Overview

Dataset statistics

Number of variables13
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory113.6 B

Variable types

Text3
Numeric4
DateTime4
Categorical2

Dataset

Description군포시 지식산업센터(구 아파트형공장) 현황으로 지식센터명, 소재지주소, 업체수, 부지면적, 건축면적, 준공연월, 지상층수, 지하층수, 데이터기준일자 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15029928/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
업체수 is highly overall correlated with 부지면적 and 2 other fieldsHigh correlation
부지면적 is highly overall correlated with 업체수 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 업체수 and 2 other fieldsHigh correlation
지상층수 is highly overall correlated with 업체수 and 1 other fieldsHigh correlation
지식센터명 has unique valuesUnique
소재지주소 has unique valuesUnique
전화번호 has unique valuesUnique
부지면적 has unique valuesUnique
건축면적 has unique valuesUnique
건축허가일자 has unique valuesUnique
건축착공일자 has unique valuesUnique
건축사용승인일 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:32:13.932790
Analysis finished2023-12-12 22:32:16.498429
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지식센터명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T07:32:16.658115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.5517241
Min length4

Characters and Unicode

Total characters190
Distinct characters85
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row삼풍데파트
2nd row삼풍프라자
3rd row군포제일공단
4th row한솔테크노타운
5th row신라테크노빌
ValueCountFrequency (%)
삼풍데파트 1
 
3.3%
삼풍프라자 1
 
3.3%
센트럴비즈파크 1
 
3.3%
군포ls지식산업센터 1
 
3.3%
에이스더블유밸리 1
 
3.3%
스카이비즈 1
 
3.3%
에이스하이테크시티군포 1
 
3.3%
엠테크노센터 1
 
3.3%
신광프라자 1
 
3.3%
군포it밸리 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T07:32:17.078125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
크 8
 
4.2%
군 7
 
3.7%
테 7
 
3.7%
타 7
 
3.7%
이 7
 
3.7%
포 7
 
3.7%
노 6
 
3.2%
워 5
 
2.6%
에 5
 
2.6%
리 5
 
2.6%
Other values (75) 126
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
89.5%
Uppercase Letter 17
 
8.9%
Decimal Number 2
 
1.1%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
크 8
 
4.7%
군 7
 
4.1%
테 7
 
4.1%
타 7
 
4.1%
이 7
 
4.1%
포 7
 
4.1%
노 6
 
3.5%
워 5
 
2.9%
에 5
 
2.9%
리 5
 
2.9%
Other values (62) 106
62.4%
Uppercase Letter
ValueCountFrequency (%)
T 4
23.5%
I 3
17.6%
S 2
11.8%
K 2
11.8%
L 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%
W 1
 
5.9%
O 1
 
5.9%
P 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
89.5%
Latin 17
 
8.9%
Common 3
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
크 8
 
4.7%
군 7
 
4.1%
테 7
 
4.1%
타 7
 
4.1%
이 7
 
4.1%
포 7
 
4.1%
노 6
 
3.5%
워 5
 
2.9%
에 5
 
2.9%
리 5
 
2.9%
Other values (62) 106
62.4%
Latin
ValueCountFrequency (%)
T 4
23.5%
I 3
17.6%
S 2
11.8%
K 2
11.8%
L 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%
W 1
 
5.9%
O 1
 
5.9%
P 1
 
5.9%
Common
ValueCountFrequency (%)
1
33.3%
1 1
33.3%
2 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
89.5%
ASCII 20
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
크 8
 
4.7%
군 7
 
4.1%
테 7
 
4.1%
타 7
 
4.1%
이 7
 
4.1%
포 7
 
4.1%
노 6
 
3.5%
워 5
 
2.9%
에 5
 
2.9%
리 5
 
2.9%
Other values (62) 106
62.4%
ASCII
ValueCountFrequency (%)
T 4
20.0%
I 3
15.0%
S 2
10.0%
K 2
10.0%
L 1
 
5.0%
E 1
 
5.0%
R 1
 
5.0%
W 1
 
5.0%
1
 
5.0%
1 1
 
5.0%
Other values (3) 3
15.0%

소재지주소
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T07:32:17.310369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length17.103448
Min length13

