Overview

Dataset statistics

Number of variables8
Number of observations1331
Missing cells21
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.9 KiB
Average record size in memory66.1 B

Variable types

Text3
Categorical2
Numeric2
DateTime1

Dataset

Description가뭄 분석 정보 제공을 위한 공단코드정보, 공단명정보, 관련 업체수 등 전국 공업단지에 대한 시설 제원정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049841/fileData.do

Alerts

업체수 has 21 (1.6%) missing valuesMissing
공업단지코드 has unique valuesUnique
업체수 has 302 (22.7%) zerosZeros

Reproduction

Analysis started2023-12-12 11:26:51.100675
Analysis finished2023-12-12 11:26:52.523071
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공업단지코드
Text

UNIQUE 

Distinct1331
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2023-12-12T20:26:52.908141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters7986
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1331 ?
Unique (%)100.0%

Sample

1st row245070
2nd row245180
3rd row245260
4th row145060
5th row448330
ValueCountFrequency (%)
245070 1
 
0.1%
248b10 1
 
0.1%
241bn0 1
 
0.1%
447500 1
 
0.1%
444830 1
 
0.1%
436050 1
 
0.1%
444490 1
 
0.1%
243050 1
 
0.1%
243060 1
 
0.1%
443290 1
 
0.1%
Other values (1321) 1321
99.2%
2023-12-12T20:26:53.574313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2025
25.4%
0 1692
21.2%
2 1255
15.7%
1 722
 
9.0%
3 509
 
6.4%
8 385
 
4.8%
6 376
 
4.7%
7 353
 
4.4%
5 310
 
3.9%
9 153
 
1.9%
Other values (26) 206
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7780
97.4%
Uppercase Letter 206
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 71
34.5%
B 46
22.3%
C 21
 
10.2%
D 5
 
2.4%
H 4
 
1.9%
G 4
 
1.9%
J 4
 
1.9%
T 3
 
1.5%
N 3
 
1.5%
W 3
 
1.5%
Other values (16) 42
20.4%
Decimal Number
ValueCountFrequency (%)
4 2025
26.0%
0 1692
21.7%
2 1255
16.1%
1 722
 
9.3%
3 509
 
6.5%
8 385
 
4.9%
6 376
 
4.8%
7 353
 
4.5%
5 310
 
4.0%
9 153
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7780
97.4%
Latin 206
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 71
34.5%
B 46
22.3%
C 21
 
10.2%
D 5
 
2.4%
H 4
 
1.9%
G 4
 
1.9%
J 4
 
1.9%
T 3
 
1.5%
N 3
 
1.5%
W 3
 
1.5%
Other values (16) 42
20.4%
Common
ValueCountFrequency (%)
4 2025
26.0%
0 1692
21.7%
2 1255
16.1%
1 722
 
9.3%
3 509
 
6.5%
8 385
 
4.9%
6 376
 
4.8%
7 353
 
4.5%
5 310
 
4.0%
9 153
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2025
25.4%
0 1692
21.2%
2 1255
15.7%
1 722
 
9.0%
3 509
 
6.4%
8 385
 
4.8%
6 376
 
4.7%
7 353
 
4.4%
5 310
 
3.9%
9 153
 
1.9%
Other values (26) 206
 
2.6%

공업단지명
Text

Distinct1307
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2023-12-12T20:26:53.932292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length9.3200601
Min length6

Characters and Unicode

Total characters12405
Distinct characters413
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1285 ?
Unique (%)96.5%

Sample

1st row전주제2일반산업단지
2nd row전주친환경첨단복합산업단지(1단계)
3rd row전주친환경첨단복합산업단지(3-1단계)
4th row전주탄소소재국가산업단지
5th row정곡농공단지
ValueCountFrequency (%)
가산일반산업단지 3
 
0.2%
검단일반산업단지 3
 
0.2%
석계2일반산업단지 2
 
0.1%
시화지구(시화mtv 2
 
0.1%
park 2
 
0.1%
안산시구역 2
 
0.1%
시화지구(1단계 2
 
0.1%
장안일반산업단지 2
 
0.1%
강동일반산업단지 2
 
0.1%
장수농공단지 2
 
0.1%
Other values (1316) 1332
98.4%
2023-12-12T20:26:54.611109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1341
 
