Overview

Dataset statistics

Number of variables20
Number of observations38
Missing cells179
Missing cells (%)23.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory171.5 B

Variable types

Categorical3
Text5
Numeric7
DateTime5

Dataset

Description경기도 파주시에 소재한 지식산업센터에 대한 데이터로서 지식산업센터명칭, 소재지주소, 위경도, 용도지역, 부지면적, 건축면적, 공장시설면적, 지원시설면적, 유치가능업체수 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15084366/fileData.do

Alerts

시군명 has constant value ""Constant
관리사무소 연락처 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도(Y) is highly overall correlated with 경도(X)High correlation
경도(X) is highly overall correlated with 위도(Y)High correlation
부지면적(제곱미터) is highly overall correlated with 공장시설면적(제곱미터) and 3 other fieldsHigh correlation
공장시설면적(제곱미터) is highly overall correlated with 부지면적(제곱미터) and 2 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
지원시설면적(제곱미터) is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
용도지역 is highly overall correlated with 부지면적(제곱미터) and 2 other fieldsHigh correlation
용도지역 is highly imbalanced (70.3%)Imbalance
건축면적(제곱미터) is highly imbalanced (82.4%)Imbalance
소재지도로명주소 has 1 (2.6%) missing valuesMissing
관리사무소 연락처 has 37 (97.4%) missing valuesMissing
공사진행상황 has 36 (94.7%) missing valuesMissing
착공일자 has 34 (89.5%) missing valuesMissing
준공일자 has 36 (94.7%) missing valuesMissing
사용승인일자 has 35 (92.1%) missing valuesMissing
지식산업센터명칭 has unique valuesUnique
소재지지번주소 has unique valuesUnique
위도(Y) has unique valuesUnique
경도(X) has unique valuesUnique
부지면적(제곱미터) has unique valuesUnique
공장시설면적(제곱미터) has unique valuesUnique
연면적(제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:39:37.291016
Analysis finished2023-12-12 16:39:44.582396
Duration7.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
파주시
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파주시 38
100.0%

Length

2023-12-13T01:39:44.697092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:39:44.843793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 38
100.0%

지식산업센터명칭
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T01:39:45.467293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.3947368
Min length2

Characters and Unicode

Total characters243
Distinct characters117
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

Unique38 ?
Unique (%)100.0%

Sample

1st row(주)에스엠디자인
2nd row엄마마음
3rd row(주)우리세움
4th row흙마당
5th row제이티스스튜디오
ValueCountFrequency (%)
주)에스엠디자인 1
 
2.5%
경인문화사 1
 
2.5%
주)에스엠디자인2 1
 
2.5%
명필름 1
 
2.5%
푸른사상사 1
 
2.5%
아카넷 1
 
2.5%
주)과학상자 1
 
2.5%
뚜르드까망 1
 
2.5%
주)두그루 1
 
2.5%
주)영화사집 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T01:39:45.925456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 18
 
7.4%
) 18
 
7.4%
주 17
 
7.0%
스 9
 
3.7%
아 6
 
2.5%
사 6
 
2.5%
자 5
 
2.1%
5
 
2.1%
출 4
 
1.6%
마 4
 
1.6%
Other values (107) 151
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 198
81.5%
Open Punctuation 18
 
7.4%
Close Punctuation 18
 
7.4%
Space Separator 5
 
2.1%
Uppercase Letter 3
 
1.2%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 17
 
8.6%
스 9
 
4.5%
아 6
 
3.0%
사 6
 
3.0%
자 5
 
2.5%
출 4
 
2.0%
마 4
 
2.0%
인 4
 
2.0%
판 4
 
2.0%
디 4
 
2.0%
Other values (100) 135
68.2%
Uppercase Letter
ValueCountFrequency (%)
J 1
33.3%
N 1
33.3%
F 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 198
81.5%
Common 42
 
17.3%
Latin 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 17
 
8.6%
스 9
 
4.5%
아 6
 
3.0%
사 6
 
3.0%
자 5
 
2.5%
출 4
 
2.0%
마 4
 
2.0%
인 4
 
2.0%
판 4
 
2.0%
디 4
 
2.0%
Other values (100) 135
68.2%
Common
ValueCountFrequency (%)
( 18
42.9%
) 18
42.9%
5
 
