Overview

Dataset statistics

Number of variables7
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory68.0 B

Variable types

Text1
Numeric6

Dataset

Description공사가 보유하고 있는 아파트 자산의 내역입니다. 지역별 , 재산세부과대상(호), 취득가액(토지)(억), 취득가액(건물)(억) , 장부가액(토지)(억), 장부가액(건물)(억),공시가격(억) 으로 구성되어 있습니다
Author서울주택도시공사
URLhttps://www.data.go.kr/data/15105564/fileData.do

Alerts

재산세 부과대상 (호) is highly overall correlated with 취득가액(토지)(억) and 4 other fieldsHigh correlation
취득가액(토지)(억) is highly overall correlated with 재산세 부과대상 (호) and 4 other fieldsHigh correlation
취득가액(건물)(억) is highly overall correlated with 재산세 부과대상 (호) and 4 other fieldsHigh correlation
장부가액(토지)(억) is highly overall correlated with 재산세 부과대상 (호) and 4 other fieldsHigh correlation
장부가액(건물)(억) is highly overall correlated with 재산세 부과대상 (호) and 4 other fieldsHigh correlation
공시가격(억) is highly overall correlated with 재산세 부과대상 (호) and 4 other fieldsHigh correlation
지역별 has unique valuesUnique
재산세 부과대상 (호) has unique valuesUnique
취득가액(건물)(억) has unique valuesUnique
공시가격(억) has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:55:22.147648
Analysis finished2023-12-12 00:55:26.462013
Duration4.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T09:55:26.580265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1363636
Min length5

Characters and Unicode

Total characters113
Distinct characters38
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row 강남구
2nd row 강동구
3rd row 서초구
4th row 송파구
5th row 강북구
ValueCountFrequency (%)
강남구 1
 
4.5%
강동구 1
 
4.5%
중랑구 1
 
4.5%
은평구 1
 
4.5%
영등포구 1
 
4.5%
양천구 1
 
4.5%
성북구 1
 
4.5%
성동구 1
 
4.5%
마포구 1
 
4.5%
동작구 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T09:55:26.861577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
38.9%
구 22
19.5%
동 4
 
3.5%
강 4
 
3.5%
서 2
 
1.8%
포 2
 
1.8%
성 2
 
1.8%
북 2
 
1.8%
천 2
 
1.8%
등 1
 
0.9%
Other values (28) 28
24.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
61.1%
Space Separator 44
38.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 22
31.9%
동 4
 
5.8%
강 4
 
5.8%
서 2
 
2.9%
포 2
 
2.9%
성 2
 
2.9%
북 2
 
2.9%
천 2
 
2.9%
등 1
 
1.4%
마 1
 
1.4%
Other values (27) 27
39.1%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
61.1%
Common 44
38.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 22
31.9%
동 4
 
5.8%
강 4
 
5.8%
서 2
 
2.9%
포 2
 
2.9%
성 2
 
2.9%
북 2
 
2.9%
천 2
 
2.9%
등 1
 
1.4%
마 1
 
1.4%
Other values (27) 27
39.1%
Common
ValueCountFrequency (%)
44
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
61.1%
ASCII 44
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
100.0%
Hangul
ValueCountFrequency (%)
구 22
31.9%
동 4
 
5.8%
강 4
 
5.8%
서 2
 
2.9%
포 2
 
2.9%
성 2
 
2.9%
북 2
 
2.9%
천 2
 
2.9%
등 1
 
1.4%
마 1
 
1.4%
Other values (27) 27
39.1%

재산세 부과대상 (호)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4636.2727
Minimum3
Maximum18771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:27.001605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13.25
Q1380.25
median3242
Q37332.5
95-th percentile11583.25
Maximum18771
Range18768
Interquartile range (IQR)6952.25

Descriptive statistics

Standard deviation5189.7006
Coefficient of variation (CV)1.1193691
Kurtosis0.83869985
Mean4636.2727
Median Absolute Deviation (MAD)3132
Skewness1.0599867
Sum101998
Variance26932992
MonotonicityNot monotonic
2023-12-12T09:55:27.116846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
8749 1
 
4.5%
13 1
 
4.5%
628 1
 
4.5%
6971 1
 
4.5%
5952 1
 
4.5%
381 1
 
4.5%
7453 1
 
4.5%
444 1
 
4.5%
471 1
 
4.5%
5705 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
3 1
4.5%
13 1
4.5%
18 1
4.5%
114 1
4.5%
170 1
4.5%
380 1
4.5%
381 1
4.5%
444 1
4.5%
471 1
4.5%
628 1
4.5%
ValueCountFrequency (%)
18771 1
4.5%
11595 1
4.5%
11360 1
4.5%
9939 1
4.5%
8749 1
4.5%
7453 1
4.5%
6971 1
4.5%
6378 1
4.5%
5952 1
4.5%
5724 1
4.5%

