Overview

Dataset statistics

Number of variables14
Number of observations135
Missing cells272
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 KiB
Average record size in memory119.0 B

Variable types

Categorical4
Text3
Numeric5
Boolean1
Unsupported1

Alerts

위생업태명 is highly overall correlated with 인허가일자 and 8 other fieldsHigh correlation
다중이용업소여부 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 8 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
인허가일자 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
폐업일자 is highly overall correlated with 영업상태명 and 3 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
다중이용업소여부 is highly imbalanced (55.8%)Imbalance
폐업일자 has 114 (84.4%) missing valuesMissing
다중이용업소여부 has 15 (11.1%) missing valuesMissing
총시설규모(㎡) has 135 (100.0%) missing valuesMissing
소재지도로명주소 has 2 (1.5%) missing valuesMissing
WGS84위도 has 2 (1.5%) missing valuesMissing
WGS84경도 has 2 (1.5%) missing valuesMissing
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:25:48.603860
Analysis finished2023-12-10 22:25:52.071695
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
동두천시
17 
여주시
11 
부천시
11 
용인시
10 
고양시
8 
Other values (21)
78 

Length

Max length4
Median length3
Mean length3.2
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
동두천시 17
 
12.6%
여주시 11
 
8.1%
부천시 11
 
8.1%
용인시 10
 
7.4%
고양시 8
 
5.9%
의정부시 7
 
5.2%
안양시 7
 
5.2%
수원시 6
 
4.4%
김포시 6
 
4.4%
하남시 5
 
3.7%
Other values (16) 47
34.8%

Length

2023-12-11T07:25:52.135798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동두천시 17
 
12.6%
여주시 11
 
8.1%
부천시 11
 
8.1%
용인시 10
 
7.4%
고양시 8
 
5.9%
의정부시 7
 
5.2%
안양시 7
 
5.2%
수원시 6
 
4.4%
김포시 6
 
4.4%
하남시 5
 
3.7%
Other values (16) 47
34.8%

사업장명
Text

Distinct131
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T07:25:52.340717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4962963
Min length2

Characters and Unicode

Total characters877
Distinct characters221
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

Unique127 ?
Unique (%)94.1%

Sample

1st row연꽃선원메밀국수
2nd row인생술집
3rd row일산면옥
4th row압구정칡냉면
5th row아리랑면옥
ValueCountFrequency (%)
팔당냉면 3
 
1.8%
함흥냉면 3
 
1.8%
개성칡냉면 2
 
1.2%
왕돈까스왕냉면 2
 
1.2%
오방냉면 2
 
1.2%
육쌈냉면 2
 
1.2%
동치미 2
 
1.2%
코다리냉면 2
 
1.2%
막국수 2
 
1.2%
매반생면 2
 
1.2%
Other values (141) 141
86.5%
2023-12-11T07:25:52.672181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 99
 
11.3%
냉 72
 
8.2%
28
 
3.2%
수 21
 
2.4%
옥 20
 
2.3%
점 19
 
2.2%
국 18
 
2.1%
칡 15
 
1.7%
막 15
 
1.7%
동 14
 
1.6%
Other values (211) 556
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
93.2%
Space Separator 28
 
3.2%
Uppercase Letter 10
 
1.1%
Open Punctuation 8
 
0.9%
Close Punctuation 8
 
0.9%
Other Punctuation 3
 
0.3%
Decimal Number 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 99
 
12.1%
냉 72
 
8.8%
수 21
 
2.6%
옥 20
 
2.4%
점 19
 
2.3%
국 18
 
2.2%
칡 15
 
1.8%
막 15
 
1.8%
동 14
 
1.7%
평 13
 
1.6%
Other values (195) 511
62.5%
Uppercase Letter
ValueCountFrequency (%)
R 2
20.0%
T 1
10.0%
O 1
10.0%
E 1
10.0%
G 1
10.0%
U 1
10.0%
B 1
10.0%
Y 1
10.0%
N 1
10.0%
Decimal Number
ValueCountFrequency (%)
9 1
33.3%
3 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 817
93.2%
Common 50
 
