Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows40
Duplicate rows (%)0.4%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

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
Dataset has 40 (0.4%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 결정일자High correlation
결정일자 is highly overall correlated with 과세년도High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (90.2%)Imbalance
시가표준액 is highly skewed (γ1 = 30.96797861)Skewed
부번 has 2774 (27.7%) zerosZeros
has 5201 (52.0%) zerosZeros

Reproduction

Analysis started2024-01-09 22:07:42.813485
Analysis finished2024-01-09 22:07:50.459976
Duration7.65 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

2024-01-10T07:07:50.506517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:50.573027image/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

2024-01-10T07:07:50.643771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:50.709596image/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

2024-01-10T07:07:50.781284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:50.853546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
5017 
2021
4983 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2021
4th row2022
5th row2021

Common Values

ValueCountFrequency (%)
2022 5017
50.2%
2021 4983
49.8%

Length

2024-01-10T07:07:50.923156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:50.993275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 5017
50.2%
2021 4983
49.8%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.3011
Minimum250
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:51.058424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median330
Q3390
95-th percentile410
Maximum410
Range160
Interquartile range (IQR)137

Descriptive statistics

Standard deviation61.07649
Coefficient of variation (CV)0.18775372
Kurtosis-1.5111443
Mean325.3011
Median Absolute Deviation (MAD)70
Skewness0.00026132579
Sum3253011
Variance3730.3377
MonotonicityNot monotonic
2024-01-10T07:07:51.145978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1820
18.2%
253 1567
15.7%
410 1309
13.1%
310 1116
11.2%
400 678
 
6.8%
340 663
 
6.6%
390 658
 
6.6%
320 483
 
4.8%
380 451
 
4.5%
330 390
 
3.9%
Other values (3) 865
8.6%
ValueCountFrequency (%)
250 1820
18.2%
253 1567
15.7%
310 1116
11.2%
320 483
 
4.8%
330 390
 
3.9%
340 663
 
6.6%
350 360
 
3.6%
360 182
 
1.8%
370 323
 
3.2%
380 451
 
4.5%
ValueCountFrequency (%)
410 1309
13.1%
400 678
6.8%
390 658
6.6%
380 451
 
4.5%
370 323
 
3.2%
360 182
 
1.8%
350 360
 
3.6%
340 663
6.6%
330 390
 
3.9%
320 483
 
4.8%

법정리
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9961
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:51.237639image/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 deviation4.3920171
Coefficient of variation (CV)0.16894908
Kurtosis0.029866124
Mean25.9961
Median Absolute Deviation (MAD)3
Skewness0.75283292
Sum259961
Variance19.289814
MonotonicityNot monotonic
2024-01-10T07:07:51.330105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21 1981
19.8%
22 1054
10.5%
28 1042
10.4%
24 890
8.9%
30 851
8.5%
23 646
 
6.5%
26 638
 
6.4%
29 602
 
6.0%
25 545
 
5.5%
27 394
 
3.9%
Other values (11) 1357
13.6%
ValueCountFrequency (%)
21 1981
19.8%
22 1054
10.5%
23 646
 
6.5%
24 890
8.9%
25 545
 
5.5%
26 638
 
6.4%
27 394
 
3.9%
28 1042
10.4%
29 602
 
6.0%
30 851
8.5%
ValueCountFrequency (%)
41 14
 
0.1%
40 19
 
0.2%
39 78
 
0.8%
38 77
 
0.8%
37 56
 
0.6%
36 103
 
1.0%
35 86
 
0.9%
34 147
1.5%
33 287
2.9%
32 188
1.9%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9874 
2
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 9874
98.7%
2 126
 
1.3%

Length

2024-01-10T07:07:51.429809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:51.498791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9874
98.7%
2 126
 
1.3%

본번
Real number (ℝ)

Distinct874
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.3991
Minimum1
Maximum1449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:51.579472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1137
median282
Q3462
95-th percentile857
Maximum1449
Range1448
Interquartile range (IQR)325

