Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells56
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Numeric2
Categorical8
Text1
Unsupported1
DateTime1

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/d49a5c95-7b12-438e-b3b0-3ee4c8aaae83

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
시도코드 has constant value ""Constant
시군구코드 has constant value ""Constant
수정일시분초 has constant value ""Constant
작업자명 has constant value ""Constant
행정동명 is highly overall correlated with 영문주소명 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 행정동명 and 1 other fieldsHigh correlation
영문주소명 is highly overall correlated with 행정동명 and 1 other fieldsHigh correlation
리명 has 26 (86.7%) missing valuesMissing
기타주소명 has 30 (100.0%) missing valuesMissing
우편번호순번 has unique valuesUnique
기타주소명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 14:14:03.462506
Analysis finished2023-12-10 14:14:05.359565
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호순번
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.5
Minimum216
Maximum245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:05.485170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum216
5-th percentile217.45
Q1223.25
median230.5
Q3237.75
95-th percentile243.55
Maximum245
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.038192661
Kurtosis-1.2
Mean230.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum6915
Variance77.5
MonotonicityStrictly increasing
2023-12-10T23:14:05.733953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
216 1
 
3.3%
232 1
 
3.3%
245 1
 
3.3%
244 1
 
3.3%
243 1
 
3.3%
242 1
 
3.3%
241 1
 
3.3%
240 1
 
3.3%
239 1
 
3.3%
238 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
216 1
3.3%
217 1
3.3%
218 1
3.3%
219 1
3.3%
220 1
3.3%
221 1
3.3%
222 1
3.3%
223 1
3.3%
224 1
3.3%
225 1
3.3%
ValueCountFrequency (%)
245 1
3.3%
244 1
3.3%
243 1
3.3%
242 1
3.3%
241 1
3.3%
240 1
3.3%
239 1
3.3%
238 1
3.3%
237 1
3.3%
236 1
3.3%

우편번호
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1040.7
Minimum1001
Maximum1085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:06.067015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1003.9
Q11010.5
median1047
Q31069.5
95-th percentile1082.1
Maximum1085
Range84
Interquartile range (IQR)59

Descriptive statistics

Standard deviation29.999023
Coefficient of variation (CV)0.028825812
Kurtosis-1.7530327
Mean1040.7
Median Absolute Deviation (MAD)29
Skewness0.048480763
Sum31221
Variance899.94138
MonotonicityNot monotonic
2023-12-10T23:14:06.278555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1018 2
 
6.7%
1010 2
 
6.7%
1015 1
 
3.3%
1014 1
 
3.3%
1056 1
 
3.3%
1050 1
 
3.3%
1012 1
 
3.3%
1068 1
 
3.3%
1076 1
 
3.3%
1005 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
1001 1
3.3%
1003 1
3.3%
1005 1
3.3%
1006 1
3.3%
1008 1
3.3%
1009 1
3.3%
1010 2
6.7%
1012 1
3.3%
1014 1
3.3%
1015 1
3.3%
ValueCountFrequency (%)
1085 1
3.3%
1083 1
3.3%
1081 1
3.3%
1076 1
3.3%
1075 1
3.3%
1073 1
3.3%
1071 1
3.3%
1070 1
3.3%
1068 1
3.3%
1063 1
3.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
서울
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 30
100.0%

Length

2023-12-10T23:14:06.602698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:06.844239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 30
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
강북구
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강북구 30
100.0%

Length

2023-12-10T23:14:07.007357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:07.187062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강북구 30
100.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
수유동
16 
우이동
7 
번동
3 
인수동
3 
번1동
 
1

Length

Max length3
Median length3
Mean length2.9
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row번동
2nd row수유동
3rd row수유동
4th row우이동
5th row수유동

Common Values

ValueCountFrequency (%)
수유동 16
53.3%
우이동 7
23.3%
번동 3
 
10.0%
인수동 3
 
10.0%
번1동 1
 
3.3%

Length

2023-12-10T23:14:07.359935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:07.529514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수유동 16
53.3%
우이동 7
23.3%
번동 3
 
