Overview

Dataset statistics

Number of variables8
Number of observations240
Missing cells243
Missing cells (%)12.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory65.5 B

Variable types

Text4
Categorical2
DateTime1
Numeric1

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

위생업태명 is highly imbalanced (96.1%)Imbalance
폐업일자 has 104 (43.3%) missing valuesMissing
전화번호 has 81 (33.8%) missing valuesMissing
도로명주소 has 58 (24.2%) missing valuesMissing
소재지면적 has 21 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-10 23:14:18.418649
Analysis finished2023-12-10 23:14:19.329731
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct234
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-11T08:14:19.526814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.5916667
Min length2

Characters and Unicode

Total characters1582
Distinct characters271
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

Unique228 ?
Unique (%)95.0%

Sample

1st row금강클린
2nd row씨엘라이프테크주식회사
3rd row(주)클린스쿨 경남동부지점
4th row합동기업(주)
5th rowVitamin C&S 경남
ValueCountFrequency (%)
주식회사 14
 
5.1%
경남 3
 
1.1%
패시픽 2
 
0.7%
씨엘코퍼레이션 2
 
0.7%
제로죤 2
 
0.7%
삼성크린테크 2
 
0.7%
코리아환경 2
 
0.7%
누리환경 2
 
0.7%
c&s 2
 
0.7%
청정나라 1
 
0.4%
Other values (241) 241
88.3%
2023-12-11T08:14:19.930682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 93
 
5.9%
) 74
 
4.7%
( 73
 
4.6%
경 43
 
2.7%
사 39
 
2.5%
업 36
 
2.3%
스 35
 
2.2%
33
 
2.1%
린 32
 
2.0%
청 31
 
2.0%
Other values (261) 1093
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1334
84.3%
Close Punctuation 74
 
4.7%
Open Punctuation 73
 
4.6%
Uppercase Letter 38
 
2.4%
Space Separator 33
 
2.1%
Lowercase Letter 14
 
0.9%
Decimal Number 10
 
0.6%
Other Punctuation 5
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 93
 
7.0%
경 43
 
3.2%
사 39
 
2.9%
업 36
 
2.7%
스 35
 
2.6%
린 32
 
2.4%
청 31
 
2.3%
크 29
 
2.2%
리 28
 
2.1%
환 27
 
2.0%
Other values (223) 941
70.5%
Uppercase Letter
ValueCountFrequency (%)
S 8
21.1%
C 6
15.8%
N 3
 
7.9%
G 3
 
7.9%
H 2
 
5.3%
K 2
 
5.3%
M 2
 
5.3%
E 2
 
5.3%
J 1
 
2.6%
B 1
 
2.6%
Other values (8) 8
21.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
28.6%
i 2
14.3%
n 2
14.3%
c 1
 
7.1%
r 1
 
7.1%
m 1
 
7.1%
a 1
 
7.1%
t 1
 
7.1%
s 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 (%)
) 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1334
84.3%
Common 196
 
12.4%
Latin 52
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 93
 
7.0%
경 43
 
3.2%
사 39
 
2.9%
업 36
 
2.7%
스 35
 
2.6%
린 32
 
2.4%
청 31
 
2.3%
크 29
 
2.2%
리 28
 
2.1%
환 27
 
2.0%
Other values (223) 941
70.5%
Latin
ValueCountFrequency (%)
S 8
15.4%
C 6
 
11.5%
e 4
 
7.7%
N 3
 
5.8%
G 3
 
5.8%
H 2
 
3.8%
K 2
 
3.8%
M 2
 
3.8%
E 2
 
3.8%
i 2
 
3.8%
Other values (17) 18
34.6%
Common
ValueCountFrequency (%)
) 74
37.8%
( 73
37.2%
33
16.8%
1 4
 
2.0%
& 3
 
1.5%
. 2
 
1.0%
3 2
 
1.0%
9 2
 
1.0%
- 1
 
0.5%
5 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1334
84.3%
ASCII 248
 
15.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 93
 
7.0%
경 43
 
3.2%
사 39
 
2.9%
업 36
 
2.7%
스 35
 
2.6%
린 32
 
2.4%
청 31
 
2.3%
크 29
 
2.2%
리 28
 
2.1%
환 27
 
2.0%
Other values (223) 941
70.5%
ASCII
ValueCountFrequency (%)
) 74
29.8%
( 73
29.4%
33
13.3%
S 8
 
