Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하는 데이터로, 일반건축물의 물건별 재산가액을 확인 할 수 있습니다.
URLhttps://www.data.go.kr/data/15080090/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 13 (0.1%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적(제곱미터)High correlation
연면적(제곱미터) is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (85.9%)Imbalance
연면적(제곱미터) is highly skewed (γ1 = 21.7648273)Skewed
법정리 has 4315 (43.1%) zerosZeros
부번 has 3627 (36.3%) zerosZeros
has 304 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-12 23:26:26.001800
Analysis finished2023-12-12 23:26:34.451056
Duration8.45 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
제주특별자치도
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 10000
100.0%

Length

2023-12-13T08:26:34.510402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:26:34.613302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서귀포시 10000
100.0%

Length

2023-12-13T08:26:34.702057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:26:34.786629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서귀포시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
50130
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50130
2nd row50130
3rd row50130
4th row50130
5th row50130

Common Values

ValueCountFrequency (%)
50130 10000
100.0%

Length

2023-12-13T08:26:34.870423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:26:34.949839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50130 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2023-12-13T08:26:35.038150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:26:35.119369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.5291
Minimum101
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:35.209720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1109
median250
Q3259
95-th percentile320
Maximum320
Range219
Interquartile range (IQR)150

Descriptive statistics

Standard deviation85.225065
Coefficient of variation (CV)0.42080405
Kurtosis-1.7038953
Mean202.5291
Median Absolute Deviation (MAD)70
Skewness-0.076055899
Sum2025291
Variance7263.3117
MonotonicityNot monotonic
2023-12-13T08:26:35.321744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
253 1320
13.2%
259 1271
12.7%
250 1209
12.1%
101 1039
10.4%
310 973
9.7%
320 912
9.1%
116 412
 
4.1%
105 406
 
4.1%
112 328
 
3.3%
119 253
 
2.5%
Other values (17) 1877
18.8%
ValueCountFrequency (%)
101 1039
10.4%
102 241
 
2.4%
103 240
 
2.4%
104 121
 
1.2%
105 406
 
4.1%
106 249
 
2.5%
107 106
 
1.1%
108 73
 
0.7%
109 85
 
0.9%
110 54
 
0.5%
ValueCountFrequency (%)
320 912
9.1%
310 973
9.7%
259 1271
12.7%
253 1320
13.2%
250 1209
12.1%
122 4
 
< 0.1%
121 61
 
0.6%
120 100
 
1.0%
119 253
 
2.5%
118 93
 
0.9%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.2381
Minimum0
Maximum33
Zeros4315
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:35.432946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q325
95-th percentile29
Maximum33
Range33
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.622074
Coefficient of variation (CV)0.88649985
Kurtosis-1.8296534
Mean14.2381
Median Absolute Deviation (MAD)8
Skewness-0.17166901
Sum142381
Variance159.31674
MonotonicityNot monotonic
2023-12-13T08:26:35.562495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 4315
43.1%
21 976
 
9.8%
27 768
 
7.7%
24 676
 
6.8%
22 616
 
6.2%
25 567
 
5.7%
26 513
 
5.1%
29 399
 
4.0%
23 389
 
3.9%
28 308
 
3.1%
Other values (4) 473
 
4.7%
ValueCountFrequency (%)
0 4315
43.1%
21 976
 
9.8%
22 616
 
6.2%
23 389
 
3.9%
24 676
 
6.8%
25 567
 
5.7%
26 513
 
5.1%
27 768
 
7.7%
28 308
 
3.1%
29 399
 
4.0%
ValueCountFrequency (%)
33 70
 
0.7%
32 66
 
0.7%
31 186
 
1.9%
30 151
 
1.5%
29 399
4.0%
28 308
3.1%
27 768
7.7%
26 513
5.1%
25 567
5.7%
24 676
6.8%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9801 
2
 
199

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 9801
98.0%
2 199
 
2.0%

Length

2023-12-13T08:26:35.667699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:26:35.762957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9801
98.0%
2 199
 
2.0%

본번
Real number (ℝ)

Distinct2604
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1199.5568
Minimum1
Maximum5623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:35.871661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68.95
Q1325
median929
Q31811
95-th percentile3077
Maximum5623
Range5622
Interquartile range (IQR)1486