Descriptive statistics

Standard deviation257.1781
Coefficient of variation (CV)0.7760374
Kurtosis1.7313649
Mean331.3991
Median Absolute Deviation (MAD)162
Skewness1.2190523
Sum3313991
Variance66140.574
MonotonicityNot monotonic
2024-01-10T07:07:51.686787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 156
 
1.6%
72 130
 
1.3%
313 86
 
0.9%
73 71
 
0.7%
332 66
 
0.7%
399 63
 
0.6%
350 55
 
0.5%
275 54
 
0.5%
303 53
 
0.5%
78 52
 
0.5%
Other values (864) 9214
92.1%
ValueCountFrequency (%)
1 50
0.5%
2 32
0.3%
3 31
0.3%
4 16
 
0.2%
5 14
 
0.1%
6 16
 
0.2%
7 11
 
0.1%
8 16
 
0.2%
9 7
 
0.1%
10 27
0.3%
ValueCountFrequency (%)
1449 1
 
< 0.1%
1448 10
0.1%
1424 7
0.1%
1418 2
 
< 0.1%
1412 3
 
< 0.1%
1406 1
 
< 0.1%
1391 1
 
< 0.1%
1387 3
 
< 0.1%
1383 4
 
< 0.1%
1374 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct210
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.4761
Minimum0
Maximum985
Zeros2774
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:51.807191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile46
Maximum985
Range985
Interquartile range (IQR)7

Descriptive statistics

Standard deviation65.553189
Coefficient of variation (CV)4.5283736
Kurtosis96.744603
Mean14.4761
Median Absolute Deviation (MAD)2
Skewness9.2910003
Sum144761
Variance4297.2206
MonotonicityNot monotonic
2024-01-10T07:07:51.911183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2774
27.7%
1 1631
16.3%
2 1027
 
10.3%
3 709
 
7.1%
4 528
 
5.3%
5 438
 
4.4%
6 315
 
3.1%
7 236
 
2.4%
9 210
 
2.1%
8 196
 
2.0%
Other values (200) 1936
19.4%
ValueCountFrequency (%)
0 2774
27.7%
1 1631
16.3%
2 1027
 
10.3%
3 709
 
7.1%
4 528
 
5.3%
5 438
 
4.4%
6 315
 
3.1%
7 236
 
2.4%
8 196
 
2.0%
9 210
 
2.1%
ValueCountFrequency (%)
985 2
 
< 0.1%
975 1
 
< 0.1%
825 1
 
< 0.1%
821 1
 
< 0.1%
808 1
 
< 0.1%
802 2
 
< 0.1%
800 1
 
< 0.1%
789 10
0.1%
786 1
 
< 0.1%
781 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean923.5405
Minimum0
Maximum9999
Zeros5201
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:52.017231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9000
Maximum9999
Range9999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2730.4645
Coefficient of variation (CV)2.9565185
Kurtosis4.9133379
Mean923.5405
Median Absolute Deviation (MAD)0
Skewness2.6281216
Sum9235405
Variance7455436.6
MonotonicityNot monotonic
2024-01-10T07:07:52.122370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5201
52.0%
1 3096
31.0%
9000 892
 
8.9%
2 284
 
2.8%
3 149
 
1.5%
9001 57
 
0.6%
4 57
 
0.6%
5 42
 
0.4%
6 22
 
0.2%
9999 17
 
0.2%
Other values (56) 183
 
1.8%
ValueCountFrequency (%)
0 5201
52.0%
1 3096
31.0%
2 284
 
2.8%
3 149
 
1.5%
4 57
 
0.6%
5 42
 
0.4%
6 22
 
0.2%
7 13
 
0.1%
8 7
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
9999 17
0.2%
9069 1
 
< 0.1%
9065 1
 
< 0.1%
9063 2
 
< 0.1%
9060 1
 
< 0.1%
9051 1
 
< 0.1%
9042 1
 
< 0.1%
9039 1
 
< 0.1%
9035 1
 
< 0.1%
9033 1
 
< 0.1%


Text

Distinct110
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:07:52.252212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9779
Min length1

