Overview

Dataset statistics

Number of variables20
Number of observations47
Missing cells35
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory166.8 B

Variable types

Categorical5
Text9
Numeric4
DateTime2

Dataset

Description경기도 부천시의 지식산업센터 정보로 지식산업센터명칭, 전화번호, 소재지(도로명,지번), 용도지역, 유치가능업체수, 공장동수, 분양형태, 준공연도 ,사용승인일 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15020749/fileData.do

Alerts

시군명 has constant value ""Constant
공사진행상황 has constant value ""Constant
데이터기준일자 has constant value ""Constant
용도지역 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
분양형태 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
층수(지하_지상) is highly overall correlated with 위도 and 1 other fieldsHigh correlation
분양형태 is highly imbalanced (85.1%)Imbalance
관리실 연락처 has 22 (46.8%) missing valuesMissing
위도 has 6 (12.8%) missing valuesMissing
경도 has 6 (12.8%) missing valuesMissing
건축면적 has 1 (2.1%) missing valuesMissing
지식산업센터명칭 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
공장시설면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:33:06.542604
Analysis finished2023-12-12 17:33:09.731334
Duration3.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
경기도 부천시
47 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 부천시
2nd row경기도 부천시
3rd row경기도 부천시
4th row경기도 부천시
5th row경기도 부천시

Common Values

ValueCountFrequency (%)
경기도 부천시 47
100.0%

Length

2023-12-13T02:33:09.838379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:33:09.951766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 47
50.0%
부천시 47
50.0%

지식산업센터명칭
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:10.184493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.1276596
Min length4

Characters and Unicode

Total characters382
Distinct characters117
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row대우테크노타운
2nd row부천콘텐츠센터
3rd row부천테크노파크1단지
4th row부천테크노파크2단지
5th row다우테크노타운
ValueCountFrequency (%)
대우테크노타운 1
 
1.9%
신중동 1
 
1.9%
서림테크노파크 1
 
1.9%
3차 1
 
1.9%
삼보테크노타워 1
 
1.9%
디클래스i 1
 
1.9%
인철테크노밸리2차 1
 
1.9%
구심이엔지 1
 
1.9%
비즈타워 1
 
1.9%
주)아이에스인터내셔날 1
 
1.9%
Other values (44) 44
81.5%
2023-12-13T02:33:10.641320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
크 48
 
12.6%
테 32
 
8.4%
노 32
 
8.4%
리 14
 
3.7%
파 14
 
3.7%
부 9
 
2.4%
천 8
 
2.1%
밸 8
 
2.1%
타 8
 
2.1%
7
 
1.8%
Other values (107) 202
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 342
89.5%
Decimal Number 12
 
3.1%
Uppercase Letter 9
 
2.4%
Space Separator 7
 
1.8%
Lowercase Letter 5
 
1.3%
Letter Number 3
 
0.8%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
크 48
 
14.0%
테 32
 
9.4%
노 32
 
9.4%
리 14
 
4.1%
파 14
 
4.1%
부 9
 
2.6%
천 8
 
2.3%
밸 8
 
2.3%
타 8
 
2.3%
벨 6
 
1.8%
Other values (86) 163
47.7%
Uppercase Letter
ValueCountFrequency (%)
I 4
44.4%
U 1
 
11.1%
C 1
 
11.1%
T 1
 
11.1%
B 1
 
11.1%
K 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
3 4
33.3%
2 3
25.0%
1 2
16.7%
4 1
 
8.3%
5 1
 
8.3%
7 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
n 1
20.0%
t 1
20.0%
r 1
20.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅰ 1
33.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
89.5%
Common 23
 
6.0%
Latin 17
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
크 48
 
14.0%
테 32
 
9.4%
노 32
 
9.4%
리 14
 
4.1%
파 14
 
4.1%
부 9
 
2.6%
천 8
 
2.3%
밸 8
 
2.3%
타 8
 
2.3%
벨 6
 
1.8%
Other values (86) 163
47.7%
Latin
ValueCountFrequency (%)
I 4
23.5%
e 2
11.8%
Ⅱ 2
11.8%
U 1
 
5.9%
C 1
 
5.9%
n 1
 
5.9%
t 1
 
5.9%
r 1
 
5.9%
T 1
 
5.9%
Ⅰ 1
 
5.9%
Other values (2) 2
11.8%
Common
ValueCountFrequency (%)
7
30.4%
3 4
17.4%
2 3
13.0%
1 2
 
8.7%
( 2
 
8.7%
) 2
 
8.7%
4 1
 
4.3%
5 1
 
4.3%
7 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 342
89.5%
ASCII 37
 
9.7%
Number Forms 3
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
크 48
 
14.0%
테 32
 
9.4%
노 32
 
9.4%
리 14
 
4.1%
파 14
 
4.1%
부 9
 
2.6%
천 8
 
2.3%
밸 8
 
2.3%
타 8
 
2.3%
벨 6
 
1.8%
Other values (86) 163
47.7%
ASCII
ValueCountFrequency (%)
7
18.9%
I 4
10.8%
3 4
10.8%
2 3
 
