Overview

Dataset statistics

Number of variables9
Number of observations82
Missing cells26
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory75.6 B

Variable types

Numeric2
Categorical2
Text4
DateTime1

Dataset

Description인천광역시 중구 관내에 위치한 에 대한 데이터 입니다. 파일명 인천광역시_중구_다중이용시설 실내공기질 관리대상시설 현황 파일내용 대상, 시설명, 연면적, 준공일 등
URLhttps://www.data.go.kr/data/15086843/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 대상High correlation
대상 is highly overall correlated with 연번High correlation
준공일 has 1 (1.2%) missing valuesMissing
소유자 has 7 (8.5%) missing valuesMissing
전화번호 has 18 (22.0%) missing valuesMissing
연번 has unique valuesUnique
연면적(미터제곱) has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:21:26.273869
Analysis finished2023-12-11 23:21:27.850968
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.5
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T08:21:27.925451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.05
Q121.25
median41.5
Q361.75
95-th percentile77.95
Maximum82
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.57386172
Kurtosis-1.2
Mean41.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3403
Variance567.16667
MonotonicityStrictly increasing
2023-12-12T08:21:28.083164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
63 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%

대상
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
업무시설
21 
실내주차장
18 
노인요양시설
10 
어린이집
8 
의료기관
7 
Other values (8)
18 

Length

Max length6
Median length4
Mean length4.597561
Min length3

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row박물관
2nd row영화상영관
3rd rowPC영업시설
4th rowPC영업시설
5th row의료기관

Common Values

ValueCountFrequency (%)
업무시설 21
25.6%
실내주차장 18
22.0%
노인요양시설 10
12.2%
어린이집 8
 
9.8%
의료기관 7
 
8.5%
지하도상가 5
 
6.1%
장례식장 3
 
3.7%
PC영업시설 2
 
2.4%
대규모점포 2
 
2.4%
목욕장업 2
 
2.4%
Other values (3) 4
 
4.9%

Length

2023-12-12T08:21:28.254514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무시설 21
25.6%
실내주차장 18
22.0%
노인요양시설 10
12.2%
어린이집 8
 
9.8%
의료기관 7
 
8.5%
지하도상가 5
 
6.1%
장례식장 3
 
3.7%
pc영업시설 2
 
2.4%
대규모점포 2
 
2.4%
목욕장업 2
 
2.4%
Other values (3) 4
 
4.9%

시설명
Text

Distinct79
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T08:21:28.470740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10.5
Mean length7.5731707
Min length3

Characters and Unicode

Total characters621
Distinct characters180
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)92.7%

Sample

1st row이민사박물관
2nd row애관극장
3rd row프랜드PC방
4th row유니넷PC존
5th row인하대병원
ValueCountFrequency (%)
공영주차장 3
 
3.1%
인하대병원 3
 
3.1%
㈜신세계이마트 2
 
2.0%
정석빌딩 2
 
2.0%
동인천점 2
 
2.0%
구립 2
 
2.0%
수인선 2
 
2.0%
중구청 2
 
2.0%
장미어린이집 1
 
1.0%
신포역 1
 
1.0%
Other values (78) 78
79.6%
2023-12-12T08:21:28.856707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 38
 
6.1%
천 32
 
5.2%
원 21
 
3.4%
16
 
2.6%
양 15
 
2.4%
장 15
 
2.4%
동 14
 
2.3%
요 13
 
2.1%
이 13
 
2.1%
지 10
 
1.6%
Other values (170) 434
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 588
94.7%
Space Separator 16
 
2.6%
Uppercase Letter 13
 
2.1%
Other Symbol 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 38
 
6.5%
천 32
 
5.4%
원 21
 
3.6%
양 15
 
2.6%
장 15
 
2.6%
동 14
 
2.4%
요 13
 
2.2%
이 13
 
2.2%
지 10
 
1.7%
어 10
 
1.7%
Other values (161) 407
69.2%
Uppercase Letter
ValueCountFrequency (%)
C 5
38.5%
P 3
23.1%
J 1
 