Characters and Unicode

Total characters496
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row경기도 군포시 당정로 57
2nd row경기도 군포시 봉성로 82-7
3rd row경기도 군포시 당정로75번길 23
4th row경기도 군포시 경수대로 455
5th row경기도 군포시 당정로 90
ValueCountFrequency (%)
경기도 29
25.0%
군포시 29
25.0%
공단로 6
 
5.2%
공단로140번길 5
 
4.3%
엘에스로 4
 
3.4%
당정로 2
 
1.7%
13 2
 
1.7%
8 1
 
0.9%
군포첨단산업2로7번길 1
 
0.9%
고산로 1
 
0.9%
Other values (36) 36
31.0%
2023-12-13T07:32:17.684410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
17.5%
경 30
 
6.0%
군 30
 
6.0%
포 30
 
6.0%
도 29
 
5.8%
시 29
 
5.8%
기 29
 
5.8%
로 28
 
5.6%
1 21
 
4.2%
2 13
 
2.6%
Other values (32) 170
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
60.1%
Decimal Number 106
 
21.4%
Space Separator 87
 
17.5%
Dash Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 30
10.1%
군 30
10.1%
포 30
10.1%
도 29
9.7%
시 29
9.7%
기 29
9.7%
로 28
9.4%
길 13
 
4.4%
번 12
 
4.0%
단 12
 
4.0%
Other values (20) 56
18.8%
Decimal Number
ValueCountFrequency (%)
1 21
19.8%
2 13
12.3%
5 13
12.3%
4 12
11.3%
3 10
9.4%
7 9
8.5%
6 9
8.5%
8 7
 
6.6%
0 7
 
6.6%
9 5
 
4.7%
Space Separator
ValueCountFrequency (%)
87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
60.1%
Common 198
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 30
10.1%
군 30
10.1%
포 30
10.1%
도 29
9.7%
시 29
9.7%
기 29
9.7%
로 28
9.4%
길 13
 
4.4%
번 12
 
4.0%
단 12
 
4.0%
Other values (20) 56
18.8%
Common
ValueCountFrequency (%)
87
43.9%
1 21
 
10.6%
2 13
 
6.6%
5 13
 
6.6%
4 12
 
6.1%
3 10
 
5.1%
7 9
 
4.5%
6 9
 
4.5%
8 7
 
3.5%
0 7
 
3.5%
Other values (2) 10
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
60.1%
ASCII 198
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
43.9%
1 21
 
10.6%
2 13
 
6.6%
5 13
 
6.6%
4 12
 
6.1%
3 10
 
5.1%
7 9
 
4.5%
6 9
 
4.5%
8 7
 
3.5%
0 7
 
3.5%
Other values (2) 10
 
5.1%
Hangul
ValueCountFrequency (%)
경 30
10.1%
군 30
10.1%
포 30
10.1%
도 29
9.7%
시 29
9.7%
기 29
9.7%
로 28
9.4%
길 13
 
4.4%
번 12
 
4.0%
단 12
 
4.0%
Other values (20) 56
18.8%

전화번호
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T07:32:17.907760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.103448
Min length12

Characters and Unicode

Total characters351
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

Unique29 ?
Unique (%)100.0%

Sample

1st row031-429-9477
2nd row031-452-9086
3rd row031-454-6617
4th row031-456-3319
5th row031-453-5942
ValueCountFrequency (%)
031-429-9477 1
 
3.4%
031-427-0091 1
 
3.4%
031-429-8366 1
 
3.4%
02-2189-9198 1
 
3.4%
031-462-9117 1
 
3.4%
031-8033-0091 1
 
3.4%
031-5183-0700 1
 
3.4%
031-470-2715 1
 
3.4%
031-451-2911 1
 
3.4%
031-8068-1111 1
 
3.4%
Other values (19) 19
65.5%
2023-12-13T07:32:18.283898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58
16.5%
1 55
15.7%
0 53
15.1%
3 41
11.7%
4 37
10.5%
7 21
 