11.9%
2 1
 
2.4%
Latin
ValueCountFrequency (%)
J 1
33.3%
N 1
33.3%
F 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 198
81.5%
ASCII 45
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 18
40.0%
) 18
40.0%
5
 
11.1%
J 1
 
2.2%
N 1
 
2.2%
F 1
 
2.2%
2 1
 
2.2%
Hangul
ValueCountFrequency (%)
주 17
 
8.6%
스 9
 
4.5%
아 6
 
3.0%
사 6
 
3.0%
자 5
 
2.5%
출 4
 
2.0%
마 4
 
2.0%
인 4
 
2.0%
판 4
 
2.0%
디 4
 
2.0%
Other values (100) 135
68.2%

소재지지번주소
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T01:39:46.159177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length17.078947
Min length15

Characters and Unicode

Total characters649
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row경기도 파주시 신촌동 742-8
2nd row경기도 파주시 서패동 472-2
3rd row경기도 파주시 문발동 516-2
4th row경기도 파주시 서패동 470-3
5th row경기도 파주시 문발동 529-2
ValueCountFrequency (%)
경기도 38
25.0%
파주시 38
25.0%
문발동 23
15.1%
서패동 11
 
7.2%
신촌동 2
 
1.3%
742-5 1
 
0.7%
470-6 1
 
0.7%
638-1 1
 
0.7%
471-4 1
 
0.7%
472-3 1
 
0.7%
Other values (35) 35
23.0%
2023-12-13T01:39:46.523050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
17.7%
동 39
 
6.0%
경 38
 
5.9%
도 38
 
5.9%
파 38
 
5.9%
주 38
 
5.9%
시 38
 
5.9%
기 38
 
5.9%
- 34
 
5.2%
3 25
 
3.9%
Other values (17) 208
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 342
52.7%
Decimal Number 157
24.2%
Space Separator 115
 
17.7%
Dash Punctuation 34
 
5.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
11.4%
경 38
11.1%
도 38
11.1%
파 38
11.1%
주 38
11.1%
시 38
11.1%
기 38
11.1%
문 24
7.0%
발 24
7.0%
서 11
 
3.2%
Other values (4) 16
4.7%
Decimal Number
ValueCountFrequency (%)
3 25
15.9%
4 23
14.6%
2 22
14.0%
6 20
12.7%
1 20
12.7%
5 14
8.9%
7 12
7.6%
9 8
 
5.1%
8 8
 
5.1%
0 5
 
3.2%
Space Separator
ValueCountFrequency (%)
115
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
52.7%
Common 307
47.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
11.4%
경 38
11.1%
도 38
11.1%
파 38
11.1%
주 38
11.1%
시 38
11.1%
기 38
11.1%
문 24
7.0%
발 24
7.0%
서 11
 
3.2%
Other values (4) 16
4.7%
Common
ValueCountFrequency (%)
115
37.5%
- 34
 
11.1%
3 25
 
8.1%
4 23
 
7.5%
2 22
 
7.2%
6 20
 
6.5%
1 20
 
6.5%
5 14
 
4.6%
7 12
 
3.9%
9 8
 
2.6%
Other values (3) 14
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 342
52.7%
ASCII 307
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
37.5%
- 34
 
11.1%
3 25
 
8.1%
4 23
 
7.5%
2 22
 
7.2%
6 20
 
6.5%
1 20
 
6.5%
5 14
 
4.6%
7 12
 
3.9%
9 8
 
2.6%
Other values (3) 14
 
4.6%
Hangul
ValueCountFrequency (%)
동 39
11.4%
경 38
11.1%
도 38
11.1%
파 38
11.1%
주 38
11.1%
시 38
11.1%
기 38
11.1%
문 24
7.0%
발 24
7.0%
서 11
 
3.2%
Other values (4) 16
4.7%

소재지도로명주소
Text

MISSING 

Distinct37
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-13T01:39:46.863992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length17.108108
Min length13

Characters and Unicode

Total characters633
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row경기도 파주시 문발로403-2
2nd row경기도 파주시 회동길 357 (서패동)
3rd row경기도 파주시 광인사길68
4th row경기도 파주시 회동길373
5th row경기도 파주시 회동길 37-14
ValueCountFrequency (%)
경기도 37
26.6%
파주시 37
26.6%
회동길 18
12.9%
문발동 4
 
