Overview

Dataset statistics

Number of variables10
Number of observations804
Missing cells45
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.5 KiB
Average record size in memory82.2 B

Variable types

Numeric2
Categorical1
Text5
DateTime2

Dataset

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

Alerts

승인업종 has 10 (1.2%) missing valuesMissing
계약시작 has 12 (1.5%) missing valuesMissing
계약종료 has 12 (1.5%) missing valuesMissing
임대료(월) has 11 (1.4%) missing valuesMissing
면적 is highly skewed (γ1 = 20.99439825)Skewed
번호 has unique valuesUnique

Reproduction

Analysis started2024-04-29 16:39:08.747373
Analysis finished2024-04-29 16:39:09.861601
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct804
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.5
Minimum1
Maximum804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-04-30T01:39:09.921122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.15
Q1201.75
median402.5
Q3603.25
95-th percentile763.85
Maximum804
Range803
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation232.2391
Coefficient of variation (CV)0.57699156
Kurtosis-1.2
Mean402.5
Median Absolute Deviation (MAD)201
Skewness0
Sum323610
Variance53935
MonotonicityStrictly increasing
2024-04-30T01:39:10.066050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
542 1
 
0.1%
532 1
 
0.1%
533 1
 
0.1%
534 1
 
0.1%
535 1
 
0.1%
536 1
 
0.1%
537 1
 
0.1%
538 1
 
0.1%
539 1
 
0.1%
Other values (794) 794
98.8%
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 (%)
804 1
0.1%
803 1
0.1%
802 1
0.1%
801 1
0.1%
800 1
0.1%
799 1
0.1%
798 1
0.1%
797 1
0.1%
796 1
0.1%
795 1
0.1%

상가유형(本)
Categorical

Distinct46
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
개별(일반)
419 
네트워크(화장품)
53 
개별(장기)
 
40
네트워크(명품)
 
38
네트워크(편의점)
 
20
Other values (41)
234 

Length

Max length11
Median length6
Mean length7.0099502
Min length6

Unique

Unique3 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
개별(일반) 419
52.1%
네트워크(화장품) 53
 
6.6%
개별(장기) 40
 
5.0%
네트워크(명품) 38
 
4.7%
네트워크(편의점) 20
 
2.5%
네트워크(커피) 20
 
2.5%
개별(대형) 18
 
2.2%
네트워크(편의점A) 14
 
1.7%
네트워크(제과) 13
 
1.6%
네트워크(일식전문점) 9
 
1.1%
Other values (36) 160
 
19.9%

Length

2024-04-30T01:39:10.218006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개별(일반 419
52.1%
네트워크(화장품 53
 
6.6%
개별(장기 40
 
5.0%
네트워크(명품 38
 
4.7%
네트워크(편의점 20
 
2.5%
네트워크(커피 20
 
2.5%
개별(대형 18
 
2.2%
네트워크(편의점a 14
 
1.7%
네트워크(제과 13
 
1.6%
네트워크(일식전문점 9
 
1.1%
Other values (36) 160
 
19.9%

상가번호
Text

Distinct803
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2024-04-30T01:39:10.510069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4824
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

Unique802 ?
Unique (%)99.8%

Sample

1st row150-03
2nd row150-05
3rd row150-07
4th row150-08
5th row150-09
ValueCountFrequency (%)
225-06 2
 
0.2%
329-04 1
 
0.1%
330-06 1
 
0.1%
327-12 1
 
0.1%
327-13 1
 
0.1%
327-14 1
 
0.1%
327-15 1
 
0.1%
327-16 1
 
0.1%
327-17 1
 
0.1%
329-01 1
 
0.1%
Other values (793) 793
98.6%
2024-04-30T01:39:10.965243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 826
17.1%
- 804
16.7%
0 778
16.1%
1 698
14.5%
3 626
13.0%
4 405
8.4%
5 182
 
3.8%
6 155
 
3.2%
8 126
 
2.6%
7 112
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4020
83.3%
Dash Punctuation 804
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 826
20.5%
0 778
19.4%
1 698
17.4%
3 626
15.6%
4 405
10.1%
5 182
 
4.5%
6 155
 
3.9%
8 126
 
3.1%
7 112
 
2.8%
9 112
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 826
17.1%
- 804
16.7%
0 778
16.1%
1 698
14.5%
3 626
13.0%
4 405
8.4%
5 182
 
