Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows42
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.
URLhttps://www.data.go.kr/data/15080610/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 42 (0.4%) duplicate rowsDuplicates
결정일자 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 결정일자High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (94.4%)Imbalance
시가표준액 is highly skewed (γ1 = 28.36354299)Skewed
연면적 is highly skewed (γ1 = 24.56773835)Skewed
부번 has 2777 (27.8%) zerosZeros

Reproduction

Analysis started2023-12-12 15:06:06.082864
Analysis finished2023-12-12 15:06:18.632004
Duration12.55 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

2023-12-13T00:06:18.686936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:18.784950image/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

2023-12-13T00:06:18.886772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:18.970107image/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

2023-12-13T00:06:19.091939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:19.238268image/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
5057 
2021
4943 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2022 5057
50.6%
2021 4943
49.4%

Length

2023-12-13T00:06:19.397038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:19.547397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 5057
50.6%
2021 4943
49.4%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.8875
Minimum250
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:19.730798image/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 deviation61.244281
Coefficient of variation (CV)0.18850919
Kurtosis-1.5206624
Mean324.8875
Median Absolute Deviation (MAD)67
Skewness0.011473873
Sum3248875
Variance3750.8619
MonotonicityNot monotonic
2023-12-13T00:06:19.928511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1827
18.3%
253 1605
16.1%
410 1299
13.0%
310 1137
11.4%
400 684
 
6.8%
390 653
 
6.5%
340 631
 
6.3%
380 467
 
4.7%
320 461
 
4.6%
330 373
 
3.7%
Other values (3) 863
8.6%
ValueCountFrequency (%)
250 1827
18.3%
253 1605
16.1%
310 1137
11.4%
320 461
 
4.6%
330 373
 
3.7%
340 631
 
6.3%
350 362
 
3.6%
360 195
 
1.9%
370 306
 
3.1%
380 467
 
4.7%
ValueCountFrequency (%)
410 1299
13.0%
400 684
6.8%
390 653
6.5%
380 467
 
4.7%
370 306
 
3.1%
360 195
 
1.9%
350 362
 
3.6%
340 631
6.3%
330 373
 
3.7%
320 461
 
4.6%

법정리
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.0286
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:20.099763image/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 deviation4.4334126
Coefficient of variation (CV)0.17032851
Kurtosis-0.032619818
Mean26.0286
Median Absolute Deviation (MAD)3
Skewness0.75201581
Sum260286
Variance19.655148
MonotonicityNot monotonic
2023-12-13T00:06:20.299912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21 1977
19.8%
22 1000
10.0%
24 980
9.8%
28 973
9.7%
30 843
8.4%
23 694
 
6.9%
29 616
 
6.2%
26 586
 
5.9%
25 506
 
5.1%
27 408
 
4.1%
Other values (11) 1417
14.2%
ValueCountFrequency (%)
21 1977
19.8%
22 1000
10.0%
23 694
 
6.9%
24 980
9.8%
25 506
 
5.1%
26 586
 
5.9%
27 408
 
4.1%
28 973
9.7%
29 616
 
6.2%
30 843
8.4%
ValueCountFrequency (%)
41 11
 
0.1%
40 15
 
0.1%
39 84
 
0.8%
38 75
 
0.8%
37 76
 
0.8%
36 111
 
1.1%
35 92
 
0.9%
34 151
1.5%
33 296
3.0%
32 194
1.9%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9888 
2
 
111
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9888
98.9%
2 111
 
1.1%
7 1
 
< 0.1%

Length

2023-12-13T00:06:20.439453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:20.546608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9888
98.9%
2 111
 
1.1%
7 1
 
< 0.1%

본번
Real number (ℝ)

Distinct882
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.9368
Minimum1
Maximum1471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:20.972844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1143
median286
Q3459
95-th percentile855
Maximum1471
Range1470
Interquartile range (IQR)316