7.7%
B 1
 
7.7%
D 1
 
7.7%
A 1
 
7.7%
M 1
 
7.7%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
95.3%
Common 16
 
2.6%
Latin 13
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 38
 
6.4%
천 32
 
5.4%
원 21
 
3.5%
양 15
 
2.5%
장 15
 
2.5%
동 14
 
2.4%
요 13
 
2.2%
이 13
 
2.2%
지 10
 
1.7%
어 10
 
1.7%
Other values (162) 411
69.4%
Latin
ValueCountFrequency (%)
C 5
38.5%
P 3
23.1%
J 1
 
7.7%
B 1
 
7.7%
D 1
 
7.7%
A 1
 
7.7%
M 1
 
7.7%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 588
94.7%
ASCII 29
 
4.7%
None 4
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 38
 
6.5%
천 32
 
5.4%
원 21
 
3.6%
양 15
 
2.6%
장 15
 
2.6%
동 14
 
2.4%
요 13
 
2.2%
이 13
 
2.2%
지 10
 
1.7%
어 10
 
1.7%
Other values (161) 407
69.2%
ASCII
ValueCountFrequency (%)
16
55.2%
C 5
 
17.2%
P 3
 
10.3%
J 1
 
3.4%
B 1
 
3.4%
D 1
 
3.4%
A 1
 
3.4%
M 1
 
3.4%
None
ValueCountFrequency (%)
㈜ 4
100.0%

연면적(미터제곱)
Real number (ℝ)

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6910.609
Minimum339.92
Maximum101876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T08:21:29.256802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum339.92
5-th percentile696.4
Q11806.25
median3469.81
Q35901.75
95-th percentile21172.919
Maximum101876
Range101536.08
Interquartile range (IQR)4095.5

Descriptive statistics

Standard deviation13229.809
Coefficient of variation (CV)1.9144201
Kurtosis34.163352
Mean6910.609
Median Absolute Deviation (MAD)1771.81
Skewness5.3334693
Sum566669.94
Variance1.7502783 × 108
MonotonicityNot monotonic
2023-12-12T08:21:29.388381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4127.0 1
 
1.2%
18140.77 1
 
1.2%
7687.0 1
 
1.2%
6901.0 1
 
1.2%
2846.0 1
 
1.2%
1161.0 1
 
1.2%
2364.0 1
 
1.2%
849.0 1
 
1.2%
742.0 1
 
1.2%
460.0 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
339.92 1
1.2%
435.0 1
1.2%
460.0 1
1.2%
600.0 1
1.2%
694.0 1
1.2%
742.0 1
1.2%
744.0 1
1.2%
849.0 1
1.2%
1021.0 1
1.2%
1045.0 1
1.2%
ValueCountFrequency (%)
101876.0 1
1.2%
48800.0 1
1.2%
39947.0 1
1.2%
26796.27 1
1.2%
21260.0 1
1.2%
19518.38 1
1.2%
18694.0 1
1.2%
18140.77 1
1.2%
12997.0 1
1.2%
9903.16 1
1.2%

준공일
Date

MISSING 

Distinct73
Distinct (%)90.1%
Missing1
Missing (%)1.2%
Memory size788.0 B
Minimum1960-09-03 00:00:00
Maximum2022-09-19 00:00:00
2023-12-12T08:21:29.517576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:21:29.654498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소유자
Text

MISSING 

Distinct54
Distinct (%)72.0%
Missing7
Missing (%)8.5%
Memory size788.0 B
2023-12-12T08:21:29.867526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length4.64
Min length2

Characters and Unicode

Total characters348
Distinct characters114
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)60.0%

Sample

1st row인천광역시장
2nd row탁경란
3rd row홍승범
4th row김태서
5th row학교법인 인하학원
ValueCountFrequency (%)
인천광역시 7
 
