Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows20
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액으로 일반건축물의 물건지, 시가표준액, 면적 등의 항목을 제공합니다.
Author경상북도 안동시
URLhttps://www.data.go.kr/data/15080284/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
데이터 기준일 has constant value ""Constant
Dataset has 20 (0.2%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (88.7%)Imbalance
법정리 has 4456 (44.6%) zerosZeros
부번 has 3476 (34.8%) zerosZeros

Reproduction

Analysis started2024-04-06 08:26:12.102152
Analysis finished2024-04-06 08:26:28.861895
Duration16.76 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
경상북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 10000
100.0%

Length

2024-04-06T17:26:28.995427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:29.177815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안동시 10000
100.0%

Length

2024-04-06T17:26:29.375548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:29.557200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안동시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47170
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47170
2nd row47170
3rd row47170
4th row47170
5th row47170

Common Values

ValueCountFrequency (%)
47170 10000
100.0%

Length

2024-04-06T17:26:29.755248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:30.065631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47170 10000
100.0%

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

2024-04-06T17:26:30.287899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:30.478000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.8732
Minimum101
Maximum430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:30.747134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile110
Q1128
median250
Q3340
95-th percentile420
Maximum430
Range329
Interquartile range (IQR)212

Descriptive statistics

Standard deviation114.12359
Coefficient of variation (CV)0.46796284
Kurtosis-1.6509378
Mean243.8732
Median Absolute Deviation (MAD)117
Skewness0.088447859
Sum2438732
Variance13024.195
MonotonicityNot monotonic
2024-04-06T17:26:31.127542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 942
 
9.4%
340 663
 
6.6%
133 548
 
5.5%
330 535
 
5.3%
310 497
 
5.0%
320 482
 
4.8%
122 449
 
4.5%
110 410
 
4.1%
350 375
 
3.8%
390 319
 
3.2%
Other values (46) 4780
47.8%
ValueCountFrequency (%)
101 83
 
0.8%
102 60
 
0.6%
103 64
 
0.6%
104 31
 
0.3%
105 45
 
0.4%
106 33
 
0.3%
107 14
 
0.1%
108 22
 
0.2%
109 49
 
0.5%
110 410
4.1%
ValueCountFrequency (%)
430 275
2.8%
420 239
 
2.4%
410 145
 
1.5%
400 240
 
2.4%
390 319
3.2%
380 314
3.1%
370 217
 
2.2%
360 301
3.0%
350 375
3.8%
340 663
6.6%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5061
Minimum0
Maximum39
Zeros4456
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:31.383378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q326
95-th percentile33
Maximum39
Range39
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13.455165
Coefficient of variation (CV)0.92755221
Kurtosis-1.706562
Mean14.5061
Median Absolute Deviation (MAD)12
Skewness-0.0093644011
Sum145061
Variance181.04147
MonotonicityNot monotonic
2024-04-06T17:26:31.682784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4456
44.6%
21 1118
 
11.2%
22 543
 
5.4%
26 457
 
4.6%
27 437
 
4.4%
23 410
 
4.1%
24 351
 
3.5%
25 340
 
3.4%
30 315
 
3.1%
29 313
 
3.1%
Other values (10) 1260
 
12.6%
ValueCountFrequency (%)
0 4456
44.6%
21 1118
 
11.2%
22 543
 
5.4%
23 410
 
4.1%
24 351
 
3.5%
25 340
 
3.4%
26 457
 
4.6%
27 437
 
4.4%
28 272
 
2.7%
29 313
 
3.1%
ValueCountFrequency (%)
39 39
 
0.4%
38 79
 
0.8%
37 145
1.5%
36 33
 
0.3%
35 34
 
0.3%
34 94
 
0.9%
33 148
1.5%
32 217
2.2%
31 199
2.0%
30 315
3.1%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9849 
2
 
151

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 9849
98.5%
2 151
 
1.5%

Length

2024-04-06T17:26:31.935690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:32.147610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9849
98.5%
2 151
 
1.5%

본번
Real number (ℝ)

Distinct1324
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.593
Minimum1
Maximum1730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:32.432433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1192
median405.5
Q3756.25
95-th percentile1330
Maximum1730
Range1729
Interquartile range (IQR)564.25

Descriptive statistics

Standard deviation403.94495
Coefficient of variation (CV)0.78497948
Kurtosis0.37394869
Mean514.593
Median Absolute Deviation (MAD)256.5
Skewness0.98558015
Sum5145930
Variance163171.52
MonotonicityNot monotonic
2024-04-06T17:26:32.747794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388 59
 
