Overview

Dataset statistics

Number of variables15
Number of observations1304
Missing cells30
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.6 KiB
Average record size in memory126.1 B

Variable types

Numeric6
Categorical3
Text3
DateTime3

Dataset

Description부산시설공단_지하도상가점포현황_20220125
Author부산시설공단
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15067617

Alerts

상가코드 is highly overall correlated with 점포코드 and 1 other fieldsHigh correlation
점포코드 is highly overall correlated with 상가코드 and 1 other fieldsHigh correlation
임대면적 is highly overall correlated with 전용면적 and 1 other fieldsHigh correlation
전용면적 is highly overall correlated with 임대면적 and 1 other fieldsHigh correlation
공용면적 is highly overall correlated with 임대면적 and 1 other fieldsHigh correlation
상가명 is highly overall correlated with 상가코드 and 1 other fieldsHigh correlation
상호 has 30 (2.3%) missing valuesMissing
점포코드 has unique valuesUnique
계약번호 has unique valuesUnique
전용면적 has 109 (8.4%) zerosZeros
공용면적 has 109 (8.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:54:55.630252
Analysis finished2023-12-10 16:55:02.354014
Duration6.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상가코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2783742
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:02.443172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q37
95-th percentile7
Maximum25
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.188134
Coefficient of variation (CV)0.60399923
Kurtosis25.367953
Mean5.2783742
Median Absolute Deviation (MAD)2
Skewness4.445261
Sum6883
Variance10.164198
MonotonicityIncreasing
2023-12-11T01:55:02.560476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 352
27.0%
5 326
25.0%
4 255
19.6%
3 176
13.5%
2 101
 
7.7%
6 69
 
5.3%
25 25
 
1.9%
ValueCountFrequency (%)
2 101
 
7.7%
3 176
13.5%
4 255
19.6%
5 326
25.0%
6 69
 
5.3%
7 352
27.0%
25 25
 
1.9%
ValueCountFrequency (%)
25 25
 
1.9%
7 352
27.0%
6 69
 
5.3%
5 326
25.0%
4 255
19.6%
3 176
13.5%
2 101
 
7.7%

상가명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
서면몰
352 
부전몰
326 
남포
255 
광복
176 
국제
101 
Other values (2)
94 

Length

Max length3
Median length3
Mean length2.5920245
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서면몰 352
27.0%
부전몰 326
25.0%
남포 255
19.6%
광복 176
13.5%
국제 101
 
7.7%
부산역 69
 
5.3%
중앙몰 25
 
1.9%

Length

2023-12-11T01:55:02.707441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:55:02.864718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서면몰 352
27.0%
부전몰 326
25.0%
남포 255
19.6%
광복 176
13.5%
국제 101
 
7.7%
부산역 69
 
5.3%
중앙몰 25
 
1.9%

블럭
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4064417
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:03.015667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3595928
Coefficient of variation (CV)0.56498057
Kurtosis8.0620141
Mean2.4064417
Median Absolute Deviation (MAD)1
Skewness1.6813903
Sum3138
Variance1.8484926
MonotonicityNot monotonic
2023-12-11T01:55:03.162696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 421
32.3%
2 307
23.5%
3 283
21.7%
4 259
19.9%
5 26
 
2.0%
11 6
 
0.5%
12 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
1 421
32.3%
2 307
23.5%
3 283
21.7%
4 259
19.9%
5 26
 
2.0%
10 1
 
0.1%
11 6
 
0.5%
12 1
 
0.1%
ValueCountFrequency (%)
12 1
 
0.1%
11 6
 
0.5%
10 1
 
0.1%
5 26
 
2.0%
4 259
19.9%
3 283
21.7%
2 307
23.5%
1 421
32.3%

점포코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1304
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5293181.4
Minimum2000100
Maximum25004300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:03.326839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000100
5-th percentile2007715
Q14005875
median5012350
Q37006125
95-th percentile7034885
Maximum25004300
Range23004200
Interquartile range (IQR)3000250

