Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells11
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory72.5 B

Variable types

Categorical2
Text3
Numeric2
DateTime1

Dataset

Description충청남도 청양군의 공공주택 현황(지역, 공공주택명, 주소, 세대수, 층수, 동수, 준공연도, 관리실 번호) 등을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=58&beforeMenuCd=DOM_000000201001001000&publicdatapk=15111798

Alerts

지역 has constant value ""Constant
세대수 is highly overall correlated with 동수High correlation
동수 is highly overall correlated with 세대수High correlation
관리실 번호 has 11 (45.8%) missing valuesMissing
공공주택명 has unique valuesUnique
주소 has unique valuesUnique
준공연도 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:06:04.501094
Analysis finished2024-01-09 21:06:05.239239
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
청양군
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청양군 24
100.0%

Length

2024-01-10T06:06:05.298742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:06:05.374734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청양군 24
100.0%

공공주택명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-10T06:06:05.524676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length4.8333333
Min length2

Characters and Unicode

Total characters116
Distinct characters70
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row은혜
2nd row애경사원
3rd row천강
4th row세아
5th row장안
ValueCountFrequency (%)
은혜 1
 
3.7%
청양필로스 1
 
3.7%
청양교월 1
 
3.7%
청양교월1단지 1
 
3.7%
어반라티 1
 
3.7%
드림팰리스3차 1
 
3.7%
드림팰리스2차 1
 
3.7%
리버파크 1
 
3.7%
드림팰리스 1
 
3.7%
드림 1
 
3.7%
Other values (17) 17
63.0%
2024-01-10T06:06:05.833844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 7
 
6.0%
드 5
 
4.3%
림 4
 
3.4%
리 4
 
3.4%
로 4
 
3.4%
양 4
 
3.4%
청 4
 
3.4%
팰 3
 
2.6%
차 3
 
2.6%
필 3
 
2.6%
Other values (60) 75
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
94.0%
Decimal Number 4
 
3.4%
Space Separator 3
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 7
 
6.4%
드 5
 
4.6%
림 4
 
3.7%
리 4
 
3.7%
로 4
 
3.7%
양 4
 
3.7%
청 4
 
3.7%
팰 3
 
2.8%
차 3
 
2.8%
필 3
 
2.8%
Other values (56) 68
62.4%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 1
25.0%
3 1
25.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
94.0%
Common 7
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 7
 
6.4%
드 5
 
4.6%
림 4
 
3.7%
리 4
 
3.7%
로 4
 
3.7%
양 4
 
3.7%
청 4
 
3.7%
팰 3
 
2.8%
차 3
 
2.8%
필 3
 
2.8%
Other values (56) 68
62.4%
Common
ValueCountFrequency (%)
3
42.9%
2 2
28.6%
1 1
 
14.3%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
94.0%
ASCII 7
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 7
 
6.4%
드 5
 
4.6%
림 4
 
3.7%
리 4
 
3.7%
로 4
 
3.7%
양 4
 
3.7%
청 4
 
3.7%
팰 3
 
2.8%
차 3
 
2.8%
필 3
 
2.8%
Other values (56) 68
62.4%
ASCII
ValueCountFrequency (%)
3
42.9%
2 2
28.6%
1 1
 
14.3%
3 1
 
14.3%

주소
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-01-10T06:06:06.009039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.583333
Min length8

Characters and Unicode

Total characters278
Distinct characters39
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

Unique24 ?
Unique (%)100.0%

Sample

1st row청양읍 읍내리218-1
2nd row정산면 서정리 산10-8
3rd row청양읍 읍내리377-5
4th row청양읍 읍내리376-8
5th row청양읍 읍내리376-11
ValueCountFrequency (%)
청양읍 22
41.5%
정산면 2
 
3.8%
읍내리 2
 
3.8%
벽천리273-4 1
 
1.9%
월촌길9 1
 
1.9%
중앙로1길19 1
 
1.9%
문화예술1길7 1
 
1.9%
문화예술1길8 1
 
1.9%
평촌1길28 1
 
1.9%
285-5 1
 
1.9%
Other values (20) 20
37.7%
2024-01-10T06:06:06.300565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
읍 34
 