6.0%
9 19
 
5.4%
5 17
 
4.8%
8 17
 
4.8%
6 17
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 293
83.5%
Dash Punctuation 58
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55
18.8%
0 53
18.1%
3 41
14.0%
4 37
12.6%
7 21
 
7.2%
9 19
 
6.5%
5 17
 
5.8%
8 17
 
5.8%
6 17
 
5.8%
2 16
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58
16.5%
1 55
15.7%
0 53
15.1%
3 41
11.7%
4 37
10.5%
7 21
 
6.0%
9 19
 
5.4%
5 17
 
4.8%
8 17
 
4.8%
6 17
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58
16.5%
1 55
15.7%
0 53
15.1%
3 41
11.7%
4 37
10.5%
7 21
 
6.0%
9 19
 
5.4%
5 17
 
4.8%
8 17
 
4.8%
6 17
 
4.8%

업체수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.7931
Minimum3
Maximum619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T07:32:18.444041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.8
Q113
median40
Q3171
95-th percentile379
Maximum619
Range616
Interquartile range (IQR)158

Descriptive statistics

Standard deviation145.06732
Coefficient of variation (CV)1.3843212
Kurtosis4.9437335
Mean104.7931
Median Absolute Deviation (MAD)31
Skewness2.1426797
Sum3039
Variance21044.527
MonotonicityNot monotonic
2023-12-13T07:32:18.576641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9 3
 
10.3%
20 2
 
6.9%
13 2
 
6.9%
24 2
 
6.9%
334 1
 
3.4%
200 1
 
3.4%
409 1
 
3.4%
173 1
 
3.4%
254 1
 
3.4%
205 1
 
3.4%
Other values (14) 14
48.3%
ValueCountFrequency (%)
3 1
 
3.4%
4 1
 
3.4%
6 1
 
3.4%
9 3
10.3%
13 2
6.9%
16 1
 
3.4%
20 2
6.9%
22 1
 
3.4%
24 2
6.9%
40 1
 
3.4%
ValueCountFrequency (%)
619 1
3.4%
409 1
3.4%
334 1
3.4%
254 1
3.4%
205 1
3.4%
200 1
3.4%
173 1
3.4%
171 1
3.4%
110 1
3.4%
87 1
3.4%

부지면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5342.0345
Minimum372
Maximum28591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T07:32:18.689777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum372
5-th percentile642.4
Q11531
median3605
Q36760
95-th percentile15632.4
Maximum28591
Range28219
Interquartile range (IQR)5229

Descriptive statistics

Standard deviation6005.5053
Coefficient of variation (CV)1.1241982
Kurtosis7.615669
Mean5342.0345
Median Absolute Deviation (MAD)2608
Skewness2.4841701
Sum154919
Variance36066094
MonotonicityNot monotonic
2023-12-13T07:32:18.809092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1001 1
 
3.4%
1984 1
 
3.4%
588 1
 
3.4%
3605 1
 
3.4%
12594 1
 
3.4%
6152 1
 
3.4%
4371 1
 
3.4%
5271 1
 
3.4%
8717 1
 
3.4%
3138 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
372 1
3.4%
588 1
3.4%
724 1
3.4%
756 1
3.4%
840 1
3.4%
997 1
3.4%
1001 1
3.4%
1531 1
3.4%
1536 1
3.4%
1984 1
3.4%
ValueCountFrequency (%)
28591 1
3.4%
17658 1
3.4%
12594 1
3.4%
8795 1
3.4%
8717 1
3.4%
8621 1
3.4%
7384 1
3.4%
6760 1
3.4%
6546 1
3.4%
6152 1
3.4%

건축면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26510.207
Minimum961
Maximum137032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T07:32:18.950395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum961
5-th percentile3025.4
Q15756
median16820
Q335426
95-th percentile92581.4
Maximum137032
Range136071
Interquartile range (IQR)29670

Descriptive statistics

Standard deviation32160.053
Coefficient of variation (CV)1.2131196
Kurtosis6.445023
Mean26510.207
Median Absolute Deviation (MAD)13118
Skewness2.4480145
Sum768796
Variance1.034269 × 109
MonotonicityNot monotonic
2023-12-13T07:32:19.092850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4305 1
 
