Overview

Dataset statistics

Number of variables10
Number of observations256
Missing cells284
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.9 KiB
Average record size in memory83.5 B

Variable types

Text4
Categorical2
DateTime1
Numeric3

Dataset

Description경상남도 김해시 건물위생관리업 현황에 대한 데이터로 사업장명,전화번호,지번주소,도로명주소 등의 항목을 제공합니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033389

Alerts

상세영업상태명 is highly overall correlated with 소재지면적 and 3 other fieldsHigh correlation
위생업태명 is highly overall correlated with 상세영업상태명High correlation
소재지면적 is highly overall correlated with 상세영업상태명High correlation
위도 is highly overall correlated with 상세영업상태명High correlation
경도 is highly overall correlated with 상세영업상태명High correlation
위생업태명 is highly imbalanced (96.3%)Imbalance
폐업일자 has 111 (43.4%) missing valuesMissing
전화번호 has 112 (43.8%) missing valuesMissing
소재지면적 has 3 (1.2%) missing valuesMissing
도로명주소 has 58 (22.7%) missing valuesMissing
소재지면적 has 18 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 23:14:09.626441
Analysis finished2023-12-10 23:14:11.261303
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct250
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T08:14:11.450778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.5351562
Min length2

Characters and Unicode

Total characters1673
Distinct characters273
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

Unique244 ?
Unique (%)95.3%

Sample

1st row토스템김해지점
2nd row(주)콘티넨탈실업
3rd row우진기업
4th row(주)청정
5th row(주)수경
ValueCountFrequency (%)
주식회사 14
 
4.9%
삼성크린테크 2
 
0.7%
김해청소 2
 
0.7%
패시픽 2
 
0.7%
씨엘코퍼레이션 2
 
0.7%
누리환경 2
 
0.7%
제로죤 2
 
0.7%
코리아환경 2
 
0.7%
경남 2
 
0.7%
영진비앤씨 1
 
0.3%
Other values (257) 257
89.2%
2023-12-11T08:14:11.897905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 98
 
5.9%
) 78
 
4.7%
( 77
 
4.6%
경 45
 
2.7%
사 40
 
2.4%
업 38
 
2.3%
린 38
 
2.3%
스 37
 
2.2%
크 34
 
2.0%
청 33
 
2.0%
Other values (263) 1155
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1420
84.9%
Close Punctuation 78
 
4.7%
Open Punctuation 77
 
4.6%
Uppercase Letter 36
 
2.2%
Space Separator 32
 
1.9%
Lowercase Letter 14
 
0.8%
Decimal Number 10
 
0.6%
Other Punctuation 5
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 98
 
6.9%
경 45
 
3.2%
사 40
 
2.8%
업 38
 
2.7%
린 38
 
2.7%
스 37
 
2.6%
크 34
 
2.4%
청 33
 
2.3%
환 30
 
2.1%
리 29
 
2.0%
Other values (225) 998
70.3%
Uppercase Letter
ValueCountFrequency (%)
S 7
19.4%
C 5
13.9%
G 3
 
8.3%
N 3
 
8.3%
E 2
 
5.6%
H 2
 
5.6%
M 2
 
5.6%
K 2
 
5.6%
O 1
 
2.8%
T 1
 
2.8%
Other values (8) 8
22.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
28.6%
i 2
14.3%
n 2
14.3%
s 1
 
7.1%
r 1
 
7.1%
c 1
 
7.1%
m 1
 
7.1%
a 1
 
7.1%
t 1
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 4
40.0%
3 2
20.0%
9 2
20.0%
5 1
 
10.0%
6 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
& 3
60.0%
. 2
40.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1420
84.9%
Common 203
 
12.1%
Latin 50
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 98
 
6.9%
경 45
 
3.2%
사 40
 
2.8%
업 38
 
2.7%
린 38
 
2.7%
스 37
 
2.6%
크 34
 
2.4%
청 33
 
2.3%
환 30
 
2.1%
리 29
 
2.0%
Other values (225) 998
70.3%
Latin
ValueCountFrequency (%)
S 7
 
14.0%
C 5
 
10.0%
e 4
 
8.0%
G 3
 
6.0%
N 3
 
6.0%
E 2
 
4.0%
i 2
 
4.0%
H 2
 
4.0%
M 2
 
4.0%
n 2
 
4.0%
Other values (17) 18
36.0%
Common
ValueCountFrequency (%)
) 78
38.4%
( 77
37.9%
32
15.8%
1 4
 