12.2%
29
 
10.4%
청 22
 
7.9%
양 22
 
7.9%
1 18
 
6.5%
리 18
 
6.5%
- 15
 
5.4%
내 12
 
4.3%
7 12
 
4.3%
6 11
 
4.0%
Other values (29) 85
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
54.3%
Decimal Number 83
29.9%
Space Separator 29
 
10.4%
Dash Punctuation 15
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
읍 34
22.5%
청 22
14.6%
양 22
14.6%
리 18
11.9%
내 12
 
7.9%
길 5
 
3.3%
정 4
 
2.6%
산 4
 
2.6%
촌 2
 
1.3%
로 2
 
1.3%
Other values (17) 26
17.2%
Decimal Number
ValueCountFrequency (%)
1 18
21.7%
7 12
14.5%
6 11
13.3%
3 11
13.3%
2 10
12.0%
8 9
10.8%
5 4
 
4.8%
9 3
 
3.6%
4 3
 
3.6%
0 2
 
2.4%
Space Separator
ValueCountFrequency (%)
29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
54.3%
Common 127
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
읍 34
22.5%
청 22
14.6%
양 22
14.6%
리 18
11.9%
내 12
 
7.9%
길 5
 
3.3%
정 4
 
2.6%
산 4
 
2.6%
촌 2
 
1.3%
로 2
 
1.3%
Other values (17) 26
17.2%
Common
ValueCountFrequency (%)
29
22.8%
1 18
14.2%
- 15
11.8%
7 12
9.4%
6 11
 
8.7%
3 11
 
8.7%
2 10
 
7.9%
8 9
 
7.1%
5 4
 
3.1%
9 3
 
2.4%
Other values (2) 5
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
54.3%
ASCII 127
45.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
읍 34
22.5%
청 22
14.6%
양 22
14.6%
리 18
11.9%
내 12
 
7.9%
길 5
 
3.3%
정 4
 
2.6%
산 4
 
2.6%
촌 2
 
1.3%
로 2
 
1.3%
Other values (17) 26
17.2%
ASCII
ValueCountFrequency (%)
29
22.8%
1 18
14.2%
- 15
11.8%
7 12
9.4%
6 11
 
8.7%
3 11
 
8.7%
2 10
 
7.9%
8 9
 
7.1%
5 4
 
3.1%
9 3
 
2.4%
Other values (2) 5
 
3.9%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.583333
Minimum18
Maximum372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:06:06.416674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18.15
Q123.75
median46.5
Q3111
95-th percentile304.1
Maximum372
Range354
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation100.16461
Coefficient of variation (CV)1.1307388
Kurtosis2.646018
Mean88.583333
Median Absolute Deviation (MAD)27.5
Skewness1.8551702
Sum2126
Variance10032.949
MonotonicityNot monotonic
2024-01-10T06:06:06.521625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
19 3
 
12.5%
18 2
 
8.3%
27 2
 
8.3%
67 1
 
4.2%
40 1
 
4.2%
127 1
 
4.2%
120 1
 
4.2%
25 1
 
4.2%
68 1
 
4.2%
29 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
18 2
8.3%
19 3
12.5%
20 1
 
4.2%
25 1
 
4.2%
27 2
8.3%
29 1
 
4.2%
40 1
 
4.2%
46 1
 
4.2%
47 1
 
4.2%
57 1
 
4.2%
ValueCountFrequency (%)
372 1
4.2%
305 1
4.2%
299 1
4.2%
154 1
4.2%
127 1
4.2%
120 1
4.2%
108 1
4.2%
95 1
4.2%
68 1
4.2%
67 1
4.2%

층수
Real number (ℝ)

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.875
Minimum5
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:06:06.627184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18.75
median10
Q315
95-th percentile15.85
Maximum24
Range19
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.4751731
Coefficient of variation (CV)0.41151017
Kurtosis1.6797254
Mean10.875
Median Absolute Deviation (MAD)3
Skewness1.013858
Sum261
Variance20.027174
MonotonicityNot monotonic
2024-01-10T06:06:06.731151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
15 5
20.8%
9 5
20.8%
10 4
16.7%
5 3
12.5%
24 1
 
4.2%
12 1
 
4.2%
16 1
 
4.2%
6 1
 
4.2%
7 1
 
4.2%
13 1
 
4.2%
ValueCountFrequency (%)
5 3
12.5%
6 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
9 5
20.8%
10 4
16.7%
12 1
 