10.8%
단 1312
 
10.6%
산 941
 
7.6%
업 793
 
6.4%
일 592
 
4.8%
반 589
 
4.7%
공 490
 
4.0%
농 466
 
3.8%
구 238
 
1.9%
) 147
 
1.2%
Other values (403) 5496
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11385
91.8%
Decimal Number 257
 
2.1%
Close Punctuation 249
 
2.0%
Open Punctuation 249
 
2.0%
Other Punctuation 101
 
0.8%
Uppercase Letter 86
 
0.7%
Lowercase Letter 48
 
0.4%
Space Separator 23
 
0.2%
Dash Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 1341
 
11.8%
단 1312
 
11.5%
산 941
 
8.3%
업 793
 
7.0%
일 592
 
5.2%
반 589
 
5.2%
공 490
 
4.3%
농 466
 
4.1%
구 238
 
2.1%
제 141
 
1.2%
Other values (346) 4482
39.4%
Uppercase Letter
ValueCountFrequency (%)
I 10
 
11.6%
G 8
 
9.3%
T 8
 
9.3%
C 6
 
7.0%
B 5
 
5.8%
K 4
 
4.7%
M 4
 
4.7%
S 4
 
4.7%
X 4
 
4.7%
D 4
 
4.7%
Other values (13) 29
33.7%
Lowercase Letter
ValueCountFrequency (%)
o 8
16.7%
l 5
10.4%
e 5
10.4%
a 4
 
8.3%
i 3
 
6.2%
t 3
 
6.2%
k 3
 
6.2%
d 3
 
6.2%
c 2
 
4.2%
n 2
 
4.2%
Other values (7) 10
20.8%
Decimal Number
ValueCountFrequency (%)
2 136
52.9%
1 50
 
19.5%
3 42
 
16.3%
4 19
 
7.4%
5 8
 
3.1%
7 1
 
0.4%
6 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
: 81
80.2%
· 17
 
16.8%
. 2
 
2.0%
& 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 147
59.0%
] 102
41.0%
Open Punctuation
ValueCountFrequency (%)
( 146
58.6%
[ 103
41.4%
Space Separator
ValueCountFrequency (%)
23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11385
91.8%
Common 886
 
7.1%
Latin 134
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 1341
 
11.8%
단 1312
 
11.5%
산 941
 
8.3%
업 793
 
7.0%
일 592
 
5.2%
반 589
 
5.2%
공 490
 
4.3%
농 466
 
4.1%
구 238
 
2.1%
제 141
 
1.2%
Other values (346) 4482
39.4%
Latin
ValueCountFrequency (%)
I 10
 
7.5%
G 8
 
6.0%
o 8
 
6.0%
T 8
 
6.0%
C 6
 
4.5%
l 5
 
3.7%
B 5
 
3.7%
e 5
 
3.7%
K 4
 
3.0%
M 4
 
3.0%
Other values (30) 71
53.0%
Common
ValueCountFrequency (%)
) 147
16.6%
( 146
16.5%
2 136
15.3%
[ 103
11.6%
] 102
11.5%
: 81
9.1%
1 50
 
5.6%
3 42
 
4.7%
23
 
2.6%
4 19
 
2.1%
Other values (7) 37
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11385
91.8%
ASCII 1003
 
8.1%
None 17
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1341
 
11.8%
단 1312
 
11.5%
산 941
 
8.3%
업 793
 
7.0%
일 592
 
5.2%
반 589
 
5.2%
공 490
 
4.3%
농 466
 
4.1%
구 238
 
2.1%
제 141
 
1.2%
Other values (346) 4482
39.4%
ASCII
ValueCountFrequency (%)
) 147
14.7%
( 146
14.6%
2 136
13.6%
[ 103
10.3%
] 102
10.2%
: 81
8.1%
1 50
 
5.0%
3 42
 
4.2%
23
 
2.3%
4 19
 
1.9%
Other values (46) 154
15.4%
None
ValueCountFrequency (%)
· 17
100.0%

공업단지구분
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
일반산업단지
737 
농공단지
474 
국가산업단지
88 
도시첨단산업단지
 
32

Length

Max length8
Median length6
Mean length5.3358377
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반산업단지
2nd row일반산업단지
3rd row일반산업단지
4th row국가산업단지
5th row농공단지

