Overview

Dataset statistics

Number of variables17
Number of observations59
Missing cells238
Missing cells (%)23.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory148.2 B

Variable types

Text3
Categorical7
Numeric4
Unsupported3

Dataset

Description- 대형마트, 백화점, 쇼핑센터,복합쇼핑몰 등 다수의 사업자로부터 납품받아 판매하는 업종의 사업장명, 인허가일자, 영업상태, 소재지주소 등의 정보를 제공합니다. - 파일에 제공되는 위도, 경도 좌표는 인포그래픽 표출을 위해 지번 주소나 도로명 주소를 이용하여 카카오 위치 서비스에서 호출한 것입니다. - 데이터 제공처: LOCALDATA
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/819

Alerts

업종구분대분류 has constant value ""Constant
데이터갱신일자 has constant value ""Constant
영업상태명 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
도로명우편번호 is highly overall correlated with 인허가일자 and 5 other fieldsHigh correlation
업종구분소분류 is highly overall correlated with 도로명우편번호High correlation
폐업일자 is highly overall correlated with 인허가일자 and 1 other fieldsHigh correlation
인허가일자 is highly overall correlated with 폐업일자 and 1 other fieldsHigh correlation
소재지면적 is highly overall correlated with 도로명우편번호High correlation
위도 is highly overall correlated with 영업상태명 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 영업상태명 and 1 other fieldsHigh correlation
인허가취소일자 is highly imbalanced (87.6%)Imbalance
폐업일자 is highly imbalanced (72.8%)Imbalance
도로명우편번호 is highly imbalanced (76.2%)Imbalance
휴업시작일자 has 59 (100.0%) missing valuesMissing
휴업종료일자 has 59 (100.0%) missing valuesMissing
재개업일자 has 59 (100.0%) missing valuesMissing
소재지면적 has 12 (20.3%) missing valuesMissing
지번주소 has 1 (1.7%) missing valuesMissing
도로명주소 has 14 (23.7%) missing valuesMissing
위도 has 17 (28.8%) missing valuesMissing
경도 has 17 (28.8%) missing valuesMissing
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 has 7 (11.9%) zerosZeros

Reproduction

Analysis started2023-12-11 19:30:56.128771
Analysis finished2023-12-11 19:30:58.420155
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct53
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T04:30:58.566530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length12
Mean length7.5084746
Min length3

Characters and Unicode

Total characters443
Distinct characters116
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)79.7%

Sample

1st row한림민속오일시장
2nd row세화민속오일시장
3rd row(주)이마트 신제주점
4th row뉴월드마트
5th row성지유니코
ValueCountFrequency (%)
신제주점 4
 
5.3%
주)이마트 4
 
5.3%
서귀포점 3
 
3.9%
주)뉴월드 3
 
3.9%
이마트 3
 
3.9%
주)신세계 2
 
2.6%
동문공설시장 2
 
2.6%
제이갤러리 2
 
2.6%
주)서귀포상설시장 2
 
2.6%
주)부귀 1
 
1.3%
Other values (50) 50
65.8%
2023-12-12T04:30:58.880707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
 
6.3%
주 28
 
6.3%
장 27
 
6.1%
( 19
 
4.3%
) 19
 
4.3%
17
 
3.8%
점 16
 
3.6%
제 13
 
2.9%
이 11
 
2.5%
신 9
 
2.0%
Other values (106) 256
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 386
87.1%
Open Punctuation 19
 
4.3%
Close Punctuation 19
 
4.3%
Space Separator 17
 
3.8%
Uppercase Letter 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
 
7.3%
주 28
 
7.3%
장 27
 
7.0%
점 16
 
4.1%
제 13
 
3.4%
이 11
 
2.8%
신 9
 
2.3%
오 9
 
2.3%
일 9
 
2.3%
마 9
 
2.3%
Other values (101) 227
58.8%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 386
87.1%
Common 55
 
12.4%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
 
7.3%
주 28
 
7.3%
장 27
 
7.0%
점 16
 
4.1%
제 13
 
3.4%
이 11
 
2.8%
신 9
 
2.3%
오 9
 
2.3%
일 9
 
2.3%
마 9
 
2.3%
Other values (101) 227
58.8%
Common
ValueCountFrequency (%)
( 19
34.5%
) 19
34.5%
17
30.9%
Latin
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 386
87.1%
ASCII 57
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
 
