Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공 시가표준금액 : 지방세 부과를 위하여 행정안전부·지방자치단체장이 일반건축물의 거래가격을 기준으로 종류·구조·용도·경과연수 등을 고려하여 평가한 금액
URLhttps://www.data.go.kr/data/15079893/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 6 (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 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (86.4%)Imbalance
동 is highly skewed (γ1 = 44.66620477)Skewed
시가표준액 is highly skewed (γ1 = 29.58774056)Skewed
연면적 is highly skewed (γ1 = 34.57754584)Skewed
부번 has 3631 (36.3%) zerosZeros

Reproduction

Analysis started2023-12-12 19:35:58.433262
Analysis finished2023-12-12 19:36:08.050419
Duration9.62 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

2023-12-13T04:36:08.157206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:08.278431image/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
청송군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청송군 10000
100.0%

Length

2023-12-13T04:36:08.380636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:08.482072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청송군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47750
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47750
2nd row47750
3rd row47750
4th row47750
5th row47750

Common Values

ValueCountFrequency (%)
47750 10000
100.0%

Length

2023-12-13T04:36:08.617499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:08.738000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47750 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

2023-12-13T04:36:08.856519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:08.951727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.756
Minimum250
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:09.062000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1315
median340
Q3360
95-th percentile370
Maximum370
Range120
Interquartile range (IQR)45

Descriptive statistics

Standard deviation38.883501
Coefficient of variation (CV)0.11791598
Kurtosis-0.012243534
Mean329.756
Median Absolute Deviation (MAD)25
Skewness-0.99133378
Sum3297560
Variance1511.9267
MonotonicityNot monotonic
2023-12-13T04:36:09.219011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
370 2468
24.7%
250 1508
15.1%
315 1418
14.2%
350 1132
11.3%
340 1044
10.4%
320 1030
10.3%
330 801
 
8.0%
360 599
 
6.0%
ValueCountFrequency (%)
250 1508
15.1%
315 1418
14.2%
320 1030
10.3%
330 801
 
8.0%
340 1044
10.4%
350 1132
11.3%
360 599
 
6.0%
370 2468
24.7%
ValueCountFrequency (%)
370 2468
24.7%
360 599
 
6.0%
350 1132
11.3%
340 1044
10.4%
330 801
 
8.0%
320 1030
10.3%
315 1418
14.2%
250 1508
15.1%

법정리
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.024
Minimum21
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:09.363533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q128
median34
Q341
95-th percentile47
Maximum50
Range29
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.1167119
Coefficient of variation (CV)0.23855843
Kurtosis-1.045649
Mean34.024
Median Absolute Deviation (MAD)6
Skewness0.027441198
Sum340240
Variance65.881012
MonotonicityNot monotonic
2023-12-13T04:36:09.557054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
36 859
 
8.6%
23 745
 
7.4%
21 621
 
6.2%
33 615
 
6.2%
44 559
 
5.6%
37 472
 
4.7%
29 411
 
4.1%
28 400
 
4.0%
43 399
 
4.0%
31 390
 
3.9%
Other values (20) 4529
45.3%
ValueCountFrequency (%)
21 621
6.2%
22 212
 
2.1%
23 745
7.4%
24 344
3.4%
25 208
 
2.1%
26 90
 
0.9%
27 250
 
2.5%
28 400
4.0%
29 411
4.1%
30 203
 
2.0%
ValueCountFrequency (%)
50 99
 
1.0%
49 248
2.5%
48 93
 
0.9%
47 142
 
1.4%
46 154
 
1.5%
45 322
3.2%
44 559
5.6%
43 399
4.0%
42 294
2.9%
41 204
 
2.0%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9809 
2
 
191

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 9809
98.1%
2 191
 
1.9%

Length

2023-12-13T04:36:09.694159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:09.794610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9809
98.1%
2 191
 
1.9%

본번
Real number (ℝ)

Distinct998
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401.3176
Minimum1
Maximum1597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:09.908018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q1150
median378
Q3592
95-th percentile859
Maximum1597
Range1596
Interquartile range (IQR)442

Descriptive statistics

Standard deviation285.54681
Coefficient of variation (CV)0.71152326
Kurtosis-0.16421132
Mean401.3176
Median Absolute Deviation (MAD)220
Skewness0.55528959
Sum4013176
Variance81536.978
MonotonicityNot monotonic
2023-12-13T04:36:10.078073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 357
 