Descriptive statistics

Standard deviation1022.989
Coefficient of variation (CV)0.85280578
Kurtosis0.92163089
Mean1199.5568
Median Absolute Deviation (MAD)644
Skewness1.088177
Sum11995568
Variance1046506.4
MonotonicityNot monotonic
2023-12-13T08:26:36.010283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2518 121
 
1.2%
745 100
 
1.0%
2476 93
 
0.9%
2180 83
 
0.8%
1605 76
 
0.8%
270 55
 
0.5%
182 55
 
0.5%
2330 55
 
0.5%
1522 54
 
0.5%
105 54
 
0.5%
Other values (2594) 9254
92.5%
ValueCountFrequency (%)
1 20
0.2%
2 17
0.2%
3 4
 
< 0.1%
4 7
 
0.1%
5 11
0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 10
0.1%
9 2
 
< 0.1%
10 5
 
0.1%
ValueCountFrequency (%)
5623 1
< 0.1%
5619 1
< 0.1%
5508 1
< 0.1%
5494 1
< 0.1%
5474 1
< 0.1%
5445 1
< 0.1%
5428 1
< 0.1%
5412 1
< 0.1%
5410 1
< 0.1%
5404 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7164
Minimum0
Maximum249
Zeros3627
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:36.431305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile15
Maximum249
Range249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.0511647
Coefficient of variation (CV)2.1663881
Kurtosis126.4876
Mean3.7164
Median Absolute Deviation (MAD)1
Skewness7.6133808
Sum37164
Variance64.821253
MonotonicityNot monotonic
2023-12-13T08:26:36.597540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3627
36.3%
1 2052
20.5%
2 893
 
8.9%
3 681
 
6.8%
4 457
 
4.6%
5 428
 
4.3%
6 301
 
3.0%
7 228
 
2.3%
9 224
 
2.2%
10 145
 
1.5%
Other values (67) 964
 
9.6%
ValueCountFrequency (%)
0 3627
36.3%
1 2052
20.5%
2 893
 
8.9%
3 681
 
6.8%
4 457
 
4.6%
5 428
 
4.3%
6 301
 
3.0%
7 228
 
2.3%
8 123
 
1.2%
9 224
 
2.2%
ValueCountFrequency (%)
249 1
< 0.1%
141 1
< 0.1%
130 1
< 0.1%
115 1
< 0.1%
99 1
< 0.1%
93 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
87 2
< 0.1%
81 1
< 0.1%


Real number (ℝ)

ZEROS 

Distinct170
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494.4387
Minimum0
Maximum9999
Zeros304
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:36.721058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile7001
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1855.2652
Coefficient of variation (CV)3.7522654
Kurtosis11.119327
Mean494.4387
Median Absolute Deviation (MAD)0
Skewness3.6067959
Sum4944387
Variance3442009.1
MonotonicityNot monotonic
2023-12-13T08:26:36.847764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7267
72.7%
2 818
 
8.2%
0 304
 
3.0%
3 226
 
2.3%
7001 146
 
1.5%
8002 110
 
1.1%
8001 102
 
1.0%
4 89
 
0.9%
8003 84
 
0.8%
5 69
 
0.7%
Other values (160) 785
 
7.8%
ValueCountFrequency (%)
0 304
 
3.0%
1 7267
72.7%
2 818
 
8.2%
3 226
 
2.3%
4 89
 
0.9%
5 69
 
0.7%
6 41
 
0.4%
7 23
 
0.2%
8 14
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
9999 1
 
< 0.1%
9001 10
 
0.1%
8013 1
 
< 0.1%
8011 1
 
< 0.1%
8009 1
 
< 0.1%
8007 6
 
0.1%
8006 6
 
0.1%
8005 18
 
0.2%
8004 28
 
0.3%
8003 84
0.8%


Text

Distinct758
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:26:37.148594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length2.7206
Min length1

Characters and Unicode

Total characters27206
Distinct characters23
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique431 ?
Unique (%)4.3%

Sample

1st row1002
2nd row101
3rd row101
4th row101
5th row0
ValueCountFrequency (%)
101 3119
31.2%
0 1739
17.4%
102 886
 