8.1%
e 2
 
5.4%
1 2
 
5.4%
( 2
 
5.4%
) 2
 
5.4%
4 1
 
2.7%
U 1
 
2.7%
Other values (9) 9
24.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅰ 1
33.3%

관리실 연락처
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing22
Missing (%)46.8%
Memory size508.0 B
2023-12-13T02:33:10.851107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters300
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

Unique25 ?
Unique (%)100.0%

Sample

1st row032-625-2963
2nd row032-621-1170
3rd row032-621-1180
4th row032-678-6272
5th row032-678-5471
ValueCountFrequency (%)
032-625-2963 1
 
4.0%
032-222-0200 1
 
4.0%
032-684-8340 1
 
4.0%
032-675-7773 1
 
4.0%
032-678-2779 1
 
4.0%
02-6474-4300 1
 
4.0%
032-342-6966 1
 
4.0%
032-326-4760 1
 
4.0%
032-624-0830 1
 
4.0%
032-678-9994 1
 
4.0%
Other values (15) 15
60.0%
2023-12-13T02:33:11.248632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 50
16.7%
0 49
16.3%
2 46
15.3%
3 43
14.3%
6 29
9.7%
7 22
7.3%
4 20
 
6.7%
1 15
 
5.0%
8 12
 
4.0%
9 10
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250
83.3%
Dash Punctuation 50
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49
19.6%
2 46
18.4%
3 43
17.2%
6 29
11.6%
7 22
8.8%
4 20
8.0%
1 15
 
6.0%
8 12
 
4.8%
9 10
 
4.0%
5 4
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 50
16.7%
0 49
16.3%
2 46
15.3%
3 43
14.3%
6 29
9.7%
7 22
7.3%
4 20
 
6.7%
1 15
 
5.0%
8 12
 
4.0%
9 10
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 50
16.7%
0 49
16.3%
2 46
15.3%
3 43
14.3%
6 29
9.7%
7 22
7.3%
4 20
 
6.7%
1 15
 
5.0%
8 12
 
4.0%
9 10
 
3.3%

소재지지번주소
Text

Distinct44
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:11.530327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length19.021277
Min length15

Characters and Unicode

Total characters894
Distinct characters48
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

Unique41 ?
Unique (%)87.2%

Sample

1st row경기도 부천시 송내동 558번지
2nd row경기도 부천시 송내동 398-4번지
3rd row경기도 부천시 삼정동 364번지 부천테크노파크
4th row경기도 부천시 약대동 192번지 부천테크노파크
5th row경기도 부천시 내동 344번지
ValueCountFrequency (%)
경기도 47
23.4%
부천시 47
23.4%
도당동 9
 
4.5%
오정동 8
 
4.0%
삼정동 6
 
3.0%
외 5
 
2.5%
옥길동 5
 
2.5%
춘의동 5
 
2.5%
1필지 4
 
2.0%
내동 4
 
2.0%
Other values (50) 61
30.3%
2023-12-13T02:33:11.963925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
154
17.2%
도 56
 
6.3%
부 49
 
5.5%
천 49
 
5.5%
경 47
 
5.3%
시 47
 
5.3%
기 47
 
5.3%
동 47
 
5.3%
지 42
 
4.7%
1 38
 
4.3%
Other values (38) 318
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 529
59.2%
Decimal Number 176
 
19.7%
Space Separator 154
 
17.2%
Dash Punctuation 35
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 56
10.6%
부 49
9.3%
천 49
9.3%
경 47
8.9%
시 47
8.9%
기 47
8.9%
동 47
8.9%
지 42
7.9%
번 37
 
7.0%
정 14
 
2.6%
Other values (26) 94
17.8%
Decimal Number
ValueCountFrequency (%)
1 38
21.6%
2 27
15.3%
9 21
11.9%
7 17
9.7%
8 15
 
8.5%
3 15
 
8.5%
6 12
 
6.8%
5 11
 
6.2%
0 10
 
5.7%
4 10
 
5.7%
Space Separator
ValueCountFrequency (%)
154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 529
59.2%
Common 365
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 56
10.6%
부 49
9.3%
천 49
9.3%
경 47
8.9%
시 47
8.9%
기 47
8.9%
동 47
8.9%
지 42
7.9%
번 37
 
7.0%
정 14
 
2.6%
Other values (26) 94
17.8%
Common
ValueCountFrequency (%)
154
42.2%
1 38
 
10.4%
- 35
 
9.6%
2 27
 
7.4%
9 21
 
5.8%
7 17
 
4.7%
8 15
 
4.1%
3 15
 
4.1%
6 12
 
3.3%
5 11
 
3.0%
Other values (2) 20
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 529
59.2%
ASCII 365
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154
42.2%
1 38
 
10.4%
- 35
 
9.6%
2 27
 
7.4%
9 21
 
5.8%
7 17
 
4.7%
8 15
 
4.1%
3 15
 
4.1%
6 12
 
3.3%
5 11
 
3.0%
Other values (2) 20
 
5.5%
Hangul
ValueCountFrequency (%)
도 56
10.6%
부 49
9.3%
천 49
9.3%
경 47
8.9%
시 47
8.9%
기 47
8.9%
동 47
8.9%
지 42
7.9%
번 37
 