Common Values

ValueCountFrequency (%)
일반산업단지 737
55.4%
농공단지 474
35.6%
국가산업단지 88
 
6.6%
도시첨단산업단지 32
 
2.4%

Length

2023-12-12T20:26:54.935758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:26:55.161816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반산업단지 737
55.4%
농공단지 474
35.6%
국가산업단지 88
 
6.6%
도시첨단산업단지 32
 
2.4%

공업단지주소
Text

Distinct1274
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2023-12-12T20:26:55.643735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length59
Mean length23.541698
Min length10

Characters and Unicode

Total characters31334
Distinct characters348
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1226 ?
Unique (%)92.1%

Sample

1st row전라북도 전주시 덕진구 팔복동 3가 일원
2nd row전라북도 전주시 덕진구 팔복동 2가 일원
3rd row전라북도 전주시 덕진구 동산동, 팔복동4가 일원
4th row전라북도 전주시 덕진구 동산동, 고랑동, 팔복동 일원
5th row경상남도 의령군 정곡면 중교리 일원
ValueCountFrequency (%)
일원 1277
 
16.8%
경상남도 206
 
2.7%
경기도 202
 
2.7%
충청남도 174
 
2.3%
경상북도 159
 
2.1%
충청북도 135
 
1.8%
전라남도 110
 
1.4%
전라북도 92
 
1.2%
강원도 76
 
1.0%
부산광역시 45
 
0.6%
Other values (2518) 5127
67.4%
2023-12-12T20:26:56.501906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6294
 
20.1%
원 1524
 
4.9%
일 1346
 
4.3%
리 1275
 
4.1%
도 1229
 
3.9%
시 966
 
3.1%
면 775
 
2.5%
동 679
 
2.2%
산 662
 
2.1%
남 633
 
2.0%
Other values (338) 15951
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22394
71.5%
Space Separator 6294
 
20.1%
Decimal Number 1829
 
5.8%
Other Punctuation 541
 
1.7%
Dash Punctuation 204
 
0.7%
Open Punctuation 28
 
0.1%
Close Punctuation 27
 
0.1%
Uppercase Letter 8
 
< 0.1%
Math Symbol 5
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 1524
 
6.8%
일 1346
 
6.0%
리 1275
 
5.7%
도 1229
 
5.5%
시 966
 
4.3%
면 775
 
3.5%
동 679
 
3.0%
산 662
 
3.0%
남 633
 
2.8%
경 633
 
2.8%
Other values (311) 12672
56.6%
Decimal Number
ValueCountFrequency (%)
1 406
22.2%
2 218
11.9%
3 177
9.7%
5 170
9.3%
0 163
8.9%
7 161
 
8.8%
4 155
 
8.5%
6 138
 
7.5%
9 123
 
6.7%
8 118
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 511
94.5%
· 28
 
5.2%
. 2
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
I 4
50.0%
A 2
25.0%
B 2
25.0%
Math Symbol
ValueCountFrequency (%)
~ 3
60.0%
< 1
 
20.0%
> 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 27
96.4%
[ 1
 
3.6%
Close Punctuation
ValueCountFrequency (%)
) 26
96.3%
] 1
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
k 2
50.0%
m 2
50.0%
Space Separator
ValueCountFrequency (%)
6294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22394
71.5%
Common 8928
 
28.5%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 1524
 
6.8%
일 1346
 
6.0%
리 1275
 
5.7%
도 1229
 
5.5%
시 966
 
4.3%
면 775
 
3.5%
동 679
 
3.0%
산 662
 
3.0%
남 633
 
2.8%
경 633
 
2.8%
Other values (311) 12672
56.6%
Common
ValueCountFrequency (%)
6294
70.5%
, 511
 
5.7%
1 406
 
4.5%
2 218
 
2.4%
- 204
 
2.3%
3 177
 
2.0%
5 170
 
1.9%
0 163
 
1.8%
7 161
 
1.8%
4 155
 
1.7%
Other values (12) 469
 
5.3%
Latin
ValueCountFrequency (%)
I 4
33.3%
A 2
16.7%
k 2
16.7%
m 2
16.7%
B 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22394
71.5%
ASCII 8912
 
28.4%
None 28
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6294
70.6%
, 511
 