3.2%
C 6
 
2.4%
e 4
 
1.6%
1 4
 
1.6%
& 3
 
1.2%
N 3
 
1.2%
G 3
 
1.2%
Other values (28) 37
14.9%

상세영업상태명
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
폐업
136 
영업
104 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 136
56.7%
영업 104
43.3%

Length

2023-12-11T08:14:20.090973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:14:20.177488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 136
56.7%
영업 104
43.3%

폐업일자
Date

MISSING 

Distinct113
Distinct (%)83.1%
Missing104
Missing (%)43.3%
Memory size2.0 KiB
Minimum2000-03-16 00:00:00
Maximum2021-06-22 00:00:00
2023-12-11T08:14:20.301955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:14:20.446885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct152
Distinct (%)95.6%
Missing81
Missing (%)33.8%
Memory size2.0 KiB
2023-12-11T08:14:20.697407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.981132
Min length9

Characters and Unicode

Total characters1905
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

Unique147 ?
Unique (%)92.5%

Sample

1st row055-332-3380
2nd row055-331-7366
3rd row055-337-2275
4th row055-313-6006
5th row055-311-6450
ValueCountFrequency (%)
055-326-9955 4
 
2.5%
055-313-2570 2
 
1.3%
050-5533-7799 2
 
1.3%
055-335-6119 2
 
1.3%
055-323-4500 2
 
1.3%
055-329-6632 1
 
0.6%
055-329-0389 1
 
0.6%
055-329-3733 1
 
0.6%
055-337-8866 1
 
0.6%
055-311-5016 1
 
0.6%
Other values (142) 142
89.3%
2023-12-11T08:14:21.068748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 379
19.9%
- 314
16.5%
3 285
15.0%
0 261
13.7%
2 152
8.0%
1 133
 
7.0%
9 82
 
4.3%
7 81
 
4.3%
6 79
 
4.1%
4 71
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1591
83.5%
Dash Punctuation 314
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 379
23.8%
3 285
17.9%
0 261
16.4%
2 152
9.6%
1 133
 
8.4%
9 82
 
5.2%
7 81
 
5.1%
6 79
 
5.0%
4 71
 
4.5%
8 68
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 379
19.9%
- 314
16.5%
3 285
15.0%
0 261
13.7%
2 152
8.0%
1 133
 
7.0%
9 82
 
4.3%
7 81
 
4.3%
6 79
 
4.1%
4 71
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 379
19.9%
- 314
16.5%
3 285
15.0%
0 261
13.7%
2 152
8.0%
1 133
 
7.0%
9 82
 
4.3%
7 81
 
4.3%
6 79
 
4.1%
4 71
 
3.7%

소재지면적
Real number (ℝ)

ZEROS 

Distinct196
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.560292
Minimum0
Maximum254.1
Zeros21
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T08:14:21.244177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.5625
median34.92
Q357.4525
95-th percentile130.3285
Maximum254.1
Range254.1
Interquartile range (IQR)35.89

Descriptive statistics

Standard deviation39.917258
Coefficient of variation (CV)0.8761414
Kurtosis4.488244
Mean45.560292
Median Absolute Deviation (MAD)16.72
Skewness1.82753
Sum10934.47
Variance1593.3875
MonotonicityNot monotonic
2023-12-11T08:14:21.396593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
8.8%
33.0 4
 
1.7%
20.0 3
 
1.2%
37.83 3
 
1.2%
36.0 3
 
1.2%
40.0 3
 
1.2%
21.6 3
 
1.2%
26.4 2
 
0.8%
23.0 2
 
0.8%
34.2 2
 
0.8%
Other values (186) 194
80.8%
ValueCountFrequency (%)
0.0 21
8.8%
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%
10.0 2
 
0.8%
11.18 1
 
0.4%
ValueCountFrequency (%)
254.1 1
0.4%
197.0 1
0.4%
191.6 1
0.4%
183.0 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%
132.72 1
0.4%

지번주소
Text

Distinct178
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-11T08:14:21.700614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length36
Mean length23.883333
Min length15

