Overview

Dataset statistics

Number of variables9
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory79.8 B

Variable types

Text3
DateTime1
Numeric4
Categorical1

Dataset

Description파일 다운로드
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-21859/F/1/datasetView.do

Alerts

면적합계 is highly overall correlated with 공장시설면적 and 2 other fieldsHigh correlation
공장시설면적 is highly overall correlated with 면적합계 and 2 other fieldsHigh correlation
지원시설면적 is highly overall correlated with 면적합계 and 2 other fieldsHigh correlation
건축형태(지상) is highly overall correlated with 면적합계 and 2 other fieldsHigh correlation
공장명 has unique valuesUnique
소재지 has unique valuesUnique
부지면적 has unique valuesUnique
면적합계 has unique valuesUnique
공장시설면적 has unique valuesUnique
지원시설면적 has 2 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-11 06:02:48.045461
Analysis finished2023-12-11 06:02:50.647625
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공장명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T15:02:50.847995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.8297872
Min length2

Characters and Unicode

Total characters415
Distinct characters103
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

Unique47 ?
Unique (%)100.0%

Sample

1st row에이스테크노타워1차
2nd row에이스테크노타워2차
3rd row에이스테크노타워3차
4th row에이스테크노타워5차
5th row에이스트윈테크노타워1차
ValueCountFrequency (%)
디지털타워 2
 
3.5%
에이스테크노타워1차 1
 
1.8%
코오롱빌란트1차 1
 
1.8%
아남빌딩 1
 
1.8%
대륭포스트타워3차 1
 
1.8%
에이스하이엔드타워2차 1
 
1.8%
벽산디지털밸리7차 1
 
1.8%
태평양물산 1
 
1.8%
한화비즈 1
 
1.8%
메트로1차 1
 
1.8%
Other values (46) 46
80.7%
2023-12-11T15:02:51.317763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
차 30
 
7.2%
타 25
 
6.0%
워 23
 
5.5%
스 20
 
4.8%
이 20
 
4.8%
1 11
 
2.7%
트 10
 
2.4%
크 10
 
2.4%
10
 
2.4%
2 10
 
2.4%
Other values (93) 246
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
81.9%
Decimal Number 31
 
7.5%
Uppercase Letter 20
 
4.8%
Space Separator 10
 
2.4%
Lowercase Letter 10
 
2.4%
Dash Punctuation 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 30
 
8.8%
타 25
 
7.4%
워 23
 
6.8%
스 20
 
5.9%
이 20
 
5.9%
트 10
 
2.9%
크 10
 
2.9%
에 9
 
2.6%
테 9
 
2.6%
노 9
 
2.6%
Other values (66) 175
51.5%
Uppercase Letter
ValueCountFrequency (%)
K 3
15.0%
T 3
15.0%
N 2
10.0%
S 2
10.0%
I 2
10.0%
H 1
 
5.0%
C 1
 
5.0%
G 1
 
5.0%
P 1
 
5.0%
V 1
 
5.0%
Other values (3) 3
15.0%
Decimal Number
ValueCountFrequency (%)
1 11
35.5%
2 10
32.3%
3 4
 
12.9%
7 2
 
6.5%
5 2
 
6.5%
8 1
 
3.2%
6 1
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 5
50.0%
n 2
 
20.0%
c 1
 
10.0%
t 1
 
10.0%
r 1
 
10.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
81.9%
Common 45
 
10.8%
Latin 30
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 30
 
8.8%
타 25
 
7.4%
워 23
 
6.8%
스 20
 
5.9%
이 20
 
5.9%
트 10
 
2.9%
크 10
 
2.9%
에 9
 
2.6%
테 9
 
2.6%
노 9
 
2.6%
Other values (66) 175
51.5%
Latin
ValueCountFrequency (%)
e 5
16.7%
K 3
 
10.0%
T 3
 
10.0%
N 2
 
6.7%
n 2
 
6.7%
S 2
 
6.7%
I 2
 
6.7%
H 1
 
3.3%
C 1
 
3.3%
G 1
 
3.3%
Other values (8) 8
26.7%
Common
ValueCountFrequency (%)
1 11
24.4%
10
22.2%
2 10
22.2%
3 4
 
