Overview

Dataset statistics

Number of variables14
Number of observations178
Missing cells326
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory118.7 B

Variable types

Categorical4
Text3
Numeric5
Boolean1
Unsupported1

Dataset

Description다단계판매(도매업유통) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=B7VR6N4340PUHZP5HGXJ14555704&infSeq=1

Alerts

다중이용업소여부 has constant value ""Constant
위생업태명 is highly overall correlated with 인허가일자 and 7 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 7 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
인허가일자 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
폐업일자 is highly overall correlated with 인허가일자 and 3 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
위생업종명 is highly imbalanced (81.5%)Imbalance
위생업태명 is highly imbalanced (81.5%)Imbalance
폐업일자 has 143 (80.3%) missing valuesMissing
다중이용업소여부 has 5 (2.8%) missing valuesMissing
총시설규모(㎡) has 178 (100.0%) missing valuesMissing
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 21:28:21.979258
Analysis finished2023-12-10 21:28:24.933647
Duration2.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
고양시
30 
안양시
29 
수원시
25 
김포시
12 
성남시
12 
Other values (17)
70 

Length

Max length4
Median length3
Mean length3.0674157
Min length3

Unique

Unique3 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 30
16.9%
안양시 29
16.3%
수원시 25
14.0%
김포시 12
 
6.7%
성남시 12
 
6.7%
용인시 12
 
6.7%
남양주시 8
 
4.5%
오산시 7
 
3.9%
부천시 7
 
3.9%
구리시 6
 
3.4%
Other values (12) 30
16.9%

Length

2023-12-11T06:28:25.005300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 30
16.9%
안양시 29
16.3%
수원시 25
14.0%
김포시 12
 
6.7%
성남시 12
 
6.7%
용인시 12
 
6.7%
남양주시 8
 
4.5%
오산시 7
 
3.9%
부천시 7
 
3.9%
구리시 6
 
3.4%
Other values (12) 30
16.9%

사업장명
Text

Distinct176
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T06:28:25.191087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length13.5
Mean length7.6741573
Min length2

Characters and Unicode

Total characters1366
Distinct characters266
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

Unique174 ?
Unique (%)97.8%

Sample

1st row(주)디엠
2nd row다현H&C
3rd row마루약품(주)
4th row애임하이
5th row(주)도윤상사
ValueCountFrequency (%)
주식회사 14
 
6.7%
그린스토어 3
 
1.4%
주)뉴트라에스씨 2
 
1.0%
비앤엘코퍼레이션 2
 
1.0%
남양우유 2
 
1.0%
광복단 2
 
1.0%
주)노바젠 1
 
0.5%
비트로시스 1
 
0.5%
교대점 1
 
0.5%
경민유통 1
 
0.5%
Other values (181) 181
86.2%
2023-12-11T06:28:25.506684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 91
 
6.7%
) 73
 
5.3%
( 72
 
5.3%
스 60
 
4.4%
이 50
 
3.7%
32
 
2.3%
사 32
 
2.3%
에 28
 
2.0%
리 27
 
2.0%
아 25
 
1.8%
Other values (256) 876
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1140
83.5%
Close Punctuation 73
 
5.3%
Open Punctuation 72
 
5.3%
Uppercase Letter 35
 
2.6%
Space Separator 32
 
2.3%
Other Punctuation 5
 
0.4%
Decimal Number 5
 
0.4%
Dash Punctuation 2
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 91
 
8.0%
스 60
 
5.3%
이 50
 
4.4%
사 32
 
2.8%
에 28
 
2.5%
리 27
 
2.4%
아 25
 
2.2%
식 25
 
2.2%
회 24
 
2.1%
유 21
 
1.8%
Other values (228) 757
66.4%
Uppercase Letter
ValueCountFrequency (%)
H 4
11.4%
G 4
11.4%
S 4
11.4%
T 3
8.6%
N 3
8.6%
C 3
8.6%
M 2
 
