Overview

Dataset statistics

Number of variables7
Number of observations369
Missing cells92
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.7 KiB
Average record size in memory57.4 B

Variable types

Categorical2
Text4
Numeric1

Dataset

Description자치구,시장명,형태,주소,전화번호,건물형(연면적),점포수(빈점포제외)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1176/S/1/datasetView.do

Alerts

전화번호 has 70 (19.0%) missing valuesMissing
건물형(연면적) has 22 (6.0%) missing valuesMissing

Reproduction

Analysis started2023-12-11 06:40:27.851073
Analysis finished2023-12-11 06:40:28.626079
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

Distinct25
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
중구
50 
종로구
27 
관악구
 
22
영등포구
 
21
동대문
 
21
Other values (20)
228 

Length

Max length4
Median length3
Mean length2.9512195
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 50
 
13.6%
종로구 27
 
7.3%
관악구 22
 
6.0%
영등포구 21
 
5.7%
동대문 21
 
5.7%
강북구 19
 
5.1%
중랑구 17
 
4.6%
동작구 16
 
4.3%
광진구 15
 
4.1%
양천구 15
 
4.1%
Other values (15) 146
39.6%

Length

2023-12-11T15:40:28.696465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 50
 
13.6%
종로구 27
 
7.3%
관악구 22
 
6.0%
영등포구 21
 
5.7%
동대문 21
 
5.7%
강북구 19
 
5.1%
중랑구 17
 
4.6%
동작구 16
 
4.3%
광진구 15
 
4.1%
양천구 15
 
4.1%
Other values (15) 146
39.6%

시장명
Text

Distinct368
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-11T15:40:28.999095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18
Mean length7.0704607
Min length4

Characters and Unicode

Total characters2609
Distinct characters273
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367 ?
Unique (%)99.5%

Sample

1st row광장시장
2nd row동대문종합시장
3rd row동대문종합시장 신관
4th row동대문종합시장D동상가
5th row신설종합시장
ValueCountFrequency (%)
골목형상점가 28
 
6.4%
상점가 8
 
1.8%
골목시장 3
 
0.7%
도깨비시장 2
 
0.5%
지하도상가 2
 
0.5%
강남시장 2
 
0.5%
동대문종합시장 2
 
0.5%
독산동 2
 
0.5%
상암동상점가 1
 
0.2%
홍대걷고싶은거리 1
 
0.2%
Other values (388) 388
88.4%
2023-12-11T15:40:29.441628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 263
 
10.1%
시 256
 
9.8%
상 123
 
4.7%
가 122
 
4.7%
동 77
 
3.0%
목 77
 
3.0%
골 74
 
2.8%
70
 
2.7%
점 66
 
2.5%
신 43
 
1.6%
Other values (263) 1438
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2451
93.9%
Space Separator 70
 
2.7%
Decimal Number 32
 
1.2%
Open Punctuation 20
 
0.8%
Close Punctuation 20
 
0.8%
Uppercase Letter 6
 
0.2%
Other Punctuation 4
 
0.2%
Lowercase Letter 4
 
0.2%
Math Symbol 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 263
 
10.7%
시 256
 
10.4%
상 123
 
5.0%
가 122
 
5.0%
동 77
 
3.1%
목 77
 
3.1%
골 74
 
3.0%
점 66
 
2.7%
신 43
 
1.8%
리 38
 
1.6%
Other values (240) 1312
53.5%
Decimal Number
ValueCountFrequency (%)
1 6
18.8%
2 5
15.6%
7 4
12.5%
5 4
12.5%
6 4
12.5%
4 4
12.5%
3 3
9.4%
0 2
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
D 2
33.3%
B 2
33.3%
A 1
16.7%
C 1
16.7%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
m 1
25.0%
a 1
25.0%
t 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
! 1
 