8.9%
- 4
 
8.9%
7 2
 
4.4%
5 2
 
4.4%
8 1
 
2.2%
6 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
81.9%
ASCII 75
 
18.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
차 30
 
8.8%
타 25
 
7.4%
워 23
 
6.8%
스 20
 
5.9%
이 20
 
5.9%
트 10
 
2.9%
크 10
 
2.9%
에 9
 
2.6%
테 9
 
2.6%
노 9
 
2.6%
Other values (66) 175
51.5%
ASCII
ValueCountFrequency (%)
1 11
14.7%
10
13.3%
2 10
13.3%
e 5
 
6.7%
3 4
 
5.3%
- 4
 
5.3%
K 3
 
4.0%
T 3
 
4.0%
7 2
 
2.7%
N 2
 
2.7%
Other values (17) 21
28.0%

소재지
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T15:02:51.605528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length19.808511
Min length17

Characters and Unicode

Total characters931
Distinct characters31
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

Unique47 ?
Unique (%)100.0%

Sample

1st row서울특별시 구로구 구로동 197-17
2nd row서울특별시 구로구 구로동 197-7
3rd row서울특별시 구로구 구로동 197-48
4th row서울특별시 구로구 구로동 197-22
5th row서울특별시 구로구 구로동 212-1
ValueCountFrequency (%)
서울특별시 47
24.4%
구로구 47
24.4%
구로동 44
22.8%
2필지 2
 
1.0%
222-8 1
 
0.5%
212-8 1
 
0.5%
182-13 1
 
0.5%
197-5 1
 
0.5%
197-47 1
 
0.5%
182-4 1
 
0.5%
Other values (47) 47
24.4%
2023-12-11T15:02:52.077865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147
15.8%
구 139
14.9%
로 92
9.9%
1 55
 
5.9%
2 54
 
5.8%
서 47
 
5.0%
울 47
 
5.0%
특 47
 
5.0%
별 47
 
5.0%
시 47
 
5.0%
Other values (21) 209
22.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 530
56.9%
Decimal Number 212
 
22.8%
Space Separator 147
 
15.8%
Dash Punctuation 42
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 139
26.2%
로 92
17.4%
서 47
 
8.9%
울 47
 
8.9%
특 47
 
8.9%
별 47
 
8.9%
시 47
 
8.9%
동 47
 
8.9%
외 3
 
0.6%
필 3
 
0.6%
Other values (9) 11
 
2.1%
Decimal Number
ValueCountFrequency (%)
1 55
25.9%
2 54
25.5%
7 23
10.8%
3 18
 
8.5%
9 14
 
6.6%
8 13
 
6.1%
0 10
 
4.7%
4 9
 
4.2%
5 9
 
4.2%
6 7
 
3.3%
Space Separator
ValueCountFrequency (%)
147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 530
56.9%
Common 401
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 139
26.2%
로 92
17.4%
서 47
 
8.9%
울 47
 
8.9%
특 47
 
8.9%
별 47
 
8.9%
시 47
 
8.9%
동 47
 
8.9%
외 3
 
0.6%
필 3
 
0.6%
Other values (9) 11
 
2.1%
Common
ValueCountFrequency (%)
147
36.7%
1 55
 
13.7%
2 54
 
13.5%
- 42
 
10.5%
7 23
 
5.7%
3 18
 
4.5%
9 14
 
3.5%
8 13
 
3.2%
0 10
 
2.5%
4 9
 
2.2%
Other values (2) 16
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 530
56.9%
ASCII 401
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
36.7%
1 55
 
13.7%
2 54
 
13.5%
- 42
 
10.5%
7 23
 
5.7%
3 18
 
4.5%
9 14
 
3.5%
8 13
 
3.2%
0 10
 
2.5%
4 9
 
2.2%
Other values (2) 16
 
4.0%
Hangul
ValueCountFrequency (%)
구 139
26.2%
로 92
17.4%
서 47
 
8.9%
울 47
 
8.9%
특 47
 
8.9%
별 47
 
8.9%
시 47
 
8.9%
동 47
 
8.9%
외 3
 
0.6%
필 3
 
0.6%
Other values (9) 11
 
2.1%

준공일
Date

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum1996-07-09 00:00:00
Maximum2021-06-29 00:00:00
2023-12-11T15:02:52.250296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:52.442970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