Characters and Unicode

Total characters5732
Distinct characters156
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)60.0%

Sample

1st row경상남도 김해시 진영읍 진영리 ****-*번지
2nd row경상남도 김해시 삼방동 ***-*번지
3rd row경상남도 김해시 부곡동 ****-* ***호
4th row경상남도 김해시 삼정동 ***
5th row경상남도 김해시 진영읍 진영리 ****
ValueCountFrequency (%)
경상남도 240
20.8%
김해시 240
20.8%
번지 201
17.4%
호 48
 
4.2%
층 39
 
3.4%
38
 
3.3%
외동 25
 
2.2%
내동 21
 
1.8%
어방동 17
 
1.5%
대청동 16
 
1.4%
Other values (118) 271
23.4%
2023-12-11T08:14:22.173944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1263
22.0%
916
16.0%
상 283
 
4.9%
동 260
 
4.5%
시 244
 
4.3%
김 243
 
4.2%
해 243
 
4.2%
도 242
 
4.2%
남 241
 
4.2%
경 240
 
4.2%
Other values (146) 1557
27.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3307
57.7%
Other Punctuation 1271
 
22.2%
Space Separator 916
 
16.0%
Dash Punctuation 224
 
3.9%
Uppercase Letter 14
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 283
 
8.6%
동 260
 
7.9%
시 244
 
7.4%
김 243
 
7.3%
해 243
 
7.3%
도 242
 
7.3%
남 241
 
7.3%
경 240
 
7.3%
지 216
 
6.5%
번 202
 
6.1%
Other values (131) 893
27.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
21.4%
B 3
21.4%
N 1
 
7.1%
P 1
 
7.1%
H 1
 
7.1%
O 1
 
7.1%
E 1
 
7.1%
I 1
 
7.1%
X 1
 
7.1%
L 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
* 1263
99.4%
, 7
 
0.6%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
916
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3307
57.7%
Common 2411
42.1%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 283
 
8.6%
동 260
 
7.9%
시 244
 
7.4%
김 243
 
7.3%
해 243
 
7.3%
도 242
 
7.3%
남 241
 
7.3%
경 240
 
7.3%
지 216
 
6.5%
번 202
 
6.1%
Other values (131) 893
27.0%
Latin
ValueCountFrequency (%)
A 3
21.4%
B 3
21.4%
N 1
 
7.1%
P 1
 
7.1%
H 1
 
7.1%
O 1
 
7.1%
E 1
 
7.1%
I 1
 
7.1%
X 1
 
7.1%
L 1
 
7.1%
Common
ValueCountFrequency (%)
* 1263
52.4%
916
38.0%
- 224
 
9.3%
, 7
 
0.3%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3307
57.7%
ASCII 2425
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1263
52.1%
916
37.8%
- 224
 
9.2%
, 7
 
0.3%
A 3
 
0.1%
B 3
 
0.1%
N 1
 
< 0.1%
P 1
 
< 0.1%
H 1
 
< 0.1%
O 1
 
< 0.1%
Other values (5) 5
 
0.2%
Hangul
ValueCountFrequency (%)
상 283
 
8.6%
동 260
 
7.9%
시 244
 
7.4%
김 243
 
7.3%
해 243
 
7.3%
도 242
 
7.3%
남 241
 
7.3%
경 240
 
7.3%
지 216
 
6.5%
번 202
 
6.1%
Other values (131) 893
27.0%

도로명주소
Text

MISSING 

Distinct170
Distinct (%)93.4%
Missing58
Missing (%)24.2%
Memory size2.0 KiB
2023-12-11T08:14:22.376207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length40.5
Mean length30.983516
Min length19

Characters and Unicode

Total characters5639
Distinct characters158
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

Unique160 ?
Unique (%)87.9%

Sample

1st row경상남도 김해시 진영읍 장등로**번길 **
2nd row경상남도 김해시 활천로***번길 *, ****호 (삼방동)
3rd row경상남도 김해시 능동로***번길 **-*, *층 ***일부호 (부곡동)
4th row경상남도 김해시 김해대로****번길 **, *층 (삼정동)
5th row경상남도 김해시 진영읍 김해대로 ***-*, ***동 ***호
ValueCountFrequency (%)
183
16.2%
경상남도 182
16.1%
김해시 182
16.1%
층 92
 
