Overview

Dataset statistics

Number of variables10
Number of observations816
Missing cells260
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.5 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 65 (8.0%) missing valuesMissing
계약시작 has 65 (8.0%) missing valuesMissing
계약종료 has 65 (8.0%) missing valuesMissing
임대료 has 65 (8.0%) missing valuesMissing
면적 is highly skewed (γ1 = 21.13002246)Skewed
NO has unique valuesUnique
상가번호 has unique valuesUnique

Reproduction

Analysis started2024-04-29 16:39:15.582524
Analysis finished2024-04-29 16:39:16.772214
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NO
Real number (ℝ)

UNIQUE 

Distinct816
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.5
Minimum1
Maximum816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-30T01:39:16.840032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.75
Q1204.75
median408.5
Q3612.25
95-th percentile775.25
Maximum816
Range815
Interquartile range (IQR)407.5

Descriptive statistics

Standard deviation235.7032
Coefficient of variation (CV)0.57699683
Kurtosis-1.2
Mean408.5
Median Absolute Deviation (MAD)204
Skewness0
Sum333336
Variance55556
MonotonicityStrictly increasing
2024-04-30T01:39:16.974666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
550 1
 
0.1%
540 1
 
0.1%
541 1
 
0.1%
542 1
 
0.1%
543 1
 
0.1%
544 1
 
0.1%
545 1
 
0.1%
546 1
 
0.1%
547 1
 
0.1%
Other values (806) 806
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 (%)
816 1
0.1%
815 1
0.1%
814 1
0.1%
813 1
0.1%
812 1
0.1%
811 1
0.1%
810 1
0.1%
809 1
0.1%
808 1
0.1%
807 1
0.1%

상가유형(本)
Categorical

Distinct50
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
개별(일반)
372 
네트워크(화장품)
53 
네트워크(명품)
42 
개별(장기)
41 
네트워크(브랜드)
 
25
Other values (45)
283 

Length

Max length11
Median length6
Mean length7.2365196
Min length6

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
개별(일반) 372
45.6%
네트워크(화장품) 53
 
6.5%
네트워크(명품) 42
 
5.1%
개별(장기) 41
 
5.0%
네트워크(브랜드) 25
 
3.1%
네트워크(편의점) 22
 
2.7%
네트워크(커피) 20
 
2.5%
네트워크(제과/언) 18
 
2.2%
네트워크(제과) 15
 
1.8%
네트워크(편의점A) 14
 
1.7%
Other values (40) 194
23.8%

Length

2024-04-30T01:39:17.110730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개별(일반 372
45.6%
네트워크(화장품 53
 
6.5%
네트워크(명품 42
 
5.1%
개별(장기 41
 
5.0%
네트워크(브랜드 25
 
3.1%
네트워크(편의점 22
 
2.7%
네트워크(커피 20
 
2.5%
네트워크(제과/언 18
 
2.2%
네트워크(제과 15
 
1.8%
네트워크(편의점a 14
 
1.7%
Other values (40) 194
23.8%

상가번호
Text

UNIQUE 

Distinct816
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-30T01:39:17.417079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4896
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

Unique816 ?
Unique (%)100.0%

Sample

1st row150-03
2nd row150-05
3rd row150-06
4th row150-07
5th row150-08
ValueCountFrequency (%)
150-03 1
 
0.1%
329-08 1
 
0.1%
330-10 1
 
0.1%
327-16 1
 
0.1%
327-17 1
 
0.1%
329-01 1
 
0.1%
329-02 1
 
0.1%
329-03 1
 
0.1%
329-04 1
 
0.1%
329-05 1
 
0.1%
Other values (806) 806
98.8%
2024-04-30T01:39:17.878697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 822
16.8%
- 816
16.7%
0 794
16.2%
1 723
14.8%
3 623
12.7%
4 406
8.3%
5 183
 
3.7%
6 159
 
3.2%
8 128
 
2.6%
9 125
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4080
83.3%
Dash Punctuation 816
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 822
20.1%
0 794
19.5%
1 723
17.7%
3 623
15.3%
4 406
10.0%
5 183
 
4.5%
6 159
 
3.9%
8 128
 
3.1%
9 125
 
3.1%
7 117
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4896
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 822
16.8%
- 816
16.7%
0 794
16.2%
1 723
14.8%
3 623
12.7%
4 406
8.3%
5 183
 
