Overview

Dataset statistics

Number of variables8
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Categorical2
Text2
DateTime1
Numeric2
Boolean1

Dataset

Description경기도 의왕시의 공동주택 현황 목록을 작성한 경기도 의왕시 공동주택현황 목록에 대한 데이터입니다.이용해 주셔서 감사합니다.
Author경기도 의왕시
URLhttps://www.data.go.kr/data/15086115/fileData.do

Alerts

시군명 has constant value ""Constant
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
공동주택명 has unique valuesUnique
지번 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:51:10.859572
Analysis finished2024-04-29 22:51:13.528359
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
의왕시
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의왕시 101
100.0%

Length

2024-04-30T07:51:13.592434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:51:13.685791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의왕시 101
100.0%

공동주택명
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-04-30T07:51:13.944990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length6.6732673
Min length2

Characters and Unicode

Total characters674
Distinct characters165
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

Unique101 ?
Unique (%)100.0%

Sample

1st rowKT이자리에
2nd row골드클래스
3rd row남양모란
4th row뉴서울국화
5th row대명구름채
ValueCountFrequency (%)
의왕백운 5
 
4.0%
해링턴 5
 
4.0%
플레이스 5
 
4.0%
1단지 3
 
2.4%
한아름 2
 
1.6%
포레움 2
 
1.6%
의왕푸르지오 2
 
1.6%
kt이자리에 1
 
0.8%
행복주택 1
 
0.8%
인덕원푸르지오엘센트로 1
 
0.8%
Other values (99) 99
78.6%
2024-04-30T07:51:14.342614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 32
 
4.7%
단 26
 
3.9%
25
 
3.7%
마 16
 
2.4%
을 15
 
2.2%
이 14
 
2.1%
레 13
 
1.9%
왕 13
 
1.9%
원 13
 
1.9%
의 13
 
1.9%
Other values (155) 494
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 590
87.5%
Decimal Number 39
 
5.8%
Space Separator 25
 
3.7%
Open Punctuation 8
 
1.2%
Close Punctuation 8
 
1.2%
Uppercase Letter 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 32
 
5.4%
단 26
 
4.4%
마 16
 
2.7%
을 15
 
2.5%
이 14
 
2.4%
레 13
 
2.2%
왕 13
 
2.2%
원 13
 
2.2%
의 13
 
2.2%
백 11
 
1.9%
Other values (140) 424
71.9%
Decimal Number
ValueCountFrequency (%)
2 11
28.2%
1 11
28.2%
4 5
12.8%
5 5
12.8%
3 3
 
7.7%
6 2
 
5.1%
9 1
 
2.6%
8 1
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
B 1
25.0%
A 1
25.0%
T 1
25.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 590
87.5%
Common 80
 
11.9%
Latin 4
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 32
 
5.4%
단 26
 
4.4%
마 16
 
2.7%
을 15
 
2.5%
이 14
 
2.4%
레 13
 
2.2%
왕 13
 
2.2%
원 13
 
2.2%
의 13
 
2.2%
백 11
 
1.9%
Other values (140) 424
71.9%
Common
ValueCountFrequency (%)
25
31.2%
2 11
13.8%
1 11
13.8%
( 8
 
10.0%
) 8
 
10.0%
4 5
 
6.2%
5 5
 
6.2%
3 3
 
3.8%
6 2
 
2.5%
9 1
 
1.2%
Latin
ValueCountFrequency (%)
K 1
25.0%
B 1
25.0%
A 1
25.0%
T 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 590
87.5%
ASCII 84
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 32
 
5.4%
단 26
 
4.4%
마 16
 
2.7%
을 15
 
2.5%
이 14
 
2.4%
레 13
 
2.2%
왕 13
 
2.2%
원 13
 
2.2%
의 13
 
2.2%
백 11
 
1.9%
Other values (140) 424
71.9%
ASCII
ValueCountFrequency (%)
25
29.8%
2 11
13.1%
1 11
13.1%
( 8
 
9.5%
) 8
 
9.5%
4 5
 
6.0%
5 5
 
6.0%
3 3
 
3.6%
6 2
 
2.4%
K 1
 
1.2%
Other values (5) 5
 
6.0%

읍면동
Categorical

Distinct8
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
오전동
28 
삼동
22 
포일동
16 
내손동
12 
학의동
8 
Other values (3)
15 

Length

Max length3
Median length3
Mean length2.7821782
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row오전동
2nd row학의동
3rd row오전동
4th row오전동
5th row오전동

