (MAD)2 × 1015
Skewness0.17668228
Sum-3.1997379 × 1017
Variance6.4406138 × 1031
MonotonicityStrictly increasing
2024-05-11T05:02:15.808495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1982318011707500001 1
 
1.8%
1991318011707500001 1
 
1.8%
2005318007607500004 1
 
1.8%
2007318011707500001 1
 
1.8%
2008318011707500001 1
 
1.8%
2008318011707500002 1
 
1.8%
2008318011707500003 1
 
1.8%
2009318011707500002 1
 
1.8%
2009318011707500003 1
 
1.8%
2011318016307500001 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1982318011707500001 1
1.8%
1991318011707500001 1
1.8%
2001318007607500001 1
1.8%
2001318007607500002 1
1.8%
2001318007607500003 1
1.8%
2001318007607500004 1
1.8%
2001318007607500005 1
1.8%
2001318007607500006 1
1.8%
2001318007607500007 1
1.8%
2001318007607500009 1
1.8%
ValueCountFrequency (%)
2023318025507500002 1
1.8%
2023318025507500001 1
1.8%
2022318023907500001 1
1.8%
2021318023907500001 1
1.8%
2020318023907500002 1
1.8%
2020318023907500001 1
1.8%
2018318016307500001 1
1.8%
2017318016307500001 1
1.8%
2013318016307500001 1
1.8%
2012318016307500002 1
1.8%

인허가일자
Date

Distinct51
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum1965-05-28 00:00:00
Maximum2023-09-05 00:00:00
2024-05-11T05:02:16.298830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:02:16.846978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
<NA>
54 
20200331
 
1

Length

Max length8
Median length4
Mean length4.0727273
Min length4

Unique

Unique1 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 54
98.2%
20200331 1
 
1.8%

Length

2024-05-11T05:02:17.365751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:17.781814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 54
98.2%
20200331 1
 
1.8%

영업상태코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
1
44 
3
7 
2
 
2
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 44
80.0%
3 7
 
12.7%
2 2
 
3.6%
4 2
 
3.6%

Length

2024-05-11T05:02:18.300092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:18.649972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 44
80.0%
3 7
 
12.7%
2 2
 
3.6%
4 2
 
3.6%

영업상태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
영업/정상
44 
폐업
7 
휴업
 
2
취소/말소/만료/정지/중지
 
2

Length

Max length14
Median length5
Mean length4.8363636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row영업/정상
3rd row휴업
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 44
80.0%
폐업 7
 
12.7%
휴업 2
 
3.6%
취소/말소/만료/정지/중지 2
 
3.6%

Length

2024-05-11T05:02:19.117542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:19.525975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 44
80.0%
폐업 7
 
12.7%
휴업 2
 
3.6%
취소/말소/만료/정지/중지 2
 
3.6%

상세영업상태코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
1
44 
3
7 
2
 
2
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 44
80.0%
3 7
 
12.7%
2 2
 
3.6%
4 2
 
3.6%

Length

2024-05-11T05:02:19.984561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:20.378092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 44
80.0%
3 7
 
12.7%
2 2
 
3.6%
4 2
 
3.6%

상세영업상태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
정상영업
44 
폐업처리
7 
휴업처리
 
2
직권취소
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업처리
2nd row정상영업
3rd row휴업처리
4th row정상영업
5th row정상영업

Common Values

ValueCountFrequency (%)
정상영업 44
80.0%
폐업처리 7
 
12.7%
휴업처리 2
 
3.6%
직권취소 2
 
3.6%

Length

2024-05-11T05:02:20.846395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:21.358355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 44
80.0%
폐업처리 7
 
12.7%
휴업처리 2
 
3.6%
직권취소 2
 
3.6%

폐업일자
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)85.7%
Missing48
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean20173457
Minimum20090825
Maximum20201014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-05-11T05:02:21.823267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090825
5-th percentile20105730
Q120165406
median20190304
Q320200622
95-th percentile20200987
Maximum20201014
Range110189
Interquartile range (IQR)35216

Descriptive statistics

Standard deviation42262.615
Coefficient of variation (CV)0.0020949615
Kurtosis1.8596002
Mean20173457
Median Absolute Deviation (MAD)10620
Skewness-1.6363751
Sum1.412142 × 108
Variance1.7861287 × 109
MonotonicityNot monotonic
2024-05-11T05:02:22.222607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20190304 2
 
3.6%
20090825 1
 
1.8%
20200924 1
 
1.8%
20140508 1
 
1.8%
20201014 1
 
1.8%
20200320 1
 
1.8%
(Missing) 48
87.3%
ValueCountFrequency (%)
20090825 1
1.8%
20140508 1
1.8%
20190304 2
3.6%
20200320 1
1.8%
20200924 1
1.8%
20201014 1
1.8%
ValueCountFrequency (%)
20201014 1
1.8%
20200924 1
1.8%
20200320 1
1.8%
20190304 2
3.6%
20140508 1
1.8%
20090825 1
1.8%