25.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2449
93.9%
Common 148
 
5.7%
Latin 10
 
0.4%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 263
 
10.7%
시 256
 
10.5%
상 123
 
5.0%
가 122
 
5.0%
동 77
 
3.1%
목 77
 
3.1%
골 74
 
3.0%
점 66
 
2.7%
신 43
 
1.8%
리 38
 
1.6%
Other values (239) 1310
53.5%
Common
ValueCountFrequency (%)
70
47.3%
( 20
 
13.5%
) 20
 
13.5%
1 6
 
4.1%
2 5
 
3.4%
7 4
 
2.7%
5 4
 
2.7%
6 4
 
2.7%
4 4
 
2.7%
, 3
 
2.0%
Other values (5) 8
 
5.4%
Latin
ValueCountFrequency (%)
D 2
20.0%
B 2
20.0%
e 1
10.0%
m 1
10.0%
a 1
10.0%
t 1
10.0%
A 1
10.0%
C 1
10.0%
Han
ValueCountFrequency (%)
舊 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2449
93.9%
ASCII 158
 
6.1%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 263
 
10.7%
시 256
 
10.5%
상 123
 
5.0%
가 122
 
5.0%
동 77
 
3.1%
목 77
 
3.1%
골 74
 
3.0%
점 66
 
2.7%
신 43
 
1.8%
리 38
 
1.6%
Other values (239) 1310
53.5%
ASCII
ValueCountFrequency (%)
70
44.3%
( 20
 
12.7%
) 20
 
12.7%
1 6
 
3.8%
2 5
 
3.2%
7 4
 
2.5%
5 4
 
2.5%
6 4
 
2.5%
4 4
 
2.5%
, 3
 
1.9%
Other values (13) 18
 
11.4%
CJK
ValueCountFrequency (%)
舊 2
100.0%

형태
Categorical

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
골목형
165 
건물형
129 
상점가
54 
지하도상가
20 
혼합형
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
골목형 165
44.7%
건물형 129
35.0%
상점가 54
 
14.6%
지하도상가 20
 
5.4%
혼합형 1
 
0.3%

Length

2023-12-11T15:40:29.609485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:40:30.031063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목형 165
44.7%
건물형 129
35.0%
상점가 54
 
14.6%
지하도상가 20
 
5.4%
혼합형 1
 
0.3%

주소
Text

Distinct366
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-11T15:40:30.317947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length26
Min length9

Characters and Unicode

Total characters9594
Distinct characters254
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique363 ?
Unique (%)98.4%

Sample

1st row종로구 창경궁로 88 (종로구 예지동 6-1)
2nd row종로구 종로 266 (종로구 종로6가 289-42)
3rd row종로구 종로272 (종로구 종로6가 289-57)
4th row종로구 종로 266 (종로구 종로6가 262-1)
5th row종로구 난계로27길 51 (종로구 숭인동 206-9)
ValueCountFrequency (%)
일대 59
 
3.1%
종로구 54
 
2.8%
중구 53
 
2.8%
관악구 21
 
1.1%
동대문구 21
 
1.1%
중랑구 19
 
1.0%
강북구 18
 
0.9%
영등포구 18
 
0.9%
일원 16
 
0.8%
광진구 15
 
0.8%
Other values (1060) 1614
84.6%
2023-12-11T15:40:30.779908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1547
 
16.1%
1 576
 
6.0%
로 442
 
4.6%
2 427
 
4.5%
동 419
 
4.4%
구 405
 
4.2%
( 337
 
3.5%
) 336
 
3.5%
3 320
 
3.3%
- 316
 
3.3%
Other values (244) 4469
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4174
43.5%
Decimal Number 2799
29.2%
Space Separator 1547
 
16.1%
Open Punctuation 337
 
3.5%
Close Punctuation 336
 
3.5%
Dash Punctuation 316
 
3.3%
Other Punctuation 59
 
0.6%
Math Symbol 19
 
0.2%
Uppercase Letter 4
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 442
 