5.7%
Latin 10
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 99
 
12.1%
냉 72
 
8.8%
수 21
 
2.6%
옥 20
 
2.4%
점 19
 
2.3%
국 18
 
2.2%
칡 15
 
1.8%
막 15
 
1.8%
동 14
 
1.7%
평 13
 
1.6%
Other values (195) 511
62.5%
Latin
ValueCountFrequency (%)
R 2
20.0%
T 1
10.0%
O 1
10.0%
E 1
10.0%
G 1
10.0%
U 1
10.0%
B 1
10.0%
Y 1
10.0%
N 1
10.0%
Common
ValueCountFrequency (%)
28
56.0%
( 8
 
16.0%
) 8
 
16.0%
& 3
 
6.0%
9 1
 
2.0%
3 1
 
2.0%
2 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 817
93.2%
ASCII 60
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 99
 
12.1%
냉 72
 
8.8%
수 21
 
2.6%
옥 20
 
2.4%
점 19
 
2.3%
국 18
 
2.2%
칡 15
 
1.8%
막 15
 
1.8%
동 14
 
1.7%
평 13
 
1.6%
Other values (195) 511
62.5%
ASCII
ValueCountFrequency (%)
28
46.7%
( 8
 
13.3%
) 8
 
13.3%
& 3
 
5.0%
R 2
 
3.3%
T 1
 
1.7%
9 1
 
1.7%
3 1
 
1.7%
O 1
 
1.7%
2 1
 
1.7%
Other values (6) 6
 
10.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20124913
Minimum19810523
Maximum20180704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T07:25:52.790217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19810523
5-th percentile19947652
Q120120311
median20160422
Q320170465
95-th percentile20180441
Maximum20180704
Range370181
Interquartile range (IQR)50154

Descriptive statistics

Standard deviation76423.607
Coefficient of variation (CV)0.0037974627
Kurtosis4.6932608
Mean20124913
Median Absolute Deviation (MAD)10381
Skewness-2.1963076
Sum2.7168633 × 109
Variance5.8405678 × 109
MonotonicityNot monotonic
2023-12-11T07:25:52.923767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160224 2
 
1.5%
20170529 2
 
1.5%
20161107 2
 
1.5%
20150901 2
 
1.5%
20150807 2
 
1.5%
20180611 2
 
1.5%
20150819 1
 
0.7%
20000217 1
 
0.7%
20160825 1
 
0.7%
19880719 1
 
0.7%
Other values (119) 119
88.1%
ValueCountFrequency (%)
19810523 1
0.7%
19810928 1
0.7%
19880719 1
0.7%
19900105 1
0.7%
19910820 1
0.7%
19940308 1
0.7%
19940508 1
0.7%
19950714 1
0.7%
19950722 1
0.7%
19971216 1
0.7%
ValueCountFrequency (%)
20180704 1
0.7%
20180612 1
0.7%
20180611 2
1.5%
20180607 1
0.7%
20180523 1
0.7%
20180510 1
0.7%
20180411 1
0.7%
20180322 1
0.7%
20180205 1
0.7%
20171227 1
0.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
운영중
114 
폐업 등
21 

Length

Max length4
Median length3
Mean length3.1555556
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 114
84.4%
폐업 등 21
 
15.6%

Length

2023-12-11T07:25:53.058847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:25:53.146129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 114
73.1%
폐업 21
 
13.5%
등 21
 
13.5%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing114
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean20171165
Minimum20150708
Maximum20180823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T07:25:53.237284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150708
5-th percentile20151125
Q120161205
median20170927
Q320180527
95-th percentile20180816
Maximum20180823
Range30115
Interquartile range (IQR)19322

Descriptive statistics

Standard deviation10120.208
Coefficient of variation (CV)0.00050171656
Kurtosis-0.58557025
Mean20171165
Median Absolute Deviation (MAD)9602
Skewness-0.73286906
Sum4.2359447 × 108
Variance1.0241861 × 108
MonotonicityNot monotonic
2023-12-11T07:25:53.390923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20180816 1
 
0.7%
20160421 1
 
0.7%
20180307 1
 
0.7%
20150708 1
 
0.7%
20170417 1
 
0.7%
20170927 1
 
0.7%
20171127 1
 
0.7%
20151125 1
 
0.7%
20180415 1
 
0.7%
20180527 1
 
0.7%
Other values (11) 11
 
8.1%
(Missing) 114
84.4%
ValueCountFrequency (%)
20150708 1
0.7%
20151125 1
0.7%
20160215 1
0.7%
20160421 1
0.7%
20161101 1
0.7%
20161205 1
0.7%
20170417 1
0.7%
20170623 1
0.7%
20170829 1
0.7%
20170918 1
0.7%
ValueCountFrequency (%)
20180823 1
0.7%
20180816 1
0.7%
20180712 1
0.7%
20180604 1
0.7%
20180529 1
0.7%
20180527 1
0.7%
20180415 1
0.7%
20180307 1
0.7%
20180123 1
0.7%
20171127 1
0.7%