Descriptive statistics

Standard deviation3188269.5
Coefficient of variation (CV)0.60233521
Kurtosis25.288949
Mean5293181.4
Median Absolute Deviation (MAD)1988200
Skewness4.4349978
Sum6.9023085 × 109
Variance1.0165062 × 1013
MonotonicityStrictly increasing
2023-12-11T01:55:03.530729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000100 1
 
0.1%
5035200 1
 
0.1%
6001800 1
 
0.1%
6001700 1
 
0.1%
6001600 1
 
0.1%
6001500 1
 
0.1%
6001400 1
 
0.1%
6001300 1
 
0.1%
6001200 1
 
0.1%
6001100 1
 
0.1%
Other values (1294) 1294
99.2%
ValueCountFrequency (%)
2000100 1
0.1%
2000200 1
0.1%
2000300 1
0.1%
2000400 1
0.1%
2000500 1
0.1%
2000600 1
0.1%
2000700 1
0.1%
2000800 1
0.1%
2000900 1
0.1%
2001000 1
0.1%
ValueCountFrequency (%)
25004300 1
0.1%
25002900 1
0.1%
25002800 1
0.1%
25002700 1
0.1%
25002600 1
0.1%
25002500 1
0.1%
25002400 1
0.1%
25002300 1
0.1%
25002200 1
0.1%
25001800 1
0.1%

점포호수
Text

Distinct1289
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T01:55:04.380149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length3.9447853
Min length2

Characters and Unicode

Total characters5144
Distinct characters48
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1274 ?
Unique (%)97.7%

Sample

1st row1-1
2nd row1-2
3rd row1-3
4th row1-4
5th row1-5
ValueCountFrequency (%)
3.1 2
 
0.2%
2.4 2
 
0.2%
2.3 2
 
0.2%
2.6 2
 
0.2%
4.1 2
 
0.2%
1.6 2
 
0.2%
3-26 2
 
0.2%
1.4 2
 
0.2%
j22 2
 
0.2%
2.2 2
 
0.2%
Other values (1279) 1285
98.5%
2023-12-11T01:55:05.130870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 778
15.1%
1 582
11.3%
2 518
10.1%
3 440
 
8.6%
. 424
 
8.2%
4 363
 
7.1%
5 284
 
5.5%
6 242
 
4.7%
가 216
 
4.2%
7 190
 
3.7%
Other values (38) 1107
21.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3011
58.5%
Dash Punctuation 778
 
15.1%
Other Letter 723
 
14.1%
Other Punctuation 426
 
8.3%
Uppercase Letter 199
 
3.9%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 216
29.9%
라 173
23.9%
나 143
19.8%
다 113
15.6%
계 19
 
2.6%
서 10
 
1.4%
동 9
 
1.2%
자 6
 
0.8%
기 4
 
0.6%
피 4
 
0.6%
Other values (18) 26
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 582
19.3%
2 518
17.2%
3 440
14.6%
4 363
12.1%
5 284
9.4%
6 242
8.0%
7 190
 
6.3%
8 156
 
5.2%
9 136
 
4.5%
0 100
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 64
32.2%
C 56
28.1%
A 54
27.1%
J 25
 
12.6%
Other Punctuation
ValueCountFrequency (%)
. 424
99.5%
/ 2
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 778
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4222
82.1%
Hangul 723
 
14.1%
Latin 199
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 216
29.9%
라 173
23.9%
나 143
19.8%
다 113
15.6%
계 19
 
2.6%
서 10
 
1.4%
동 9
 
1.2%
자 6
 
0.8%
기 4
 
0.6%
피 4
 
0.6%
Other values (18) 26
 
3.6%
Common
ValueCountFrequency (%)
- 778
18.4%
1 582
13.8%
2 518
12.3%
3 440
10.4%
. 424
10.0%
4 363
8.6%
5 284
 