2.9%
서패동 3
 
2.2%
회동길445-2 1
 
0.7%
530-20 1
 
0.7%
337-16 1
 
0.7%
445-3 1
 
0.7%
325-6 1
 
0.7%
Other values (35) 35
25.2%
2023-12-13T01:39:47.305851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
17.1%
동 39
 
6.2%
경 37
 
5.8%
도 37
 
5.8%
파 37
 
5.8%
주 37
 
5.8%
시 37
 
5.8%
기 37
 
5.8%
길 35
 
5.5%
회 30
 
4.7%
Other values (34) 199
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
57.5%
Decimal Number 129
 
20.4%
Space Separator 108
 
17.1%
Dash Punctuation 16
 
2.5%
Open Punctuation 8
 
1.3%
Close Punctuation 8
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
10.7%
경 37
10.2%
도 37
10.2%
파 37
10.2%
주 37
10.2%
시 37
10.2%
기 37
10.2%
길 35
9.6%
회 30
8.2%
발 5
 
1.4%
Other values (20) 33
9.1%
Decimal Number
ValueCountFrequency (%)
5 19
14.7%
4 19
14.7%
1 18
14.0%
3 17
13.2%
2 15
11.6%
7 12
9.3%
0 11
8.5%
6 7
 
5.4%
8 6
 
4.7%
9 5
 
3.9%
Space Separator
ValueCountFrequency (%)
108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
57.5%
Common 269
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
10.7%
경 37
10.2%
도 37
10.2%
파 37
10.2%
주 37
10.2%
시 37
10.2%
기 37
10.2%
길 35
9.6%
회 30
8.2%
발 5
 
1.4%
Other values (20) 33
9.1%
Common
ValueCountFrequency (%)
108
40.1%
5 19
 
7.1%
4 19
 
7.1%
1 18
 
6.7%
3 17
 
6.3%
- 16
 
5.9%
2 15
 
5.6%
7 12
 
4.5%
0 11
 
4.1%
( 8
 
3.0%
Other values (4) 26
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
57.5%
ASCII 269
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
40.1%
5 19
 
7.1%
4 19
 
7.1%
1 18
 
6.7%
3 17
 
6.3%
- 16
 
5.9%
2 15
 
5.6%
7 12
 
4.5%
0 11
 
4.1%
( 8
 
3.0%
Other values (4) 26
 
9.7%
Hangul
ValueCountFrequency (%)
동 39
10.7%
경 37
10.2%
도 37
10.2%
파 37
10.2%
주 37
10.2%
시 37
10.2%
기 37
10.2%
길 35
9.6%
회 30
8.2%
발 5
 
1.4%
Other values (20) 33
9.1%

위도(Y)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.71673
Minimum37.70307
Maximum37.728838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:47.458954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.70307
5-th percentile37.706051
Q137.710585
median37.717001
Q337.721221
95-th percentile37.724246
Maximum37.728838
Range0.0257684
Interquartile range (IQR)0.01063658

Descriptive statistics

Standard deviation0.0064342568
Coefficient of variation (CV)0.00017059424
Kurtosis-0.59912842
Mean37.71673
Median Absolute Deviation (MAD)0.0046643
Skewness-0.33379169
Sum1433.2357
Variance4.1399661 × 10-5
MonotonicityNot monotonic
2023-12-13T01:39:47.602814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
37.7288382 1
 
2.6%
37.7286117 1
 
2.6%
37.7209616 1
 
2.6%
37.7160638 1
 
2.6%
37.7162467 1
 
2.6%
37.7084685 1
 
2.6%
37.7228272 1
 
2.6%
37.7204661 1
 
2.6%
37.7213239 1
 
2.6%
37.7060662 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
37.7030698 1
2.6%
37.7059662 1
2.6%
37.7060662 1
2.6%
37.7072328 1
2.6%
37.7074948 1
2.6%
37.7084685 1
2.6%
37.7092335 1
2.6%
37.7102704 1
2.6%
37.7105591 1
2.6%
37.71057726 1
2.6%
ValueCountFrequency (%)
37.7288382 1
2.6%
37.7286117 1
2.6%
37.7234751 1
2.6%
37.7232383 1
2.6%
37.7230314 1
2.6%
37.7228797 1
2.6%
37.7228272 1
2.6%
37.722007 1
2.6%
37.7213239 1
2.6%
37.7212228 1
2.6%

경도(X)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.69088
Minimum126.68375
Maximum126.69464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:47.750283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68375
5-th percentile126.68519
Q1126.68794
median126.69225
Q3126.69338
95-th percentile126.69414
Maximum126.69464
Range0.010891
Interquartile range (IQR)0.00543495

