Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows62
Duplicate rows (%)0.6%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric6
Text3

Dataset

Description2021년~2022년 서천군 일반건축물 시가표준액에 대한 과세자료로, 물건지 및 시가표준액, 연면적을 포함한 과세자료입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=346&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080610

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
결정일자 has constant value ""Constant
Dataset has 62 (0.6%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (90.9%)Imbalance
연면적 is highly skewed (γ1 = 30.20924627)Skewed
부번 has 2716 (27.2%) zerosZeros

Reproduction

Analysis started2024-01-09 22:08:11.140329
Analysis finished2024-01-09 22:08:15.676354
Duration4.54 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
충청남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 10000
100.0%

Length

2024-01-10T07:08:15.725799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:15.791792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 10000
100.0%

Length

2024-01-10T07:08:15.863600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:15.939185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44770
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 10000
100.0%

Length

2024-01-10T07:08:16.030755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:16.121453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 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

2024-01-10T07:08:16.214347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:16.301591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.3387
Minimum250
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:16.388141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median320
Q3390
95-th percentile410
Maximum410
Range160
Interquartile range (IQR)137

Descriptive statistics

Standard deviation61.251555
Coefficient of variation (CV)0.18885059
Kurtosis-1.5208304
Mean324.3387
Median Absolute Deviation (MAD)67
Skewness0.027895349
Sum3243387
Variance3751.753
MonotonicityNot monotonic
2024-01-10T07:08:16.502973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1783
17.8%
253 1689
16.9%
410 1272
12.7%
310 1116
11.2%
400 719
7.2%
340 638
 
6.4%
390 630
 
6.3%
320 485
 
4.9%
380 463
 
4.6%
330 391
 
3.9%
Other values (3) 814
8.1%
ValueCountFrequency (%)
250 1783
17.8%
253 1689
16.9%
310 1116
11.2%
320 485
 
4.9%
330 391
 
3.9%
340 638
 
6.4%
350 348
 
3.5%
360 177
 
1.8%
370 289
 
2.9%
380 463
 
4.6%
ValueCountFrequency (%)
410 1272
12.7%
400 719
7.2%
390 630
6.3%
380 463
 
4.6%
370 289
 
2.9%
360 177
 
1.8%
350 348
 
3.5%
340 638
6.4%
330 391
 
3.9%
320 485
 
4.9%

법정리
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9681
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:16.627778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median25
Q329
95-th percentile34
Maximum41
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4208412
Coefficient of variation (CV)0.17024123
Kurtosis-0.063350939
Mean25.9681
Median Absolute Deviation (MAD)3
Skewness0.74267815
Sum259681
Variance19.543837
MonotonicityNot monotonic
2024-01-10T07:08:16.754187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21 2029
20.3%
22 1094
10.9%
28 977
9.8%
24 886
8.9%
30 867
8.7%
23 657
 
6.6%
29 593
 
5.9%
26 586
 
5.9%
25 511
 
5.1%
27 410
 
4.1%
Other values (11) 1390
13.9%
ValueCountFrequency (%)
21 2029
20.3%
22 1094
10.9%
23 657
 
6.6%
24 886
8.9%
25 511
 
5.1%
26 586
 
5.9%
27 410
 
4.1%
28 977
9.8%
29 593
 
5.9%
30 867
8.7%
ValueCountFrequency (%)
41 10
 
0.1%
40 22
 
0.2%
39 69
 
0.7%
38 72
 
0.7%
37 65
 
0.7%
36 100
 
1.0%
35 101
 
1.0%
34 174
1.7%
33 266
2.7%
32 193
1.9%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9885 
2
 
115

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 9885
98.9%
2 115
 
1.1%

Length

2024-01-10T07:08:16.881435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:16.956491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9885
98.9%
2 115
 
1.1%

본번
Real number (ℝ)

Distinct872
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.409
Minimum1
Maximum1449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:17.057266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1144
median290
Q3464
95-th percentile855
Maximum1449
Range1448
Interquartile range (IQR)320

Descriptive statistics

Standard deviation251.00011
Coefficient of variation (CV)0.75282943
Kurtosis1.6446323
Mean333.409
Median Absolute Deviation (MAD)160
Skewness1.182452
Sum3334090
Variance63001.054
MonotonicityNot monotonic
2024-01-10T07:08:17.202129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 159
 
