Overview

Dataset statistics

Number of variables13
Number of observations324
Missing cells1064
Missing cells (%)25.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory110.4 B

Variable types

Categorical3
Text3
DateTime1
Unsupported3
Numeric3

Dataset

Description휴게음식점(과자점) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8IMJM07MQXGQYV1KTN1F13483726&infSeq=1

Alerts

위생업태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
영업상태명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
시군명 is highly imbalanced (68.8%)Imbalance
영업상태명 is highly imbalanced (53.5%)Imbalance
다중이용업소여부 has 324 (100.0%) missing valuesMissing
총시설규모(㎡) has 324 (100.0%) missing valuesMissing
위생업종명 has 324 (100.0%) missing valuesMissing
소재지도로명주소 has 42 (13.0%) missing valuesMissing
소재지우편번호 has 14 (4.3%) missing valuesMissing
WGS84위도 has 18 (5.6%) missing valuesMissing
WGS84경도 has 18 (5.6%) missing valuesMissing
다중이용업소여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported
위생업종명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-10 20:32:28.319803
Analysis finished2024-05-10 20:32:32.948558
Duration4.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
부천시
266 
용인시
 
6
고양시
 
4
구리시
 
4
남양주시
 
4
Other values (22)
40 

Length

Max length4
Median length3
Mean length3.0216049
Min length3

Unique

Unique8 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
부천시 266
82.1%
용인시 6
 
1.9%
고양시 4
 
1.2%
구리시 4
 
1.2%
남양주시 4
 
1.2%
과천시 3
 
0.9%
광명시 3
 
0.9%
의정부시 3
 
0.9%
양주시 3
 
0.9%
시흥시 2
 
0.6%
Other values (17) 26
 
8.0%

Length

2024-05-10T20:32:33.162449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시 266
82.1%
용인시 6
 
1.9%
고양시 4
 
1.2%
구리시 4
 
1.2%
남양주시 4
 
1.2%
과천시 3
 
0.9%
광명시 3
 
0.9%
의정부시 3
 
0.9%
양주시 3
 
0.9%
안산시 2
 
0.6%
Other values (17) 26
 
8.0%

사업장명
Text

Distinct299
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-10T20:32:33.615792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length6.6635802
Min length2

Characters and Unicode

Total characters2159
Distinct characters379
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

Unique284 ?
Unique (%)87.7%

Sample

1st row파이팬
2nd row움트
3rd row던킨도너츠마두점
4th row나무와베이커리
5th row호밀호두
ValueCountFrequency (%)
던킨도너츠 11
 
3.0%
빵굽는마을 4
 
1.1%
호밀호두 3
 
0.8%
브레드하우스 3
 
0.8%
케익이벤트 3
 
0.8%
하나도너츠 3
 
0.8%
소풍가는날 2
 
0.6%
중동점 2
 
0.6%
롯데백화점 2
 
0.6%
크리스피크림도넛 2
 
0.6%
Other values (313) 327
90.3%
2024-05-10T20:32:34.505830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 90
 
4.2%
이 72
 
3.3%
스 67
 
3.1%
리 54
 
2.5%
도 40
 
1.9%
39
 
1.8%
베 38
 
1.8%
과 34
 
1.6%
자 32
 
1.5%
커 31
 
1.4%
Other values (369) 1662
77.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1991
92.2%
Space Separator 39
 
1.8%
Close Punctuation 31
 
1.4%
Open Punctuation 31
 
1.4%
Decimal Number 23
 
1.1%
Uppercase Letter 22
 
1.0%
Lowercase Letter 18
 
0.8%
Dash Punctuation 2
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 90
 
4.5%
이 72
 
3.6%
스 67
 
3.4%
리 54
 
2.7%
도 40
 
2.0%
베 38
 
1.9%
과 34
 
1.7%
자 32
 
1.6%
커 31
 
1.6%
너 30
 
1.5%
Other values (335) 1503
75.5%
Lowercase Letter
ValueCountFrequency (%)
r 3
16.7%
a 3
16.7%
e 2
11.1%
l 2
11.1%
d 1
 