Descriptive statistics

Standard deviation253.22692
Coefficient of variation (CV)0.7651821
Kurtosis1.9929106
Mean330.9368
Median Absolute Deviation (MAD)157
Skewness1.2637477
Sum3309368
Variance64123.871
MonotonicityNot monotonic
2023-12-13T00:06:21.122745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 148
 
1.5%
72 130
 
1.3%
313 103
 
1.0%
303 77
 
0.8%
332 63
 
0.6%
73 62
 
0.6%
350 62
 
0.6%
25 59
 
0.6%
243 52
 
0.5%
416 51
 
0.5%
Other values (872) 9193
91.9%
ValueCountFrequency (%)
1 35
0.4%
2 24
0.2%
3 33
0.3%
4 10
 
0.1%
5 22
0.2%
6 12
 
0.1%
7 13
 
0.1%
8 12
 
0.1%
9 5
 
0.1%
10 24
0.2%
ValueCountFrequency (%)
1471 1
 
< 0.1%
1449 5
0.1%
1448 2
 
< 0.1%
1441 1
 
< 0.1%
1424 5
0.1%
1418 1
 
< 0.1%
1414 2
 
< 0.1%
1412 6
0.1%
1406 2
 
< 0.1%
1391 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct208
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.7306
Minimum0
Maximum975
Zeros2777
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:21.296994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile48
Maximum975
Range975
Interquartile range (IQR)7

Descriptive statistics

Standard deviation65.195298
Coefficient of variation (CV)4.4258413
Kurtosis92.677933
Mean14.7306
Median Absolute Deviation (MAD)2
Skewness9.0662551
Sum147306
Variance4250.4269
MonotonicityNot monotonic
2023-12-13T00:06:21.446212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2777
27.8%
1 1648
16.5%
2 1028
 
10.3%
3 695
 
7.0%
4 566
 
5.7%
5 399
 
4.0%
6 300
 
3.0%
7 230
 
2.3%
8 185
 
1.8%
9 183
 
1.8%
Other values (198) 1989
19.9%
ValueCountFrequency (%)
0 2777
27.8%
1 1648
16.5%
2 1028
 
10.3%
3 695
 
7.0%
4 566
 
5.7%
5 399
 
4.0%
6 300
 
3.0%
7 230
 
2.3%
8 185
 
1.8%
9 183
 
1.8%
ValueCountFrequency (%)
975 1
 
< 0.1%
866 1
 
< 0.1%
833 1
 
< 0.1%
825 1
 
< 0.1%
821 2
 
< 0.1%
802 1
 
< 0.1%
789 14
0.1%
781 3
 
< 0.1%
766 1
 
< 0.1%
756 5
 
0.1%


Text

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:06:21.587991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.3184
Min length1

Characters and Unicode

Total characters13184
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.3%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row2
ValueCountFrequency (%)
0 5212
52.1%
1 3075
30.7%
9000 861
 
8.6%
2 353
 
3.5%
3 129
 
1.3%
4 55
 
0.5%
9001 54
 
0.5%
9999 29
 
0.3%
5 27
 
0.3%
6 17
 
0.2%
Other values (63) 189
 
1.9%
2023-12-13T00:06:21.826549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8069
61.2%
1 3259
24.7%
9 1118
 
8.5%
2 385
 
2.9%
3 153
 
1.2%
4 72
 
0.5%
5 41
 
0.3%
6 41
 
0.3%
7 25
 
0.2%
8 19
 
0.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13182
> 99.9%
Modifier Symbol 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8069
61.2%
1 3259
24.7%
9 1118
 
8.5%
2 385
 
2.9%
3 153
 
1.2%
4 72
 
0.5%
5 41
 
0.3%
6 41
 
0.3%
7 25
 
0.2%
8 19
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8069
61.2%
1 3259
24.7%
9 1118
 
8.5%
2 385
 
2.9%
3 153
 
1.2%
4 72
 
0.5%
5 41
 
0.3%
6 41
 
0.3%
7 25
 
0.2%
8 19
 
0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8069
61.2%
1 3259
24.7%
9 1118
 