7.0%
정 14
 
2.6%
Other values (26) 94
17.8%

소재지도로명주소
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:12.316318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length31
Mean length26.255319
Min length14

Characters and Unicode

Total characters1234
Distinct characters87
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row경기도 부천시 경인로133번길 14 (송내동)
2nd row경기도 부천시 경인로60번길 40 (송내동)
3rd row경기도 부천시 삼작로 22， 부천테크노파크 (삼정동)
4th row경기도 부천시 송내대로 388， 부천테크노파크 (약대동)
5th row경기도 부천시 삼작로233번길 57 (내동， 다우테크노타운)
ValueCountFrequency (%)
부천시 47
 
18.1%
경기도 46
 
17.8%
오정동 7
 
2.7%
신흥로511번길 7
 
2.7%
도당동 5
 
1.9%
외 5
 
1.9%
도당동， 4
 
1.5%
1필지 4
 
1.5%
삼정동 4
 
1.5%
내동， 3
 
1.2%
Other values (101) 127
49.0%
2023-12-13T02:33:12.744758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212
 
17.2%
천 64
 
5.2%
부 62
 
5.0%
도 59
 
4.8%
경 49
 
4.0%
시 47
 
3.8%
기 47
 
3.8%
로 46
 
3.7%
1 45
 
3.6%
동 41
 
3.3%
Other values (77) 562
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 711
57.6%
Space Separator 212
 
17.2%
Decimal Number 201
 
16.3%
Close Punctuation 41
 
3.3%
Open Punctuation 41
 
3.3%
Other Punctuation 14
 
1.1%
Dash Punctuation 10
 
0.8%
Uppercase Letter 2
 
0.2%
Lowercase Letter 1
 
0.1%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 64
 
9.0%
부 62
 
8.7%
도 59
 
8.3%
경 49
 
6.9%
시 47
 
6.6%
기 47
 
6.6%
로 46
 
6.5%
동 41
 
5.8%
길 25
 
3.5%
번 22
 
3.1%
Other values (59) 249
35.0%
Decimal Number
ValueCountFrequency (%)
1 45
22.4%
3 34
16.9%
2 28
13.9%
5 20
10.0%
8 18
 
9.0%
0 14
 
7.0%
7 12
 
6.0%
9 11
 
5.5%
6 10
 
5.0%
4 9
 
4.5%
Space Separator
ValueCountFrequency (%)
212
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Other Punctuation
ValueCountFrequency (%)
， 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 711
57.6%
Common 519
42.1%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 64
 
9.0%
부 62
 
8.7%
도 59
 
8.3%
경 49
 
6.9%
시 47
 
6.6%
기 47
 
6.6%
로 46
 
6.5%
동 41
 
5.8%
길 25
 
3.5%
번 22
 
3.1%
Other values (59) 249
35.0%
Common
ValueCountFrequency (%)
212
40.8%
1 45
 
8.7%
) 41
 
7.9%
( 41
 
7.9%
3 34
 
6.6%
2 28
 
5.4%
5 20
 
3.9%
8 18
 
3.5%
0 14
 
2.7%
， 14
 
2.7%
Other values (5) 52
 
10.0%
Latin
ValueCountFrequency (%)
I 2
50.0%
y 1
25.0%
Ⅱ 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 711
57.6%
ASCII 508
41.2%
None 14
 
1.1%
Number Forms 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
41.7%
1 45
 
8.9%
) 41
 
8.1%
( 41
 
8.1%
3 34
 
6.7%
2 28
 
5.5%
5 20
 
3.9%
8 18
 
3.5%
0 14
 
2.8%
7 12
 
2.4%
Other values (6) 43
 
8.5%
Hangul
ValueCountFrequency (%)
천 64
 
9.0%
부 62
 
8.7%
도 59
 
8.3%
경 49
 
6.9%
시 47
 
6.6%
기 47
 
6.6%
로 46
 
6.5%
동 41
 
5.8%
길 25
 
3.5%
번 22
 
3.1%
Other values (59) 249
35.0%
None
ValueCountFrequency (%)
， 14
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)97.6%
Missing6
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean37.510151
Minimum37.466419
Maximum37.528504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T02:33:12.902025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.466419
5-th percentile37.467796
Q137.501477
median37.512926
Q337.523177
95-th percentile37.528462
Maximum37.528504
Range0.06208537
Interquartile range (IQR)0.02170061

Descriptive statistics

Standard deviation0.017349245
Coefficient of variation (CV)0.00046252134
Kurtosis0.80801823
Mean37.510151
Median Absolute Deviation (MAD)0.01144956
Skewness-1.1568587
Sum1537.9162
Variance0.00030099631
MonotonicityNot monotonic
2023-12-13T02:33:13.039153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
37.52797858 2
 