8.9%
201 673
 
6.7%
103 288
 
2.9%
8101 253
 
2.5%
301 230
 
2.3%
202 169
 
1.7%
104 122
 
1.2%
401 81
 
0.8%
Other values (750) 2444
24.4%
2023-12-13T08:26:37.586394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10434
38.4%
0 9101
33.5%
2 3053
 
11.2%
3 1356
 
5.0%
8 700
 
2.6%
4 690
 
2.5%
5 609
 
2.2%
6 467
 
1.7%
7 374
 
1.4%
9 266
 
1.0%
Other values (13) 156
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27050
99.4%
Uppercase Letter 136
 
0.5%
Other Letter 8
 
< 0.1%
Space Separator 4
 
< 0.1%
Dash Punctuation 4
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10434
38.6%
0 9101
33.6%
2 3053
 
11.3%
3 1356
 
5.0%
8 700
 
2.6%
4 690
 
2.6%
5 609
 
2.3%
6 467
 
1.7%
7 374
 
1.4%
9 266
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
B 56
41.2%
A 37
27.2%
C 16
 
11.8%
D 15
 
11.0%
E 9
 
6.6%
G 2
 
1.5%
F 1
 
0.7%
Other Letter
ValueCountFrequency (%)
4
50.0%
4
50.0%
Lowercase Letter
ValueCountFrequency (%)
b 2
50.0%
a 2
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27058
99.5%
Latin 140
 
0.5%
Hangul 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10434
38.6%
0 9101
33.6%
2 3053
 
11.3%
3 1356
 
5.0%
8 700
 
2.6%
4 690
 
2.6%
5 609
 
2.3%
6 467
 
1.7%
7 374
 
1.4%
9 266
 
1.0%
Other values (2) 8
 
< 0.1%
Latin
ValueCountFrequency (%)
B 56
40.0%
A 37
26.4%
C 16
 
11.4%
D 15
 
10.7%
E 9
 
6.4%
G 2
 
1.4%
b 2
 
1.4%
a 2
 
1.4%
F 1
 
0.7%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27198
> 99.9%
Hangul 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10434
38.4%
0 9101
33.5%
2 3053
 
11.2%
3 1356
 
5.0%
8 700
 
2.6%
4 690
 
2.5%
5 609
 
2.2%
6 467
 
1.7%
7 374
 
1.4%
9 266
 
1.0%
Other values (11) 148
 
0.5%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%
Distinct9808
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:26:37.874760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length35
Mean length28.7152
Min length21

Characters and Unicode

Total characters287152
Distinct characters234
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

Unique9646 ?
Unique (%)96.5%

Sample

1st row[ 김정문화로41번길 10-6 ] 0001동 1002호
2nd row제주특별자치도 서귀포시 표선면 성읍리 1624-17 1동 101호
3rd row제주특별자치도 서귀포시 동홍동 872 1동 101호
4th row[ 평화로 534-63 ] 0001동 0101호
5th row제주특별자치도 서귀포시 표선면 가시리 2981-1 1동
ValueCountFrequency (%)
9496
 
15.2%
제주특별자치도 5252
 
8.4%
서귀포시 5252
 
8.4%
0001동 4239
 
6.8%
1동 3028
 
4.9%
101호 1615
 
2.6%
0101호 1504
 
2.4%
남원읍 792
 
1.3%
대정읍 747
 
1.2%
성산읍 724
 
1.2%
Other values (6347) 29692
47.6%
2023-12-13T08:26:38.282461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52341
18.2%
0 31540
 
11.0%
1 26381
 
9.2%
12663
 
4.4%
2 9777
 
3.4%
9067
 
3.2%
6546
 
2.3%
3 5811
 
2.0%
5529
 
1.9%
5517
 
1.9%
Other values (224) 121980
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124628
43.4%
Decimal Number 96275
33.5%
Space Separator 52341
18.2%
Close Punctuation 4748
 
1.7%
Open Punctuation 4748
 
1.7%
Dash Punctuation 4272
 
1.5%
Uppercase Letter 136
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12663
 