10.6%
동 419
 
10.0%
구 405
 
9.7%
길 254
 
6.1%
대 153
 
3.7%
일 117
 
2.8%
중 96
 
2.3%
종 74
 
1.8%
가 63
 
1.5%
천 57
 
1.4%
Other values (223) 2094
50.2%
Decimal Number
ValueCountFrequency (%)
1 576
20.6%
2 427
15.3%
3 320
11.4%
4 259
9.3%
5 239
8.5%
6 232
8.3%
7 204
 
7.3%
8 188
 
6.7%
0 181
 
6.5%
9 173
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
C 1
25.0%
A 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 57
96.6%
/ 2
 
3.4%
Space Separator
ValueCountFrequency (%)
1547
100.0%
Open Punctuation
ValueCountFrequency (%)
( 337
100.0%
Close Punctuation
ValueCountFrequency (%)
) 336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 316
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5413
56.4%
Hangul 4174
43.5%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 442
 
10.6%
동 419
 
10.0%
구 405
 
9.7%
길 254
 
6.1%
대 153
 
3.7%
일 117
 
2.8%
중 96
 
2.3%
종 74
 
1.8%
가 63
 
1.5%
천 57
 
1.4%
Other values (223) 2094
50.2%
Common
ValueCountFrequency (%)
1547
28.6%
1 576
 
10.6%
2 427
 
7.9%
( 337
 
6.2%
) 336
 
6.2%
3 320
 
5.9%
- 316
 
5.8%
4 259
 
4.8%
5 239
 
4.4%
6 232
 
4.3%
Other values (7) 824
15.2%
Latin
ValueCountFrequency (%)
m 3
42.9%
B 2
28.6%
C 1
 
14.3%
A 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5420
56.5%
Hangul 4174
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1547
28.5%
1 576
 
10.6%
2 427
 
7.9%
( 337
 
6.2%
) 336
 
6.2%
3 320
 
5.9%
- 316
 
5.8%
4 259
 
4.8%
5 239
 
4.4%
6 232
 
4.3%
Other values (11) 831
15.3%
Hangul
ValueCountFrequency (%)
로 442
 
10.6%
동 419
 
10.0%
구 405
 
9.7%
길 254
 
6.1%
대 153
 
3.7%
일 117
 
2.8%
중 96
 
2.3%
종 74
 
1.8%
가 63
 
1.5%
천 57
 
1.4%
Other values (223) 2094
50.2%

전화번호
Text

MISSING 

Distinct296
Distinct (%)99.0%
Missing70
Missing (%)19.0%
Memory size3.0 KiB
2023-12-11T15:40:31.111326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length11
Mean length11.672241
Min length11

Characters and Unicode

Total characters3490
Distinct characters20
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)98.0%

Sample

1st row02-2272-0967
2nd row02-2262-0211
3rd row02-2262-0211
4th row02-2279-8751~2
5th row02-2234-7151
ValueCountFrequency (%)
02-989-4730 2
 
0.7%
02-2262-0211 2
 
0.7%
02-2232-9559 2
 
0.7%
02-409-6500 1
 
0.3%
02-3272-8688 1
 
0.3%
02-2065-7212 1
 
0.3%
02-2666-4080 1
 
0.3%
02-2646-5656 1
 
0.3%
02-2606-4500 1
 
0.3%
02-3661-8993 1
 
0.3%
Other values (286) 286
95.7%
2023-12-11T15:40:31.582691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 621
17.8%
- 606
17.4%
0 528
15.1%
5 242
 
6.9%
6 240
 
6.9%
3 233
 
6.7%
4 222
 
6.4%
7 207
 
5.9%
9 194
 
5.6%
8 194
 
5.6%
Other values (10) 203
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2866
82.1%
Dash Punctuation 606
 
17.4%
Other Punctuation 5
 
0.1%
Math Symbol 3
 
0.1%
Open Punctuation 2
 
0.1%
Other Letter 2
 
0.1%
Close Punctuation 2
 
0.1%
Space Separator 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 621
21.7%
0 528
18.4%
5 242
 
8.4%
6 240
 
8.4%
3 233
 
8.1%
4 222
 
7.7%
7 207
 
7.2%
9 194
 
6.8%
8 194
 
6.8%
1 185
 
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 4
80.0%
? 1
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
C 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 606
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Letter
ValueCountFrequency (%)
동 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3486
99.9%
Hangul 2
 