8.5%
2 385
 
2.9%
3 153
 
1.2%
4 72
 
0.5%
5 41
 
0.3%
6 41
 
0.3%
7 25
 
0.2%
8 19
 
0.1%
Other values (2) 2
 
< 0.1%


Real number (ℝ)

Distinct106
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.2367
Minimum0
Maximum8102
Zeros42
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:21.947963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1101
median101
Q3103
95-th percentile301
Maximum8102
Range8102
Interquartile range (IQR)2

Descriptive statistics

Standard deviation930.77954
Coefficient of variation (CV)4.0427071
Kurtosis67.084799
Mean230.2367
Median Absolute Deviation (MAD)0
Skewness8.2859065
Sum2302367
Variance866350.55
MonotonicityNot monotonic
2023-12-13T00:06:22.110389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 5128
51.3%
102 1694
 
16.9%
103 774
 
7.7%
201 700
 
7.0%
104 318
 
3.2%
301 187
 
1.9%
105 160
 
1.6%
8101 120
 
1.2%
106 107
 
1.1%
202 103
 
1.0%
Other values (96) 709
 
7.1%
ValueCountFrequency (%)
0 42
0.4%
1 95
0.9%
2 21
 
0.2%
3 11
 
0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
81 2
 
< 0.1%
ValueCountFrequency (%)
8102 16
 
0.2%
8101 120
1.2%
8001 1
 
< 0.1%
3001 1
 
< 0.1%
1402 1
 
< 0.1%
1010 3
 
< 0.1%
1004 1
 
< 0.1%
1003 1
 
< 0.1%
1001 3
 
< 0.1%
903 1
 
< 0.1%
Distinct8329
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:06:22.378305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length27.1103
Min length20

Characters and Unicode

Total characters271103
Distinct characters172
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

Unique7060 ?
Unique (%)70.6%

Sample

1st row[ 부사로 153 ] 0001동 0101호
2nd row충청남도 서천군 서면 신합리 473-26 101호
3rd row충청남도 서천군 마서면 당선리 359-3 101호
4th row[ 사곡로 173 ] 0000동 0101호
5th row충청남도 서천군 마서면 덕암리 229-8 2동 102호
ValueCountFrequency (%)
8608
 
13.6%
충청남도 5696
 
9.0%
서천군 5696
 
9.0%
101호 2823
 
4.5%
0000동 2705
 
4.3%
0101호 2305
 
3.6%
1동 1997
 
3.2%
장항읍 1155
 
1.8%
0001동 1079
 
1.7%
102호 952
 
1.5%
Other values (4365) 30230
47.8%
2023-12-13T00:06:22.812464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53247
19.6%
0 33873
 
12.5%
1 26004
 
9.6%
10019
 
3.7%
8821
 
3.3%
2 8722
 
3.2%
7857
 
2.9%
7392
 
2.7%
6209
 
2.3%
6186
 
2.3%
Other values (162) 102773
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108427
40.0%
Decimal Number 95220
35.1%
Space Separator 53247
19.6%
Dash Punctuation 5601
 
2.1%
Close Punctuation 4304
 
1.6%
Open Punctuation 4304
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10019
 
9.2%
8821
 
8.1%
7857
 
7.2%
7392
 
6.8%
6209
 
5.7%
6186
 
5.7%
6041
 
5.6%
5956
 
5.5%
5768
 
5.3%
5719
 
5.3%
Other values (148) 38459
35.5%
Decimal Number
ValueCountFrequency (%)
0 33873
35.6%
1 26004
27.3%
2 8722
 
9.2%
3 5857
 
6.2%
4 4378
 
4.6%
5 3899
 
4.1%
9 3649
 
3.8%
7 3032
 
3.2%
6 3013
 
3.2%
8 2793
 
2.9%
Space Separator
ValueCountFrequency (%)
53247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5601
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4304
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162676
60.0%
Hangul 108427
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10019
 
