Overview

Dataset statistics

Number of variables11
Number of observations1551
Missing cells784
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.0 KiB
Average record size in memory91.1 B

Variable types

Numeric3
Categorical4
Text2
DateTime2

Dataset

Description서울교통공사의 상가임대 현황(면적,업종,계약일,임대료 등) 데이터입니다. 해당 데이터는 상가 유형, 호선, 역사명, 상가번호, 면적(㎡), 영업업종, 계약시작일자, 계약종료일자, 월임대료, 사업진행 단계 정보를 포함하고 있습니다.(데이터 제공 요청으로 인해 2015년 이후 데이터 순차적으로 업데이트 합니다. )* 상가유형- 복합: 일정면적 이상을 임차인의 비용으로 일괄 개발하여 직영 또는 위탁 운영하는 상가- 네트워크: 다수역에 다수점표를 일괄 임대차하여 운영하는 상가- 소송상가: 계약이 종료되었으나 명도거부하여 명도소송 중인 상가
Author서울교통공사
URLhttps://www.data.go.kr/data/15071329/fileData.do

Alerts

상가유형 is highly overall correlated with 사업진행단계High correlation
사업진행단계 is highly overall correlated with 상가유형High correlation
연번 is highly overall correlated with 호선High correlation
호선 is highly overall correlated with 연번High correlation
사업진행단계 is highly imbalanced (66.2%)Imbalance
면적(제곱미터) has 59 (3.8%) missing valuesMissing
계약시작일자 has 201 (13.0%) missing valuesMissing
계약종료일자 has 201 (13.0%) missing valuesMissing
월임대료 has 323 (20.8%) missing valuesMissing
면적(제곱미터) is highly skewed (γ1 = 30.60614399)Skewed
연번 has unique valuesUnique
상가번호 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:54:10.151338
Analysis finished2024-04-06 08:54:15.868495
Duration5.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean776
Minimum1
Maximum1551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-04-06T17:54:16.019794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile78.5
Q1388.5
median776
Q31163.5
95-th percentile1473.5
Maximum1551
Range1550
Interquartile range (IQR)775

Descriptive statistics

Standard deviation447.87945
Coefficient of variation (CV)0.57716424
Kurtosis-1.2
Mean776
Median Absolute Deviation (MAD)388
Skewness0
Sum1203576
Variance200596
MonotonicityStrictly increasing
2024-04-06T17:54:16.291775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1020 1
 
0.1%
1042 1
 
0.1%
1041 1
 
0.1%
1040 1
 
0.1%
1039 1
 
0.1%
1038 1
 
0.1%
1037 1
 
0.1%
1036 1
 
0.1%
1035 1
 
0.1%
Other values (1541) 1541
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1551 1
0.1%
1550 1
0.1%
1549 1
0.1%
1548 1
0.1%
1547 1
0.1%
1546 1
0.1%
1545 1
0.1%
1544 1
0.1%
1543 1
0.1%
1542 1
0.1%

상가유형
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
개별(일반)
632 
네트워크
425 
복합
252 
공실
124 
임대진행
73 
Other values (2)
 
45

Length

Max length6
Median length4
Mean length4.3752418
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개별(일반)
2nd row네트워크
3rd row개별(일반)
4th row개별(일반)
5th row개별(일반)

Common Values

ValueCountFrequency (%)
개별(일반) 632
40.7%
네트워크 425
27.4%
복합 252
 
16.2%
공실 124
 
8.0%
임대진행 73
 
4.7%
개별(대형) 35
 
2.3%
소송상가 10
 
0.6%

Length

2024-04-06T17:54:16.620815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:54:16.911466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별(일반 632
40.7%
네트워크 425
27.4%
복합 252
 
16.2%
공실 124
 
8.0%
임대진행 73
 
4.7%
개별(대형 35
 
2.3%
소송상가 10
 
0.6%

호선
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
7호선
361 
2호선
295 
5호선
291 
6호선
186 
3호선
180 
Other values (3)
238 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row1호선
3rd row1호선
4th row1호선
5th row1호선

