Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공합니다. (법정동, 물건지, 시가표준액, 연면적, 기준일자)
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15080086&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 9 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (95.9%)Imbalance
동 is highly skewed (γ1 = 53.75463967)Skewed
부번 has 1198 (12.0%) zerosZeros
동 has 6267 (62.7%) zerosZeros

Reproduction

Analysis started2024-01-28 12:00:45.117825
Analysis finished2024-01-28 12:00:49.776389
Duration4.66 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
인천광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 10000
100.0%

Length

2024-01-28T21:00:49.825699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:49.899221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계양구 10000
100.0%

Length

2024-01-28T21:00:49.977978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:50.052425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계양구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
28245
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28245
2nd row28245
3rd row28245
4th row28245
5th row28245

Common Values

ValueCountFrequency (%)
28245 10000
100.0%

Length

2024-01-28T21:00:50.129412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:50.200421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28245 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

2024-01-28T21:00:50.284758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:50.363531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.0626
Minimum101
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:50.439133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median103
Q3104
95-th percentile113
Maximum123
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.1852877
Coefficient of variation (CV)0.040218942
Kurtosis8.1531701
Mean104.0626
Median Absolute Deviation (MAD)1
Skewness2.8014576
Sum1040626
Variance17.516633
MonotonicityNot monotonic
2024-01-28T21:00:50.533502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
102 3034
30.3%
103 2754
27.5%
101 1301
13.0%
104 675
 
6.8%
106 449
 
4.5%
105 439
 
4.4%
107 262
 
2.6%
110 176
 
1.8%
109 140
 
1.4%
123 129
 
1.3%
Other values (12) 641
 
6.4%
ValueCountFrequency (%)
101 1301
13.0%
102 3034
30.3%
103 2754
27.5%
104 675
 
6.8%
105 439
 
4.4%
106 449
 
4.5%
107 262
 
2.6%
108 57
 
0.6%
109 140
 
1.4%
110 176
 
1.8%
ValueCountFrequency (%)
123 129
1.3%
122 47
 
0.5%
121 6
 
0.1%
120 61
0.6%
119 49
 
0.5%
118 43
 
0.4%
117 43
 
0.4%
116 57
0.6%
114 54
0.5%
113 41
 
0.4%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-01-28T21:00:50.630248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:50.701784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9926 
2
 
72
3
 
2

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 9926
99.3%
2 72
 
0.7%
3 2
 
< 0.1%

Length

2024-01-28T21:00:50.781126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:50.862178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9926
99.3%
2 72
 
0.7%
3 2
 
< 0.1%

본번
Real number (ℝ)

Distinct673
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.6997
Minimum1
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:50.966736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1198
median595
Q3920
95-th percentile1076.05
Maximum1109
Range1108
Interquartile range (IQR)722

Descriptive statistics

Standard deviation377.03495
Coefficient of variation (CV)0.67123937
Kurtosis-1.6132775
Mean561.6997
Median Absolute Deviation (MAD)361
Skewness-0.069358535
Sum5616997
Variance142155.35
MonotonicityNot monotonic
2024-01-28T21:00:51.079843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
899 560
 
5.6%
1062 266
 
2.7%
926 254
 
2.5%
1082 149
 
1.5%
1063 139
 
1.4%
392 134
 
1.3%
1083 122
 
1.2%
1074 121
 
1.2%
148 112
 
1.1%
679 110
 
1.1%
Other values (663) 8033
80.3%
ValueCountFrequency (%)
1 11
 
0.1%
2 4
 
< 0.1%
3 17
0.2%
4 17
0.2%
5 22
0.2%
6 34
0.3%
7 5
 
0.1%
8 2
 
< 0.1%
9 13
 
0.1%
10 23
0.2%
ValueCountFrequency (%)
1109 2
 
< 0.1%
1086 19
 
0.2%
1085 22
 
0.2%
1084 38
 
0.4%
1083 122
1.2%
1082 149
1.5%
1081 71
0.7%
1080 57
 
0.6%
1079 6
 
0.1%
1078 12
 
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct162
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.9862
Minimum0
Maximum485
Zeros1198
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:51.209415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312
95-th percentile56
Maximum485
Range485
Interquartile range (IQR)11