3.4%
9435 1
 
3.4%
4986 1
 
3.4%
27341 1
 
3.4%
30866 1
 
3.4%
38240 1
 
3.4%
36302 1
 
3.4%
20549 1
 
3.4%
35006 1
 
3.4%
5756 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
961 1
3.4%
2981 1
3.4%
3092 1
3.4%
3178 1
3.4%
4305 1
3.4%
4561 1
3.4%
4986 1
3.4%
5756 1
3.4%
6077 1
3.4%
6165 1
3.4%
ValueCountFrequency (%)
137032 1
3.4%
122317 1
3.4%
47978 1
3.4%
47083 1
3.4%
38240 1
3.4%
36672 1
3.4%
36302 1
3.4%
35426 1
3.4%
35006 1
3.4%
30866 1
3.4%

건축허가일자
Date

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum1991-12-12 00:00:00
Maximum2021-06-08 00:00:00
2023-12-13T07:32:19.201792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:19.319882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

건축착공일자
Date

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum1991-12-27 00:00:00
Maximum2021-09-30 00:00:00
2023-12-13T07:32:19.446867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:19.575464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

준공연월
Date

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum1992-07-01 00:00:00
Maximum2023-02-01 00:00:00
2023-12-13T07:32:19.693236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:19.803012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

건축사용승인일
Date

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum1992-07-27 00:00:00
Maximum2023-01-10 00:00:00
2023-12-13T07:32:19.917101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:20.046162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3793103
Minimum4
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T07:32:20.154982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median7
Q311
95-th percentile14.6
Maximum34
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.7346926
Coefficient of variation (CV)0.61141943
Kurtosis11.922541
Mean9.3793103
Median Absolute Deviation (MAD)2
Skewness3.003283
Sum272
Variance32.8867
MonotonicityNot monotonic
2023-12-13T07:32:20.258203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 6
20.7%
5 4
13.8%
6 4
13.8%
14 3
10.3%
10 2
 
6.9%
9 2
 
6.9%
8 2
 
6.9%
13 2
 
6.9%
11 1
 
3.4%
15 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
4 1
 
3.4%
5 4
13.8%
6 4
13.8%
7 6
20.7%
8 2
 
6.9%
9 2
 
6.9%
10 2
 
6.9%
11 1
 
3.4%
13 2
 
6.9%
14 3
10.3%
ValueCountFrequency (%)
34 1
 
3.4%
15 1
 
3.4%
14 3
10.3%
13 2
 
6.9%
11 1
 
3.4%
10 2
 
6.9%
9 2
 
6.9%
8 2
 
6.9%
7 6
20.7%
6 4
13.8%

지하층수
Categorical

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
1
13 
2
8 
3
4 
0
4 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 13
44.8%
2 8
27.6%
3 4
 
13.8%
0 4
 
13.8%

Length

2023-12-13T07:32:20.367640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:32:20.488029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 13
44.8%
2 8
27.6%
3 4
 
13.8%
0 4
 
13.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-07-31
29 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 29
100.0%

Length

2023-12-13T07:32:20.617776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:32:20.728579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 29
100.0%

Interactions

2023-12-13T07:32:15.716055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:14.325341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:14.995752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.357444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.801754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:14.423162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.086922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.449277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.886019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:14.533450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.172472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.534124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:16.038600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:14.899092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.273399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:15.636304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:32:20.821657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지식센터명소재지주소전화번호업체수부지면적건축면적건축허가일자건축착공일자준공연월건축사용승인일지상층수지하층수
지식센터명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업체수1.0001.0001.0001.0000.8090.8551.0001.0000.9091.0000.8270.583
부지면적1.0001.0001.0000.8091.0000.9841.0001.0000.9431.0000.6690.476
건축면적1.0001.0001.0000.8550.9841.0001.0001.0000.8171.0000.6520.428
건축허가일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
건축착공일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
준공연월1.0001.0001.0000.9090.9430.8171.0001.0001.0001.0000.8020.848
건축사용승인일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지상층수1.0001.0001.0000.8270.6690.6521.0001.0000.8021.0001.0000.421
지하층수1.0001.0001.0000.5830.4760.4281.0001.0000.8481.0000.4211.000
2023-12-13T07:32:20.996105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체수부지면적건축면적지상층수지하층수
업체수1.0000.7560.8540.7120.251
부지면적0.7561.0000.9410.4890.330
건축면적0.8540.9411.0000.6240.295
지상층수0.7120.4890.6241.0000.061
지하층수0.2510.3300.2950.0611.000