휴업시작일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
<NA>
53 
20190701
 
1
20080529
 
1

Length

Max length8
Median length4
Mean length4.1454545
Min length4

Unique

Unique2 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
96.4%
20190701 1
 
1.8%
20080529 1
 
1.8%

Length

2024-05-11T05:02:22.711440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:23.069881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
96.4%
20190701 1
 
1.8%
20080529 1
 
1.8%

휴업종료일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
<NA>
53 
20200630
 
1
20090829
 
1

Length

Max length8
Median length4
Mean length4.1454545
Min length4

Unique

Unique2 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
96.4%
20200630 1
 
1.8%
20090829 1
 
1.8%

Length

2024-05-11T05:02:23.504676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:23.877493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
96.4%
20200630 1
 
1.8%
20090829 1
 
1.8%

재개업일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing54
Missing (%)98.2%
Memory size572.0 B
Minimum2009-08-21 00:00:00
Maximum2009-08-21 00:00:00
2024-05-11T05:02:24.232513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:02:24.591401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

전화번호
Text

MISSING 

Distinct52
Distinct (%)98.1%
Missing2
Missing (%)3.6%
Memory size572.0 B
2024-05-11T05:02:25.125804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.584906
Min length8

Characters and Unicode

Total characters561
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

Unique51 ?
Unique (%)96.2%

Sample

1st row02-2630-8800
2nd row0226708832
3rd row02 7896052
4th row0226302653
5th row0226338050
ValueCountFrequency (%)
02 16
 
22.2%
02-2006-2676 2
 
2.8%
8546 1
 
1.4%
02-2630-8800 1
 
1.4%
02-832-8546 1
 
1.4%
02-2630-2651 1
 
1.4%
2166-6800 1
 
1.4%
02-2634-0602 1
 
1.4%
02-2634-7654 1
 
1.4%
26382000 1
 
1.4%
Other values (46) 46
63.9%
2024-05-11T05:02:26.430572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 109
19.4%
2 100
17.8%
6 53
9.4%
8 53
9.4%
3 50
8.9%
- 46
8.2%
5 34
 
6.1%
4 33
 
5.9%
7 30
 
5.3%
1 22
 
3.9%
Other values (2) 31
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 495
88.2%
Dash Punctuation 46
 
8.2%
Space Separator 20
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 109
22.0%
2 100
20.2%
6 53
10.7%
8 53
10.7%
3 50
10.1%
5 34
 
6.9%
4 33
 
6.7%
7 30
 
6.1%
1 22
 
4.4%
9 11
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 109
19.4%
2 100
17.8%
6 53
9.4%
8 53
9.4%
3 50
8.9%
- 46
8.2%
5 34
 
6.1%
4 33
 
5.9%
7 30
 
5.3%
1 22
 
3.9%
Other values (2) 31
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 109
19.4%
2 100
17.8%
6 53
9.4%
8 53
9.4%
3 50
8.9%
- 46
8.2%
5 34
 
6.1%
4 33
 
5.9%
7 30
 
5.3%
1 22
 
3.9%
Other values (2) 31
 
5.5%

소재지면적
Real number (ℝ)

MISSING  ZEROS 

Distinct35
Distinct (%)94.6%
Missing18
Missing (%)32.7%
Infinite0
Infinite (%)0.0%
Mean13116.563
Minimum0
Maximum94319.57
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-05-11T05:02:27.042838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162.56
Q11075.83
median3972
Q310000.33
95-th percentile59383.684
Maximum94319.57
Range94319.57
Interquartile range (IQR)8924.5

Descriptive statistics

Standard deviation22446.52
Coefficient of variation (CV)1.7113111
Kurtosis6.2710596
Mean13116.563
Median Absolute Deviation (MAD)3723.8
Skewness2.5481194
Sum485312.83
Variance5.0384627 × 108
MonotonicityNot monotonic
2024-05-11T05:02:28.016280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 2
 
3.6%
3536.87 2
 
3.6%
18016.52 1
 
1.8%
248.2 1
 
1.8%
2796.71 1
 
1.8%
1398.36 1
 
1.8%
390.2 1
 
1.8%
37286.0 1
 
1.8%
3003.29 1
 
1.8%
3520.04 1
 
1.8%
Other values (25) 25
45.5%
(Missing) 18
32.7%
ValueCountFrequency (%)
0.0 2
3.6%
203.2 1
1.8%
240.5 1
1.8%
245.5 1
1.8%
248.2 1
1.8%
390.2 1
1.8%
396.3 1
1.8%
561.4 1
1.8%
1075.83 1
1.8%
1398.36 1
1.8%
ValueCountFrequency (%)
94319.57 1
1.8%
84573.34 1
1.8%
53086.27 1
1.8%
41973.81 1
1.8%
37286.0 1
1.8%
35687.74 1
1.8%
18016.52 1
1.8%
12066.16 1
1.8%
11893.83 1
1.8%
10000.33 1
1.8%