부지면적
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-11T15:02:52.736805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1276596
Min length3

Characters and Unicode

Total characters194
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

Unique47 ?
Unique (%)100.0%

Sample

1st row5584
2nd row4959
3rd row6488
4th row6390
5th row6467
ValueCountFrequency (%)
5584 1
 
2.1%
11832 1
 
2.1%
12820 1
 
2.1%
10305 1
 
2.1%
7270 1
 
2.1%
1497 1
 
2.1%
7087 1
 
2.1%
6740 1
 
2.1%
3660 1
 
2.1%
6320 1
 
2.1%
Other values (37) 37
78.7%
2023-12-11T15:02:53.280813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 28
14.4%
6 27
13.9%
7 22
11.3%
0 21
10.8%
1 20
10.3%
5 16
8.2%
4 15
7.7%
9 15
7.7%
2 15
7.7%
3 14
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 193
99.5%
Other Punctuation 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 28
14.5%
6 27
14.0%
7 22
11.4%
0 21
10.9%
1 20
10.4%
5 16
8.3%
4 15
7.8%
9 15
7.8%
2 15
7.8%
3 14
7.3%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 28
14.4%
6 27
13.9%
7 22
11.3%
0 21
10.8%
1 20
10.3%
5 16
8.2%
4 15
7.7%
9 15
7.7%
2 15
7.7%
3 14
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 28
14.4%
6 27
13.9%
7 22
11.3%
0 21
10.8%
1 20
10.3%
5 16
8.2%
4 15
7.7%
9 15
7.7%
2 15
7.7%
3 14
7.2%

면적합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42195.598
Minimum2969.37
Maximum92905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T15:02:53.478060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2969.37
5-th percentile8352
Q128606
median41049
Q353464.5
95-th percentile85781
Maximum92905
Range89935.63
Interquartile range (IQR)24858.5

Descriptive statistics

Standard deviation22547.251
Coefficient of variation (CV)0.53435079
Kurtosis0.064102751
Mean42195.598
Median Absolute Deviation (MAD)12804
Skewness0.41869588
Sum1983193.1
Variance5.0837853 × 108
MonotonicityNot monotonic
2023-12-11T15:02:53.640471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
34233.0 1
 
2.1%
25215.0 1
 
2.1%
92605.0 1
 
2.1%
87365.0 1
 
2.1%
46627.0 1
 
2.1%
8349.0 1
 
2.1%
51040.0 1
 
2.1%
43023.0 1
 
2.1%
25438.0 1
 
2.1%
46550.0 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
2969.37 1
2.1%
4876.0 1
2.1%
8349.0 1
2.1%
8359.0 1
2.1%
9177.0 1
2.1%
11788.0 1
2.1%
14831.0 1
2.1%
18660.0 1
2.1%
20231.0 1
2.1%
21107.0 1
2.1%
ValueCountFrequency (%)
92905.0 1
2.1%
92605.0 1
2.1%
87365.0 1
2.1%
82085.0 1
2.1%
81900.0 1
2.1%
74726.0 1
2.1%
60133.0 1
2.1%
57122.0 1
2.1%
56904.0 1
2.1%
54774.0 1
2.1%

공장시설면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37260.092
Minimum2237.25
Maximum82444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T15:02:53.814795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2237.25
5-th percentile7294.1
Q124984.5
median37596
Q346210.04
95-th percentile74451.8
Maximum82444
Range80206.75
Interquartile range (IQR)21225.54

Descriptive statistics

Standard deviation19344.751
Coefficient of variation (CV)0.51918151
Kurtosis-0.007900394
Mean37260.092
Median Absolute Deviation (MAD)11034
Skewness0.29813863
Sum1751224.3
Variance3.7421939 × 108
MonotonicityNot monotonic
2023-12-11T15:02:54.014891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
32789.0 1
 