Descriptive statistics

Standard deviation0.0031246319
Coefficient of variation (CV)2.4663432 × 10-5
Kurtosis-0.49947837
Mean126.69088
Median Absolute Deviation (MAD)0.0014115
Skewness-0.87888974
Sum4814.2535
Variance9.7633246 × 10-6
MonotonicityNot monotonic
2023-12-13T01:39:47.918605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
126.69081 1
 
2.6%
126.690342 1
 
2.6%
126.693391 1
 
2.6%
126.691268 1
 
2.6%
126.692322 1
 
2.6%
126.68445 1
 
2.6%
126.693633 1
 
2.6%
126.693428 1
 
2.6%
126.694139 1
 
2.6%
126.685674 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
126.683747 1
2.6%
126.68445 1
2.6%
126.685323 1
2.6%
126.685674 1
2.6%
126.685791 1
2.6%
126.687302 1
2.6%
126.687509 1
2.6%
126.687562 1
2.6%
126.687811 1
2.6%
126.687934 1
2.6%
ValueCountFrequency (%)
126.694638 1
2.6%
126.694154 1
2.6%
126.694139 1
2.6%
126.693901 1
2.6%
126.693892 1
2.6%
126.693633 1
2.6%
126.693501 1
2.6%
126.693428 1
2.6%
126.693391 1
2.6%
126.693379 1
2.6%

용도지역
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
준공업
36 
준주거
 
2

Length

Max length4
Median length3
Mean length3.0526316
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
준공업 36
94.7%
준주거 2
 
5.3%

Length

2023-12-13T01:39:48.080251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:39:48.183391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준공업 36
94.7%
준주거 2
 
5.3%

부지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1679.3029
Minimum659.6
Maximum10654.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:48.293859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum659.6
5-th percentile674.65
Q1753.6
median1027.655
Q31577.65
95-th percentile3739.39
Maximum10654.4
Range9994.8
Interquartile range (IQR)824.05

Descriptive statistics

Standard deviation1840.5165
Coefficient of variation (CV)1.0960003
Kurtosis16.021079
Mean1679.3029
Median Absolute Deviation (MAD)345.505
Skewness3.7422981
Sum63813.51
Variance3387500.8
MonotonicityNot monotonic
2023-12-13T01:39:48.448174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1460.6 1
 
2.6%
679.9 1
 
2.6%
661.9 1
 
2.6%
687.7 1
 
2.6%
659.6 1
 
2.6%
1345.4 1
 
2.6%
990.7 1
 
2.6%
3243.6 1
 
2.6%
688.4 1
 
2.6%
1019.0 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
659.6 1
2.6%
661.9 1
2.6%
676.9 1
2.6%
678.3 1
2.6%
679.9 1
2.6%
684.4 1
2.6%
687.7 1
2.6%
688.4 1
2.6%
690.6 1
2.6%
700.8 1
2.6%
ValueCountFrequency (%)
10654.4 1
2.6%
6257.6 1
2.6%
3295.0 1
2.6%
3259.4 1
2.6%
3243.6 1
2.6%
2479.0 1
2.6%
1953.0 1
2.6%
1679.3 1
2.6%
1651.9 1
2.6%
1596.5 1
2.6%

건축면적(제곱미터)
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
<NA>
37 
3669.52
 
1

Length

Max length7
Median length4
Mean length4.0789474
Min length4

Unique

Unique1 ?
Unique (%)2.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 37
97.4%
3669.52 1
 
2.6%

Length

2023-12-13T01:39:48.638123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:39:48.753297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 37
97.4%
3669.52 1
 
2.6%

공장시설면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2792.4218
Minimum675.76
Maximum22048.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:48.873900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum675.76
5-th percentile921.0055
Q11361.2125
median2004.575
Q32514.1275
95-th percentile7193.7905
Maximum22048.29
Range21372.53
Interquartile range (IQR)1152.915

Descriptive statistics

Standard deviation3628.4091
Coefficient of variation (CV)1.2993771
Kurtosis22.324365
Mean2792.4218
Median Absolute Deviation (MAD)642.655
Skewness4.4306206
Sum106112.03
Variance13165352
MonotonicityNot monotonic
2023-12-13T01:39:49.033963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2005.98 1
 
