Overview

Dataset statistics

Number of variables6
Number of observations1960
Missing cells42
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.6 KiB
Average record size in memory53.1 B

Variable types

Text1
Numeric5

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 아파트매매 거래현황의 연도별 매입자연령대별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068661/fileData.do

Alerts

2019 is highly overall correlated with 2020 and 3 other fieldsHigh correlation
2020 is highly overall correlated with 2019 and 3 other fieldsHigh correlation
2021 is highly overall correlated with 2019 and 3 other fieldsHigh correlation
2022 is highly overall correlated with 2019 and 3 other fieldsHigh correlation
2023 is highly overall correlated with 2019 and 3 other fieldsHigh correlation
2019 is highly skewed (γ1 = 20.45399655)Skewed
2023 is highly skewed (γ1 = 20.06984504)Skewed
지역 및 거래현황 has unique valuesUnique
2019 has 167 (8.5%) zerosZeros
2020 has 136 (6.9%) zerosZeros
2021 has 164 (8.4%) zerosZeros
2022 has 213 (10.9%) zerosZeros
2023 has 222 (11.3%) zerosZeros

Reproduction

Analysis started2024-03-23 05:39:18.026712
Analysis finished2024-03-23 05:39:24.600484
Duration6.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역 및 거래현황
Text

UNIQUE 

Distinct1960
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2024-03-23T14:39:24.944810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length10.539286
Min length6

Characters and Unicode

Total characters20657
Distinct characters153
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

Unique1960 ?
Unique (%)100.0%

Sample

1st row전국 /20대이하
2nd row전국 /30대
3rd row전국 /40대
4th row전국 /50대
5th row전국 /60대
ValueCountFrequency (%)
경기 343
 
8.8%
경북 182
 
4.6%
서울 182
 
4.6%
경남 168
 
4.3%
전남 161
 
4.1%
강원 133
 
3.4%
충남 126
 
3.2%
부산 119
 
3.0%
전북 119
 
3.0%
충북 112
 
2.9%
Other values (1688) 2275
58.0%
2024-03-23T14:39:25.754341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1960
 
9.5%
/ 1960
 
9.5%
대 1820
 
8.8%
0 1680
 
8.1%
구 812
 
3.9%
시 777
 
3.8%
경 714
 
3.5%
기 637
 
3.1%
군 609
 
2.9%
남 574
 
2.8%
Other values (143) 9114
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13377
64.8%
Decimal Number 3360
 
16.3%
Space Separator 1960
 
9.5%
Other Punctuation 1960
 
9.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 1820
 
13.6%
구 812
 
6.1%
시 777
 
5.8%
경 714
 
5.3%
기 637
 
4.8%
군 609
 
4.6%
남 574
 
4.3%
이 567
 
4.2%
북 469
 
3.5%
전 350
 
2.6%
Other values (134) 6048
45.2%
Decimal Number
ValueCountFrequency (%)
0 1680
50.0%
7 280
 
8.3%
3 280
 
8.3%
5 280
 
8.3%
6 280
 
8.3%
4 280
 
8.3%
2 280
 
8.3%
Space Separator
ValueCountFrequency (%)
1960
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13377
64.8%
Common 7280
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1820
 
13.6%
구 812
 
6.1%
시 777
 
5.8%
경 714
 
5.3%
기 637
 
4.8%
군 609
 
4.6%
남 574
 
4.3%
이 567
 
4.2%
북 469
 
3.5%
전 350
 
2.6%
Other values (134) 6048
45.2%
Common
ValueCountFrequency (%)
1960
26.9%
/ 1960
26.9%
0 1680
23.1%
7 280
 
3.8%
3 280
 
3.8%
5 280
 
3.8%
6 280
 
3.8%
4 280
 
3.8%
2 280
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13377
64.8%
ASCII 7280
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1960
26.9%
/ 1960
26.9%
0 1680
23.1%
7 280
 
3.8%
3 280
 
3.8%
5 280
 
3.8%
6 280
 
3.8%
4 280
 
3.8%
2 280
 
3.8%
Hangul
ValueCountFrequency (%)
대 1820
 
13.6%
구 812
 
6.1%
시 777
 
5.8%
경 714
 
5.3%
기 637
 
4.8%
군 609
 
4.6%
남 574
 
4.3%
이 567
 
4.2%
북 469
 
3.5%
전 350
 
2.6%
Other values (134) 6048
45.2%

2019
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct241
Distinct (%)12.4%
Missing14
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean68.326824
Minimum0
Maximum12645
Zeros167
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-03-23T14:39:26.077051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q343
95-th percentile175.25
Maximum12645
Range12645
Interquartile range (IQR)40