소재지우편번호
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing41
Missing (%)74.5%
Memory size572.0 B
2024-05-11T05:02:28.562636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length6.5
Min length6

Characters and Unicode

Total characters91
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

Unique14 ?
Unique (%)100.0%

Sample

1st row150043
2nd row150-723
3rd row150-991
4th row150010
5th row150798
ValueCountFrequency (%)
150043 1
 
7.1%
150-723 1
 
7.1%
150-991 1
 
7.1%
150010 1
 
7.1%
150798 1
 
7.1%
150-985 1
 
7.1%
150820 1
 
7.1%
150-833 1
 
7.1%
150-847 1
 
7.1%
150102 1
 
7.1%
Other values (4) 4
28.6%
2024-05-11T05:02:29.507224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22
24.2%
1 19
20.9%
5 16
17.6%
- 7
 
7.7%
8 7
 
7.7%
7 5
 
5.5%
3 4
 
4.4%
9 4
 
4.4%
2 3
 
3.3%
4 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
92.3%
Dash Punctuation 7
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22
26.2%
1 19
22.6%
5 16
19.0%
8 7
 
8.3%
7 5
 
6.0%
3 4
 
4.8%
9 4
 
4.8%
2 3
 
3.6%
4 2
 
2.4%
6 2
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 91
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22
24.2%
1 19
20.9%
5 16
17.6%
- 7
 
7.7%
8 7
 
7.7%
7 5
 
5.5%
3 4
 
4.4%
9 4
 
4.4%
2 3
 
3.3%
4 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22
24.2%
1 19
20.9%
5 16
17.6%
- 7
 
7.7%
8 7
 
7.7%
7 5
 
5.5%
3 4
 
4.4%
9 4
 
4.4%
2 3
 
3.3%
4 2
 
2.2%

지번주소
Text

MISSING 

Distinct47
Distinct (%)90.4%
Missing3
Missing (%)5.5%
Memory size572.0 B
2024-05-11T05:02:30.444304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32.5
Mean length24.634615
Min length19

Characters and Unicode

Total characters1281
Distinct characters76
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)84.6%

Sample

1st row서울특별시 영등포구 당산동3가 2번지 7호
2nd row서울특별시 영등포구 영등포동 618-496
3rd row서울특별시 영등포구 여의도동 60호
4th row서울특별시 영등포구 양평동3가 65호
5th row서울특별시 영등포구 영등포동5가 20호
ValueCountFrequency (%)
서울특별시 52
20.8%
영등포구 52
20.8%
신길동 7
 
2.8%
여의도동 6
 
2.4%
영등포동5가 6
 
2.4%
20호 5
 
2.0%
호 4
 
1.6%
문래동3가 4
 
1.6%
대림동 4
 
1.6%
7호 4
 
1.6%
Other values (84) 106
42.4%
2024-05-11T05:02:32.094714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
258
20.1%
포 67
 
5.2%
등 65
 
5.1%
영 65
 
5.1%
동 53
 
4.1%
서 52
 
4.1%
울 52
 
4.1%
구 52
 
4.1%
시 52
 
4.1%
별 52
 
4.1%
Other values (66) 513
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 819
63.9%
Space Separator 258
 
20.1%
Decimal Number 188
 
14.7%
Uppercase Letter 9
 
0.7%
Dash Punctuation 5
 
0.4%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 67
 
8.2%
등 65
 
7.9%
영 65
 
7.9%
동 53
 
6.5%
서 52
 
6.3%
울 52
 
6.3%
구 52
 
6.3%
시 52
 
6.3%
별 52
 
6.3%
특 52
 
6.3%
Other values (47) 257
31.4%
Decimal Number
ValueCountFrequency (%)
2 31
16.5%
1 31
16.5%
5 26
13.8%
3 23
12.2%
6 17
9.0%
4 16
8.5%
7 14
7.4%
0 13
6.9%
9 13
6.9%
8 4
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
S 5
55.6%
L 1
 
11.1%
C 1
 
11.1%
K 1
 
11.1%
A 1
 
11.1%
Space Separator
ValueCountFrequency (%)
258
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 819
63.9%
Common 453
35.4%
Latin 9
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 67
 