2.0%
& 3
 
1.5%
3 2
 
1.0%
9 2
 
1.0%
. 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1420
84.9%
ASCII 253
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 98
 
6.9%
경 45
 
3.2%
사 40
 
2.8%
업 38
 
2.7%
린 38
 
2.7%
스 37
 
2.6%
크 34
 
2.4%
청 33
 
2.3%
환 30
 
2.1%
리 29
 
2.0%
Other values (225) 998
70.3%
ASCII
ValueCountFrequency (%)
) 78
30.8%
( 77
30.4%
32
12.6%
S 7
 
2.8%
C 5
 
2.0%
1 4
 
1.6%
e 4
 
1.6%
& 3
 
1.2%
G 3
 
1.2%
N 3
 
1.2%
Other values (28) 37
14.6%

상세영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
폐업
145 
<NA>
111 

Length

Max length4
Median length2
Mean length2.8671875
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 145
56.6%
<NA> 111
43.4%

Length

2023-12-11T08:14:12.068124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:14:12.190052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 145
56.6%
na 111
43.4%

폐업일자
Date

MISSING 

Distinct122
Distinct (%)84.1%
Missing111
Missing (%)43.4%
Memory size2.1 KiB
Minimum2000-03-16 00:00:00
Maximum2022-05-31 00:00:00
2023-12-11T08:14:12.296152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:12.461077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct139
Distinct (%)96.5%
Missing112
Missing (%)43.8%
Memory size2.1 KiB
2023-12-11T08:14:12.725744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.076389
Min length12

Characters and Unicode

Total characters1739
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)93.8%

Sample

1st row055-327-7179
2nd row055-332-6365
3rd row055-332-6617
4th row055-322-9495
5th row055-328-1050
ValueCountFrequency (%)
055-326-9955 3
 
2.1%
055-335-6119 2
 
1.4%
055-313-2570 2
 
1.4%
055-323-4500 2
 
1.4%
055-337-8866 1
 
0.7%
055-311-5016 1
 
0.7%
055-311-4517 1
 
0.7%
055-322-3119 1
 
0.7%
055-324-0110 1
 
0.7%
055-333-3378 1
 
0.7%
Other values (129) 129
89.6%
2023-12-11T08:14:13.125435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 349
20.1%
- 288
16.6%
3 254
14.6%
0 244
14.0%
2 144
8.3%
1 115
 
6.6%
7 80
 
4.6%
4 71
 
4.1%
9 70
 
4.0%
6 69
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1451
83.4%
Dash Punctuation 288
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 349
24.1%
3 254
17.5%
0 244
16.8%
2 144
9.9%
1 115
 
7.9%
7 80
 
5.5%
4 71
 
4.9%
9 70
 
4.8%
6 69
 
4.8%
8 55
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 349
20.1%
- 288
16.6%
3 254
14.6%
0 244
14.0%
2 144
8.3%
1 115
 
6.6%
7 80
 
4.6%
4 71
 
4.1%
9 70
 
4.0%
6 69
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 349
20.1%
- 288
16.6%
3 254
14.6%
0 244
14.0%
2 144
8.3%
1 115
 
6.6%
7 80
 
4.6%
4 71
 
4.1%
9 70
 
4.0%
6 69
 
4.0%

소재지면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct212
Distinct (%)83.8%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean46.853162
Minimum0
Maximum254.1
Zeros18
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T08:14:13.288507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.88
median36
Q361.1
95-th percentile130.458
Maximum254.1
Range254.1
Interquartile range (IQR)39.22

Descriptive statistics

Standard deviation40.167676
Coefficient of variation (CV)0.85730982
Kurtosis4.2166131
Mean46.853162
Median Absolute Deviation (MAD)16.55
Skewness1.7938999
Sum11853.85
Variance1613.4422
MonotonicityNot monotonic
2023-12-11T08:14:13.426019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
7.0%
40.0 3
 
1.2%
37.83 3
 
1.2%
20.0 3
 
1.2%
10.0 3
 
1.2%
36.0 3
 
1.2%
21.6 3
 
1.2%
33.0 3
 
1.2%
46.2 2
 
0.8%
26.4 2
 
0.8%
Other values (202) 210
82.0%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
0.0 18
7.0%
4.0 1
 