취득가액(토지)(억)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3189.7727
Minimum5
Maximum12226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:27.253398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20.9
Q178
median1217
Q36809.75
95-th percentile9475.6
Maximum12226
Range12221
Interquartile range (IQR)6731.75

Descriptive statistics

Standard deviation3893.4819
Coefficient of variation (CV)1.2206142
Kurtosis-0.3669635
Mean3189.7727
Median Absolute Deviation (MAD)1188
Skewness0.99223513
Sum70175
Variance15159201
MonotonicityNot monotonic
2023-12-12T09:55:27.377674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
78 2
 
9.1%
7225 1
 
4.5%
49 1
 
4.5%
739 1
 
4.5%
3072 1
 
4.5%
7857 1
 
4.5%
184 1
 
4.5%
1695 1
 
4.5%
383 1
 
4.5%
459 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
5 1
4.5%
20 1
4.5%
38 1
4.5%
39 1
4.5%
49 1
4.5%
78 2
9.1%
184 1
4.5%
383 1
4.5%
459 1
4.5%
739 1
4.5%
ValueCountFrequency (%)
12226 1
4.5%
9510 1
4.5%
8822 1
4.5%
7857 1
4.5%
7417 1
4.5%
7225 1
4.5%
5564 1
4.5%
3072 1
4.5%
2639 1
4.5%
2076 1
4.5%

취득가액(건물)(억)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4057
Minimum6
Maximum13591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:27.506811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19.25
Q1151.75
median2115.5
Q37370.5
95-th percentile12752.2
Maximum13591
Range13585
Interquartile range (IQR)7218.75

Descriptive statistics

Standard deviation4643.5638
Coefficient of variation (CV)1.1445807
Kurtosis-0.65515183
Mean4057
Median Absolute Deviation (MAD)2094
Skewness0.82769631
Sum89254
Variance21562685
MonotonicityNot monotonic
2023-12-12T09:55:27.626687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5911 1
 
4.5%
24 1
 
4.5%
594 1
 
4.5%
5111 1
 
4.5%
8117 1
 
4.5%
305 1
 
4.5%
5132 1
 
4.5%
417 1
 
4.5%
423 1
 
4.5%
3822 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
6 1
4.5%
19 1
4.5%
24 1
4.5%
38 1
4.5%
46 1
4.5%
145 1
4.5%
172 1
4.5%
305 1
4.5%
417 1
4.5%
423 1
4.5%
ValueCountFrequency (%)
13591 1
4.5%
12863 1
4.5%
10647 1
4.5%
10377 1
4.5%
8117 1
4.5%
7857 1
4.5%
5911 1
4.5%
5132 1
4.5%
5111 1
4.5%
3822 1
4.5%

장부가액(토지)(억)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3189.7727
Minimum5
Maximum12226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:27.746697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20.9
Q178
median1217
Q36809.75
95-th percentile9475.6
Maximum12226
Range12221
Interquartile range (IQR)6731.75

Descriptive statistics

Standard deviation3893.4819
Coefficient of variation (CV)1.2206142
Kurtosis-0.3669635
Mean3189.7727
Median Absolute Deviation (MAD)1188
Skewness0.99223513
Sum70175
Variance15159201
MonotonicityNot monotonic
2023-12-12T09:55:27.875089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
78 2
 
9.1%
7225 1
 
4.5%
49 1
 
4.5%
739 1
 
4.5%
3072 1
 
4.5%
7857 1
 
4.5%
184 1
 
4.5%
1695 1
 
4.5%
383 1
 
4.5%
459 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
5 1
4.5%
20 1
4.5%
38 1
4.5%
39 1
4.5%
49 1
4.5%
78 2
9.1%
184 1
4.5%
383 1
4.5%
459 1
4.5%
739 1
4.5%
ValueCountFrequency (%)
12226 1
4.5%
9510 1
4.5%
8822 1
4.5%
7857 1
4.5%
7417 1
4.5%
7225 1
4.5%
5564 1
4.5%
3072 1
4.5%
2639 1
4.5%
2076 1
4.5%

장부가액(건물)(억)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2669.9545
Minimum3
Maximum10146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:27.990346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10.15
Q135.25
median745.5
Q34531
95-th percentile8683.35
Maximum10146
Range10143
Interquartile range (IQR)4495.75

Descriptive statistics

Standard deviation3330.1552
Coefficient of variation (CV)1.2472704
Kurtosis-0.25698876
Mean2669.9545
Median Absolute Deviation (MAD)739
Skewness1.0370116
Sum58739
Variance11089934
MonotonicityNot monotonic
2023-12-12T09:55:28.109049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15 2
 
