Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공(시도명,시군구명,자치단체코드,과세년도,법정동,법정리,특수지,본번,부번,동,호,물건지,시가표준액,연면적,기준일자)
URLhttps://www.data.go.kr/data/15080165/fileData.do

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
시군구명 is highly overall correlated with 법정동 and 1 other fieldsHigh correlation
자치단체코드 is highly overall correlated with 법정동 and 1 other fieldsHigh correlation
법정동 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 특수지High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly overall correlated with 본번High correlation
특수지 is highly imbalanced (96.4%)Imbalance
시가표준액 is highly skewed (γ1 = 21.22531423)Skewed
연면적 is highly skewed (γ1 = 22.333813)Skewed
부번 has 3589 (35.9%) zerosZeros
동 has 154 (1.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:40:21.558675
Analysis finished2023-12-12 02:40:27.899076
Duration6.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 10000
100.0%

Length

2023-12-12T11:40:27.971215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:28.075989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 10000
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시팔달구
3915 
수원시영통구
3887 
수원시권선구
2198 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수원시팔달구
2nd row수원시영통구
3rd row수원시팔달구
4th row수원시팔달구
5th row수원시영통구

Common Values

ValueCountFrequency (%)
수원시팔달구 3915
39.1%
수원시영통구 3887
38.9%
수원시권선구 2198
22.0%

Length

2023-12-12T11:40:28.188031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:28.296915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수원시팔달구 3915
39.1%
수원시영통구 3887
38.9%
수원시권선구 2198
22.0%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41115
3915 
41117
3887 
41113
2198 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41115
2nd row41117
3rd row41115
4th row41115
5th row41117

Common Values

ValueCountFrequency (%)
41115 3915
39.1%
41117 3887
38.9%
41113 2198
22.0%

Length

2023-12-12T11:40:28.443609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:28.563638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41115 3915
39.1%
41117 3887
38.9%
41113 2198
22.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-12T11:40:28.692689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:28.802751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.8697
Minimum101
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:28.928262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1104
median128
Q3135
95-th percentile141
Maximum141
Range40
Interquartile range (IQR)31

Descriptive statistics

Standard deviation15.542152
Coefficient of variation (CV)0.12753089
Kurtosis-1.6782371
Mean121.8697
Median Absolute Deviation (MAD)13
Skewness-0.24480052
Sum1218697
Variance241.55848
MonotonicityNot monotonic
2023-12-12T11:40:29.082011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
141 1278
12.8%
134 1049
10.5%
101 924
 
9.2%
135 921
 
9.2%
102 796
 
8.0%
128 646
 
6.5%
104 614
 
6.1%
105 541
 
5.4%
103 445
 
4.5%
106 326
 
3.3%
Other values (19) 2460
24.6%
ValueCountFrequency (%)
101 924
9.2%
102 796
8.0%
103 445
4.5%
104 614
6.1%
105 541
5.4%
106 326
 
3.3%
107 241
 
2.4%
120 44
 
0.4%
121 150
 
1.5%
122 99
 
1.0%
ValueCountFrequency (%)
141 1278
12.8%
140 119
 
1.2%
139 6
 
0.1%
138 242
 
2.4%
137 297
 
3.0%
136 109
 
1.1%
135 921
9.2%
134 1049
10.5%
133 139
 
1.4%
132 103
 
1.0%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T11:40:29.236776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:29.349552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9941 
2
 
42
7
 
17

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 9941
99.4%
2 42
 
0.4%
7 17
 
0.2%

Length

2023-12-12T11:40:29.483615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:40:29.595703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9941
99.4%
2 42
 
0.4%
7 17
 
0.2%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct830
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676.3297
Minimum1
Maximum7801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:29.740891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q1112
median718
Q31118
95-th percentile1375
Maximum7801
Range7800
Interquartile range (IQR)1006