Descriptive statistics

Standard deviation458.22929
Coefficient of variation (CV)6.7064333
Kurtosis478.66451
Mean68.326824
Median Absolute Deviation (MAD)11
Skewness20.453997
Sum132964
Variance209974.08
MonotonicityNot monotonic
2024-03-23T14:39:26.335034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 189
 
9.6%
0 167
 
8.5%
2 107
 
5.5%
3 91
 
4.6%
4 75
 
3.8%
5 69
 
3.5%
7 58
 
3.0%
6 49
 
2.5%
8 49
 
2.5%
9 44
 
2.2%
Other values (231) 1048
53.5%
ValueCountFrequency (%)
0 167
8.5%
1 189
9.6%
2 107
5.5%
3 91
4.6%
4 75
 
3.8%
5 69
 
3.5%
6 49
 
2.5%
7 58
 
3.0%
8 49
 
2.5%
9 44
 
2.2%
ValueCountFrequency (%)
12645 1
0.1%
9983 1
0.1%
8799 1
0.1%
4607 1
0.1%
3532 1
0.1%
2643 1
0.1%
2379 1
0.1%
2041 1
0.1%
1755 1
0.1%
1700 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct330
Distinct (%)17.0%
Missing14
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean117.9851
Minimum0
Maximum20886
Zeros136
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-03-23T14:39:26.541854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median21
Q374.75
95-th percentile301
Maximum20886
Range20886
Interquartile range (IQR)70.75

Descriptive statistics

Standard deviation781.81745
Coefficient of variation (CV)6.6264085
Kurtosis446.15354
Mean117.9851
Median Absolute Deviation (MAD)20
Skewness19.733267
Sum229599
Variance611238.53
MonotonicityNot monotonic
2024-03-23T14:39:26.759912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 153
 
7.8%
0 136
 
6.9%
2 118
 
6.0%
3 69
 
3.5%
5 64
 
3.3%
4 51
 
2.6%
6 45
 
2.3%
12 33
 
1.7%
9 32
 
1.6%
8 28
 
1.4%
Other values (320) 1217
62.1%
ValueCountFrequency (%)
0 136
6.9%
1 153
7.8%
2 118
6.0%
3 69
3.5%
4 51
 
2.6%
5 64
3.3%
6 45
 
2.3%
7 26
 
1.3%
8 28
 
1.4%
9 32
 
1.6%
ValueCountFrequency (%)
20886 1
0.1%
17304 1
0.1%
14582 1
0.1%
8499 1
0.1%
6590 1
0.1%
5490 1
0.1%
4567 1
0.1%
3796 1
0.1%
3124 1
0.1%
3005 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct263
Distinct (%)13.5%
Missing7
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean78.777266
Minimum0
Maximum12895
Zeros164
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-03-23T14:39:27.006313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q351
95-th percentile208.2
Maximum12895
Range12895
Interquartile range (IQR)48

Descriptive statistics

Standard deviation507.68759
Coefficient of variation (CV)6.4445952
Kurtosis431.69739
Mean78.777266
Median Absolute Deviation (MAD)14
Skewness19.458978
Sum153852
Variance257746.68
MonotonicityNot monotonic
2024-03-23T14:39:27.330432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 177
 
9.0%
0 164
 
8.4%
2 110
 
5.6%
3 80
 
4.1%
6 58
 
3.0%
5 53
 
2.7%
4 48
 
2.4%
8 43
 
2.2%
9 39
 
2.0%
11 39
 
2.0%
Other values (253) 1142
58.3%
ValueCountFrequency (%)
0 164
8.4%
1 177
9.0%
2 110
5.6%
3 80
4.1%
4 48
 
2.4%
5 53
 
2.7%
6 58
 
3.0%
7 38
 
1.9%
8 43
 
2.2%
9 39
 
2.0%
ValueCountFrequency (%)
12895 1
0.1%
12029 1
0.1%
9230 1
0.1%
6066 1
0.1%
3718 1
0.1%
3667 1
0.1%
3091 1
0.1%
2669 1
0.1%
2481 1
0.1%
1876 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct174
Distinct (%)8.9%
Missing7
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean33.481311
Minimum0
Maximum5176
Zeros213
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-03-23T14:39:27.658712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q322
95-th percentile94.4
Maximum5176
Range5176
Interquartile range (IQR)20

