Overview

Dataset statistics

Number of variables5
Number of observations7905
Missing cells547
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory324.4 KiB
Average record size in memory42.0 B

Variable types

Categorical2
Numeric2
Text1

Dataset

Description주택분양보증을 받아 분양한 전체 민간 신규아파트 분양가격 동향으로 지역별, 면적별 분양가격 등의 자료를 제공합니다. 해당 데이터는 주택도시보증공사 홈페이지 및 통계청 KOSIS에서도 확인가능하오니 참고하시기 바랍니다.
URLhttps://www.data.go.kr/data/15061057/fileData.do

Alerts

분양가격(제곱미터) has 547 (6.9%) missing valuesMissing

Reproduction

Analysis started2023-12-11 23:15:41.711320
Analysis finished2023-12-11 23:15:42.672852
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
서울
 
465
인천
 
465
경기
 
465
부산
 
465
대구
 
465
Other values (12)
5580 

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 465
 
5.9%
인천 465
 
5.9%
경기 465
 
5.9%
부산 465
 
5.9%
대구 465
 
5.9%
광주 465
 
5.9%
대전 465
 
5.9%
울산 465
 
5.9%
세종 465
 
5.9%
강원 465
 
5.9%
Other values (7) 3255
41.2%

Length

2023-12-12T08:15:42.745255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 465
 
5.9%
강원 465
 
5.9%
경남 465
 
5.9%
경북 465
 
5.9%
전남 465
 
5.9%
전북 465
 
5.9%
충남 465
 
5.9%
충북 465
 
5.9%
세종 465
 
5.9%
인천 465
 
5.9%
Other values (7) 3255
41.2%

규모구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
모든면적
1581 
전용면적 60제곱미터이하
1581 
전용면적 60제곱미터초과 85제곱미터이하
1581 
전용면적 85제곱미터초과 102제곱미터이하
1581 
전용면적 102제곱미터초과
1581 

Length

Max length23
Median length14
Mean length15.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row모든면적
2nd row전용면적 60제곱미터이하
3rd row전용면적 60제곱미터초과 85제곱미터이하
4th row전용면적 85제곱미터초과 102제곱미터이하
5th row전용면적 102제곱미터초과

Common Values

ValueCountFrequency (%)
모든면적 1581
20.0%
전용면적 60제곱미터이하 1581
20.0%
전용면적 60제곱미터초과 85제곱미터이하 1581
20.0%
전용면적 85제곱미터초과 102제곱미터이하 1581
20.0%
전용면적 102제곱미터초과 1581
20.0%

Length

2023-12-12T08:15:42.880980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:43.000206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전용면적 6324
36.4%
모든면적 1581
 
9.1%
60제곱미터이하 1581
 
9.1%
60제곱미터초과 1581
 
9.1%
85제곱미터이하 1581
 
9.1%
85제곱미터초과 1581
 
9.1%
102제곱미터이하 1581
 
9.1%
102제곱미터초과 1581
 
9.1%

연도
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.129
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T08:15:43.116745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2023
Maximum2023
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2683236
Coefficient of variation (CV)0.0011234169
Kurtosis-1.1292067
Mean2019.129
Median Absolute Deviation (MAD)2
Skewness0.0060765719
Sum15961215
Variance5.1452919
MonotonicityIncreasing
2023-12-12T08:15:43.328884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2016 1020
12.9%
2017 1020
12.9%
2018 1020
12.9%
2019 1020
12.9%
2020 1020
12.9%
2021 1020
12.9%
2022 1020
12.9%
2023 510
6.5%
2015 255
 
3.2%
ValueCountFrequency (%)
2015 255
 
3.2%
2016 1020
12.9%
2017 1020
12.9%
2018 1020
12.9%
2019 1020
12.9%
2020 1020
12.9%
2021 1020
12.9%
2022 1020
12.9%
2023 510
6.5%
ValueCountFrequency (%)
2023 510
6.5%
2022 1020
12.9%
2021 1020
12.9%
2020 1020
12.9%
2019 1020
12.9%
2018 1020
12.9%
2017 1020
12.9%
2016 1020
12.9%
2015 255
 