Descriptive statistics

Standard deviation543.42944
Coefficient of variation (CV)0.80349781
Kurtosis20.483985
Mean676.3297
Median Absolute Deviation (MAD)430.5
Skewness1.9276847
Sum6763297
Variance295315.55
MonotonicityNot monotonic
2023-12-12T11:40:29.926855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016 303
 
3.0%
627 219
 
2.2%
94 189
 
1.9%
1191 187
 
1.9%
989 143
 
1.4%
1352 131
 
1.3%
27 129
 
1.3%
40 129
 
1.3%
1118 122
 
1.2%
986 118
 
1.2%
Other values (820) 8330
83.3%
ValueCountFrequency (%)
1 23
 
0.2%
2 7
 
0.1%
3 29
 
0.3%
4 15
 
0.1%
5 38
 
0.4%
6 21
 
0.2%
7 28
 
0.3%
8 54
0.5%
9 15
 
0.1%
10 99
1.0%
ValueCountFrequency (%)
7801 4
 
< 0.1%
6101 8
 
0.1%
5602 1
 
< 0.1%
5301 4
 
< 0.1%
1419 1
 
< 0.1%
1412 2
 
< 0.1%
1411 1
 
< 0.1%
1408 3
 
< 0.1%
1407 1
 
< 0.1%
1406 82
0.8%

부번
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5519
Minimum0
Maximum745
Zeros3589
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:30.076476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile31
Maximum745
Range745
Interquartile range (IQR)6

Descriptive statistics

Standard deviation67.504816
Coefficient of variation (CV)5.3780556
Kurtosis102.74201
Mean12.5519
Median Absolute Deviation (MAD)2
Skewness9.9455747
Sum125519
Variance4556.9002
MonotonicityNot monotonic
2023-12-12T11:40:30.228169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3589
35.9%
1 1383
 
13.8%
2 982
 
9.8%
3 706
 
7.1%
4 473
 
4.7%
6 377
 
3.8%
5 337
 
3.4%
7 198
 
2.0%
11 192
 
1.9%
8 188
 
1.9%
Other values (150) 1575
15.8%
ValueCountFrequency (%)
0 3589
35.9%
1 1383
 
13.8%
2 982
 
9.8%
3 706
 
7.1%
4 473
 
4.7%
5 337
 
3.4%
6 377
 
3.8%
7 198
 
2.0%
8 188
 
1.9%
9 160
 
1.6%
ValueCountFrequency (%)
745 10
 
0.1%
741 46
0.5%
736 18
 
0.2%
705 1
 
< 0.1%
699 1
 
< 0.1%
692 1
 
< 0.1%
668 2
 
< 0.1%
443 2
 
< 0.1%
397 2
 
< 0.1%
264 1
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.174
Minimum0
Maximum9905
Zeros154
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:30.397492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile102
Maximum9905
Range9905
Interquartile range (IQR)0

Descriptive statistics

Standard deviation989.24933
Coefficient of variation (CV)8.0313161
Kurtosis79.847203
Mean123.174
Median Absolute Deviation (MAD)0
Skewness8.999506
Sum1231740
Variance978614.24
MonotonicityNot monotonic
2023-12-12T11:40:30.569857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8330
83.3%
2 463
 
4.6%
0 154
 
1.5%
3 135
 
1.4%
101 122
 
1.2%
102 98
 
1.0%
103 79
 
0.8%
800 42
 
0.4%
99 37
 
0.4%
203 34
 
0.3%
Other values (124) 506
 
5.1%
ValueCountFrequency (%)
0 154
 
1.5%
1 8330
83.3%
2 463
 
4.6%
3 135
 
1.4%
4 18
 
0.2%
5 30
 
0.3%
6 13
 
0.1%
7 7
 
0.1%
8 11
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
9905 2
 
< 0.1%
9904 2
 
< 0.1%
9903 8
0.1%
9902 4
 
< 0.1%
9901 16
0.2%
9009 2
 
< 0.1%
9008 1
 
< 0.1%
9007 4
 
< 0.1%
9005 3
 
< 0.1%
9004 2
 
< 0.1%

호
Text

Distinct1786
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:40:30.952095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.0924
Min length4