4.3%
37.51969809 1
 
2.1%
37.52787702 1
 
2.1%
37.52796486 1
 
2.1%
37.52846222 1
 
2.1%
37.52846527 1
 
2.1%
37.51375683 1
 
2.1%
37.49928384 1
 
2.1%
37.50163981 1
 
2.1%
37.52850437 1
 
2.1%
Other values (30) 30
63.8%
(Missing) 6
 
12.8%
ValueCountFrequency (%)
37.466419 1
2.1%
37.46764023 1
2.1%
37.46779646 1
2.1%
37.481952 1
2.1%
37.48488193 1
2.1%
37.48652838 1
2.1%
37.49928384 1
2.1%
37.50024013 1
2.1%
37.50080247 1
2.1%
37.50141974 1
2.1%
ValueCountFrequency (%)
37.52850437 1
2.1%
37.52846527 1
2.1%
37.52846222 1
2.1%
37.52846008 1
2.1%
37.52797858 2
4.3%
37.52796486 1
2.1%
37.52787702 1
2.1%
37.52657271 1
2.1%
37.52605357 1
2.1%
37.52317731 1
2.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)97.6%
Missing6
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean126.78065
Minimum126.75407
Maximum126.82758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T02:33:13.158822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.75407
5-th percentile126.76164
Q1126.77485
median126.77643
Q3126.787
95-th percentile126.82418
Maximum126.82758
Range0.0735104
Interquartile range (IQR)0.0121516

Descriptive statistics

Standard deviation0.015884302
Coefficient of variation (CV)0.00012528964
Kurtosis2.983379
Mean126.78065
Median Absolute Deviation (MAD)0.007886
Skewness1.4520942
Sum5198.0067
Variance0.00025231106
MonotonicityNot monotonic
2023-12-13T02:33:13.287619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
126.7753546 2
 
4.3%
126.783209 1
 
2.1%
126.7764308 1
 
2.1%
126.7760564 1
 
2.1%
126.774852 1
 
2.1%
126.7753499 1
 
2.1%
126.775594 1
 
2.1%
126.7923868 1
 
2.1%
126.7914595 1
 
2.1%
126.7612931 1
 
2.1%
Other values (30) 30
63.8%
(Missing) 6
 
12.8%
ValueCountFrequency (%)
126.7540716 1
2.1%
126.7612931 1
2.1%
126.7616386 1
2.1%
126.7622788 1
2.1%
126.7630935 1
2.1%
126.7640485 1
2.1%
126.7647743 1
2.1%
126.7670606 1
2.1%
126.7695398 1
2.1%
126.7704323 1
2.1%
ValueCountFrequency (%)
126.827582 1
2.1%
126.8252307 1
2.1%
126.8241776 1
2.1%
126.7923868 1
2.1%
126.7914595 1
2.1%
126.7901098 1
2.1%
126.7895268 1
2.1%
126.7893346 1
2.1%
126.7883159 1
2.1%
126.7880588 1
2.1%

용도지역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size508.0 B
일반공업
19 
준주거
15 
준공업
7 
근린상업
4 
제2종일반주거
 
1

Length

Max length7
Median length4
Mean length3.5531915
Min length2

Unique

Unique2 ?
Unique (%)4.3%

Sample

1st row준주거
2nd row제2종일반주거
3rd row근린상업
4th row근린상업
5th row주거

Common Values

ValueCountFrequency (%)
일반공업 19
40.4%
준주거 15
31.9%
준공업 7
 
14.9%
근린상업 4
 
8.5%
제2종일반주거 1
 
2.1%
주거 1
 
2.1%

Length

2023-12-13T02:33:13.420315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:33:13.519967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반공업 19
40.4%
준주거 15
31.9%
준공업 7
 
14.9%
근린상업 4
 
8.5%
제2종일반주거 1
 
2.1%
주거 1
 
2.1%

부지면적
Text

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:13.730745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.4468085
Min length3

Characters and Unicode

Total characters209
Distinct characters12
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

Unique45 ?
Unique (%)95.7%

Sample

1st row1388
2nd row4671
3rd row32451
4th row31790
5th row2437
ValueCountFrequency (%)
2784 2
 
4.3%
2579 1
 
2.1%
3506.2 1
 
2.1%
2486 1
 
2.1%
1477 1
 
2.1%
1061 1
 
2.1%
1319 1
 
2.1%
19410 1
 
2.1%
490 1
 
2.1%
1344 1
 
2.1%
Other values (36) 36
76.6%
2023-12-13T02:33:14.427139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32
15.3%
2 26
12.4%
4 23
11.0%
3 20
9.6%
5 20
9.6%
8 19
9.1%
7 18
8.6%
0 17
8.1%
9 16
7.7%
6 9
 
4.3%
Other values (2) 9
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
95.7%
Other Punctuation 9
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
16.0%
2 26
13.0%
4 23
11.5%
3 20
10.0%
5 20
10.0%
8 19
9.5%
7 18
9.0%
0 17
8.5%
9 16
8.0%
6 9
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 6
66.7%
, 3
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 209
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32
15.3%
2 26
12.4%
4 23
11.0%
3 20
9.6%
5 20
9.6%
8 19
9.1%
7 18
8.6%
0 17
8.1%
9 16
7.7%
6 9
 