5.6%
y 1
 
5.6%
o 1
 
5.6%
t 1
 
5.6%
s 1
 
5.6%
c 1
 
5.6%
Other values (2) 2
11.1%
Uppercase Letter
ValueCountFrequency (%)
G 5
22.7%
L 4
18.2%
H 2
 
9.1%
O 2
 
9.1%
B 2
 
9.1%
S 2
 
9.1%
V 1
 
4.5%
T 1
 
4.5%
E 1
 
4.5%
C 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 7
30.4%
3 5
21.7%
5 5
21.7%
4 4
17.4%
7 2
 
8.7%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1991
92.2%
Common 128
 
5.9%
Latin 40
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 90
 
4.5%
이 72
 
3.6%
스 67
 
3.4%
리 54
 
2.7%
도 40
 
2.0%
베 38
 
1.9%
과 34
 
1.7%
자 32
 
1.6%
커 31
 
1.6%
너 30
 
1.5%
Other values (335) 1503
75.5%
Latin
ValueCountFrequency (%)
G 5
 
12.5%
L 4
 
10.0%
r 3
 
7.5%
a 3
 
7.5%
H 2
 
5.0%
O 2
 
5.0%
e 2
 
5.0%
l 2
 
5.0%
B 2
 
5.0%
S 2
 
5.0%
Other values (13) 13
32.5%
Common
ValueCountFrequency (%)
39
30.5%
) 31
24.2%
( 31
24.2%
2 7
 
5.5%
3 5
 
3.9%
5 5
 
3.9%
4 4
 
3.1%
7 2
 
1.6%
- 2
 
1.6%
# 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1991
92.2%
ASCII 168
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 90
 
4.5%
이 72
 
3.6%
스 67
 
3.4%
리 54
 
2.7%
도 40
 
2.0%
베 38
 
1.9%
과 34
 
1.7%
자 32
 
1.6%
커 31
 
1.6%
너 30
 
1.5%
Other values (335) 1503
75.5%
ASCII
ValueCountFrequency (%)
39
23.2%
) 31
18.5%
( 31
18.5%
2 7
 
4.2%
3 5
 
3.0%
5 5
 
3.0%
G 5
 
3.0%
4 4
 
2.4%
L 4
 
2.4%
r 3
 
1.8%
Other values (24) 34
20.2%

인허가일자
Date

Distinct301
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1976-02-26 00:00:00
Maximum2015-05-08 00:00:00
2024-05-10T20:32:35.126955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:35.583712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
폐업
292 
영업
32 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 292
90.1%
영업 32
 
9.9%

Length

2024-05-10T20:32:36.003361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T20:32:36.245070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 292
90.1%
영업 32
 
9.9%

다중이용업소여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing324
Missing (%)100.0%
Memory size3.0 KiB

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing324
Missing (%)100.0%
Memory size3.0 KiB

위생업종명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing324
Missing (%)100.0%
Memory size3.0 KiB

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
과자점
324 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
과자점 324
100.0%

Length

2024-05-10T20:32:36.447410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T20:32:36.674589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과자점 324
100.0%

소재지도로명주소
Text

MISSING 

Distinct253
Distinct (%)89.7%
Missing42
Missing (%)13.0%
Memory size2.7 KiB
2024-05-10T20:32:37.261669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length51
Mean length25.542553
Min length17

Characters and Unicode

Total characters7203
Distinct characters228
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

Unique240 ?
Unique (%)85.1%

Sample

1st row경기도 고양시 일산동구 경의로 19, 현대밀라트 C동 103(일부)호 (백석동)
2nd row경기도 고양시 일산서구 호수로 817 (대화동, 레이킨스몰현대백화점 지하1층일부)
3rd row경기도 고양시 일산동구 중앙로 1172, 1층 103호 (마두동, 일산그랜드프라자빌딩)
4th row경기도 고양시 일산동구 산두로 117, 1층 일부호 (정발산동)
5th row경기도 과천시 별양로 28 (원문동, 래미안슈르B상가 1048)
ValueCountFrequency (%)
경기도 282
 