Characters and Unicode

Total characters40924
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1126 ?
Unique (%)11.3%

Sample

1st row0322
2nd row0202
3rd row0702
4th row1010
5th row0110
ValueCountFrequency (%)
0101 999
 
10.0%
0201 559
 
5.6%
0102 388
 
3.9%
0301 379
 
3.8%
8101 359
 
3.6%
0401 240
 
2.4%
0103 215
 
2.1%
0202 161
 
1.6%
0501 157
 
1.6%
0001 135
 
1.4%
Other values (1776) 6408
64.1%
2023-12-12T11:40:31.472816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16390
40.0%
1 9648
23.6%
2 3855
 
9.4%
3 2398
 
5.9%
4 1814
 
4.4%
8 1753
 
4.3%
5 1402
 
3.4%
6 1169
 
2.9%
7 980
 
2.4%
9 778
 
1.9%
Other values (9) 737
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40187
98.2%
Uppercase Letter 606
 
1.5%
Dash Punctuation 115
 
0.3%
Lowercase Letter 16
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16390
40.8%
1 9648
24.0%
2 3855
 
9.6%
3 2398
 
6.0%
4 1814
 
4.5%
8 1753
 
4.4%
5 1402
 
3.5%
6 1169
 
2.9%
7 980
 
2.4%
9 778
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
A 313
51.7%
C 129
21.3%
B 106
 
17.5%
D 30
 
5.0%
L 13
 
2.1%
E 10
 
1.7%
G 5
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40302
98.5%
Latin 622
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16390
40.7%
1 9648
23.9%
2 3855
 
9.6%
3 2398
 
6.0%
4 1814
 
4.5%
8 1753
 
4.3%
5 1402
 
3.5%
6 1169
 
2.9%
7 980
 
2.4%
9 778
 
1.9%
Latin
ValueCountFrequency (%)
A 313
50.3%
C 129
20.7%
B 106
 
17.0%
D 30
 
4.8%
b 16
 
2.6%
L 13
 
2.1%
E 10
 
1.6%
G 5
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16390
40.0%
1 9648
23.6%
2 3855
 
9.4%
3 2398
 
5.9%
4 1814
 
4.4%
8 1753
 
4.3%
5 1402
 
3.4%
6 1169
 
2.9%
7 980
 
2.4%
9 778
 
1.9%
Other values (9) 737
 
1.8%

물건지
Text

Distinct9607
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:40:31.832220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length26.2887
Min length20

Characters and Unicode

Total characters262887
Distinct characters135
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9261 ?
Unique (%)92.6%

Sample

1st row[ 인계로166번길 48-21 ] 0001동 0322호
2nd row경기도 수원시영통구 매탄동 520-1 1동 202호
3rd row[ 갓매산로 51 ] 0001동 0702호
4th row[ 매산로 24 ] 0001동 1010호
5th row[ 매탄로108번길 16 ] 0001동 0110호
ValueCountFrequency (%)
14404
24.0%
0001동 6644
 
11.1%
경기도 2798
 
4.7%
1동 1686
 
2.8%
수원시영통구 1344
 
2.2%
수원시팔달구 1058
 
1.8%
0101호 742
 
1.2%
0201호 419
 
0.7%
수원시권선구 396
 
0.7%
8101호 359
 
0.6%
Other values (4163) 30167
50.3%
2023-12-12T11:40:32.340805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50017
19.0%
0 40192
15.3%
1 26970
 
10.3%
동 12827
 
4.9%
호 10526
 
4.0%
2 9603
 
3.7%
로 7410
 
2.8%
[ 7202
 
2.7%
] 7202
 
2.7%
3 6080
 
2.3%
Other values (125) 84858
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110048
41.9%
Other Letter 84830
32.3%
Space Separator 50017
19.0%
Open Punctuation 7202
 