Common Values

ValueCountFrequency (%)
오전동 28
27.7%
삼동 22
21.8%
포일동 16
15.8%
내손동 12
11.9%
학의동 8
 
7.9%
청계동 6
 
5.9%
왕곡동 5
 
5.0%
고천동 4
 
4.0%

Length

2024-04-30T07:51:14.485914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:51:14.607737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오전동 28
27.7%
삼동 22
21.8%
포일동 16
15.8%
내손동 12
11.9%
학의동 8
 
7.9%
청계동 6
 
5.9%
왕곡동 5
 
5.0%
고천동 4
 
4.0%

지번
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-04-30T07:51:14.868637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.8811881
Min length5

Characters and Unicode

Total characters695
Distinct characters32
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

Unique101 ?
Unique (%)100.0%

Sample

1st row오전동 333
2nd row학의동 823-2
3rd row오전동358-19
4th row오전동838-3
5th row오전동28
ValueCountFrequency (%)
포일동 8
 
6.3%
학의동 8
 
6.3%
오전동 5
 
3.9%
삼동 3
 
2.4%
삼동244-1 1
 
0.8%
441-2 1
 
0.8%
오전동104-20 1
 
0.8%
오전동230 1
 
0.8%
607 1
 
0.8%
594 1
 
0.8%
Other values (97) 97
76.4%
2024-04-30T07:51:15.274832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 99
 
14.2%
1 64
 
9.2%
2 41
 
5.9%
6 38
 
5.5%
- 37
 
5.3%
8 37
 
5.3%
4 35
 
5.0%
3 31
 
4.5%
5 29
 
4.2%
7 27
 
3.9%
Other values (22) 257
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 351
50.5%
Other Letter 281
40.4%
Dash Punctuation 37
 
5.3%
Space Separator 26
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 99
35.2%
오 26
 
9.3%
전 26
 
9.3%
삼 22
 
7.8%
일 16
 
5.7%
포 16
 
5.7%
내 12
 
4.3%
손 12
 
4.3%
학 8
 
2.8%
의 8
 
2.8%
Other values (10) 36
 
12.8%
Decimal Number
ValueCountFrequency (%)
1 64
18.2%
2 41
11.7%
6 38
10.8%
8 37
10.5%
4 35
10.0%
3 31
8.8%
5 29
8.3%
7 27
7.7%
9 25
 
7.1%
0 24
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 414
59.6%
Hangul 281
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 99
35.2%
오 26
 
9.3%
전 26
 
9.3%
삼 22
 
7.8%
일 16
 
5.7%
포 16
 
5.7%
내 12
 
4.3%
손 12
 
4.3%
학 8
 
2.8%
의 8
 
2.8%
Other values (10) 36
 
12.8%
Common
ValueCountFrequency (%)
1 64
15.5%
2 41
9.9%
6 38
9.2%
- 37
8.9%
8 37
8.9%
4 35
8.5%
3 31
7.5%
5 29
7.0%
7 27
6.5%
26
6.3%
Other values (2) 49
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414
59.6%
Hangul 281
40.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 99
35.2%
오 26
 
9.3%
전 26
 
9.3%
삼 22
 
7.8%
일 16
 
5.7%
포 16
 
5.7%
내 12
 
4.3%
손 12
 
4.3%
학 8
 
2.8%
의 8
 
2.8%
Other values (10) 36
 
12.8%
ASCII
ValueCountFrequency (%)
1 64
15.5%
2 41
9.9%
6 38
9.2%
- 37
8.9%
8 37
8.9%
4 35
8.5%
3 31
7.5%
5 29
7.0%
7 27
6.5%
26
6.3%
Other values (2) 49
11.8%

사용검사일자
Date

Distinct90
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size940.0 B
Minimum1978-12-13 00:00:00
Maximum2020-12-14 00:00:00
2024-04-30T07:51:15.411943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:15.561986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6732673
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-30T07:51:15.687881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile12
Maximum38
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5823094
Coefficient of variation (CV)0.98396728
Kurtosis14.930526
Mean5.6732673
Median Absolute Deviation (MAD)3
Skewness3.2380688
Sum573
Variance31.162178
MonotonicityNot monotonic
2024-04-30T07:51:15.787899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 21
20.8%
4 13
12.9%
3 10
9.9%
7 9
8.9%
8 9
8.9%
2 8
 