17.2%
부천시 226
 
13.8%
원미구 190
 
11.6%
1층 30
 
1.8%
길주로 29
 
1.8%
부일로 27
 
1.7%
오정구 25
 
1.5%
300 14
 
0.9%
중동 13
 
0.8%
일부 11
 
0.7%
Other values (496) 788
48.2%
2024-05-10T20:32:38.318606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1354
18.8%
부 325
 
4.5%
1 298
 
4.1%
도 292
 
4.1%
경 291
 
4.0%
시 284
 
3.9%
기 284
 
3.9%
로 281
 
3.9%
천 267
 
3.7%
구 245
 
3.4%
Other values (218) 3282
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4335
60.2%
Space Separator 1354
 
18.8%
Decimal Number 1162
 
16.1%
Other Punctuation 105
 
1.5%
Close Punctuation 104
 
1.4%
Open Punctuation 104
 
1.4%
Dash Punctuation 26
 
0.4%
Uppercase Letter 13
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 325
 
7.5%
도 292
 
6.7%
경 291
 
6.7%
시 284
 
6.6%
기 284
 
6.6%
로 281
 
6.5%
천 267
 
6.2%
구 245
 
5.7%
원 221
 
5.1%
미 206
 
4.8%
Other values (196) 1639
37.8%
Decimal Number
ValueCountFrequency (%)
1 298
25.6%
2 149
12.8%
0 122
10.5%
3 121
10.4%
4 94
 
8.1%
7 89
 
7.7%
5 78
 
6.7%
8 78
 
6.7%
9 68
 
5.9%
6 65
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 5
38.5%
T 2
 
15.4%
A 2
 
15.4%
C 1
 
7.7%
E 1
 
7.7%
O 1
 
7.7%
L 1
 
7.7%
Space Separator
ValueCountFrequency (%)
1354
100.0%
Other Punctuation
ValueCountFrequency (%)
, 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4335
60.2%
Common 2855
39.6%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 325
 
7.5%
도 292
 
6.7%
경 291
 
6.7%
시 284
 
6.6%
기 284
 
6.6%
로 281
 
6.5%
천 267
 
6.2%
구 245
 
5.7%
원 221
 
5.1%
미 206
 
4.8%
Other values (196) 1639
37.8%
Common
ValueCountFrequency (%)
1354
47.4%
1 298
 
10.4%
2 149
 
5.2%
0 122
 
4.3%
3 121
 
4.2%
, 105
 
3.7%
) 104
 
3.6%
( 104
 
3.6%
4 94
 
3.3%
7 89
 
3.1%
Other values (5) 315
 
11.0%
Latin
ValueCountFrequency (%)
B 5
38.5%
T 2
 
15.4%
A 2
 
15.4%
C 1
 
7.7%
E 1
 
7.7%
O 1
 
7.7%
L 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4335
60.2%
ASCII 2868
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1354
47.2%
1 298
 
10.4%
2 149
 
5.2%
0 122
 
4.3%
3 121
 
4.2%
, 105
 
3.7%
) 104
 
3.6%
( 104
 
3.6%
4 94
 
3.3%
7 89
 
3.1%
Other values (12) 328
 
11.4%
Hangul
ValueCountFrequency (%)
부 325
 
7.5%
도 292
 
6.7%
경 291
 
6.7%
시 284
 
6.6%
기 284
 
6.6%
로 281
 
6.5%
천 267
 
6.2%
구 245
 
5.7%
원 221
 
5.1%
미 206
 
4.8%
Other values (196) 1639
37.8%

소재지지번주소
Text

Distinct310
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-10T20:32:38.852641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length42
Mean length26.895062
Min length16

Characters and Unicode

Total characters8714
Distinct characters233
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