7.3%
주 28
 
7.3%
장 27
 
7.0%
점 16
 
4.1%
제 13
 
3.4%
이 11
 
2.8%
신 9
 
2.3%
오 9
 
2.3%
일 9
 
2.3%
마 9
 
2.3%
Other values (101) 227
58.8%
ASCII
ValueCountFrequency (%)
( 19
33.3%
) 19
33.3%
17
29.8%
L 1
 
1.8%
G 1
 
1.8%

업종구분대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
대규모점포
59 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대규모점포 59
100.0%

Length

2023-12-12T04:30:58.990212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:30:59.068652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대규모점포 59
100.0%

업종구분소분류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size604.0 B
시장
18 
그 밖의 대규모점포
17 
대형마트
16 
백화점
4 
쇼핑센터
2 

Length

Max length10
Median length4
Mean length5.0169492
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시장
2nd row시장
3rd row대형마트
4th row대형마트
5th row시장

Common Values

ValueCountFrequency (%)
시장 18
30.5%
그 밖의 대규모점포 17
28.8%
대형마트 16
27.1%
백화점 4
 
6.8%
쇼핑센터 2
 
3.4%
전문점 2
 
3.4%

Length

2023-12-12T04:30:59.158092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:30:59.255813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시장 18
19.4%
그 17
18.3%
밖의 17
18.3%
대규모점포 17
18.3%
대형마트 16
17.2%
백화점 4
 
4.3%
쇼핑센터 2
 
2.2%
전문점 2
 
2.2%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19922217
Minimum19541109
Maximum20210726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T04:30:59.413177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19541109
5-th percentile19631217
Q119835825
median19960516
Q320020674
95-th percentile20117559
Maximum20210726
Range669617
Interquartile range (IQR)184849.5

Descriptive statistics

Standard deviation157326.67
Coefficient of variation (CV)0.0078970464
Kurtosis0.33009129
Mean19922217
Median Absolute Deviation (MAD)80008
Skewness-0.80460555
Sum1.1754108 × 109
Variance2.4751681 × 1010
MonotonicityNot monotonic
2023-12-12T04:30:59.586706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19541109 3
 
5.1%
19650115 2
 
3.4%
19991229 2
 
3.4%
20020425 2
 
3.4%
20020924 1
 
1.7%
19641229 1
 
1.7%
20010130 1
 
1.7%
20010728 1
 
1.7%
20020319 1
 
1.7%
20040524 1
 
1.7%
Other values (44) 44
74.6%
ValueCountFrequency (%)
19541109 3
5.1%
19641229 1
 
1.7%
19650115 2
3.4%
19650426 1
 
1.7%
19750306 1
 
1.7%
19750501 1
 
1.7%
19750506 1
 
1.7%
19780501 1
 
1.7%
19780515 1
 
1.7%
19800310 1
 
1.7%
ValueCountFrequency (%)
20210726 1
1.7%
20201125 1
1.7%
20180810 1
1.7%
20110531 1
1.7%
20070808 1
1.7%
20060519 1
1.7%
20051227 1
1.7%
20051128 1
1.7%
20050428 1
1.7%
20050408 1
1.7%

인허가취소일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size604.0 B
<NA>
58 
20010723
 
1

Length

Max length8
Median length4
Mean length4.0677966
Min length4

Unique

Unique1 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 58
98.3%
20010723 1
 
1.7%

Length

2023-12-12T04:30:59.742094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:30:59.979938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 58
98.3%
20010723 1
 
1.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size604.0 B
영업/정상
44 
휴업
6 
폐업
6 
취소/말소/만료/정지/중지
 
3

Length

Max length14
Median length5
Mean length4.8474576
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 44
74.6%
휴업 6
 
10.2%
폐업 6
 
10.2%
취소/말소/만료/정지/중지 3
 
5.1%

Length

2023-12-12T04:31:00.098747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:31:00.198683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 44
74.6%
휴업 6
 
10.2%
폐업 6
 
10.2%
취소/말소/만료/정지/중지 3
 
5.1%

폐업일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size604.0 B
<NA>
53 
20060331
 
2
20160120
 
1
20011120
 
1
20021111
 
1

Length

Max length8
Median length4
Mean length4.4067797
Min length4

Unique

Unique4 ?
Unique (%)6.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
89.8%
20060331 2
 