8.2%
등 65
 
7.9%
영 65
 
7.9%
동 53
 
6.5%
서 52
 
6.3%
울 52
 
6.3%
구 52
 
6.3%
시 52
 
6.3%
별 52
 
6.3%
특 52
 
6.3%
Other values (47) 257
31.4%
Common
ValueCountFrequency (%)
258
57.0%
2 31
 
6.8%
1 31
 
6.8%
5 26
 
5.7%
3 23
 
5.1%
6 17
 
3.8%
4 16
 
3.5%
7 14
 
3.1%
0 13
 
2.9%
9 13
 
2.9%
Other values (4) 11
 
2.4%
Latin
ValueCountFrequency (%)
S 5
55.6%
L 1
 
11.1%
C 1
 
11.1%
K 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 819
63.9%
ASCII 462
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
258
55.8%
2 31
 
6.7%
1 31
 
6.7%
5 26
 
5.6%
3 23
 
5.0%
6 17
 
3.7%
4 16
 
3.5%
7 14
 
3.0%
0 13
 
2.8%
9 13
 
2.8%
Other values (9) 20
 
4.3%
Hangul
ValueCountFrequency (%)
포 67
 
8.2%
등 65
 
7.9%
영 65
 
7.9%
동 53
 
6.5%
서 52
 
6.3%
울 52
 
6.3%
구 52
 
6.3%
시 52
 
6.3%
별 52
 
6.3%
특 52
 
6.3%
Other values (47) 257
31.4%

도로명주소
Text

MISSING 

Distinct40
Distinct (%)95.2%
Missing13
Missing (%)23.6%
Memory size572.0 B
2024-05-11T05:02:33.007919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length28.714286
Min length23

Characters and Unicode

Total characters1206
Distinct characters89
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

Unique38 ?
Unique (%)90.5%

Sample

1st row서울특별시 영등포구 경인로 846 (영등포동)
2nd row서울특별시 영등포구 63로 50 (여의도동)
3rd row서울특별시 영등포구 선유로 156 (양평동3가)
4th row서울특별시 영등포구 영등포로37길 4 (영등포동5가)
5th row서울특별시 영등포구 여의대로 128 (여의도동)
ValueCountFrequency (%)
서울특별시 42
 
18.1%
영등포구 42
 
18.1%
신길동 8
 
3.4%
대림동 6
 
2.6%
여의도동 5
 
2.2%
영등포로 5
 
2.2%
당산로 4
 
1.7%
신길로 4
 
1.7%
1층 4
 
1.7%
영중로 4
 
1.7%
Other values (83) 108
46.6%
2024-05-11T05:02:34.624581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
 
15.9%
영 61
 
5.1%
포 59
 
4.9%
등 57
 
4.7%
동 46
 
3.8%
서 43
 
3.6%
) 43
 
3.6%
( 43
 
3.6%
울 42
 
3.5%
로 42
 
3.5%
Other values (79) 578
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 755
62.6%
Space Separator 192
 
15.9%
Decimal Number 148
 
12.3%
Close Punctuation 43
 
3.6%
Open Punctuation 43
 
3.6%
Other Punctuation 12
 
1.0%
Uppercase Letter 9
 
0.7%
Math Symbol 2
 
0.2%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 61
 
8.1%
포 59
 
7.8%
등 57
 
7.5%
동 46
 
6.1%
서 43
 
5.7%
울 42
 
5.6%
로 42
 
5.6%
구 42
 
5.6%
시 42
 
5.6%
별 42
 
5.6%
Other values (58) 279
37.0%
Decimal Number
ValueCountFrequency (%)
1 34
23.0%
3 23
15.5%
2 17
11.5%
5 15
10.1%
6 14
9.5%
4 12
 
8.1%
0 11
 
7.4%
8 9
 
6.1%
9 8
 
5.4%
7 5
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
S 5
55.6%
K 1
 
11.1%
C 1
 
11.1%
L 1
 
11.1%
A 1
 
11.1%
Space Separator
ValueCountFrequency (%)
192
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 755
62.6%
Common 442
36.7%
Latin 9
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 61
 
8.1%
포 59
 
7.8%
등 57
 
7.5%
동 46
 
6.1%
서 43
 
5.7%
울 42
 
5.6%
로 42
 
5.6%
구 42
 
5.6%
시 42
 
5.6%
별 42
 
5.6%
Other values (58) 279
37.0%
Common
ValueCountFrequency (%)
192
43.4%
) 43
 
9.7%
( 43
 
9.7%
1 34
 
7.7%
3 23
 
5.2%
2 17
 
3.8%
5 15
 
3.4%
6 14
 
3.2%
, 12
 
2.7%
4 12
 
2.7%
Other values (6) 37
 
8.4%
Latin
ValueCountFrequency (%)
S 5
55.6%
K 1
 
11.1%
C 1
 
11.1%
L 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 755
62.6%
ASCII 451
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
42.6%
) 43
 