8.8%
중구청장 6
 
7.5%
천성욱 4
 
5.0%
학교법인 4
 
5.0%
인하학원 3
 
3.8%
국가 2
 
2.5%
대표자 2
 
2.5%
강권일 2
 
2.5%
한국철도시설공단 2
 
2.5%
중구청 2
 
2.5%
Other values (46) 46
57.5%
2023-12-12T08:21:30.194284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 22
 
6.3%
천 18
 
5.2%
시 13
 
3.7%
청 10
 
2.9%
장 10
 
2.9%
광 9
 
2.6%
역 9
 
2.6%
중 9
 
2.6%
학 8
 
2.3%
김 8
 
2.3%
Other values (104) 232
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 336
96.6%
Other Symbol 6
 
1.7%
Space Separator 5
 
1.4%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 22
 
6.5%
천 18
 
5.4%
시 13
 
3.9%
청 10
 
3.0%
장 10
 
3.0%
광 9
 
2.7%
역 9
 
2.7%
중 9
 
2.7%
학 8
 
2.4%
김 8
 
2.4%
Other values (101) 220
65.5%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
98.3%
Common 6
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 22
 
6.4%
천 18
 
5.3%
시 13
 
3.8%
청 10
 
2.9%
장 10
 
2.9%
광 9
 
2.6%
역 9
 
2.6%
중 9
 
2.6%
학 8
 
2.3%
김 8
 
2.3%
Other values (102) 226
66.1%
Common
ValueCountFrequency (%)
5
83.3%
, 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 336
96.6%
None 6
 
1.7%
ASCII 6
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 22
 
6.5%
천 18
 
5.4%
시 13
 
3.9%
청 10
 
3.0%
장 10
 
3.0%
광 9
 
2.7%
역 9
 
2.7%
중 9
 
2.7%
학 8
 
2.4%
김 8
 
2.4%
Other values (101) 220
65.5%
None
ValueCountFrequency (%)
㈜ 6
100.0%
ASCII
ValueCountFrequency (%)
5
83.3%
, 1
 
16.7%

위치
Text

Distinct73
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T08:21:30.474949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length18.134146
Min length13

Characters and Unicode

Total characters1487
Distinct characters69
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

Unique66 ?
Unique (%)80.5%

Sample

1st row인천광역시 중구 월미로 329
2nd row인천광역시 중구 개항로 63-2
3rd row인천광역시 중구 우현로87번길 17
4th row인천광역시 중구 홍예문로 90
5th row인천광역시 중구 인항로 27
ValueCountFrequency (%)
인천광역시 82
24.6%
중구 82
24.6%
제물량로 8
 
2.4%
지하 6
 
1.8%
참외전로 5
 
1.5%
인중로 5
 
1.5%
서해대로 5
 
1.5%
인항로 5
 
1.5%
홍예문로 4
 
1.2%
90 4
 
1.2%
Other values (99) 128
38.3%
2023-12-12T08:21:30.899076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
252
16.9%
인 93
 
6.3%
중 88
 
5.9%
광 82
 
5.5%
역 82
 
5.5%
시 82
 
5.5%
구 82
 
5.5%
천 82
 
5.5%
로 81
 
5.4%
1 48
 
3.2%
Other values (59) 515
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 957
64.4%
Decimal Number 266
 
17.9%
Space Separator 252
 
16.9%
Dash Punctuation 12
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 93
9.7%
중 88
 
9.2%
광 82
 
8.6%
역 82
 
8.6%
시 82
 
8.6%
구 82
 
8.6%
천 82
 
8.6%
로 81
 
8.5%
길 33
 
3.4%
번 32
 
3.3%
Other values (47) 220
23.0%
Decimal Number
ValueCountFrequency (%)
1 48
18.0%
2 46
17.3%
3 31
11.7%
6 30
11.3%
7 30
11.3%
4 20
7.5%
9 20
7.5%
0 16
 
