Overview

Dataset statistics

Number of variables11
Number of observations1564
Missing cells1112
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory139.1 KiB
Average record size in memory91.1 B

Variable types

Numeric3
Categorical4
Text2
DateTime2

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-12927/F/1/datasetView.do

Alerts

사업진행단계 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
상가유형 is highly overall correlated with 사업진행단계High correlation
연번 is highly overall correlated with 호선High correlation
면적(제곱미터) is highly overall correlated with 사업진행단계High correlation
호선 is highly overall correlated with 연번High correlation
사업진행단계 is highly imbalanced (56.9%)Imbalance
면적(제곱미터) has 62 (4.0%) missing valuesMissing
계약시작일자 has 310 (19.8%) missing valuesMissing
계약종료일자 has 310 (19.8%) missing valuesMissing
월임대료 has 430 (27.5%) missing valuesMissing
면적(제곱미터) is highly skewed (γ1 = 30.71786094)Skewed
연번 has unique valuesUnique
상가번호 has unique valuesUnique

Reproduction

Analysis started2024-04-29 16:39:45.563543
Analysis finished2024-04-29 16:39:47.303206
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1564
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean782.5
Minimum1
Maximum1564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-04-30T01:39:47.370968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile79.15
Q1391.75
median782.5
Q31173.25
95-th percentile1485.85
Maximum1564
Range1563
Interquartile range (IQR)781.5

Descriptive statistics

Standard deviation451.63223
Coefficient of variation (CV)0.57716578
Kurtosis-1.2
Mean782.5
Median Absolute Deviation (MAD)391
Skewness0
Sum1223830
Variance203971.67
MonotonicityStrictly increasing
2024-04-30T01:39:47.747946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1029 1
 
0.1%
1051 1
 
0.1%
1050 1
 
0.1%
1049 1
 
0.1%
1048 1
 
0.1%
1047 1
 
0.1%
1046 1
 
0.1%
1045 1
 
0.1%
1044 1
 
0.1%
Other values (1554) 1554
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 (%)
1564 1
0.1%
1563 1
0.1%
1562 1
0.1%
1561 1
0.1%
1560 1
0.1%
1559 1
0.1%
1558 1
0.1%
1557 1
0.1%
1556 1
0.1%
1555 1
0.1%

상가유형
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
개별(일반)
597 
네트워크
419 
복합
201 
임대진행
168 
공실
126 
Other values (2)
 
53

Length

Max length6
Median length4
Mean length4.3938619
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) 597
38.2%
네트워크 419
26.8%
복합 201
 
12.9%
임대진행 168
 
10.7%
공실 126
 
8.1%
개별(대형) 38
 
2.4%
소송상가 15
 
1.0%

Length

2024-04-30T01:39:47.882482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:39:47.997528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별(일반 597
38.2%
네트워크 419
26.8%
복합 201
 
12.9%
임대진행 168
 
10.7%
공실 126
 
8.1%
개별(대형 38
 
2.4%
소송상가 15
 
1.0%

호선
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
7호선
370 
5호선
301 
2호선
294 
6호선
185 
3호선
172 
Other values (3)
242 

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 370
23.7%
5호선 301
19.2%
2호선 294
18.8%
6호선 185
11.8%
3호선 172
11.0%
4호선 147
 
9.4%
8호선 65
 
4.2%
1호선 30
 
1.9%

Length

2024-04-30T01:39:48.127895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:39:48.239294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7호선 370
23.7%
5호선 301
19.2%
2호선 294
18.8%
6호선 185
11.8%
3호선 172
11.0%
4호선 147
 
9.4%
8호선 65
 
4.2%
1호선 30
 
1.9%

역명
Text

Distinct238
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2024-04-30T01:39:48.456127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length4.9942455
Min length3

Characters and Unicode

Total characters7811
Distinct characters204
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
 
2.9%
고속터미널(3)역 39
 
2.5%
공덕(5)역 29
 
1.9%
천호(5)역 29
 
1.9%
잠실(8)역 26
 
1.7%
사당(4)역 25
 
1.6%
노원(7)역 22
 
1.4%
잠실(2)역 21
 
1.3%
미아사거리역 19
 
1.2%
마들역 19
 
1.2%
Other values (228) 1289
82.4%
2024-04-30T01:39:48.765810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 1590
 