5.7%
K 2
 
5.7%
D 2
 
5.7%
B 1
 
2.9%
Other values (7) 7
20.0%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
9 1
20.0%
5 1
20.0%
3 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
50.0%
s 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Other Punctuation
ValueCountFrequency (%)
& 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1140
83.5%
Common 189
 
13.8%
Latin 37
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 91
 
8.0%
스 60
 
5.3%
이 50
 
4.4%
사 32
 
2.8%
에 28
 
2.5%
리 27
 
2.4%
아 25
 
2.2%
식 25
 
2.2%
회 24
 
2.1%
유 21
 
1.8%
Other values (228) 757
66.4%
Latin
ValueCountFrequency (%)
H 4
10.8%
G 4
10.8%
S 4
10.8%
T 3
 
8.1%
N 3
 
8.1%
C 3
 
8.1%
M 2
 
5.4%
K 2
 
5.4%
D 2
 
5.4%
i 1
 
2.7%
Other values (9) 9
24.3%
Common
ValueCountFrequency (%)
) 73
38.6%
( 72
38.1%
32
16.9%
& 5
 
2.6%
- 2
 
1.1%
2 2
 
1.1%
9 1
 
0.5%
5 1
 
0.5%
3 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1140
83.5%
ASCII 226
 
16.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 91
 
8.0%
스 60
 
5.3%
이 50
 
4.4%
사 32
 
2.8%
에 28
 
2.5%
리 27
 
2.4%
아 25
 
2.2%
식 25
 
2.2%
회 24
 
2.1%
유 21
 
1.8%
Other values (228) 757
66.4%
ASCII
ValueCountFrequency (%)
) 73
32.3%
( 72
31.9%
32
14.2%
& 5
 
2.2%
H 4
 
1.8%
G 4
 
1.8%
S 4
 
1.8%
T 3
 
1.3%
N 3
 
1.3%
C 3
 
1.3%
Other values (18) 23
 
10.2%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20159193
Minimum20050725
Maximum20180827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T06:28:25.668498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050725
5-th percentile20128656
Q120150723
median20160514
Q320171111
95-th percentile20180703
Maximum20180827
Range130102
Interquartile range (IQR)20387.75

Descriptive statistics

Standard deviation21202.905
Coefficient of variation (CV)0.0010517735
Kurtosis9.4296446
Mean20159193
Median Absolute Deviation (MAD)10050
Skewness-2.5059032
Sum3.5883363 × 109
Variance4.4956317 × 108
MonotonicityNot monotonic
2023-12-11T06:28:25.824661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150803 4
 
2.2%
20150602 2
 
1.1%
20150206 2
 
1.1%
20180126 2
 
1.1%
20160502 2
 
1.1%
20180529 2
 
1.1%
20150902 2
 
1.1%
20160720 2
 
1.1%
20171121 2
 
1.1%
20150723 2
 
1.1%
Other values (148) 156
87.6%
ValueCountFrequency (%)
20050725 1
0.6%
20060608 1
0.6%
20061009 1
0.6%
20070329 1
0.6%
20100629 1
0.6%
20101006 1
0.6%
20111013 1
0.6%
20120229 1
0.6%
20120316 1
0.6%
20130128 1
0.6%
ValueCountFrequency (%)
20180827 1
0.6%
20180823 1
0.6%
20180822 1
0.6%
20180821 1
0.6%
20180816 1
0.6%
20180731 1
0.6%
20180709 1
0.6%
20180705 1
0.6%
20180704 1
0.6%
20180703 1
0.6%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
운영중
143 
폐업 등
35 

Length

Max length4
Median length3
Mean length3.1966292
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 143
80.3%
폐업 등 35
 
19.7%

Length

2023-12-11T06:28:25.970592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:28:26.091674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 143
67.1%
폐업 35
 