3.8%
6 155
 
3.2%
8 126
 
2.6%
7 112
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 826
17.1%
- 804
16.7%
0 778
16.1%
1 698
14.5%
3 626
13.0%
4 405
8.4%
5 182
 
3.8%
6 155
 
3.2%
8 126
 
2.6%
7 112
 
2.3%

역명
Text

Distinct106
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2024-04-30T01:39:11.157193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length4.6778607
Min length3

Characters and Unicode

Total characters3761
Distinct characters135
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

Unique6 ?
Unique (%)0.7%

Sample

1st row서울(1)역
2nd row서울(1)역
3rd row서울(1)역
4th row서울(1)역
5th row서울(1)역
ValueCountFrequency (%)
사당(4)역 33
 
4.1%
총신대입구역 21
 
2.6%
역삼역 20
 
2.5%
잠실역 20
 
2.5%
미아사거리역 19
 
2.4%
을지로입구역 17
 
2.1%
안국역 17
 
2.1%
선릉역 16
 
2.0%
신사역 15
 
1.9%
을지로3가(2)역 15
 
1.9%
Other values (96) 611
76.0%
2024-04-30T01:39:11.459594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 839
22.3%
( 179
 
4.8%
) 179
 
4.8%
대 154
 
4.1%
구 110
 
2.9%
신 103
 
2.7%
사 95
 
2.5%
로 89
 
2.4%
4 82
 
2.2%
입 79
 
2.1%
Other values (125) 1852
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3169
84.3%
Decimal Number 234
 
6.2%
Open Punctuation 179
 
4.8%
Close Punctuation 179
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 839
26.5%
대 154
 
4.9%
구 110
 
3.5%
신 103
 
3.3%
사 95
 
3.0%
로 89
 
2.8%
입 79
 
2.5%
문 72
 
2.3%
동 71
 
2.2%
당 62
 
2.0%
Other values (118) 1495
47.2%
Decimal Number
ValueCountFrequency (%)
4 82
35.0%
3 68
29.1%
2 55
23.5%
1 23
 
9.8%
5 6
 
2.6%
Open Punctuation
ValueCountFrequency (%)
( 179
100.0%
Close Punctuation
ValueCountFrequency (%)
) 179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3169
84.3%
Common 592
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 839
26.5%
대 154
 
4.9%
구 110
 
3.5%
신 103
 
3.3%
사 95
 
3.0%
로 89
 
2.8%
입 79
 
2.5%
문 72
 
2.3%
동 71
 
2.2%
당 62
 
2.0%
Other values (118) 1495
47.2%
Common
ValueCountFrequency (%)
( 179
30.2%
) 179
30.2%
4 82
13.9%
3 68
 
11.5%
2 55
 
9.3%
1 23
 
3.9%
5 6
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3169
84.3%
ASCII 592
 
15.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 839
26.5%
대 154
 
4.9%
구 110
 
3.5%
신 103
 
3.3%
사 95
 
3.0%
로 89
 
2.8%
입 79
 
2.5%
문 72
 
2.3%
동 71
 
2.2%
당 62
 
2.0%
Other values (118) 1495
47.2%
ASCII
ValueCountFrequency (%)
( 179
30.2%
) 179
30.2%
4 82
13.9%
3 68
 
11.5%
2 55
 
9.3%
1 23
 
3.9%
5 6
 
1.0%

면적
Real number (ℝ)

SKEWED 

Distinct480
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.336789
Minimum8
Maximum3788.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-04-30T01:39:11.594466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11.9
Q116.68
median21.95
Q330.70075
95-th percentile64.4855
Maximum3788.6
Range3780.6
Interquartile range (IQR)14.02075

Descriptive statistics

Standard deviation149.59267
Coefficient of variation (CV)4.0065757
Kurtosis502.86397
Mean37.336789
Median Absolute Deviation (MAD)6.65
Skewness20.994398
Sum30018.778
Variance22377.967
MonotonicityNot monotonic
2024-04-30T01:39:11.713419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 19
 
