Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액에 대한 데이터로 물건지, 시가표준 금액, 연면적, 결정일자 등의 항목을 제공합니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15079937/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 13 (0.1%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 과세년도High correlation
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 (95.3%)Imbalance
시가표준액 is highly skewed (γ1 = 26.77975095)Skewed
연면적 is highly skewed (γ1 = 28.59475382)Skewed
법정리 has 8327 (83.3%) zerosZeros
부번 has 3731 (37.3%) zerosZeros
has 700 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-12 19:24:05.921432
Analysis finished2023-12-12 19:24:13.801758
Duration7.88 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
전라남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 10000
100.0%

Length

2023-12-13T04:24:13.861818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:13.942856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여수시 10000
100.0%

Length

2023-12-13T04:24:14.021167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:14.108947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여수시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
46130
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46130
2nd row46130
3rd row46130
4th row46130
5th row46130

Common Values

ValueCountFrequency (%)
46130 10000
100.0%

Length

2023-12-13T04:24:14.192486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:14.269876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46130 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
7778 
2018
2222 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2018
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 7778
77.8%
2018 2222
 
22.2%

Length

2023-12-13T04:24:14.361333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:14.451597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 7778
77.8%
2018 2222
 
22.2%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.6838
Minimum101
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:14.559210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile108
Q1118
median128
Q3144
95-th percentile330
Maximum360
Range259
Interquartile range (IQR)26

Descriptive statistics

Standard deviation69.257774
Coefficient of variation (CV)0.44486179
Kurtosis1.823251
Mean155.6838
Median Absolute Deviation (MAD)13
Skewness1.842898
Sum1556838
Variance4796.6393
MonotonicityNot monotonic
2023-12-13T04:24:14.694798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 564
 
5.6%
120 553
 
5.5%
128 526
 
5.3%
250 481
 
4.8%
144 418
 
4.2%
108 406
 
4.1%
115 389
 
3.9%
320 361
 
3.6%
141 360
 
3.6%
133 342
 
3.4%
Other values (48) 5600
56.0%
ValueCountFrequency (%)
101 36
 
0.4%
102 56
 
0.6%
103 86
 
0.9%
104 75
 
0.8%
105 33
 
0.3%
106 16
 
0.2%
107 84
 
0.8%
108 406
4.1%
109 13
 
0.1%
110 207
2.1%
ValueCountFrequency (%)
360 87
 
0.9%
350 78
 
0.8%
340 115
 
1.1%
330 242
2.4%
320 361
3.6%
310 309
3.1%
250 481
4.8%
151 18
 
0.2%
150 72
 
0.7%
149 19
 
0.2%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2351
Minimum0
Maximum30
Zeros8327
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:14.817640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile28
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.5426482
Coefficient of variation (CV)2.2532285
Kurtosis1.5960785
Mean4.2351
Median Absolute Deviation (MAD)0
Skewness1.8608311
Sum42351
Variance91.062134
MonotonicityNot monotonic
2023-12-13T04:24:14.933700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 8327
83.3%
21 320
 
3.2%
30 242
 
2.4%
22 188
 
1.9%
28 179
 
1.8%
29 146
 
1.5%
24 134
 
1.3%
25 134
 
1.3%
27 134
 
1.3%
26 99
 
1.0%
ValueCountFrequency (%)
0 8327
83.3%
21 320
 
3.2%
22 188
 
1.9%
23 97
 
1.0%
24 134
 
1.3%
25 134
 
1.3%
26 99
 
1.0%
27 134
 
1.3%
28 179
 
1.8%
29 146
 
1.5%
ValueCountFrequency (%)
30 242
2.4%
29 146
1.5%
28 179
1.8%
27 134
1.3%
26 99
 
1.0%
25 134
1.3%
24 134
1.3%
23 97
 
1.0%
22 188
1.9%
21 320
3.2%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9911 
2
 