0.4%
4.35 1
 
0.4%
5.59 1
 
0.4%
6.6 1
 
0.4%
7.26 1
 
0.4%
8.36 1
 
0.4%
8.5 1
 
0.4%
9.16 1
 
0.4%
10.0 3
 
1.2%
ValueCountFrequency (%)
254.1 1
0.4%
197.0 1
0.4%
191.6 1
0.4%
183.0 1
0.4%
180.32 1
0.4%
157.42 1
0.4%
157.0 1
0.4%
146.76 1
0.4%
143.77 1
0.4%
134.29 1
0.4%

도로명주소
Text

MISSING 

Distinct191
Distinct (%)96.5%
Missing58
Missing (%)22.7%
Memory size2.1 KiB
2023-12-11T08:14:13.768342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length40.5
Mean length31.267677
Min length19

Characters and Unicode

Total characters6191
Distinct characters175
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

Unique184 ?
Unique (%)92.9%

Sample

1st row경상남도 김해시 전하로99번길 3, 2층 (흥동)
2nd row경상남도 김해시 인제로51번길 50 (삼정동)
3rd row경상남도 김해시 가야로 183, 삼계위너스타운 109호 (삼계동)
4th row경상남도 김해시 번화1로79번길 2, 센터빌딩 1003호 (대청동)
5th row경상남도 김해시 월산로 111-67
ValueCountFrequency (%)
경상남도 198
 
16.0%
김해시 198
 
16.0%
1층 47
 
3.8%
2층 25
 
2.0%
외동 21
 
1.7%
삼방동 16
 
1.3%
내동 16
 
1.3%
3층 16
 
1.3%
대청동 15
 
1.2%
삼계동 12
 
1.0%
Other values (413) 674
54.4%
2023-12-11T08:14:14.226176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1040
 
16.8%
1 299
 
4.8%
상 230
 
3.7%
해 226
 
3.7%
김 223
 
3.6%
동 222
 
3.6%
시 201
 
3.2%
도 200
 
3.2%
, 199
 
3.2%
남 199
 
3.2%
Other values (165) 3152
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3317
53.6%
Decimal Number 1208
 
19.5%
Space Separator 1040
 
16.8%
Other Punctuation 200
 
3.2%
Close Punctuation 181
 
2.9%
Open Punctuation 181
 
2.9%
Dash Punctuation 53
 
0.9%
Uppercase Letter 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 230
 
6.9%
해 226
 
6.8%
김 223
 
6.7%
동 222
 
6.7%
시 201
 
6.1%
도 200
 
6.0%
남 199
 
6.0%
경 198
 
6.0%
로 198
 
6.0%
번 122
 
3.7%
Other values (140) 1298
39.1%
Decimal Number
ValueCountFrequency (%)
1 299
24.8%
2 183
15.1%
3 139
11.5%
0 126
10.4%
4 93
 
7.7%
5 88
 
7.3%
6 80
 
6.6%
9 71
 
5.9%
7 66
 
5.5%
8 63
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
N 1
9.1%
I 1
9.1%
E 1
9.1%
O 1
9.1%
X 1
9.1%
P 1
9.1%
H 1
9.1%
Other Punctuation
ValueCountFrequency (%)
, 199
99.5%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1040
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%
Open Punctuation
ValueCountFrequency (%)
( 181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3317
53.6%
Common 2863
46.2%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 230
 
6.9%
해 226
 
6.8%
김 223
 
6.7%
동 222
 
6.7%
시 201
 
6.1%
도 200
 
6.0%
남 199
 
6.0%
경 198
 
6.0%
로 198
 
6.0%
번 122
 
3.7%
Other values (140) 1298
39.1%
Common
ValueCountFrequency (%)
1040
36.3%
1 299
 
10.4%
, 199
 
7.0%
2 183
 
6.4%
) 181
 
6.3%
( 181
 
6.3%
3 139
 
4.9%
0 126
 
4.4%
4 93
 
3.2%
5 88
 
3.1%
Other values (6) 334
 
11.7%
Latin
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
N 1
9.1%
I 1
9.1%
E 1
9.1%
O 1
9.1%
X 1
9.1%
P 1
9.1%
H 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3317
53.6%
ASCII 2874
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1040
36.2%
1 299
 