1.6%
72 126
 
1.3%
313 102
 
1.0%
275 82
 
0.8%
73 60
 
0.6%
332 59
 
0.6%
303 58
 
0.6%
304 54
 
0.5%
78 52
 
0.5%
334 52
 
0.5%
Other values (862) 9196
92.0%
ValueCountFrequency (%)
1 40
0.4%
2 15
 
0.1%
3 30
0.3%
4 11
 
0.1%
5 11
 
0.1%
6 13
 
0.1%
7 14
 
0.1%
8 10
 
0.1%
9 6
 
0.1%
10 27
0.3%
ValueCountFrequency (%)
1449 3
< 0.1%
1448 3
< 0.1%
1424 5
0.1%
1414 1
 
< 0.1%
1412 4
< 0.1%
1403 2
 
< 0.1%
1391 1
 
< 0.1%
1387 3
< 0.1%
1383 1
 
< 0.1%
1378 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.1752
Minimum0
Maximum866
Zeros2716
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:17.349862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile48
Maximum866
Range866
Interquartile range (IQR)7

Descriptive statistics

Standard deviation62.125211
Coefficient of variation (CV)4.3826691
Kurtosis98.202584
Mean14.1752
Median Absolute Deviation (MAD)2
Skewness9.3087693
Sum141752
Variance3859.5419
MonotonicityNot monotonic
2024-01-10T07:08:17.484858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2716
27.2%
1 1630
16.3%
2 1076
 
10.8%
3 685
 
6.9%
4 574
 
5.7%
5 442
 
4.4%
6 280
 
2.8%
7 242
 
2.4%
9 193
 
1.9%
8 191
 
1.9%
Other values (194) 1971
19.7%
ValueCountFrequency (%)
0 2716
27.2%
1 1630
16.3%
2 1076
 
10.8%
3 685
 
6.9%
4 574
 
5.7%
5 442
 
4.4%
6 280
 
2.8%
7 242
 
2.4%
8 191
 
1.9%
9 193
 
1.9%
ValueCountFrequency (%)
866 1
 
< 0.1%
825 1
 
< 0.1%
821 1
 
< 0.1%
808 1
 
< 0.1%
802 1
 
< 0.1%
789 11
0.1%
786 1
 
< 0.1%
781 3
 
< 0.1%
756 6
0.1%
755 1
 
< 0.1%

동
Text

Distinct88
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:08:17.633896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters40000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.4%

Sample

1st row0001
2nd row0000
3rd row0002
4th row0000
5th row0001
ValueCountFrequency (%)
0000 5000
50.0%
0001 2975
29.7%
9000 1033
 
10.3%
0002 361
 
3.6%
0003 168
 
1.7%
0004 75
 
0.7%
9001 62
 
0.6%
0006 32
 
0.3%
0005 25
 
0.2%
9002 20
 
0.2%
Other values (79) 250
 
2.5%
2024-01-10T07:08:17.849240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34608
86.5%
1 3201
 
8.0%
9 1301
 
3.3%
2 416
 
1.0%
3 205
 
0.5%
4 112
 
0.3%
6 60
 
0.1%
5 48
 
0.1%
7 26
 
0.1%
8 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39999
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34608
86.5%
1 3201
 
8.0%
9 1301
 
3.3%
2 416
 
1.0%
3 205
 
0.5%
4 112
 
0.3%
6 60
 
0.2%
5 48
 
0.1%
7 26
 
0.1%
8 22
 
0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34608
86.5%
1 3201
 
8.0%
9 1301
 
3.3%
2 416
 
1.0%
3 205
 
0.5%
4 112
 
0.3%
6 60
 
0.1%
5 48
 
0.1%
7 26
 
0.1%
8 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34608
86.5%
1 3201
 
8.0%
9 1301
 
3.3%
2 416
 
1.0%
3 205
 
0.5%
4 112
 
0.3%
6 60
 
0.1%
5 48
 
0.1%
7 26
 
0.1%
8 22
 
0.1%

호
Text

Distinct116
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:08:17.984541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.005
Min length1

Characters and Unicode

Total characters40050
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.6%

Sample

1st row0101
2nd row0101
3rd row0101
4th row0102
5th row0101
ValueCountFrequency (%)
0101 5110
51.1%
0102 1675
 
16.8%
0103 750
 
7.5%
0201 685
 
6.9%
0104 359
 
3.6%
0301 189
 
1.9%
0105 174
 
1.7%
0202 121
 
1.2%
8101 119
 
1.2%
0106 110
 
1.1%
Other values (106) 708
 
7.1%
2024-01-10T07:08:18.224990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20046
50.1%
1 15024
37.5%
2 2760
 
6.9%
3 1042
 
2.6%
4 474
 
1.2%
8 214
 
0.5%
5 211
 
0.5%
6 137
 
0.3%
7 82
 
0.2%
9 59
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40049
> 99.9%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20046
50.1%
1 15024
37.5%
2 2760
 