다중이용업소여부
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.7%
Missing15
Missing (%)11.1%
Memory size402.0 B
False
109 
True
11 
(Missing)
15 
ValueCountFrequency (%)
False 109
80.7%
True 11
 
8.1%
(Missing) 15
 
11.1%
2023-12-11T07:25:53.490771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing135
Missing (%)100.0%
Memory size1.3 KiB

위생업종명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
일반음식점
120 
<NA>
15 

Length

Max length5
Median length5
Mean length4.8888889
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 120
88.9%
<NA> 15
 
11.1%

Length

2023-12-11T07:25:53.591201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:25:53.689720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 120
88.9%
na 15
 
11.1%

위생업태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
냉면집
120 
<NA>
15 

Length

Max length4
Median length3
Mean length3.1111111
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
냉면집 120
88.9%
<NA> 15
 
11.1%

Length

2023-12-11T07:25:53.802909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:25:53.913258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
냉면집 120
88.9%
na 15
 
11.1%

소재지도로명주소
Text

MISSING 

Distinct132
Distinct (%)99.2%
Missing2
Missing (%)1.5%
Memory size1.2 KiB
2023-12-11T07:25:54.168790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length39
Mean length30.834586
Min length14

Characters and Unicode

Total characters4101
Distinct characters242
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

Unique131 ?
Unique (%)98.5%

Sample

1st row경기도 가평군 북면 백둔로342번길 101-28, 2동
2nd row경기도 고양시 덕양구 혜음로 85 (고양동,(1층))
3rd row경기도 고양시 일산서구 탄중로221번길 4, 1층 (탄현동)
4th row경기도 고양시 덕양구 화중로138번길 49 (화정동, 1층일부)
5th row경기도 고양시 일산서구 경의로 953, 2동 1(전체)층 (덕이동)
ValueCountFrequency (%)
경기도 133
 
15.1%
1층 53
 
6.0%
동두천시 17
 
1.9%
부천시 11
 
1.3%
생연동 11
 
1.3%
용인시 10
 
1.1%
여주시 10
 
1.1%
고양시 8
 
0.9%
의정부시 7
 
0.8%
안양시 7
 
0.8%
Other values (437) 611
69.6%
2023-12-11T07:25:54.636053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
746
 
18.2%
1 211
 
5.1%
동 155
 
3.8%
경 142
 
3.5%
기 141
 
3.4%
도 137
 
3.3%
시 137
 
3.3%
, 133
 
3.2%
로 131
 
3.2%
( 121
 
3.0%
Other values (232) 2047
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2282
55.6%
Space Separator 746
 
18.2%
Decimal Number 670
 
16.3%
Other Punctuation 134
 
3.3%
Open Punctuation 121
 
3.0%
Close Punctuation 121
 
3.0%
Dash Punctuation 16
 
0.4%
Uppercase Letter 10
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 155
 
6.8%
경 142
 
6.2%
기 141
 
6.2%
도 137
 
6.0%
시 137
 
6.0%
로 131
 
5.7%
층 88
 
3.9%
길 47
 
2.1%
구 45
 
2.0%
부 44
 
1.9%
Other values (210) 1215
53.2%
Decimal Number
ValueCountFrequency (%)
1 211
31.5%
2 109
16.3%
4 63
 
9.4%
0 58
 
8.7%
3 57
 
8.5%
5 49
 
7.3%
9 37
 
5.5%
7 33
 
4.9%
6 29
 
4.3%
8 24
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
40.0%
A 2
20.0%
S 2
20.0%
N 1
 
10.0%
H 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 133
99.3%
& 1
 
0.7%
Space Separator
ValueCountFrequency (%)
746
100.0%
Open Punctuation
ValueCountFrequency (%)
( 121
100.0%
Close Punctuation
ValueCountFrequency (%)
) 121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2282
55.6%
Common 1808
44.1%
Latin 11
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 155
 