2.7%
Close Punctuation 7202
 
2.7%
Dash Punctuation 2966
 
1.1%
Uppercase Letter 606
 
0.2%
Lowercase Letter 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12827
15.1%
호 10526
 
12.4%
로 7410
 
8.7%
원 3856
 
4.5%
번 3621
 
4.3%
길 3621
 
4.3%
수 3186
 
3.8%
경 2903
 
3.4%
구 2855
 
3.4%
도 2799
 
3.3%
Other values (103) 31226
36.8%
Decimal Number
ValueCountFrequency (%)
0 40192
36.5%
1 26970
24.5%
2 9603
 
8.7%
3 6080
 
5.5%
4 5451
 
5.0%
5 5043
 
4.6%
8 4768
 
4.3%
6 4197
 
3.8%
9 3930
 
3.6%
7 3814
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A 313
51.7%
C 129
21.3%
B 106
 
17.5%
D 30
 
5.0%
L 13
 
2.1%
E 10
 
1.7%
G 5
 
0.8%
Space Separator
ValueCountFrequency (%)
50017
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7202
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7202
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2966
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177435
67.5%
Hangul 84830
32.3%
Latin 622
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12827
15.1%
호 10526
 
12.4%
로 7410
 
8.7%
원 3856
 
4.5%
번 3621
 
4.3%
길 3621
 
4.3%
수 3186
 
3.8%
경 2903
 
3.4%
구 2855
 
3.4%
도 2799
 
3.3%
Other values (103) 31226
36.8%
Common
ValueCountFrequency (%)
50017
28.2%
0 40192
22.7%
1 26970
15.2%
2 9603
 
5.4%
[ 7202
 
4.1%
] 7202
 
4.1%
3 6080
 
3.4%
4 5451
 
3.1%
5 5043
 
2.8%
8 4768
 
2.7%
Other values (4) 14907
 
8.4%
Latin
ValueCountFrequency (%)
A 313
50.3%
C 129
20.7%
B 106
 
17.0%
D 30
 
4.8%
b 16
 
2.6%
L 13
 
2.1%
E 10
 
1.6%
G 5
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178057
67.7%
Hangul 84830
32.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50017
28.1%
0 40192
22.6%
1 26970
15.1%
2 9603
 
5.4%
[ 7202
 
4.0%
] 7202
 
4.0%
3 6080
 
3.4%
4 5451
 
3.1%
5 5043
 
2.8%
8 4768
 
2.7%
Other values (12) 15529
 
8.7%
Hangul
ValueCountFrequency (%)
동 12827
15.1%
호 10526
 
12.4%
로 7410
 
8.7%
원 3856
 
4.5%
번 3621
 
4.3%
길 3621
 
4.3%
수 3186
 
3.8%
경 2903
 
3.4%
구 2855
 
3.4%
도 2799
 
3.3%
Other values (103) 31226
36.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7655
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0185835 × 108
Minimum26240
Maximum1.8855111 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:32.501379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26240
5-th percentile2296314
Q113706280
median36937700
Q389510795
95-th percentile2.856461 × 108
Maximum1.8855111 × 1010
Range1.8855085 × 1010
Interquartile range (IQR)75804515

Descriptive statistics

Standard deviation3.9886779 × 108
Coefficient of variation (CV)3.9159068
Kurtosis695.60423
Mean1.0185835 × 108
Median Absolute Deviation (MAD)28925815
Skewness21.225314
Sum1.0185835 × 1012
Variance1.5909551 × 1017
MonotonicityNot monotonic
2023-12-12T11:40:32.658043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96340040 45
 