3.6%
859 281
 
2.8%
69 74
 
0.7%
54 70
 
0.7%
521 53
 
0.5%
384 51
 
0.5%
40 44
 
0.4%
428 43
 
0.4%
567 42
 
0.4%
422 40
 
0.4%
Other values (988) 8945
89.5%
ValueCountFrequency (%)
1 28
 
0.3%
2 357
3.6%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 5
 
0.1%
6 10
 
0.1%
7 10
 
0.1%
8 12
 
0.1%
9 8
 
0.1%
10 5
 
0.1%
ValueCountFrequency (%)
1597 2
< 0.1%
1532 3
< 0.1%
1529 1
 
< 0.1%
1518 1
 
< 0.1%
1515 2
< 0.1%
1506 2
< 0.1%
1505 3
< 0.1%
1477 1
 
< 0.1%
1461 1
 
< 0.1%
1457 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct137
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2043
Minimum0
Maximum272
Zeros3631
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:10.249752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile25
Maximum272
Range272
Interquartile range (IQR)4

Descriptive statistics

Standard deviation19.352892
Coefficient of variation (CV)3.1192708
Kurtosis61.222239
Mean6.2043
Median Absolute Deviation (MAD)1
Skewness7.045137
Sum62043
Variance374.53441
MonotonicityNot monotonic
2023-12-13T04:36:10.424067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3631
36.3%
1 1811
18.1%
2 951
 
9.5%
3 724
 
7.2%
4 488
 
4.9%
5 352
 
3.5%
6 285
 
2.9%
8 197
 
2.0%
7 180
 
1.8%
9 141
 
1.4%
Other values (127) 1240
 
12.4%
ValueCountFrequency (%)
0 3631
36.3%
1 1811
18.1%
2 951
 
9.5%
3 724
 
7.2%
4 488
 
4.9%
5 352
 
3.5%
6 285
 
2.9%
7 180
 
1.8%
8 197
 
2.0%
9 141
 
1.4%
ValueCountFrequency (%)
272 1
 
< 0.1%
271 1
 
< 0.1%
254 1
 
< 0.1%
241 2
< 0.1%
240 2
< 0.1%
236 1
 
< 0.1%
219 1
 
< 0.1%
218 3
< 0.1%
210 4
< 0.1%
208 2
< 0.1%

동
Real number (ℝ)

SKEWED 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6346
Minimum0
Maximum5000
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:10.533705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5000
Range5000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation111.77955
Coefficient of variation (CV)30.754293
Kurtosis1994.2905
Mean3.6346
Median Absolute Deviation (MAD)0
Skewness44.666205
Sum36346
Variance12494.668
MonotonicityNot monotonic
2023-12-13T04:36:10.637572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 9391
93.9%
2 421
 
4.2%
3 117
 
1.2%
4 18
 
0.2%
0 15
 
0.1%
5 11
 
0.1%
6 10
 
0.1%
5000 5
 
0.1%
7 3
 
< 0.1%
100 2
 
< 0.1%
Other values (5) 7
 
0.1%
ValueCountFrequency (%)
0 15
 
0.1%
1 9391
93.9%
2 421
 
4.2%
3 117
 
1.2%
4 18
 
0.2%
5 11
 
0.1%
6 10
 
0.1%
7 3
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
5000 5
0.1%
107 1
 
< 0.1%
103 2
 
< 0.1%
100 2
 
< 0.1%
12 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
7 3
 
< 0.1%
6 10
0.1%
5 11
0.1%

호
Text

Distinct510
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:10.793780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0086
Min length1

Characters and Unicode

Total characters30086
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique339 ?
Unique (%)3.4%

Sample

1st row201
2nd row105
3rd row101
4th row101
5th row101
ValueCountFrequency (%)
101 4163
41.6%
102 2068
20.7%
103 935
 
9.3%
201 474
 
4.7%
104 443
 
4.4%
105 255
 
2.5%
106 124
 
1.2%
202 124
 
1.2%
301 92
 
0.9%
8101 86
 
0.9%
Other values (500) 1236
 
12.4%
2023-12-13T04:36:11.075997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13895
46.2%
0 9374
31.2%
2 3219
 
10.7%
3 1401
 
4.7%
4 688
 
2.3%
5 428
 
1.4%
8 345
 
1.1%
6 294
 
1.0%
7 210
 
0.7%
9 138
 
0.5%
Other values (4) 94
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29992
99.7%
Other Letter 69
 
0.2%
Dash Punctuation 25
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13895
46.3%
0 9374
31.3%
2 3219
 