6.7%
6 242
 
5.7%
7 190
 
4.5%
8 156
 
3.7%
Other values (6) 245
 
5.8%
Latin
ValueCountFrequency (%)
B 64
32.2%
C 56
28.1%
A 54
27.1%
J 25
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4421
85.9%
Hangul 723
 
14.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 778
17.6%
1 582
13.2%
2 518
11.7%
3 440
10.0%
. 424
9.6%
4 363
8.2%
5 284
 
6.4%
6 242
 
5.5%
7 190
 
4.3%
8 156
 
3.5%
Other values (10) 444
10.0%
Hangul
ValueCountFrequency (%)
가 216
29.9%
라 173
23.9%
나 143
19.8%
다 113
15.6%
계 19
 
2.6%
서 10
 
1.4%
동 9
 
1.2%
자 6
 
0.8%
기 4
 
0.6%
피 4
 
0.6%
Other values (18) 26
 
3.6%

상호
Text

MISSING 

Distinct947
Distinct (%)74.3%
Missing30
Missing (%)2.3%
Memory size10.3 KiB
2023-12-11T01:55:05.592780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length19
Mean length5.3069074
Min length1

Characters and Unicode

Total characters6761
Distinct characters595
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique727 ?
Unique (%)57.1%

Sample

1st rowMoonA 갤러리
2nd row낭만공방
3rd rowMoonA 갤러리
4th row낭만공방
5th row동목인물화아카데미
ValueCountFrequency (%)
유진안경 10
 
0.7%
크로커다일 8
 
0.5%
갤러리 8
 
0.5%
남포점 8
 
0.5%
루이스(saint 7
 
0.5%
louis 7
 
0.5%
세인트 7
 
0.5%
서울종합커튼 6
 
0.4%
리뱅 5
 
0.3%
서면점 5
 
0.3%
Other values (1061) 1413
95.2%
2023-12-11T01:55:06.253427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
 
3.1%
이 173
 
2.6%
) 170
 
2.5%
( 170
 
2.5%
스 143
 
2.1%
리 134
 
2.0%
아 94
 
1.4%
o 75
 
1.1%
점 73
 
1.1%
미 71
 
1.1%
Other values (585) 5448
80.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4888
72.3%
Lowercase Letter 591
 
8.7%
Uppercase Letter 446
 
6.6%
Space Separator 210
 
3.1%
Decimal Number 174
 
2.6%
Close Punctuation 170
 
2.5%
Open Punctuation 170
 
2.5%
Dash Punctuation 54
 
0.8%
Other Punctuation 54
 
0.8%
Other Number 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 173
 
3.5%
스 143
 
2.9%
리 134
 
2.7%
아 94
 
1.9%
점 73
 
1.5%
미 71
 
1.5%
라 66
 
1.4%
지 60
 
1.2%
드 59
 
1.2%
로 56
 
1.1%
Other values (510) 3959
81.0%
Uppercase Letter
ValueCountFrequency (%)
A 46
 
10.3%
O 45
 
10.1%
S 41
 
9.2%
L 32
 
7.2%
N 26
 
5.8%
M 26
 
5.8%
C 25
 
5.6%
P 24
 
5.4%
I 23
 
5.2%
H 22
 
4.9%
Other values (16) 136
30.5%
Lowercase Letter
ValueCountFrequency (%)
o 75
12.7%
e 66
11.2%
n 54
 
9.1%
i 53
 
9.0%
a 50
 
8.5%
l 39
 
6.6%
s 32
 
5.4%
t 31
 
5.2%
u 28
 
4.7%
r 26
 
4.4%
Other values (15) 137
23.2%
Decimal Number
ValueCountFrequency (%)
1 55
31.6%
2 38
21.8%
3 24
13.8%
0 19
 