9.2%
8821
 
8.1%
7857
 
7.2%
7392
 
6.8%
6209
 
5.7%
6186
 
5.7%
6041
 
5.6%
5956
 
5.5%
5768
 
5.3%
5719
 
5.3%
Other values (148) 38459
35.5%
Common
ValueCountFrequency (%)
53247
32.7%
0 33873
20.8%
1 26004
16.0%
2 8722
 
5.4%
3 5857
 
3.6%
- 5601
 
3.4%
4 4378
 
2.7%
] 4304
 
2.6%
[ 4304
 
2.6%
5 3899
 
2.4%
Other values (4) 12487
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162676
60.0%
Hangul 108427
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53247
32.7%
0 33873
20.8%
1 26004
16.0%
2 8722
 
5.4%
3 5857
 
3.6%
- 5601
 
3.4%
4 4378
 
2.7%
] 4304
 
2.6%
[ 4304
 
2.6%
5 3899
 
2.4%
Other values (4) 12487
 
7.7%
Hangul
ValueCountFrequency (%)
10019
 
9.2%
8821
 
8.1%
7857
 
7.2%
7392
 
6.8%
6209
 
5.7%
6186
 
5.7%
6041
 
5.6%
5956
 
5.5%
5768
 
5.3%
5719
 
5.3%
Other values (148) 38459
35.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8220
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44703541
Minimum30000
Maximum9.5958802 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:22.950993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30000
5-th percentile434640
Q12016000
median7323435
Q335792610
95-th percentile1.7562697 × 108
Maximum9.5958802 × 109
Range9.5958502 × 109
Interquartile range (IQR)33776610

Descriptive statistics

Standard deviation1.9831321 × 108
Coefficient of variation (CV)4.4361857
Kurtosis1151.88
Mean44703541
Median Absolute Deviation (MAD)6626790
Skewness28.363543
Sum4.4703541 × 1011
Variance3.9328129 × 1016
MonotonicityNot monotonic
2023-12-13T00:06:23.078930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2502000 27
 
0.3%
2250000 26
 
0.3%
2016000 22
 
0.2%
2376000 21
 
0.2%
23984250 21
 
0.2%
2142000 17
 
0.2%
288000 17
 
0.2%
2610000 16
 
0.2%
24679200 16
 
0.2%
1764000 15
 
0.1%
Other values (8210) 9802
98.0%
ValueCountFrequency (%)
30000 2
< 0.1%
31200 1
< 0.1%
56750 1
< 0.1%
66120 1
< 0.1%
67110 1
< 0.1%
77500 1
< 0.1%
79500 1
< 0.1%
81000 1
< 0.1%
82600 2
< 0.1%
86800 1
< 0.1%
ValueCountFrequency (%)
9595880160 1
< 0.1%
9331488000 1
< 0.1%
5463954080 1
< 0.1%
4832088750 1
< 0.1%
3631169990 1
< 0.1%
3464663800 1
< 0.1%
3281695200 1
< 0.1%
3006617190 1
< 0.1%
2734405630 1
< 0.1%
2383593150 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4829
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.30426
Minimum0.95
Maximum26269.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:06:23.229883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile12
Q129.9825
median79.34
Q3182.5625
95-th percentile569.354
Maximum26269.01
Range26268.06
Interquartile range (IQR)152.58

Descriptive statistics

Standard deviation533.17212
Coefficient of variation (CV)3.0588588
Kurtosis888.62383
Mean174.30426
Median Absolute Deviation (MAD)59.405
Skewness24.567738
Sum1743042.6
Variance284272.51
MonotonicityNot monotonic
2023-12-13T00:06:23.371330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 638
 