Characters and Unicode

Total characters29779
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

Unique44 ?
Unique (%)0.4%

Sample

1st row101
2nd row101
3rd row102
4th row101
5th row101
ValueCountFrequency (%)
101 5119
51.2%
102 1728
 
17.3%
103 760
 
7.6%
201 646
 
6.5%
104 333
 
3.3%
105 178
 
1.8%
301 178
 
1.8%
106 125
 
1.2%
8101 125
 
1.2%
202 106
 
1.1%
Other values (100) 702
 
7.0%
2024-01-10T07:07:52.479735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14987
50.3%
0 9782
32.8%
2 2763
 
9.3%
3 1057
 
3.5%
4 450
 
1.5%
5 225
 
0.8%
8 223
 
0.7%
6 155
 
0.5%
7 78
 
0.3%
9 58
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29778
> 99.9%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14987
50.3%
0 9782
32.8%
2 2763
 
9.3%
3 1057
 
3.5%
4 450
 
1.5%
5 225
 
0.8%
8 223
 
0.7%
6 155
 
0.5%
7 78
 
0.3%
9 58
 
0.2%
Other Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29778
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14987
50.3%
0 9782
32.8%
2 2763
 
9.3%
3 1057
 
3.5%
4 450
 
1.5%
5 225
 
0.8%
8 223
 
0.7%
6 155
 
0.5%
7 78
 
0.3%
9 58
 
0.2%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29778
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14987
50.3%
0 9782
32.8%
2 2763
 
9.3%
3 1057
 
3.5%
4 450
 
1.5%
5 225
 
0.8%
8 223
 
0.7%
6 155
 
0.5%
7 78
 
0.3%
9 58
 
0.2%
Hangul
ValueCountFrequency (%)
1
100.0%
Distinct8235
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:07:52.732405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length27.1238
Min length19

Characters and Unicode

Total characters271238
Distinct characters169
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

Unique6885 ?
Unique (%)68.8%

Sample

1st row충청남도 서천군 마서면 송석리 578-2 1동 101호
2nd row충청남도 서천군 한산면 마양리 257-3 1동 101호
3rd row[ 충서로460번길 29-4 ] 0000동 0102호
4th row충청남도 서천군 한산면 지현리 172-5 2동 101호
5th row[ 마명송림길135번길 71-2 ] 0000동 0101호
ValueCountFrequency (%)
8648
 
13.7%
충청남도 5676
 
9.0%
서천군 5676
 
9.0%
101호 2809
 
4.4%
0000동 2762
 
4.4%
0101호 2309
 
3.6%
1동 2003
 
3.2%
장항읍 1149
 
1.8%
0001동 1093
 
1.7%
102호 977
 
1.5%
Other values (4291) 30194
47.7%
2024-01-10T07:07:53.106877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53296
19.6%
0 34142
 
12.6%
1 26099
 
9.6%
10004
 
3.7%
8780
 
3.2%
2 8591
 
3.2%
7921
 
2.9%
7382
 
2.7%
6189
 
2.3%
6154
 
2.3%
Other values (159) 102680
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108323
39.9%
Decimal Number 95381
35.2%
Space Separator 53296
19.6%
Dash Punctuation 5590
 
2.1%
Close Punctuation 4324
 
1.6%
Open Punctuation 4324
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10004
 
9.2%
8780
 
8.1%
7921
 
7.3%
7382
 
6.8%
6189
 
5.7%
6154
 
5.7%
6031
 
5.6%
5958
 
5.5%
5745
 
5.3%
5698
 
5.3%
Other values (145) 38461
35.5%
Decimal Number
ValueCountFrequency (%)
0 34142
35.8%
1 26099
27.4%
2 8591
 