16.4%
등 35
 
16.4%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)94.3%
Missing143
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean20168242
Minimum20150421
Maximum20180723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T06:28:26.207333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150421
5-th percentile20150978
Q120160562
median20170228
Q320180118
95-th percentile20180616
Maximum20180723
Range30302
Interquartile range (IQR)19557

Descriptive statistics

Standard deviation9911.269
Coefficient of variation (CV)0.00049142949
Kurtosis-1.035802
Mean20168242
Median Absolute Deviation (MAD)9817
Skewness-0.25416879
Sum7.0588848 × 108
Variance98233253
MonotonicityNot monotonic
2023-12-11T06:28:26.374068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20170221 2
 
1.1%
20170228 2
 
1.1%
20160516 1
 
0.6%
20160411 1
 
0.6%
20180119 1
 
0.6%
20180131 1
 
0.6%
20160711 1
 
0.6%
20160118 1
 
0.6%
20171227 1
 
0.6%
20160705 1
 
0.6%
Other values (23) 23
 
12.9%
(Missing) 143
80.3%
ValueCountFrequency (%)
20150421 1
0.6%
20150914 1
0.6%
20151005 1
0.6%
20151012 1
0.6%
20160105 1
0.6%
20160108 1
0.6%
20160118 1
0.6%
20160411 1
0.6%
20160516 1
0.6%
20160607 1
0.6%
ValueCountFrequency (%)
20180723 1
0.6%
20180618 1
0.6%
20180615 1
0.6%
20180321 1
0.6%
20180308 1
0.6%
20180223 1
0.6%
20180131 1
0.6%
20180125 1
0.6%
20180119 1
0.6%
20180118 1
0.6%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing5
Missing (%)2.8%
Memory size488.0 B
False
173 
(Missing)
 
5
ValueCountFrequency (%)
False 173
97.2%
(Missing) 5
 
2.8%
2023-12-11T06:28:26.490430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing178
Missing (%)100.0%
Memory size1.7 KiB

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
건강기능식품일반판매업
173 
<NA>
 
5

Length

Max length11
Median length11
Mean length10.803371
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건강기능식품일반판매업 173
97.2%
<NA> 5
 
2.8%

Length

2023-12-11T06:28:26.594174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:28:26.699034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건강기능식품일반판매업 173
97.2%
na 5
 
2.8%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
도매업(유통)
173 
<NA>
 
5

Length

Max length7
Median length7
Mean length6.9157303
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도매업(유통)
2nd row도매업(유통)
3rd row도매업(유통)
4th row도매업(유통)
5th row<NA>

Common Values

ValueCountFrequency (%)
도매업(유통) 173
97.2%
<NA> 5
 
2.8%

Length

2023-12-11T06:28:27.039175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:28:27.163080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도매업(유통 173
97.2%
na 5
 
2.8%

소재지도로명주소
Text

Distinct174
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T06:28:27.398414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length43.5
Mean length35.994382
Min length18

Characters and Unicode

Total characters6407
Distinct characters286
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)95.5%

Sample

1st row경기도 고양시 덕양구 삼송로136번길 81, 1층 1호 (삼송동)
2nd row경기도 고양시 일산서구 송포로425번길 97-11, 1층 (가좌동)
3rd row경기도 고양시 덕양구 토당로 20, 2층 (토당동)
4th row경기도 고양시 일산서구 송산로515번길 47, 1층 (덕이동)
5th row경기도 고양시 일산서구 송산로214번길 58, 1동 (구산동)
ValueCountFrequency (%)
경기도 178
 
13.5%
고양시 30
 
2.3%
1층 29
 
2.2%
안양시 29
 
2.2%
수원시 25
 
1.9%
동안구 18
 
1.4%
덕양구 17
 
1.3%
2층 13
 
1.0%
용인시 12
 
0.9%
일부 12
 
0.9%
Other values (609) 957
72.5%
2023-12-11T06:28:27.824137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1143
 