4.3%
Other values (2) 9
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32
15.3%
2 26
12.4%
4 23
11.0%
3 20
9.6%
5 20
9.6%
8 19
9.1%
7 18
8.6%
0 17
8.1%
9 16
7.7%
6 9
 
4.3%
Other values (2) 9
 
4.3%

건축면적
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing1
Missing (%)2.1%
Memory size508.0 B
2023-12-13T02:33:14.698890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.4782609
Min length6

Characters and Unicode

Total characters344
Distinct characters12
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

Unique46 ?
Unique (%)100.0%

Sample

1st row4013.54
2nd row3058.69
3rd row74787.42
4th row89106.19
5th row7970.49
ValueCountFrequency (%)
4013.54 1
 
2.2%
4210.3 1
 
2.2%
4732.58 1
 
2.2%
4515.35 1
 
2.2%
5518.28 1
 
2.2%
181159.81 1
 
2.2%
1326.32 1
 
2.2%
5742.81 1
 
2.2%
3878.9 1
 
2.2%
13358.95 1
 
2.2%
Other values (36) 36
78.3%
2023-12-13T02:33:15.137079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 46
13.4%
3 40
11.6%
4 36
10.5%
1 34
9.9%
5 34
9.9%
8 32
9.3%
2 29
8.4%
7 27
7.8%
9 27
7.8%
0 19
5.5%
Other values (2) 20
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297
86.3%
Other Punctuation 47
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 40
13.5%
4 36
12.1%
1 34
11.4%
5 34
11.4%
8 32
10.8%
2 29
9.8%
7 27
9.1%
9 27
9.1%
0 19
6.4%
6 19
6.4%
Other Punctuation
ValueCountFrequency (%)
. 46
97.9%
, 1
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 46
13.4%
3 40
11.6%
4 36
10.5%
1 34
9.9%
5 34
9.9%
8 32
9.3%
2 29
8.4%
7 27
7.8%
9 27
7.8%
0 19
5.5%
Other values (2) 20
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 46
13.4%
3 40
11.6%
4 36
10.5%
1 34
9.9%
5 34
9.9%
8 32
9.3%
2 29
8.4%
7 27
7.8%
9 27
7.8%
0 19
5.5%
Other values (2) 20
5.8%

층수(지하_지상)
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
B1/6
9 
B1/15
7 
B1/7
4 
B0/5
3 
B3/10
3 
Other values (15)
21 

Length

Max length5
Median length4
Mean length4.3829787
Min length4

Unique

Unique9 ?
Unique (%)19.1%

Sample

1st rowB1/15
2nd rowB1/4
3rd rowB1/9
4th rowB1/15
5th rowB1/6

Common Values

ValueCountFrequency (%)
B1/6 9
19.1%
B1/15 7
14.9%
B1/7 4
 
8.5%
B0/5 3
 
6.4%
B3/10 3
 
6.4%
B0/4 2
 
4.3%
B4/8 2
 
4.3%
B2/10 2
 
4.3%
B1/5 2
 
4.3%
B2/7 2
 
4.3%
Other values (10) 11
23.4%

Length

2023-12-13T02:33:15.301565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b1/6 9
19.1%
b1/15 7
14.9%
b1/7 4
 
8.5%
b0/5 3
 
6.4%
b3/10 3
 
6.4%
b1/5 2
 
4.3%
b2/7 2
 
4.3%
b1/9 2
 
4.3%
b2/10 2
 
4.3%
b4/8 2
 
4.3%
Other values (10) 11
23.4%

공장시설면적
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:15.531147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.3191489
Min length6

Characters and Unicode

Total characters344
Distinct characters12
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

Unique47 ?
Unique (%)100.0%

Sample

1st row3045.5
2nd row1098.616
3rd row69871.72
4th row69782.15
5th row5733.04
ValueCountFrequency (%)
3045.5 1
 
2.1%
3338.1 1
 
2.1%
4085.6 1
 
2.1%
4191.99 1
 
2.1%
5518.28 1
 
2.1%
170755.24 1
 
2.1%
1151.56 1
 
2.1%
5266.45 1
 
2.1%
3878.9 1
 
2.1%
9366.96 1
 
2.1%
Other values (37) 37
78.7%
2023-12-13T02:33:16.013668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 46
13.4%
1 42
12.2%
8 34
9.9%
5 33
9.6%
4 32
9.3%
9 32
9.3%
3 28
8.1%
6 25
7.3%
2 24
7.0%
7 24
7.0%
Other values (2) 24
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 295
85.8%
Other Punctuation 49
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 42
14.2%
8 34
11.5%
5 33
11.2%
4 32
10.8%
9 32
10.8%
3 28
9.5%
6 25
8.5%
2 24
8.1%
7 24
8.1%
0 21
7.1%
Other Punctuation
ValueCountFrequency (%)
. 46
93.9%
, 3
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 46
13.4%
1 42
12.2%
8 34
9.9%
5 33
9.6%
4 32
9.3%
9 32
9.3%
3 28
8.1%
6 25
7.3%
2 24
7.0%
7 24
7.0%
Other values (2) 24
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 46
13.4%
1 42
12.2%
8 34
9.9%
5 33
9.6%
4 32
9.3%
9 32
9.3%
3 28
8.1%
6 25
7.3%
2 24
7.0%
7 24
7.0%
Other values (2) 24
7.0%