10.4%
, 199
 
6.9%
2 183
 
6.4%
) 181
 
6.3%
( 181
 
6.3%
3 139
 
4.8%
0 126
 
4.4%
4 93
 
3.2%
5 88
 
3.1%
Other values (15) 345
 
12.0%
Hangul
ValueCountFrequency (%)
상 230
 
6.9%
해 226
 
6.8%
김 223
 
6.7%
동 222
 
6.7%
시 201
 
6.1%
도 200
 
6.0%
남 199
 
6.0%
경 198
 
6.0%
로 198
 
6.0%
번 122
 
3.7%
Other values (140) 1298
39.1%

지번주소
Text

Distinct243
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T08:14:14.488909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length35
Mean length22.910156
Min length16

Characters and Unicode

Total characters5865
Distinct characters167
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)90.6%

Sample

1st row경상남도 김해시 삼방동 202-5
2nd row경상남도 김해시 어방동 1113-8
3rd row경상남도 김해시 봉황동 24-3
4th row경상남도 김해시 외동 1244-7
5th row경상남도 김해시 흥동 52-7 2층
ValueCountFrequency (%)
경상남도 256
20.8%
김해시 256
20.8%
외동 28
 
2.3%
내동 20
 
1.6%
2층 19
 
1.5%
어방동 18
 
1.5%
삼방동 18
 
1.5%
삼계동 18
 
1.5%
대청동 17
 
1.4%
동상동 16
 
1.3%
Other values (386) 564
45.9%
2023-12-11T08:14:15.006866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1181
20.1%
1 323
 
5.5%
상 300
 
5.1%
동 278
 
4.7%
시 260
 
4.4%
김 259
 
4.4%
해 259
 
4.4%
도 258
 
4.4%
남 257
 
4.4%
경 256
 
4.4%
Other values (157) 2234
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3084
52.6%
Decimal Number 1339
22.8%
Space Separator 1181
 
20.1%
Dash Punctuation 234
 
4.0%
Uppercase Letter 14
 
0.2%
Other Punctuation 13
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 300
 
9.7%
동 278
 
9.0%
시 260
 
8.4%
김 259
 
8.4%
해 259
 
8.4%
도 258
 
8.4%
남 257
 
8.3%
경 256
 
8.3%
호 64
 
2.1%
삼 59
 
1.9%
Other values (133) 834
27.0%
Decimal Number
ValueCountFrequency (%)
1 323
24.1%
2 167
12.5%
0 145
10.8%
3 123
 
9.2%
6 118
 
8.8%
5 105
 
7.8%
4 100
 
7.5%
7 95
 
7.1%
8 95
 
7.1%
9 68
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 3
21.4%
A 3
21.4%
X 1
 
7.1%
N 1
 
7.1%
E 1
 
7.1%
O 1
 
7.1%
I 1
 
7.1%
P 1
 
7.1%
H 1
 
7.1%
L 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 12
92.3%
/ 1
 
7.7%
Space Separator
ValueCountFrequency (%)
1181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3084
52.6%
Common 2767
47.2%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 300
 
9.7%
동 278
 
9.0%
시 260
 
8.4%
김 259
 
8.4%
해 259
 
8.4%
도 258
 
8.4%
남 257
 
8.3%
경 256
 
8.3%
호 64
 
2.1%
삼 59
 
1.9%
Other values (133) 834
27.0%
Common
ValueCountFrequency (%)
1181
42.7%
1 323
 
11.7%
- 234
 
8.5%
2 167
 
6.0%
0 145
 
5.2%
3 123
 
4.4%
6 118
 
4.3%
5 105
 
3.8%
4 100
 
3.6%
7 95
 
3.4%
Other values (4) 176
 
6.4%
Latin
ValueCountFrequency (%)
B 3
21.4%
A 3
21.4%
X 1
 
7.1%
N 1
 
7.1%
E 1
 
7.1%
O 1
 
7.1%
I 1
 
7.1%
P 1
 
7.1%
H 1
 
7.1%
L 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3084
52.6%
ASCII 2781
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1181
42.5%
1 323
 
11.6%
- 234
 
8.4%
2 167
 
6.0%
0 145
 
5.2%
3 123
 
4.4%
6 118
 
4.2%
5 105
 
3.8%
4 100
 
3.6%
7 95
 
3.4%
Other values (14) 190
 
6.8%
Hangul
ValueCountFrequency (%)
상 300
 
9.7%
동 278
 
9.0%
시 260
 
8.4%
김 259
 
8.4%
해 259
 
8.4%
도 258
 
8.4%
남 257
 
8.3%
경 256
 
8.3%
호 64
 
2.1%
삼 59
 
1.9%
Other values (133) 834
27.0%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
건물위생관리업
255 
건물위생관리업 기타
 