6.9%
3 1042
 
2.6%
4 474
 
1.2%
8 214
 
0.5%
5 211
 
0.5%
6 137
 
0.3%
7 82
 
0.2%
9 59
 
0.1%
Other Letter
ValueCountFrequency (%)
비 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40049
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20046
50.1%
1 15024
37.5%
2 2760
 
6.9%
3 1042
 
2.6%
4 474
 
1.2%
8 214
 
0.5%
5 211
 
0.5%
6 137
 
0.3%
7 82
 
0.2%
9 59
 
0.1%
Hangul
ValueCountFrequency (%)
비 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40049
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20046
50.1%
1 15024
37.5%
2 2760
 
6.9%
3 1042
 
2.6%
4 474
 
1.2%
8 214
 
0.5%
5 211
 
0.5%
6 137
 
0.3%
7 82
 
0.2%
9 59
 
0.1%
Hangul
ValueCountFrequency (%)
비 1
100.0%

물건지
Text

Distinct9261
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:08:18.467235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length27.1433
Min length20

Characters and Unicode

Total characters271433
Distinct characters171
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

Unique8802 ?
Unique (%)88.0%

Sample

1st row충청남도 서천군 비인면 성북리 28-7 1동 101호
2nd row[ 충절로 124 ] 0000동 0101호
3rd row충청남도 서천군 서면 도둔리 1222-46 2동 101호
4th row[ 사곡안길 15 ] 0000동 0102호
5th row충청남도 서천군 종천면 장구리 86-21 1동 101호
ValueCountFrequency (%)
8776
 
13.9%
충청남도 5612
 
8.9%
서천군 5612
 
8.9%
101호 2807
 
4.4%
0000동 2669
 
4.2%
0101호 2303
 
3.6%
1동 1899
 
3.0%
장항읍 1138
 
1.8%
0001동 1077
 
1.7%
9000동 1033
 
1.6%
Other values (4494) 30414
48.0%
2024-01-10T07:08:18.832129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53341
19.7%
0 34450
 
12.7%
1 25871
 
9.5%
호 10012
 
3.7%
서 8741
 
3.2%
2 8695
 
3.2%
동 8016
 
3.0%
천 7313
 
2.7%
도 6102
 
2.2%
군 6089
 
2.2%
Other values (161) 102803
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107802
39.7%
Decimal Number 95938
35.3%
Space Separator 53341
19.7%
Dash Punctuation 5576
 
2.1%
Close Punctuation 4388
 
1.6%
Open Punctuation 4388
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 10012
 
9.3%
서 8741
 
8.1%
동 8016
 
7.4%
천 7313
 
6.8%
도 6102
 
5.7%
군 6089
 
5.6%
충 5985
 
5.6%
남 5885
 
5.5%
청 5680
 
5.3%
리 5630
 
5.2%
Other values (147) 38349
35.6%
Decimal Number
ValueCountFrequency (%)
0 34450
35.9%
1 25871
27.0%
2 8695
 
9.1%
3 5808
 
6.1%
4 4533
 
4.7%
5 3867
 
4.0%
9 3852
 
4.0%
6 3046
 
3.2%
7 3005
 
3.1%
8 2811
 
2.9%
Space Separator
ValueCountFrequency (%)
53341
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5576
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4388
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 163631
60.3%
Hangul 107802
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 10012
 
9.3%
서 8741
 
8.1%
동 8016
 
7.4%
천 7313
 
6.8%
도 6102
 
5.7%
군 6089
 
5.6%
충 5985
 
5.6%
남 5885
 
5.5%
청 5680
 
5.3%
리 5630
 
5.2%
Other values (147) 38349
35.6%
Common
ValueCountFrequency (%)
53341
32.6%
0 34450
21.1%
1 25871
15.8%
2 8695
 
5.3%
3 5808
 
3.5%
- 5576
 
3.4%
4 4533
 
2.8%
] 4388
 
2.7%
[ 4388
 
2.7%
5 3867
 
2.4%
Other values (4) 12714
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163631
60.3%
Hangul 107802
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53341
32.6%
0 34450
21.1%
1 25871
15.8%
2 8695
 