88
3
 
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 9911
99.1%
2 88
 
0.9%
3 1
 
< 0.1%

Length

2023-12-13T04:24:15.046872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:15.130965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9911
99.1%
2 88
 
0.9%
3 1
 
< 0.1%

본번
Real number (ℝ)

Distinct1460
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean532.3788
Minimum1
Maximum2239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:15.235643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1178
median419
Q3774
95-th percentile1406
Maximum2239
Range2238
Interquartile range (IQR)596

Descriptive statistics

Standard deviation451.49824
Coefficient of variation (CV)0.84807705
Kurtosis0.53122604
Mean532.3788
Median Absolute Deviation (MAD)300
Skewness1.0305977
Sum5323788
Variance203850.66
MonotonicityNot monotonic
2023-12-13T04:24:15.369382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
805 110
 
1.1%
280 106
 
1.1%
37 101
 
1.0%
70 96
 
1.0%
7 93
 
0.9%
400 91
 
0.9%
1 86
 
0.9%
323 72
 
0.7%
62 68
 
0.7%
172 68
 
0.7%
Other values (1450) 9109
91.1%
ValueCountFrequency (%)
1 86
0.9%
2 32
 
0.3%
3 9
 
0.1%
4 45
0.4%
5 11
 
0.1%
6 14
 
0.1%
7 93
0.9%
8 12
 
0.1%
9 23
 
0.2%
10 18
 
0.2%
ValueCountFrequency (%)
2239 1
 
< 0.1%
2208 1
 
< 0.1%
2207 1
 
< 0.1%
2187 1
 
< 0.1%
2177 1
 
< 0.1%
2154 1
 
< 0.1%
2149 5
0.1%
2146 2
 
< 0.1%
2145 1
 
< 0.1%
2144 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3962
Minimum0
Maximum585
Zeros3731
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:15.507300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile26
Maximum585
Range585
Interquartile range (IQR)6

Descriptive statistics

Standard deviation36.76161
Coefficient of variation (CV)4.3783628
Kurtosis167.08562
Mean8.3962
Median Absolute Deviation (MAD)1
Skewness12.111375
Sum83962
Variance1351.416
MonotonicityNot monotonic
2023-12-13T04:24:15.644878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3731
37.3%
1 1426
 
14.3%
2 729
 
7.3%
3 534
 
5.3%
4 418
 
4.2%
5 357
 
3.6%
6 331
 
3.3%
7 285
 
2.9%
8 247
 
2.5%
9 202
 
2.0%
Other values (162) 1740
17.4%
ValueCountFrequency (%)
0 3731
37.3%
1 1426
 
14.3%
2 729
 
7.3%
3 534
 
5.3%
4 418
 
4.2%
5 357
 
3.6%
6 331
 
3.3%
7 285
 
2.9%
8 247
 
2.5%
9 202
 
2.0%
ValueCountFrequency (%)
585 1
 
< 0.1%
584 3
< 0.1%
582 1
 
< 0.1%
578 2
< 0.1%
576 1
 
< 0.1%
569 2
< 0.1%
564 1
 
< 0.1%
563 1
 
< 0.1%
560 1
 
< 0.1%
558 2
< 0.1%


Real number (ℝ)

ZEROS 

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.8952
Minimum0
Maximum9000
Zeros700
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:15.807138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile15.05
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation350.93343
Coefficient of variation (CV)7.8167251
Kurtosis393.38287
Mean44.8952
Median Absolute Deviation (MAD)0
Skewness17.880846
Sum448952
Variance123154.28
MonotonicityNot monotonic
2023-12-13T04:24:15.952930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7858
78.6%
0 700
 