10.7%
3 1401
 
4.7%
4 688
 
2.3%
5 428
 
1.4%
8 345
 
1.2%
6 294
 
1.0%
7 210
 
0.7%
9 138
 
0.5%
Other Letter
ValueCountFrequency (%)
에 25
36.2%
스 25
36.2%
비 19
27.5%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30017
99.8%
Hangul 69
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13895
46.3%
0 9374
31.2%
2 3219
 
10.7%
3 1401
 
4.7%
4 688
 
2.3%
5 428
 
1.4%
8 345
 
1.1%
6 294
 
1.0%
7 210
 
0.7%
9 138
 
0.5%
Hangul
ValueCountFrequency (%)
에 25
36.2%
스 25
36.2%
비 19
27.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30017
99.8%
Hangul 69
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13895
46.3%
0 9374
31.2%
2 3219
 
10.7%
3 1401
 
4.7%
4 688
 
2.3%
5 428
 
1.4%
8 345
 
1.1%
6 294
 
1.0%
7 210
 
0.7%
9 138
 
0.5%
Hangul
ValueCountFrequency (%)
에 25
36.2%
스 25
36.2%
비 19
27.5%

물건지
Text

Distinct9872
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:11.370146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length27.5626
Min length20

Characters and Unicode

Total characters275626
Distinct characters222
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9766 ?
Unique (%)97.7%

Sample

1st row경상북도 청송군 현서면 구산리 36-1 1동 201호
2nd row경상북도 청송군 현서면 구산리 100-6 1동 105호
3rd row경상북도 청송군 안덕면 문거리 629-2 1동 101호
4th row경상북도 청송군 진보면 진안리 126-1 1동 101호
5th row경상북도 청송군 진보면 진안리 384-21 1동 101호
ValueCountFrequency (%)
7004
 
10.5%
경상북도 6498
 
9.8%
청송군 6498
 
9.8%
1동 6016
 
9.0%
0001동 3375
 
5.1%
101호 2498
 
3.8%
진보면 1681
 
2.5%
0101호 1665
 
2.5%
102호 1399
 
2.1%
청송읍 929
 
1.4%
Other values (4531) 29044
43.6%
2023-12-13T04:36:11.784565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56607
20.5%
1 29784
 
10.8%
0 25573
 
9.3%
동 10735
 
3.9%
호 10005
 
3.6%
2 8657
 
3.1%
송 7824
 
2.8%
청 7788
 
2.8%
도 6763
 
2.5%
상 6669
 
2.4%
Other values (212) 105221
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117336
42.6%
Decimal Number 88889
32.2%
Space Separator 56607
20.5%
Dash Punctuation 5790
 
2.1%
Close Punctuation 3502
 
1.3%
Open Punctuation 3502
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10735
 
9.1%
호 10005
 
8.5%
송 7824
 
6.7%
청 7788
 
6.6%
도 6763
 
5.8%
상 6669
 
5.7%
경 6606
 
5.6%
리 6558
 
5.6%
북 6540
 
5.6%
군 6511
 
5.5%
Other values (198) 41337
35.2%
Decimal Number
ValueCountFrequency (%)
1 29784
33.5%
0 25573
28.8%
2 8657
 
9.7%
3 5253
 
5.9%
4 4850
 
5.5%
5 3623
 
4.1%
6 3058
 
3.4%
8 2830
 
3.2%
7 2679
 
3.0%
9 2582
 
2.9%
Space Separator
ValueCountFrequency (%)
56607
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5790
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3502
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 158290
57.4%
Hangul 117336
42.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10735
 
9.1%
호 10005
 
8.5%
송 7824
 
6.7%
청 7788
 
6.6%
도 6763
 
5.8%
상 6669
 
5.7%
경 6606
 
5.6%
리 6558
 
5.6%
북 6540
 
5.6%
군 6511
 
5.5%
Other values (198) 41337
35.2%
Common
ValueCountFrequency (%)
56607
35.8%
1 29784
18.8%
0 25573
16.2%
2 8657
 
5.5%
- 5790
 
3.7%
3 5253
 
3.3%
4 4850
 
3.1%
5 3623
 
2.3%
] 3502
 
2.2%
[ 3502
 
2.2%
Other values (4) 11149
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158290
57.4%
Hangul 117336
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56607
35.8%
1 29784
18.8%
0 25573
16.2%
2 8657
 