0.4%
23020350 43
 
0.4%
31797960 37
 
0.4%
31661760 37
 
0.4%
63197140 30
 
0.3%
49599930 29
 
0.3%
27030000 25
 
0.2%
36177980 24
 
0.2%
13706280 24
 
0.2%
32470000 24
 
0.2%
Other values (7645) 9682
96.8%
ValueCountFrequency (%)
26240 1
< 0.1%
38610 1
< 0.1%
42640 1
< 0.1%
50220 1
< 0.1%
95880 1
< 0.1%
97980 1
< 0.1%
99660 1
< 0.1%
110160 1
< 0.1%
115320 1
< 0.1%
123200 1
< 0.1%
ValueCountFrequency (%)
18855110800 1
< 0.1%
10948398720 1
< 0.1%
10350522100 1
< 0.1%
8734208340 1
< 0.1%
7850817890 1
< 0.1%
7642476490 1
< 0.1%
7629909920 1
< 0.1%
7383520610 1
< 0.1%
7345622970 1
< 0.1%
5308275420 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6699
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.83139
Minimum0.27
Maximum29250.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:40:32.833102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile7.15697
Q128.4613
median59.98
Q3128.7228
95-th percentile514.32112
Maximum29250.87
Range29250.6
Interquartile range (IQR)100.2615

Descriptive statistics

Standard deviation646.4906
Coefficient of variation (CV)3.9221329
Kurtosis749.01549
Mean164.83139
Median Absolute Deviation (MAD)39.33
Skewness22.333813
Sum1648313.9
Variance417950.1
MonotonicityNot monotonic
2023-12-12T11:40:33.007431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.3198 51
 
0.5%
28.4613 47
 
0.5%
18.0 46
 
0.5%
28.29 38
 
0.4%
46.02 38
 
0.4%
66.45 30
 
0.3%
54.62 29
 
0.3%
27.0 27
 
0.3%
31.8 26
 
0.3%
38.2 25
 
0.2%
Other values (6689) 9643
96.4%
ValueCountFrequency (%)
0.27 1
 
< 0.1%
0.41 1
 
< 0.1%
0.52 1
 
< 0.1%
0.53 1
 
< 0.1%
0.55 1
 
< 0.1%
0.58 1
 
< 0.1%
0.61 1
 
< 0.1%
0.67 3
< 0.1%
0.81 2
< 0.1%
0.9359 1
 
< 0.1%
ValueCountFrequency (%)
29250.87 1
< 0.1%
23195.76 1
< 0.1%
20359.46 1
< 0.1%
14930.128 1
< 0.1%
13432.94 1
< 0.1%
12369.78 1
< 0.1%
9252.5321 1
< 0.1%
8960.8968 1
< 0.1%
8414.5797 1
< 0.1%
8370.73 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-06-01 00:00:00
Maximum2022-06-01 00:00:00
2023-12-12T11:40:33.145253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:33.255254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T11:40:26.716965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.089079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.686726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.320376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.153329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.894055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.857700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.190817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.785254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.504247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.279069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.035822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.991150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.288252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.881660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.649000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.396935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.180886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:27.109113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.386290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.993692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.769460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.501325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.323505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:27.226153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.473232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.087714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.890656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.619475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.474266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:27.344511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:23.595342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:24.196095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.012103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:25.751295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:40:26.591171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:40:33.367714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드법정동특수지본번부번동시가표준액연면적
시군구명1.0001.0000.8630.2310.3940.1720.1140.0470.077
자치단체코드1.0001.0000.8630.2310.3940.1720.1140.0470.077
법정동0.8630.8631.0000.1320.2900.3160.0560.0470.045
특수지0.2310.2310.1321.0000.7190.0000.3200.0000.000
본번0.3940.3940.2900.7191.0000.1190.4380.0000.000
부번0.1720.1720.3160.0000.1191.0000.0000.0000.000
동0.1140.1140.0560.3200.4380.0001.0000.0000.000
시가표준액0.0470.0470.0470.0000.0000.0000.0001.0000.906
연면적0.0770.0770.0450.0000.0000.0000.0000.9061.000
2023-12-12T11:40:33.533210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명특수지자치단체코드
시군구명1.0000.0741.000
특수지0.0741.0000.074
자치단체코드1.0000.0741.000
2023-12-12T11:40:33.650593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적시군구명자치단체코드특수지
법정동1.0000.0580.164-0.008-0.063-0.0720.8220.8220.077
본번0.0581.000-0.3260.1130.1860.0320.3250.3250.708
부번0.164-0.3261.000-0.212-0.0270.0710.1170.1170.000
동-0.0080.113-0.2121.000-0.022-0.0370.0850.0850.255
시가표준액-0.0630.186-0.027-0.0221.0000.8890.0310.0310.000
연면적-0.0720.0320.071-0.0370.8891.0000.0340.0340.000
시군구명0.8220.3250.1170.0850.0310.0341.0001.0000.074
자치단체코드0.8220.3250.1170.0850.0310.0341.0001.0000.074
특수지0.0770.7080.0000.2550.0000.0000.0740.0741.000

