Overview

Dataset statistics

Number of variables6
Number of observations73
Missing cells6
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory51.8 B

Variable types

Text3
Numeric2
DateTime1

Dataset

Description경상남도 창녕군의 공동주택 현황에 대한 데이터를 포함하고 있습니다. (명칭 위치, 세대수, 동수, 층수, 사용승인일자)
Author경상남도 창녕군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15024747

Alerts

위치 has 1 (1.4%) missing valuesMissing
세대수 has 1 (1.4%) missing valuesMissing
동수 has 1 (1.4%) missing valuesMissing
층수 has 1 (1.4%) missing valuesMissing
사용승인일자 has 2 (2.7%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:47:00.866399
Analysis finished2023-12-10 23:47:02.165570
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명칭
Text

Distinct72
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-11T08:47:02.323863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.3972603
Min length1

Characters and Unicode

Total characters467
Distinct characters118
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)97.3%

Sample

1st row홍남이브빌
2nd row도원아파트
3rd row동아더프라임
4th row남지덕진6차휴먼빌
5th row남지덕진7차휴먼빌
ValueCountFrequency (%)
동문아파트 2
 
2.7%
청호아파트 1
 
1.4%
동호빌리지 1
 
1.4%
목련아파트 1
 
1.4%
삼우아파트(2동 1
 
1.4%
장미아파트 1
 
1.4%
대흥아파트 1
 
1.4%
반도아파트 1
 
1.4%
남중파크 1
 
1.4%
신창아파트 1
 
1.4%
Other values (63) 63
85.1%
2023-12-11T08:47:02.665219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 52
 
11.1%
파 51
 
10.9%
트 49
 
10.5%
빌 18
 
3.9%
동 16
 
3.4%
라 12
 
2.6%
우 10
 
2.1%
이 9
 
1.9%
지 9
 
1.9%
남 8
 
1.7%
Other values (108) 233
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 441
94.4%
Decimal Number 11
 
2.4%
Close Punctuation 5
 
1.1%
Open Punctuation 5
 
1.1%
Space Separator 4
 
0.9%
Letter Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 52
 
11.8%
파 51
 
11.6%
트 49
 
11.1%
빌 18
 
4.1%
동 16
 
3.6%
라 12
 
2.7%
우 10
 
2.3%
이 9
 
2.0%
지 9
 
2.0%
남 8
 
1.8%
Other values (99) 207
46.9%
Decimal Number
ValueCountFrequency (%)
2 4
36.4%
1 3
27.3%
3 2
18.2%
7 1
 
9.1%
6 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 441
94.4%
Common 25
 
5.4%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 52
 
11.8%
파 51
 
11.6%
트 49
 
11.1%
빌 18
 
4.1%
동 16
 
3.6%
라 12
 
2.7%
우 10
 
2.3%
이 9
 
2.0%
지 9
 
2.0%
남 8
 
1.8%
Other values (99) 207
46.9%
Common
ValueCountFrequency (%)
) 5
20.0%
( 5
20.0%
2 4
16.0%
4
16.0%
1 3
12.0%
3 2
 
8.0%
7 1
 
4.0%
6 1
 
4.0%
Latin
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 441
94.4%
ASCII 25
 
5.4%
Number Forms 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 52
 
11.8%
파 51
 
11.6%
트 49
 
11.1%
빌 18
 
4.1%
동 16
 
3.6%
라 12
 
2.7%
우 10
 
2.3%
이 9
 
2.0%
지 9
 
2.0%
남 8
 
1.8%
Other values (99) 207
46.9%
ASCII
ValueCountFrequency (%)
) 5
20.0%
( 5
20.0%
2 4
16.0%
4
16.0%
1 3
12.0%
3 2
 
8.0%
7 1
 
4.0%
6 1
 
4.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

위치
Text

MISSING 

Distinct72
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Memory size716.0 B
2023-12-11T08:47:02.924461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length25
Mean length16.847222
Min length13

Characters and Unicode

Total characters1213
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)100.0%

Sample

1st row창녕군 창녕읍 낙영길 12
2nd row창녕군 창녕읍 창밀로 12-8
3rd row창녕군 남지읍 남고길 47(남지리 817-1)
4th row창녕군 남지읍 남포길 23
5th row창녕군 남지읍 남지중앙1길 10
ValueCountFrequency (%)
창녕군 72
24.7%
창녕읍 31
 
10.6%
남지읍 25
 
8.6%
영산면 10
 
3.4%
성내뒷들길 6
 
2.1%
남지리 5
 
1.7%
낙동로 5
 
1.7%
마산리 4
 
1.4%
군청길 4
 
1.4%
20 3
 
1.0%
Other values (109) 127
43.5%
2023-12-11T08:47:03.270585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
220
18.1%
창 108
 