9.5%
( 43
 
9.5%
1 34
 
7.5%
3 23
 
5.1%
2 17
 
3.8%
5 15
 
3.3%
6 14
 
3.1%
, 12
 
2.7%
4 12
 
2.7%
Other values (11) 46
 
10.2%
Hangul
ValueCountFrequency (%)
영 61
 
8.1%
포 59
 
7.8%
등 57
 
7.5%
동 46
 
6.1%
서 43
 
5.7%
울 42
 
5.6%
로 42
 
5.6%
구 42
 
5.6%
시 42
 
5.6%
별 42
 
5.6%
Other values (58) 279
37.0%

도로명우편번호
Text

MISSING 

Distinct28
Distinct (%)93.3%
Missing25
Missing (%)45.5%
Memory size572.0 B
2024-05-11T05:02:35.188079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.8666667
Min length5

Characters and Unicode

Total characters176
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

Unique26 ?
Unique (%)86.7%

Sample

1st row07306
2nd row07387
3rd row07387
4th row150-723
5th row150045
ValueCountFrequency (%)
07264 2
 
6.7%
07387 2
 
6.7%
150102 1
 
3.3%
07362 1
 
3.3%
07273 1
 
3.3%
07335 1
 
3.3%
07392 1
 
3.3%
150103 1
 
3.3%
07413 1
 
3.3%
150876 1
 
3.3%
Other values (18) 18
60.0%
2024-05-11T05:02:36.229464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41
23.3%
1 23
13.1%
5 23
13.1%
7 20
11.4%
3 16
 
9.1%
8 14
 
8.0%
2 11
 
6.2%
- 9
 
5.1%
6 7
 
4.0%
9 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167
94.9%
Dash Punctuation 9
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
24.6%
1 23
13.8%
5 23
13.8%
7 20
12.0%
3 16
 
9.6%
8 14
 
8.4%
2 11
 
6.6%
6 7
 
4.2%
9 7
 
4.2%
4 5
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41
23.3%
1 23
13.1%
5 23
13.1%
7 20
11.4%
3 16
 
9.1%
8 14
 
8.0%
2 11
 
6.2%
- 9
 
5.1%
6 7
 
4.0%
9 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41
23.3%
1 23
13.1%
5 23
13.1%
7 20
11.4%
3 16
 
9.1%
8 14
 
8.0%
2 11
 
6.2%
- 9
 
5.1%
6 7
 
4.0%
9 7
 
4.0%

사업장명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-05-11T05:02:36.922750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.4909091
Min length4

Characters and Unicode

Total characters522
Distinct characters137
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row코레일유통주식회사
2nd row롯데백화점 영등포점
3rd row63씨티
4th row코스트코홀세일
5th row영등포지하상가
ValueCountFrequency (%)
홈플러스(주 6
 
6.4%
익스프레스 5
 
5.3%
the 3
 
3.2%
영등포점 3
 
3.2%
여의도점 2
 
2.1%
영등포 2
 
2.1%
양평점 2
 
2.1%
롯데슈퍼 2
 
2.1%
fresh 2
 
2.1%
gs 2
 
2.1%
Other values (64) 65
69.1%
2024-05-11T05:02:38.218741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
7.5%
스 24
 
4.6%
점 21
 
4.0%
주 16
 
3.1%
영 15
 
2.9%
) 15
 
2.9%
( 15
 
2.9%
포 13
 
2.5%
등 12
 
2.3%
가 11
 
2.1%
Other values (127) 341
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 402
77.0%
Uppercase Letter 44
 
8.4%
Space Separator 39
 
7.5%
Close Punctuation 15
 
2.9%
Open Punctuation 15
 
2.9%
Decimal Number 4
 
0.8%
Lowercase Letter 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 24
 
6.0%
점 21
 
5.2%
주 16
 
4.0%
영 15
 
3.7%
포 13
 
3.2%
등 12
 
3.0%
가 11
 
2.7%
시 11
 
2.7%
신 10
 
2.5%
장 9
 
2.2%
Other values (101) 260
64.7%
Uppercase Letter
ValueCountFrequency (%)
H 6
13.6%
E 6
13.6%
S 5
11.4%
F 3
 
6.8%
T 3
 
6.8%
I 3
 
6.8%
G 3
 
6.8%
C 2
 
4.5%
U 2
 
4.5%
L 2
 
4.5%
Other values (8) 9
20.5%
Decimal Number
ValueCountFrequency (%)
3 2
50.0%
2 1
25.0%
6 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
l 2
66.7%
a 1
33.3%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 402
77.0%
Common 73
 