지원시설면적
Text

Distinct44
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:16.231446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.2978723
Min length1

Characters and Unicode

Total characters296
Distinct characters12
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

Unique43 ?
Unique (%)91.5%

Sample

1st row968.04
2nd row1434.734
3rd row4915.7
4th row19324.04
5th row2237.46
ValueCountFrequency (%)
0 4
 
8.5%
968.04 1
 
2.1%
198.18 1
 
2.1%
1434.734 1
 
2.1%
872.2 1
 
2.1%
646.98 1
 
2.1%
323.36 1
 
2.1%
10404.57 1
 
2.1%
174.76 1
 
2.1%
476.36 1
 
2.1%
Other values (34) 34
72.3%
2023-12-13T02:33:16.592406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 42
14.2%
1 37
12.5%
6 33
11.1%
4 28
9.5%
3 25
8.4%
9 24
8.1%
2 24
8.1%
8 21
7.1%
0 20
6.8%
7 20
6.8%
Other values (2) 22
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 252
85.1%
Other Punctuation 44
 
14.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
14.7%
6 33
13.1%
4 28
11.1%
3 25
9.9%
9 24
9.5%
2 24
9.5%
8 21
8.3%
0 20
7.9%
7 20
7.9%
5 20
7.9%
Other Punctuation
ValueCountFrequency (%)
. 42
95.5%
, 2
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42
14.2%
1 37
12.5%
6 33
11.1%
4 28
9.5%
3 25
8.4%
9 24
8.1%
2 24
8.1%
8 21
7.1%
0 20
6.8%
7 20
6.8%
Other values (2) 22
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42
14.2%
1 37
12.5%
6 33
11.1%
4 28
9.5%
3 25
8.4%
9 24
8.1%
2 24
8.1%
8 21
7.1%
0 20
6.8%
7 20
6.8%
Other values (2) 22
7.4%

유치가능업체수
Text

Distinct33
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-13T02:33:16.808417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2553191
Min length1

Characters and Unicode

Total characters106
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

Unique23 ?
Unique (%)48.9%

Sample

1st row483
2nd row26
3rd row400
4th row450
5th row28
ValueCountFrequency (%)
6 5
 
10.6%
400 3
 
6.4%
483 2
 
4.3%
100 2
 
4.3%
26 2
 
4.3%
9 2
 
4.3%
11 2
 
4.3%
260 2
 
4.3%
28 2
 
4.3%
7 2
 
4.3%
Other values (23) 23
48.9%
2023-12-13T02:33:17.145892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
18.9%
1 15
14.2%
6 12
11.3%
3 12
11.3%
4 10
9.4%
2 10
9.4%
8 9
8.5%
5 9
8.5%
7 5
 
4.7%
9 3
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
99.1%
Other Punctuation 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
19.0%
1 15
14.3%
6 12
11.4%
3 12
11.4%
4 10
9.5%
2 10
9.5%
8 9
8.6%
5 9
8.6%
7 5
 
4.8%
9 3
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
18.9%
1 15
14.2%
6 12
11.3%
3 12
11.3%
4 10
9.4%
2 10
9.4%
8 9
8.5%
5 9
8.5%
7 5
 
4.7%
9 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20
18.9%
1 15
14.2%
6 12
11.3%
3 12
11.3%
4 10
9.4%
2 10
9.4%
8 9
8.5%
5 9
8.5%
7 5
 
4.7%
9 3
 
2.8%

공장동수
Real number (ℝ)

Distinct6
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5957447
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T02:33:17.301077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.623999
Coefficient of variation (CV)1.017706
Kurtosis15.544771
Mean1.5957447
Median Absolute Deviation (MAD)0
Skewness3.6219417
Sum75
Variance2.6373728
MonotonicityNot monotonic
2023-12-13T02:33:17.427614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 39
83.0%
4 4
 
8.5%
5 1
 
2.1%
3 1
 
2.1%
2 1
 
2.1%
10 1
 
2.1%
ValueCountFrequency (%)
1 39
83.0%
2 1
 
2.1%
3 1
 
2.1%
4 4
 
8.5%
5 1
 
2.1%
10 1
 
2.1%
ValueCountFrequency (%)
10 1
 
2.1%
5 1
 
2.1%
4 4
 
8.5%
3 1
 
2.1%
2 1
 
2.1%
1 39
83.0%

분양형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
분양
46 
임대
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row분양
2nd row임대
3rd row분양
4th row분양
5th row분양

Common Values

ValueCountFrequency (%)
분양 46
97.9%
임대 1
 
2.1%

Length

2023-12-13T02:33:17.581055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:33:17.697814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분양 46
97.9%
임대 1
 
2.1%

공사진행상황
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
사용승인
47 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사용승인 47
100.0%