9.1%
3607 1
 
4.5%
10 1
 
4.5%
347 1
 
4.5%
3248 1
 
4.5%
4839 1
 
4.5%
129 1
 
4.5%
2764 1
 
4.5%
293 1
 
4.5%
226 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
3 1
4.5%
10 1
4.5%
13 1
4.5%
15 2
9.1%
33 1
4.5%
42 1
4.5%
129 1
4.5%
226 1
4.5%
293 1
4.5%
347 1
4.5%
ValueCountFrequency (%)
10146 1
4.5%
8730 1
4.5%
7797 1
4.5%
7264 1
4.5%
6063 1
4.5%
4839 1
4.5%
3607 1
4.5%
3248 1
4.5%
2764 1
4.5%
2011 1
4.5%

공시가격(억)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15792.136
Minimum12
Maximum58940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:55:28.218112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile36.5
Q1593
median9029
Q321131.25
95-th percentile47295.65
Maximum58940
Range58928
Interquartile range (IQR)20538.25

Descriptive statistics

Standard deviation18943.31
Coefficient of variation (CV)1.1995407
Kurtosis-0.22041977
Mean15792.136
Median Absolute Deviation (MAD)8892.5
Skewness1.0078846
Sum347427
Variance3.5884898 × 108
MonotonicityNot monotonic
2023-12-12T09:55:28.578929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
36748 1
 
4.5%
65 1
 
4.5%
1854 1
 
4.5%
16204 1
 
4.5%
22122 1
 
4.5%
1331 1
 
4.5%
17186 1
 
4.5%
776 1
 
4.5%
1325 1
 
4.5%
16452 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
12 1
4.5%
35 1
4.5%
65 1
4.5%
200 1
4.5%
287 1
4.5%
561 1
4.5%
689 1
4.5%
776 1
4.5%
1325 1
4.5%
1331 1
4.5%
ValueCountFrequency (%)
58940 1
4.5%
47378 1
4.5%
45731 1
4.5%
43387 1
4.5%
36748 1
4.5%
22122 1
4.5%
18159 1
4.5%
17985 1
4.5%
17186 1
4.5%
16452 1
4.5%

Interactions

2023-12-12T09:55:25.668004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.423676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.011395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.649792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.329333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.010053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.776864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.561689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.103960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.750528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.456865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.136492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.891473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.659187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.223002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.885556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.583025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.238168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.997479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.746733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.352574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.010653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.689366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.348742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:26.086634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.833069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.467598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.114527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.798584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.460201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:26.164578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:22.916718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:23.551033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.209319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:24.909936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:25.568807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:55:28.656780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역별재산세 부과대상 (호)취득가액(토지)(억)취득가액(건물)(억)장부가액(토지)(억)장부가액(건물)(억)공시가격(억)
지역별1.0001.0001.0001.0001.0001.0001.000
재산세 부과대상 (호)1.0001.0000.9100.8660.9100.9100.925
취득가액(토지)(억)1.0000.9101.0000.9181.0000.9500.949
취득가액(건물)(억)1.0000.8660.9181.0000.9180.9540.989
장부가액(토지)(억)1.0000.9101.0000.9181.0000.9500.949
장부가액(건물)(억)1.0000.9100.9500.9540.9501.0000.939
공시가격(억)1.0000.9250.9490.9890.9490.9391.000
2023-12-12T09:55:28.768704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재산세 부과대상 (호)취득가액(토지)(억)취득가액(건물)(억)장부가액(토지)(억)장부가액(건물)(억)공시가격(억)
재산세 부과대상 (호)1.0000.8860.8890.8860.8830.922
취득가액(토지)(억)0.8861.0000.9831.0000.9690.976
취득가액(건물)(억)0.8890.9831.0000.9830.9840.983
장부가액(토지)(억)0.8861.0000.9831.0000.9690.976
장부가액(건물)(억)0.8830.9690.9840.9691.0000.968
공시가격(억)0.9220.9760.9830.9760.9681.000

Missing values

2023-12-12T09:55:26.285480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:55:26.409900image/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

지역별재산세 부과대상 (호)취득가액(토지)(억)취득가액(건물)(억)장부가액(토지)(억)장부가액(건물)(억)공시가격(억)
0강남구8749722559117225360736748
1강동구1136074171286374171014647378
2서초구57248822106478822726443387
3송파구99399510103779510779745731
4강북구183819381535
5강서구18771122261359112226873058940
6관악구380781727842561
7광진구3565312
8구로구6378556478575564606318159
9금천구11439383913200
지역별재산세 부과대상 (호)취득가액(토지)(억)취득가액(건물)(억)장부가액(토지)(억)장부가액(건물)(억)공시가격(억)
12동대문구132024201565
13동작구17049464910287
14마포구5705263938222639201116452
15성동구4714594234592261325
16성북구444383417383293776
17양천구7453169551321695276417186
18영등포구3811843051841291331
19은평구5952785781177857483922122
20중랑구6971307251113072324816204
21의정부시6287395947393471854