14.0%
Latin 47
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 24
 
6.0%
점 21
 
5.2%
주 16
 
4.0%
영 15
 
3.7%
포 13
 
3.2%
등 12
 
3.0%
가 11
 
2.7%
시 11
 
2.7%
신 10
 
2.5%
장 9
 
2.2%
Other values (101) 260
64.7%
Latin
ValueCountFrequency (%)
H 6
12.8%
E 6
12.8%
S 5
10.6%
F 3
 
6.4%
T 3
 
6.4%
I 3
 
6.4%
G 3
 
6.4%
C 2
 
4.3%
U 2
 
4.3%
l 2
 
4.3%
Other values (10) 12
25.5%
Common
ValueCountFrequency (%)
39
53.4%
) 15
 
20.5%
( 15
 
20.5%
3 2
 
2.7%
2 1
 
1.4%
6 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 402
77.0%
ASCII 120
 
23.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
32.5%
) 15
 
12.5%
( 15
 
12.5%
H 6
 
5.0%
E 6
 
5.0%
S 5
 
4.2%
F 3
 
2.5%
T 3
 
2.5%
I 3
 
2.5%
G 3
 
2.5%
Other values (16) 22
18.3%
Hangul
ValueCountFrequency (%)
스 24
 
6.0%
점 21
 
5.2%
주 16
 
4.0%
영 15
 
3.7%
포 13
 
3.2%
등 12
 
3.0%
가 11
 
2.7%
시 11
 
2.7%
신 10
 
2.5%
장 9
 
2.2%
Other values (101) 260
64.7%

최종수정일자
Date

Distinct39
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2007-07-07 12:24:36
Maximum2024-04-24 16:07:18
2024-05-11T05:02:38.800030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:02:39.237041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)

데이터갱신구분
Categorical

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
U
29 
I
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
U 29
52.7%
I 26
47.3%

Length

2024-05-11T05:02:39.656301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:40.109094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 29
52.7%
i 26
47.3%

데이터갱신일자
Date

Distinct25
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-05-11T05:02:40.508149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:02:40.977360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

업태구분명
Categorical

Distinct7
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size572.0 B
그 밖의 대규모점포
29 
구분없음
9 
대형마트
7 
쇼핑센터
4 
백화점
3 
Other values (2)
3 

Length

Max length10
Median length10
Mean length7.0909091
Min length3

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row그 밖의 대규모점포
2nd row백화점
3rd row그 밖의 대규모점포
4th row그 밖의 대규모점포
5th row그 밖의 대규모점포

Common Values

ValueCountFrequency (%)
그 밖의 대규모점포 29
52.7%
구분없음 9
 
16.4%
대형마트 7
 
12.7%
쇼핑센터 4
 
7.3%
백화점 3
 
5.5%
전문점 2
 
3.6%
복합쇼핑몰 1
 
1.8%

Length

2024-05-11T05:02:41.631562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:42.078449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
그 29
25.7%
밖의 29
25.7%
대규모점포 29
25.7%
구분없음 9
 
8.0%
대형마트 7
 
6.2%
쇼핑센터 4
 
3.5%
백화점 3
 
2.7%
전문점 2
 
1.8%
복합쇼핑몰 1
 
0.9%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)87.8%
Missing14
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean191584.88
Minimum189785.75
Maximum194632.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-05-11T05:02:42.564377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189785.75
5-th percentile189877.03
Q1190721.42
median191580.65
Q3192108.93
95-th percentile193592
Maximum194632.53
Range4846.7739
Interquartile range (IQR)1387.5079

Descriptive statistics

Standard deviation1173.8413
Coefficient of variation (CV)0.0061270036
Kurtosis-0.091259704
Mean191584.88
Median Absolute Deviation (MAD)851.13994
Skewness0.55802183
Sum7854980.3
Variance1377903.3
MonotonicityNot monotonic
2024-05-11T05:02:43.112198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
193393.096553574 2
 
3.6%
191800.728214995 2
 
3.6%
189877.032334127 2
 
3.6%
190916.911377676 2
 
3.6%
190408.509134733 2
 
3.6%
193592.000380036 1
 
1.8%
192558.271784626 1
 
1.8%
190352.321686588 1
 
1.8%
193326.931018911 1
 
1.8%
192275.299730624 1
 
1.8%
Other values (26) 26
47.3%
(Missing) 14
25.5%
ValueCountFrequency (%)
189785.752470051 1
1.8%
189825.743146319 1
1.8%
189877.032334127 2
3.6%
190307.528282719 1
1.8%
190352.321686588 1
1.8%
190408.509134733 2
3.6%
190555.768991595 1
1.8%
190644.120684734 1
1.8%
190721.417931753 1
1.8%
190729.506453391 1
1.8%
ValueCountFrequency (%)
194632.526367463 1
1.8%
193674.743726431 1
1.8%
193592.000380036 1
1.8%
193393.096553574 2
3.6%
193326.931018911 1
1.8%
192712.769279545 1
1.8%
192643.702398007 1
1.8%
192558.271784626 1
1.8%
192275.299730624 1
1.8%
192108.925858666 1
1.8%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)87.8%
Missing14
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean445951.2
Minimum443681.77
Maximum447965.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-05-11T05:02:43.520961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum443681.77
5-th percentile443809.77
Q1444944.15
median446218.44
Q3446801.78
95-th percentile447298.02
Maximum447965.65
Range4283.8788
Interquartile range (IQR)1857.6285