Common Values

ValueCountFrequency (%)
7호선 361
23.3%
2호선 295
19.0%
5호선 291
18.8%
6호선 186
12.0%
3호선 180
11.6%
4호선 146
9.4%
8호선 65
 
4.2%
1호선 27
 
1.7%

Length

2024-04-06T17:54:17.153818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:54:17.402084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7호선 361
23.3%
2호선 295
19.0%
5호선 291
18.8%
6호선 186
12.0%
3호선 180
11.6%
4호선 146
9.4%
8호선 65
 
4.2%
1호선 27
 
1.7%

역명
Text

Distinct237
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2024-04-06T17:54:17.887673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.002579
Min length3

Characters and Unicode

Total characters7759
Distinct characters203
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

Unique30 ?
Unique (%)1.9%

Sample

1st row서울(1)역
2nd row서울(1)역
3rd row시청(1)역
4th row시청(1)역
5th row시청(1)역
ValueCountFrequency (%)
오목교역 46
 
3.0%
고속터미널(3)역 39
 
2.5%
공덕(5)역 29
 
1.9%
천호(5)역 27
 
1.7%
잠실(8)역 26
 
1.7%
사당(4)역 25
 
1.6%
노원(7)역 23
 
1.5%
잠실(2)역 21
 
1.4%
미아사거리역 19
 
1.2%
마들역 19
 
1.2%
Other values (227) 1277
82.3%
2024-04-06T17:54:18.663228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 1577
 
20.3%
( 549
 
7.1%
) 549
 
7.1%
대 200
 
2.6%
구 175
 
2.3%
신 121
 
1.6%
2 116
 
1.5%
입 109
 
1.4%
3 108
 
1.4%
사 108
 
1.4%
Other values (193) 4147
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6069
78.2%
Decimal Number 592
 
7.6%
Open Punctuation 549
 
7.1%
Close Punctuation 549
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 1577
26.0%
대 200
 
3.3%
구 175
 
2.9%
신 121
 
2.0%
입 109
 
1.8%
사 108
 
1.8%
동 93
 
1.5%
공 92
 
1.5%
산 89
 
1.5%
미 89
 
1.5%
Other values (183) 3416
56.3%
Decimal Number
ValueCountFrequency (%)
2 116
19.6%
3 108
18.2%
5 103
17.4%
7 93
15.7%
6 64
10.8%
4 63
10.6%
8 31
 
5.2%
1 14
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 549
100.0%
Close Punctuation
ValueCountFrequency (%)
) 549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6069
78.2%
Common 1690
 
21.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 1577
26.0%
대 200
 
3.3%
구 175
 
2.9%
신 121
 
2.0%
입 109
 
1.8%
사 108
 
1.8%
동 93
 
1.5%
공 92
 
1.5%
산 89
 
1.5%
미 89
 
1.5%
Other values (183) 3416
56.3%
Common
ValueCountFrequency (%)
( 549
32.5%
) 549
32.5%
2 116
 
6.9%
3 108
 
6.4%
5 103
 
6.1%
7 93
 
5.5%
6 64
 
3.8%
4 63
 
3.7%
8 31
 
1.8%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6069
78.2%
ASCII 1690
 
21.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 1577
26.0%
대 200
 
3.3%
구 175
 
2.9%
신 121
 
2.0%
입 109
 
1.8%
사 108
 
1.8%
동 93
 
1.5%
공 92
 
1.5%
산 89
 
1.5%
미 89
 
1.5%
Other values (183) 3416
56.3%
ASCII
ValueCountFrequency (%)
( 549
32.5%
) 549
32.5%
2 116
 
6.9%
3 108
 
6.4%
5 103
 
6.1%
7 93
 
5.5%
6 64
 
3.8%
4 63
 
3.7%
8 31
 
1.8%
1 14
 
0.8%

상가번호
Text

UNIQUE 

Distinct1551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2024-04-06T17:54:19.282481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10857
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1551 ?
Unique (%)100.0%