Descriptive statistics

Standard deviation209.17345
Coefficient of variation (CV)6.2474688
Kurtosis420.09951
Mean33.481311
Median Absolute Deviation (MAD)6
Skewness19.328067
Sum65389
Variance43753.531
MonotonicityNot monotonic
2024-03-23T14:39:28.025218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 226
 
11.5%
0 213
 
10.9%
2 157
 
8.0%
3 110
 
5.6%
4 101
 
5.2%
5 80
 
4.1%
6 79
 
4.0%
7 61
 
3.1%
8 52
 
2.7%
9 47
 
2.4%
Other values (164) 827
42.2%
ValueCountFrequency (%)
0 213
10.9%
1 226
11.5%
2 157
8.0%
3 110
5.6%
4 101
5.2%
5 80
 
4.1%
6 79
 
4.0%
7 61
 
3.1%
8 52
 
2.7%
9 47
 
2.4%
ValueCountFrequency (%)
5176 1
0.1%
4742 1
0.1%
4266 1
0.1%
2767 1
0.1%
1373 1
0.1%
1162 1
0.1%
1114 1
0.1%
1027 1
0.1%
942 1
0.1%
791 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct205
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.60051
Minimum0
Maximum8319
Zeros222
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-03-23T14:39:28.375670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q330
95-th percentile130.1
Maximum8319
Range8319
Interquartile range (IQR)28

Descriptive statistics

Standard deviation333.25962
Coefficient of variation (CV)6.7188748
Kurtosis451.18184
Mean49.60051
Median Absolute Deviation (MAD)7
Skewness20.069845
Sum97217
Variance111061.97
MonotonicityNot monotonic
2024-03-23T14:39:28.731073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 233
 
11.9%
0 222
 
11.3%
2 141
 
7.2%
3 97
 
4.9%
4 78
 
4.0%
5 74
 
3.8%
6 51
 
2.6%
8 50
 
2.6%
7 46
 
2.3%
9 42
 
2.1%
Other values (195) 926
47.2%
ValueCountFrequency (%)
0 222
11.3%
1 233
11.9%
2 141
7.2%
3 97
4.9%
4 78
 
4.0%
5 74
 
3.8%
6 51
 
2.6%
7 46
 
2.3%
8 50
 
2.6%
9 42
 
2.1%
ValueCountFrequency (%)
8319 1
0.1%
8109 1
0.1%
6518 1
0.1%
3953 1
0.1%
2271 1
0.1%
2190 1
0.1%
1669 1
0.1%
1659 1
0.1%
1255 1
0.1%
962 1
0.1%

Interactions

2024-03-23T14:39:23.070260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:18.674651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:19.539429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:20.445746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:21.605601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:23.296919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:18.845035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:19.691483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:20.768685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:21.807032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:23.464098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:18.991988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:19.833931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:21.049770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:22.303128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:23.645497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:19.201545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:20.022331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:21.234141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:22.603539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:23.830041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:19.386578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:20.247218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:21.418919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:22.839673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:39:28.917824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20192020202120222023
20191.0000.9810.9410.9300.944
20200.9811.0000.9540.9470.985
20210.9410.9541.0000.9970.994
20220.9300.9470.9971.0000.994
20230.9440.9850.9940.9941.000
2024-03-23T14:39:29.132517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20192020202120222023
20191.0000.9710.9290.8820.932
20200.9711.0000.9460.8960.940
20210.9290.9461.0000.9540.951
20220.8820.8960.9541.0000.933
20230.9320.9400.9510.9331.000

Missing values

2024-03-23T14:39:24.080908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:39:24.303620image/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.
2024-03-23T14:39:24.503417image/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

지역 및 거래현황20192020202120222023
0전국 /20대이하15593005266911621255
1전국 /30대9983173041202947428109
2전국 /40대12645208861289551768319
3전국 /50대879914582923042666518
4전국 /60대46078499606627673953
5전국 /70대이상20413796309113731659
6전국 /기타175531241876942469
7서울 /20대이하1302061434371
8서울 /30대149221931223276814
9서울 /40대170021131069265826
지역 및 거래현황20192020202120222023
1950제주 제주시/60대1720231611
1951제주 제주시/70대이상581175
1952제주 제주시/기타78572
1953제주 서귀포시/20대이하23422
1954제주 서귀포시/30대815201211
1955제주 서귀포시/40대1120281513
1956제주 서귀포시/50대91520119
1957제주 서귀포시/60대681177
1958제주 서귀포시/70대이상24632
1959제주 서귀포시/기타36223