Descriptive statistics

Standard deviation1175.6389
Coefficient of variation (CV)0.0026362501
Kurtosis-0.66301379
Mean445951.2
Median Absolute Deviation (MAD)786.92297
Skewness-0.6194734
Sum18283999
Variance1382126.9
MonotonicityNot monotonic
2024-05-11T05:02:43.965134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
446218.443828439 2
 
3.6%
443809.773854649 2
 
3.6%
446801.78225662 2
 
3.6%
443681.766720649 2
 
3.6%
447298.022173646 2
 
3.6%
447092.629432527 1
 
1.8%
444233.81892942 1
 
1.8%
447095.641206789 1
 
1.8%
446973.914570276 1
 
1.8%
443891.073398846 1
 
1.8%
Other values (26) 26
47.3%
(Missing) 14
25.5%
ValueCountFrequency (%)
443681.766720649 2
3.6%
443809.773854649 2
3.6%
443891.073398846 1
1.8%
444233.81892942 1
1.8%
444571.769293729 1
1.8%
444884.804266378 1
1.8%
444885.168731883 1
1.8%
444933.158731651 1
1.8%
444944.153786024 1
1.8%
444953.005202762 1
1.8%
ValueCountFrequency (%)
447965.645513185 1
1.8%
447298.022173646 2
3.6%
447296.226305826 1
1.8%
447289.548355449 1
1.8%
447095.641206789 1
1.8%
447092.629432527 1
1.8%
447064.885774633 1
1.8%
446973.914570276 1
1.8%
446801.78225662 2
3.6%
446796.882113561 1
1.8%

점포구분명
Categorical

Distinct3
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
<NA>
39 
대규모점포
14 
준대규모점포
 
2

Length

Max length6
Median length4
Mean length4.3272727
Min length4

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> 39
70.9%
대규모점포 14
 
25.5%
준대규모점포 2
 
3.6%

Length

2024-05-11T05:02:44.521260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:02:44.882977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 39
70.9%
대규모점포 14
 