3.4%
20160120 1
 
1.7%
20011120 1
 
1.7%
20021111 1
 
1.7%
20060918 1
 
1.7%

Length

2023-12-12T04:31:00.320016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:31:00.435946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
89.8%
20060331 2
 
3.4%
20160120 1
 
1.7%
20011120 1
 
1.7%
20021111 1
 
1.7%
20060918 1
 
1.7%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing59
Missing (%)100.0%
Memory size663.0 B

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing59
Missing (%)100.0%
Memory size663.0 B

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing59
Missing (%)100.0%
Memory size663.0 B

소재지면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)83.0%
Missing12
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean4308.8036
Minimum0
Maximum18067
Zeros7
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T04:31:00.551852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1998.255
median3220
Q36992.2
95-th percentile11139.671
Maximum18067
Range18067
Interquartile range (IQR)5993.945

Descriptive statistics

Standard deviation3959.1759
Coefficient of variation (CV)0.91885736
Kurtosis1.8101829
Mean4308.8036
Median Absolute Deviation (MAD)2712
Skewness1.2084747
Sum202513.77
Variance15675074
MonotonicityNot monotonic
2023-12-12T04:31:00.677851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 7
 
11.9%
3963.0 2
 
3.4%
7537.19 2
 
3.4%
513.0 1
 
1.7%
4065.0 1
 
1.7%
3136.0 1
 
1.7%
6747.0 1
 
1.7%
500.0 1
 
1.7%
3529.0 1
 
1.7%
508.0 1
 
1.7%
Other values (29) 29
49.2%
(Missing) 12
20.3%
ValueCountFrequency (%)
0.0 7
11.9%
500.0 1
 
1.7%
508.0 1
 
1.7%
513.0 1
 
1.7%
825.0 1
 
1.7%
996.51 1
 
1.7%
1000.0 1
 
1.7%
1429.74 1
 
1.7%
1880.0 1
 
1.7%
2057.49 1
 
1.7%
ValueCountFrequency (%)
18067.0 1
1.7%
12437.0 1
1.7%
11235.53 1
1.7%
10916.0 1
1.7%
8942.96 1
1.7%
8834.54 1
1.7%
8014.22 1
1.7%
8014.0 1
1.7%
7548.0 1
1.7%
7537.19 2
3.4%

지번주소
Text

MISSING 

Distinct52
Distinct (%)89.7%
Missing1
Missing (%)1.7%
Memory size604.0 B
2023-12-12T04:31:01.013098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length25.189655
Min length18

Characters and Unicode

Total characters1461
Distinct characters79
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

Unique47 ?
Unique (%)81.0%

Sample

1st row제주특별자치도 제주시 한림읍 대림리 1698번지 4호
2nd row제주특별자치도 제주시 구좌읍 세화리 1500번지 5호
3rd row제주특별자치도 제주시 연동 303번지 17 호
4th row제주특별자치도 제주시 삼도이동 1259호
5th row제주특별자치도 제주시 도두1동 1212번지
ValueCountFrequency (%)
제주특별자치도 58
19.7%
제주시 42
 
14.3%
서귀포시 16
 
5.4%
호 11
 
3.7%
연동 9
 
3.1%
노형동 6
 
2.0%
일도일동 5
 
1.7%
1호 5
 
1.7%
서귀동 4
 
1.4%
273번지 4
 
1.4%
Other values (98) 134
45.6%
2023-12-12T04:31:01.560217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
244
16.7%
제 101
 
6.9%
주 101
 
6.9%
도 80
 
5.5%
1 59
 
4.0%
별 58
 
4.0%
치 58
 
4.0%
특 58
 
4.0%
시 58
 
4.0%
자 58
 
4.0%
Other values (69) 586
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 961
65.8%
Decimal Number 250
 
17.1%
Space Separator 244
 
16.7%
Dash Punctuation 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 101
 
10.5%
주 101
 
10.5%
도 80
 
8.3%
별 58
 
6.0%
치 58
 
6.0%
특 58
 
6.0%
시 58
 
6.0%
자 58
 
6.0%
동 51
 
5.3%
호 47
 
4.9%
Other values (57) 291
30.3%
Decimal Number
ValueCountFrequency (%)
1 59
23.6%
2 48
19.2%
3 23
 
9.2%
9 22
 
8.8%
8 21
 
8.4%
7 19
 
7.6%
5 17
 
6.8%
4 16
 
6.4%
0 15
 
6.0%
6 10
 
4.0%
Space Separator
ValueCountFrequency (%)
244
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 961
65.8%
Common 500
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 101
 