10.0%
인수동 3
 
10.0%
번1동 1
 
3.3%

리명
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2023-12-10T23:14:07.817814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters24
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row대우아파트
2nd row코스타타워빌딩
3rd row통일연구원
4th row아카데미하우스
ValueCountFrequency (%)
대우아파트 1
25.0%
코스타타워빌딩 1
25.0%
통일연구원 1
25.0%
아카데미하우스 1
25.0%
2023-12-10T23:14:08.403430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2
 
8.3%
스 2
 
8.3%
타 2
 
8.3%
우 2
 
8.3%
대 1
 
4.2%
일 1
 
4.2%
미 1
 
4.2%
데 1
 
4.2%
카 1
 
4.2%
원 1
 
4.2%
Other values (10) 10
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2
 
8.3%
스 2
 
8.3%
타 2
 
8.3%
우 2
 
8.3%
대 1
 
4.2%
일 1
 
4.2%
미 1
 
4.2%
데 1
 
4.2%
카 1
 
4.2%
원 1
 
4.2%
Other values (10) 10
41.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2
 
8.3%
스 2
 
8.3%
타 2
 
8.3%
우 2
 
8.3%
대 1
 
4.2%
일 1
 
4.2%
미 1
 
4.2%
데 1
 
4.2%
카 1
 
4.2%
원 1
 
4.2%
Other values (10) 10
41.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2
 
8.3%
스 2
 
8.3%
타 2
 
8.3%
우 2
 
8.3%
대 1
 
4.2%
일 1
 
4.2%
미 1
 
4.2%
데 1
 
4.2%
카 1
 
4.2%
원 1
 
4.2%
Other values (10) 10
41.7%

기타주소명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

영문주소명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Suyu-dong; Gangbuk-gu; Seoul
16 
Ui-dong; Gangbuk-gu; Seoul
6 
Beon-dong; Gangbuk-gu; Seoul
3 
Daewoo Apt.Ui-dong; Gangbuk-gu; Seoul
 
1
Costar Tower Bldg.Beon1-dong; Gangbuk-gu; Seoul
 
1
Other values (3)
3 

Length

Max length50
Median length28
Mean length29.7
Min length26

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st rowBeon-dong; Gangbuk-gu; Seoul
2nd rowSuyu-dong; Gangbuk-gu; Seoul
3rd rowSuyu-dong; Gangbuk-gu; Seoul
4th rowDaewoo Apt.Ui-dong; Gangbuk-gu; Seoul
5th rowSuyu-dong; Gangbuk-gu; Seoul

Common Values

ValueCountFrequency (%)
Suyu-dong; Gangbuk-gu; Seoul 16
53.3%
Ui-dong; Gangbuk-gu; Seoul 6
 
20.0%
Beon-dong; Gangbuk-gu; Seoul 3
 
10.0%
Daewoo Apt.Ui-dong; Gangbuk-gu; Seoul 1
 
3.3%
Costar Tower Bldg.Beon1-dong; Gangbuk-gu; Seoul 1
 
3.3%
Korea Institute for National UnificationInsu-dong; 1
 
3.3%
Academy HouseInsu-dong; Gangbuk-gu; Seoul 1
 
3.3%
Insu-dong; Gangbuk-gu; Seoul 1
 
3.3%

Length

2023-12-10T23:14:08.663147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:08.917504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 29
30.2%
gangbuk-gu 29
30.2%
suyu-dong 16
16.7%
ui-dong 6
 
6.2%
beon-dong 3
 
3.1%
institute 1
 
1.0%
houseinsu-dong 1
 
1.0%
academy 1
 
1.0%
unificationinsu-dong 1
 
1.0%
national 1
 
1.0%
Other values (8) 8
 
8.3%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 30
100.0%

Length

2023-12-10T23:14:09.189350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:09.336992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1003
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1003
2nd row1003
3rd row1003
4th row1003
5th row1003

Common Values

ValueCountFrequency (%)
1003 30
100.0%

Length

2023-12-10T23:14:09.514041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:09.769017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1003 30
100.0%