Length

2023-12-13T02:33:17.818537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:33:17.922866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사용승인 47
100.0%

준공연도
Real number (ℝ)

Distinct17
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4468
Minimum2000
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T02:33:18.036514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2002.3
Q12005
median2017
Q32019
95-th percentile2022.7
Maximum2023
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.5780246
Coefficient of variation (CV)0.0037637074
Kurtosis-1.4276297
Mean2013.4468
Median Absolute Deviation (MAD)5
Skewness-0.43774959
Sum94632
Variance57.426457
MonotonicityNot monotonic
2023-12-13T02:33:18.172269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2017 8
17.0%
2004 4
 
8.5%
2022 4
 
8.5%
2018 4
 
8.5%
2023 3
 
6.4%
2003 3
 
6.4%
2005 3
 
6.4%
2016 3
 
6.4%
2019 3
 
6.4%
2021 2
 
4.3%
Other values (7) 10
21.3%
ValueCountFrequency (%)
2000 2
4.3%
2002 1
 
2.1%
2003 3
6.4%
2004 4
8.5%
2005 3
6.4%
2006 2
4.3%
2007 1
 
2.1%
2008 1
 
2.1%
2011 1
 
2.1%
2016 3
6.4%
ValueCountFrequency (%)
2023 3
 
6.4%
2022 4
8.5%
2021 2
 
4.3%
2020 2
 
4.3%
2019 3
 
6.4%
2018 4
8.5%
2017 8
17.0%
2016 3
 
6.4%
2011 1
 
2.1%
2008 1
 
2.1%

사용승인일
Date

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum2000-04-30 00:00:00
Maximum2023-06-01 00:00:00
2023-12-13T02:33:18.345934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.539500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum2023-07-17 00:00:00
Maximum2023-07-17 00:00:00
2023-12-13T02:33:18.692216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.813980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T02:33:08.755713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:07.571577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:07.982557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.404938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.840891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:07.682865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.088518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.498981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.942506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:07.796541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.227913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.597178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:09.020625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:07.881839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.318043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:08.674483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:33:18.918187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지식산업센터명칭관리실 연락처소재지지번주소소재지도로명주소위도경도용도지역부지면적건축면적층수(지하_지상)공장시설면적지원시설면적유치가능업체수공장동수분양형태준공연도사용승인일
지식산업센터명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리실 연락처1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0000.0000.9200.9891.0001.0000.9771.0000.9840.9551.0001.0000.9851.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0000.0001.0001.0000.8020.8181.0001.0000.8841.0000.9550.8890.0000.7970.5901.000
경도1.0001.0000.9201.0000.8021.0000.6991.0001.0000.7701.0000.3860.7120.2620.0000.4661.000
용도지역1.0001.0000.9891.0000.8180.6991.0001.0001.0000.8521.0000.9220.4540.7001.0000.7231.000
부지면적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.9921.0001.0001.0001.000
건축면적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
층수(지하_지상)1.0001.0000.9771.0000.8840.7700.8521.0001.0001.0001.0000.9660.9420.0001.0000.0001.000
공장시설면적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지원시설면적1.0001.0000.9841.0000.9550.3860.9220.9951.0000.9661.0001.0000.9921.0001.0000.9760.995
유치가능업체수1.0001.0000.9551.0000.8890.7120.4540.9921.0000.9421.0000.9921.0000.5360.0000.7900.992
공장동수1.0001.0001.0001.0000.0000.2620.7001.0001.0000.0001.0001.0000.5361.0000.0000.4531.000
분양형태1.0001.0001.0001.0000.7970.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0001.000
준공연도1.0001.0000.9851.0000.5900.4660.7231.0001.0000.0001.0000.9760.7900.4530.0001.0001.000
사용승인일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9950.9921.0001.0001.0001.000
2023-12-13T02:33:19.128167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층수(지하_지상)용도지역분양형태
층수(지하_지상)1.0000.4880.775
용도지역0.4881.0000.955
분양형태0.7750.9551.000
2023-12-13T02:33:19.244898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도공장동수준공연도용도지역층수(지하_지상)분양형태
위도1.000-0.5250.061-0.1370.6150.5340.566
경도-0.5251.000-0.2920.2570.5010.3970.000
공장동수0.061-0.2921.000-0.4750.3150.0000.000
준공연도-0.1370.257-0.4751.0000.3070.0000.000
용도지역0.6150.5010.3150.3071.0000.4880.955
층수(지하_지상)0.5340.3970.0000.0000.4881.0000.775
분양형태0.5660.0000.0000.0000.9550.7751.000

Missing values

2023-12-13T02:33:09.164927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:33:09.463196image/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.
2023-12-13T02:33:09.643230image/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