Unique302 ?
Unique (%)93.2%

Sample

1st row경기도 고양시 일산동구 백석동 1317 현대밀라트 C동 103(일부)호
2nd row경기도 고양시 일산서구 대화동 2602 레이킨스몰현대백화점 지하1층일부
3rd row경기도 고양시 일산동구 마두동 802-1 일산그랜드프라자빌딩 1층 103호
4th row경기도 고양시 일산동구 정발산동 1353 1층 일부
5th row경기도 과천시 원문동 4 래미안슈르B상가 1048
ValueCountFrequency (%)
경기도 324
 
16.9%
부천시 266
 
13.9%
원미구 225
 
11.8%
중동 81
 
4.2%
상동 59
 
3.1%
1층 36
 
1.9%
심곡동 30
 
1.6%
오정구 30
 
1.6%
지하1층 17
 
0.9%
원미동 17
 
0.9%
Other values (554) 827
43.3%
2024-05-10T20:32:39.834131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1903
21.8%
1 560
 
6.4%
동 358
 
4.1%
도 333
 
3.8%
시 326
 
3.7%
기 326
 
3.7%
경 325
 
3.7%
부 313
 
3.6%
구 285
 
3.3%
천 276
 
3.2%
Other values (223) 3709
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4729
54.3%
Space Separator 1903
21.8%
Decimal Number 1739
 
20.0%
Dash Punctuation 213
 
2.4%
Other Punctuation 53
 
0.6%
Open Punctuation 31
 
0.4%
Close Punctuation 31
 
0.4%
Uppercase Letter 15
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 358
 
7.6%
도 333
 
7.0%
시 326
 
6.9%
기 326
 
6.9%
경 325
 
6.9%
부 313
 
6.6%
구 285
 
6.0%
천 276
 
5.8%
원 263
 
5.6%
미 244
 
5.2%
Other values (201) 1680
35.5%
Decimal Number
ValueCountFrequency (%)
1 560
32.2%
4 189
 
10.9%
0 183
 
10.5%
2 172
 
9.9%
5 138
 
7.9%
3 130
 
7.5%
6 124
 
7.1%
7 94
 
5.4%
8 93
 
5.3%
9 56
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
B 5
33.3%
A 4
26.7%
T 2
 
13.3%
E 1
 
6.7%
O 1
 
6.7%
L 1
 
6.7%
C 1
 
6.7%
Space Separator
ValueCountFrequency (%)
1903
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4729
54.3%
Common 3970
45.6%
Latin 15
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 358
 
7.6%
도 333
 
7.0%
시 326
 
6.9%
기 326
 
6.9%
경 325
 
6.9%
부 313
 
6.6%
구 285
 
6.0%
천 276
 
5.8%
원 263
 
5.6%
미 244
 
5.2%
Other values (201) 1680
35.5%
Common
ValueCountFrequency (%)
1903
47.9%
1 560
 
14.1%
- 213
 
5.4%
4 189
 
4.8%
0 183
 
4.6%
2 172
 
4.3%
5 138
 
3.5%
3 130
 
3.3%
6 124
 
3.1%
7 94
 
2.4%
Other values (5) 264
 
6.6%
Latin
ValueCountFrequency (%)
B 5
33.3%
A 4
26.7%
T 2
 
13.3%
E 1
 
6.7%
O 1
 
6.7%
L 1
 
6.7%
C 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4729
54.3%
ASCII 3985
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1903
47.8%
1 560
 
14.1%
- 213
 
5.3%
4 189
 
4.7%
0 183
 
4.6%
2 172
 
4.3%
5 138
 
3.5%
3 130
 
3.3%
6 124
 
3.1%
7 94
 
2.4%
Other values (12) 279
 
7.0%
Hangul
ValueCountFrequency (%)
동 358
 
7.6%
도 333
 
7.0%
시 326
 
6.9%
기 326
 
6.9%
경 325
 
6.9%
부 313
 
6.6%
구 285
 
6.0%
천 276
 
5.8%
원 263
 
5.6%
미 244
 
5.2%
Other values (201) 1680
35.5%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct162
Distinct (%)52.3%
Missing14
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean14458.165
Minimum10068
Maximum18445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-10T20:32:40.205075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10068
5-th percentile11924.7
Q114526.25
median14555.5
Q314624.75
95-th percentile16068.4
Maximum18445
Range8377
Interquartile range (IQR)98.5