10.2%
9067
 
7.3%
6546
 
5.3%
5529
 
4.4%
5517
 
4.4%
5435
 
4.4%
5426
 
4.4%
5397
 
4.3%
5373
 
4.3%
5282
 
4.2%
Other values (201) 58393
46.9%
Decimal Number
ValueCountFrequency (%)
0 31540
32.8%
1 26381
27.4%
2 9777
 
10.2%
3 5811
 
6.0%
4 4632
 
4.8%
5 4253
 
4.4%
8 3977
 
4.1%
7 3668
 
3.8%
6 3609
 
3.7%
9 2627
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
B 56
41.2%
A 37
27.2%
C 16
 
11.8%
D 15
 
11.0%
E 9
 
6.6%
G 2
 
1.5%
F 1
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
b 2
50.0%
Space Separator
ValueCountFrequency (%)
52341
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4748
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4748
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162384
56.5%
Hangul 124628
43.4%
Latin 140
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12663
 
10.2%
9067
 
7.3%
6546
 
5.3%
5529
 
4.4%
5517
 
4.4%
5435
 
4.4%
5426
 
4.4%
5397
 
4.3%
5373
 
4.3%
5282
 
4.2%
Other values (201) 58393
46.9%
Common
ValueCountFrequency (%)
52341
32.2%
0 31540
19.4%
1 26381
16.2%
2 9777
 
6.0%
3 5811
 
3.6%
] 4748
 
2.9%
[ 4748
 
2.9%
4 4632
 
2.9%
- 4272
 
2.6%
5 4253
 
2.6%
Other values (4) 13881
 
8.5%
Latin
ValueCountFrequency (%)
B 56
40.0%
A 37
26.4%
C 16
 
11.4%
D 15
 
10.7%
E 9
 
6.4%
a 2
 
1.4%
b 2
 
1.4%
G 2
 
1.4%
F 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162524
56.6%
Hangul 124628
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52341
32.2%
0 31540
19.4%
1 26381
16.2%
2 9777
 
6.0%
3 5811
 
3.6%
] 4748
 
2.9%
[ 4748
 
2.9%
4 4632
 
2.9%
- 4272
 
2.6%
5 4253
 
2.6%
Other values (13) 14021
 
8.6%
Hangul
ValueCountFrequency (%)
12663
 
10.2%
9067
 
7.3%
6546
 
5.3%
5529
 
4.4%
5517
 
4.4%
5435
 
4.4%
5426
 
4.4%
5397
 
4.3%
5373
 
4.3%
5282
 
4.2%
Other values (201) 58393
46.9%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7241
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56351008
Minimum34000
Maximum5.5598877 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:38.403262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34000
5-th percentile408690
Q11852892.5
median19020075
Q355985458
95-th percentile1.9081061 × 108
Maximum5.5598877 × 109
Range5.5598537 × 109
Interquartile range (IQR)54132565

Descriptive statistics

Standard deviation1.7113852 × 108
Coefficient of variation (CV)3.0370091
Kurtosis341.94773
Mean56351008
Median Absolute Deviation (MAD)18258525
Skewness14.917656
Sum5.6351008 × 1011
Variance2.9288395 × 1016
MonotonicityNot monotonic
2023-12-13T08:26:38.581014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990000 95
 
0.9%
924000 64
 
0.6%
50755770 53
 
0.5%
132513650 51
 
0.5%
742500 47
 
0.5%
90369720 30
 
0.3%
57166560 23
 
0.2%
59047130 22
 
0.2%
42504700 21
 
0.2%
152390690 20
 
0.2%
Other values (7231) 9574
95.7%
ValueCountFrequency (%)
34000 1
< 0.1%
42640 1
< 0.1%
46500 1
< 0.1%
50700 1
< 0.1%
57600 1
< 0.1%
60000 1
< 0.1%
74400 1
< 0.1%
75600 1
< 0.1%
76160 1
< 0.1%
76800 2
< 0.1%
ValueCountFrequency (%)
5559887730 1
< 0.1%
5309887280 1
< 0.1%
4559756250 1
< 0.1%
3934753900 1
< 0.1%
3905551650 1
< 0.1%
2894865090 1
< 0.1%
2638465240 1
< 0.1%
2591470570 1
< 0.1%
2581452720 1
< 0.1%
2288473560 1
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5076
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.11872
Minimum0.26
Maximum17408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:26:38.700597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile12.9695
Q135.79
median66
Q3119.01
95-th percentile354.258
Maximum17408
Range17407.74
Interquartile range (IQR)83.22