2.6%
935.83 1
 
2.6%
1330.84 1
 
2.6%
1254.61 1
 
2.6%
1106.94 1
 
2.6%
1404.84 1
 
2.6%
2003.17 1
 
2.6%
3021.93 1
 
2.6%
1057.35 1
 
2.6%
1327.0 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
675.76 1
2.6%
837.0 1
2.6%
935.83 1
2.6%
1057.35 1
2.6%
1106.94 1
2.6%
1254.61 1
2.6%
1327.0 1
2.6%
1330.84 1
2.6%
1347.95 1
2.6%
1361.05 1
2.6%
ValueCountFrequency (%)
22048.29 1
2.6%
8949.27 1
2.6%
6884.0 1
2.6%
6438.0 1
2.6%
3584.85 1
2.6%
3216.0 1
2.6%
3021.93 1
2.6%
2753.0 1
2.6%
2545.9 1
2.6%
2534.09 1
2.6%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4059.0497
Minimum929.24
Maximum31232.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:49.181554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum929.24
5-th percentile1129.791
Q11665.7675
median2404.88
Q32969.115
95-th percentile14565.459
Maximum31232.53
Range30303.29
Interquartile range (IQR)1303.3475

Descriptive statistics

Standard deviation6368.8628
Coefficient of variation (CV)1.5690527
Kurtosis12.981479
Mean4059.0497
Median Absolute Deviation (MAD)709.5
Skewness3.626955
Sum154243.89
Variance40562413
MonotonicityNot monotonic
2023-12-13T01:39:49.339472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2474.25 1
 
2.6%
1145.46 1
 
2.6%
1617.53 1
 
2.6%
1564.0 1
 
2.6%
1362.69 1
 
2.6%
1734.21 1
 
2.6%
2503.27 1
 
2.6%
3769.7 1
 
2.6%
1226.26 1
 
2.6%
1539.0 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
929.24 1
2.6%
1041.0 1
2.6%
1145.46 1
2.6%
1226.26 1
2.6%
1362.69 1
2.6%
1539.0 1
2.6%
1564.0 1
2.6%
1617.53 1
2.6%
1639.76 1
2.6%
1658.02 1
2.6%
ValueCountFrequency (%)
31232.53 1
2.6%
27257.37 1
2.6%
12325.71 1
2.6%
8009.0 1
2.6%
4397.13 1
2.6%
4375.0 1
2.6%
3769.7 1
2.6%
3138.0 1
2.6%
3090.18 1
2.6%
2981.05 1
2.6%

지원시설면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean672.34211
Minimum169
Maximum5209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:49.488531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum169
5-th percentile209.1
Q1281
median372
Q3504
95-th percentile1998.15
Maximum5209
Range5040
Interquartile range (IQR)223

Descriptive statistics

Standard deviation954.34625
Coefficient of variation (CV)1.4194355
Kurtosis15.122313
Mean672.34211
Median Absolute Deviation (MAD)121.5
Skewness3.7375625
Sum25549
Variance910776.77
MonotonicityNot monotonic
2023-12-13T01:39:49.614061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
504 2
 
5.3%
210 1
 
2.6%
287 1
 
2.6%
309 1
 
2.6%
256 1
 
2.6%
329 1
 
2.6%
500 1
 
2.6%
748 1
 
2.6%
169 1
 
2.6%
468 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
169 1
2.6%
204 1
2.6%
210 1
2.6%
213 1
2.6%
218 1
2.6%
239 1
2.6%
249 1
2.6%
253 1
2.6%
256 1
2.6%
279 1
2.6%
ValueCountFrequency (%)
5209 1
2.6%
3376 1
2.6%
1755 1
2.6%
1572 1
2.6%
1159 1
2.6%
822 1
2.6%
748 1
2.6%
544 1
2.6%
522 1
2.6%
504 2
5.3%

유치가능업체수
Real number (ℝ)

Distinct14
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.657895
Minimum6
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:39:49.728513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q18
median9
Q311.75
95-th percentile74.5
Maximum321
Range315
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation68.508148
Coefficient of variation (CV)2.5699009
Kurtosis16.115607
Mean26.657895
Median Absolute Deviation (MAD)1.5
Skewness4.1395823
Sum1013
Variance4693.3663
MonotonicityNot monotonic
2023-12-13T01:39:49.848081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 8
21.1%
8 6
15.8%
10 5
13.2%
7 3
 