시군명지식산업센터명칭관리실 연락처소재지지번주소소재지도로명주소위도경도용도지역부지면적건축면적층수(지하_지상)공장시설면적지원시설면적유치가능업체수공장동수분양형태공사진행상황준공연도사용승인일데이터기준일자
0경기도 부천시대우테크노타운<NA>경기도 부천시 송내동 558번지경기도 부천시 경인로133번길 14 (송내동)37.484882126.770432준주거13884013.54B1/153045.5968.044831분양사용승인20112011-01-202023-07-17
1경기도 부천시부천콘텐츠센터032-625-2963경기도 부천시 송내동 398-4번지경기도 부천시 경인로60번길 40 (송내동)37.481952126.761639제2종일반주거46713058.69B1/41098.6161434.734261임대사용승인20172017-07-282023-07-17
2경기도 부천시부천테크노파크1단지032-621-1170경기도 부천시 삼정동 364번지 부천테크노파크경기도 부천시 삼작로 22， 부천테크노파크 (삼정동)37.519214126.763093근린상업3245174787.42B1/969871.724915.74004분양사용승인20002000-04-302023-07-17
3경기도 부천시부천테크노파크2단지032-621-1180경기도 부천시 약대동 192번지 부천테크노파크경기도 부천시 송내대로 388， 부천테크노파크 (약대동)37.516901126.762279근린상업3179089106.19B1/1569782.1519324.044504분양사용승인20002000-10-242023-07-17
4경기도 부천시다우테크노타운032-678-6272경기도 부천시 내동 344번지경기도 부천시 삼작로233번길 57 (내동， 다우테크노타운)37.522876126.787004주거24377970.49B1/65733.042237.46281분양사용승인20022002-07-262023-07-17
5경기도 부천시다우테크노타운Ⅱ032-678-5471경기도 부천시 내동 196번지경기도 부천시 옥산로 276-1 (내동， 다우테크노타운Ⅱ)37.518259126.780778일반공업10444058.8B1/62627.11365.92281분양사용승인20032003-05-102023-07-17
6경기도 부천시송내테크노밸리032-651-7317경기도 부천시 송내동 299-5번지 외 1필지경기도 부천시 송내대로30번길 13 (송내동) (총 2 필지) 외 1필지37.486528126.754072준공업327814866.28B1/77986.656879.63371분양사용승인20032003-10-112023-07-17
7경기도 부천시뉴테크노타운<NA>경기도 부천시 내동 212-28번지경기도 부천시 오정로 183-9 (내동， 뉴테크노타운)37.526054126.780409일반공업9923638.7B1/61918.41717.13121분양사용승인20032003-12-302023-07-17
8경기도 부천시부천테크노파크제4공장032-234-3140경기도 부천시 약대동 193번지경기도 부천시 평천로 655 (약대동， 부천 테크노파크)37.517606126.764049근린상업2652177056.96B1/1574492.542270.824004분양사용승인20042004-03-262023-07-17
9경기도 부천시부천테크노파크제3공장032-234-3040경기도 부천시 삼정동 365번지 외 1필지경기도 부천시 석천로 345 (삼정동， 부천테크노파크) 외 1필지37.519756126.764774근린상업42937<NA>B1/9102124.219956.544005분양사용승인20042004-03-292023-07-17
시군명지식산업센터명칭관리실 연락처소재지지번주소소재지도로명주소위도경도용도지역부지면적건축면적층수(지하_지상)공장시설면적지원시설면적유치가능업체수공장동수분양형태공사진행상황준공연도사용승인일데이터기준일자
37경기도 부천시우성테크노파크<NA>경기도 부천시 옥길동 794-1경기도 부천시 부광로 220 (옥길동)37.46764126.825231준주거590845554.4B3/1031885.6113661.953351분양사용승인20202020-11-032023-07-17
38경기도 부천시신중동 더퍼스트 지식산업센터<NA>경기도 부천시 약대동 177경기도 부천시 정주로 53 (약대동)37.509192126.776328일반공업412929045.39B1/1320342.5048702.8861351분양사용승인20212021-02-042023-07-17
39경기도 부천시부천테크노밸리 U1 Center<NA>경기도 부천시 원미동 39-1번지경기도 부천시 조마루로385번길 96 (원미동)37.500802126.79011준공업10635104779.18B3/20100140.646638.543501분양사용승인20212021-06-212023-07-17
40경기도 부천시광양프런티어밸리5차지식산업센터02-6474-4300경기도 부천시 약대동 177경기도 부천시 양지로 237 (옥길동， 광양프런티어밸리 5차 지식산업센터)37.466419126.827582준주거5107.936421.33B2/1031417.025004.311001분양사용승인20222022-01-262023-07-17
41경기도 부천시레노부르크032-678-2779경기도 부천시 오정동 798경기도 부천시 신흥로511번길 180<NA><NA>준주거7817.138372.32B1/728070.5510301.771871분양사용승인20232023-05-252023-07-17
42경기도 부천시진택하이테크밸리032-675-7773경기도 부천시 삼정동 71-26경기도 부천시 오정로 110<NA><NA>일반공업1,3523,455.39B1/82,557.55897.84271분양사용승인20232023-03-272023-07-17
43경기도 부천시골든IT타워<NA>경기도 부천시 옥길동 794-1경기 부천시 부광로 220<NA><NA>준주거5,58836843.89B3/1026,237.4310,606.461001분양사용승인20222022-03-102023-07-17
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