5.7%
1 406
 
4.6%
2 218
 
2.4%
- 204
 
2.3%
3 177
 
2.0%
5 170
 
1.9%
0 163
 
1.8%
7 161
 
1.8%
4 155
 
1.7%
Other values (16) 453
 
5.1%
Hangul
ValueCountFrequency (%)
원 1524
 
6.8%
일 1346
 
6.0%
리 1275
 
5.7%
도 1229
 
5.5%
시 966
 
4.3%
면 775
 
3.5%
동 679
 
3.0%
산 662
 
3.0%
남 633
 
2.8%
경 633
 
2.8%
Other values (311) 12672
56.6%
None
ValueCountFrequency (%)
· 28
100.0%

행정동코드
Real number (ℝ)

Distinct713
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3060251 × 109
Minimum1.1500603 × 109
Maximum5.01306 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-12T20:26:56.788914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1500603 × 109
5-th percentile2.771032 × 109
Q14.159036 × 109
median4.421034 × 109
Q34.713033 × 109
95-th percentile4.8730355 × 109
Maximum5.01306 × 109
Range3.8629997 × 109
Interquartile range (IQR)5.53997 × 108

Descriptive statistics

Standard deviation6.0654198 × 108
Coefficient of variation (CV)0.14085891
Kurtosis3.8508175
Mean4.3060251 × 109
Median Absolute Deviation (MAD)2.66001 × 108
Skewness-1.9295051
Sum5.7313194 × 1012
Variance3.6789318 × 1017
MonotonicityNot monotonic
2023-12-12T20:26:57.082360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2644056000 20
 
1.5%
4713025900 13
 
1.0%
4157025600 10
 
0.8%
2671025300 10
 
0.8%
4213025000 9
 
0.7%
4377025300 9
 
0.7%
4825025000 7
 
0.5%
4825032000 7
 
0.5%
4421025000 7
 
0.5%
4311425300 7
 
0.5%
Other values (703) 1232
92.6%
ValueCountFrequency (%)
1150060300 1
 
0.1%
1153052000 2
0.2%
1153079000 1
 
0.1%
1174052000 1
 
0.1%
2632053000 1
 
0.1%
2635052000 1
 
0.1%
2635061000 1
 
0.1%
2638057200 2
0.2%
2638060100 2
0.2%
2644053500 3
0.2%
ValueCountFrequency (%)
5013060000 1
0.1%
5013025000 1
0.1%
5011063000 1
0.1%
5011025600 2
0.2%
5011025000 1
0.1%
4889040000 1
0.1%
4889035000 1
0.1%
4889034000 1
0.1%
4888040000 2
0.2%
4888037000 2
0.2%

지정일자
Date

Distinct1050
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
Minimum1964-04-15 00:00:00
Maximum2020-12-30 00:00:00
2023-12-12T20:26:57.311424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:57.542498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업체수
Real number (ℝ)

MISSING  ZEROS 

Distinct186
Distinct (%)14.2%
Missing21
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean77.154198
Minimum0
Maximum10685
Zeros302
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-12T20:26:57.810236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q327
95-th percentile208.55
Maximum10685
Range10685
Interquartile range (IQR)26

Descriptive statistics

Standard deviation470.37459
Coefficient of variation (CV)6.0965521
Kurtosis270.30778
Mean77.154198
Median Absolute Deviation (MAD)8
Skewness14.739397
Sum101072
Variance221252.26
MonotonicityNot monotonic
2023-12-12T20:26:58.061730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 302
22.7%
1 100
 
7.5%
6 47
 
3.5%
2 44
 
3.3%
3 42
 
3.2%
7 34
 
2.6%
4 33
 
2.5%
5 30
 
2.3%
9 29
 
2.2%
8 29
 
2.2%
Other values (176) 620
46.6%
ValueCountFrequency (%)
0 302
22.7%
1 100
 
7.5%
2 44
 
3.3%
3 42
 
3.2%
4 33
 
2.5%
5 30
 
2.3%
6 47
 
3.5%
7 34
 
2.6%
8 29
 
2.2%
9 29
 
2.2%
ValueCountFrequency (%)
10685 1
 
0.1%
6986 1
 
0.1%
6816 1
 
0.1%
3075 1
 
0.1%
2721 1
 
0.1%
2720 3
0.2%
2498 1
 
0.1%
2496 1
 
0.1%
2284 1
 
0.1%
2212 1
 
0.1%

분양상태
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
완료
909 
분양중
290 
분양계획
132 

Length

Max length4
Median length2
Mean length2.4162284
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row완료
2nd row완료
3rd row완료
4th row분양계획
5th row완료