10.5%
주 101
 
10.5%
도 80
 
8.3%
별 58
 
6.0%
치 58
 
6.0%
특 58
 
6.0%
시 58
 
6.0%
자 58
 
6.0%
동 51
 
5.3%
호 47
 
4.9%
Other values (57) 291
30.3%
Common
ValueCountFrequency (%)
244
48.8%
1 59
 
11.8%
2 48
 
9.6%
3 23
 
4.6%
9 22
 
4.4%
8 21
 
4.2%
7 19
 
3.8%
5 17
 
3.4%
4 16
 
3.2%
0 15
 
3.0%
Other values (2) 16
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 961
65.8%
ASCII 500
34.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244
48.8%
1 59
 
11.8%
2 48
 
9.6%
3 23
 
4.6%
9 22
 
4.4%
8 21
 
4.2%
7 19
 
3.8%
5 17
 
3.4%
4 16
 
3.2%
0 15
 
3.0%
Other values (2) 16
 
3.2%
Hangul
ValueCountFrequency (%)
제 101
 
10.5%
주 101
 
10.5%
도 80
 
8.3%
별 58
 
6.0%
치 58
 
6.0%
특 58
 
6.0%
시 58
 
6.0%
자 58
 
6.0%
동 51
 
5.3%
호 47
 
4.9%
Other values (57) 291
30.3%

도로명주소
Text

MISSING 

Distinct35
Distinct (%)77.8%
Missing14
Missing (%)23.7%
Memory size604.0 B
2023-12-12T04:31:01.895928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length27.511111
Min length23

Characters and Unicode

Total characters1238
Distinct characters98
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

Unique28 ?
Unique (%)62.2%

Sample

1st row제주특별자치도 제주시 한림읍 한수풀로4길 10
2nd row제주특별자치도 제주시 구좌읍 해맞이해안로 1412
3rd row제주특별자치도 제주시 1100로 3348 (노형동)
4th row제주특별자치도 제주시 탑동로 38 (삼도이동)
5th row제주특별자치도 제주시 오일장서길 26 (도두일동)
ValueCountFrequency (%)
제주특별자치도 45
19.4%
제주시 32
 
13.8%
서귀포시 13
 
5.6%
연동 6
 
2.6%
노형동 6
 
2.6%
일도일동 4
 
1.7%
서귀동 4
 
1.7%
38 4
 
1.7%
52 3
 
1.3%
1100로 3
 
1.3%
Other values (80) 112
48.3%
2023-12-12T04:31:02.454543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
187
 
15.1%
주 81
 
6.5%
제 79
 
6.4%
도 59
 
4.8%
동 52
 
4.2%
특 45
 
3.6%
별 45
 
3.6%
자 45
 
3.6%
치 45
 
3.6%
시 45
 
3.6%
Other values (88) 555
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 812
65.6%
Space Separator 187
 
15.1%
Decimal Number 154
 
12.4%
Close Punctuation 39
 
3.2%
Open Punctuation 39
 
3.2%
Other Punctuation 5
 
0.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 81
 
10.0%
제 79
 
9.7%
도 59
 
7.3%
동 52
 
6.4%
특 45
 
5.5%
별 45
 
5.5%
자 45
 
5.5%
치 45
 
5.5%
시 45
 
5.5%
로 43
 
5.3%
Other values (73) 273
33.6%
Decimal Number
ValueCountFrequency (%)
1 42
27.3%
2 23
14.9%
3 19
12.3%
0 17
11.0%
4 16
 
10.4%
8 10
 
6.5%
9 10
 
6.5%
6 9
 
5.8%
5 5
 
3.2%
7 3
 
1.9%
Space Separator
ValueCountFrequency (%)
187
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 812
65.6%
Common 426
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 81
 
10.0%
제 79
 
9.7%
도 59
 
7.3%
동 52
 
6.4%
특 45
 
5.5%
별 45
 
5.5%
자 45
 
5.5%
치 45
 
5.5%
시 45
 
5.5%
로 43
 
5.3%
Other values (73) 273
33.6%
Common
ValueCountFrequency (%)
187
43.9%
1 42
 
9.9%
) 39
 
9.2%
( 39
 
9.2%
2 23
 
5.4%
3 19
 
4.5%
0 17
 
4.0%
4 16
 
3.8%
8 10
 
2.3%
9 10
 
2.3%
Other values (5) 24
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 812
65.6%
ASCII 426
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187
43.9%
1 42
 