Descriptive statistics

Standard deviation1114.8535
Coefficient of variation (CV)0.077108923
Kurtosis5.9025668
Mean14458.165
Median Absolute Deviation (MAD)67.5
Skewness-0.75650635
Sum4482031
Variance1242898.3
MonotonicityNot monotonic
2024-05-10T20:32:40.759489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14548 20
 
6.2%
14623 14
 
4.3%
14546 13
 
4.0%
14542 8
 
2.5%
14544 7
 
2.2%
14571 6
 
1.9%
14637 6
 
1.9%
14635 5
 
1.5%
14545 5
 
1.5%
14598 5
 
1.5%
Other values (152) 221
68.2%
(Missing) 14
 
4.3%
ValueCountFrequency (%)
10068 1
0.3%
10077 1
0.3%
10391 1
0.3%
10406 1
0.3%
10414 1
0.3%
10449 1
0.3%
10864 1
0.3%
11435 1
0.3%
11456 1
0.3%
11506 1
0.3%
ValueCountFrequency (%)
18445 1
 
0.3%
18429 1
 
0.3%
18102 1
 
0.3%
17943 1
 
0.3%
17915 1
 
0.3%
17570 1
 
0.3%
17373 1
 
0.3%
17113 1
 
0.3%
17023 5
1.5%
16704 1
 
0.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct251
Distinct (%)82.0%
Missing18
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean37.491311
Minimum36.979379
Maximum37.84177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-10T20:32:41.131913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.979379
5-th percentile37.294218
Q137.487884
median37.497857
Q337.506969
95-th percentile37.630116
Maximum37.84177
Range0.86239052
Interquartile range (IQR)0.01908537

Descriptive statistics

Standard deviation0.093605595
Coefficient of variation (CV)0.0024967278
Kurtosis10.0244
Mean37.491311
Median Absolute Deviation (MAD)0.0097249831
Skewness-1.3926567
Sum11472.341
Variance0.0087620075
MonotonicityNot monotonic
2024-05-10T20:32:41.526802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5025517408 15
 
4.6%
37.4888795916 10
 
3.1%
37.5043171668 9
 
2.8%
37.2907221232 5
 
1.5%
37.6026921337 3
 
0.9%
37.4916093636 2
 
0.6%
37.5067802392 2
 
0.6%
37.5158292022 2
 
0.6%
37.5029949535 2
 
0.6%
37.5043271458 2
 
0.6%
Other values (241) 254
78.4%
(Missing) 18
 
5.6%
ValueCountFrequency (%)
36.9793791444 1
0.3%
36.9887914679 1
0.3%
37.0136374384 1
0.3%
37.1758134947 1
0.3%
37.1994162658 1
0.3%
37.2024090096 1
0.3%
37.2303018745 1
0.3%
37.2532216172 1
0.3%
37.2773344789 1
0.3%
37.290433036 1
0.3%
ValueCountFrequency (%)
37.8417696599 1
0.3%
37.8372449352 1
0.3%
37.8091775782 1
0.3%
37.7529146143 1
0.3%
37.7486653842 1
0.3%
37.7299319376 1
0.3%
37.7235943623 1
0.3%
37.7083495916 1
0.3%
37.6982583254 1
0.3%
37.6681262943 1
0.3%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct251
Distinct (%)82.0%
Missing18
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean126.82783
Minimum126.6437
Maximum127.63609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-10T20:32:41.883608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6437
5-th percentile126.75137
Q1126.76275
median126.78078
Q3126.8052
95-th percentile127.15373
Maximum127.63609
Range0.99238987
Interquartile range (IQR)0.042443498