1

Length

Max length10
Median length7
Mean length7.0117188
Min length7

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 255
99.6%
건물위생관리업 기타 1
 
0.4%

Length

2023-12-11T08:14:15.154047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:14:15.258346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 256
99.6%
기타 1
 
0.4%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.233828
Minimum35.169322
Maximum35.325108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T08:14:15.582092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.169322
5-th percentile35.192815
Q135.226579
median35.233076
Q335.243501
95-th percentile35.267929
Maximum35.325108
Range0.15578557
Interquartile range (IQR)0.016922475

Descriptive statistics

Standard deviation0.024118165
Coefficient of variation (CV)0.00068451731
Kurtosis2.2296965
Mean35.233828
Median Absolute Deviation (MAD)0.00963967
Skewness0.64719965
Sum9019.86
Variance0.00058168589
MonotonicityNot monotonic
2023-12-11T08:14:15.737807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.2318007 4
 
1.6%
35.23965639 4
 
1.6%
35.24356241 3
 
1.2%
35.22160871 3
 
1.2%
35.22975733 3
 
1.2%
35.23125389 3
 
1.2%
35.26116473 3
 
1.2%
35.22817228 3
 
1.2%
35.2305751 2
 
0.8%
35.20323576 2
 
0.8%
Other values (209) 226
88.3%
ValueCountFrequency (%)
35.1693221 1
0.4%
35.17995757 1
0.4%
35.18862746 1
0.4%
35.18869945 1
0.4%
35.18927002 1
0.4%
35.1897253 1
0.4%
35.1900322 1
0.4%
35.19040661 1
0.4%
35.19148042 1
0.4%
35.19161245 1
0.4%
ValueCountFrequency (%)
35.32510767 1
0.4%
35.3149905 1
0.4%
35.30898626 1
0.4%
35.30823516 1
0.4%
35.30580434 1
0.4%
35.3032939 1
0.4%
35.3026071 1
0.4%
35.30188822 1
0.4%
35.30088751 1
0.4%
35.30071858 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.86262
Minimum128.71861
Maximum128.93219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T08:14:15.910993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.71861
5-th percentile128.79328
Q1128.85167
median128.87068
Q3128.89007
95-th percentile128.9125
Maximum128.93219
Range0.2135797
Interquartile range (IQR)0.038392925

Descriptive statistics

Standard deviation0.041028312
Coefficient of variation (CV)0.00031838801
Kurtosis1.0608261
Mean128.86262
Median Absolute Deviation (MAD)0.0194183
Skewness-1.1003959
Sum32988.83
Variance0.0016833224
MonotonicityNot monotonic
2023-12-11T08:14:16.038148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.88293 4
 
1.6%
128.9006385 4
 
1.6%
128.9086697 3
 
1.2%
128.8485584 3
 
1.2%
128.893586 3
 
1.2%
128.8688292 3
 
1.2%
128.872237 3
 
1.2%
128.8798806 3
 
1.2%
128.8939245 2
 
0.8%
128.8013521 2
 
0.8%
Other values (209) 226
88.3%
ValueCountFrequency (%)
128.7186067 1
0.4%
128.7301679 1
0.4%
128.7325938 1
0.4%
128.7398049 1
0.4%
128.7416298 1
0.4%
128.750406 1
0.4%
128.7547649 2
0.8%
128.7682775 1
0.4%
128.7684286 1
0.4%
128.7732338 1
0.4%
ValueCountFrequency (%)
128.9321864 1
0.4%
128.9252255 1
0.4%
128.9244034 1
0.4%
128.9239473 1
0.4%
128.9225676 1
0.4%
128.9215653 1
0.4%
128.9180856 1
0.4%
128.9167671 1
0.4%
128.9149221 1
0.4%
128.9146363 1
0.4%

Interactions

2023-12-11T08:14:10.589254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.092433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.365342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.665219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.179267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.442274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.768843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.276097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:10.518993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:14:16.116337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지면적위생업태명위도경도
소재지면적1.0000.0000.2660.156
위생업태명0.0001.0000.0000.000
위도0.2660.0001.0000.863
경도0.1560.0000.8631.000
2023-12-11T08:14:16.194954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상세영업상태명위생업태명
상세영업상태명1.0001.000
위생업태명1.0001.000
2023-12-11T08:14:16.273174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지면적위도경도상세영업상태명위생업태명
소재지면적1.000-0.163-0.1371.0000.000
위도-0.1631.0000.2911.0000.000
경도-0.1370.2911.0001.0000.000
상세영업상태명1.0001.0001.0001.0001.000
위생업태명0.0000.0000.0001.0001.000