17.8%
1 268
 
4.2%
, 224
 
3.5%
동 220
 
3.4%
시 191
 
3.0%
도 189
 
2.9%
경 186
 
2.9%
기 186
 
2.9%
로 171
 
2.7%
2 161
 
2.5%
Other values (276) 3468
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3546
55.3%
Space Separator 1143
 
17.8%
Decimal Number 1116
 
17.4%
Other Punctuation 226
 
3.5%
Open Punctuation 156
 
2.4%
Close Punctuation 156
 
2.4%
Dash Punctuation 47
 
0.7%
Uppercase Letter 17
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 220
 
6.2%
시 191
 
5.4%
도 189
 
5.3%
경 186
 
5.2%
기 186
 
5.2%
로 171
 
4.8%
양 124
 
3.5%
구 121
 
3.4%
호 115
 
3.2%
안 93
 
2.6%
Other values (251) 1950
55.0%
Decimal Number
ValueCountFrequency (%)
1 268
24.0%
2 161
14.4%
0 134
12.0%
3 125
11.2%
4 93
 
8.3%
5 91
 
8.2%
6 81
 
7.3%
7 61
 
5.5%
8 59
 
5.3%
9 43
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
T 2
 
11.8%
B 2
 
11.8%
W 1
 
5.9%
P 1
 
5.9%
L 1
 
5.9%
E 1
 
5.9%
C 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 224
99.1%
@ 2
 
0.9%
Space Separator
ValueCountFrequency (%)
1143
100.0%
Open Punctuation
ValueCountFrequency (%)
( 156
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3546
55.3%
Common 2844
44.4%
Latin 17
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 220
 
6.2%
시 191
 
5.4%
도 189
 
5.3%
경 186
 
5.2%
기 186
 
5.2%
로 171
 
4.8%
양 124
 
3.5%
구 121
 
3.4%
호 115
 
3.2%
안 93
 
2.6%
Other values (251) 1950
55.0%
Common
ValueCountFrequency (%)
1143
40.2%
1 268
 
9.4%
, 224
 
7.9%
2 161
 
5.7%
( 156
 
5.5%
) 156
 
5.5%
0 134
 
4.7%
3 125
 
4.4%
4 93
 
3.3%
5 91
 
3.2%
Other values (6) 293
 
10.3%
Latin
ValueCountFrequency (%)
A 5
29.4%
I 3
17.6%
T 2
 
11.8%
B 2
 
11.8%
W 1
 
5.9%
P 1
 
5.9%
L 1
 
5.9%
E 1
 
5.9%
C 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3546
55.3%
ASCII 2861
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1143
40.0%
1 268
 
9.4%
, 224
 
7.8%
2 161
 
5.6%
( 156
 
5.5%
) 156
 
5.5%
0 134
 
4.7%
3 125
 
4.4%
4 93
 
3.3%
5 91
 
3.2%
Other values (15) 310
 
10.8%
Hangul
ValueCountFrequency (%)
동 220
 
6.2%
시 191
 
5.4%
도 189
 
5.3%
경 186
 
5.2%
기 186
 
5.2%
로 171
 
4.8%
양 124
 
3.5%
구 121
 
3.4%
호 115
 
3.2%
안 93
 
2.6%
Other values (251) 1950
55.0%

소재지지번주소
Text

Distinct174
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T06:28:28.096857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length38
Mean length29.820225
Min length17

Characters and Unicode

Total characters5308
Distinct characters257
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)95.5%

Sample

1st row경기도 고양시 덕양구 삼송동 30-40번지
2nd row경기도 고양시 일산서구 가좌동 25-1번지
3rd row경기도 고양시 덕양구 토당동 869-1번지 태양빌딩
4th row경기도 고양시 일산서구 덕이동 944번지 1층
5th row경기도 고양시 일산서구 구산동 625-35번지 1동
ValueCountFrequency (%)
경기도 178
 