25.5%
준대규모점포 2
 
3.6%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
03180000198231801170750000119821020<NA>3폐업3폐업처리20090825<NA><NA><NA>02-2630-88005380.0150043서울특별시 영등포구 당산동3가 2번지 7호<NA><NA>코레일유통주식회사2009-10-15 14:50:41I2018-08-31 23:59:59.0그 밖의 대규모점포191272.120991447064.885775<NA>
1318000019913180117075000011991-05-01<NA>1영업/정상1정상영업<NA><NA><NA><NA>022670883253086.27<NA>서울특별시 영등포구 영등포동 618-496서울특별시 영등포구 경인로 846 (영등포동)07306롯데백화점 영등포점2024-04-22 14:45:43U2023-12-03 22:04:00.0백화점191741.345848445970.307641<NA>
23180000200131800760750000119850727<NA>2휴업2휴업처리<NA><NA><NA><NA>02 7896052<NA><NA>서울특별시 영등포구 여의도동 60호서울특별시 영등포구 63로 50 (여의도동)<NA>63씨티2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포194632.526367446401.926526<NA>
33180000200131800760750000219941007<NA>1영업/정상1정상영업<NA><NA><NA><NA>0226302653<NA><NA>서울특별시 영등포구 양평동3가 65호서울특별시 영등포구 선유로 156 (양평동3가)<NA>코스트코홀세일2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포190408.509135447298.022174<NA>
43180000200131800760750000319970916<NA>1영업/정상1정상영업<NA><NA><NA><NA>0226338050<NA><NA>서울특별시 영등포구 영등포동5가 20호<NA><NA>영등포지하상가2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA><NA>
53180000200131800760750000419800409<NA>1영업/정상1정상영업<NA><NA><NA><NA>02 6788817<NA><NA>서울특별시 영등포구 영등포동5가 20호<NA><NA>영등포역앞지하상가2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA><NA>
63180000200131800760750000519830909<NA>1영업/정상1정상영업<NA><NA><NA><NA>02 6788586<NA><NA>서울특별시 영등포구 영등포동5가 20호<NA><NA>영등포로타리지하상가2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA><NA>
73180000200131800760750000619871127<NA>1영업/정상1정상영업<NA><NA><NA><NA>0226340536<NA><NA>서울특별시 영등포구 영등포동5가 20호<NA><NA>삼구시장2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA><NA>
83180000200131800760750000719710809<NA>1영업/정상1정상영업<NA><NA><NA><NA>0226354846<NA><NA>서울특별시 영등포구 영등포동5가 24호서울특별시 영등포구 영등포로37길 4 (영등포동5가)<NA>동남시장2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포191590.878296446489.419538<NA>
93180000200131800760750000919740515<NA>1영업/정상1정상영업<NA><NA><NA><NA>0226339047<NA><NA>서울특별시 영등포구 문래동3가 16호<NA><NA>영일시장2007-07-07 12:24:36I2018-08-31 23:59:59.0그 밖의 대규모점포<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)점포구분명
453180000201231801630750000220120613<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-6333-570037286.0150876서울특별시 영등포구 여의도동 23번지서울특별시 영등포구 국제금융로 10 (여의도동)150876아이에프씨몰(IFC Mall)2018-02-20 09:08:41I2018-08-31 23:59:59.0복합쇼핑몰193326.931019446973.91457대규모점포
463180000201331801630750000120130902<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2609-20023003.29<NA>서울특별시 영등포구 대림동 717 대림동 쌍용 플래티넘-S서울특별시 영등포구 대림로29길 13 (대림동, 대림동 쌍용 플래티넘-S)07413두암상가2020-09-07 11:17:23U2020-09-09 02:40:00.0그 밖의 대규모점포190916.911378443681.766721대규모점포
473180000201731801630750000120170425<NA>1영업/정상1정상영업<NA><NA><NA><NA>2145817818016.52<NA><NA>서울특별시 영등포구 선유로 138 (양평동3가)150103롯데쇼핑(주) 롯데마트 양평점2022-05-10 19:18:57U2021-12-04 23:02:00.0대형마트190352.321687447095.641207<NA>
483180000201831801630750000120180614<NA>3폐업3폐업처리20200320<NA><NA><NA><NA>3520.04<NA>서울특별시 영등포구 신길동 2363번지 메트하임서울특별시 영등포구 신풍로 93, 메트하임 (신길동)07392메트하임2020-03-20 09:44:30U2020-03-22 02:40:00.0그 밖의 대규모점포192558.271785444233.818929대규모점포
493180000202031802390750000120200102<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2090-613384573.34<NA>서울특별시 영등포구 여의도동 22번지서울특별시 영등포구 여의대로 108 (여의도동)07335THE HYUNDAI SEOUL2020-12-18 18:28:54U2020-12-20 02:40:00.0백화점193592.00038447092.629433대규모점포
503180000202031802390750000220201012<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2038-85753613.32<NA>서울특별시 영등포구 양평동1가 247 영등포 중흥 S-CLASS서울특별시 영등포구 선유서로24길 6 (양평동1가, 영등포 중흥 S-CLASS)07273영등포 중흥 에스클래스2021-04-07 10:37:13U2021-04-09 02:40:00.0그 밖의 대규모점포189877.032334446801.782257대규모점포
513180000202131802390750000120211228<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2006-2676561.4<NA>서울특별시 영등포구 신길동 4902-6서울특별시 영등포구 신길로 162, 1층 일부 (신길동)07362GS THE FRESH 신길사러가점2022-04-25 14:49:28U2021-12-03 22:08:00.0구분없음192108.925859444944.153786<NA>
52318000020223180239075000012022-12-14<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-585-05805824.25<NA>서울특별시 영등포구 당산동2가 165 한화포레나당산서울특별시 영등포구 당산로 83 (당산동2가, 한화포레나당산)07264포레나당산동역세권청년주택 상업시설2024-01-08 09:44:36U2023-11-30 23:00:00.0그 밖의 대규모점포<NA><NA><NA>
53318000020233180255075000012023-05-25<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-2006-2676203.2<NA>서울특별시 영등포구 문래동6가 33서울특별시 영등포구 문래북로 8, 1층 101~106호 (문래동6가)07280GS THE FRESH 문래점2023-10-06 14:22:08U2022-10-31 00:08:00.0구분없음189825.743146446574.176568<NA>
54318000020233180255075000022023-09-05<NA>1영업/정상1정상영업<NA><NA><NA><NA>02-380-50421075.83<NA>서울특별시 영등포구 당산동2가 165 한화포레나당산서울특별시 영등포구 당산로 83, 지하 102~103호 (당산동2가, 한화포레나당산)07264(주)이마트에브리데이 영등포구청점2023-10-06 14:22:36U2022-10-31 00:08:00.0구분없음<NA><NA><NA>