2.4%
25.0 13
 
1.6%
21.0 12
 
1.5%
12.0 11
 
1.4%
14.41 11
 
1.4%
11.0 11
 
1.4%
16.0 11
 
1.4%
20.5 10
 
1.2%
29.0 9
 
1.1%
15.5 8
 
1.0%
Other values (470) 689
85.7%
ValueCountFrequency (%)
8.0 1
0.1%
9.41 1
0.1%
9.66 1
0.1%
9.8 2
0.2%
9.92 1
0.1%
10.0 2
0.2%
10.2 1
0.1%
10.38 1
0.1%
10.4 1
0.1%
10.44 1
0.1%
ValueCountFrequency (%)
3788.6 1
0.1%
1351.0 1
0.1%
861.0 1
0.1%
808.0 1
0.1%
555.0 1
0.1%
408.0 1
0.1%
248.9 1
0.1%
240.3 1
0.1%
233.0 1
0.1%
169.0 2
0.2%

상가명
Text

Distinct800
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2024-04-30T01:39:11.900887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length10.717662
Min length2

Characters and Unicode

Total characters8617
Distinct characters148
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique797 ?
Unique (%)99.1%

Sample

1st row서울(1)역 상가03,04호 합산
2nd row서울(1)역 상가05호
3rd row서울(1)역 상가07호
4th row서울(1)역 상가08호
5th row서울(1)역 상가09호
ValueCountFrequency (%)
상가01호 73
 
4.5%
상가02호 66
 
4.1%
상가04호 59
 
3.6%
상가03호 59
 
3.6%
합산 59
 
3.6%
상가06호 50
 
3.1%
상가05호 48
 
3.0%
상가07호 40
 
2.5%
상가08호 37
 
2.3%
사당(4)역 33
 
2.0%
Other values (235) 1093
67.6%
2024-04-30T01:39:12.210524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
814
 
9.4%
가 793
 
9.2%
역 775
 
9.0%
상 750
 
8.7%
호 744
 
8.6%
0 599
 
7.0%
1 328
 
3.8%
2 215
 
2.5%
3 192
 
2.2%
4 186
 
2.2%
Other values (138) 3221
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5522
64.1%
Decimal Number 1864
 
21.6%
Space Separator 814
 
9.4%
Open Punctuation 176
 
2.0%
Close Punctuation 176
 
2.0%
Other Punctuation 65
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 793
14.4%
역 775
14.0%
상 750
13.6%
호 744
13.5%
대 154
 
2.8%
합 116
 
2.1%
구 109
 
2.0%
신 103
 
1.9%
사 95
 
1.7%
로 89
 
1.6%
Other values (124) 1794
32.5%
Decimal Number
ValueCountFrequency (%)
0 599
32.1%
1 328
17.6%
2 215
 
11.5%
3 192
 
10.3%
4 186
 
10.0%
5 91
 
4.9%
6 84
 
4.5%
7 68
 
3.6%
8 56
 
3.0%
9 45
 
2.4%
Space Separator
ValueCountFrequency (%)
814
100.0%
Open Punctuation
ValueCountFrequency (%)
( 176
100.0%
Close Punctuation
ValueCountFrequency (%)
) 176
100.0%
Other Punctuation
ValueCountFrequency (%)
, 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5522
64.1%
Common 3095
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 793
14.4%
역 775
14.0%
상 750
13.6%
호 744
13.5%
대 154
 
2.8%
합 116
 
2.1%
구 109
 
2.0%
신 103
 
1.9%
사 95
 
1.7%
로 89
 
1.6%
Other values (124) 1794
32.5%
Common
ValueCountFrequency (%)
814
26.3%
0 599
19.4%
1 328
10.6%
2 215
 
6.9%
3 192
 
6.2%
4 186
 
6.0%
( 176
 
5.7%
) 176
 
5.7%
5 91
 
2.9%
6 84
 
2.7%
Other values (4) 234
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5522
64.1%
ASCII 3095
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
814
26.3%
0 599
19.4%
1 328
10.6%
2 215
 
6.9%
3 192
 
6.2%
4 186
 
6.0%
( 176
 
5.7%
) 176
 
5.7%
5 91
 
2.9%
6 84
 
2.7%
Other values (4) 234
 
7.6%
Hangul
ValueCountFrequency (%)
가 793
14.4%
역 775
14.0%
상 750
13.6%
호 744
13.5%
대 154
 
2.8%
합 116
 
2.1%
구 109
 
2.0%
신 103
 
1.9%
사 95
 
1.7%
로 89
 
1.6%
Other values (124) 1794
32.5%

승인업종
Text

MISSING 

Distinct98
Distinct (%)12.3%
Missing10
Missing (%)1.2%
Memory size6.4 KiB
2024-04-30T01:39:12.377378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.3816121
Min length2