16.2%
고양시 30
 
2.7%
안양시 29
 
2.6%
수원시 25
 
2.3%
동안구 18
 
1.6%
1층 17
 
1.5%
덕양구 17
 
1.5%
일부 13
 
1.2%
용인시 12
 
1.1%
성남시 12
 
1.1%
Other values (492) 751
68.1%
2023-12-11T06:28:28.556471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
924
 
17.4%
1 256
 
4.8%
동 207
 
3.9%
지 206
 
3.9%
기 186
 
3.5%
도 182
 
3.4%
경 180
 
3.4%
시 180
 
3.4%
번 178
 
3.4%
- 147
 
2.8%
Other values (247) 2662
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3139
59.1%
Decimal Number 1074
 
20.2%
Space Separator 924
 
17.4%
Dash Punctuation 147
 
2.8%
Uppercase Letter 15
 
0.3%
Other Punctuation 5
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 207
 
6.6%
지 206
 
6.6%
기 186
 
5.9%
도 182
 
5.8%
경 180
 
5.7%
시 180
 
5.7%
번 178
 
5.7%
양 118
 
3.8%
구 117
 
3.7%
호 82
 
2.6%
Other values (224) 1503
47.9%
Decimal Number
ValueCountFrequency (%)
1 256
23.8%
2 137
12.8%
0 129
12.0%
5 99
 
9.2%
9 92
 
8.6%
3 86
 
8.0%
4 85
 
7.9%
6 78
 
7.3%
7 61
 
5.7%
8 51
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 4
26.7%
I 4
26.7%
B 2
13.3%
T 2
13.3%
P 1
 
6.7%
W 1
 
6.7%
E 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
@ 2
40.0%
Space Separator
ValueCountFrequency (%)
924
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 147
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3139
59.1%
Common 2154
40.6%
Latin 15
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 207
 
6.6%
지 206
 
6.6%
기 186
 
5.9%
도 182
 
5.8%
경 180
 
5.7%
시 180
 
5.7%
번 178
 
5.7%
양 118
 
3.8%
구 117
 
3.7%
호 82
 
2.6%
Other values (224) 1503
47.9%
Common
ValueCountFrequency (%)
924
42.9%
1 256
 
11.9%
- 147
 
6.8%
2 137
 
6.4%
0 129
 
6.0%
5 99
 
4.6%
9 92
 
4.3%
3 86
 
4.0%
4 85
 
3.9%
6 78
 
3.6%
Other values (6) 121
 
5.6%
Latin
ValueCountFrequency (%)
A 4
26.7%
I 4
26.7%
B 2
13.3%
T 2
13.3%
P 1
 
6.7%
W 1
 
6.7%
E 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3139
59.1%
ASCII 2169
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
924
42.6%
1 256
 
11.8%
- 147
 
6.8%
2 137
 
6.3%
0 129
 
5.9%
5 99
 
4.6%
9 92
 
4.2%
3 86
 
4.0%
4 85
 
3.9%
6 78
 
3.6%
Other values (13) 136
 
6.3%
Hangul
ValueCountFrequency (%)
동 207
 
6.6%
지 206
 
6.6%
기 186
 
5.9%
도 182
 
5.8%
경 180
 
5.7%
시 180
 
5.7%
번 178
 
5.7%
양 118
 
3.8%
구 117
 
3.7%
호 82
 
2.6%
Other values (224) 1503
47.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412382.53
Minimum10914
Maximum483010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T06:28:28.687001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10914
5-th percentile16686.05
Q1414133.25
median435840
Q3447285
95-th percentile472501
Maximum483010
Range472096
Interquartile range (IQR)33151.75

Descriptive statistics

Standard deviation104277.19
Coefficient of variation (CV)0.2528652
Kurtosis10.583122
Mean412382.53
Median Absolute Deviation (MAD)20016
Skewness-3.4438304
Sum73404090
Variance1.0873733 × 1010
MonotonicityNot monotonic
2023-12-11T06:28:28.818082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
472501 3
 