Descriptive statistics

Standard deviation344.94107
Coefficient of variation (CV)2.7350506
Kurtosis815.44109
Mean126.11872
Median Absolute Deviation (MAD)34.12
Skewness21.764827
Sum1261187.2
Variance118984.34
MonotonicityNot monotonic
2023-12-13T08:26:38.811043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 286
 
2.9%
18.0 157
 
1.6%
49.5 97
 
1.0%
153.55 71
 
0.7%
27.0 67
 
0.7%
71.487 53
 
0.5%
33.0 41
 
0.4%
50.0 41
 
0.4%
19.8 36
 
0.4%
119.695 35
 
0.4%
Other values (5066) 9116
91.2%
ValueCountFrequency (%)
0.26 1
 
< 0.1%
0.93 1
 
< 0.1%
1.1121 5
0.1%
1.145 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 12
0.1%
1.26 1
 
< 0.1%
1.33 3
 
< 0.1%
1.35 1
 
< 0.1%
1.38 3
 
< 0.1%
ValueCountFrequency (%)
17408.0 1
< 0.1%
10175.49 1
< 0.1%
9788.35 1
< 0.1%
7444.5 1
< 0.1%
5155.5 1
< 0.1%
4898.42 1
< 0.1%
4833.85 1
< 0.1%
4591.2 1
< 0.1%
4157.6 1
< 0.1%
3998.83 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-06-01 00:00:00
Maximum2021-06-01 00:00:00
2023-12-13T08:26:38.900666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:38.969833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:26:33.389336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.028085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.062593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.059733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.121466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.968524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.727523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.485952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.106465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.157147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.198323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.216817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.087890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.818413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.606339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.222688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.294994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.353997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.319203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.185999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.908138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.707259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.324661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.439386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.524118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.443221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.293838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.998272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.814235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.447056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.574158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.652074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.547475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.408232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.095841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.929788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.871655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.718852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.788080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.733957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.516521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.191249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:34.031829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:28.953402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:29.885262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:30.949210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:31.847486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:32.631159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:26:33.286323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:26:39.030509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적(제곱미터)
법정동1.0000.6790.1020.2840.0590.0880.0120.008
법정리0.6791.0000.0400.3690.0590.1370.0640.041
특수지0.1020.0401.0000.2440.0000.1740.0920.108
본번0.2840.3690.2441.0000.1120.1960.0380.046
부번0.0590.0590.0000.1121.0000.0680.0560.072
0.0880.1370.1740.1960.0681.0000.0280.000
시가표준액0.0120.0640.0920.0380.0560.0281.0000.885
연면적(제곱미터)0.0080.0410.1080.0460.0720.0000.8851.000
2023-12-13T08:26:39.126338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적(제곱미터)특수지
법정동1.0000.7250.114-0.1870.065-0.191-0.0210.068
법정리0.7251.0000.121-0.1650.050-0.214-0.0240.049
본번0.1140.1211.000-0.1600.1070.0050.0380.187
부번-0.187-0.165-0.1601.000-0.1170.024-0.0710.000
0.0650.0500.107-0.1171.0000.1760.0990.213
시가표준액-0.191-0.2140.0050.0240.1761.0000.6680.092
연면적(제곱미터)-0.021-0.0240.038-0.0710.0990.6681.0000.078
특수지0.0680.0490.1870.0000.2130.0920.0781.000