3.7%
6 159
 
3.2%
8 128
 
2.6%
9 125
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 822
16.8%
- 816
16.7%
0 794
16.2%
1 723
14.8%
3 623
12.7%
4 406
8.3%
5 183
 
3.7%
6 159
 
3.2%
8 128
 
2.6%
9 125
 
2.6%

역명
Text

Distinct109
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-30T01:39:18.190413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length4.6642157
Min length3

Characters and Unicode

Total characters3806
Distinct characters140
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.3%

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
 
4.0%
잠실역 23
 
2.8%
총신대입구역 21
 
2.6%
미아삼거리역 20
 
2.4%
역삼역 20
 
2.4%
안국역 17
 
2.1%
을지로입구역 17
 
2.1%
동대문(4)역 16
 
2.0%
홍제역 16
 
2.0%
선릉역 16
 
2.0%
Other values (99) 619
75.7%
2024-04-30T01:39:18.643979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 847
22.3%
( 174
 
4.6%
) 174
 
4.6%
대 150
 
3.9%
구 115
 
3.0%
신 106
 
2.8%
로 90
 
2.4%
입 84
 
2.2%
4 81
 
2.1%
3 73
 
1.9%
Other values (130) 1912
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3221
84.6%
Decimal Number 235
 
6.2%
Open Punctuation 174
 
4.6%
Close Punctuation 174
 
4.6%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 847
26.3%
대 150
 
4.7%
구 115
 
3.6%
신 106
 
3.3%
로 90
 
2.8%
입 84
 
2.6%
사 73
 
2.3%
당 68
 
2.1%
동 68
 
2.1%
문 63
 
2.0%
Other values (119) 1557
48.3%
Decimal Number
ValueCountFrequency (%)
4 81
34.5%
3 73
31.1%
2 46
19.6%
1 25
 
10.6%
5 6
 
2.6%
0 2
 
0.9%
7 1
 
0.4%
6 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3221
84.6%
Common 585
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 847
26.3%
대 150
 
4.7%
구 115
 
3.6%
신 106
 
3.3%
로 90
 
2.8%
입 84
 
2.6%
사 73
 
2.3%
당 68
 
2.1%
동 68
 
2.1%
문 63
 
2.0%
Other values (119) 1557
48.3%
Common
ValueCountFrequency (%)
( 174
29.7%
) 174
29.7%
4 81
13.8%
3 73
12.5%
2 46
 
7.9%
1 25
 
4.3%
5 6
 
1.0%
0 2
 
0.3%
2
 
0.3%
7 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3221
84.6%
ASCII 585
 
15.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 847
26.3%
대 150
 
4.7%
구 115
 
3.6%
신 106
 
3.3%
로 90
 
2.8%
입 84
 
2.6%
사 73
 
2.3%
당 68
 
2.1%
동 68
 
2.1%
문 63
 
2.0%
Other values (119) 1557
48.3%
ASCII
ValueCountFrequency (%)
( 174
29.7%
) 174
29.7%
4 81
13.8%
3 73
12.5%
2 46
 
7.9%
1 25
 
4.3%
5 6
 
1.0%
0 2
 
0.3%
2
 
0.3%
7 1
 
0.2%

면적
Real number (ℝ)

SKEWED 

Distinct486
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.202779
Minimum8
Maximum3788.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-30T01:39:18.770945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11.72
Q116.4675
median21.91
Q330.7
95-th percentile66.46
Maximum3788.6
Range3780.6
Interquartile range (IQR)14.2325

Descriptive statistics

Standard deviation148.54052
Coefficient of variation (CV)3.9927264
Kurtosis509.70868
Mean37.202779
Median Absolute Deviation (MAD)6.89
Skewness21.130022
Sum30357.468
Variance22064.286
MonotonicityNot monotonic
2024-04-30T01:39:18.882054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 19
 
2.3%
25.0 14
 
1.7%
11.0 12
 
1.5%
14.41 12
 
1.5%
21.0 12
 
1.5%
12.0 11
 
1.3%
16.0 11
 
1.3%
20.5 10
 
1.2%
29.0 9
 
1.1%
37.7 8
 
1.0%
Other values (476) 698
85.5%
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 3
0.4%
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