Missing values

2023-12-13T07:32:16.219140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:32:16.418893image/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

지식센터명소재지주소전화번호업체수부지면적건축면적건축허가일자건축착공일자준공연월건축사용승인일지상층수지하층수데이터기준일자
0삼풍데파트경기도 군포시 당정로 57031-429-947720100143051991-12-121991-12-271992-07-011992-07-27522023-07-31
1삼풍프라자경기도 군포시 봉성로 82-7031-452-908624198494351993-09-241993-11-171994-11-011994-11-23722023-07-31
2군포제일공단경기도 군포시 당정로75번길 23031-454-6617604177168201995-12-301996-02-061997-04-011997-04-14612023-07-31
3한솔테크노타운경기도 군포시 경수대로 455031-456-33191107384354261996-11-181996-12-041998-08-011998-09-241032023-07-31
4신라테크노빌경기도 군포시 당정로 90031-453-5942486760299381995-04-101995-06-131998-09-011998-09-29912023-07-31
5동영벤처스텔경기도 군포시 공단로 97031-456-1771672429811998-11-271999-08-302000-04-012000-04-11712023-07-31
6금봉테크노벨리 1경기도 군포시 산본로48번길 32031-453-424813153661651999-08-102000-03-172001-06-012001-06-25912023-07-31
7예성팩토링경기도 군포시 공단로 223031-459-64501699745612001-03-212001-04-182001-11-012001-11-26712023-07-31
8한림벤처타운경기도 군포시 공단로 284031-454-9778764542222322002-04-102002-04-182003-05-012003-03-17712023-07-31
9에이프로테크노피아경기도 군포시 엘에스로115번길 60031-441-4001375631782002-08-142002-09-192003-05-012003-05-20512023-07-31
지식센터명소재지주소전화번호업체수부지면적건축면적건축허가일자건축착공일자준공연월건축사용승인일지상층수지하층수데이터기준일자
19군포신일IT유토경기도 군포시 엘에스로 13031-689-34101718795479782008-09-182010-03-122011-10-012011-09-151532023-07-31
20군포IT밸리경기도 군포시 고산로148번길 17031-8068-1111619176581370322010-10-072010-12-102013-06-012013-06-283432023-07-31
21신광프라자경기도 군포시 공단로140번길 32-19031-451-29119313857562015-08-042015-08-212016-02-012016-02-26402023-07-31
22엠테크노센터경기도 군포시 공단로140번길 46031-470-27152058717350062015-11-102016-07-012018-02-012018-02-271322023-07-31
23에이스하이테크시티군포경기도 군포시 공단로140번길 52031-5183-07002545271205492017-05-252017-09-052019-09-012019-09-061422023-07-31
24스카이비즈경기도 군포시 농심로 2031-8033-00911734371363022018-04-232018-05-302020-04-012020-04-091422023-07-31
25에이스더블유밸리경기도 군포시 군포첨단산업2로7번길 8031-462-91174096152382402019-04-122019-04-182021-07-012021-07-281332023-07-31
26군포LS지식산업센터경기도 군포시 공단로140번길 2702-2189-9198912594308662020-05-222020-09-072022-01-012022-01-28622023-07-31
27센트럴비즈파크경기도 군포시 공단로140번길 38031-429-83662003605273412020-01-312020-09-032022-07-012022-07-211412023-07-31
28성우빌딩경기도 군포시 공단로 356-16031-362-31002058849862021-06-082021-09-302023-02-012023-01-10712023-07-31