Descriptive statistics

Standard deviation0.13490274
Coefficient of variation (CV)0.0010636683
Kurtosis7.4939124
Mean126.82783
Median Absolute Deviation (MAD)0.018707732
Skewness2.6544408
Sum38809.314
Variance0.01819875
MonotonicityNot monotonic
2024-05-10T20:32:42.503064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7753741701 15
 
4.6%
126.7552924952 10
 
3.1%
126.7620745903 9
 
2.8%
127.1967269237 5
 
1.5%
127.1437935543 3
 
0.9%
126.7902194211 2
 
0.6%
126.7538475044 2
 
0.6%
126.8024259921 2
 
0.6%
126.7625175247 2
 
0.6%
126.7550485645 2
 
0.6%
Other values (241) 254
78.4%
(Missing) 18
 
5.6%
ValueCountFrequency (%)
126.6437024926 1
0.3%
126.6814042268 1
0.3%
126.7177022879 1
0.3%
126.728861451 1
0.3%
126.7436401576 1
0.3%
126.7483596581 1
0.3%
126.7499440433 1
0.3%
126.7500809494 1
0.3%
126.750220724 1
0.3%
126.7503291688 1
0.3%
ValueCountFrequency (%)
127.6360923603 1
0.3%
127.4471528681 1
0.3%
127.3196020976 1
0.3%
127.265052381 1
0.3%
127.2611487693 1
0.3%
127.257234533 1
0.3%
127.2549271307 1
0.3%
127.2063333952 1
0.3%
127.2061040778 1
0.3%
127.2011999987 1
0.3%

Interactions

2024-05-10T20:32:31.105909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:29.617447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:30.365745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:31.369356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:29.861704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:30.612175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:31.608441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:30.112403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:32:30.853599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T20:32:42.937320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명소재지우편번호WGS84위도WGS84경도
시군명1.0000.7880.9970.9940.999
영업상태명0.7881.0000.7770.5880.528
소재지우편번호0.9970.7771.0000.8650.827
WGS84위도0.9940.5880.8651.0000.843
WGS84경도0.9990.5280.8270.8431.000
2024-05-10T20:32:43.376134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명
시군명1.0000.674
영업상태명0.6741.000
2024-05-10T20:32:43.726828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명영업상태명
소재지우편번호1.000-0.823-0.0350.9550.608
WGS84위도-0.8231.000-0.1630.8500.585
WGS84경도-0.035-0.1631.0000.9060.524
시군명0.9550.8500.9061.0000.674
영업상태명0.6080.5850.5240.6741.000

Missing values

2024-05-10T20:32:31.991714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T20:32:32.450951image/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.
2024-05-10T20:32:32.786870image/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