5.5%
- 5790
 
3.7%
3 5253
 
3.3%
4 4850
 
3.1%
5 3623
 
2.3%
] 3502
 
2.2%
[ 3502
 
2.2%
Other values (4) 11149
 
7.0%
Hangul
ValueCountFrequency (%)
동 10735
 
9.1%
호 10005
 
8.5%
송 7824
 
6.7%
청 7788
 
6.6%
도 6763
 
5.8%
상 6669
 
5.7%
경 6606
 
5.6%
리 6558
 
5.6%
북 6540
 
5.6%
군 6511
 
5.5%
Other values (198) 41337
35.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7832
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31406350
Minimum18200
Maximum6.9921 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:11.909246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18200
5-th percentile397425
Q11835880
median7357675
Q326716590
95-th percentile1.1704542 × 108
Maximum6.9921 × 109
Range6.9920818 × 109
Interquartile range (IQR)24880710

Descriptive statistics

Standard deviation1.1150645 × 108
Coefficient of variation (CV)3.5504428
Kurtosis1587.3149
Mean31406350
Median Absolute Deviation (MAD)6608875
Skewness29.587741
Sum3.140635 × 1011
Variance1.2433688 × 1016
MonotonicityNot monotonic
2023-12-13T04:36:12.266660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72197190 200
 
2.0%
990000 45
 
0.4%
48532120 34
 
0.3%
15408000 31
 
0.3%
27627690 27
 
0.3%
905200 25
 
0.2%
936000 24
 
0.2%
18967200 21
 
0.2%
756000 21
 
0.2%
49248720 21
 
0.2%
Other values (7822) 9551
95.5%
ValueCountFrequency (%)
18200 1
< 0.1%
24000 2
< 0.1%
28000 1
< 0.1%
30360 1
< 0.1%
31360 1
< 0.1%
33600 1
< 0.1%
38400 2
< 0.1%
45000 1
< 0.1%
46000 1
< 0.1%
47250 1
< 0.1%
ValueCountFrequency (%)
6992100000 1
< 0.1%
2258864380 1
< 0.1%
2033455000 1
< 0.1%
1999369560 1
< 0.1%
1874897180 1
< 0.1%
1836096000 1
< 0.1%
1441000000 1
< 0.1%
1367757430 1
< 0.1%
1312094200 1
< 0.1%
1306130200 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4666
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.44967
Minimum0.75
Maximum20565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:12.389032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile10
Q133
median66.27
Q3130.54
95-th percentile395.688
Maximum20565
Range20564.25
Interquartile range (IQR)97.54

Descriptive statistics

Standard deviation305.73668
Coefficient of variation (CV)2.4567096
Kurtosis2072.3424
Mean124.44967
Median Absolute Deviation (MAD)39.375
Skewness34.577546
Sum1244496.7
Variance93474.92
MonotonicityNot monotonic
2023-12-13T04:36:12.515551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 486
 
4.9%
82.89 209
 
2.1%
66.0 119
 
1.2%
36.0 56
 
0.6%
20.0 49
 
0.5%
48.0 47
 
0.5%
49.0 45
 
0.4%
144.0 44
 
0.4%
35.0 44
 
0.4%
198.0 43
 
0.4%
Other values (4656) 8858
88.6%
ValueCountFrequency (%)
0.75 1
 
< 0.1%
0.88 1
 
< 0.1%
1.0 1
 
< 0.1%
1.32 1
 
< 0.1%
1.36 1
 
< 0.1%
1.4 1
 
< 0.1%
1.44 5
0.1%
1.49 1
 
< 0.1%
1.5 2
 
< 0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
20565.0 1
< 0.1%
7206.48 1
< 0.1%
6626.88 1
< 0.1%
6114.28 1
< 0.1%
3995.0 1
< 0.1%
3358.99 1
< 0.1%
3050.1 1
< 0.1%
2920.0 1
< 0.1%
2846.71 1
< 0.1%
2750.0 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-01
2nd row2022-06-01
3rd row2022-06-01
4th row2022-06-01
5th row2022-06-01

Common Values

ValueCountFrequency (%)
2022-06-01 10000
100.0%

Length

2023-12-13T04:36:12.627193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:12.723785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-01 10000
100.0%