Distinct801
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-30T01:39:19.093213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length10.368873
Min length2

Characters and Unicode

Total characters8461
Distinct characters158
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

Unique788 ?
Unique (%)96.6%

Sample

1st row서울(1)역 상가03,04호 합산
2nd row서울(1)역 상가05호
3rd row서울(1)역 상가06호
4th row서울(1)역 상가7호
5th row서울(1)역
ValueCountFrequency (%)
상가01호 76
 
4.7%
상가02호 68
 
4.2%
합산 63
 
3.9%
상가03호 61
 
3.8%
상가04호 53
 
3.3%
상가05호 45
 
2.8%
상가06호 44
 
2.7%
상가07호 36
 
2.2%
상가08호 32
 
2.0%
사당(4)역 32
 
2.0%
Other values (261) 1097
68.3%
2024-04-30T01:39:19.423404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
793
 
9.4%
가 764
 
9.0%
역 745
 
8.8%
상 719
 
8.5%
호 714
 
8.4%
0 581
 
6.9%
1 325
 
3.8%
2 201
 
2.4%
3 196
 
2.3%
4 177
 
2.1%
Other values (148) 3246
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5456
64.5%
Decimal Number 1801
 
21.3%
Space Separator 793
 
9.4%
Open Punctuation 172
 
2.0%
Close Punctuation 172
 
2.0%
Other Punctuation 67
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 764
14.0%
역 745
13.7%
상 719
 
13.2%
호 714
 
13.1%
대 150
 
2.7%
합 120
 
2.2%
구 115
 
2.1%
신 104
 
1.9%
로 90
 
1.6%
입 83
 
1.5%
Other values (134) 1852
33.9%
Decimal Number
ValueCountFrequency (%)
0 581
32.3%
1 325
18.0%
2 201
 
11.2%
3 196
 
10.9%
4 177
 
9.8%
5 85
 
4.7%
6 76
 
4.2%
7 63
 
3.5%
8 51
 
2.8%
9 46
 
2.6%
Space Separator
ValueCountFrequency (%)
793
100.0%
Open Punctuation
ValueCountFrequency (%)
( 172
100.0%
Close Punctuation
ValueCountFrequency (%)
) 172
100.0%
Other Punctuation
ValueCountFrequency (%)
, 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5456
64.5%
Common 3005
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 764
14.0%
역 745
13.7%
상 719
 
13.2%
호 714
 
13.1%
대 150
 
2.7%
합 120
 
2.2%
구 115
 
2.1%
신 104
 
1.9%
로 90
 
1.6%
입 83
 
1.5%
Other values (134) 1852
33.9%
Common
ValueCountFrequency (%)
793
26.4%
0 581
19.3%
1 325
10.8%
2 201
 
6.7%
3 196
 
6.5%
4 177
 
5.9%
( 172
 
5.7%
) 172
 
5.7%
5 85
 
2.8%
6 76
 
2.5%
Other values (4) 227
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5456
64.5%
ASCII 3005
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
793
26.4%
0 581
19.3%
1 325
10.8%
2 201
 
6.7%
3 196
 
6.5%
4 177
 
5.9%
( 172
 
5.7%
) 172
 
5.7%
5 85
 
2.8%
6 76
 
2.5%
Other values (4) 227
 
7.6%
Hangul
ValueCountFrequency (%)
가 764
14.0%
역 745
13.7%
상 719
 
13.2%
호 714
 
13.1%
대 150
 
2.7%
합 120
 
2.2%
구 115
 
2.1%
신 104
 
1.9%
로 90
 
1.6%
입 83
 
1.5%
Other values (134) 1852
33.9%

승인업종
Text

MISSING 

Distinct99
Distinct (%)13.2%
Missing65
Missing (%)8.0%
Memory size6.5 KiB
2024-04-30T01:39:19.600744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.4740346
Min length1

Characters and Unicode

Total characters2609
Distinct characters137
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)7.3%

Sample

1st row제과
2nd row편의점
3rd row화장품
4th row커피전문점
5th row커피전문점
ValueCountFrequency (%)
화장품 129
17.0%
편의점 92
12.2%
여성의류 92
12.2%
커피전문점 46
 