Descriptive statistics

Standard deviation32.45315
Coefficient of variation (CV)2.3203693
Kurtosis79.730027
Mean13.9862
Median Absolute Deviation (MAD)3
Skewness7.3653437
Sum139862
Variance1053.2069
MonotonicityNot monotonic
2024-01-28T21:00:51.330664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1625
16.2%
0 1198
 
12.0%
2 962
 
9.6%
4 679
 
6.8%
3 656
 
6.6%
6 479
 
4.8%
7 410
 
4.1%
5 405
 
4.0%
8 296
 
3.0%
27 290
 
2.9%
Other values (152) 3000
30.0%
ValueCountFrequency (%)
0 1198
12.0%
1 1625
16.2%
2 962
9.6%
3 656
6.6%
4 679
6.8%
5 405
 
4.0%
6 479
 
4.8%
7 410
 
4.1%
8 296
 
3.0%
9 212
 
2.1%
ValueCountFrequency (%)
485 3
< 0.1%
483 1
 
< 0.1%
476 1
 
< 0.1%
475 1
 
< 0.1%
472 3
< 0.1%
451 3
< 0.1%
422 2
< 0.1%
418 2
< 0.1%
416 3
< 0.1%
415 2
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3866
Minimum0
Maximum7004
Zeros6267
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:51.446208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum7004
Range7004
Interquartile range (IQR)1

Descriptive statistics

Standard deviation124.17325
Coefficient of variation (CV)19.442778
Kurtosis3021.5365
Mean6.3866
Median Absolute Deviation (MAD)0
Skewness53.75464
Sum63866
Variance15418.995
MonotonicityNot monotonic
2024-01-28T21:00:51.558713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6267
62.7%
1 2847
28.5%
2 376
 
3.8%
3 121
 
1.2%
4 68
 
0.7%
101 60
 
0.6%
201 50
 
0.5%
102 34
 
0.3%
301 21
 
0.2%
203 20
 
0.2%
Other values (48) 136
 
1.4%
ValueCountFrequency (%)
0 6267
62.7%
1 2847
28.5%
2 376
 
3.8%
3 121
 
1.2%
4 68
 
0.7%
5 8
 
0.1%
6 7
 
0.1%
7 3
 
< 0.1%
8 10
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
7004 1
 
< 0.1%
7001 2
 
< 0.1%
801 1
 
< 0.1%
303 1
 
< 0.1%
301 21
0.2%
221 2
 
< 0.1%
203 20
 
0.2%
202 15
 
0.1%
201 50
0.5%
178 1
 
< 0.1%

호
Text

Distinct1121
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-28T21:00:51.888976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0346
Min length1

Characters and Unicode

Total characters30346
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

Unique643 ?
Unique (%)6.4%

Sample

1st row213
2nd row102
3rd row1110
4th row101
5th row101
ValueCountFrequency (%)
101 1429
 
14.3%
201 815
 
8.2%
102 545
 
5.5%
8101 485
 
4.9%
301 476
 
4.8%
1 386
 
3.9%
401 276
 
2.8%
103 249
 
2.5%
202 210
 
2.1%
2 210
 
2.1%
Other values (1111) 4919
49.2%
2024-01-28T21:00:52.339200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10239
33.7%
0 8478
27.9%
2 3658
 
12.1%
3 2109
 
6.9%
8 1445
 
4.8%
4 1390
 
4.6%
5 953
 
3.1%
7 803
 
2.6%
6 767
 
2.5%
9 496
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30338
> 99.9%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10239
33.7%
0 8478
27.9%
2 3658
 
12.1%
3 2109
 
7.0%
8 1445
 
4.8%
4 1390
 
4.6%
5 953
 
3.1%
7 803
 
2.6%
6 767
 
2.5%
9 496
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10239
33.7%
0 8478
27.9%
2 3658
 
12.1%
3 2109
 
6.9%
8 1445
 
4.8%
4 1390
 
4.6%
5 953
 
3.1%
7 803
 
2.6%
6 767
 
2.5%
9 496
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10239
33.7%
0 8478
27.9%
2 3658
 
12.1%
3 2109
 
6.9%
8 1445
 
4.8%
4 1390
 
4.6%
5 953
 
3.1%
7 803
 
2.6%
6 767
 
2.5%
9 496
 
1.6%

물건지
Text

Distinct9617
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-28T21:00:52.584135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length25.1969
Min length17