Characters and Unicode

Total characters2685
Distinct characters138
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

Unique55 ?
Unique (%)6.9%

Sample

1st row제과
2nd row편의점
3rd row화장품
4th row커피전문점
5th row커피전문점
ValueCountFrequency (%)
화장품 125
15.6%
여성의류 111
13.8%
편의점 89
11.1%
제과 70
 
8.7%
액세서리 68
 
8.5%
의류 48
 
6.0%
커피전문점 42
 
5.2%
커피 36
 
4.5%
언더웨어 19
 
2.4%
인쇄 12
 
1.5%
Other values (85) 182
22.7%
2024-04-30T01:39:12.667693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 263
 
9.8%
류 174
 
6.5%
점 153
 
5.7%
품 152
 
5.7%
화 135
 
5.0%
장 129
 
4.8%
성 115
 
4.3%
여 113
 
4.2%
편 91
 
3.4%
제 79
 
2.9%
Other values (128) 1281
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2627
97.8%
Other Punctuation 16
 
0.6%
Close Punctuation 14
 
0.5%
Open Punctuation 14
 
0.5%
Space Separator 11
 
0.4%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 263
 
10.0%
류 174
 
6.6%
점 153
 
5.8%
품 152
 
5.8%
화 135
 
5.1%
장 129
 
4.9%
성 115
 
4.4%
여 113
 
4.3%
편 91
 
3.5%
제 79
 
3.0%
Other values (122) 1223
46.6%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
V 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2627
97.8%
Common 55
 
2.0%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 263
 
10.0%
류 174
 
6.6%
점 153
 
5.8%
품 152
 
5.8%
화 135
 
5.1%
장 129
 
4.9%
성 115
 
4.4%
여 113
 
4.3%
편 91
 
3.5%
제 79
 
3.0%
Other values (122) 1223
46.6%
Common
ValueCountFrequency (%)
, 16
29.1%
) 14
25.5%
( 14
25.5%
11
20.0%
Latin
ValueCountFrequency (%)
D 2
66.7%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2627
97.8%
ASCII 58
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 263
 
10.0%
류 174
 
6.6%
점 153
 
5.8%
품 152
 
5.8%
화 135
 
5.1%
장 129
 
4.9%
성 115
 
4.4%
여 113
 
4.3%
편 91
 
3.5%
제 79
 
3.0%
Other values (122) 1223
46.6%
ASCII
ValueCountFrequency (%)
, 16
27.6%
) 14
24.1%
( 14
24.1%
11
19.0%
D 2
 
3.4%
V 1
 
1.7%

계약시작
Date

MISSING 

Distinct171
Distinct (%)21.6%
Missing12
Missing (%)1.5%
Memory size6.4 KiB
Minimum2002-04-29 00:00:00
Maximum2015-04-21 00:00:00
2024-04-30T01:39:12.788334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:12.909882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약종료
Date

MISSING 

Distinct190
Distinct (%)24.0%
Missing12
Missing (%)1.5%
Memory size6.4 KiB
Minimum2013-07-03 00:00:00
Maximum2020-01-31 00:00:00
2024-04-30T01:39:13.215738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:13.324337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

임대료(월)
Text

MISSING 

Distinct666
Distinct (%)84.0%
Missing11
Missing (%)1.4%
Memory size6.4 KiB
2024-04-30T01:39:13.584461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.9029004
Min length1

Characters and Unicode

Total characters6267
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

Unique630 ?
Unique (%)79.4%

Sample

1st row8,837,400
2nd row10,065,000
3rd row7,439,600
4th row5,245,800
5th row5,040,000
ValueCountFrequency (%)
0 91
 
11.5%
2,200,000 3
 
0.4%
4,751,726 3
 
0.4%
25,010,000 2
 
0.3%
1,010,000 2
 
0.3%
17,646,975 2
 
0.3%
5,010,000 2
 
0.3%
2,210,000 2
 
0.3%
15,510,000 2
 
0.3%
8,020,000 2
 
0.3%
Other values (655) 681
86.0%
2024-04-30T01:39:13.969393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1735
27.7%
, 1282
20.5%
1 497
 
7.9%
2 436
 
7.0%
3 358
 
5.7%
5 343
 
5.5%
4 342
 
5.5%
6 325
 
5.2%
7 318
 
5.1%
8 317
 
5.1%
Other values (2) 314
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4983
79.5%
Other Punctuation 1282
 