7.9%
11 3
 
7.9%
6 3
 
7.9%
21 2
 
5.3%
12 2
 
5.3%
34 1
 
2.6%
19 1
 
2.6%
Other values (4) 4
10.5%
ValueCountFrequency (%)
6 3
 
7.9%
7 3
 
7.9%
8 6
15.8%
9 8
21.1%
10 5
13.2%
11 3
 
7.9%
12 2
 
5.3%
13 1
 
2.6%
14 1
 
2.6%
19 1
 
2.6%
ValueCountFrequency (%)
321 1
 
2.6%
304 1
 
2.6%
34 1
 
2.6%
21 2
 
5.3%
19 1
 
2.6%
14 1
 
2.6%
13 1
 
2.6%
12 2
 
5.3%
11 3
7.9%
10 5
13.2%

관리사무소 연락처
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing37
Missing (%)97.4%
Memory size436.0 B
2023-12-13T01:39:49.986959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters8
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

Unique1 ?
Unique (%)100.0%

Sample

1st row031-947-5400
ValueCountFrequency (%)
031-947-5400 1
100.0%
2023-12-13T01:39:50.246711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
25.0%
- 2
16.7%
4 2
16.7%
3 1
 
8.3%
1 1
 
8.3%
9 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
83.3%
Dash Punctuation 2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
30.0%
4 2
20.0%
3 1
 
10.0%
1 1
 
10.0%
9 1
 
10.0%
7 1
 
10.0%
5 1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
25.0%
- 2
16.7%
4 2
16.7%
3 1
 
8.3%
1 1
 
8.3%
9 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
25.0%
- 2
16.7%
4 2
16.7%
3 1
 
8.3%
1 1
 
8.3%
9 1
 
8.3%
7 1
 
8.3%
5 1
 
8.3%

공사진행상황
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing36
Missing (%)94.7%
Memory size436.0 B
2023-12-13T01:39:50.388411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row공사중
2nd row사용승인
ValueCountFrequency (%)
공사중 1
50.0%
사용승인 1
50.0%
2023-12-13T01:39:50.637738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 2
28.6%
공 1
14.3%
중 1
14.3%
용 1
14.3%
승 1
14.3%
인 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 2
28.6%
공 1
14.3%
중 1
14.3%
용 1
14.3%
승 1
14.3%
인 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 2
28.6%
공 1
14.3%
중 1
14.3%
용 1
14.3%
승 1
14.3%
인 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 2
28.6%
공 1
14.3%
중 1
14.3%
용 1
14.3%
승 1
14.3%
인 1
14.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum2023-07-20 00:00:00
Maximum2023-07-20 00:00:00
2023-12-13T01:39:50.764398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:50.877664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

허가일자
Date

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum2019-02-25 00:00:00
Maximum2023-04-27 00:00:00
2023-12-13T01:39:50.993213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:51.115719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

착공일자
Date

MISSING 

Distinct4
Distinct (%)100.0%
Missing34
Missing (%)89.5%
Memory size436.0 B
Minimum2019-02-26 00:00:00
Maximum2021-03-16 00:00:00
2023-12-13T01:39:51.237795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:51.350359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

준공일자
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing36
Missing (%)94.7%
Memory size436.0 B
Minimum2020-11-04 00:00:00
Maximum2021-11-29 00:00:00
2023-12-13T01:39:51.463693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:51.582152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