10.9%
5 13
 
7.5%
4 12
 
6.9%
9 5
 
2.9%
7 4
 
2.3%
8 3
 
1.7%
6 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 30
55.6%
& 9
 
16.7%
# 6
 
11.1%
' 3
 
5.6%
, 3
 
5.6%
: 2
 
3.7%
/ 1
 
1.9%
Space Separator
ValueCountFrequency (%)
210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Other Number
ValueCountFrequency (%)
² 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4889
72.3%
Latin 1038
 
15.4%
Common 834
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 173
 
3.5%
스 143
 
2.9%
리 134
 
2.7%
아 94
 
1.9%
점 73
 
1.5%
미 71
 
1.5%
라 66
 
1.3%
지 60
 
1.2%
드 59
 
1.2%
로 56
 
1.1%
Other values (511) 3960
81.0%
Latin
ValueCountFrequency (%)
o 75
 
7.2%
e 66
 
6.4%
n 54
 
5.2%
i 53
 
5.1%
a 50
 
4.8%
A 46
 
4.4%
O 45
 
4.3%
S 41
 
3.9%
l 39
 
3.8%
L 32
 
3.1%
Other values (42) 537
51.7%
Common
ValueCountFrequency (%)
210
25.2%
) 170
20.4%
( 170
20.4%
1 55
 
6.6%
- 54
 
6.5%
2 38
 
4.6%
. 30
 
3.6%
3 24
 
2.9%
0 19
 
2.3%
5 13
 
1.6%
Other values (12) 51
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4888
72.3%
ASCII 1869
 
27.6%
None 3
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
 
11.2%
) 170
 
9.1%
( 170
 
9.1%
o 75
 
4.0%
e 66
 
3.5%
1 55
 
2.9%
n 54
 
2.9%
- 54
 
2.9%
i 53
 
2.8%
a 50
 
2.7%
Other values (62) 912
48.8%
Hangul
ValueCountFrequency (%)
이 173
 
3.5%
스 143
 
2.9%
리 134
 
2.7%
아 94
 
1.9%
점 73
 
1.5%
미 71
 
1.5%
라 66
 
1.4%
지 60
 
1.2%
드 59
 
1.2%
로 56
 
1.1%
Other values (510) 3959
81.0%
None
ValueCountFrequency (%)
² 2
66.7%
㈜ 1
33.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

업태
Categorical

Distinct18
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
소매
754 
소매업
189 
서비스
81 
도소매
 
69
도,소매
 
69
Other values (13)
142 

Length

Max length7
Median length2
Mean length2.5429448
Min length2

Unique

Unique4 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
소매 754
57.8%
소매업 189
 
14.5%
서비스 81
 
6.2%
도소매 69
 
5.3%
도,소매 69
 
5.3%
건설 41
 
3.1%
도,소매업 28
 
2.1%
도매업/소매업 25
 
1.9%
음식 17
 
1.3%
도매 11
 
0.8%
Other values (8) 20
 
1.5%

Length

2023-12-11T01:55:06.454062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소매 754
57.8%
소매업 189
 
14.5%
서비스 81
 
6.2%
도소매 69
 
5.3%
도,소매 69
 
5.3%
건설 41
 
3.1%
도,소매업 28
 
2.1%
도매업/소매업 25
 
1.9%
음식 17
 
1.3%
도매 11
 
0.8%
Other values (8) 20
 
1.5%

업종
Categorical

Distinct41
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
의류
597 
잡화
134 
기타
119 
악세사리
66 
미술
 
50
Other values (36)
338 

Length

Max length5
Median length2
Mean length2.1832822
Min length2

Unique

Unique11 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
의류 597
45.8%
잡화 134
 
10.3%
기타 119
 
9.1%
악세사리 66
 
5.1%
미술 50
 
3.8%
가발 45
 
3.5%
화장품 44
 
3.4%
신발 35
 
2.7%
내의 27
 
2.1%
안경 23
 
1.8%
Other values (31) 164
 
12.6%

Length

2023-12-11T01:55:06.636427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
의류 597
45.8%
잡화 134
 