8.1%
호 62
 
5.5%
외동 18
 
1.6%
내동 17
 
1.5%
삼방동 14
 
1.2%
대청동 14
 
1.2%
김해대로****번길 12
 
1.1%
Other values (158) 355
31.4%
2023-12-11T08:14:22.731362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1087
19.3%
949
16.8%
상 213
 
3.8%
해 210
 
3.7%
김 207
 
3.7%
동 202
 
3.6%
시 185
 
3.3%
도 184
 
3.3%
남 183
 
3.2%
경 182
 
3.2%
Other values (148) 2037
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3033
53.8%
Other Punctuation 1270
22.5%
Space Separator 949
 
16.8%
Close Punctuation 164
 
2.9%
Open Punctuation 164
 
2.9%
Dash Punctuation 48
 
0.9%
Uppercase Letter 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 213
 
7.0%
해 210
 
6.9%
김 207
 
6.8%
동 202
 
6.7%
시 185
 
6.1%
도 184
 
6.1%
남 183
 
6.0%
경 182
 
6.0%
로 182
 
6.0%
번 110
 
3.6%
Other values (132) 1175
38.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
O 1
9.1%
X 1
9.1%
I 1
9.1%
P 1
9.1%
H 1
9.1%
E 1
9.1%
N 1
9.1%
Other Punctuation
ValueCountFrequency (%)
* 1087
85.6%
, 182
 
14.3%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
949
100.0%
Close Punctuation
ValueCountFrequency (%)
) 164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3033
53.8%
Common 2595
46.0%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 213
 
7.0%
해 210
 
6.9%
김 207
 
6.8%
동 202
 
6.7%
시 185
 
6.1%
도 184
 
6.1%
남 183
 
6.0%
경 182
 
6.0%
로 182
 
6.0%
번 110
 
3.6%
Other values (132) 1175
38.7%
Latin
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
O 1
9.1%
X 1
9.1%
I 1
9.1%
P 1
9.1%
H 1
9.1%
E 1
9.1%
N 1
9.1%
Common
ValueCountFrequency (%)
* 1087
41.9%
949
36.6%
, 182
 
7.0%
) 164
 
6.3%
( 164
 
6.3%
- 48
 
1.8%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3033
53.8%
ASCII 2606
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1087
41.7%
949
36.4%
, 182
 
7.0%
) 164
 
6.3%
( 164
 
6.3%
- 48
 
1.8%
B 2
 
0.1%
A 2
 
0.1%
/ 1
 
< 0.1%
O 1
 
< 0.1%
Other values (6) 6
 
0.2%
Hangul
ValueCountFrequency (%)
상 213
 
7.0%
해 210
 
6.9%
김 207
 
6.8%
동 202
 
6.7%
시 185
 
6.1%
도 184
 
6.1%
남 183
 
6.0%
경 182
 
6.0%
로 182
 
6.0%
번 110
 
3.6%
Other values (132) 1175
38.7%

위생업태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
건물위생관리업
239 
건물위생관리업 기타
 
1

Length

Max length10
Median length7
Mean length7.0125
Min length7

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 239
99.6%
건물위생관리업 기타 1
 
0.4%

Length

2023-12-11T08:14:22.857808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:14:22.947878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 240
99.6%
기타 1
 
0.4%

Interactions

2023-12-11T08:14:18.832510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:14:23.001428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상세영업상태명소재지면적위생업태명
상세영업상태명1.0000.0000.000
소재지면적0.0001.0000.000
위생업태명0.0000.0001.000
2023-12-11T08:14:23.078347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상세영업상태명위생업태명
상세영업상태명1.0000.000
위생업태명0.0001.000
2023-12-11T08:14:23.152186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지면적상세영업상태명위생업태명
소재지면적1.0000.0000.000
상세영업상태명0.0001.0000.000
위생업태명0.0000.0001.000

Missing values

2023-12-11T08:14:18.978014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:14:19.164378image/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.
2023-12-11T08:14:19.274545image/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