0.6%
734 45
 
0.4%
108 43
 
0.4%
791 43
 
0.4%
333 42
 
0.4%
773 41
 
0.4%
235 40
 
0.4%
180 40
 
0.4%
189 38
 
0.4%
1647 37
 
0.4%
Other values (1314) 9572
95.7%
ValueCountFrequency (%)
1 25
0.2%
2 6
 
0.1%
3 6
 
0.1%
4 8
 
0.1%
5 27
0.3%
6 7
 
0.1%
7 9
 
0.1%
8 4
 
< 0.1%
9 7
 
0.1%
10 12
0.1%
ValueCountFrequency (%)
1730 2
 
< 0.1%
1728 2
 
< 0.1%
1725 5
 
0.1%
1724 13
0.1%
1723 1
 
< 0.1%
1713 1
 
< 0.1%
1700 1
 
< 0.1%
1677 2
 
< 0.1%
1669 16
0.2%
1666 3
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4208
Minimum0
Maximum353
Zeros3476
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:33.082315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile36
Maximum353
Range353
Interquartile range (IQR)6

Descriptive statistics

Standard deviation25.344079
Coefficient of variation (CV)3.0096997
Kurtosis59.870138
Mean8.4208
Median Absolute Deviation (MAD)1
Skewness6.8735975
Sum84208
Variance642.32236
MonotonicityNot monotonic
2024-04-06T17:26:33.445973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3476
34.8%
1 1687
16.9%
2 790
 
7.9%
3 652
 
6.5%
4 398
 
4.0%
5 378
 
3.8%
6 270
 
2.7%
7 260
 
2.6%
8 193
 
1.9%
9 170
 
1.7%
Other values (170) 1726
17.3%
ValueCountFrequency (%)
0 3476
34.8%
1 1687
16.9%
2 790
 
7.9%
3 652
 
6.5%
4 398
 
4.0%
5 378
 
3.8%
6 270
 
2.7%
7 260
 
2.6%
8 193
 
1.9%
9 170
 
1.7%
ValueCountFrequency (%)
353 1
< 0.1%
352 2
< 0.1%
350 1
< 0.1%
339 1
< 0.1%
322 1
< 0.1%
319 1
< 0.1%
315 1
< 0.1%
302 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%


Real number (ℝ)

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.4599
Minimum0
Maximum9999
Zeros57
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:33.831539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation830.68993
Coefficient of variation (CV)9.7202305
Kurtosis102.81731
Mean85.4599
Median Absolute Deviation (MAD)0
Skewness10.179695
Sum854599
Variance690045.76
MonotonicityNot monotonic
2024-04-06T17:26:34.101915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8937
89.4%
2 428
 
4.3%
3 131
 
1.3%
4 63
 
0.6%
9000 60
 
0.6%
0 57
 
0.6%
5 32
 
0.3%
6 19
 
0.2%
7 17
 
0.2%
301 17
 
0.2%
Other values (71) 239
 
2.4%
ValueCountFrequency (%)
0 57
 
0.6%
1 8937
89.4%
2 428
 
4.3%
3 131
 
1.3%
4 63
 
0.6%
5 32
 
0.3%
6 19
 
0.2%
7 17
 
0.2%
8 10
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
9999 3
 
< 0.1%
9001 2
 
< 0.1%
9000 60
0.6%
8004 2
 
< 0.1%
8003 1
 
< 0.1%
8002 1
 
< 0.1%
8001 4
 
< 0.1%
7006 4
 
< 0.1%
7005 3
 
< 0.1%
7004 2
 
< 0.1%


Text

Distinct256
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:26:34.578315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.6231
Min length1

Characters and Unicode

Total characters26231
Distinct characters14
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)1.2%

Sample

1st row101
2nd row201
3rd row6
4th row1
5th row102
ValueCountFrequency (%)
101 3255
32.6%
1 1113
 
11.1%
102 1076
 
10.8%
201 854
 
8.5%
103 457
 
4.6%
2 392
 
3.9%
301 384
 
3.8%
104 236
 
2.4%
8101 221
 
2.2%
3 218
 
2.2%
Other values (246) 1794
17.9%
2024-04-06T17:26:35.270204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12233
46.6%
0 7772
29.6%
2 2959
 
11.3%
3 1317
 
5.0%
4 612
 
2.3%
5 411
 
1.6%
8 408
 
1.6%
6 260
 
1.0%
7 172
 
0.7%
9 74
 
0.3%
Other values (4) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26218
> 99.9%
Dash Punctuation 10
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12233
46.7%
0 7772
29.6%
2 2959
 