7.0%
2 554
 
5.5%
3 160
 
1.6%
901 108
 
1.1%
4 56
 
0.6%
5 37
 
0.4%
101 32
 
0.3%
902 25
 
0.2%
7 23
 
0.2%
Other values (105) 447
 
4.5%
ValueCountFrequency (%)
0 700
 
7.0%
1 7858
78.6%
2 554
 
5.5%
3 160
 
1.6%
4 56
 
0.6%
5 37
 
0.4%
6 22
 
0.2%
7 23
 
0.2%
8 15
 
0.1%
9 12
 
0.1%
ValueCountFrequency (%)
9000 2
 
< 0.1%
8002 4
< 0.1%
8001 7
0.1%
7001 1
 
< 0.1%
3002 5
0.1%
3001 3
< 0.1%
999 2
 
< 0.1%
990 1
 
< 0.1%
976 1
 
< 0.1%
974 3
< 0.1%


Text

Distinct265
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:24:16.295467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0011
Min length3

Characters and Unicode

Total characters40011
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

Unique113 ?
Unique (%)1.1%

Sample

1st row0104
2nd row8101
3rd row0101
4th row0101
5th row0102
ValueCountFrequency (%)
0101 3478
34.8%
0102 1285
 
12.8%
0201 1228
 
12.3%
0301 550
 
5.5%
0103 505
 
5.1%
8101 405
 
4.0%
0202 236
 
2.4%
0104 230
 
2.3%
0401 211
 
2.1%
0105 137
 
1.4%
Other values (255) 1735
17.3%
2023-12-13T04:24:17.035794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19599
49.0%
1 12945
32.4%
2 3660
 
9.1%
3 1507
 
3.8%
4 731
 
1.8%
8 659
 
1.6%
5 433
 
1.1%
6 237
 
0.6%
7 143
 
0.4%
9 89
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40003
> 99.9%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19599
49.0%
1 12945
32.4%
2 3660
 
9.1%
3 1507
 
3.8%
4 731
 
1.8%
8 659
 
1.6%
5 433
 
1.1%
6 237
 
0.6%
7 143
 
0.4%
9 89
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19599
49.0%
1 12945
32.4%
2 3660
 
9.1%
3 1507
 
3.8%
4 731
 
1.8%
8 659
 
1.6%
5 433
 
1.1%
6 237
 
0.6%
7 143
 
0.4%
9 89
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19599
49.0%
1 12945
32.4%
2 3660
 
9.1%
3 1507
 
3.8%
4 731
 
1.8%
8 659
 
1.6%
5 433
 
1.1%
6 237
 
0.6%
7 143
 
0.4%
9 89
 
0.2%
Distinct8769
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:24:17.421613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length24.8644
Min length17

Characters and Unicode

Total characters248644
Distinct characters229
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

Unique8246 ?
Unique (%)82.5%

Sample

1st row[ 여자대동길 3-1 ] 0001동 0104호
2nd row[ 상암로 624 ] 0001동 8101호
3rd row전라남도 여수시 중흥동 1741 1동 101호
4th row전라남도 여수시 평여동 205-12 1동 101호
5th row전라남도 여수시 서교동 280-9 1동 102호
ValueCountFrequency (%)
9898
 
16.2%
전라남도 5051
 
8.3%
여수시 5051
 
8.3%
0001동 4227
 
6.9%
1동 3631
 
6.0%
101호 1774
 
2.9%
0101호 1705
 
2.8%
0201호 763
 
1.3%
0102호 660
 
1.1%
102호 626
 
1.0%
Other values (4111) 27628
45.3%
2023-12-13T04:24:18.002822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51014
20.5%
0 32038
 
12.9%
1 28611
 
11.5%
14377
 
5.8%
10303
 
4.1%
2 8800
 
3.5%
5979
 
2.4%
5812
 
2.3%
5648
 
2.3%
3 5426
 
2.2%
Other values (219) 80636
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93001
37.4%
Other Letter 90547
36.4%
Space Separator 51014
20.5%
Open Punctuation 4949
 
2.0%
Close Punctuation 4949
 
2.0%
Dash Punctuation 4184
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14377
15.9%
10303
11.4%
5979
 
6.6%
5812
 
6.4%
5648
 
6.2%
5339
 
5.9%
5248
 
5.8%
5235
 
5.8%
5113
 
5.6%
2525
 
2.8%
Other values (205) 24968
27.6%
Decimal Number
ValueCountFrequency (%)
0 32038
34.4%
1 28611
30.8%
2 8800
 