사업장명상세영업상태명폐업일자전화번호소재지면적지번주소도로명주소위생업태명
0금강클린폐업2019-11-12<NA>26.4경상남도 김해시 진영읍 진영리 ****-*번지경상남도 김해시 진영읍 장등로**번길 **건물위생관리업
1씨엘라이프테크주식회사폐업2019-05-01<NA>37.83경상남도 김해시 삼방동 ***-*번지경상남도 김해시 활천로***번길 *, ****호 (삼방동)건물위생관리업
2(주)클린스쿨 경남동부지점폐업2020-10-15<NA>20.0경상남도 김해시 부곡동 ****-* ***호경상남도 김해시 능동로***번길 **-*, *층 ***일부호 (부곡동)건물위생관리업
3합동기업(주)폐업2021-01-25<NA>157.0경상남도 김해시 삼정동 ***경상남도 김해시 김해대로****번길 **, *층 (삼정동)건물위생관리업
4Vitamin C&S 경남폐업2020-08-11<NA>36.0경상남도 김해시 진영읍 진영리 ****경상남도 김해시 진영읍 김해대로 ***-*, ***동 ***호건물위생관리업
5에어인터네셔널폐업2021-01-12<NA>67.3경상남도 김해시 삼방동 ***-*경상남도 김해시 활천로***번길 *, 지하*층 (삼방동)건물위생관리업
6우진ENG폐업2012-11-27<NA>40.0경상남도 김해시 부원동 ***-*번지 영남빌딩*층경상남도 김해시 김해대로****번길 * (부원동, 영남빌딩*층)건물위생관리업
7(주)에이스레일폐업2012-12-31055-332-3380191.6경상남도 김해시 부원동 ***-**번지 애국빌딩*층경상남도 김해시 김해대로 **** (부원동, 애국빌딩*층)건물위생관리업
8가림기술(주)폐업2011-08-19055-331-7366134.29경상남도 김해시 불암동 ***-**번지 *층<NA>건물위생관리업
9김해청소전문업체폐업2011-09-05055-337-227528.98경상남도 김해시 동상동 ***-**번지<NA>건물위생관리업
사업장명상세영업상태명폐업일자전화번호소재지면적지번주소도로명주소위생업태명
230(주)레오팔레스영업<NA>055-321-2216183.0경상남도 김해시 대성동 ***번지경상남도 김해시 구지로***번길 **, ***,***호 (대성동)건물위생관리업
231코리아환경영업<NA><NA>74.48경상남도 김해시 봉황동 **-**번지경상남도 김해시 가락로**번길 **, *층 (봉황동)건물위생관리업
232웅진이디티(주)영업<NA>055-336-118834.2경상남도 김해시 풍유동 **-**번지경상남도 김해시 전하로*번길 * (풍유동)건물위생관리업
233유한회사 김해늘푸른사람들영업<NA>055-329-637030.0경상남도 김해시 외동 **번지 김해지역자활센터 ***호경상남도 김해시 분성로***번길 **, *층 ***호 (외동, 김해지역자활센터)건물위생관리업
234주식회사 은광 비엠씨영업<NA>055-333-5111104.09경상남도 김해시 풍유동 ***-*번지 *층경상남도 김해시 칠산로***번길 ** (풍유동, *층)건물위생관리업
235제로죤영업<NA>055-335-611940.46경상남도 김해시 동상동 ***-**번지경상남도 김해시 분성로***번길 *-** (동상동)건물위생관리업
236부은환경영업<NA><NA>19.45경상남도 김해시 안동 ***-*번지경상남도 김해시 삼안로**번길 **, *층 (안동)건물위생관리업
237한라종합건설(주)영업<NA>055-312-6133143.77경상남도 김해시 대청동 **-*번지 비전타워 ***호경상남도 김해시 대청로***번길 **, ***호 (대청동, 비전타워)건물위생관리업
238나성개발영업<NA><NA>42.23경상남도 김해시 대청동 ***-*번지경상남도 김해시 대청로***번길 **-**, *층 (대청동)건물위생관리업
239중앙산업보건센터(주)영업<NA>055-338-284887.25경상남도 김해시 외동 ***-*번지 김해한솔빌리지 상가동 ***호경상남도 김해시 분성로 **-**, ***호 (외동, 김해한솔빌리지)건물위생관리업