11.3%
3 1317
 
5.0%
4 612
 
2.3%
5 411
 
1.6%
8 408
 
1.6%
6 260
 
1.0%
7 172
 
0.7%
9 74
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26228
> 99.9%
Latin 2
 
< 0.1%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12233
46.6%
0 7772
29.6%
2 2959
 
11.3%
3 1317
 
5.0%
4 612
 
2.3%
5 411
 
1.6%
8 408
 
1.6%
6 260
 
1.0%
7 172
 
0.7%
9 74
 
0.3%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26230
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12233
46.6%
0 7772
29.6%
2 2959
 
11.3%
3 1317
 
5.0%
4 612
 
2.3%
5 411
 
1.6%
8 408
 
1.6%
6 260
 
1.0%
7 172
 
0.7%
9 74
 
0.3%
Other values (3) 12
 
< 0.1%
Hangul
ValueCountFrequency (%)
1
100.0%
Distinct9690
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:26:36.099941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length28.1598
Min length20

Characters and Unicode

Total characters281598
Distinct characters319
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9476 ?
Unique (%)94.8%

Sample

1st row경상북도 안동시 임하면 천전리 51 1동 101호
2nd row경상북도 안동시 송현동 555-3 1동 201호
3rd row경상북도 안동시 북후면 장기리 173-1 1동 6호
4th row경상북도 안동시 풍천면 광덕리 141-1 2동 1호
5th row경상북도 안동시 직곡길 7 0001동 0102호
ValueCountFrequency (%)
경상북도 10000
 
15.6%
안동시 10000
 
15.6%
1동 5071
 
7.9%
0001동 3866
 
6.0%
101호 1801
 
2.8%
0101호 1454
 
2.3%
풍산읍 745
 
1.2%
1호 732
 
1.1%
102호 690
 
1.1%
0201호 502
 
0.8%
Other values (5175) 29251
45.6%
2024-04-06T17:26:37.901686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54112
19.2%
1 28536
 
10.1%
0 27426
 
9.7%
22623
 
8.0%
10798
 
3.8%
10733
 
3.8%
10545
 
3.7%
10463
 
3.7%
10342
 
3.7%
10159
 
3.6%
Other values (309) 85861
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134422
47.7%
Decimal Number 88203
31.3%
Space Separator 54112
19.2%
Dash Punctuation 4859
 
1.7%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22623
16.8%
10798
 
8.0%
10733
 
8.0%
10545
 
7.8%
10463
 
7.8%
10342
 
7.7%
10159
 
7.6%
10148
 
7.5%
4304
 
3.2%
3280
 
2.4%
Other values (295) 31027
23.1%
Decimal Number
ValueCountFrequency (%)
1 28536
32.4%
0 27426
31.1%
2 7933
 
9.0%
3 5396
 
6.1%
4 3888
 
4.4%
5 3499
 
4.0%
6 3113
 
3.5%
8 2960
 
3.4%
7 2903
 
3.3%
9 2549
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
54112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4859
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147174
52.3%
Hangul 134422
47.7%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22623
16.8%
10798
 
8.0%
10733
 
8.0%
10545
 
7.8%
10463
 
7.8%
10342
 
7.7%
10159
 
7.6%
10148
 
7.5%
4304
 
3.2%
3280
 
2.4%
Other values (295) 31027
23.1%
Common
ValueCountFrequency (%)
54112
36.8%
1 28536
19.4%
0 27426
18.6%
2 7933
 
5.4%
3 5396
 
3.7%
- 4859
 
3.3%
4 3888
 
2.6%
5 3499
 
2.4%
6 3113
 
2.1%
8 2960
 
2.0%
Other values (2) 5452
 
3.7%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147176
52.3%
Hangul 134422
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54112
36.8%
1 28536
19.4%
0 27426
18.6%
2 7933
 
5.4%
3 5396
 
3.7%
- 4859
 
3.3%
4 3888
 
2.6%
5 3499
 
2.4%
6 3113
 
2.1%
8 2960
 
2.0%
Other values (4) 5454
 
3.7%
Hangul
ValueCountFrequency (%)
22623
16.8%
10798
 
8.0%
10733
 
8.0%
10545
 
7.8%
10463
 
7.8%
10342
 
7.7%
10159
 
7.6%
10148
 
7.5%
4304
 
3.2%
3280
 
2.4%
Other values (295) 31027
23.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8467
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56523059
Minimum20650
Maximum6.1104471 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:38.213301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20650
5-th percentile388785
Q12166000
median12783030
Q349246955
95-th percentile2.1492847 × 108
Maximum6.1104471 × 109
Range6.1104264 × 109
Interquartile range (IQR)47080955