10.3%
기타 119
 
9.1%
악세사리 66
 
5.1%
미술 50
 
3.8%
가발 45
 
3.5%
화장품 44
 
3.4%
신발 35
 
2.7%
내의 27
 
2.1%
안경 23
 
1.8%
Other values (31) 164
 
12.6%

임대면적
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.560933
Minimum0
Maximum465.08
Zeros8
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:06.866823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.56
Q125.12
median25.12
Q329.931
95-th percentile58.357
Maximum465.08
Range465.08
Interquartile range (IQR)4.811

Descriptive statistics

Standard deviation20.697825
Coefficient of variation (CV)0.72469007
Kurtosis177.26773
Mean28.560933
Median Absolute Deviation (MAD)3.97
Skewness10.142846
Sum37243.457
Variance428.39995
MonotonicityNot monotonic
2023-12-11T01:55:07.073623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.12 387
29.7%
28.81 101
 
7.7%
12.56 101
 
7.7%
17.0 93
 
7.1%
28.09 83
 
6.4%
31.441 66
 
5.1%
29.931 64
 
4.9%
58.357 35
 
2.7%
39.127 19
 
1.5%
13.22 17
 
1.3%
Other values (157) 338
25.9%
ValueCountFrequency (%)
0.0 8
0.6%
5.45 3
 
0.2%
6.04 5
0.4%
6.25 1
 
0.1%
6.34 5
0.4%
6.54 2
 
0.2%
9.25 1
 
0.1%
9.52 1
 
0.1%
9.58 1
 
0.1%
11.83 1
 
0.1%
ValueCountFrequency (%)
465.08 1
 
0.1%
286.836 1
 
0.1%
217.63 1
 
0.1%
149.66 1
 
0.1%
148.06 1
 
0.1%
119.0 1
 
0.1%
118.99 1
 
0.1%
99.329 4
0.3%
98.17 1
 
0.1%
94.79 1
 
0.1%

전용면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct163
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.723635
Minimum0
Maximum314.56
Zeros109
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:07.271434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.5975
median17.88
Q318.24
95-th percentile33.86
Maximum314.56
Range314.56
Interquartile range (IQR)2.6425

Descriptive statistics

Standard deviation13.979471
Coefficient of variation (CV)0.78874742
Kurtosis174.59899
Mean17.723635
Median Absolute Deviation (MAD)1.13
Skewness9.6778354
Sum23111.62
Variance195.42562
MonotonicityNot monotonic
2023-12-11T01:55:07.473082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.15 242
18.6%
17.88 145
 
11.1%
0.0 109
 
8.4%
16.75 101
 
7.7%
8.94 86
 
6.6%
18.24 70
 
5.4%
17.37 64
 
4.9%
20.0 42
 
3.2%
20.3 41
 
3.1%
33.86 35
 
2.7%
Other values (153) 369
28.3%
ValueCountFrequency (%)
0.0 109
8.4%
3.88 4
 
0.3%
4.3 5
 
0.4%
4.51 5
 
0.4%
4.66 2
 
0.2%
5.9 1
 
0.1%
6.59 1
 
0.1%
6.82 1
 
0.1%
7.34 1
 
0.1%
7.75 1
 
0.1%
ValueCountFrequency (%)
314.56 1
0.1%
166.44 1
0.1%
154.94 1
0.1%
101.22 1
0.1%
100.14 1
0.1%
84.72 1
0.1%
80.48 1
0.1%
66.4 1
0.1%
64.11 1
0.1%
62.52 1
0.1%

공용면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct161
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5473773
Minimum0
Maximum150.52
Zeros109
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T01:55:07.714695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.97
median7.24
Q312.21
95-th percentile24.064
Maximum150.52
Range150.52
Interquartile range (IQR)5.24

Descriptive statistics

Standard deviation8.3216662
Coefficient of variation (CV)0.87161803
Kurtosis88.763556
Mean9.5473773
Median Absolute Deviation (MAD)3.62
Skewness6.4628453
Sum12449.78
Variance69.250129
MonotonicityNot monotonic
2023-12-11T01:55:07.896728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.97 242
18.6%
7.24 145
 