시군명사업장명인허가일자영업상태명다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시파이팬2009-10-29영업<NA><NA><NA>과자점경기도 고양시 일산동구 경의로 19, 현대밀라트 C동 103(일부)호 (백석동)경기도 고양시 일산동구 백석동 1317 현대밀라트 C동 103(일부)호1044937.638044126.789214
1고양시움트2012-03-30영업<NA><NA><NA>과자점경기도 고양시 일산서구 호수로 817 (대화동, 레이킨스몰현대백화점 지하1층일부)경기도 고양시 일산서구 대화동 2602 레이킨스몰현대백화점 지하1층일부1039137.667979126.751624
2고양시던킨도너츠마두점20150330폐업<NA><NA><NA>과자점경기도 고양시 일산동구 중앙로 1172, 1층 103호 (마두동, 일산그랜드프라자빌딩)경기도 고양시 일산동구 마두동 802-1 일산그랜드프라자빌딩 1층 103호1041437.651732126.778393
3고양시나무와베이커리2013-08-26폐업<NA><NA><NA>과자점경기도 고양시 일산동구 산두로 117, 1층 일부호 (정발산동)경기도 고양시 일산동구 정발산동 1353 1층 일부1040637.668126126.785605
4과천시호밀호두20121011영업<NA><NA><NA>과자점경기도 과천시 별양로 28 (원문동, 래미안슈르B상가 1048)경기도 과천시 원문동 4 래미안슈르B상가 10481383537.421222126.991225
5과천시파리바게뜨20081001폐업<NA><NA><NA>과자점경기도 과천시 별양로 28 (원문동, 래미안슈르상가1001,1002,1044호)경기도 과천시 원문동 4 래미안슈르상가1001,1002,1044호1383537.421222126.991225
6과천시피자헛과천우면점2013-06-11폐업<NA><NA><NA>과자점경기도 과천시 중앙로 417, 2층 (과천동)경기도 과천시 과천동 455-16 2층1381337.450705126.999539
7광명시던킨도너츠광명하안점2007-05-29영업<NA><NA><NA>과자점경기도 광명시 하안로 289 (하안동,,1층)경기도 광명시 하안동 51 ,1층1430537.461695126.880193
8광명시던킨도너츠 철산점2007-03-27폐업<NA><NA><NA>과자점경기도 광명시 철산로 23 (철산동,,대광빌딩 1층)경기도 광명시 철산동 245-6 ,대광빌딩 1층1423637.476623126.8685
9광명시우메마루20060802폐업<NA><NA><NA>과자점경기도 광명시 하안로288번길 15 (하안동,,주은프라자1층)경기도 광명시 하안동 61-1 ,주은프라자1층1430637.462251126.881342
시군명사업장명인허가일자영업상태명다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
314의정부시아임파이20130404폐업<NA><NA><NA>과자점경기도 의정부시 동일로 395 (장암동, 장암2단지주공아파트 종합상가동 207호)경기도 의정부시 장암동 14-131171937.723594127.060107
315의정부시던킨도너츠20070621폐업<NA><NA><NA>과자점경기도 의정부시 평화로 635 (의정부동, 미림프라자 107, 108호)경기도 의정부시 의정부동 235-171168637.748665127.044862
316이천시송사부수제쌀고로케(이천터미널점)2013-11-15영업<NA><NA><NA>과자점경기도 이천시 이섭대천로 1200, 1층 152호 (중리동)경기도 이천시 중리동 219 (1층) 152호1737337.277334127.447153
317파주시더(THE)가미20111018폐업<NA><NA><NA>과자점경기도 파주시 오도로 158, 1층 일부 (오도동)경기도 파주시 오도동 293-8 1층 일부1086437.752915126.717702
318평택시브레드44520130111영업<NA><NA><NA>과자점경기도 평택시 안중읍 안현로서8길 35 (1층)경기도 평택시 안중읍 현화리 835-1 1층1794336.979379126.921824
319평택시커피나라20090827영업<NA><NA><NA>과자점경기도 평택시 조개터로16번길 1 (합정동,(1층))경기도 평택시 합정동 917-20 (1층)1791536.988791127.101919
320하남시메밀만두20131203영업<NA><NA><NA>과자점경기도 하남시 하남대로801번길 68, 1층 (신장동)경기도 하남시 신장동 427-452 1층1295737.538246127.206333
321하남시하남(만)휴게소(드림빵빵)2010-08-10폐업<NA><NA><NA>과자점경기도 하남시 중부고속도로 117 (천현동)경기도 하남시 천현동 2671301837.530341127.206104
322화성시배스킨라빈스 동탄홈플러스20101206영업<NA><NA><NA>과자점경기도 화성시 동탄중앙로 200 (반송동, (메타폴리스 상가동 지하4층 일부))경기도 화성시 반송동 98 (메타폴리스 상가동 지하4층 일부)1844537.202409127.06803
323화성시떡보의하루 병점.동탄점2007-08-28폐업<NA><NA><NA>과자점경기도 화성시 10용사로 343-2 (능동, (폴리클리닉 107호))경기도 화성시 능동 1152-5 (폴리클리닉 107호)1842937.199416127.055821