행정동코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
10030070
16 
10030071
7 
10030062
3 
10030072
3 
10030059
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row10030062
2nd row10030070
3rd row10030070
4th row10030071
5th row10030070

Common Values

ValueCountFrequency (%)
10030070 16
53.3%
10030071 7
23.3%
10030062 3
 
10.0%
10030072 3
 
10.0%
10030059 1
 
3.3%

Length

2023-12-10T23:14:09.944329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:10.124081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10030070 16
53.3%
10030071 7
23.3%
10030062 3
 
10.0%
10030072 3
 
10.0%
10030059 1
 
3.3%

수정일시분초
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-09-23 11:17:48
Maximum2021-09-23 11:17:48
2023-12-10T23:14:10.288710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:10.448576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

작업자명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
KEDSYSTEM
30 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYSTEM 30
100.0%

Length

2023-12-10T23:14:10.642729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:10.868710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsystem 30
100.0%

Interactions

2023-12-10T23:14:04.295053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:03.981407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:04.459726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:04.127529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:14:10.975175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호순번우편번호행정동명리명영문주소명행정동코드
우편번호순번1.0000.5740.3991.0000.4020.399
우편번호0.5741.0000.6881.0000.7030.688
행정동명0.3990.6881.0001.0001.0001.000
리명1.0001.0001.0001.0001.0001.000
영문주소명0.4020.7031.0001.0001.0001.000
행정동코드0.3990.6881.0001.0001.0001.000
2023-12-10T23:14:11.162283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명행정동코드영문주소명
행정동명1.0001.0000.938
행정동코드1.0001.0000.938
영문주소명0.9380.9381.000
2023-12-10T23:14:11.352835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호순번우편번호행정동명영문주소명행정동코드
우편번호순번1.000-0.3960.1130.1740.113
우편번호-0.3961.0000.4950.3100.495
행정동명0.1130.4951.0000.9381.000
영문주소명0.1740.3100.9381.0000.938
행정동코드0.1130.4951.0000.9381.000

Missing values

2023-12-10T23:14:04.763300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:05.142197image/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

우편번호순번우편번호시도명시군구명행정동명리명기타주소명영문주소명시도코드시군구코드행정동코드수정일시분초작업자명
02161058서울강북구번동<NA><NA>Beon-dong; Gangbuk-gu; Seoul11003100300622021-09-23 11:17:48KEDSYSTEM
12171062서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
22181071서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
32191006서울강북구우이동대우아파트<NA>Daewoo Apt.Ui-dong; Gangbuk-gu; Seoul11003100300712021-09-23 11:17:48KEDSYSTEM
42201073서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
52211075서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
62221081서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
72231023서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
82241054서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
92251083서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
우편번호순번우편번호시도명시군구명행정동명리명기타주소명영문주소명시도코드시군구코드행정동코드수정일시분초작업자명
202361044서울강북구우이동<NA><NA>Ui-dong; Gangbuk-gu; Seoul11003100300712021-09-23 11:17:48KEDSYSTEM
212371005서울강북구우이동<NA><NA>Ui-dong; Gangbuk-gu; Seoul11003100300712021-09-23 11:17:48KEDSYSTEM
222381076서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
232391068서울강북구번동<NA><NA>Beon-dong; Gangbuk-gu; Seoul11003100300622021-09-23 11:17:48KEDSYSTEM
242401010서울강북구우이동<NA><NA>Ui-dong; Gangbuk-gu; Seoul11003100300712021-09-23 11:17:48KEDSYSTEM
252411012서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
262421010서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
272431050서울강북구수유동<NA><NA>Suyu-dong; Gangbuk-gu; Seoul11003100300702021-09-23 11:17:48KEDSYSTEM
282441056서울강북구번동<NA><NA>Beon-dong; Gangbuk-gu; Seoul11003100300622021-09-23 11:17:48KEDSYSTEM
292451014서울강북구인수동<NA><NA>Insu-dong; Gangbuk-gu; Seoul11003100300722021-09-23 11:17:48KEDSYSTEM