11.1%
0.0 109
 
8.4%
12.06 101
 
7.7%
3.62 87
 
6.7%
13.2 70
 
5.4%
12.56 65
 
5.0%
8.09 42
 
3.2%
7.79 41
 
3.1%
24.49 35
 
2.7%
Other values (151) 367
28.1%
ValueCountFrequency (%)
0.0 109
8.4%
1.57 3
 
0.2%
1.74 5
 
0.4%
1.83 5
 
0.4%
1.88 2
 
0.2%
2.37 1
 
0.1%
2.66 1
 
0.1%
2.76 1
 
0.1%
3.49 15
 
1.2%
3.62 87
6.7%
ValueCountFrequency (%)
150.52 1
 
0.1%
120.4 1
 
0.1%
62.69 1
 
0.1%
48.44 1
 
0.1%
47.92 1
 
0.1%
41.58 4
0.3%
38.51 1
 
0.1%
37.01 1
 
0.1%
34.28 1
 
0.1%
32.97 1
 
0.1%

계약번호
Text

UNIQUE 

Distinct1304
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T01:55:08.274266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13040
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

Unique1304 ?
Unique (%)100.0%

Sample

1st row202005-033
2nd row201912-088
3rd row202004-397
4th row201912-089
5th row202004-398
ValueCountFrequency (%)
202005-033 1
 
0.1%
202003-653 1
 
0.1%
202007-087 1
 
0.1%
202007-086 1
 
0.1%
202007-152 1
 
0.1%
202006-197 1
 
0.1%
202006-196 1
 
0.1%
202006-195 1
 
0.1%
202006-194 1
 
0.1%
202006-193 1
 
0.1%
Other values (1294) 1294
99.2%
2023-12-11T01:55:08.806034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4154
31.9%
2 2793
21.4%
- 1304
 
10.0%
1 902
 
6.9%
4 823
 
6.3%
3 628
 
4.8%
7 536
 
4.1%
6 519
 
4.0%
5 482
 
3.7%
9 450
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11736
90.0%
Dash Punctuation 1304
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4154
35.4%
2 2793
23.8%
1 902
 
7.7%
4 823
 
7.0%
3 628
 
5.4%
7 536
 
4.6%
6 519
 
4.4%
5 482
 
4.1%
9 450
 
3.8%
8 449
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 1304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4154
31.9%
2 2793
21.4%
- 1304
 
10.0%
1 902
 
6.9%
4 823
 
6.3%
3 628
 
4.8%
7 536
 
4.1%
6 519
 
4.0%
5 482
 
3.7%
9 450
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4154
31.9%
2 2793
21.4%
- 1304
 
10.0%
1 902
 
6.9%
4 823
 
6.3%
3 628
 
4.8%
7 536
 
4.1%
6 519
 
4.0%
5 482
 
3.7%
9 450
 
3.5%

최초계약일
Date

Distinct137
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Minimum2008-12-20 00:00:00
Maximum2020-09-14 00:00:00
2023-12-11T01:55:09.008489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:09.197123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약시작일
Date

Distinct28
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Minimum2018-07-09 00:00:00
Maximum2020-09-14 00:00:00
2023-12-11T01:55:09.385545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:09.598253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

계약종료일
Date

Distinct28
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Minimum2020-09-30 00:00:00
Maximum2021-09-13 00:00:00
2023-12-11T01:55:09.807081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:10.000773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