6.8%
경 142
 
6.2%
기 141
 
6.2%
도 137
 
6.0%
시 137
 
6.0%
로 131
 
5.7%
층 88
 
3.9%
길 47
 
2.1%
구 45
 
2.0%
부 44
 
1.9%
Other values (210) 1215
53.2%
Common
ValueCountFrequency (%)
746
41.3%
1 211
 
11.7%
, 133
 
7.4%
( 121
 
6.7%
) 121
 
6.7%
2 109
 
6.0%
4 63
 
3.5%
0 58
 
3.2%
3 57
 
3.2%
5 49
 
2.7%
Other values (6) 140
 
7.7%
Latin
ValueCountFrequency (%)
B 4
36.4%
A 2
18.2%
S 2
18.2%
N 1
 
9.1%
e 1
 
9.1%
H 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2282
55.6%
ASCII 1819
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
746
41.0%
1 211
 
11.6%
, 133
 
7.3%
( 121
 
6.7%
) 121
 
6.7%
2 109
 
6.0%
4 63
 
3.5%
0 58
 
3.2%
3 57
 
3.1%
5 49
 
2.7%
Other values (12) 151
 
8.3%
Hangul
ValueCountFrequency (%)
동 155
 
6.8%
경 142
 
6.2%
기 141
 
6.2%
도 137
 
6.0%
시 137
 
6.0%
로 131
 
5.7%
층 88
 
3.9%
길 47
 
2.1%
구 45
 
2.0%
부 44
 
1.9%
Other values (210) 1215
53.2%

소재지지번주소
Text

Distinct133
Distinct (%)99.3%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
2023-12-11T07:25:54.951685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38.5
Mean length25.970149
Min length15

Characters and Unicode

Total characters3480
Distinct characters216
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

Unique132 ?
Unique (%)98.5%

Sample

1st row경기도 가평군 북면 백둔리 261-4번지 2동
2nd row경기도 고양시 덕양구 고양동 8-4번지 (1층)
3rd row경기도 고양시 일산서구 탄현동 1498-10번지 1층
4th row경기도 고양시 덕양구 화정동 885-7번지 1층일부
5th row경기도 고양시 일산서구 덕이동 322-1번지 1층전체 2동
ValueCountFrequency (%)
경기도 134
 
18.2%
1층 28
 
3.8%
동두천시 17
 
2.3%
생연동 11
 
1.5%
여주시 11
 
1.5%
부천시 11
 
1.5%
용인시 10
 
1.4%
고양시 8
 
1.1%
의정부시 7
 
1.0%
수원시 6
 
0.8%
Other values (355) 492
66.9%
2023-12-11T07:25:55.503506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
601
 
17.3%
1 194
 
5.6%
지 157
 
4.5%
동 151
 
4.3%
기 141
 
4.1%
경 137
 
3.9%
도 137
 
3.9%
시 136
 
3.9%
번 135
 
3.9%
- 104
 
3.0%
Other values (206) 1587
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2026
58.2%
Decimal Number 709
 
20.4%
Space Separator 601
 
17.3%
Dash Punctuation 104
 
3.0%
Other Punctuation 16
 
0.5%
Uppercase Letter 11
 
0.3%
Close Punctuation 6
 
0.2%
Open Punctuation 6
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 157
 
7.7%
동 151
 
7.5%
기 141
 
7.0%
경 137
 
6.8%
도 137
 
6.8%
시 136
 
6.7%
번 135
 
6.7%
층 52
 
2.6%
구 43
 
2.1%
부 36
 
1.8%
Other values (182) 901
44.5%
Decimal Number
ValueCountFrequency (%)
1 194
27.4%
2 84
11.8%
4 70
 
9.9%
0 59
 
8.3%
3 58
 
8.2%
5 58
 
8.2%
7 54
 
7.6%
6 50
 
7.1%
8 48
 
6.8%
9 34
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
B 3
27.3%
A 2
18.2%
F 2
18.2%
S 2
18.2%
H 1
 
9.1%
N 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 14
87.5%
. 1
 
6.2%
& 1
 
6.2%
Space Separator
ValueCountFrequency (%)
601
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2026
58.2%
Common 1442
41.4%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 157
 