5.3%
3 5808
 
3.5%
- 5576
 
3.4%
4 4533
 
2.8%
] 4388
 
2.7%
[ 4388
 
2.7%
5 3867
 
2.4%
Other values (4) 12714
 
7.8%
Hangul
ValueCountFrequency (%)
호 10012
 
9.3%
서 8741
 
8.1%
동 8016
 
7.4%
천 7313
 
6.8%
도 6102
 
5.7%
군 6089
 
5.6%
충 5985
 
5.6%
남 5885
 
5.5%
청 5680
 
5.3%
리 5630
 
5.2%
Other values (147) 38349
35.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8147
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41159674
Minimum25200
Maximum6.2832702 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:18.945940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25200
5-th percentile480000
Q12066625
median7737500
Q335038200
95-th percentile1.6774433 × 108
Maximum6.2832702 × 109
Range6.283245 × 109
Interquartile range (IQR)32971575

Descriptive statistics

Standard deviation1.4513057 × 108
Coefficient of variation (CV)3.526038
Kurtosis549.47098
Mean41159674
Median Absolute Deviation (MAD)6953820
Skewness18.687473
Sum4.1159674 × 1011
Variance2.1062883 × 1016
MonotonicityNot monotonic
2024-01-10T07:08:19.048459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2376000 53
 
0.5%
2610000 39
 
0.4%
2142000 38
 
0.4%
1908000 33
 
0.3%
1674000 31
 
0.3%
252000 20
 
0.2%
1440000 19
 
0.2%
23984250 16
 
0.2%
24679200 16
 
0.2%
43088760 14
 
0.1%
Other values (8137) 9721
97.2%
ValueCountFrequency (%)
25200 1
< 0.1%
60160 1
< 0.1%
65140 1
< 0.1%
66120 1
< 0.1%
70300 1
< 0.1%
70800 1
< 0.1%
75600 1
< 0.1%
79200 1
< 0.1%
79500 1
< 0.1%
81000 2
< 0.1%
ValueCountFrequency (%)
6283270240 1
< 0.1%
3741983900 1
< 0.1%
3651313550 1
< 0.1%
3464663800 1
< 0.1%
3418440000 1
< 0.1%
3210070900 1
< 0.1%
2534241280 1
< 0.1%
2481532790 1
< 0.1%
2338143280 1
< 0.1%
1998317620 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5263
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.48954
Minimum0.95
Maximum30208.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:19.152305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile12.838
Q131.5
median81.825
Q3181.69
95-th percentile503.297
Maximum30208.03
Range30207.08
Interquartile range (IQR)150.19

Descriptive statistics

Standard deviation500.47674
Coefficient of variation (CV)2.9703729
Kurtosis1482.8035
Mean168.48954
Median Absolute Deviation (MAD)60.825
Skewness30.209246
Sum1684895.4
Variance250476.97
MonotonicityNot monotonic
2024-01-10T07:08:19.254561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 650
 
6.5%
27.0 116
 
1.2%
16.5 66
 
0.7%
36.0 48
 
0.5%
198.0 46
 
0.5%
15.0 45
 
0.4%
10.0 42
 
0.4%
9.0 41
 
0.4%
24.0 40
 
0.4%
12.0 39
 
0.4%
Other values (5253) 8867
88.7%
ValueCountFrequency (%)
0.95 1
< 0.1%
1.16 1
< 0.1%
1.2 1
< 0.1%
1.35 1
< 0.1%
1.7 2
< 0.1%
1.76 1
< 0.1%
1.88 1
< 0.1%
1.98 1
< 0.1%
2.0 1
< 0.1%
2.11 1
< 0.1%
ValueCountFrequency (%)
30208.03 1
< 0.1%
14010.0 1
< 0.1%
13325.63 1
< 0.1%
13056.0 1
< 0.1%
10603.52 1
< 0.1%
6882.0 1
< 0.1%
6511.3 1
< 0.1%
5949.1 1
< 0.1%
5804.95 1
< 0.1%
5611.095 1
< 0.1%

결정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20210601
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 10000
100.0%

Length

2024-01-10T07:08:19.353125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:19.421645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 10000
100.0%

Interactions

2024-01-10T07:08:14.945715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.385625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.860255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.335023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.801887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.241800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:15.031103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.462434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.940487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.414327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.876210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.320284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:15.115410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.555888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.026209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.497054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.953053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.401812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:15.192271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.634870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.104641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.578759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.025826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.476342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:15.267890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.707050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.179368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.649205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.092246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.547124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:15.344238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:12.783768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.256916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:13.728838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.169295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:14.628707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:08:19.467792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.5320.0870.4060.1650.3460.0400.000
법정리0.5321.0000.0260.4820.2540.1170.0390.000
특수지0.0870.0261.0000.2380.0000.0000.0000.000
본번0.4060.4820.2381.0000.1780.1760.1360.080
부번0.1650.2540.0000.1781.0000.0000.0000.000
동0.3460.1170.0000.1760.0001.0000.0000.000
시가표준액0.0400.0390.0000.1360.0000.0001.0000.860
연면적0.0000.0000.0000.0800.0000.0000.8601.000
2024-01-10T07:08:19.557655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.117-0.100-0.059-0.130-0.0390.073
법정리0.1171.000-0.036-0.102-0.106-0.0050.006
본번-0.100-0.0361.0000.0170.1400.0270.183
부번-0.059-0.1020.0171.0000.1140.0250.000
시가표준액-0.130-0.1060.1400.1141.0000.7010.000
연면적-0.039-0.0050.0270.0250.7011.0000.000
특수지0.0730.0060.1830.0000.0000.0001.000