20.4%
( 547
 
7.0%
) 547
 
7.0%
대 204
 
2.6%
구 178
 
2.3%
신 122
 
1.6%
2 114
 
1.5%
입 112
 
1.4%
5 109
 
1.4%
사 107
 
1.4%
Other values (194) 4181
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6127
78.4%
Decimal Number 590
 
7.6%
Open Punctuation 547
 
7.0%
Close Punctuation 547
 
7.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 1590
26.0%
대 204
 
3.3%
구 178
 
2.9%
신 122
 
2.0%
입 112
 
1.8%
사 107
 
1.7%
동 94
 
1.5%
산 92
 
1.5%
공 92
 
1.5%
미 89
 
1.5%
Other values (184) 3447
56.3%
Decimal Number
ValueCountFrequency (%)
2 114
19.3%
5 109
18.5%
3 107
18.1%
7 91
15.4%
4 62
10.5%
6 61
10.3%
8 31
 
5.3%
1 15
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 547
100.0%
Close Punctuation
ValueCountFrequency (%)
) 547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6127
78.4%
Common 1684
 
21.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 1590
26.0%
대 204
 
3.3%
구 178
 
2.9%
신 122
 
2.0%
입 112
 
1.8%
사 107
 
1.7%
동 94
 
1.5%
산 92
 
1.5%
공 92
 
1.5%
미 89
 
1.5%
Other values (184) 3447
56.3%
Common
ValueCountFrequency (%)
( 547
32.5%
) 547
32.5%
2 114
 
6.8%
5 109
 
6.5%
3 107
 
6.4%
7 91
 
5.4%
4 62
 
3.7%
6 61
 
3.6%
8 31
 
1.8%
1 15
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6127
78.4%
ASCII 1684
 
21.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 1590
26.0%
대 204
 
3.3%
구 178
 
2.9%
신 122
 
2.0%
입 112
 
1.8%
사 107
 
1.7%
동 94
 
1.5%
산 92
 
1.5%
공 92
 
1.5%
미 89
 
1.5%
Other values (184) 3447
56.3%
ASCII
ValueCountFrequency (%)
( 547
32.5%
) 547
32.5%
2 114
 
6.8%
5 109
 
6.5%
3 107
 
6.4%
7 91
 
5.4%
4 62
 
3.7%
6 61
 
3.6%
8 31
 
1.8%
1 15
 
0.9%

상가번호
Text

UNIQUE 

Distinct1564
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2024-04-30T01:39:49.100898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10948
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

Unique1564 ?
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%
626-126 1
 
0.1%
629-101 1
 
0.1%
628-308 1
 
0.1%
628-307 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%
Other values (1554) 1554
99.4%
2024-04-30T01:39:49.496386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2261
20.7%
2 1604
14.7%
- 1564
14.3%
0 1532
14.0%
3 1035
9.5%
4 727
 
6.6%
5 628
 
5.7%
7 608
 
5.6%
6 467
 
4.3%
8 271
 
2.5%
Other values (2) 251
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9381
85.7%
Dash Punctuation 1564
 
14.3%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2261
24.1%
2 1604
17.1%
0 1532
16.3%
3 1035
11.0%
4 727
 
7.7%
5 628
 
6.7%
7 608
 
6.5%
6 467
 
5.0%
8 271
 
2.9%
9 248
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 1564
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10945
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2261
20.7%
2 1604
14.7%
- 1564
14.3%
0 1532
14.0%
3 1035
9.5%
4 727
 
6.6%
5 628
 
5.7%
7 608
 
5.6%
6 467
 
4.3%
8 271
 
2.5%
Latin
ValueCountFrequency (%)
M 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2261
20.7%
2 1604
14.7%
- 1564
14.3%
0 1532
14.0%
3 1035
9.5%
4 727
 