9.5%
3 5426
 
5.8%
4 3970
 
4.3%
5 3296
 
3.5%
7 2970
 
3.2%
8 2771
 
3.0%
6 2769
 
3.0%
9 2350
 
2.5%
Space Separator
ValueCountFrequency (%)
51014
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4949
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4949
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 158097
63.6%
Hangul 90547
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14377
15.9%
10303
11.4%
5979
 
6.6%
5812
 
6.4%
5648
 
6.2%
5339
 
5.9%
5248
 
5.8%
5235
 
5.8%
5113
 
5.6%
2525
 
2.8%
Other values (205) 24968
27.6%
Common
ValueCountFrequency (%)
51014
32.3%
0 32038
20.3%
1 28611
18.1%
2 8800
 
5.6%
3 5426
 
3.4%
[ 4949
 
3.1%
] 4949
 
3.1%
- 4184
 
2.6%
4 3970
 
2.5%
5 3296
 
2.1%
Other values (4) 10860
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158097
63.6%
Hangul 90547
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51014
32.3%
0 32038
20.3%
1 28611
18.1%
2 8800
 
5.6%
3 5426
 
3.4%
[ 4949
 
3.1%
] 4949
 
3.1%
- 4184
 
2.6%
4 3970
 
2.5%
5 3296
 
2.1%
Other values (4) 10860
 
6.9%
Hangul
ValueCountFrequency (%)
14377
15.9%
10303
11.4%
5979
 
6.6%
5812
 
6.4%
5648
 
6.2%
5339
 
5.9%
5248
 
5.8%
5235
 
5.8%
5113
 
5.6%
2525
 
2.8%
Other values (205) 24968
27.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8926
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57673173
Minimum28800
Maximum1.1671656 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:18.194142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28800
5-th percentile612415
Q14998510
median19824400
Q356689828
95-th percentile1.9398998 × 108
Maximum1.1671656 × 1010
Range1.1671627 × 1010
Interquartile range (IQR)51691318

Descriptive statistics

Standard deviation2.0428475 × 108
Coefficient of variation (CV)3.5421105
Kurtosis1195.4067
Mean57673173
Median Absolute Deviation (MAD)17329180
Skewness26.779751
Sum5.7673173 × 1011
Variance4.1732259 × 1016
MonotonicityNot monotonic
2023-12-13T04:24:18.345918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25194400 21
 
0.2%
5133150 17
 
0.2%
22917340 16
 
0.2%
6642000 14
 
0.1%
17719200 14
 
0.1%
10838900 12
 
0.1%
4998510 12
 
0.1%
6092600 11
 
0.1%
31860000 10
 
0.1%
6048000 10
 
0.1%
Other values (8916) 9863
98.6%
ValueCountFrequency (%)
28800 1
< 0.1%
36000 1
< 0.1%
39000 1
< 0.1%
40000 1
< 0.1%
40320 1
< 0.1%
43200 1
< 0.1%
45000 2
< 0.1%
46500 1
< 0.1%
52270 1
< 0.1%
60000 1
< 0.1%
ValueCountFrequency (%)
11671655800 1
< 0.1%
5158340490 1
< 0.1%
4670263080 1
< 0.1%
4606916320 1
< 0.1%
4474421950 1
< 0.1%
3524062080 1
< 0.1%
3348299950 1
< 0.1%
2853346650 1
< 0.1%
2835024690 1
< 0.1%
2789396790 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5863
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.19662
Minimum0.24
Maximum26430.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:18.553128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.24
5-th percentile8.6875
Q132.6775
median74.5
Q3147.9075
95-th percentile493.509
Maximum26430.38
Range26430.14
Interquartile range (IQR)115.23