Characters and Unicode

Total characters251969
Distinct characters128
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

Unique9271 ?
Unique (%)92.7%

Sample

1st row인천광역시 계양구 작전동 444-2 213호
2nd row[ 효서로 234 ] 0000동 0102호
3rd row인천광역시 계양구 계산동 1062 3동 1110호
4th row인천광역시 계양구 작전동 619-5 1동 101호
5th row[ 새풀로1번길 4 ] 0001동 0101호
ValueCountFrequency (%)
13392
22.9%
0000동 4801
 
8.2%
인천광역시 3304
 
5.6%
계양구 3304
 
5.6%
0001동 1707
 
2.9%
1동 1140
 
1.9%
0101호 988
 
1.7%
계산동 980
 
1.7%
작전동 915
 
1.6%
장제로 901
 
1.5%
Other values (3415) 27125
46.3%
2024-01-28T21:00:52.951024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48557
19.3%
0 42252
16.8%
1 20132
 
8.0%
동 12173
 
4.8%
호 9953
 
4.0%
2 8779
 
3.5%
[ 6696
 
2.7%
] 6696
 
2.7%
로 6462
 
2.6%
계 5828
 
2.3%
Other values (118) 84441
33.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101252
40.2%
Other Letter 85380
33.9%
Space Separator 48557
19.3%
Open Punctuation 6696
 
2.7%
Close Punctuation 6696
 
2.7%
Dash Punctuation 3388
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12173
14.3%
호 9953
 
11.7%
로 6462
 
7.6%
계 5828
 
6.8%
양 4316
 
5.1%
천 3486
 
4.1%
시 3433
 
4.0%
역 3316
 
3.9%
인 3305
 
3.9%
구 3304
 
3.9%
Other values (104) 29804
34.9%
Decimal Number
ValueCountFrequency (%)
0 42252
41.7%
1 20132
19.9%
2 8779
 
8.7%
3 5710
 
5.6%
8 4625
 
4.6%
4 4617
 
4.6%
5 4302
 
4.2%
7 3945
 
3.9%
6 3867
 
3.8%
9 3023
 
3.0%
Space Separator
ValueCountFrequency (%)
48557
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 6696
100.0%
Close Punctuation
ValueCountFrequency (%)
] 6696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166589
66.1%
Hangul 85380
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12173
14.3%
호 9953
 
11.7%
로 6462
 
7.6%
계 5828
 
6.8%
양 4316
 
5.1%
천 3486
 
4.1%
시 3433
 
4.0%
역 3316
 
3.9%
인 3305
 
3.9%
구 3304
 
3.9%
Other values (104) 29804
34.9%
Common
ValueCountFrequency (%)
48557
29.1%
0 42252
25.4%
1 20132
12.1%
2 8779
 
5.3%
[ 6696
 
4.0%
] 6696
 
4.0%
3 5710
 
3.4%
8 4625
 
2.8%
4 4617
 
2.8%
5 4302
 
2.6%
Other values (4) 14223
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166589
66.1%
Hangul 85380
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48557
29.1%
0 42252
25.4%
1 20132
12.1%
2 8779
 
5.3%
[ 6696
 
4.0%
] 6696
 
4.0%
3 5710
 
3.4%
8 4625
 
2.8%
4 4617
 
2.8%
5 4302
 
2.6%
Other values (4) 14223
 
8.5%
Hangul
ValueCountFrequency (%)
동 12173
14.3%
호 9953
 
11.7%
로 6462
 
7.6%
계 5828
 
6.8%
양 4316
 
5.1%
천 3486
 
4.1%
시 3433
 
4.0%
역 3316
 
3.9%
인 3305
 
3.9%
구 3304
 
3.9%
Other values (104) 29804
34.9%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7880
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88177802
Minimum19000
Maximum5.5433569 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:53.072287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19000
5-th percentile1543912
Q111296910
median34136500
Q387210878
95-th percentile3.0917347 × 108
Maximum5.5433569 × 109
Range5.5433379 × 109
Interquartile range (IQR)75913968