Interactions

2023-12-11T01:55:01.053504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:56.682289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.355075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.270259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.194645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.121393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:01.194224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:56.769341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.478973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.431458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.376542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.268079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:01.335741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:56.900493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.597018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.608518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.520981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.456475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:01.491974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.007255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.703287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.772625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.656798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.606020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:01.620138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.122042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.878452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.886501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.810855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.738385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:01.787934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:57.242056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:58.041693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.030212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:59.964528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:00.889980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:55:10.131954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상가코드상가명블럭점포코드업태업종임대면적전용면적공용면적계약시작일계약종료일
상가코드1.0001.0000.3071.0000.3880.5510.4610.4890.4710.9940.994
상가명1.0001.0000.3491.0000.5010.7470.6050.6210.6270.9670.967
블럭0.3070.3491.0000.3070.4680.6400.1370.1380.1320.8290.829
점포코드1.0001.0000.3071.0000.3870.5500.4610.4890.4720.9940.994
업태0.3880.5010.4680.3871.0000.8710.1670.1550.1880.7380.738
업종0.5510.7470.6400.5500.8711.0000.4480.5340.4350.7580.758
임대면적0.4610.6050.1370.4610.1670.4481.0000.9980.9990.5350.535
전용면적0.4890.6210.1380.4890.1550.5340.9981.0000.9960.5640.564
공용면적0.4710.6270.1320.4720.1880.4350.9990.9961.0000.5350.535
계약시작일0.9940.9670.8290.9940.7380.7580.5350.5640.5351.0001.000
계약종료일0.9940.9670.8290.9940.7380.7580.5350.5640.5351.0001.000
2023-12-11T01:55:10.364411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태업종상가명
업태1.0000.4280.250
업종0.4281.0000.410
상가명0.2500.4101.000
2023-12-11T01:55:10.553895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상가코드블럭점포코드임대면적전용면적공용면적상가명업태업종
상가코드1.0000.0710.977-0.3120.076-0.1870.9990.2200.307
블럭0.0711.0000.260-0.053-0.002-0.0700.2160.2050.333
점포코드0.9770.2601.000-0.3010.076-0.1810.9990.2200.307
임대면적-0.312-0.053-0.3011.0000.7690.9530.2490.0750.195
전용면적0.076-0.0020.0760.7691.0000.6970.2580.0690.244
공용면적-0.187-0.070-0.1810.9530.6971.0000.2620.0840.188
상가명0.9990.2160.9990.2490.2580.2621.0000.2500.410
업태0.2200.2050.2200.0750.0690.0840.2501.0000.428
업종0.3070.3330.3070.1950.2440.1880.4100.4281.000

Missing values

2023-12-11T01:55:01.982476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:55:02.242404image/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.

Sample

상가코드상가명블럭점포코드점포호수상호업태업종임대면적전용면적공용면적계약번호최초계약일계약시작일계약종료일
02국제120001001-1MoonA 갤러리서비스미술12.790.00.0202005-0332013-05-012020-05-012021-04-30
12국제120002001-2낭만공방서비스미술12.790.00.0201912-0882010-12-012019-12-012020-11-30
22국제120003001-3MoonA 갤러리서비스미술12.790.00.0202004-3972013-04-012020-04-012021-03-31
32국제120004001-4낭만공방서비스미술12.790.00.0201912-0892010-12-012019-12-012020-11-30
42국제120005001-5동목인물화아카데미서비스미술14.180.00.0202004-3982010-04-012020-04-012021-03-31
52국제120006001-6미술의거리서비스미술14.180.00.0202006-1762016-06-012020-06-012021-05-31
62국제120007001-7동목인물화아카데미서비스미술17.00.00.0202004-3992010-04-012020-04-012021-03-31
72국제120008001-8윤희배아뜰리에서비스미술17.00.00.0202007-0922017-07-012020-07-012021-06-30
82국제120009001-9신화남헤어아트갤러리소매업기타17.00.00.0202008-1042020-08-012020-08-012021-07-31
92국제120010001-101-10서비스미술17.00.00.0202001-0782019-01-012020-01-012020-12-31
상가코드상가명블럭점포코드점포호수상호업태업종임대면적전용면적공용면적계약번호최초계약일계약시작일계약종료일
129425중앙몰125001800J18씨에네 3소매의류92.4462.5229.92201912-1262017-12-172019-12-172020-12-16
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