Missing values

2023-12-13T08:26:34.183349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:26:34.368416image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적(제곱미터)기준일자
73835제주특별자치도서귀포시50130202110201745111002[ 김정문화로41번길 10-6 ] 0001동 1002호5891879067.6452021-06-01
5200제주특별자치도서귀포시5013020213202311624171101제주특별자치도 서귀포시 표선면 성읍리 1624-17 1동 101호207740044.22021-06-01
55534제주특별자치도서귀포시5013020211050187201101제주특별자치도 서귀포시 동홍동 872 1동 101호99000066.02021-06-01
746제주특별자치도서귀포시501302021310261151251101[ 평화로 534-63 ] 0001동 0101호3327048075.962021-06-01
6338제주특별자치도서귀포시5013020213202412981110제주특별자치도 서귀포시 표선면 가시리 2981-1 1동32724036.362021-06-01
69164제주특별자치도서귀포시5013020212592113920117제주특별자치도 서귀포시 성산읍 성산리 392 1동 17호9216371090.692021-06-01
77285제주특별자치도서귀포시5013020211010132031501[ 서문로41번길 10-1 ] 0001동 0501호10463803.25372021-06-01
33399제주특별자치도서귀포시5013020212532325201101제주특별자치도 서귀포시 남원읍 한남리 산 5-20 1동 101호85859460192.512021-06-01
69811제주특별자치도서귀포시50130202125922161111622[ 고성오조로 108 ] 0001동 0622호2859262037.422021-06-01
14014제주특별자치도서귀포시501302021310271141207002101제주특별자치도 서귀포시 안덕면 서광리 1412 7002동 101호2390500035.02021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적(제곱미터)기준일자
34586제주특별자치도서귀포시501302021253211239001521제주특별자치도 서귀포시 남원읍 남원리 2390 1동 521호5904713089.332021-06-01
42858제주특별자치도서귀포시50130202125022164621101[ 최남단해안로 119 ] 0001동 0101호103449400209.22021-06-01
66864제주특별자치도서귀포시501302021320211159111101[ 표선동서로193번길 43 ] 0001동 0101호245399660256.9632021-06-01
7004제주특별자치도서귀포시5013020213202611601522제주특별자치도 서귀포시 표선면 토산리 16 1동 522호4350702065.822021-06-01
46352제주특별자치도서귀포시5013020211170114321101[ 중산간서로356번길 113-9 ] 0001동 0101호127485084.992021-06-01
61040제주특별자치도서귀포시5013020211100186901308[ 칠십리로485번길 27 ] 0001동 0308호818664035.442021-06-01
26072제주특별자치도서귀포시5013020212532712287000제주특별자치도 서귀포시 남원읍 위미리 228792400066.02021-06-01
70730제주특별자치도서귀포시501302021253271462701151152제주특별자치도 서귀포시 남원읍 위미리 4627 115동 1152호5404043069.912021-06-01
53553제주특별자치도서귀포시501302021107011300121101[ 516로 466 ] 0001동 0101호2791377065.992021-06-01
24127제주특별자치도서귀포시50130202125924184011101[ 신양로 8 ] 0001동 0101호652536038.162021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적(제곱미터)기준일자# duplicates
5제주특별자치도서귀포시50130202111401249011제주특별자치도 서귀포시 대포동 249 1동 1호2480481020.72021-06-013
10제주특별자치도서귀포시50130202125324198821201제주특별자치도 서귀포시 남원읍 수망리 988-2 1동 201호2033115023.452021-06-013
0제주특별자치도서귀포시5013020211070111231101제주특별자치도 서귀포시 상효동 112-3 1동 101호4465735042.722021-06-012
1제주특별자치도서귀포시5013020211070111231201제주특별자치도 서귀포시 상효동 112-3 1동 201호1739826019.142021-06-012
2제주특별자치도서귀포시5013020211100142501101제주특별자치도 서귀포시 보목동 425 1동 101호1261638023.942021-06-012
3제주특별자치도서귀포시5013020211100142501201제주특별자치도 서귀포시 보목동 425 1동 201호1123037021.312021-06-012
4제주특별자치도서귀포시50130202111401249011제주특별자치도 서귀포시 대포동 249 1동 1호2419524023.222021-06-012
6제주특별자치도서귀포시50130202112101188801201제주특별자치도 서귀포시 하예동 1888 1동 201호6159008078.162021-06-012
7제주특별자치도서귀포시501302021250251551191202제주특별자치도 서귀포시 대정읍 일과리 551-19 1동 202호1474473054.012021-06-012
8제주특별자치도서귀포시501302021250251602340제주특별자치도 서귀포시 대정읍 일과리 602-3 4동97650065.12021-06-012