Missing values

2023-12-11T08:14:10.878003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:14:11.023386image/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.
2023-12-11T08:14:11.186400image/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

사업장명상세영업상태명폐업일자전화번호소재지면적도로명주소지번주소위생업태명위도경도
0토스템김해지점폐업2002-10-17055-327-71790.0<NA>경상남도 김해시 삼방동 202-5건물위생관리업35.245943128.912081
1(주)콘티넨탈실업폐업2000-03-16055-332-63650.0<NA>경상남도 김해시 어방동 1113-8건물위생관리업35.229594128.903449
2우진기업폐업2002-10-17<NA>0.0<NA>경상남도 김해시 봉황동 24-3건물위생관리업35.22888128.880493
3(주)청정폐업2002-10-17055-332-66170.0<NA>경상남도 김해시 외동 1244-7건물위생관리업35.231744128.858277
4(주)수경<NA><NA>055-322-949570.01경상남도 김해시 전하로99번길 3, 2층 (흥동)경상남도 김해시 흥동 52-7 2층건물위생관리업35.221157128.858923
5신한용역폐업2002-10-17055-328-10500.0<NA>경상남도 김해시 부원동 671-8건물위생관리업35.229125128.887592
6김해청소개발폐업2002-10-17055-336-72490.0<NA>경상남도 김해시 동상동 937-4건물위생관리업35.234513128.884344
7대일용역폐업2004-10-12055-322-41770.0<NA>경상남도 김해시 부원동 619-7건물위생관리업35.228451128.887376
8청호기업(주)폐업2002-10-17055-326-04040.0<NA>경상남도 김해시 외동 1225-2건물위생관리업35.233029128.854951
9경남공영(주)폐업2002-10-17055-326-41600.0<NA>경상남도 김해시 동상동 1038-2 동상동민회관 201호건물위생관리업35.233178128.884395
사업장명상세영업상태명폐업일자전화번호소재지면적도로명주소지번주소위생업태명위도경도
246김해청소 한빛환경<NA><NA><NA>9.16경상남도 김해시 삼계중앙로 51, 명성산업개발 빌딩 8층 801호 (삼계동)경상남도 김해시 삼계동 1487-9건물위생관리업35.260931128.870839
247홈앤베이비크린<NA><NA><NA>180.32경상남도 김해시 분성로172번길 64, 2층 201호 (외동, 진풍아트빌라)경상남도 김해시 외동 1373 진풍아트빌라건물위생관리업35.227895128.87087
248금관환경산업<NA><NA><NA>38.7경상남도 김해시 금관대로1134번길 34, 2층 (외동)경상남도 김해시 외동 913-2건물위생관리업35.231302128.851911
249(주)장웅건설<NA><NA><NA>106.4경상남도 김해시 삼계중앙로 59, 701호 (삼계동)경상남도 김해시 삼계동 1487-6건물위생관리업35.261016128.871642
250스마트환경<NA><NA>055-328-040725.03경상남도 김해시 삼안로279번길 13-4 (삼방동)경상남도 김해시 삼방동 661-3건물위생관리업35.250754128.908871
251우림관리청소<NA><NA><NA>15.06경상남도 김해시 삼안로111번길 19, 116-2호 (안동)경상남도 김해시 안동 278건물위생관리업35.237737128.914922
252단디 클린<NA><NA><NA>29.4경상남도 김해시 삼안로195번길 20-14, 1층 (삼방동)경상남도 김해시 삼방동 190-3건물위생관리업35.245628128.909715
253유앤아이<NA><NA><NA>28.75경상남도 김해시 인제로188번길 6, 힐튼타워 402호 (어방동)경상남도 김해시 어방동 523 힐튼타워건물위생관리업35.245071128.904577
254광이나크린<NA><NA><NA>10.0경상남도 김해시 함박로 164, 동일아파트상가 204동 116호 (외동)경상남도 김해시 외동 1261-1 동일아파트상가건물위생관리업35.232469128.871633
255(주)시영테크<NA><NA>055-337-152268.62경상남도 김해시 흥동로171번길 34, 1층 (흥동)경상남도 김해시 흥동 5-5건물위생관리업35.223633128.870626