Missing values

2024-01-10T07:08:15.450881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:08:15.597676image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
19018충청남도서천군44770202140022128700010101충청남도 서천군 비인면 성북리 28-7 1동 101호33024000192.020210601
10247충청남도서천군447702021253211158300000101[ 충절로 124 ] 0000동 0101호4281984090.7220210601
20647충청남도서천군44770202141028112224600020101충청남도 서천군 서면 도둔리 1222-46 2동 101호70187130326.320210601
6659충청남도서천군4477020212532213049400000102[ 사곡안길 15 ] 0000동 0102호4772643054.2520210601
15133충청남도서천군447702021390251862100010101충청남도 서천군 종천면 장구리 86-21 1동 101호43088760290.1620210601
12699충청남도서천군447702021340211751490000103[ 충절로 1156 ] 9000동 0103호9120008.020210601
10310충청남도서천군447702021310361417000020102[ 당선길166번길 23 ] 0002동 0102호66120033.0620210601
16867충청남도서천군447702021400261467300000101[ 충서로 1410-50 ] 0000동 0101호5553660132.2320210601
2593충청남도서천군44770202125025178300010101충청남도 서천군 장항읍 장암리 78-3 1동 101호2125204.6220210601
7609충청남도서천군447702021320291199000000101[ 활산로141번길 61-13 ] 0000동 0101호145152040.3220210601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
6271충청남도서천군44770202125024163700010101[ 신창동로 81 ] 0001동 0101호690000092.020210601
5343충청남도서천군447702021253221386001090101충청남도 서천군 서천읍 사곡리 386 109동 101호100232800144.064420210601
21261충청남도서천군44770202141026189700010102충청남도 서천군 서면 신합리 89-7 1동 102호1431120071.220210601
4373충청남도서천군447702021250211292500000101[ 장산로317번길 30-1 ] 0000동 0101호171600026.020210601
16870충청남도서천군447702021400211551100020103충청남도 서천군 비인면 성내리 551-1 2동 103호307427099.1720210601
1749충청남도서천군4477020212502411501500000201충청남도 서천군 장항읍 신창리 150-15 201호24570000113.7520210601
18366충청남도서천군447702021370271198100000101[ 구동길 94 ] 0000동 0101호607983081.3920210601
20383충청남도서천군4477020214102811288000000101[ 춘장대로151번길 51 ] 0000동 0101호1444520093.820210601
6553충청남도서천군44770202125323188200000103충청남도 서천군 서천읍 오석리 88-2 103호28890000270.020210601
13692충청남도서천군447702021350271352700010102충청남도 서천군 마산면 관포리 352-7 1동 102호188500032.520210601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자# duplicates
21충청남도서천군447702021390241557000010101충청남도 서천군 종천면 당정리 557 1동 101호36288540307.53202106015
46충청남도서천군4477020214102514858700010101충청남도 서천군 서면 원두리 485-87 1동 101호19205480169.96202106015
51충청남도서천군4477020214102814361400010101[ 춘장대길7번길 15 ] 0001동 0101호46800018.0202106015
0충청남도서천군44770202125022133600000000000000충청남도 서천군 장항읍 창선2리 336136800018.0202106013
11충청남도서천군447702021330221211200010101충청남도 서천군 기산면 신산리 211-2 1동 101호45091200184.8202106013
22충청남도서천군447702021390241562000010101충청남도 서천군 종천면 당정리 562 1동 101호29066940246.33202106013
1충청남도서천군44770202125022133600000000000000충청남도 서천군 장항읍 창선2리 336273600036.0202106012
2충청남도서천군447702021250241420200010101충청남도 서천군 장항읍 신창리 420-2 1동 101호262800052.56202106012
3충청남도서천군4477020212503014092000010101충청남도 서천군 장항읍 원수리 409-20 1동 101호1102428097.56202106012
4충청남도서천군447702021253211315000010101충청남도 서천군 서천읍 군사리 315 1동 101호196320032.72202106012