4.2%
13 1
 
4.2%
15 5
20.8%
16 1
 
4.2%
ValueCountFrequency (%)
24 1
 
4.2%
16 1
 
4.2%
15 5
20.8%
13 1
 
4.2%
12 1
 
4.2%
10 4
16.7%
9 5
20.8%
8 1
 
4.2%
7 1
 
4.2%
6 1
 
4.2%

동수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
18 
2
4 
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 18
75.0%
2 4
 
16.7%
4 1
 
4.2%
3 1
 
4.2%

Length

2024-01-10T06:06:06.880600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:06:06.981642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18
75.0%
2 4
 
16.7%
4 1
 
4.2%
3 1
 
4.2%

준공연도
Date

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum1992-04-28 00:00:00
Maximum2023-10-18 00:00:00
2024-01-10T06:06:07.069664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:06:07.177493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

관리실 번호
Text

MISSING 

Distinct12
Distinct (%)92.3%
Missing11
Missing (%)45.8%
Memory size324.0 B
2024-01-10T06:06:07.334124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters156
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

Unique11 ?
Unique (%)84.6%

Sample

1st row041-943-6889
2nd row041-942-5871
3rd row041-942-3183
4th row041-943-7795
5th row041-944-0102
ValueCountFrequency (%)
041-943-9300 2
15.4%
041-943-6889 1
7.7%
041-942-5871 1
7.7%
041-942-3183 1
7.7%
041-943-7795 1
7.7%
041-944-0102 1
7.7%
041-943-6801 1
7.7%
041-943-8767 1
7.7%
041-943-0880 1
7.7%
041-942-4500 1
7.7%
Other values (2) 2
15.4%
2024-01-10T06:06:07.598130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 31
19.9%
- 26
16.7%
0 24
15.4%
1 17
10.9%
9 17
10.9%
3 12
 
7.7%
8 8
 
5.1%
7 8
 
5.1%
2 5
 
3.2%
5 5
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
83.3%
Dash Punctuation 26
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 31
23.8%
0 24
18.5%
1 17
13.1%
9 17
13.1%
3 12
 
9.2%
8 8
 
6.2%
7 8
 
6.2%
2 5
 
3.8%
5 5
 
3.8%
6 3
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 31
19.9%
- 26
16.7%
0 24
15.4%
1 17
10.9%
9 17
10.9%
3 12
 
7.7%
8 8
 
5.1%
7 8
 
5.1%
2 5
 
3.2%
5 5
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 31
19.9%
- 26
16.7%
0 24
15.4%
1 17
10.9%
9 17
10.9%
3 12
 
7.7%
8 8
 
5.1%
7 8
 
5.1%
2 5
 
3.2%
5 5
 
3.2%

Interactions

2024-01-10T06:06:04.904429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:06:04.761445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:06:04.980086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:06:04.830151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:06:07.692752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공공주택명주소세대수층수동수준공연도관리실 번호
공공주택명1.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.000
세대수1.0001.0001.0000.0000.9331.0000.903
층수1.0001.0000.0001.0000.0001.0000.718
동수1.0001.0000.9330.0001.0001.0001.000
준공연도1.0001.0001.0001.0001.0001.0001.000
관리실 번호1.0001.0000.9030.7181.0001.0001.000
2024-01-10T06:06:07.796119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수층수동수
세대수1.0000.4960.845
층수0.4961.0000.000
동수0.8450.0001.000

Missing values

2024-01-10T06:06:05.086884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:06:05.192651image/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

지역공공주택명주소세대수층수동수준공연도관리실 번호
0청양군은혜청양읍 읍내리218-1671011992-04-28041-943-6889
1청양군애경사원정산면 서정리 산10-895521994-07-15041-942-5871
2청양군천강청양읍 읍내리377-51542411996-11-27041-942-3183
3청양군세아청양읍 읍내리376-81081511997-01-29041-943-7795
4청양군장안청양읍 읍내리376-112991522000-01-03041-944-0102
5청양군읍내주공청양읍 읍내리3063721542002-02-25041-943-6801
6청양군디엠이스빌청양읍 읍내리371-6471212004-01-09041-943-8767
7청양군읍내휴먼시아청양읍 읍내리4763051532009-12-11041-943-0880
8청양군청양필로스캐슬청양읍 벽천리273571512012-06-07041-942-4500
9청양군에코빌2차청양읍 읍내리286-120512013-12-02<NA>
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