Descriptive statistics

Standard deviation2.1367681 × 108
Coefficient of variation (CV)2.4232494
Kurtosis149.07225
Mean88177802
Median Absolute Deviation (MAD)28034320
Skewness9.5711039
Sum8.8177802 × 1011
Variance4.5657778 × 1016
MonotonicityNot monotonic
2024-01-28T21:00:53.183181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34074000 64
 
0.6%
2255870 52
 
0.5%
33389790 52
 
0.5%
36065400 44
 
0.4%
106696890 38
 
0.4%
22101120 33
 
0.3%
10588750 33
 
0.3%
37412970 33
 
0.3%
27256710 26
 
0.3%
2391340 24
 
0.2%
Other values (7870) 9601
96.0%
ValueCountFrequency (%)
19000 3
< 0.1%
22800 1
 
< 0.1%
34200 1
 
< 0.1%
37620 1
 
< 0.1%
38000 3
< 0.1%
39000 2
< 0.1%
39520 1
 
< 0.1%
44000 1
 
< 0.1%
45600 2
< 0.1%
49140 1
 
< 0.1%
ValueCountFrequency (%)
5543356930 1
< 0.1%
4970054400 1
< 0.1%
4514321640 1
< 0.1%
4498135440 1
< 0.1%
3235248850 1
< 0.1%
3152258110 1
< 0.1%
2924602420 1
< 0.1%
2922685060 1
< 0.1%
2844750370 1
< 0.1%
2700849820 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6307
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.43571
Minimum0.2601
Maximum16390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-28T21:00:53.610578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2601
5-th percentile6.199
Q136.3175
median81.6
Q3168.64025
95-th percentile586.7535
Maximum16390
Range16389.74
Interquartile range (IQR)132.32275

Descriptive statistics

Standard deviation415.61475
Coefficient of variation (CV)2.3556158
Kurtosis369.05268
Mean176.43571
Median Absolute Deviation (MAD)57.582
Skewness13.789465
Sum1764357.1
Variance172735.62
MonotonicityNot monotonic
2024-01-28T21:00:53.717250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.0 80
 
0.8%
18.0 65
 
0.7%
163.2 63
 
0.6%
9.85 62
 
0.6%
3.83 52
 
0.5%
51.29 52
 
0.5%
55.4 44
 
0.4%
38.37 33
 
0.3%
57.47 33
 
0.3%
44.61 26
 
0.3%
Other values (6297) 9490
94.9%
ValueCountFrequency (%)
0.2601 1
 
< 0.1%
0.3 1
 
< 0.1%
0.3068 4
< 0.1%
0.4193 7
0.1%
0.5 3
< 0.1%
0.51 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 1
 
< 0.1%
0.99 1
 
< 0.1%
ValueCountFrequency (%)
16390.0 1
< 0.1%
12802.21 1
< 0.1%
7276.8 1
< 0.1%
7164.0579 1
< 0.1%
7097.99 1
< 0.1%
7072.54 1
< 0.1%
6097.69 1
< 0.1%
5277.83 1
< 0.1%
3993.77 1
< 0.1%
3987.77 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-04-18
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-18
2nd row2023-04-18
3rd row2023-04-18
4th row2023-04-18
5th row2023-04-18

Common Values

ValueCountFrequency (%)
2023-04-18 10000
100.0%

Length

2024-01-28T21:00:53.819504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:00:53.891600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04-18 10000
100.0%

Interactions

2024-01-28T21:00:48.998169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.308549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.765218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.253845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.958421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.499568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:49.083838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.380580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.842694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.328501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.045017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.584753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:49.161394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.456036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.923817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.405106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.129482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.676903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:49.238954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.529338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.021020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.478664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.217377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.752788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:49.321424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.610820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.098781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.812547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.327125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.838022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:49.402851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:46.684449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.177999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:47.885099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.408207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:00:48.914724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T21:00:53.944573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.2660.7220.2420.0000.0650.043
특수지0.2661.0000.1870.0000.0000.0000.000
본번0.7220.1871.0000.3950.0650.0930.078
부번0.2420.0000.3951.0000.0000.0340.051
동0.0000.0000.0650.0001.0000.0000.000
시가표준액0.0650.0000.0930.0340.0001.0000.857
연면적0.0430.0000.0780.0510.0000.8571.000
2024-01-28T21:00:54.034606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.455-0.1310.0710.0230.0310.165
본번-0.4551.0000.025-0.0080.004-0.0960.113
부번-0.1310.0251.000-0.076-0.0310.0520.000
동0.071-0.008-0.0761.0000.0390.1020.000
시가표준액0.0230.004-0.0310.0391.0000.8750.000
연면적0.031-0.0960.0520.1020.8751.0000.000
특수지0.1650.1130.0000.0000.0000.0001.000