9.0%
3 5759
 
6.0%
4 4343
 
4.6%
5 3811
 
4.0%
9 3697
 
3.9%
6 3082
 
3.2%
7 3058
 
3.2%
8 2799
 
2.9%
Space Separator
ValueCountFrequency (%)
53296
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5590
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4324
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4324
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162915
60.1%
Hangul 108323
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10004
 
9.2%
8780
 
8.1%
7921
 
7.3%
7382
 
6.8%
6189
 
5.7%
6154
 
5.7%
6031
 
5.6%
5958
 
5.5%
5745
 
5.3%
5698
 
5.3%
Other values (145) 38461
35.5%
Common
ValueCountFrequency (%)
53296
32.7%
0 34142
21.0%
1 26099
16.0%
2 8591
 
5.3%
3 5759
 
3.5%
- 5590
 
3.4%
4 4343
 
2.7%
] 4324
 
2.7%
[ 4324
 
2.7%
5 3811
 
2.3%
Other values (4) 12636
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162915
60.1%
Hangul 108323
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53296
32.7%
0 34142
21.0%
1 26099
16.0%
2 8591
 
5.3%
3 5759
 
3.5%
- 5590
 
3.4%
4 4343
 
2.7%
] 4324
 
2.7%
[ 4324
 
2.7%
5 3811
 
2.3%
Other values (4) 12636
 
7.8%
Hangul
ValueCountFrequency (%)
10004
 
9.2%
8780
 
8.1%
7921
 
7.3%
7382
 
6.8%
6189
 
5.7%
6154
 
5.7%
6031
 
5.6%
5958
 
5.5%
5745
 
5.3%
5698
 
5.3%
Other values (145) 38461
35.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8226
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43372476
Minimum25200
Maximum9.5958802 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:53.224034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25200
5-th percentile440946
Q12066625
median7670955
Q336209025
95-th percentile1.7465052 × 108
Maximum9.5958802 × 109
Range9.595855 × 109
Interquartile range (IQR)34142400

Descriptive statistics

Standard deviation1.8439803 × 108
Coefficient of variation (CV)4.2514987
Kurtosis1423.1464
Mean43372476
Median Absolute Deviation (MAD)6950955
Skewness30.967979
Sum4.3372476 × 1011
Variance3.4002632 × 1016
MonotonicityNot monotonic
2024-01-10T07:07:53.333519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2376000 36
 
0.4%
2502000 31
 
0.3%
2016000 28
 
0.3%
2250000 24
 
0.2%
2142000 23
 
0.2%
1764000 19
 
0.2%
2610000 19
 
0.2%
23984250 18
 
0.2%
1512000 18
 
0.2%
1908000 16
 
0.2%
Other values (8216) 9768
97.7%
ValueCountFrequency (%)
25200 1
< 0.1%
56750 1
< 0.1%
60000 1
< 0.1%
70300 1
< 0.1%
70800 1
< 0.1%
72000 1
< 0.1%
72710 1
< 0.1%
75600 2
< 0.1%
79200 1
< 0.1%
80960 1
< 0.1%
ValueCountFrequency (%)
9595880160 1
< 0.1%
9331488000 1
< 0.1%
3849067700 1
< 0.1%
3631169990 1
< 0.1%
3464663800 1
< 0.1%
2535161200 1
< 0.1%
2026366000 1
< 0.1%
1979190200 1
< 0.1%
1901190000 1
< 0.1%
1807298090 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4822
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.98631
Minimum1.16
Maximum15552.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:07:53.435985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.16
5-th percentile12.0665
Q130.6575
median81
Q3182.555
95-th percentile510.7365
Maximum15552.48
Range15551.32
Interquartile range (IQR)151.8975

Descriptive statistics

Standard deviation419.54151
Coefficient of variation (CV)2.5275669
Kurtosis604.64201
Mean165.98631
Median Absolute Deviation (MAD)61
Skewness19.713896
Sum1659863.1
Variance176015.08
MonotonicityNot monotonic
2024-01-10T07:07:53.544635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 615
 