9.9%
) 39
 
9.2%
( 39
 
9.2%
2 23
 
5.4%
3 19
 
4.5%
0 17
 
4.0%
4 16
 
3.8%
8 10
 
2.3%
9 10
 
2.3%
Other values (5) 24
 
5.6%
Hangul
ValueCountFrequency (%)
주 81
 
10.0%
제 79
 
9.7%
도 59
 
7.3%
동 52
 
6.4%
특 45
 
5.5%
별 45
 
5.5%
자 45
 
5.5%
치 45
 
5.5%
시 45
 
5.5%
로 43
 
5.3%
Other values (73) 273
33.6%

도로명우편번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size604.0 B
<NA>
54 
690802
 
1
63082
 
1
697839
 
1
63572
 
1

Length

Max length6
Median length4
Mean length4.1186441
Min length4

Unique

Unique5 ?
Unique (%)8.5%

Sample

1st row<NA>
2nd row<NA>
3rd row690802
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 54
91.5%
690802 1
 
1.7%
63082 1
 
1.7%
697839 1
 
1.7%
63572 1
 
1.7%
63522 1
 
1.7%

Length

2023-12-12T04:31:02.665246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:31:02.820935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 54
91.5%
690802 1
 
1.7%
63082 1
 
1.7%
697839 1
 
1.7%
63572 1
 
1.7%
63522 1
 
1.7%

데이터갱신일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
2022-10-28
59 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-28
2nd row2022-10-28
3rd row2022-10-28
4th row2022-10-28
5th row2022-10-28

Common Values

ValueCountFrequency (%)
2022-10-28 59
100.0%

Length

2023-12-12T04:31:02.967482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:31:03.091546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-28 59
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)85.7%
Missing17
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean33.440203
Minimum33.220607
Maximum33.526186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T04:31:03.226650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.220607
5-th percentile33.248268
Q133.42784
median33.488012
Q333.511086
95-th percentile33.517975
Maximum33.526186
Range0.30557908
Interquartile range (IQR)0.083246236

Descriptive statistics

Standard deviation0.10524513
Coefficient of variation (CV)0.0031472635
Kurtosis-0.24507383
Mean33.440203
Median Absolute Deviation (MAD)0.024075811
Skewness-1.2374431
Sum1404.4885
Variance0.011076538
MonotonicityNot monotonic
2023-12-12T04:31:03.406311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
33.5179748474968 3
 
5.1%
33.4849650136857 2
 
3.4%
33.4824421892687 2
 
3.4%
33.2483380261564 2
 
3.4%
33.5120874337174 2
 
3.4%
33.322717197345 1
 
1.7%
33.4852623358443 1
 
1.7%
33.4921061280708 1
 
1.7%
33.2505828963154 1
 
1.7%
33.2482640388818 1
 
1.7%
Other values (26) 26
44.1%
(Missing) 17
28.8%
ValueCountFrequency (%)
33.22060696936 1
1.7%
33.2218491788575 1
1.7%
33.2482640388818 1
1.7%
33.2483380261564 2
3.4%
33.2505828963154 1
1.7%
33.2604859888121 1
1.7%
33.2665213365088 1
1.7%
33.3049726791712 1
1.7%
33.322717197345 1
1.7%
33.4198324657691 1
1.7%
ValueCountFrequency (%)
33.5261860508541 1
 
1.7%
33.5226529036892 1
 
1.7%
33.5179748474968 3
5.1%
33.5131588774745 1
 
1.7%
33.5127966491588 1
 
1.7%
33.5122603460718 1
 
1.7%
33.5120874337174 2
3.4%
33.5113511633008 1
 
1.7%
33.5102911768693 1
 
1.7%
33.5060362925481 1
 
1.7%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)85.7%
Missing17
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean126.52693
Minimum126.24821
Maximum126.93323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T04:31:03.863541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.24821
5-th percentile126.2766
Q1126.48378
median126.52118
Q3126.53794
95-th percentile126.85595
Maximum126.93323
Range0.68502152
Interquartile range (IQR)0.054162661

Descriptive statistics

Standard deviation0.14079185
Coefficient of variation (CV)0.0011127422
Kurtosis3.0562706
Mean126.52693
Median Absolute Deviation (MAD)0.030982268
Skewness1.0979841
Sum5314.1312
Variance0.019822346
MonotonicityNot monotonic
2023-12-12T04:31:04.023675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
126.521177288784 3
 