7.7%
동 151
 
7.5%
기 141
 
7.0%
경 137
 
6.8%
도 137
 
6.8%
시 136
 
6.7%
번 135
 
6.7%
층 52
 
2.6%
구 43
 
2.1%
부 36
 
1.8%
Other values (182) 901
44.5%
Common
ValueCountFrequency (%)
601
41.7%
1 194
 
13.5%
- 104
 
7.2%
2 84
 
5.8%
4 70
 
4.9%
0 59
 
4.1%
3 58
 
4.0%
5 58
 
4.0%
7 54
 
3.7%
6 50
 
3.5%
Other values (7) 110
 
7.6%
Latin
ValueCountFrequency (%)
B 3
25.0%
A 2
16.7%
F 2
16.7%
S 2
16.7%
e 1
 
8.3%
H 1
 
8.3%
N 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2026
58.2%
ASCII 1454
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
601
41.3%
1 194
 
13.3%
- 104
 
7.2%
2 84
 
5.8%
4 70
 
4.8%
0 59
 
4.1%
3 58
 
4.0%
5 58
 
4.0%
7 54
 
3.7%
6 50
 
3.4%
Other values (14) 122
 
8.4%
Hangul
ValueCountFrequency (%)
지 157
 
7.7%
동 151
 
7.5%
기 141
 
7.0%
경 137
 
6.8%
도 137
 
6.8%
시 136
 
6.7%
번 135
 
6.7%
층 52
 
2.6%
구 43
 
2.1%
부 36
 
1.8%
Other values (182) 901
44.5%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)78.4%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean417183.43
Minimum14415
Maximum483800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T07:25:55.685390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14415
5-th percentile14548
Q1426356.25
median450346
Q3470730.5
95-th percentile483030
Maximum483800
Range469385
Interquartile range (IQR)44374.25

Descriptive statistics

Standard deviation123023.26
Coefficient of variation (CV)0.29489008
Kurtosis6.8848281
Mean417183.43
Median Absolute Deviation (MAD)21478.5
Skewness-2.8913739
Sum55902580
Variance1.5134721 × 1010
MonotonicityNot monotonic
2023-12-11T07:25:55.854407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483030 10
 
7.4%
483100 3
 
2.2%
469101 3
 
2.2%
467020 2
 
1.5%
443800 2
 
1.5%
446908 2
 
1.5%
448811 2
 
1.5%
14416 2
 
1.5%
480090 2
 
1.5%
469107 2
 
1.5%
Other values (95) 104
77.0%
ValueCountFrequency (%)
14415 1
0.7%
14416 2
1.5%
14420 1
0.7%
14478 1
0.7%
14495 1
0.7%
14548 2
1.5%
14705 1
0.7%
14725 1
0.7%
14757 1
0.7%
411808 1
0.7%
ValueCountFrequency (%)
483800 1
 
0.7%
483120 1
 
0.7%
483100 3
 
2.2%
483030 10
7.4%
483020 2
 
1.5%
482811 1
 
0.7%
482030 1
 
0.7%
480839 1
 
0.7%
480823 1
 
0.7%
480815 1
 
0.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct131
Distinct (%)98.5%
Missing2
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean37.513077
Minimum37.002037
Maximum37.947731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T07:25:56.004068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.002037
5-th percentile37.223134
Q137.327407
median37.478489
Q337.697322
95-th percentile37.903914
Maximum37.947731
Range0.9456934
Interquartile range (IQR)0.36991554

Descriptive statistics

Standard deviation0.23593702
Coefficient of variation (CV)0.0062894606
Kurtosis-0.75118824
Mean37.513077
Median Absolute Deviation (MAD)0.1790798
Skewness0.17284754
Sum4989.2392
Variance0.055666277
MonotonicityNot monotonic
2023-12-11T07:25:56.168542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5378987689 2
 