1.7%
443810 3
 
1.7%
412827 3
 
1.7%
410837 3
 
1.7%
431807 3
 
1.7%
430817 3
 
1.7%
431811 3
 
1.7%
430842 3
 
1.7%
463847 3
 
1.7%
423836 2
 
1.1%
Other values (127) 149
83.7%
ValueCountFrequency (%)
10914 1
0.6%
13813 1
0.6%
14504 2
1.1%
14544 2
1.1%
14609 1
0.6%
14625 1
0.6%
14720 1
0.6%
17033 1
0.6%
17036 1
0.6%
410820 1
0.6%
ValueCountFrequency (%)
483010 1
 
0.6%
480865 1
 
0.6%
480839 1
 
0.6%
480831 1
 
0.6%
472938 1
 
0.6%
472927 1
 
0.6%
472901 1
 
0.6%
472501 3
1.7%
472060 2
1.1%
471857 2
1.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.447138
Minimum36.952243
Maximum37.882293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T06:28:28.958684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.952243
5-th percentile37.171417
Q137.299333
median37.401327
Q337.626275
95-th percentile37.700755
Maximum37.882293
Range0.93004964
Interquartile range (IQR)0.32694277

Descriptive statistics

Standard deviation0.18249103
Coefficient of variation (CV)0.0048732971
Kurtosis-0.73646547
Mean37.447138
Median Absolute Deviation (MAD)0.13948044
Skewness-0.065934199
Sum6665.5906
Variance0.033302976
MonotonicityNot monotonic
2023-12-11T06:28:29.079399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2555242912 3
 
1.7%
37.4013274083 3
 
1.7%
37.3681645175 3
 
1.7%
37.3813658336 3
 
1.7%
37.6753452441 2
 
1.1%
37.5128657079 2
 
1.1%
37.3917961106 2
 
1.1%
37.4773883075 2
 
1.1%
37.6039559156 2
 
1.1%
37.4015014234 1
 
0.6%
Other values (155) 155
87.1%
ValueCountFrequency (%)
36.9522429054 1
0.6%
36.972093045 1
0.6%
37.0665101715 1
0.6%
37.0931684877 1
0.6%
37.0941096332 1
0.6%
37.1440990471 1
0.6%
37.1454761283 1
0.6%
37.1499161659 1
0.6%
37.1710165557 1
0.6%
37.1714881498 1
0.6%
ValueCountFrequency (%)
37.882292543 1
0.6%
37.7802430586 1
0.6%
37.7544679809 1
0.6%
37.7519787725 1
0.6%
37.736027333 1
0.6%
37.7359404788 1
0.6%
37.7314725355 1
0.6%
37.7288075105 1
0.6%
37.7054312676 1
0.6%
37.699929877 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96483
Minimum126.57468
Maximum127.64389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T06:28:29.201342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.57468
5-th percentile126.68667
Q1126.83318
median126.97334
Q3127.07927
95-th percentile127.21348
Maximum127.64389
Range1.0692133
Interquartile range (IQR)0.24608445

Descriptive statistics

Standard deviation0.17244318
Coefficient of variation (CV)0.0013581964
Kurtosis0.90944294
Mean126.96483
Median Absolute Deviation (MAD)0.12668851
Skewness0.30422144
Sum22599.74
Variance0.029736651
MonotonicityNot monotonic
2023-12-11T06:28:29.357740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0743122195 3
 