6.6%
5 628
 
5.7%
7 608
 
5.6%
6 467
 
4.3%
8 271
 
2.5%
Other values (2) 251
 
2.3%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct816
Distinct (%)54.3%
Missing62
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean50.729148
Minimum7.61
Maximum7475.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-04-30T01:39:49.629731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.61
5-th percentile13.0205
Q122
median31.135
Q344
95-th percentile101.3595
Maximum7475.19
Range7467.58
Interquartile range (IQR)22

Descriptive statistics

Standard deviation208.43356
Coefficient of variation (CV)4.1087534
Kurtosis1076.5379
Mean50.729148
Median Absolute Deviation (MAD)10.315
Skewness30.717861
Sum76195.18
Variance43444.548
MonotonicityNot monotonic
2024-04-30T01:39:49.746090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 45
 
2.9%
33.0 32
 
2.0%
40.0 26
 
1.7%
20.0 20
 
1.3%
50.0 18
 
1.2%
35.0 16
 
1.0%
25.0 16
 
1.0%
37.0 16
 
1.0%
31.0 15
 
1.0%
32.0 15
 
1.0%
Other values (806) 1283
82.0%
(Missing) 62
 
4.0%
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 size12.3 KiB
패션잡화
392 
<NA>
294 
식음료
264 
기타
183 
편의점
171 
Other values (8)
260 

Length

Max length5
Median length4
Mean length3.3670077
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
패션잡화 392
25.1%
<NA> 294
18.8%
식음료 264
16.9%
기타 183
11.7%
편의점 171
10.9%
플라워 74
 
4.7%
화장품 65
 
4.2%
의약업 37
 
2.4%
사무실 33
 
2.1%
공유오피스 19
 
1.2%
Other values (3) 32
 
2.0%

Length

2024-04-30T01:39:49.884102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
패션잡화 392
25.1%
na 294
18.8%
식음료 264
16.9%
기타 183
11.7%
편의점 171
10.9%
플라워 74
 
4.7%
화장품 65
 
4.2%
의약업 37
 
2.4%
사무실 33
 
2.1%
공유오피스 19
 
1.2%
Other values (3) 32
 
2.0%

계약시작일자
Date

MISSING 

Distinct344
Distinct (%)27.4%
Missing310
Missing (%)19.8%
Memory size12.3 KiB
Minimum2016-03-15 00:00:00
Maximum2023-03-20 00:00:00
2024-04-30T01:39:50.006548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:50.137612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약종료일자
Date

MISSING 

Distinct360
Distinct (%)28.7%
Missing310
Missing (%)19.8%
Memory size12.3 KiB
Minimum2022-02-05 00:00:00
Maximum2028-08-10 00:00:00
2024-04-30T01:39:50.276682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:50.422266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

월임대료
Real number (ℝ)

MISSING 

Distinct1021
Distinct (%)90.0%
Missing430
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean5438492.5
Minimum153600
Maximum2.8792277 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-04-30T01:39:50.595956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153600
5-th percentile699300
Q11775341.5
median3341605.5
Q36307049
95-th percentile14947647
Maximum2.8792277 × 108
Range2.8776917 × 108
Interquartile range (IQR)4531707.5

Descriptive statistics

Standard deviation11127406
Coefficient of variation (CV)2.0460461
Kurtosis398.48713
Mean5438492.5
Median Absolute Deviation (MAD)1900125
Skewness17.271368
Sum6.1672505 × 109
Variance1.2381917 × 1014
MonotonicityNot monotonic
2024-04-30T01:39:50.797763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1700000 8
 
0.5%
1619070 5
 
0.3%
500000 5
 
0.3%
2500000 5
 
0.3%
2200000 4
 
0.3%
2300000 4
 
0.3%
2810000 4
 
0.3%
1810000 4
 
0.3%
4310000 3
 
0.2%
5500000 3
 
0.2%
Other values (1011) 1089
69.6%
(Missing) 430
 
27.5%
ValueCountFrequency (%)
153600 1
0.1%
163900 1
0.1%
186000 1
0.1%
233500 1
0.1%
242291 1
0.1%
300000 1
0.1%
302500 1
0.1%
311667 1
0.1%
328100 1
0.1%
330000 1
0.1%
ValueCountFrequency (%)
287922774 1
0.1%
157003070 1
0.1%
70933119 1
0.1%
61517300 1
0.1%
48204012 1
0.1%
46465000 1
0.1%
44049396 1
0.1%
40100000 1
0.1%
29358258 1
0.1%
29064529 1
0.1%