사용승인일자
Date

MISSING 

Distinct3
Distinct (%)100.0%
Missing35
Missing (%)92.1%
Memory size436.0 B
Minimum2020-10-08 00:00:00
Maximum2021-11-29 00:00:00
2023-12-13T01:39:51.738738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:51.997767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2023-12-13T01:39:42.639585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:37.965992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.709430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.567723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.383018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.135819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.843493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.800015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.075896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.813876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.699664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.480260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.238379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.939905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.935193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.184364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.916981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.830368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.602055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.342088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.043075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:43.052588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.291744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.019622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.936960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.711442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.436092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.181224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:43.217841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.394526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.146939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.042988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.814764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.525032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.296231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:43.365154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.515548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.304588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.153911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.923911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.630451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.431687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:43.488038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:38.613151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:39.405469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:40.283758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.030633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:41.729030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:39:42.547860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:39:52.184590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지식산업센터명칭소재지지번주소소재지도로명주소위도(Y)경도(X)용도지역부지면적(제곱미터)공장시설면적(제곱미터)연면적(제곱미터)지원시설면적(제곱미터)유치가능업체수공사진행상황허가일자착공일자준공일자사용승인일자
지식산업센터명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0000.0001.000
위도(Y)1.0001.0001.0001.0000.9000.0000.0000.0000.0000.0000.0000.0000.5411.000NaN1.000
경도(X)1.0001.0001.0000.9001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.000
용도지역1.0001.0001.0000.0000.0001.0000.5400.2960.8340.8340.2070.0001.0001.000NaN1.000
부지면적(제곱미터)1.0001.0001.0000.0000.0000.5401.0000.9540.8770.8841.0000.0000.9731.0000.0001.000
공장시설면적(제곱미터)1.0001.0001.0000.0000.0000.2960.9541.0000.9561.0000.7050.0001.0001.000NaN0.000
연면적(제곱미터)1.0001.0001.0000.0000.0000.8340.8770.9561.0000.9991.0000.0001.0001.000NaN0.000
지원시설면적(제곱미터)1.0001.0001.0000.0000.0000.8340.8841.0000.9991.0001.0000.0001.0001.0000.0001.000
유치가능업체수1.0001.0001.0000.0000.0000.2071.0000.7051.0001.0001.0000.0001.0001.0000.0001.000
공사진행상황0.0000.000NaN0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000NaNNaN
허가일자1.0001.0001.0000.5410.0001.0000.9731.0001.0001.0001.0000.0001.0001.0000.0001.000
착공일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.000
준공일자0.0000.0000.000NaN0.000NaN0.000NaNNaN0.0000.000NaN0.0000.0001.0000.000
사용승인일자1.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.000NaN1.0001.0000.0001.000
2023-12-13T01:39:52.435708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축면적(제곱미터)용도지역
건축면적(제곱미터)1.000NaN
용도지역NaN1.000
2023-12-13T01:39:52.609406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도(Y)경도(X)부지면적(제곱미터)공장시설면적(제곱미터)연면적(제곱미터)지원시설면적(제곱미터)유치가능업체수용도지역건축면적(제곱미터)
위도(Y)1.0000.716-0.1220.0690.0820.1830.2050.000NaN
경도(X)0.7161.000-0.0770.1470.1540.2320.0470.000NaN
부지면적(제곱미터)-0.122-0.0771.0000.8150.8220.7530.3240.626NaN
공장시설면적(제곱미터)0.0690.1470.8151.0000.9880.8730.3040.347NaN
연면적(제곱미터)0.0820.1540.8220.9881.0000.9180.3290.603NaN
지원시설면적(제곱미터)0.1830.2320.7530.8730.9181.0000.4550.603NaN
유치가능업체수0.2050.0470.3240.3040.3290.4551.0000.130NaN
용도지역0.0000.0000.6260.3470.6030.6030.1301.000NaN
건축면적(제곱미터)NaNNaNNaNNaNNaNNaNNaNNaN1.000

Missing values

2023-12-13T01:39:43.712061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:39:44.139745image/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.
2023-12-13T01:39:44.401909image/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