Sample

1st row150-107
2nd row150-109
3rd row151-101
4th row151-103
5th row151-104
ValueCountFrequency (%)
150-107 1
 
0.1%
618-004 1
 
0.1%
628-301 1
 
0.1%
628-165 1
 
0.1%
627-106 1
 
0.1%
627-105 1
 
0.1%
627-104 1
 
0.1%
627-103 1
 
0.1%
627-102 1
 
0.1%
627-101 1
 
0.1%
Other values (1541) 1541
99.4%
2024-04-06T17:54:20.174256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2247
20.7%
2 1600
14.7%
- 1551
14.3%
0 1518
14.0%
3 1037
9.6%
4 714
 
6.6%
5 605
 
5.6%
7 596
 
5.5%
6 465
 
4.3%
8 274
 
2.5%
Other values (2) 250
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9302
85.7%
Dash Punctuation 1551
 
14.3%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2247
24.2%
2 1600
17.2%
0 1518
16.3%
3 1037
11.1%
4 714
 
7.7%
5 605
 
6.5%
7 596
 
6.4%
6 465
 
5.0%
8 274
 
2.9%
9 246
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 1551
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10853
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2247
20.7%
2 1600
14.7%
- 1551
14.3%
0 1518
14.0%
3 1037
9.6%
4 714
 
6.6%
5 605
 
5.6%
7 596
 
5.5%
6 465
 
4.3%
8 274
 
2.5%
Latin
ValueCountFrequency (%)
M 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2247
20.7%
2 1600
14.7%
- 1551
14.3%
0 1518
14.0%
3 1037
9.6%
4 714
 
6.6%
5 605
 
5.6%
7 596
 
5.5%
6 465
 
4.3%
8 274
 
2.5%
Other values (2) 250
 
2.3%

면적(제곱미터)
Real number (ℝ)

MISSING  SKEWED 

Distinct813
Distinct (%)54.5%
Missing59
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean50.980643
Minimum7.61
Maximum7475.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-04-06T17:54:20.512597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.61
5-th percentile13
Q122
median31
Q344
95-th percentile106.023
Maximum7475.19
Range7467.58
Interquartile range (IQR)22

Descriptive statistics

Standard deviation209.1424
Coefficient of variation (CV)4.1023884
Kurtosis1068.9982
Mean50.980643
Median Absolute Deviation (MAD)10.2
Skewness30.606144
Sum76063.12
Variance43740.544
MonotonicityNot monotonic
2024-04-06T17:54:20.846703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 43
 
2.8%
33.0 37
 
2.4%
40.0 26
 
1.7%
20.0 19
 
1.2%
35.0 17
 
1.1%
37.0 16
 
1.0%
25.0 16
 
1.0%
50.0 16
 
1.0%
45.0 15
 
1.0%
31.0 15
 
1.0%
Other values (803) 1272
82.0%
(Missing) 59
 
3.8%
ValueCountFrequency (%)
7.61 1
0.1%
8.0 1
0.1%
8.15 1
0.1%
8.25 1
0.1%
9.01 1
0.1%
9.05 1
0.1%
9.06 1
0.1%
9.2 1
0.1%
9.36 1
0.1%
9.41 1
0.1%
ValueCountFrequency (%)
7475.19 1
0.1%
1351.0 1
0.1%
1260.58 1
0.1%
900.39 1
0.1%
871.4 1
0.1%
867.64 1
0.1%
849.0 1
0.1%
808.0 1
0.1%
708.0 1
0.1%
592.0 1
0.1%

영업업종
Categorical

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
패션잡화
413 
식음료
305 
<NA>
197 
기타
189 
편의점
175 
Other values (8)
272 

Length

Max length5
Median length4
Mean length3.3146357
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사무실
2nd row패션잡화
3rd row패션잡화
4th row기타
5th row플라워

Common Values

ValueCountFrequency (%)
패션잡화 413
26.6%
식음료 305
19.7%
<NA> 197
12.7%
기타 189
12.2%
편의점 175
11.3%
플라워 76
 