Descriptive statistics

Standard deviation1.850614 × 108
Coefficient of variation (CV)3.2740867
Kurtosis349.55176
Mean56523059
Median Absolute Deviation (MAD)11983030
Skewness15.10416
Sum5.6523059 × 1011
Variance3.424772 × 1016
MonotonicityNot monotonic
2024-04-06T17:26:38.482249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960000 48
 
0.5%
800000 21
 
0.2%
828000 20
 
0.2%
1600000 19
 
0.2%
936000 17
 
0.2%
480000 17
 
0.2%
720000 16
 
0.2%
1920000 16
 
0.2%
918000 15
 
0.1%
1750000 15
 
0.1%
Other values (8457) 9796
98.0%
ValueCountFrequency (%)
20650 1
< 0.1%
22500 2
< 0.1%
24480 1
< 0.1%
26390 1
< 0.1%
27720 1
< 0.1%
34500 1
< 0.1%
35280 1
< 0.1%
36000 1
< 0.1%
37440 1
< 0.1%
38500 1
< 0.1%
ValueCountFrequency (%)
6110447080 1
< 0.1%
5579325710 1
< 0.1%
4701327470 1
< 0.1%
4700651720 1
< 0.1%
3859198270 1
< 0.1%
3490040920 1
< 0.1%
3268609360 1
< 0.1%
3268303810 1
< 0.1%
3123725540 1
< 0.1%
2669305500 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6024
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.5438
Minimum0.9
Maximum12569.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:26:38.790421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile12.7585
Q140.32
median89.255
Q3192
95-th percentile591.6255
Maximum12569.23
Range12568.33
Interquartile range (IQR)151.68

Descriptive statistics

Standard deviation368.74891
Coefficient of variation (CV)2.0424346
Kurtosis246.88523
Mean180.5438
Median Absolute Deviation (MAD)59.77
Skewness11.722407
Sum1805438
Variance135975.76
MonotonicityNot monotonic
2024-04-06T17:26:39.055856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 298
 
3.0%
192.0 73
 
0.7%
96.0 43
 
0.4%
160.0 43
 
0.4%
66.0 40
 
0.4%
36.0 36
 
0.4%
32.0 31
 
0.3%
33.0 30
 
0.3%
350.0 28
 
0.3%
84.0 28
 
0.3%
Other values (6014) 9350
93.5%
ValueCountFrequency (%)
0.9 2
 
< 0.1%
1.0 3
< 0.1%
1.08 2
 
< 0.1%
1.14 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 1
 
< 0.1%
1.3 1
 
< 0.1%
1.44 6
0.1%
1.5 2
 
< 0.1%
1.56 1
 
< 0.1%
ValueCountFrequency (%)
12569.23 1
< 0.1%
8828.85 1
< 0.1%
7909.04 1
< 0.1%
7900.255 1
< 0.1%
6697.97 1
< 0.1%
6697.87 1
< 0.1%
5931.79 1
< 0.1%
5328.0 1
< 0.1%
5162.3 1
< 0.1%
5046.7729 1
< 0.1%

데이터 기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-12-31
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 10000
100.0%

Length

2024-04-06T17:26:39.294149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:26:39.538610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 10000
100.0%

Interactions

2024-04-06T17:26:26.619610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:16.590340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:18.418831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:20.036468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:21.691956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:23.290957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:25.243879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:26.816287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:16.802556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:18.694527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:20.248983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:22.012181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:23.469174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:25.441829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:27.028816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:17.179321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:18.930292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:20.509758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:22.256215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:23.666493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:25.643501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:27.235194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:17.509540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:19.159029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:20.764844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:22.486498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:23.873576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:25.840127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:27.446254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:17.729072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:19.379954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:20.984718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:22.687449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:24.099851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:26.046706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:27.661923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:17.957642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:19.578972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:21.192459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:22.893605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:24.374078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:26.229770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:27.916807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:18.224604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:19.786451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:21.455074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:23.086230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:24.667068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:26:26.412608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:26:39.658947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.7030.0900.3620.1490.0860.0620.048
법정리0.7031.0000.1550.2830.1340.0730.0480.075
특수지0.0900.1551.0000.2640.0000.0000.0000.021
본번0.3620.2830.2641.0000.2610.1350.1140.072
부번0.1490.1340.0000.2611.0000.1790.0000.000
0.0860.0730.0000.1350.1791.0000.0000.000
시가표준액0.0620.0480.0000.1140.0000.0001.0000.911
연면적0.0480.0750.0210.0720.0000.0000.9111.000
2024-04-06T17:26:39.915999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.7450.153-0.399-0.011-0.310-0.0080.097
법정리0.7451.0000.059-0.395-0.028-0.3600.0100.111
본번0.1530.0591.000-0.120-0.0170.1240.0610.202
부번-0.399-0.395-0.1201.000-0.0470.128-0.0710.000
-0.011-0.028-0.017-0.0471.000-0.002-0.0620.000
시가표준액-0.310-0.3600.1240.128-0.0021.0000.6130.000
연면적-0.0080.0100.061-0.071-0.0620.6131.0000.021
특수지0.0970.1110.2020.0000.0000.0000.0211.000