6.4%
27.0 105
 
1.1%
16.5 74
 
0.7%
12.0 60
 
0.6%
15.0 57
 
0.6%
10.0 54
 
0.5%
24.0 52
 
0.5%
36.0 51
 
0.5%
198.0 45
 
0.4%
9.0 39
 
0.4%
Other values (4819) 8825
88.2%
ValueCountFrequency (%)
0.95 1
< 0.1%
1.12 1
< 0.1%
1.16 1
< 0.1%
1.29 1
< 0.1%
1.6 2
< 0.1%
1.7 1
< 0.1%
1.88 1
< 0.1%
2.0 2
< 0.1%
2.25 1
< 0.1%
2.28 1
< 0.1%
ValueCountFrequency (%)
26269.01 1
< 0.1%
15552.48 2
< 0.1%
14622.49 1
< 0.1%
13806.73 1
< 0.1%
13325.63 1
< 0.1%
13056.0 1
< 0.1%
6511.3 1
< 0.1%
6171.25 1
< 0.1%
5962.93 2
< 0.1%
5611.095 1
< 0.1%

결정일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20220601
5057 
20210601
4943 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20220601 5057
50.6%
20210601 4943
49.4%

Length

2023-12-13T00:06:23.480835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:06:23.557350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220601 5057
50.6%
20210601 4943
49.4%

Interactions

2023-12-13T00:06:17.370851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:08.405191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.564588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:10.923566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.904886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:13.221932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:16.296047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.462320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:08.544884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.668605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.057388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:12.010899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:13.509085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:16.436460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.557131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:08.673127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.787138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.206587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:12.097064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:13.886084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:16.594943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.666905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:08.786059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.902981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.294790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:12.450941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:14.223295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:16.714839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.749572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:08.915284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.994477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.378669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:12.543178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:14.545521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:16.827485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:18.117706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.359849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:10.618776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.738472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:13.018157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:15.286012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.212621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:18.214237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:09.464146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:10.776908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:11.819711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:13.112267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:15.941268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:06:17.293297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:06:23.617987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적결정일자
과세년도1.0000.0080.0000.0000.0000.0140.1010.0000.0140.0121.000
법정동0.0081.0000.5440.0910.3990.1680.3240.0680.0130.0210.008
법정리0.0000.5441.0000.0490.4780.2610.1270.0500.0460.0240.000
특수지0.0000.0910.0491.0000.2020.0000.0000.0000.0000.0000.000
본번0.0000.3990.4780.2021.0000.1790.1260.0340.1070.0660.000
부번0.0140.1680.2610.0000.1791.0000.0000.0000.0000.0000.014
0.1010.3240.1270.0000.1260.0001.0000.0770.0000.0000.101
0.0000.0680.0500.0000.0340.0000.0771.0000.0000.0000.000
시가표준액0.0140.0130.0460.0000.1070.0000.0000.0001.0000.9020.014
연면적0.0120.0210.0240.0000.0660.0000.0000.0000.9021.0000.012
결정일자1.0000.0080.0000.0000.0000.0140.1010.0000.0140.0121.000
2023-12-13T00:06:23.732179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지결정일자과세년도
특수지1.0000.0000.000
결정일자0.0001.0001.000
과세년도0.0001.0001.000
2023-12-13T00:06:23.823336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지결정일자
법정동1.0000.109-0.100-0.075-0.126-0.111-0.0270.0060.0630.006
법정리0.1091.000-0.037-0.125-0.108-0.0930.0040.0000.0340.000
본번-0.100-0.0371.000-0.0080.0600.1350.0370.0000.1220.000
부번-0.075-0.125-0.0081.0000.0300.1240.0510.0110.0000.011
-0.126-0.1080.0600.0301.0000.0820.0020.0250.0000.025
시가표준액-0.111-0.0930.1350.1240.0821.0000.7110.0100.0000.010
연면적-0.0270.0040.0370.0510.0020.7111.0000.0090.0000.009
과세년도0.0060.0000.0000.0110.0250.0100.0091.0000.0001.000
특수지0.0630.0340.1220.0000.0000.0000.0000.0001.0000.000
결정일자0.0060.0000.0000.0110.0250.0100.0091.0000.0001.000