8.9%
녕 107
 
8.8%
군 76
 
6.3%
읍 56
 
4.6%
1 52
 
4.3%
지 43
 
3.5%
남 42
 
3.5%
2 37
 
3.1%
길 35
 
2.9%
Other values (64) 437
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 712
58.7%
Decimal Number 240
 
19.8%
Space Separator 220
 
18.1%
Dash Punctuation 34
 
2.8%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 108
15.2%
녕 107
15.0%
군 76
10.7%
읍 56
 
7.9%
지 43
 
6.0%
남 42
 
5.9%
길 35
 
4.9%
로 24
 
3.4%
산 23
 
3.2%
리 17
 
2.4%
Other values (49) 181
25.4%
Decimal Number
ValueCountFrequency (%)
1 52
21.7%
2 37
15.4%
3 25
10.4%
9 22
9.2%
0 19
 
7.9%
8 18
 
7.5%
4 18
 
7.5%
5 17
 
7.1%
7 17
 
7.1%
6 15
 
6.2%
Space Separator
ValueCountFrequency (%)
220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 712
58.7%
Common 501
41.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 108
15.2%
녕 107
15.0%
군 76
10.7%
읍 56
 
7.9%
지 43
 
6.0%
남 42
 
5.9%
길 35
 
4.9%
로 24
 
3.4%
산 23
 
3.2%
리 17
 
2.4%
Other values (49) 181
25.4%
Common
ValueCountFrequency (%)
220
43.9%
1 52
 
10.4%
2 37
 
7.4%
- 34
 
6.8%
3 25
 
5.0%
9 22
 
4.4%
0 19
 
3.8%
8 18
 
3.6%
4 18
 
3.6%
5 17
 
3.4%
Other values (5) 39
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 712
58.7%
ASCII 501
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220
43.9%
1 52
 
10.4%
2 37
 
7.4%
- 34
 
6.8%
3 25
 
5.0%
9 22
 
4.4%
0 19
 
3.8%
8 18
 
3.6%
4 18
 
3.6%
5 17
 
3.4%
Other values (5) 39
 
7.8%
Hangul
ValueCountFrequency (%)
창 108
15.2%
녕 107
15.0%
군 76
10.7%
읍 56
 
7.9%
지 43
 
6.0%
남 42
 
5.9%
길 35
 
4.9%
로 24
 
3.4%
산 23
 
3.2%
리 17
 
2.4%
Other values (49) 181
25.4%

세대수
Text

MISSING 

Distinct47
Distinct (%)65.3%
Missing1
Missing (%)1.4%
Memory size716.0 B
2023-12-11T08:47:03.443663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length2.4305556
Min length2

Characters and Unicode

Total characters175
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

Unique36 ?
Unique (%)50.0%

Sample

1st row68
2nd row120
3rd row443
4th row168
5th row264
ValueCountFrequency (%)
19 9
 
12.7%
10 4
 
5.6%
68 3
 
4.2%
30 3
 
4.2%
16 3
 
4.2%
42 3
 
4.2%
18 3
 
4.2%
88 2
 
2.8%
12 2
 
2.8%
28 2
 
2.8%
Other values (36) 37
52.1%
2023-12-11T08:47:03.738110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 38
21.7%
9 18
10.3%
8 18
10.3%
4 16
9.1%
16
9.1%
2 15
 
8.6%
0 14
 
8.0%
6 14
 
8.0%
3 11
 
6.3%
5 9
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
90.9%
Space Separator 16
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38
23.9%
9 18
11.3%
8 18
11.3%
4 16
10.1%
2 15
 
9.4%
0 14
 
8.8%
6 14
 
8.8%
3 11
 
6.9%
5 9
 
5.7%
7 6
 
3.8%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38
21.7%
9 18
10.3%
8 18
10.3%
4 16
9.1%
16
9.1%
2 15
 
8.6%
0 14
 
8.0%
6 14
 
8.0%
3 11
 
6.3%
5 9
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38
21.7%
9 18
10.3%
8 18
10.3%
4 16
9.1%
16
9.1%
2 15
 
8.6%
0 14
 
8.0%
6 14
 
8.0%
3 11
 
6.3%
5 9
 
5.1%

동수
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)12.5%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean1.9583333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-11T08:47:03.844145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.45
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7637788
Coefficient of variation (CV)0.90065298
Kurtosis9.0932249
Mean1.9583333
Median Absolute Deviation (MAD)0
Skewness2.8530843
Sum141
Variance3.1109155
MonotonicityNot monotonic
2023-12-11T08:47:03.939421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 42
57.5%
2 15
 