1.5%
37.3250031722 2
 
1.5%
37.2975989851 1
 
0.7%
37.3901364426 1
 
0.7%
37.7171736297 1
 
0.7%
37.787887632 1
 
0.7%
37.4032515899 1
 
0.7%
37.2922284458 1
 
0.7%
37.2929772182 1
 
0.7%
37.2978343196 1
 
0.7%
Other values (121) 121
89.6%
(Missing) 2
 
1.5%
ValueCountFrequency (%)
37.0020374776 1
0.7%
37.00601352 1
0.7%
37.0135245127 1
0.7%
37.0377046339 1
0.7%
37.053409437 1
0.7%
37.1262540886 1
0.7%
37.2094801437 1
0.7%
37.2322368905 1
0.7%
37.2526400801 1
0.7%
37.2622130116 1
0.7%
ValueCountFrequency (%)
37.9477308749 1
0.7%
37.9474434839 1
0.7%
37.9460514156 1
0.7%
37.9125078988 1
0.7%
37.9089200145 1
0.7%
37.904148872 1
0.7%
37.9039152617 1
0.7%
37.9039129468 1
0.7%
37.9008502282 1
0.7%
37.8993716777 1
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct131
Distinct (%)98.5%
Missing2
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean127.05038
Minimum126.58503
Maximum127.65316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T07:25:56.304132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58503
5-th percentile126.71743
Q1126.86171
median127.0541
Q3127.1254
95-th percentile127.5812
Maximum127.65316
Range1.068129
Interquartile range (IQR)0.26369467

Descriptive statistics

Standard deviation0.24455854
Coefficient of variation (CV)0.0019248942
Kurtosis0.31462333
Mean127.05038
Median Absolute Deviation (MAD)0.13474508
Skewness0.62401454
Sum16897.701
Variance0.059808878
MonotonicityNot monotonic
2023-12-11T07:25:56.514244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2043086265 2
 
1.5%
127.1081092919 2
 
1.5%
127.6400458684 1
 
0.7%
126.9613293346 1
 
0.7%
126.9715192364 1
 
0.7%
127.0704363804 1
 
0.7%
127.5486419038 1
 
0.7%
127.6531572174 1
 
0.7%
127.6300410904 1
 
0.7%
127.6392226362 1
 
0.7%
Other values (121) 121
89.6%
(Missing) 2
 
1.5%
ValueCountFrequency (%)
126.5850282365 1
0.7%
126.5898157068 1
0.7%
126.5922864263 1
0.7%
126.6178064103 1
0.7%
126.6432497855 1
0.7%
126.6850215085 1
0.7%
126.7037706706 1
0.7%
126.7265416247 1
0.7%
126.7268826218 1
0.7%
126.7384854514 1
0.7%
ValueCountFrequency (%)
127.6531572174 1
0.7%
127.6400458684 1
0.7%
127.6392226362 1
0.7%
127.6380791212 1
0.7%
127.6336485902 1
0.7%
127.63163099 1
0.7%
127.6300410904 1
0.7%
127.5486419038 1
0.7%
127.5322750445 1
0.7%
127.4726057434 1
0.7%