2.1%
23407.0 1
 
2.1%
74468.0 1
 
2.1%
82444.0 1
 
2.1%
42657.0 1
 
2.1%
8349.0 1
 
2.1%
41080.0 1
 
2.1%
39488.0 1
 
2.1%
20533.0 1
 
2.1%
37280.0 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
2237.25 1
2.1%
4793.0 1
2.1%
6842.0 1
2.1%
8349.0 1
2.1%
9177.0 1
2.1%
11361.0 1
2.1%
13161.0 1
2.1%
14351.0 1
2.1%
16334.0 1
2.1%
20059.0 1
2.1%
ValueCountFrequency (%)
82444.0 1
2.1%
77538.0 1
2.1%
74468.0 1
2.1%
74414.0 1
2.1%
64921.0 1
2.1%
59587.0 1
2.1%
58274.0 1
2.1%
54106.0 1
2.1%
49464.0 1
2.1%
49029.0 1
2.1%

지원시설면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4920.8251
Minimum0
Maximum22498
Zeros2
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T15:02:54.265516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.384
Q11582
median4004
Q36074
95-th percentile15683.9
Maximum22498
Range22498
Interquartile range (IQR)4492

Descriptive statistics

Standard deviation4820.6738
Coefficient of variation (CV)0.97964745
Kurtosis4.4500608
Mean4920.8251
Median Absolute Deviation (MAD)2401
Skewness1.9621756
Sum231278.78
Variance23238896
MonotonicityNot monotonic
2023-12-11T15:02:54.445926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 2
 
4.3%
1444.0 1
 
2.1%
427.0 1
 
2.1%
18137.0 1
 
2.1%
4921.0 1
 
2.1%
3970.0 1
 
2.1%
9960.0 1
 
2.1%
3535.0 1
 
2.1%
4905.0 1
 
2.1%
9270.0 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
0.0 2
4.3%
42.12 1
2.1%
83.0 1
2.1%
427.0 1
2.1%
480.0 1
2.1%
971.0 1
2.1%
1048.0 1
2.1%
1412.0 1
2.1%
1444.0 1
2.1%
1517.0 1
2.1%
ValueCountFrequency (%)
22498.0 1
2.1%
18491.0 1
2.1%
18137.0 1
2.1%
9960.0 1
2.1%
9805.0 1
2.1%
9738.66 1
2.1%
9270.0 1
2.1%
9000.0 1
2.1%
7440.0 1
2.1%
6995.0 1
2.1%

건축형태(지하)
Categorical

Distinct5
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
2
25 
3
10 
4
8 
1
3 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
2 25
53.2%
3 10
 
21.3%
4 8
 
17.0%
1 3
 
6.4%
5 1
 
2.1%

Length

2023-12-11T15:02:54.619334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:02:55.084170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 25
53.2%
3 10
 
21.3%
4 8
 
17.0%
1 3
 
6.4%
5 1
 
2.1%

건축형태(지상)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.425532
Minimum6
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-11T15:02:55.206776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.3
Q111
median14
Q315
95-th percentile19.7
Maximum20
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4812834
Coefficient of variation (CV)0.2593032
Kurtosis-0.23014958
Mean13.425532
Median Absolute Deviation (MAD)2
Skewness0.045576506
Sum631
Variance12.119334
MonotonicityNot monotonic
2023-12-11T15:02:55.359831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
14 13
27.7%
10 6
12.8%
13 5
 