Common Values

ValueCountFrequency (%)
완료 909
68.3%
분양중 290
 
21.8%
분양계획 132
 
9.9%

Length

2023-12-12T20:26:58.287402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:26:58.457272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완료 909
68.3%
분양중 290
 
21.8%
분양계획 132
 
9.9%

Interactions

2023-12-12T20:26:52.027706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:51.767648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:52.156649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:51.910982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:26:58.573039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공업단지구분행정동코드업체수분양상태
공업단지구분1.0000.5130.2270.222
행정동코드0.5131.0000.4540.000
업체수0.2270.4541.0000.000
분양상태0.2220.0000.0001.000
2023-12-12T20:26:59.158837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공업단지구분분양상태
공업단지구분1.0000.211
분양상태0.2111.000
2023-12-12T20:26:59.269567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드업체수공업단지구분분양상태
행정동코드1.000-0.1110.2470.000
업체수-0.1111.0000.1870.000
공업단지구분0.2470.1871.0000.211
분양상태0.0000.0000.2111.000

Missing values

2023-12-12T20:26:52.302417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:26:52.457656image/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.

Sample

공업단지코드공업단지명공업단지구분공업단지주소행정동코드지정일자업체수분양상태
0245070전주제2일반산업단지일반산업단지전라북도 전주시 덕진구 팔복동 3가 일원45113600001984-03-14 00:00:0027완료
1245180전주친환경첨단복합산업단지(1단계)일반산업단지전라북도 전주시 덕진구 팔복동 2가 일원45113600002008-01-31 00:00:0047완료
2245260전주친환경첨단복합산업단지(3-1단계)일반산업단지전라북도 전주시 덕진구 동산동, 팔복동4가 일원45113665002011-12-01 00:00:002완료
3145060전주탄소소재국가산업단지국가산업단지전라북도 전주시 덕진구 동산동, 고랑동, 팔복동 일원45113665002019-09-05 00:00:000분양계획
4448330정곡농공단지농공단지경상남도 의령군 정곡면 중교리 일원48720360001989-07-08 00:00:003완료
5248870정곡백곡일반산업단지일반산업단지경상남도 의령군 정곡면 백곡리 일원48720360002013-04-04 00:00:000완료
6426010정관농공단지농공단지부산광역시 기장군 정관면 달산리, 예림리, 방곡리 일원26710256001987-02-25 00:00:0026완료
7226060정관일반산업단지일반산업단지부산광역시 기장군 정관면 달산리 일원26710256002001-10-25 00:00:00204완료
8226190정관코리일반산업단지일반산업단지부산광역시 기장군 정관면 달산리 일원26710256002010-10-20 00:00:003완료
9241B80정남일반산업단지일반산업단지경기도 화성시 정남면 음양리 632번지 일원41590410002015-12-15 00:00:0058완료
공업단지코드공업단지명공업단지구분공업단지주소행정동코드지정일자업체수분양상태
1321241520금곡일반산업단지일반산업단지경기도 남양주시 진접읍 금곡리 산140-5일원41360253002002-09-16 00:00:0024완료
1322449030금능농공단지농공단지제주특별자치도 제주시 한림읍 금능리 일원50110250001992-11-11 00:00:0017완료
1323241140금산일반산업단지일반산업단지경기도 안성시 일죽면 금산리 일원41550390001993-10-09 00:00:002완료
1324244010금산일반산업단지일반산업단지충청남도 금산군 제원면 수당리, 명암리 일원44710320001992-07-29 00:00:001완료
1325448280금서농공단지농공단지경상남도 산청군 금서면 매촌리 1289번지 일원48860340001992-04-28 00:00:0011완료
1326448680금서제2농공단지농공단지경상남도 산청군 금서면 매촌리 1321번지 일원48860340002008-04-08 00:00:005완료
1327444290금성농공단지농공단지충청남도 금산군 금성면 하신리 일원44710310001990-02-02 00:00:0022완료
1328446120금성농공단지농공단지전라남도 담양군 금성면 봉서리 일원46710370001990-10-23 00:00:0030완료
1329448760금성조선농공단지농공단지경상남도 하동군 금성면 갈사리 204-1번지 일원48850420002009-12-07 00:00:000완료
1330443030금성테크노빌[구:금성농공]농공단지충청북도 제천시 금성면 양화리 234번지 일원43150310001988-11-19 00:00:0010완료