0.1%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 621
17.8%
- 606
17.4%
0 528
15.1%
5 242
 
6.9%
6 240
 
6.9%
3 233
 
6.7%
4 222
 
6.4%
7 207
 
5.9%
9 194
 
5.6%
8 194
 
5.6%
Other values (7) 199
 
5.7%
Latin
ValueCountFrequency (%)
A 1
50.0%
C 1
50.0%
Hangul
ValueCountFrequency (%)
동 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3488
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 621
17.8%
- 606
17.4%
0 528
15.1%
5 242
 
6.9%
6 240
 
6.9%
3 233
 
6.7%
4 222
 
6.4%
7 207
 
5.9%
9 194
 
5.6%
8 194
 
5.6%
Other values (9) 201
 
5.8%
Hangul
ValueCountFrequency (%)
동 2
100.0%

건물형(연면적)
Text

MISSING 

Distinct340
Distinct (%)98.0%
Missing22
Missing (%)6.0%
Memory size3.0 KiB
2023-12-11T15:40:31.972770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.7002882
Min length3

Characters and Unicode

Total characters1631
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)96.3%

Sample

1st row18975
2nd row61476
3rd row13533
4th row12452
5th row1691.18
ValueCountFrequency (%)
4735 3
 
0.9%
3947 2
 
0.6%
2320 2
 
0.6%
12099 2
 
0.6%
3778 2
 
0.6%
27603 2
 
0.6%
1150 1
 
0.3%
2739 1
 
0.3%
2241 1
 
0.3%
3582 1
 
0.3%
Other values (330) 330
95.1%
2023-12-11T15:40:32.579209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 222
13.6%
2 191
11.7%
6 162
9.9%
5 161
9.9%
3 159
9.7%
9 149
9.1%
7 142
8.7%
4 138
8.5%
0 123
7.5%
8 121
7.4%
Other values (4) 63
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1568
96.1%
Other Punctuation 61
 
3.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 222
14.2%
2 191
12.2%
6 162
10.3%
5 161
10.3%
3 159
10.1%
9 149
9.5%
7 142
9.1%
4 138
8.8%
0 123
7.8%
8 121
7.7%
Other Punctuation
ValueCountFrequency (%)
. 52
85.2%
, 9
 
14.8%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1631
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 222
13.6%
2 191
11.7%
6 162
9.9%
5 161
9.9%
3 159
9.7%
9 149
9.1%
7 142
8.7%
4 138
8.5%
0 123
7.5%
8 121
7.4%
Other values (4) 63
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 222
13.6%
2 191
11.7%
6 162
9.9%
5 161
9.9%
3 159
9.7%
9 149
9.1%
7 142
8.7%
4 138
8.5%
0 123
7.5%
8 121
7.4%
Other values (4) 63
 
3.9%

점포수(빈점포제외)
Real number (ℝ)

Distinct183
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.95461
Minimum2
Maximum4190.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-11T15:40:32.799709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q146
median76
Q3135
95-th percentile602.6
Maximum4190.25
Range4188.25
Interquartile range (IQR)89

Descriptive statistics

Standard deviation342.29209
Coefficient of variation (CV)2.208983
Kurtosis73.063497
Mean154.95461
Median Absolute Deviation (MAD)38
Skewness7.5471858
Sum57178.25
Variance117163.87
MonotonicityNot monotonic
2023-12-11T15:40:32.941115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.0 9
 