20.5%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1735
34.8%
1 497
 
10.0%
2 436
 
8.7%
3 358
 
7.2%
5 343
 
6.9%
4 342
 
6.9%
6 325
 
6.5%
7 318
 
6.4%
8 317
 
6.4%
9 312
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 1282
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1735
27.7%
, 1282
20.5%
1 497
 
7.9%
2 436
 
7.0%
3 358
 
5.7%
5 343
 
5.5%
4 342
 
5.5%
6 325
 
5.2%
7 318
 
5.1%
8 317
 
5.1%
Other values (2) 314
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1735
27.7%
, 1282
20.5%
1 497
 
7.9%
2 436
 
7.0%
3 358
 
5.7%
5 343
 
5.5%
4 342
 
5.5%
6 325
 
5.2%
7 318
 
5.1%
8 317
 
5.1%
Other values (2) 314
 
5.0%

Interactions

2024-04-30T01:39:09.394317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:09.209198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:09.479433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:09.296680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:39:14.055711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호상가유형(本)면적승인업종
번호1.0000.7110.0000.333
상가유형(本)0.7111.0000.3460.875
면적0.0000.3461.0000.968
승인업종0.3330.8750.9681.000
2024-04-30T01:39:14.129477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호면적상가유형(本)
번호1.0000.0600.324
면적0.0601.0000.162
상가유형(本)0.3240.1621.000

Missing values

2024-04-30T01:39:09.590691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:39:09.711012image/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.
2024-04-30T01:39:09.806507image/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

번호상가유형(本)상가번호역명면적상가명승인업종계약시작계약종료임대료(월)
01개별(일반)150-03서울(1)역29.46서울(1)역 상가03,04호 합산제과2010.11.152015.11.148,837,400
12개별(일반)150-05서울(1)역27.6서울(1)역 상가05호편의점2010.11.152015.11.1410,065,000
23네트워크(화장품A)150-07서울(1)역33.0서울(1)역 상가07호화장품2012.06.012015.07.117,439,600
34네트워크(푸드A)150-08서울(1)역33.0서울(1)역 상가08호커피전문점2012.07.062015.08.155,245,800
45네트워크(미니샵A)150-09서울(1)역12.0서울(1)역 상가09호커피전문점2012.05.212015.07.105,040,000
56개별(일반)151-01시청(1)역19.18시청(1)역 상가01호액세서리2013.03.182016.03.174,693,700
67네트워크(화장품)151-02시청(1)역15.03시청(1)역 상가02호화장품2008.07.042013.07.030
78개별(무상)151-03시청(1)역57.6시청(1)역 상가03호장애인생산품,잡화2015.02.012020.01.310
89네트워크(편의점A)151-04시청(1)역25.0시청(1)역 상가04호편의점2012.05.242015.07.039,732,800
910네트워크(푸드D)151-05시청(1)역25.0시청(1)역 상가05호커피전문점2012.06.282015.08.076,002,800
번호상가유형(本)상가번호역명면적상가명승인업종계약시작계약종료임대료(월)
794795복합(사당(4)역)433-33사당(4)역13.5사당(4)역 상가33호핸드폰2009.12.312016.02.271,701,472
795796복합(사당(4)역)433-34사당(4)역15.0사당(4)역 상가34호언더웨어2009.12.312016.02.271,890,525
796797복합(사당(4)역)433-35사당(4)역20.0사당(4)역 상가35호제과2009.12.312016.02.272,520,699
797798복합(사당(4)역)433-36사당(4)역10.0사당(4)역 상가36호여성의류2009.12.312016.02.271,260,350
798799복합(사당(4)역)433-37사당(4)역15.0사당(4)역 상가37호화장품2009.12.312016.02.271,890,525
799800복합(사당(4)역)433-38사당(4)역23.1사당(4)역 상가38호액세서리2009.12.312016.02.272,911,408
800801개별(대형)433-39사당(4)역861.0사당(4)역 상가39호아울렛매장 등2014.02.032017.02.2367,000,000
801802개별(대형)433-40사당(4)역408.0사당(4)역 상가40호화장품,의류,제과2011.08.012016.09.1586,075,590
802803네트워크(편의점)433-41사당(4)역22.0사당(4)역 상가41호편의점2011.08.292016.10.2818,487,100
803804네트워크(푸드D)433-42사당(4)역24.8사당(4)역 상가42호커피전문점2012.06.282015.08.078,179,410