10.6%
15 4
 
8.5%
12 3
 
6.4%
20 3
 
6.4%
18 3
 
6.4%
7 2
 
4.3%
19 2
 
4.3%
11 2
 
4.3%
Other values (4) 4
 
8.5%
ValueCountFrequency (%)
6 1
 
2.1%
7 2
 
4.3%
8 1
 
2.1%
9 1
 
2.1%
10 6
12.8%
11 2
 
4.3%
12 3
 
6.4%
13 5
 
10.6%
14 13
27.7%
15 4
 
8.5%
ValueCountFrequency (%)
20 3
 
6.4%
19 2
 
4.3%
18 3
 
6.4%
17 1
 
2.1%
15 4
 
8.5%
14 13
27.7%
13 5
 
10.6%
12 3
 
6.4%
11 2
 
4.3%
10 6
12.8%

Interactions

2023-12-11T15:02:49.832031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:48.449798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:48.913861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.361016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.947201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:48.557195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.032441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.469679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:50.072286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:48.685501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.156097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.595373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:50.198786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:48.786940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.250825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:02:49.703424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:02:55.514570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공장명소재지준공일부지면적면적합계공장시설면적지원시설면적건축형태(지하)건축형태(지상)
공장명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.000
준공일1.0001.0001.0001.0000.9830.9481.0001.0001.000
부지면적1.0001.0001.0001.0001.0001.0001.0001.0001.000
면적합계1.0001.0000.9831.0001.0000.9820.6920.5000.576
공장시설면적1.0001.0000.9481.0000.9821.0000.5590.5830.458
지원시설면적1.0001.0001.0001.0000.6920.5591.0000.1920.545
건축형태(지하)1.0001.0001.0001.0000.5000.5830.1921.0000.818
건축형태(지상)1.0001.0001.0001.0000.5760.4580.5450.8181.000
2023-12-11T15:02:55.700233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적합계공장시설면적지원시설면적건축형태(지상)건축형태(지하)
면적합계1.0000.9900.7340.7640.214
공장시설면적0.9901.0000.6650.7230.263
지원시설면적0.7340.6651.0000.7510.108
건축형태(지상)0.7640.7230.7511.0000.450
건축형태(지하)0.2140.2630.1080.4501.000

Missing values

2023-12-11T15:02:50.379788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:02:50.573503image/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

공장명소재지준공일부지면적면적합계공장시설면적지원시설면적건축형태(지하)건축형태(지상)
0에이스테크노타워1차서울특별시 구로구 구로동 197-171998-09-30558434233.032789.01444.0210
1에이스테크노타워2차서울특별시 구로구 구로동 197-72000-10-16495925215.023407.01808.0210
2에이스테크노타워3차서울특별시 구로구 구로동 197-482001-09-21648838080.035501.02579.0212
3에이스테크노타워5차서울특별시 구로구 구로동 197-222002-08-03639040128.038567.01561.0213
4에이스트윈테크노타워1차서울특별시 구로구 구로동 212-12003-03-19646741724.036724.05000.0214
5에이스트윈테크노타워2차서울특별시 구로구 구로동 212-302003-05-20635741049.036094.04955.0214
6에이스테크노타워8차서울특별시 구로구 구로동 191-72004-06-18677842434.040449.01985.0214
7벽산디지털밸리1차서울특별시 구로구 구로동 212-162002-04-22552034122.032710.01412.0210
8벽산디지털밸리3차서울특별시 구로구 구로동 212-132004-05-31568938567.037596.0971.0312
9동일테크노타운1차서울특별시 구로구 구로동 8261996-07-0916859177.09177.00.028
공장명소재지준공일부지면적면적합계공장시설면적지원시설면적건축형태(지하)건축형태(지상)
37파트너스타워2차서울특별시 구로구 구로동 1128-32010-02-16266218660.013161.05499.0314
38JnK 디지털타워서울특별시 구로구 구로3동 222-32012-02-15789856904.049464.07440.0418
39대륭포스트타워7차서울특별시 구로구 구로동 170-10 외 2필지2012-12-11608649587.040587.09000.0420
40G플러스 코오롱 디지털타워서울특별시 구로구 구로동 222-312013-07-24211046966.040471.06495.0418
41대명밸리온서울특별시 구로구 구로동 614-502018-07-20434638461.034350.04111.0413
42NHN KCP서울특별시 구로구 구로동 222-222015-08-24297514831.014351.0480.027
43지하이시티서울특별시 구로구 구로동 2372018-08-20705648035.041040.06995.0319
44오닉스타워서울특별시 구로구 구로동 197-302018-12-2110828359.06842.01517.0511
45그린빌딩 지식산업센터서울특별시 구로구 구로동 614-532020-04-068502969.372237.2542.1216
46구로 SK V1 center서울특별시 구로구 항동 산51-1 일원2021-06-298,31754090.7444352.089738.66411