7.9%
5 7
 
6.9%
6 7
 
6.9%
9 4
 
4.0%
12 3
 
3.0%
Other values (6) 10
9.9%
ValueCountFrequency (%)
1 21
20.8%
2 8
 
7.9%
3 10
9.9%
4 13
12.9%
5 7
 
6.9%
6 7
 
6.9%
7 9
8.9%
8 9
8.9%
9 4
 
4.0%
10 3
 
3.0%
ValueCountFrequency (%)
38 1
 
1.0%
32 1
 
1.0%
19 1
 
1.0%
18 1
 
1.0%
12 3
 
3.0%
11 3
 
3.0%
10 3
 
3.0%
9 4
4.0%
8 9
8.9%
7 9
8.9%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.21782
Minimum25
Maximum2540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-30T07:51:15.921659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile35
Q1182
median344
Q3520
95-th percentile1074
Maximum2540
Range2515
Interquartile range (IQR)338

Descriptive statistics

Standard deviation455.69491
Coefficient of variation (CV)1.0212387
Kurtosis9.1974874
Mean446.21782
Median Absolute Deviation (MAD)166
Skewness2.7868193
Sum45068
Variance207657.85
MonotonicityNot monotonic
2024-04-30T07:51:16.072701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 2
 
2.0%
447 2
 
2.0%
25 2
 
2.0%
370 2
 
2.0%
520 2
 
2.0%
586 2
 
2.0%
269 2
 
2.0%
50 2
 
2.0%
30 2
 
2.0%
166 2
 
2.0%
Other values (80) 81
80.2%
ValueCountFrequency (%)
25 2
2.0%
28 1
1.0%
30 2
2.0%
35 1
1.0%
48 1
1.0%
49 1
1.0%
50 2
2.0%
54 1
1.0%
70 1
1.0%
76 1
1.0%
ValueCountFrequency (%)
2540 1
1.0%
2422 1
1.0%
2200 1
1.0%
1774 1
1.0%
1614 1
1.0%
1074 1
1.0%
1068 1
1.0%
998 1
1.0%
958 1
1.0%
840 1
1.0%

의무관리대상여부
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size233.0 B
True
80 
False
21 
ValueCountFrequency (%)
True 80
79.2%
False 21
 
20.8%
2024-04-30T07:51:16.181419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-04-30T07:51:13.149082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:12.875118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:13.237519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:13.064228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:51:16.278309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동사용검사일자동수세대수의무관리대상여부
읍면동1.0001.0000.2940.4970.354
사용검사일자1.0001.0000.3210.9381.000
동수0.2940.3211.0000.8090.553
세대수0.4970.9380.8091.0000.653
의무관리대상여부0.3541.0000.5530.6531.000
2024-04-30T07:51:16.374267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의무관리대상여부읍면동
의무관리대상여부1.0000.256
읍면동0.2561.000
2024-04-30T07:51:16.452003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수세대수읍면동의무관리대상여부
동수1.0000.8330.1640.392
세대수0.8331.0000.1830.480
읍면동0.1640.1831.0000.256
의무관리대상여부0.3920.4800.2561.000

Missing values

2024-04-30T07:51:13.347226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:51:13.472689image/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

시군명공동주택명읍면동지번사용검사일자동수세대수의무관리대상여부
0의왕시KT이자리에오전동오전동 3332004-11-293182Y
1의왕시골드클래스학의동학의동 823-22019-05-038420Y
2의왕시남양모란오전동오전동358-191999-12-241128N
3의왕시뉴서울국화오전동오전동838-31994-12-152220Y
4의왕시대명구름채오전동오전동282004-12-216518Y
5의왕시대명솔채오전동오전동8872009-11-068445Y
6의왕시대영포일동포일동537-61981-10-24128N
7의왕시대우이안삼동삼동462-202003-09-229688Y
8의왕시대원칸타빌1단지내손동내손동7952002-10-194394Y
9의왕시대원칸타빌2단지내손동내손동7992002-10-194322Y
시군명공동주택명읍면동지번사용검사일자동수세대수의무관리대상여부
91의왕시한일나래아파트포일동포일동533-11997-10-301214Y
92의왕시해모로오전동오전동27-12004-08-3011998Y
93의왕시호수마을1단지포일동포일동6382009-07-108450Y
94의왕시호수마을2단지포일동포일동 6432009-10-306447Y
95의왕시효성청솔삼동삼동130-11997-09-094472Y
96의왕시휴먼시아 청계마을3단지청계동청계동9722007-07-117506Y
97의왕시휴먼시아청계마을1단지청계동청계동9822009-11-107266Y
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