Interactions

2023-12-13T04:36:06.805994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.134425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.123726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.126998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.030759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.113202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.938607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.941179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.259566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.267260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.261496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.131589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.240131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.062303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:07.061550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.402524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.435683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.392340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.249961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.368511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.188621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:07.192436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.544058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.610242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.533657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.357012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.501712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.316109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:07.300649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.681180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.747520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.653582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.772031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.611010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.450250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:07.397924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.819381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:02.859128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.777329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:04.887706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.723411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.561415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:07.515537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:01.987899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.008986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:03.912986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.019416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:05.837083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:06.690452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:36:12.792541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.8690.0580.5020.1710.0220.0160.020
법정리0.8691.0000.1560.5380.1960.0340.0430.019
특수지0.0580.1561.0000.2770.0000.0000.0000.000
본번0.5020.5380.2771.0000.2410.0000.0190.118
부번0.1710.1960.0000.2411.0000.0000.0000.000
동0.0220.0340.0000.0000.0001.0000.0000.000
시가표준액0.0160.0430.0000.0190.0000.0001.0000.924
연면적0.0200.0190.0000.1180.0000.0000.9241.000
2023-12-13T04:36:12.921193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번동시가표준액연면적특수지
법정동1.0000.827-0.2860.0740.044-0.0280.0120.076
법정리0.8271.000-0.2830.0130.043-0.0980.0110.119
본번-0.286-0.2831.000-0.004-0.0320.0460.0060.213
부번0.0740.013-0.0041.000-0.055-0.048-0.0650.000
동0.0440.043-0.032-0.0551.000-0.025-0.0060.000
시가표준액-0.028-0.0980.046-0.048-0.0251.0000.6760.000
연면적0.0120.0110.006-0.065-0.0060.6761.0000.000
특수지0.0760.1190.2130.0000.0000.0000.0001.000

Missing values

2023-12-13T04:36:07.689258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:36:07.937396image/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
4048경상북도청송군4775020223404313611201경상북도 청송군 현서면 구산리 36-1 1동 201호436650058.222022-06-01
4725경상북도청송군47750202234043110061105경상북도 청송군 현서면 구산리 100-6 1동 105호1126320043.322022-06-01
1983경상북도청송군47750202235036162921101경상북도 청송군 안덕면 문거리 629-2 1동 101호9069480170.82022-06-01
13254경상북도청송군47750202237036112611101경상북도 청송군 진보면 진안리 126-1 1동 101호510450068.062022-06-01
10212경상북도청송군477502022370361384211101경상북도 청송군 진보면 진안리 384-21 1동 101호1092881049.242022-06-01
1964경상북도청송군4775020223404415901108경상북도 청송군 현서면 화목리 59 1동 108호3640000130.02022-06-01
11329경상북도청송군4775020223703613081102경상북도 청송군 진보면 진안리 30-8 1동 102호1435205.982022-06-01
1778경상북도청송군47750202235036140921101경상북도 청송군 안덕면 문거리 409-2 1동 101호17009890150.532022-06-01
4431경상북도청송군4775020223703611401102[ 영당로 43 ] 0001동 0102호1128600054.02022-06-01
2354경상북도청송군4775020223403913901101경상북도 청송군 현서면 월정리 39 1동 101호489445040.452022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
1735경상북도청송군47750202235036125521102경상북도 청송군 안덕면 문거리 255-2 1동 102호110880023.12022-06-01
9365경상북도청송군477502022250241524122201[ 청송로 4586-6 ] 0002동 0201호66600018.02022-06-01
6115경상북도청송군47750202231527182812105[ 부동로 1013 ] 0002동 0105호1971206.162022-06-01
5545경상북도청송군47750202233028173481101경상북도 청송군 현동면 도평리 734-8 1동 101호493245043.652022-06-01
4395경상북도청송군47750202235043123702103경상북도 청송군 안덕면 덕성리 237 2동 103호1720500114.72022-06-01
4095경상북도청송군47750202235033143341103경상북도 청송군 안덕면 명당리 433-4 1동 103호15134000216.22022-06-01
13270경상북도청송군477502022370361126341105[ 진안남2길 5 ] 0001동 0105호2518402048.132022-06-01
2918경상북도청송군47750202234046172201101[ 대거리길 375-9 ] 0001동 0101호1282400056.02022-06-01
12171경상북도청송군477502022370441203160경상북도 청송군 진보면 광덕리 2 3동 160호407740070.32022-06-01
13245경상북도청송군47750202237036112581101[ 진보로 132 ] 0001동 0101호25743900123.02022-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0경상북도청송군47750202225023171601101경상북도 청송군 청송읍 금곡리 716 1동 101호406080027.02022-06-012
1경상북도청송군47750202233032212951101경상북도 청송군 현동면 개일리 산 129-5 1동 101호142200018.02022-06-012
2경상북도청송군477502022350351174011경상북도 청송군 안덕면 장전리 174 1동 1호2713200099.752022-06-012
3경상북도청송군47750202236038125201101경상북도 청송군 파천면 황목리 252 1동 101호315360018.02022-06-012
4경상북도청송군47750202237038156001101[ 돈골신법길 120-110 ] 0001동 0101호622080019.22022-06-012
5경상북도청송군47750202237041140321101[ 상고산길 160-41 ] 0001동 0101호420112034.722022-06-012