Missing values

2024-01-28T21:00:49.520326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T21:00:49.698491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
15305인천광역시계양구2824520211030144420213인천광역시 계양구 작전동 444-2 213호1159978037.062023-04-18
21314인천광역시계양구2824520211030143830102[ 효서로 234 ] 0000동 0102호620001.02023-04-18
4826인천광역시계양구282452021102011062031110인천광역시 계양구 계산동 1062 3동 1110호113978880163.22023-04-18
26547인천광역시계양구2824520211030161951101인천광역시 계양구 작전동 619-5 1동 101호21750000108.752023-04-18
17290인천광역시계양구2824520211010156851101[ 새풀로1번길 4 ] 0001동 0101호178520940431.212023-04-18
353인천광역시계양구2824520211010123270102인천광역시 계양구 효성동 232-7 102호254704027.012023-04-18
29927인천광역시계양구2824520211100138760101인천광역시 계양구 동양동 387-6 101호62255080199.922023-04-18
11145인천광역시계양구28245202110301899104021[ 장제로 738 ] 0000동 4021호1277100011.882023-04-18
23897인천광역시계양구2824520211040113120102[ 아나지로 509 ] 0000동 0102호164160086.42023-04-18
3702인천광역시계양구2824520211020195660805[ 계양대로 188 ] 0000동 0805호5356281062.212023-04-18
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
722인천광역시계양구28245202110201926270352인천광역시 계양구 계산동 926-27 352호2050880054.42023-04-18
20880인천광역시계양구28245202110201961101501[ 계산천동로 21 ] 0001동 0501호106039960213.062023-04-18
26967인천광역시계양구2824520211040155740101[ 아나지로 502 ] 0000동 0101호45383620127.52023-04-18
13508인천광역시계양구2824520211050167004059[ 임학동로 38 ] 0000동 4059호1118978021.772023-04-18
21550인천광역시계양구28245202110301476130101[ 계양대로13번길 22 ] 0000동 0101호88869060393.42023-04-18
2198인천광역시계양구2824520211010199110301인천광역시 계양구 효성동 99-11 301호999168039.032023-04-18
21312인천광역시계양구2824520211030143820202[ 계양대로 41 ] 0000동 0202호195030029.552023-04-18
7832인천광역시계양구2824520211020196891601[ 계산로103번길 9 ] 0001동 0601호1102500045.02023-04-18
24349인천광역시계양구2824520211040114201102인천광역시 계양구 서운동 142 1동 102호64800036.02023-04-18
32362인천광역시계양구2824520211200125500301인천광역시 계양구 둑실동 255 301호5900400003960.02023-04-18

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0인천광역시계양구282452021101011952501[ 효서로 212 ] 0000동 0001호45014200110.62023-04-183
1인천광역시계양구28245202110101322312인천광역시 계양구 효성동 322-3 1동 2호33106200239.92023-04-182
2인천광역시계양구28245202110101511111인천광역시 계양구 효성동 511-1 1동 1호92590000492.52023-04-182
3인천광역시계양구282452021101016656957101인천광역시 계양구 효성동 665-69 5동 7101호1215000054.02023-04-182
4인천광역시계양구2824520211030170972301인천광역시 계양구 작전동 709-7 2동 301호199332490721.4352023-04-182
5인천광역시계양구28245202110301906201[ 계양문화로29번길 9 ] 0000동 0001호2883234073.742023-04-182
6인천광역시계양구28245202110301906201[ 계양문화로29번길 9 ] 0000동 0001호120642560219.392023-04-182
7인천광역시계양구28245202110701378013[ 병방시장로61번길 5-1 ] 0001동 0003호208092019.252023-04-182
8인천광역시계양구2824520211130164821101인천광역시 계양구 하야동 64-82 1동 101호181546200233.22023-04-182