5.1%
126.480431633456 2
 
3.4%
126.481834000763 2
 
3.4%
126.509117350341 2
 
3.4%
126.528242265925 2
 
3.4%
126.830382623505 1
 
1.7%
126.481459449471 1
 
1.7%
126.495612196879 1
 
1.7%
126.423947496672 1
 
1.7%
126.563721580326 1
 
1.7%
Other values (26) 26
44.1%
(Missing) 17
28.8%
ValueCountFrequency (%)
126.248212763464 1
1.7%
126.254876540046 1
1.7%
126.27450372708 1
1.7%
126.31635202613 1
1.7%
126.423947496672 1
1.7%
126.474888392629 1
1.7%
126.480431633456 2
3.4%
126.481459449471 1
1.7%
126.481834000763 2
3.4%
126.489615229505 1
1.7%
ValueCountFrequency (%)
126.933234284568 1
1.7%
126.912814725435 1
1.7%
126.857296969585 1
1.7%
126.830382623505 1
1.7%
126.573290349854 1
1.7%
126.565868332827 1
1.7%
126.563721580326 1
1.7%
126.559934556059 1
1.7%
126.551618962642 1
1.7%
126.541979975895 1
1.7%

Interactions

2023-12-12T04:30:57.723283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:56.880004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.170172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.448421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.795809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:56.949479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.246733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.519576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.870558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.023774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.315743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.588883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.941556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.097063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.379944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:57.651970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:31:04.133375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명업종구분소분류인허가일자영업상태명폐업일자소재지면적지번주소도로명주소도로명우편번호위도경도
사업장명1.0000.9040.9570.7531.0000.9630.9970.9961.0001.0001.000
업종구분소분류0.9041.0000.4670.4940.5730.2150.0000.7071.0000.6710.329
인허가일자0.9570.4671.0000.508NaN0.4130.9970.9051.0000.5170.342
영업상태명0.7530.4940.5081.000NaN0.1440.9180.000NaNNaNNaN
폐업일자1.0000.573NaNNaN1.0000.6471.0001.000NaNNaNNaN
소재지면적0.9630.2150.4130.1440.6471.0000.8540.8171.0000.0000.000
지번주소0.9970.0000.9970.9181.0000.8541.0001.0001.0001.0001.000
도로명주소0.9960.7070.9050.0001.0000.8171.0001.0001.0001.0001.000
도로명우편번호1.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.000
위도1.0000.6710.517NaNNaN0.0001.0001.0001.0001.0000.840
경도1.0000.3290.342NaNNaN0.0001.0001.0001.0000.8401.000
2023-12-12T04:31:04.286484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명인허가취소일자도로명우편번호업종구분소분류폐업일자
영업상태명1.000NaN1.0000.3311.000
인허가취소일자NaN1.000NaNNaNNaN
도로명우편번호1.000NaN1.0001.000NaN
업종구분소분류0.331NaN1.0001.0000.000
폐업일자1.000NaNNaN0.0001.000
2023-12-12T04:31:04.388709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자소재지면적위도경도업종구분소분류인허가취소일자영업상태명폐업일자도로명우편번호
인허가일자1.0000.327-0.242-0.4160.250NaN0.2151.0001.000
소재지면적0.3271.000-0.064-0.3920.115NaN0.0790.0001.000
위도-0.242-0.0641.0000.3360.4710.0001.0000.0001.000
경도-0.416-0.3920.3361.0000.2200.0001.0000.0001.000
업종구분소분류0.2500.1150.4710.2201.000NaN0.3310.0001.000
인허가취소일자NaNNaN0.0000.000NaN1.000NaN0.0000.000
영업상태명0.2150.0791.0001.0000.331NaN1.0001.0001.000
폐업일자1.0000.0000.0000.0000.0000.0001.0001.0000.000
도로명우편번호1.0001.0001.0001.0001.0000.0001.0000.0001.000