6.2%
27.0 114
 
1.1%
16.5 63
 
0.6%
36.0 54
 
0.5%
198.0 52
 
0.5%
9.0 50
 
0.5%
24.0 48
 
0.5%
15.0 45
 
0.4%
10.0 43
 
0.4%
12.0 40
 
0.4%
Other values (4812) 8876
88.8%
ValueCountFrequency (%)
1.16 1
 
< 0.1%
1.29 1
 
< 0.1%
1.3 1
 
< 0.1%
1.35 1
 
< 0.1%
1.76 1
 
< 0.1%
2.11 1
 
< 0.1%
2.16 1
 
< 0.1%
2.25 1
 
< 0.1%
2.28 1
 
< 0.1%
2.4 3
< 0.1%
ValueCountFrequency (%)
15552.48 2
< 0.1%
13806.73 1
< 0.1%
13325.63 1
< 0.1%
7612.27 1
< 0.1%
7485.0 1
< 0.1%
5949.1 1
< 0.1%
5213.8 1
< 0.1%
4875.31 1
< 0.1%
4564.39 1
< 0.1%
4368.62 1
< 0.1%

결정일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20220601
5017 
20210601
4983 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220601
2nd row20220601
3rd row20210601
4th row20220601
5th row20210601

Common Values

ValueCountFrequency (%)
20220601 5017
50.2%
20210601 4983
49.8%

Length

2024-01-10T07:07:53.648276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:07:53.729838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220601 5017
50.2%
20210601 4983
49.8%

Interactions

2024-01-10T07:07:49.221678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.153185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.897925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.593954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.289587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.180004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.527624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.302046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.240731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.978126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.674971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.363991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.369533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.605718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.384409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.329956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.058933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.760068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.444174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.565262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.690741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.723105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.408838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.137396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.837658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.516656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.746422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.776317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.798473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.494278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.210907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.909155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.583970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.901887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.848015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:50.025294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.742044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.427180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.131858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.022147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.200382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.065516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:50.104216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:44.818589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:45.509388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:46.208409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:47.092111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:48.362231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:49.142132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:07:54.021650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적결정일자
과세년도1.0000.0250.0190.0000.0170.0260.0820.0000.0001.000
법정동0.0251.0000.5420.1130.4080.1570.1600.0590.0530.025
법정리0.0190.5421.0000.0720.4850.2500.0770.0650.0710.019
특수지0.0000.1130.0721.0000.2400.0000.0000.0000.0000.000
본번0.0170.4080.4850.2401.0000.1610.0000.1440.1040.017
부번0.0260.1570.2500.0000.1611.0000.0000.0000.0000.026
0.0820.1600.0770.0000.0000.0001.0000.0000.0000.082
시가표준액0.0000.0590.0650.0000.1440.0000.0001.0000.8840.000
연면적0.0000.0530.0710.0000.1040.0000.0000.8841.0000.000
결정일자1.0000.0250.0190.0000.0170.0260.0820.0000.0001.000
2024-01-10T07:07:54.111232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지결정일자
과세년도1.0000.0001.000
특수지0.0001.0000.000
결정일자1.0000.0001.000
2024-01-10T07:07:54.184203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지결정일자
법정동1.0000.121-0.119-0.0610.022-0.115-0.0290.0180.0930.018
법정리0.1211.000-0.040-0.1010.021-0.097-0.0070.0230.0410.023
본번-0.119-0.0401.0000.0120.0200.1460.0360.0130.1840.013
부번-0.061-0.1010.0121.000-0.0480.1120.0360.0200.0000.020
0.0220.0210.020-0.0481.000-0.025-0.1340.0910.0000.091
시가표준액-0.115-0.0970.1460.112-0.0251.0000.7080.0000.0000.000
연면적-0.029-0.0070.0360.036-0.1340.7081.0000.0000.0000.000
과세년도0.0180.0230.0130.0200.0910.0000.0001.0000.0001.000
특수지0.0930.0410.1840.0000.0000.0000.0000.0001.0000.000
결정일자0.0180.0230.0130.0200.0910.0000.0001.0000.0001.000