Missing values

2023-12-12T11:40:27.525345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:40:27.790064image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
10050경기도수원시팔달구411152022141011119010322[ 인계로166번길 48-21 ] 0001동 0322호2830278040.262022-06-01
58681경기도수원시영통구41117202210101520110202경기도 수원시영통구 매탄동 520-1 1동 202호156095540699.982022-06-01
71230경기도수원시팔달구4111520221350127010702[ 갓매산로 51 ] 0001동 0702호315752830500.482022-06-01
76260경기도수원시팔달구4111520221350165311010[ 매산로 24 ] 0001동 1010호3234285026.1042022-06-01
34418경기도수원시영통구411172022101011262210110[ 매탄로108번길 16 ] 0001동 0110호1083267036.262022-06-01
51069경기도수원시영통구41117202210101494710102[ 신원로 244 ] 0001동 0102호1980004.42022-06-01
3160경기도수원시영통구4111720221040110160181113[ 광교중앙로 170 ] 0001동 81113호6939083076.7982022-06-01
5496경기도수원시팔달구4111520221410111221110207[ 효원로 299 ] 0001동 0207호2343062047.042022-06-01
16561경기도수원시팔달구411152022140015621010401[ 경수대로 600 ] 0001동 0401호50906600151.962022-06-01
21841경기도수원시영통구411172022102012241400401[ 삼성로267번길 32 ] 0000동 0401호138223800178.35332022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
30616경기도수원시영통구411172022101011266110503[ 매탄로108번길 38 ] 0001동 0503호61477650100.752022-06-01
53113경기도수원시팔달구4111520221260110241818052경기도 수원시팔달구 구천동 10-24 1동 818052호1836987018.172022-06-01
7495경기도수원시팔달구4111520221410111221111802[ 효원로 299 ] 0001동 1802호5967428057.882022-06-01
46359경기도수원시권선구411132022135011406210112[ 호매실로104번길 24-10 ] 0001동 0112호1632248025.2672022-06-01
15400경기도수원시영통구4111720221030113320203B227경기도 수원시영통구 이의동 1332 203동 B227호155989410139.0282022-06-01
55664경기도수원시영통구41117202210101416010104경기도 수원시영통구 매탄동 416 1동 104호2142367401015.342022-06-01
329경기도수원시영통구411172022104011016010302[ 광교중앙로 170 ] 0001동 0302호7993760075.22022-06-01
18132경기도수원시팔달구41115202214101960910101경기도 수원시팔달구 인계동 960-9 1동 101호16563400138.492022-06-01
15249경기도수원시팔달구41115202214101954110501[ 경수대로565번길 21 ] 0001동 0501호8910782098.23462022-06-01
70979경기도수원시영통구41117202210501978710502[ 영통로 215 ] 0001동 0502호85727600159.82032022-06-01