2.4%
64.0 8
 
2.2%
69.0 6
 
1.6%
30.0 6
 
1.6%
60.0 5
 
1.4%
101.0 5
 
1.4%
87.0 5
 
1.4%
110.0 4
 
1.1%
71.0 4
 
1.1%
175.0 4
 
1.1%
Other values (173) 313
84.8%
ValueCountFrequency (%)
2.0 1
 
0.3%
4.0 1
 
0.3%
5.0 1
 
0.3%
6.0 1
 
0.3%
7.0 3
0.8%
8.0 3
0.8%
9.0 1
 
0.3%
10.0 3
0.8%
11.0 3
0.8%
12.0 3
0.8%
ValueCountFrequency (%)
4190.25 1
0.3%
3270.0 1
0.3%
1915.0 1
0.3%
1450.0 1
0.3%
1214.0 1
0.3%
1151.0 1
0.3%
1110.0 1
0.3%
1050.0 1
0.3%
1048.0 1
0.3%
1004.0 1
0.3%

Interactions

2023-12-11T15:40:28.250322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:40:33.030747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구형태점포수(빈점포제외)
자치구1.0000.5750.000
형태0.5751.0000.000
점포수(빈점포제외)0.0000.0001.000
2023-12-11T15:40:33.143796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구형태
자치구1.0000.279
형태0.2791.000
2023-12-11T15:40:33.248296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
점포수(빈점포제외)자치구형태
점포수(빈점포제외)1.0000.0000.000
자치구0.0001.0000.279
형태0.0000.2791.000

Missing values

2023-12-11T15:40:28.354236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:40:28.471479image/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.
2023-12-11T15:40:28.575121image/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

자치구시장명형태주소전화번호건물형(연면적)점포수(빈점포제외)
0종로구광장시장건물형종로구 창경궁로 88 (종로구 예지동 6-1)02-2272-096718975482.0
1종로구동대문종합시장건물형종로구 종로 266 (종로구 종로6가 289-42)02-2262-0211614763270.0
2종로구동대문종합시장 신관건물형종로구 종로272 (종로구 종로6가 289-57)02-2262-021113533286.0
3종로구동대문종합시장D동상가건물형종로구 종로 266 (종로구 종로6가 262-1)02-2279-8751~2124521048.0
4종로구신설종합시장건물형종로구 난계로27길 51 (종로구 숭인동 206-9)02-2234-71511691.18141.0
5종로구종각지하쇼핑센터지하도상가종로구 종로 지하 73 (종로구 종로2가 11-1 일대)02-722-8970472369.0
6종로구종오지하쇼핑센터지하도상가종로구 종로 지하 200 (종로구 종로4가 176-1 일대)<NA>416565.0
7종로구종로4가지하쇼핑센터지하도상가종로구 창경궁로 지하 81 (종로구 예지동 187-2 일대)02-2275-20762997102.0
8종로구마전교지하쇼핑센터(구한일상가)지하도상가종로구 동호로 지하 398 (종로구 종로5가 194-1 일대)02-2269-3380121029.0
9종로구동대문지하쇼핑센터지하도상가종로구 율곡로 지하 308 (종로구 종로6가 287-1 일대)02-2290-6545228548.0
자치구시장명형태주소전화번호건물형(연면적)점포수(빈점포제외)
359송파구가락골골목형상점가상점가송이로20길 12-16, 2층 (가락동 86-4)02-400-568812,607.5191.0
360강동구암사종합시장골목형강동구 성암로11길 25 (암사동 501-17)02-442-104017394114.0
361강동구길동복조리시장골목형강동구 천중로52길37일대070-4896-25442050151.0
362강동구둔촌역전통시장골목형강동구 풍성로58길 34 (성내동 428-9)02-6052-5657395587.0
363강동구성내전통시장골목형강동구 천호대로162길 65 (성내동 144-16)02-475-8272270076.0
364강동구명일전통시장골목형강동구 양재대로138길 일대02-475-7095186260.0
365강동구고분다리전통시장골목형강동구 구천면로34길 10 (천호동 393-33)02-6227-0045146885.0
366강동구로데오거리상점가상점가강동구 천호대로157길 14 (천호동 454-50)02-478-71566273150.0
367강동구장신구 특화거리 상점가상점가강동구 성내로 83 (성내동 545-6)02-475-91403366109.0
368강동구고덕 골목형상점가상점가강동구 고덕로83길 18일대02-429-13766699104.0