Missing values

2024-01-10T07:07:50.231156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:07:50.387656image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
30976충청남도서천군44770202231025157821101충청남도 서천군 마서면 송석리 578-2 1동 101호5798100193.2720220601
35984충청남도서천군44770202234036125731101충청남도 서천군 한산면 마양리 257-3 1동 101호200508045.5720220601
16089충청남도서천군44770202139023130300102[ 충서로460번길 29-4 ] 0000동 0102호187680036.820210601
35098충청남도서천군44770202234021117252101충청남도 서천군 한산면 지현리 172-5 2동 101호143200057.2820220601
13495충청남도서천군4477020213503622930101[ 마명송림길135번길 71-2 ] 0000동 0101호232897047.5320210601
6239충청남도서천군44770202125321150340101[ 서천로84번길 2 ] 0000동 0101호1998433094.9620210601
1991충청남도서천군447702021250261330017107충청남도 서천군 장항읍 화천리 330 17동 107호15645600147.620210601
28269충청남도서천군447702022253211187120101충청남도 서천군 서천읍 군사리 187-12 101호176400012.020220601
39549충청남도서천군44770202239026130150201충청남도 서천군 종천면 화산리 301-5 201호3662379085.7720220601
41077충청남도서천군44770202240031137931101[ 갯벌체험로695번길 9-33 ] 0001동 0101호224580011.420220601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
40190충청남도서천군447702022380291144340201[ 종판로901번길 12 ] 0000동 0201호7392000168.020220601
20058충청남도서천군44770202141024146201101충청남도 서천군 서면 부사리 462 1동 101호304831510999.1220210601
15600충청남도서천군44770202139029144200101충청남도 서천군 종천면 석촌리 442 101호1293600084.020210601
15592충청남도서천군447702021390251645470103충청남도 서천군 종천면 장구리 645-47 103호860940601247.7420210601
12278충청남도서천군44770202135024137411[ 한마로912번길 41 ] 0001동 0001호3983364086.2220210601
39438충청남도서천군4477020223803111370101충청남도 서천군 판교면 수성리 13-7 101호135000090.020220601
29109충청남도서천군4477020222532213850106201충청남도 서천군 서천읍 사곡리 385 106동 201호166881330268.7320220601
43892충청남도서천군44770202241024123201102충청남도 서천군 서면 부사리 23-20 1동 102호5402804.4820220601
10237충청남도서천군447702021253211754111101[ 서천로 144-2 ] 0001동 0101호649800085.520210601
16106충청남도서천군44770202139022111729000101충청남도 서천군 종천면 랑평리 117-2 9000동 101호190800018.020210601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자# duplicates
5충청남도서천군44770202139024156201101충청남도 서천군 종천면 당정리 562 1동 101호29066940246.33202106014
17충청남도서천군447702021410281138302101충청남도 서천군 서면 도둔리 1383 2동 101호731214024.62202106013
38충청남도서천군4477020224102811201101충청남도 서천군 서면 도둔리 1-20 1동 101호175863018.91202206013
39충청남도서천군4477020224102811021201101충청남도 서천군 서면 도둔리 1021-20 1동 101호133500089.0202206013
0충청남도서천군44770202125022118731101충청남도 서천군 장항읍 창선2리 187-3 1동 101호90432000192.0202106012
1충청남도서천군44770202125024142119000101충청남도 서천군 장항읍 신창리 421-1 9000동 101호788400018.0202106012
2충청남도서천군4477020212502713352101[ 장마로185번길 24-41 ] 0002동 0101호9000018.0202106012
3충청남도서천군44770202133032112701101[ 장산로 1673-13 ] 0001동 0101호37000074.0202106012
4충청남도서천군4477020213402116011101충청남도 서천군 한산면 지현리 60-1 1동 101호332000040.0202106012
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