Missing values

2023-12-13T00:06:18.364352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:06:18.550255image/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
18583충청남도서천군447702021410281106291101[ 부사로 153 ] 0001동 0101호239075510367.9520210601
41413충청남도서천군447702022410261473260101충청남도 서천군 서면 신합리 473-26 101호5806680099.620220601
10085충청남도서천군44770202131036135930101충청남도 서천군 마서면 당선리 359-3 101호640500091.520210601
28249충청남도서천군44770202225330167090101[ 사곡로 173 ] 0000동 0101호78673920199.6820220601
31438충청남도서천군44770202231030122982102충청남도 서천군 마서면 덕암리 229-8 2동 102호1384250018.7220220601
30874충청남도서천군44770202231023115501201[ 마서로 400 ] 0001동 0201호412391680518.0820220601
3340충청남도서천군447702021250241171200102충청남도 서천군 장항읍 신창리 171-20 102호385560075.620210601
38185충청남도서천군447702022400291312218101[ 충서로 622 ] 0001동 8101호2544300056.5420220601
4653충청남도서천군44770202125322131301301충청남도 서천군 서천읍 사곡리 313 1동 301호383400018.020210601
14117충청남도서천군44770202134039113811101충청남도 서천군 한산면 용산리 138-1 1동 101호39670039.6720210601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
2191충청남도서천군44770202125022153910202충청남도 서천군 장항읍 창선2리 539-1 202호150708019.8320210601
33731충청남도서천군44770202235032114809000101충청남도 서천군 마산면 벽오리 148 9000동 101호8618400342.020220601
18714충청남도서천군447702021410281103911101충청남도 서천군 서면 도둔리 1039-1 1동 101호17308210153.1720210601
24023충청남도서천군44770202225030199370103[ 장항공단길 77 ] 0000동 0103호131100057.020220601
27286충청남도서천군44770202225321150261201[ 군청로 29 ] 0001동 0201호852533045.5920220601
22207충청남도서천군44770202225030145031102충청남도 서천군 장항읍 원수리 450-3 1동 102호611535001036.520220601
35466충청남도서천군447702022330211245179999101[ 기산길 62 ] 9999동 0101호31872250132.2520220601
19941충청남도서천군44770202141022127552201충청남도 서천군 서면 마량리 275-5 2동 201호349800066.020210601
36961충청남도서천군4477020223402119781104충청남도 서천군 한산면 지현리 97-8 1동 104호3100715061.1420220601
38728충청남도서천군44770202238030133910102[ 저산길33번길 43 ] 0000동 0102호92554066.1120220601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자# duplicates
12충청남도서천군447702021410281436141102[ 춘장대길7번길 15 ] 0001동 0102호11520012.0202106016
39충청남도서천군447702022410281436141102[ 춘장대길7번길 15 ] 0001동 0102호12240012.0202206015
10충청남도서천군44770202141026130641101충청남도 서천군 서면 신합리 306-4 1동 101호28800012.0202106013
13충청남도서천군447702021410281138302101충청남도 서천군 서면 도둔리 1383 2동 101호731214024.62202106013
21충청남도서천군44770202231030173801101충청남도 서천군 마서면 덕암리 738 1동 101호20520009.0202206013
40충청남도서천군447702022410281138302101충청남도 서천군 서면 도둔리 1383 2동 101호765682024.62202206013
0충청남도서천군447702021250291378010충청남도 서천군 장항읍 옥산리 378 1동24804630219.51202106012
1충청남도서천군44770202125030122431101충청남도 서천군 장항읍 원수리 224-3 1동 101호67108800196.8202106012
2충청남도서천군44770202131031175501201충청남도 서천군 마서면 송내리 755 1동 201호205788000342.98202106012
3충청남도서천군44770202131037119111101충청남도 서천군 마서면 어리 191-1 1동 101호25287502.89202106012