Interactions

2023-12-11T07:25:50.960490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.298358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.734865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.149948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.550448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:51.045856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.380202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.813299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.233658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.632630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:51.138654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.471585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.912590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.317849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.715789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:51.234148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.549486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.992453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.397445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.800701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:51.329621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.634278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.077596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.475360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.883089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:25:56.622564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자다중이용업소여부소재지우편번호WGS84위도WGS84경도
시군명1.0000.0000.0000.5880.0001.0000.9560.944
인허가일자0.0001.0000.0000.8440.0000.1360.0000.000
영업상태명0.0000.0001.000NaN0.0760.0000.0000.262
폐업일자0.5880.844NaN1.000NaN0.0000.4310.000
다중이용업소여부0.0000.0000.076NaN1.0000.0850.0000.000
소재지우편번호1.0000.1360.0000.0000.0851.0000.7230.802
WGS84위도0.9560.0000.0000.4310.0000.7231.0000.719
WGS84경도0.9440.0000.2620.0000.0000.8020.7191.000
2023-12-11T07:25:56.758360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업태명다중이용업소여부위생업종명영업상태명시군명
위생업태명1.0001.0001.0001.0001.000
다중이용업소여부1.0001.0001.0000.0480.000
위생업종명1.0001.0001.0001.0001.000
영업상태명1.0000.0481.0001.0000.000
시군명1.0000.0001.0000.0001.000
2023-12-11T07:25:56.861248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명다중이용업소여부위생업종명위생업태명
인허가일자1.0000.475-0.153-0.2160.0310.0000.0000.0001.0001.000
폐업일자0.4751.000-0.051-0.0950.0690.0001.0001.0001.0001.000
소재지우편번호-0.153-0.0511.0000.3060.6790.9080.0000.1051.0001.000
WGS84위도-0.216-0.0950.3061.000-0.2730.7200.0000.0001.0001.000
WGS84경도0.0310.0690.679-0.2731.0000.6800.1940.0001.0001.000
시군명0.0000.0000.9080.7200.6801.0000.0000.0001.0001.000
영업상태명0.0001.0000.0000.0000.1940.0001.0000.0481.0001.000
다중이용업소여부0.0001.0000.1050.0000.0000.0000.0481.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T07:25:51.460734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:25:51.630240image/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.
2023-12-11T07:25:51.983834image/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
0가평군연꽃선원메밀국수20160705폐업 등20170829N<NA>일반음식점냉면집경기도 가평군 북면 백둔로342번길 101-28, 2동경기도 가평군 북면 백둔리 261-4번지 2동47784237.912508127.472606
1고양시인생술집20050830운영중<NA>N<NA>일반음식점냉면집경기도 고양시 덕양구 혜음로 85 (고양동,(1층))경기도 고양시 덕양구 고양동 8-4번지 (1층)41280037.708134126.901914
2고양시일산면옥19950714운영중<NA>N<NA>일반음식점냉면집경기도 고양시 일산서구 탄중로221번길 4, 1층 (탄현동)경기도 고양시 일산서구 탄현동 1498-10번지 1층41184137.693609126.76981
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5고양시삼복냉면20070420운영중<NA>N<NA>일반음식점냉면집경기도 고양시 덕양구 화중로130번길 5-15 (화정동, 1층전체)경기도 고양시 덕양구 화정동 897-9번지 1층전체41282637.639101126.832909
6고양시오실장강남면옥 본점19971216운영중<NA>N<NA>일반음식점냉면집경기도 고양시 일산서구 현중로26번길 45-2 (탄현동)경기도 고양시 일산서구 탄현동 1500-12번지41184137.693614126.770683
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9광명시봉평메밀마당광명소하점20170214운영중<NA>N<NA>일반음식점냉면집경기도 광명시 소하일로52번길 2, 1층 101호 (소하동)경기도 광명시 소하동 1312번지 1층 101호42305037.449605126.888399
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
125평택시박군자진주냉면(평택점)20170510운영중<NA>N<NA>일반음식점냉면집경기도 평택시 도일유통길 20-8 (도일동)경기도 평택시 도일동 1172-3번지45909037.037705127.099628
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127평택시백두산냉면20160503운영중<NA>N<NA>일반음식점냉면집경기도 평택시 평택1로 109, 상가동 110호 (통복동, 신명보람아파트)경기도 평택시 통복동 539번지 외1필지, 신명보람아파트 상가동 110호45075937.002037127.089824
128하남시오방냉면20160421운영중<NA>N<NA>일반음식점냉면집경기도 하남시 신평로 45, 1층 (신장동)경기도 하남시 신장동 427-17번지46582037.537899127.204309
129하남시해주냉면20160425운영중<NA>N<NA>일반음식점냉면집경기도 하남시 하남대로 747, 1층 (신장동)경기도 하남시 신장동 570번지 외 2필지46503037.538009127.21265
130하남시천천푸드20150622운영중<NA>N<NA>일반음식점냉면집경기도 하남시 역말로 39, 1층 (덕풍동)경기도 하남시 덕풍동 432-7번지46580637.535692127.201374
131하남시시골막국수20160829운영중<NA>N<NA>일반음식점냉면집경기도 하남시 미사대로 750, 2층 (신장동, 스타필드하남 쇼핑센터)경기도 하남시 신장동 616번지 스타필드하남 쇼핑센터 2층46503037.545418127.223761
132하남시오방냉면20160411폐업 등20160421N<NA>일반음식점냉면집경기도 하남시 신평로 45, 1층 (신장동)경기도 하남시 신장동 427-17번지46582037.537899127.204309
133화성시이화면옥20130725운영중<NA>N<NA>일반음식점냉면집<NA>경기도 화성시 우정읍 조암리 340-19번지 외 1필지 1층 일부445955<NA><NA>
134화성시청학동칡냉면20170512운영중<NA>N<NA>일반음식점냉면집경기도 화성시 효행로 466, 1층 (안녕동)경기도 화성시 안녕동 188-508번지44538037.20948126.985573