Descriptive statistics

Standard deviation471.50301
Coefficient of variation (CV)3.0578039
Kurtosis1294.709
Mean154.19662
Median Absolute Deviation (MAD)49.325
Skewness28.594754
Sum1541966.2
Variance222315.09
MonotonicityNot monotonic
2023-12-13T04:24:18.717404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 165
 
1.7%
27.0 104
 
1.0%
36.0 41
 
0.4%
9.0 36
 
0.4%
12.0 35
 
0.4%
15.0 32
 
0.3%
15.3 30
 
0.3%
30.0 30
 
0.3%
54.0 29
 
0.3%
84.0 28
 
0.3%
Other values (5853) 9470
94.7%
ValueCountFrequency (%)
0.24 1
 
< 0.1%
0.45 1
 
< 0.1%
1.0 4
< 0.1%
1.08 1
 
< 0.1%
1.2 4
< 0.1%
1.27 1
 
< 0.1%
1.32 1
 
< 0.1%
1.37 1
 
< 0.1%
1.44 3
< 0.1%
1.5 7
0.1%
ValueCountFrequency (%)
26430.38 1
< 0.1%
19140.41 1
< 0.1%
10246.7 1
< 0.1%
8778.69 1
< 0.1%
8675.23 1
< 0.1%
7568.78 1
< 0.1%
7175.97 1
< 0.1%
6899.58 1
< 0.1%
6053.06 1
< 0.1%
5313.0 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20170601
7778 
20180601
2222 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20170601
2nd row20180601
3rd row20170601
4th row20170601
5th row20170601

Common Values

ValueCountFrequency (%)
20170601 7778
77.8%
20180601 2222
 
22.2%

Length

2023-12-13T04:24:18.858207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:18.968012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20170601 7778
77.8%
20180601 2222
 
22.2%

Interactions

2023-12-13T04:24:12.859739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:07.750828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.469800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.318047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.458374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.318945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.107817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.947345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:07.848456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.567227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.407534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.569070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.429774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.226669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:13.042325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:07.961137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.709686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.870307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.708789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.555135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.325006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:13.125110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.063682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.840128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.972117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.823762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.654512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.428615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:13.218365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.169943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.974248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.113796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.926245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.756852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.535940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:13.296543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.262498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.094098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.240616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.065200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.873012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.641625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:13.385859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:08.371430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:09.223505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:10.354155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.190822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:11.998918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:12.754261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:24:19.054076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.3710.3070.0090.2980.0520.0650.0180.0001.000
법정동0.3711.0000.6860.1580.5070.0880.1150.0000.0000.371
법정리0.3070.6861.0000.1060.2930.0860.0540.0000.0000.307
특수지0.0090.1580.1061.0000.0850.0000.0000.0000.0000.009
본번0.2980.5070.2930.0851.0000.0720.1710.2820.1520.298
부번0.0520.0880.0860.0000.0721.0000.0000.0000.0000.052
0.0650.1150.0540.0000.1710.0001.0000.0000.0000.065
시가표준액0.0180.0000.0000.0000.2820.0000.0001.0000.9540.018
연면적0.0000.0000.0000.0000.1520.0000.0000.9541.0000.000
기준일자1.0000.3710.3070.0090.2980.0520.0650.0180.0001.000
2023-12-13T04:24:19.215184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도기준일자특수지
과세년도1.0001.0000.014
기준일자1.0001.0000.014
특수지0.0140.0141.000
2023-12-13T04:24:19.316694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.6410.143-0.0840.286-0.120-0.0100.4530.1190.453
법정리0.6411.0000.1590.0070.057-0.160-0.0220.2040.1240.204
본번0.1430.1591.000-0.1730.049-0.013-0.0240.2290.0500.229
부번-0.0840.007-0.1731.000-0.1890.003-0.0220.0530.0000.053
0.2860.0570.049-0.1891.000-0.031-0.0310.0470.0000.047
시가표준액-0.120-0.160-0.0130.003-0.0311.0000.8310.0130.0000.013
연면적-0.010-0.022-0.024-0.022-0.0310.8311.0000.0000.0000.000
과세년도0.4530.2040.2290.0530.0470.0130.0001.0000.0141.000
특수지0.1190.1240.0500.0000.0000.0000.0000.0141.0000.014
기준일자0.4530.2040.2290.0530.0470.0130.0001.0000.0141.000