Missing values

2024-04-06T17:26:28.218422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:26:28.649643image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터 기준일
39586경상북도안동시4717020223802215101101경상북도 안동시 임하면 천전리 51 1동 101호7958920194.122022-12-31
13400경상북도안동시4717020221360155531201경상북도 안동시 송현동 555-3 1동 201호89811000153.02022-12-31
22393경상북도안동시471702022320221173116경상북도 안동시 북후면 장기리 173-1 1동 6호25120000160.02022-12-31
32958경상북도안동시471702022340241141121경상북도 안동시 풍천면 광덕리 141-1 2동 1호102960019.82022-12-31
13144경상북도안동시4717020221360148611102경상북도 안동시 직곡길 7 0001동 0102호142116310144.22022-12-31
44486경상북도안동시47170202233024115131102경상북도 안동시 경북대로 957-15 0001동 0102호2383920079.22022-12-31
7416경상북도안동시4717020221090160131502경상북도 안동시 경동로 744 0001동 0502호149040034.52022-12-31
15772경상북도안동시47170202212601138161201경상북도 안동시 서동문로 87-4 0001동 0201호544275030.752022-12-31
35821경상북도안동시4717020221330194212201경상북도 안동시 옥동 942-1 2동 201호91924560115.922022-12-31
6620경상북도안동시4717020221180121221301경상북도 안동시 대석동 212-2 1동 301호134400032.02022-12-31
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터 기준일
8166경상북도안동시47170202211001459211경상북도 안동시 경동로 1098 0001동 0001호54210041.72022-12-31
11493경상북도안동시471702022133011275321101경상북도 안동시 하이마로 247-1 0001동 0101호4428270066.02022-12-31
22330경상북도안동시47170202231031134511101경상북도 안동시 와룡면 주하리 345-1 1동 101호260400028.02022-12-31
12468경상북도안동시4717020221340141321102경상북도 안동시 이천동 413-2 1동 102호62235480420.512022-12-31
16802경상북도안동시4717020221270199651101경상북도 안동시 평화동 99-65 1동 101호7923125091.742022-12-31
30803경상북도안동시471702022340231428011경상북도 안동시 풍천면 구담리 428 1동 1호29302041.862022-12-31
36856경상북도안동시471702022430211730012경상북도 안동시 녹전면 신평리 730 1동 2호120528050.222022-12-31
28220경상북도안동시471702022360221117013경상북도 안동시 남후면 무릉리 117 1동 3호37248031.042022-12-31
36474경상북도안동시47170202241035159023경상북도 안동시 예안면 구룡리 59 2동 3호15033600129.62022-12-31
43057경상북도안동시47170202213301104081101경상북도 안동시 은행나무로 46-35 0001동 0101호30940170300.392022-12-31

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터 기준일# duplicates
3경상북도안동시4717020221360211801101경상북도 안동시 송현동 산 118 1동 101호60894000199.02022-12-313
0경상북도안동시471702022119013031121경상북도 안동시 중앙시장1길 48-1 0002동 0001호83328014.882022-12-312
1경상북도안동시4717020221340183041101경상북도 안동시 이천동 830-4 1동 101호67767840199.22022-12-312
2경상북도안동시47170202213601599118101경상북도 안동시 송현동 599-1 1동 8101호43329600153.02022-12-312
4경상북도안동시4717020221410128871101경상북도 안동시 수상동 288-7 1동 101호529920038.42022-12-312
5경상북도안동시47170202225022110811301경상북도 안동시 풍산읍 상리리 108-1 1동 301호68813280337.322022-12-312
6경상북도안동시471702022250251113201101경상북도 안동시 풍산읍 수리 1132 1동 101호31050000150.02022-12-312
7경상북도안동시47170202225027145111101경상북도 안동시 풍산읍 계평리 451-1 1동 101호753600125.62022-12-312
8경상북도안동시47170202225030175841101경상북도 안동시 풍산읍 노리 758-4 1동 101호4588155058.52022-12-312
9경상북도안동시47170202225034131401101경상북도 안동시 풍산읍 신양리 314 1동 101호25704000216.02022-12-312