Missing values

2023-12-12T04:30:58.052333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:30:58.220196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T04:30:58.342833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사업장명업종구분대분류업종구분소분류인허가일자인허가취소일자영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지면적지번주소도로명주소도로명우편번호데이터갱신일자위도경도
0한림민속오일시장대규모점포시장20020924<NA>영업/정상<NA><NA><NA><NA>2900.0제주특별자치도 제주시 한림읍 대림리 1698번지 4호제주특별자치도 제주시 한림읍 한수풀로4길 10<NA>2022-10-2833.419832126.274504
1세화민속오일시장대규모점포시장19650115<NA>영업/정상<NA><NA><NA><NA>3183.0제주특별자치도 제주시 구좌읍 세화리 1500번지 5호제주특별자치도 제주시 구좌읍 해맞이해안로 1412<NA>2022-10-2833.526186126.857297
2(주)이마트 신제주점대규모점포대형마트20110531<NA>영업/정상<NA><NA><NA><NA>7148.4<NA>제주특별자치도 제주시 1100로 3348 (노형동)6908022022-10-2833.484965126.480432
3뉴월드마트대규모점포대형마트19961022<NA>영업/정상<NA><NA><NA><NA><NA>제주특별자치도 제주시 연동 303번지 17 호<NA><NA>2022-10-2833.486961126.497115
4성지유니코대규모점포시장19961031<NA>영업/정상<NA><NA><NA><NA>12437.0제주특별자치도 제주시 삼도이동 1259호제주특별자치도 제주시 탑동로 38 (삼도이동)<NA>2022-10-2833.517975126.521177
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49고성오일시장대규모점포시장19840419<NA>영업/정상<NA><NA><NA><NA>825.0제주특별자치도 서귀포시 성산읍 고성리 1188번지 2호제주특별자치도 서귀포시 성산읍 고성오조로 93<NA>2022-10-2833.451862126.912815
50서귀포향토오일시장대규모점포시장19950929<NA>영업/정상<NA><NA><NA><NA>10916.0제주특별자치도 서귀포시 동홍동 774번지 3호제주특별자치도 서귀포시 토평서로11번길 150 (동홍동)<NA>2022-10-2833.266521126.57329
51홈플러스(주) 서귀포점대규모점포대형마트20051227<NA>영업/정상<NA><NA><NA><NA>6441.0제주특별자치도 서귀포시 동홍동 1560번지 1호제주특별자치도 서귀포시 중앙로 180 (동홍동)6978392022-10-2833.260486126.559935
52(주)이마트 서귀포점대규모점포대형마트20060519<NA>영업/정상<NA><NA><NA><NA>7537.19제주특별자치도 서귀포시 법환동 841번지 2 호 외 21필지제주특별자치도 서귀포시 일주동로 9209 (법환동,외 21필지)<NA>2022-10-2833.248338126.509117
53대정오일시장대규모점포시장19831001<NA>영업/정상<NA><NA><NA><NA>7548.0제주특별자치도 서귀포시 대정읍 하모리 1089번지 15호제주특별자치도 서귀포시 대정읍 신영로36번길 65<NA>2022-10-2833.220607126.248213
54(주)이마트 서귀포점대규모점포대형마트20180810<NA>영업/정상<NA><NA><NA><NA>7537.19제주특별자치도 서귀포시 법환동 841번지 2호 이마트서귀포점제주특별자치도 서귀포시 일주동로 9209, 이마트서귀포점 (법환동)635722022-10-2833.248338126.509117
55(주)서귀포상설시장대규모점포그 밖의 대규모점포19750506<NA>휴업<NA><NA><NA><NA><NA>제주특별자치도 서귀포시 서귀동 273번지 4호제주특별자치도 서귀포시 중앙로42번길 26 (서귀동)<NA>2022-10-28<NA><NA>
56(주)서귀포상설시장대규모점포그 밖의 대규모점포20050428<NA>휴업<NA><NA><NA><NA>1880.0제주특별자치도 서귀포시 서귀동 273번지 4호제주특별자치도 서귀포시 중앙로42번길 26 (서귀동)<NA>2022-10-28<NA><NA>
57목화백화점대규모점포그 밖의 대규모점포19780515<NA>취소/말소/만료/정지/중지<NA><NA><NA><NA>0.0제주특별자치도 서귀포시 서귀동 273번지 3호제주특별자치도 서귀포시 중앙로42번길 32 (서귀동)<NA>2022-10-28<NA><NA>
58제주 프리미엄 전문점대규모점포전문점20201125<NA>영업/정상<NA><NA><NA><NA>8834.54제주특별자치도 서귀포시 안덕면 서광리 산 24제주특별자치도 서귀포시 안덕면 신화역사로304번길 38635222022-10-2833.304973126.316352