1.7%
126.9131289596 3
 
1.7%
127.1063150979 3
 
1.7%
126.938032977 3
 
1.7%
126.7506431142 2
 
1.1%
126.7512183778 2
 
1.1%
126.9517714335 2
 
1.1%
126.8667408384 2
 
1.1%
127.1326509698 2
 
1.1%
126.8975469686 1
 
0.6%
Other values (155) 155
87.1%
ValueCountFrequency (%)
126.5746807386 1
0.6%
126.6053522005 1
0.6%
126.6241704315 1
0.6%
126.6409693666 1
0.6%
126.6508935416 1
0.6%
126.6556420549 1
0.6%
126.6639552034 1
0.6%
126.6767629586 1
0.6%
126.6836346379 1
0.6%
126.6872079523 1
0.6%
ValueCountFrequency (%)
127.6438940339 1
0.6%
127.5466417097 1
0.6%
127.3404099109 1
0.6%
127.3168335058 1
0.6%
127.2666069632 1
0.6%
127.2422536 1
0.6%
127.2399925319 1
0.6%
127.2354043965 1
0.6%
127.2334372937 1
0.6%
127.209955815 1
0.6%

Interactions

2023-12-11T06:28:24.162821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:22.662732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.030973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.392807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.790725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.243170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:22.737000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.104606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.466608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.878340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.320226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:22.809832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.181040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.541686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.954290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.412493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:22.892850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.247844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.614690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.029070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.492306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:22.958511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.327012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:23.698519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:24.092119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:28:29.448031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자소재지우편번호WGS84위도WGS84경도
시군명1.0000.2340.0200.5750.9790.9680.942
인허가일자0.2341.0000.2590.6780.0000.0710.000
영업상태명0.0200.2591.000NaN0.0390.0000.000
폐업일자0.5750.678NaN1.0000.0000.3590.000
소재지우편번호0.9790.0000.0390.0001.0000.7500.909
WGS84위도0.9680.0710.0000.3590.7501.0000.645
WGS84경도0.9420.0000.0000.0000.9090.6451.000
2023-12-11T06:28:29.557044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업태명영업상태명위생업종명시군명
위생업태명1.0001.0001.0001.000
영업상태명1.0001.0001.0000.000
위생업종명1.0001.0001.0001.000
시군명1.0000.0001.0001.000
2023-12-11T06:28:29.661408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명위생업종명위생업태명
인허가일자1.0000.519-0.0120.1030.0200.0870.2441.0001.000
폐업일자0.5191.0000.048-0.0330.1090.2481.0001.0001.000
소재지우편번호-0.0120.0481.000-0.4450.8390.8740.0201.0001.000
WGS84위도0.103-0.033-0.4451.000-0.5180.8000.0001.0001.000
WGS84경도0.0200.1090.839-0.5181.0000.7210.0001.0001.000
시군명0.0870.2480.8740.8000.7211.0000.0001.0001.000
영업상태명0.2441.0000.0200.0000.0000.0001.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T06:28:24.612138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:28:24.775246image/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.
2023-12-11T06:28:24.884037image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시(주)디엠20160902운영중<NA>N<NA>건강기능식품일반판매업도매업(유통)경기도 고양시 덕양구 삼송로136번길 81, 1층 1호 (삼송동)경기도 고양시 덕양구 삼송동 30-40번지41208037.652938126.893288
1고양시다현H&C20070329운영중<NA>N<NA>건강기능식품일반판매업도매업(유통)경기도 고양시 일산서구 송포로425번길 97-11, 1층 (가좌동)경기도 고양시 일산서구 가좌동 25-1번지41144037.69993126.723659
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시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
168파주시메디미학20161128운영중<NA>N<NA>건강기능식품일반판매업도매업(유통)경기도 파주시 번영로 55, 108동 14층 1404호 (금촌동)경기도 파주시 금촌동 972번지 새꽃마을뜨란채 108동 14층 1404호1091437.754468126.765818
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171파주시파주유통20150306폐업 등20171120N<NA>건강기능식품일반판매업도매업(유통)경기도 파주시 탄현면 성동로 7-26, 1동 3호경기도 파주시 탄현면 성동리 668-2번지 1동 3호41384137.780243126.687208
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