시군명지식산업센터명칭소재지지번주소소재지도로명주소위도(Y)경도(X)용도지역부지면적(제곱미터)건축면적(제곱미터)공장시설면적(제곱미터)연면적(제곱미터)지원시설면적(제곱미터)유치가능업체수관리사무소 연락처공사진행상황데이터기준일자허가일자착공일자준공일자사용승인일자
0파주시(주)에스엠디자인경기도 파주시 신촌동 742-8경기도 파주시 문발로403-237.728838126.69081준공업1460.6<NA>2005.982474.2546834<NA><NA>2023-07-202019-02-25<NA><NA><NA>
1파주시엄마마음경기도 파주시 서패동 472-2경기도 파주시 회동길 357 (서패동)37.716554126.692593준공업1385.9<NA>2534.092981.054477<NA><NA>2023-07-202019-02-25<NA><NA><NA>
2파주시(주)우리세움경기도 파주시 문발동 516-2경기도 파주시 광인사길6837.710607126.685791준공업1521.1<NA>2454.242933.3147919<NA><NA>2023-07-202019-02-25<NA><NA><NA>
3파주시흙마당경기도 파주시 서패동 470-3경기도 파주시 회동길37337.717068126.692886준공업963.9<NA>2041.692533.254929<NA><NA>2023-07-202019-02-25<NA><NA><NA>
4파주시제이티스스튜디오경기도 파주시 문발동 529-2경기도 파주시 회동길 37-1437.705966126.685323준공업926.4<NA>1600.541839.9623911<NA><NA>2023-07-202019-02-25<NA><NA><NA>
5파주시(주)임창정(예스아이엠)경기도 파주시 문발동 513-10경기도 파주시 회동길19237.710559126.687934준공업1651.9<NA>2210.232732.6252214<NA><NA>2023-07-202019-02-25<NA><NA><NA>
6파주시(주)첨단경기도 파주시 서패동 470-1경기도 파주시 심학산로 1037.717193126.692364준공업966.2<NA>2064.542457.423939<NA><NA>2023-07-202019-02-25<NA><NA><NA>
7파주시아트팩토리NJF경기도 파주시 문발동 626-11경기도 파주시 회동길48037.721217126.694638준공업10654.4<NA>22048.2927257.375209321<NA><NA>2023-07-202019-02-262019-02-262020-11-042020-11-04
8파주시경인문화사경기도 파주시 문발동 638-4경기도 파주시 회동길 445-137.720503126.693892준공업1679.3<NA>3584.854397.1382210<NA><NA>2023-07-202019-05-09<NA><NA><NA>
9파주시(주)에이치컴퍼니경기도 파주시 문발동 632-4경기도 파주시 회동길 521-1 (문발동)37.723031126.692498준공업1020.8<NA>2304.842808.685049<NA><NA>2023-07-202019-05-10<NA><NA><NA>
시군명지식산업센터명칭소재지지번주소소재지도로명주소위도(Y)경도(X)용도지역부지면적(제곱미터)건축면적(제곱미터)공장시설면적(제곱미터)연면적(제곱미터)지원시설면적(제곱미터)유치가능업체수관리사무소 연락처공사진행상황데이터기준일자허가일자착공일자준공일자사용승인일자
28파주시몽스패밀리경기도 파주시 문발동 639-1경기도 파주시 회동길47137.721324126.694139준공업688.4<NA>1057.351226.261699<NA><NA>2023-07-202021-08-11<NA><NA><NA>
29파주시(주)정윤인터내셔날경기도 파주시 신촌동 742-5경기도 파주시 재두루미길6837.728612126.690342준공업679.9<NA>935.831145.462109<NA><NA>2023-07-202021-10-08<NA><NA><NA>
30파주시(주)하다니경기도 파주시 문발동 529-3경기도 파주시 회동길57-937.706066126.685674준공업1019.0<NA>1327.01539.02138<NA><NA>2023-07-202021-07-14<NA><NA><NA>
31파주시(주)무카스경기도 파주시 문발동 635-1경기도 파주시 회동길 49537.722007126.693501준공업1389.0<NA>3216.04375.011598<NA><NA>2023-07-202022-11-23<NA><NA><NA>
32파주시(주)오브제스퀘어경기도 파주시 서패동 469<NA>37.717209126.69149준공업3259.4<NA>8949.2712325.71337621<NA>공사중2023-07-202023-01-052021-03-16<NA><NA>
33파주시(주)플린키경기도 파주시 문발동 513-14경기도 파주시 회동길 16037.709234126.687509준공업2479.0<NA>2753.03138.03856<NA><NA>2023-07-202023-02-02<NA><NA><NA>
34파주시자음과모음경기도 파주시 서패동 469-1경기도 파주시 회동길 325-2037.716928126.691038준공업1313.9<NA>2029.912533.950413<NA><NA>2023-07-202023-04-26<NA><NA><NA>
35파주시돌베개경기도 파주시 문발동 532-4경기도 파주시 회동길 77-2037.707233126.687302준공업912.0<NA>837.01041.02046<NA><NA>2023-07-202023-04-27<NA><NA><NA>
36파주시명필름경기도 파주시 문발동 627경기도 파주시 회동길 530-2037.723475126.693336준공업3295.0<NA>6438.08009.0157212<NA><NA>2023-07-202023-03-13<NA><NA><NA>
37파주시운정한강듀클래스경기도 파주시 와동동 1484, 1484-2경기도 파주시 가람로116번길 107 (와동동)37.710577126.687958준주거6257.63669.526884.031232.531755304031-947-5400사용승인2023-07-202019-03-272019-04-10<NA>2020-10-08