3.2%

월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4516129
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2023-12-12T08:15:43.475329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4937361
Coefficient of variation (CV)0.5415291
Kurtosis-1.2474394
Mean6.4516129
Median Absolute Deviation (MAD)3
Skewness0.036738122
Sum51000
Variance12.206192
MonotonicityNot monotonic
2023-12-12T08:15:43.599369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 680
8.6%
11 680
8.6%
12 680
8.6%
1 680
8.6%
2 680
8.6%
3 680
8.6%
4 680
8.6%
5 680
8.6%
6 680
8.6%
7 595
7.5%
Other values (2) 1190
15.1%
ValueCountFrequency (%)
1 680
8.6%
2 680
8.6%
3 680
8.6%
4 680
8.6%
5 680
8.6%
6 680
8.6%
7 595
7.5%
8 595
7.5%
9 595
7.5%
10 680
8.6%
ValueCountFrequency (%)
12 680
8.6%
11 680
8.6%
10 680
8.6%
9 595
7.5%
8 595
7.5%
7 595
7.5%
6 680
8.6%
5 680
8.6%
4 680
8.6%
3 680
8.6%

분양가격(제곱미터)
Text

MISSING 

Distinct2766
Distinct (%)37.6%
Missing547
Missing (%)6.9%
Memory size61.9 KiB
2023-12-12T08:15:44.001513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9940201
Min length1

Characters and Unicode

Total characters29388
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

Unique1234 ?
Unique (%)16.8%

Sample

1st row5841
2nd row5652
3rd row5882
4th row5721
5th row5879
ValueCountFrequency (%)
3395 20
 
0.3%
2667 17
 
0.2%
2221 17
 
0.2%
2657 17
 
0.2%
3226 17
 
0.2%
2729 16
 
0.2%
2743 16
 
0.2%
3804 15
 
0.2%
2680 15
 
0.2%
4303 15
 
0.2%
Other values (2754) 7157
97.7%
2023-12-12T08:15:44.618822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5104
17.4%
3 4599
15.6%
4 3282
11.2%
6 2592
8.8%
5 2545
8.7%
8 2280
7.8%
7 2279
7.8%
1 2257
7.7%
9 2195
7.5%
0 2188
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29321
99.8%
Space Separator 67
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5104
17.4%
3 4599
15.7%
4 3282
11.2%
6 2592
8.8%
5 2545
8.7%
8 2280
7.8%
7 2279
7.8%
1 2257
7.7%
9 2195
7.5%
0 2188
7.5%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5104
17.4%
3 4599
15.6%
4 3282
11.2%
6 2592
8.8%
5 2545
8.7%
8 2280
7.8%
7 2279
7.8%
1 2257
7.7%
9 2195
7.5%
0 2188
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5104
17.4%
3 4599
15.6%
4 3282
11.2%
6 2592
8.8%
5 2545
8.7%
8 2280
7.8%
7 2279
7.8%
1 2257
7.7%
9 2195
7.5%
0 2188
7.4%

Interactions

2023-12-12T08:15:42.251062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:42.034238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:42.369773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:42.138902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:15:44.762550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명규모구분연도월
지역명1.0000.0000.0000.000
규모구분0.0001.0000.0000.000
연도0.0000.0001.0000.183
월0.0000.0000.1831.000
2023-12-12T08:15:44.903550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모구분지역명
규모구분1.0000.000
지역명0.0001.000
2023-12-12T08:15:45.004687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월지역명규모구분
연도1.000-0.1600.0000.000
월-0.1601.0000.0000.000
지역명0.0000.0001.0000.000
규모구분0.0000.0000.0001.000

Missing values

2023-12-12T08:15:42.512854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:15:42.620028image/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

지역명규모구분연도월분양가격(제곱미터)
0서울모든면적2015105841
1서울전용면적 60제곱미터이하2015105652
2서울전용면적 60제곱미터초과 85제곱미터이하2015105882
3서울전용면적 85제곱미터초과 102제곱미터이하2015105721
4서울전용면적 102제곱미터초과2015105879
5인천모든면적2015103163
6인천전용면적 60제곱미터이하2015103488
7인천전용면적 60제곱미터초과 85제곱미터이하2015103119
8인천전용면적 85제곱미터초과 102제곱미터이하2015103545
9인천전용면적 102제곱미터초과2015103408
지역명규모구분연도월분양가격(제곱미터)
7895경남모든면적202363532
7896경남전용면적 60제곱미터이하202363883
7897경남전용면적 60제곱미터초과 85제곱미터이하202363496
7898경남전용면적 85제곱미터초과 102제곱미터이하202364655
7899경남전용면적 102제곱미터초과202364293
7900제주모든면적202367326
7901제주전용면적 60제곱미터이하202367381
7902제주전용면적 60제곱미터초과 85제곱미터이하202367084
7903제주전용면적 85제곱미터초과 102제곱미터이하202366639
7904제주전용면적 102제곱미터초과202367506