20.5%
3 8
 
11.0%
4 2
 
2.7%
7 1
 
1.4%
5 1
 
1.4%
10 1
 
1.4%
9 1
 
1.4%
6 1
 
1.4%
(Missing) 1
 
1.4%
ValueCountFrequency (%)
1 42
57.5%
2 15
 
20.5%
3 8
 
11.0%
4 2
 
2.7%
5 1
 
1.4%
6 1
 
1.4%
7 1
 
1.4%
9 1
 
1.4%
10 1
 
1.4%
ValueCountFrequency (%)
10 1
 
1.4%
9 1
 
1.4%
7 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
4 2
 
2.7%
3 8
 
11.0%
2 15
 
20.5%
1 42
57.5%

층수
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)18.1%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean8.5416667
Minimum5
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-11T08:47:04.026719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median8
Q310
95-th percentile15
Maximum21
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9358858
Coefficient of variation (CV)0.46078663
Kurtosis1.4404732
Mean8.5416667
Median Absolute Deviation (MAD)3
Skewness1.2313113
Sum615
Variance15.491197
MonotonicityNot monotonic
2023-12-11T08:47:04.124941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5 28
38.4%
10 10
 
13.7%
9 8
 
11.0%
8 6
 
8.2%
12 4
 
5.5%
15 3
 
4.1%
6 3
 
4.1%
20 2
 
2.7%
14 2
 
2.7%
13 2
 
2.7%
Other values (3) 4
 
5.5%
ValueCountFrequency (%)
5 28
38.4%
6 3
 
4.1%
7 1
 
1.4%
8 6
 
8.2%
9 8
 
11.0%
10 10
 
13.7%
11 2
 
2.7%
12 4
 
5.5%
13 2
 
2.7%
14 2
 
2.7%
ValueCountFrequency (%)
21 1
 
1.4%
20 2
 
2.7%
15 3
 
4.1%
14 2
 
2.7%
13 2
 
2.7%
12 4
 
5.5%
11 2
 
2.7%
10 10
13.7%
9 8
11.0%
8 6
8.2%

사용승인일자
Date

MISSING 

Distinct67
Distinct (%)94.4%
Missing2
Missing (%)2.7%
Memory size716.0 B
Minimum1987-03-30 00:00:00
Maximum2019-05-28 00:00:00
2023-12-11T08:47:04.256185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:04.396235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T08:47:01.430227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:01.280964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:01.510688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:01.349492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:47:04.493526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명칭위치세대수동수층수사용승인일자
명칭1.0001.0000.9881.0000.9781.000
위치1.0001.0001.0001.0001.0001.000
세대수0.9881.0001.0000.9970.8530.996
동수1.0001.0000.9971.0000.4890.997
층수0.9781.0000.8530.4891.0000.983
사용승인일자1.0001.0000.9960.9970.9831.000
2023-12-11T08:47:04.616432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수층수
동수1.0000.225
층수0.2251.000

Missing values

2023-12-11T08:47:01.623685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:47:01.725894image/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.
2023-12-11T08:47:02.109276image/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

명칭위치세대수동수층수사용승인일자
0홍남이브빌창녕군 창녕읍 낙영길 12682122006-03-22
1도원아파트창녕군 창녕읍 창밀로 12-8120210<NA>
2동아더프라임창녕군 남지읍 남고길 47(남지리 817-1)4437202010-12-09
3남지덕진6차휴먼빌창녕군 남지읍 남포길 231682142008-10-29
4남지덕진7차휴먼빌창녕군 남지읍 남지중앙1길 102643152010-04-14
5현대1차아파트창녕군 창녕읍 우포2로 11991512151992-12-24
6영산상가아파트창녕군 영산면 연지길 19421151992-08-04
7창녕주공아파트창녕군 창녕읍 말흘5길 333385142007-10-11
8푸른마을아파트창녕군 남지읍 동포로 44(남지리293-1)1822132005-03-16
9청목아파트창녕군 창녕읍 말흘리 748561132015-11-02
명칭위치세대수동수층수사용승인일자
63삼우공영아파트창녕군 남지읍 동포1길 2110151989-11-28
64호반아파트창녕군 대합면 십이리1길 19-1510152002-01-17
65대림아파트창녕군 영산면 성내뒷들길 3110151996-03-05
66남지이원웰스타아파트창녕군 남지읍 마산리 954-2번지 외2필지2303212016-07-14
67나인캐슬빌라창녕군 남지읍 마산리 695-224352016-02-24
68르마레남지아파트창녕군 남지읍 남지리 986-639282017-01-23
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