Missing values

2023-12-13T04:24:13.514030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:24:13.713545image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
55305전라남도여수시461302017350301742910104[ 여자대동길 3-1 ] 0001동 0104호587520028.820170601
85092전라남도여수시46130201815001481118101[ 상암로 624 ] 0001동 8101호2593184073.6720180601
51053전라남도여수시461302017144011741010101전라남도 여수시 중흥동 1741 1동 101호1921654044.3820170601
25266전라남도여수시461302017143012051210101전라남도 여수시 평여동 205-12 1동 101호301227012.7120170601
4417전라남도여수시46130201711301280910102전라남도 여수시 서교동 280-9 1동 102호436392052.220170601
10242전라남도여수시46130201713301381918101[ 새터로 80 ] 0001동 8101호2489459090.4620170601
3971전라남도여수시46130201711001490310301[ 충무로 54 ] 0001동 0301호88077000157.020170601
12981전라남도여수시4613020171280198210101전라남도 여수시 학동 98-2 1동 101호52979540129.8220170601
66817전라남도여수시461302017130017251610401[ 장성1길 26 ] 0001동 0401호5333685096.4520170601
3720전라남도여수시46130201710601463110102[ 진남로 96 ] 0001동 0102호1478402.120170601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
66718전라남도여수시4613020171300144112031104전라남도 여수시 안산동 441-1 203동 1104호2194500079.820170601
44743전라남도여수시461302017139011221210002[ 좌수영로 813 ] 0001동 0002호10419504.5520170601
2225전라남도여수시461302017104011014000101[ 관문서1길 28-1 ] 0000동 0101호1485000090.020170601
72490전라남도여수시46130201810801505100101[ 중앙로 48-1 ] 0000동 0101호2805737067.320180601
35945전라남도여수시46130201711801331410101전라남도 여수시 신월동 33-14 1동 101호75200075.220170601
5558전라남도여수시46130201711001544110101전라남도 여수시 충무동 544-1 1동 101호16703280210.920170601
17521전라남도여수시461302017142011020101전라남도 여수시 월하동 1 2동 101호11182604.4220170601
82627전라남도여수시461302018115012552310102[ 봉산남1길 26-1 ] 0001동 0102호1196801.620180601
10848전라남도여수시46130201713301129610101[ 신월로 6-1 ] 0001동 0101호95653440120.4420170601
54540전라남도여수시4613020173102211178210401[ 죽림중앙로 13-38 ] 0001동 0401호6061190083.9520170601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
0전라남도여수시46130201712401940018101[ 해양경찰로 122 ] 0001동 8101호1255120023.2201706012
1전라남도여수시4613020171280136110504[ 시청로 34 ] 0001동 0504호4572018076.97201706012
2전라남도여수시461302017134011558210101[ 신월로 284-1 ] 0001동 0101호726450021.75201706012
3전라남도여수시4613020171410148810101전라남도 여수시 화치동 48-8 1동 101호183600012.0201706012
4전라남도여수시46130201714101482130101전라남도 여수시 화치동 48-21 3동 101호26730000198.0201706012
5전라남도여수시461302017141011293010101전라남도 여수시 화치동 1293 1동 101호1102016042.88201706012
6전라남도여수시46130201714101141009000102전라남도 여수시 화치동 1410 900동 102호517500037.5201706012
7전라남도여수시46130201714301287010101전라남도 여수시 평여동 287 1동 101호6460508.85201706012
8전라남도여수시46130201714301287120101전라남도 여수시 평여동 287-1 2동 101호429300027.0201706012
9전라남도여수시461302017144011721030101전라남도 여수시 중흥동 1721 3동 101호17226008.7201706012