6.1%
제과점 43
 
5.7%
커피 36
 
4.8%
의류 30
 
4.0%
악세서리 26
 
3.4%
제과 23
 
3.0%
액세서리 20
 
2.6%
Other values (86) 220
29.1%
2024-04-30T01:39:19.873617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 237
 
9.1%
점 204
 
7.8%
품 156
 
6.0%
류 144
 
5.5%
화 136
 
5.2%
장 133
 
5.1%
성 100
 
3.8%
여 95
 
3.6%
편 94
 
3.6%
커 82
 
3.1%
Other values (127) 1228
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2549
97.7%
Close Punctuation 19
 
0.7%
Open Punctuation 19
 
0.7%
Other Punctuation 13
 
0.5%
Space Separator 9
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 237
 
9.3%
점 204
 
8.0%
품 156
 
6.1%
류 144
 
5.6%
화 136
 
5.3%
장 133
 
5.2%
성 100
 
3.9%
여 95
 
3.7%
편 94
 
3.7%
커 82
 
3.2%
Other values (123) 1168
45.8%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2549
97.7%
Common 60
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 237
 
9.3%
점 204
 
8.0%
품 156
 
6.1%
류 144
 
5.6%
화 136
 
5.3%
장 133
 
5.2%
성 100
 
3.9%
여 95
 
3.7%
편 94
 
3.7%
커 82
 
3.2%
Other values (123) 1168
45.8%
Common
ValueCountFrequency (%)
) 19
31.7%
( 19
31.7%
, 13
21.7%
9
15.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2549
97.7%
ASCII 60
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 237
 
9.3%
점 204
 
8.0%
품 156
 
6.1%
류 144
 
5.6%
화 136
 
5.3%
장 133
 
5.2%
성 100
 
3.9%
여 95
 
3.7%
편 94
 
3.7%
커 82
 
3.2%
Other values (123) 1168
45.8%
ASCII
ValueCountFrequency (%)
) 19
31.7%
( 19
31.7%
, 13
21.7%
9
15.0%

계약시작
Date

MISSING 

Distinct138
Distinct (%)18.4%
Missing65
Missing (%)8.0%
Memory size6.5 KiB
Minimum2002-04-29 00:00:00
Maximum2014-01-09 00:00:00
2024-04-30T01:39:20.000017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:20.117368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약종료
Date

MISSING 

Distinct153
Distinct (%)20.4%
Missing65
Missing (%)8.0%
Memory size6.5 KiB
Minimum2013-07-03 00:00:00
Maximum2017-11-30 00:00:00
2024-04-30T01:39:20.230953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:20.385844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

임대료
Text

MISSING 

Distinct669
Distinct (%)89.1%
Missing65
Missing (%)8.0%
Memory size6.5 KiB
2024-04-30T01:39:20.687267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.4527297
Min length2

Characters and Unicode

Total characters7099
Distinct characters15
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

Unique635 ?
Unique (%)84.6%

Sample

1st row8,837,400
2nd row10,065,000
3rd row 7,439,600
4th row 5,245,800
5th row 5,040,000
ValueCountFrequency (%)
소송중 47
 
6.3%
880,000 3
 
0.4%
4,751,726 3
 
0.4%
2,499,200 3
 
0.4%
3,099,800 2
 
0.3%
2,210,000 2
 
0.3%
15,510,000 2
 
0.3%
4,329,600 2
 
0.3%
599,500 2
 
0.3%
2,009,700 2
 
0.3%
Other values (657) 683
90.9%
2024-04-30T01:39:21.082469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1488
21.0%
, 1284
18.1%
780
11.0%
1 488
 
6.9%
2 414
 
5.8%
9 410
 
5.8%
3 385
 
5.4%
5 364
 
5.1%
7 355
 
5.0%
4 337
 
4.7%
Other values (5) 794
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4894
68.9%
Other Punctuation 1284
 
18.1%
Space Separator 780
 
11.0%
Other Letter 141
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1488
30.4%
1 488
 
10.0%
2 414
 
8.5%
9 410
 
8.4%
3 385
 
7.9%
5 364
 
7.4%
7 355
 
7.3%
4 337
 
6.9%
8 328
 
6.7%
6 325
 
6.6%
Other Letter
ValueCountFrequency (%)
소 47
33.3%
송 47
33.3%
중 47
33.3%
Other Punctuation
ValueCountFrequency (%)
, 1284
100.0%
Space Separator
ValueCountFrequency (%)
780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6958
98.0%
Hangul 141
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1488
21.4%
, 1284
18.5%
780
11.2%
1 488
 