4.9%
화장품 66
 
4.3%
의약업 50
 
3.2%
사무실 30
 
1.9%
무인프린트 22
 
1.4%
Other values (3) 28
 
1.8%

Length

2024-04-06T17:54:21.183468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
패션잡화 413
26.6%
식음료 305
19.7%
na 197
12.7%
기타 189
12.2%
편의점 175
11.3%
플라워 76
 
4.9%
화장품 66
 
4.3%
의약업 50
 
3.2%
사무실 30
 
1.9%
무인프린트 22
 
1.4%
Other values (3) 28
 
1.8%

계약시작일자
Date

MISSING 

Distinct386
Distinct (%)28.6%
Missing201
Missing (%)13.0%
Memory size12.2 KiB
Minimum2016-03-15 00:00:00
Maximum2024-03-12 00:00:00
2024-04-06T17:54:21.434911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:21.778820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약종료일자
Date

MISSING 

Distinct402
Distinct (%)29.8%
Missing201
Missing (%)13.0%
Memory size12.2 KiB
Minimum2022-05-23 00:00:00
Maximum2029-04-11 00:00:00
2024-04-06T17:54:22.053609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:22.320527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

월임대료
Real number (ℝ)

MISSING 

Distinct1105
Distinct (%)90.0%
Missing323
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean5250423.7
Minimum153600
Maximum2.8792277 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-04-06T17:54:22.580487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153600
5-th percentile698700
Q11738062.5
median3182814.5
Q35953997.5
95-th percentile15066450
Maximum2.8792277 × 108
Range2.8776917 × 108
Interquartile range (IQR)4215935

Descriptive statistics

Standard deviation10465423
Coefficient of variation (CV)1.9932531
Kurtosis441.91789
Mean5250423.7
Median Absolute Deviation (MAD)1777329.5
Skewness17.571804
Sum6.4475203 × 109
Variance1.0952508 × 1014
MonotonicityNot monotonic
2024-04-06T17:54:22.922389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1700000 9
 
0.6%
2500000 5
 
0.3%
1619070 5
 
0.3%
2200000 4
 
0.3%
500000 4
 
0.3%
4286585 3
 
0.2%
2300000 3
 
0.2%
3674216 3
 
0.2%
450000 3
 
0.2%
4310000 3
 
0.2%
Other values (1095) 1186
76.5%
(Missing) 323
 
20.8%
ValueCountFrequency (%)
153600 1
0.1%
163900 1
0.1%
186000 1
0.1%
196467 1
0.1%
233500 1
0.1%
300000 1
0.1%
302500 1
0.1%
311667 1
0.1%
330000 1
0.1%
337800 1
0.1%
ValueCountFrequency (%)
287922774 1
0.1%
86110000 1
0.1%
84900600 1
0.1%
70980000 1
0.1%
61517300 1
0.1%
54194684 1
0.1%
48204012 1
0.1%
46465000 1
0.1%
44049396 1
0.1%
40100000 1
0.1%

사업진행단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
<NA>
1354 
2단계. 시설물 점검
 
83
3단계. 감정평가
 
50
1단계. 사업계획 수립중
 
41
5단계. 입찰공고
 
23

Length

Max length13
Median length4
Mean length4.8478401
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1354
87.3%
2단계. 시설물 점검 83
 
5.4%
3단계. 감정평가 50
 
3.2%
1단계. 사업계획 수립중 41
 
2.6%
5단계. 입찰공고 23
 
1.5%

Length

2024-04-06T17:54:23.301692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:54:23.563825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1354
72.3%
2단계 83
 