사업진행단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
<NA>
1270 
3단계. 감정평가
 
96
2단계. 시설물 점검
 
70
1단계. 사업계획 수립중
 
56
4단계. 방침수립
 
38

Length

Max length13
Median length4
Mean length5.1726343
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> 1270
81.2%
3단계. 감정평가 96
 
6.1%
2단계. 시설물 점검 70
 
4.5%
1단계. 사업계획 수립중 56
 
3.6%
4단계. 방침수립 38
 
2.4%
5단계. 입찰공고 34
 
2.2%

Length

2024-04-30T01:39:50.959906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:39:51.096937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1270
64.0%
3단계 96
 
4.8%
감정평가 96
 
4.8%
2단계 70
 
3.5%
시설물 70
 
3.5%
점검 70
 
3.5%
1단계 56
 
2.8%
사업계획 56
 
2.8%
수립중 56
 
2.8%
4단계 38
 
1.9%
Other values (3) 106
 
5.3%

Interactions

2024-04-30T01:39:46.607307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.055459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.304740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.709596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.138309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.403291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.799994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.224622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:46.508404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:39:51.178176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상가유형호선면적(제곱미터)영업업종월임대료사업진행단계
연번1.0000.3720.9370.0000.3090.0180.604
상가유형0.3721.0000.3190.3300.5410.3821.000
호선0.9370.3191.0000.0480.3020.1120.380
면적(제곱미터)0.0000.3300.0481.0000.2360.741NaN
영업업종0.3090.5410.3020.2361.0000.000NaN
월임대료0.0180.3820.1120.7410.0001.000NaN
사업진행단계0.6041.0000.380NaNNaNNaN1.000
2024-04-30T01:39:51.286998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업진행단계영업업종상가유형호선
사업진행단계1.000NaN0.9950.243
영업업종NaN1.0000.3360.132
상가유형0.9950.3361.0000.176
호선0.2430.1320.1761.000
2024-04-30T01:39:51.382124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)월임대료상가유형호선영업업종사업진행단계
연번1.0000.374-0.0700.1980.8110.1350.291
면적(제곱미터)0.3741.0000.2730.2350.0300.1081.000
월임대료-0.0700.2731.0000.1510.0680.0000.000
상가유형0.1980.2350.1511.0000.1760.3360.995
호선0.8110.0300.0680.1761.0000.1320.243
영업업종0.1350.1080.0000.3360.1321.0000.000
사업진행단계0.2911.0000.0000.9950.2430.0001.000

Missing values

2024-04-30T01:39:46.920916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:39:47.089502image/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.
2024-04-30T01:39:47.222799image/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
15541555네트워크8호선남한산성입구역822-20354.76밀키트2022-05-022027-07-312155500<NA>
15551556네트워크8호선남한산성입구역822-20434.18패션잡화2018-05-092023-07-071977511<NA>
15561557개별(일반)8호선남한산성입구역822-20517.0식음료2022-04-282027-05-281200000<NA>
15571558개별(일반)8호선단대오거리역823-10236.78기타2021-01-212026-02-201700000<NA>
15581559네트워크8호선단대오거리역823-20132.5편의점2022-02-032027-05-046235459<NA>
15591560개별(일반)8호선단대오거리역823-20354.03식음료2018-08-312023-09-297630000<NA>
15601561개별(일반)8호선단대오거리역823-20475.09패션잡화2021-03-182026-04-173780000<NA>
15611562네트워크8호선신흥역824-10140.0편의점2022-02-032027-05-044391715<NA>
15621563네트워크8호선수진역825-10140.0편의점2022-02-032027-05-044036829<NA>
15631564네트워크8호선모란역826-10150.0편의점2022-02-032027-05-043770665<NA>