6.0%
8 13
 
4.9%
5 12
 
4.5%
Space Separator
ValueCountFrequency (%)
252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 957
64.4%
Common 530
35.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 93
9.7%
중 88
 
9.2%
광 82
 
8.6%
역 82
 
8.6%
시 82
 
8.6%
구 82
 
8.6%
천 82
 
8.6%
로 81
 
8.5%
길 33
 
3.4%
번 32
 
3.3%
Other values (47) 220
23.0%
Common
ValueCountFrequency (%)
252
47.5%
1 48
 
9.1%
2 46
 
8.7%
3 31
 
5.8%
6 30
 
5.7%
7 30
 
5.7%
4 20
 
3.8%
9 20
 
3.8%
0 16
 
3.0%
8 13
 
2.5%
Other values (2) 24
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 957
64.4%
ASCII 530
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
252
47.5%
1 48
 
9.1%
2 46
 
8.7%
3 31
 
5.8%
6 30
 
5.7%
7 30
 
5.7%
4 20
 
3.8%
9 20
 
3.8%
0 16
 
3.0%
8 13
 
2.5%
Other values (2) 24
 
4.5%
Hangul
ValueCountFrequency (%)
인 93
9.7%
중 88
 
9.2%
광 82
 
8.6%
역 82
 
8.6%
시 82
 
8.6%
구 82
 
8.6%
천 82
 
8.6%
로 81
 
8.5%
길 33
 
3.4%
번 32
 
3.3%
Other values (47) 220
23.0%

전화번호
Text

MISSING 

Distinct54
Distinct (%)84.4%
Missing18
Missing (%)22.0%
Memory size788.0 B
2023-12-12T08:21:31.157016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.875
Min length9

Characters and Unicode

Total characters760
Distinct characters12
Distinct categories3 ?
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 (%)73.4%

Sample

1st row032-440-4704
2nd row032-761-7177
3rd row032-890-2693
4th row032-770-1351
5th row032-270-8422
ValueCountFrequency (%)
1833-6140 4
 
6.2%
032-890-2693 3
 
4.6%
032-760-7187 2
 
3.1%
032-773-6035 2
 
3.1%
032-451-1100 2
 
3.1%
032-763-8146 2
 
3.1%
032-770-1351 2
 
3.1%
032-760-6016 1
 
1.5%
032-760-7089 1
 
1.5%
032-891-5966 1
 
1.5%
Other values (45) 45
69.2%
2023-12-12T08:21:31.582414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 124
16.3%
0 110
14.5%
3 100
13.2%
7 95
12.5%
2 85
11.2%
6 54
7.1%
1 53
7.0%
8 48
 
6.3%
4 34
 
4.5%
5 33
 
4.3%
Other values (2) 24
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 634
83.4%
Dash Punctuation 124
 
16.3%
Space Separator 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110
17.4%
3 100
15.8%
7 95
15.0%
2 85
13.4%
6 54
8.5%
1 53
8.4%
8 48
7.6%
4 34
 
5.4%
5 33
 
5.2%
9 22
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 124
16.3%
0 110
14.5%
3 100
13.2%
7 95
12.5%
2 85
11.2%
6 54
7.1%
1 53
7.0%
8 48
 
6.3%
4 34
 
4.5%
5 33
 
4.3%
Other values (2) 24
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 124
16.3%
0 110
14.5%
3 100
13.2%
7 95
12.5%
2 85
11.2%
6 54
7.1%
1 53
7.0%
8 48
 
6.3%
4 34
 
4.5%
5 33
 
4.3%
Other values (2) 24
 
3.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-08-19
82 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-19
2nd row2023-08-19
3rd row2023-08-19
4th row2023-08-19
5th row2023-08-19

Common Values

ValueCountFrequency (%)
2023-08-19 82
100.0%

Length

2023-12-12T08:21:31.718203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:21:31.803786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-19 82
100.0%