4.4%
시설물 83
 
4.4%
점검 83
 
4.4%
3단계 50
 
2.7%
감정평가 50
 
2.7%
1단계 41
 
2.2%
사업계획 41
 
2.2%
수립중 41
 
2.2%
5단계 23
 
1.2%

Interactions

2024-04-06T17:54:12.730069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:11.059302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:12.010861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:12.935785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:11.323648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:12.306913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:13.128771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:11.809662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:12.528345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:54:23.728245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상가유형호선면적(제곱미터)영업업종월임대료사업진행단계
연번1.0000.3130.9300.0000.2950.0520.423
상가유형0.3131.0000.2890.2800.5200.1941.000
호선0.9300.2891.0000.0390.2950.0000.447
면적(제곱미터)0.0000.2800.0391.0000.2030.6930.000
영업업종0.2950.5200.2950.2031.0000.000NaN
월임대료0.0520.1940.0000.6930.0001.000NaN
사업진행단계0.4231.0000.4470.000NaNNaN1.000
2024-04-06T17:54:23.951298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상가유형영업업종호선사업진행단계
상가유형1.0000.3190.1590.995
영업업종0.3191.0000.129NaN
호선0.1590.1291.0000.210
사업진행단계0.995NaN0.2101.000
2024-04-06T17:54:24.153898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)월임대료상가유형호선영업업종사업진행단계
연번1.0000.376-0.0700.1640.7950.1280.261
면적(제곱미터)0.3761.0000.3030.1960.0250.0920.000
월임대료-0.0700.3031.0000.1590.0000.0000.000
상가유형0.1640.1960.1591.0000.1590.3190.995
호선0.7950.0250.0000.1591.0000.1290.210
영업업종0.1280.0920.0000.3190.1291.0000.000
사업진행단계0.2610.0000.0000.9950.2100.0001.000

Missing values

2024-04-06T17:54:13.485632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:54:15.274827image/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.
2024-04-06T17:54:15.616779image/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

연번상가유형호선역명상가번호면적(제곱미터)영업업종계약시작일자계약종료일자월임대료사업진행단계
01개별(일반)1호선서울(1)역150-10733.0사무실2019-06-202024-06-19380100<NA>
12네트워크1호선서울(1)역150-10912.0패션잡화2023-01-162028-05-163225582<NA>
23개별(일반)1호선시청(1)역151-10129.73패션잡화2022-08-022027-08-023166600<NA>
34개별(일반)1호선시청(1)역151-10357.6기타2020-02-012025-01-311858300<NA>
45개별(일반)1호선시청(1)역151-10425.0플라워2020-12-312026-01-302470600<NA>
56네트워크1호선시청(1)역151-10525.0식음료2021-06-032026-08-024145884<NA>
67개별(일반)1호선시청(1)역151-10614.0패션잡화2017-09-192024-11-171805400<NA>
78개별(일반)1호선시청(1)역151-10722.0패션잡화2020-09-182025-10-182613800<NA>
89공실1호선종각역152-10136.85<NA><NA><NA><NA>1단계. 사업계획 수립중
910개별(일반)1호선종각역152-10418.64플라워2023-02-092028-03-113471600<NA>
연번상가유형호선역명상가번호면적(제곱미터)영업업종계약시작일자계약종료일자월임대료사업진행단계
15411542네트워크8호선남한산성입구역822-20354.76식음료2024-01-152029-03-252060400<NA>
15421543네트워크8호선남한산성입구역822-20434.18패션잡화2023-07-172028-08-311809246<NA>
15431544개별(일반)8호선남한산성입구역822-20517.0식음료2022-04-282027-05-281200000<NA>
15441545개별(일반)8호선단대오거리역823-10236.78기타2021-01-212026-02-201700000<NA>
15451546네트워크8호선단대오거리역823-20132.5편의점2022-02-032027-05-046235459<NA>
15461547공실8호선단대오거리역823-20354.03<NA><NA><NA><NA>2단계. 시설물 점검
15471548개별(일반)8호선단대오거리역823-20475.09패션잡화2021-03-182026-04-173780000<NA>
15481549네트워크8호선신흥역824-10140.0편의점2022-02-032027-05-044391715<NA>
15491550네트워크8호선수진역825-10140.0편의점2022-02-032027-05-044036829<NA>
15501551네트워크8호선모란역826-10150.0편의점2022-02-032027-05-043770665<NA>