7.0%
2 414
 
5.9%
9 410
 
5.9%
3 385
 
5.5%
5 364
 
5.2%
7 355
 
5.1%
4 337
 
4.8%
Other values (2) 653
9.4%
Hangul
ValueCountFrequency (%)
소 47
33.3%
송 47
33.3%
중 47
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6958
98.0%
Hangul 141
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1488
21.4%
, 1284
18.5%
780
11.2%
1 488
 
7.0%
2 414
 
5.9%
9 410
 
5.9%
3 385
 
5.5%
5 364
 
5.2%
7 355
 
5.1%
4 337
 
4.8%
Other values (2) 653
9.4%
Hangul
ValueCountFrequency (%)
소 47
33.3%
송 47
33.3%
중 47
33.3%

Interactions

2024-04-30T01:39:16.257330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:16.085917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:16.348353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:16.178432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:39:21.175559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NO상가유형(本)면적승인업종
NO1.0000.7290.0000.405
상가유형(本)0.7291.0000.3610.916
면적0.0000.3611.0000.990
승인업종0.4050.9160.9901.000
2024-04-30T01:39:21.555449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NO면적상가유형(本)
NO1.0000.0560.317
면적0.0561.0000.160
상가유형(本)0.3170.1601.000

Missing values

2024-04-30T01:39:16.485521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:39:16.614614image/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.
2024-04-30T01:39:16.721294image/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

NO상가유형(本)상가번호역명면적상가명승인업종계약시작계약종료임대료
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(브랜드)150-06서울(1)역20.7서울(1)역 상가06호<NA><NA><NA><NA>
34네트워크(화장품A)150-07서울(1)역33.0서울(1)역 상가7호화장품2012.06.012015.07.117,439,600
45네트워크(푸드A)150-08서울(1)역33.0서울(1)역커피전문점2012.07.062015.08.155,245,800
56네트워크(미니샵A)150-09서울(1)역12.0서울(1)역커피전문점2012.05.212015.07.105,040,000
67개별(일반)151-01시청(1)역19.18시청(1)역 상가01호액세서리2013.03.182016.03.174,693,700
78네트워크(화장품)151-02시청(1)역15.03시청(1)역 상가02호화장품2008.07.042013.07.036,667,131
89개별(무상)151-03시청(1)역57.6시청(1)역 상가03호장애인생산품,잡화2010.01.012015.01.310
910네트워크(편의점A)151-04시청(1)역25.0시청(1)역편의점2012.05.242015.07.039,732,800
NO상가유형(本)상가번호역명면적상가명승인업종계약시작계약종료임대료
806807복합(사당(4)역)433-33사당(4)역13.5사당(4)역 상가33호핸드폰2009.12.312016.02.271,701,472
807808복합(사당(4)역)433-34사당(4)역15.0사당(4)역 상가34호언더웨어2009.12.312016.02.271,890,525
808809복합(사당(4)역)433-35사당(4)역20.0사당(4)역 상가35호제과점2009.12.312016.02.272,520,699
809810복합(사당(4)역)433-36사당(4)역10.0사당(4)역 상가36호여성의류2009.12.312016.02.271,260,350
810811복합(사당(4)역)433-37사당(4)역15.0사당(4)역 상가37호화장품2009.12.312016.02.271,890,525
811812복합(사당(4)역)433-38사당(4)역23.1사당(4)역 상가38호악세서리2009.12.312016.02.272,911,408
812813개별(대형)433-39사당(4)역861.0사당(4)역 상가39호아울렛매장 등2013.02.012016.03.1275,999,000
813814개별(대형)433-40사당(4)역408.0사당(4)역 상가40호화장품,의류,제과2011.08.012014.09.1583,666,000
814815네트워크(편의점)433-41사당(4)역22.0사당(4)역 상가41호편의점2011.08.292014.10.2818,354,892
815816네트워크(푸드D)433-42사당(4)역24.8사당(4)커피전문점2012.06.282015.08.078,179,410