Interactions

2023-12-12T08:21:27.291327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:21:27.102571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:21:27.396922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:21:27.195857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:21:31.877798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대상시설명연면적(미터제곱)준공일소유자위치전화번호
연번1.0000.8820.7930.0000.9460.9310.8160.961
대상0.8821.0000.8980.0940.9820.9780.0000.955
시설명0.7930.8981.0000.0000.9990.9890.9990.997
연면적(미터제곱)0.0000.0940.0001.0000.0000.0000.0000.000
준공일0.9460.9820.9990.0001.0000.9970.9790.997
소유자0.9310.9780.9890.0000.9971.0000.9890.994
위치0.8160.0000.9990.0000.9790.9891.0000.998
전화번호0.9610.9550.9970.0000.9970.9940.9981.000
2023-12-12T08:21:32.010701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연면적(미터제곱)대상
연번1.0000.3320.617
연면적(미터제곱)0.3321.0000.048
대상0.6170.0481.000

Missing values

2023-12-12T08:21:27.530421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:21:27.663397image/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.
2023-12-12T08:21:27.789113image/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

연번대상시설명연면적(미터제곱)준공일소유자위치전화번호데이터기준일자
01박물관이민사박물관4127.02009-07-31인천광역시장인천광역시 중구 월미로 329032-440-47042023-08-19
12영화상영관애관극장4351.01960-09-03탁경란인천광역시 중구 개항로 63-2032-761-71772023-08-19
23PC영업시설프랜드PC방339.922017-08-11홍승범인천광역시 중구 우현로87번길 17<NA>2023-08-19
34PC영업시설유니넷PC존435.02018-09-13김태서인천광역시 중구 홍예문로 90<NA>2023-08-19
45의료기관인하대병원101876.01996-03-22학교법인 인하학원인천광역시 중구 인항로 27032-890-26932023-08-19
56의료기관가천대부속 동인천길병원3234.02016-04-22학교법인 가천경원학원인천광역시 중구 큰우물로 21032-770-13512023-08-19
67의료기관인천기독병원12997.01972-09-14변동일인천광역시 중구 답동로30번길 10032-270-84222023-08-19
78의료기관예지요양병원1944.02003-10-24염기훈인천광역시 중구 개항로 82032-773-60352023-08-19
89의료기관가천의대부속 길한방병원1916.571978-11-14송윤경인천광역시 중구 큰우물로 21032-770-13512023-08-19
910의료기관인천삼성요양병원2917.161996-08-31윤여준인천광역시 중구 우현로62번길 30032-721-75752023-08-19
연번대상시설명연면적(미터제곱)준공일소유자위치전화번호데이터기준일자
7273업무시설인천일보 인천본사3991.91994-07-09㈜인천일보인천광역시 중구 인중로 226<NA>2023-08-19
7374업무시설인천중부소방서3204.431985-11-19인천광역시인천광역시 중구 인중로 204<NA>2023-08-19
7475업무시설인천출입국관리사무소9903.162006-11-13국가인천광역시 중구 서해대로 393032-890-63002023-08-19
7576업무시설인천본부세관7888.721992-11-12국가인천광역시 중구 서해대로 339032-452-31142023-08-19
7677업무시설벤츠한성자동차인천서비스센터4121.592003-10-29한성인베스트먼트㈜인천광역시 중구 축항대로86번길 56032-770-77212023-08-19
7778업무시설인천지방해양수산청4360.642017-10-25<NA>인천광역시 중구 서해대로 365-7<NA>2023-08-19
7879업무시설만수리움채4686.062020-11-03<NA>인천광역시 중구 제물량로101번길 20<NA>2023-08-19
7980업무시설라희메트로시티19518.382021-06-04<NA>인천광역시 중구 제물량로 24<NA>2023-08-19
8081업무시설대동캐슬4327.632019-11-06<NA>인천광역시 중구 제물량로113번길 38-27<NA>2023-08-19
8182업무시설휴먼캐슬오피스텔4459.242018-08-28<NA>인천광역시 중구 참외전로174번길 4<NA>2023-08-19