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

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공합니다. (법정동, 물건지, 시가표준액, 연면적, 기준일자)
Author인천광역시 계양구
URLhttps://www.data.go.kr/data/15080086/fileData.do

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 (95.2%)Imbalance
동 is highly skewed (γ1 = 42.69640967)Skewed
연면적 is highly skewed (γ1 = 20.69238664)Skewed
부번 has 1241 (12.4%) zerosZeros
동 has 6264 (62.6%) zerosZeros

Reproduction

Analysis started2023-12-12 01:10:42.872783
Analysis finished2023-12-12 01:10:49.797062
Duration6.92 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

2023-12-12T10:10:49.879300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:50.006463image/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

2023-12-12T10:10:50.143801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:50.280569image/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

2023-12-12T10:10:50.410788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:50.553725image/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

2023-12-12T10:10:50.714507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:50.848984image/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%
Mean104.0348
Minimum101
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:51.026178image/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 deviation4.1254686
Coefficient of variation (CV)0.039654698
Kurtosis8.1456882
Mean104.0348
Median Absolute Deviation (MAD)1
Skewness2.7930908
Sum1040348
Variance17.019491
MonotonicityNot monotonic
2023-12-12T10:10:51.222976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
102 3061
30.6%
103 2755
27.6%
101 1304
13.0%
104 615
 
6.2%
105 485
 
4.9%
106 470
 
4.7%
107 226
 
2.3%
110 177
 
1.8%
109 133
 
1.3%
123 113
 
1.1%
Other values (12) 661
 
6.6%
ValueCountFrequency (%)
101 1304
13.0%
102 3061
30.6%
103 2755
27.6%
104 615
 
6.2%
105 485
 
4.9%
106 470
 
4.7%
107 226
 
2.3%
108 65
 
0.7%
109 133
 
1.3%
110 177
 
1.8%
ValueCountFrequency (%)
123 113
1.1%
122 40
 
0.4%
121 11
 
0.1%
120 55
0.5%
119 60
0.6%
118 38
 
0.4%
117 47
0.5%
116 65
0.7%
114 49
0.5%
113 46
0.5%

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

2023-12-12T10:10:51.378645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:51.477838image/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
9913 
2
 
80
3
 
7

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 9913
99.1%
2 80
 
0.8%
3 7
 
0.1%

Length

2023-12-12T10:10:51.630096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:10:51.750188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9913
99.1%
2 80
 
0.8%
3 7
 
0.1%

본번
Real number (ℝ)

Distinct673
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.4625
Minimum1
Maximum1110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:51.902078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1195
median595
Q3918
95-th percentile1078
Maximum1110
Range1109
Interquartile range (IQR)723

Descriptive statistics

Standard deviation379.03591
Coefficient of variation (CV)0.67508678
Kurtosis-1.6139598
Mean561.4625
Median Absolute Deviation (MAD)361
Skewness-0.077148396
Sum5614625
Variance143668.22
MonotonicityNot monotonic
2023-12-12T10:10:52.063006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
899 592
 
5.9%
1062 305
 
3.0%
926 222
 
2.2%
1083 130
 
1.3%
1082 127
 
1.3%
392 121
 
1.2%
1063 115
 
1.1%
148 111
 
1.1%
1074 110
 
1.1%
856 103
 
1.0%
Other values (663) 8064
80.6%
ValueCountFrequency (%)
1 12
0.1%
2 14
0.1%
3 7
 
0.1%
4 22
0.2%
5 23
0.2%
6 29
0.3%
7 9
 
0.1%
8 3
 
< 0.1%
9 12
0.1%
10 27
0.3%
ValueCountFrequency (%)
1110 1
 
< 0.1%
1087 1
 
< 0.1%
1086 16
 
0.2%
1085 31
 
0.3%
1084 37
 
0.4%
1083 130
1.3%
1082 127
1.3%
1081 83
0.8%
1080 52
 
0.5%
1079 8
 
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.271
Minimum0
Maximum485
Zeros1241
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:52.234084image/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 deviation30.76736
Coefficient of variation (CV)2.3183905
Kurtosis89.273422
Mean13.271
Median Absolute Deviation (MAD)3
Skewness7.6380808
Sum132710
Variance946.63042
MonotonicityNot monotonic
2023-12-12T10:10:52.404828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1673
16.7%
0 1241
12.4%
2 1019
 
10.2%
3 675
 
6.8%
4 642
 
6.4%
6 503
 
5.0%
5 434
 
4.3%
7 388
 
3.9%
27 277
 
2.8%
8 271
 
2.7%
Other values (150) 2877
28.8%
ValueCountFrequency (%)
0 1241
12.4%
1 1673
16.7%
2 1019
10.2%
3 675
6.8%
4 642
 
6.4%
5 434
 
4.3%
6 503
 
5.0%
7 388
 
3.9%
8 271
 
2.7%
9 201
 
2.0%
ValueCountFrequency (%)
485 2
< 0.1%
483 3
< 0.1%
482 1
 
< 0.1%
476 3
< 0.1%
472 2
< 0.1%
451 1
 
< 0.1%
422 1
 
< 0.1%
418 4
< 0.1%
415 1
 
< 0.1%
388 1
 
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.733
Minimum0
Maximum7003
Zeros6264
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:52.567726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7003
Range7003
Interquartile range (IQR)1

Descriptive statistics

Standard deviation158.89567
Coefficient of variation (CV)20.54774
Kurtosis1874.906
Mean7.733
Median Absolute Deviation (MAD)0
Skewness42.69641
Sum77330
Variance25247.834
MonotonicityNot monotonic
2023-12-12T10:10:52.715208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 6264
62.6%
1 2864
28.6%
2 382
 
3.8%
3 111
 
1.1%
4 75
 
0.8%
101 60
 
0.6%
201 44
 
0.4%
301 35
 
0.4%
102 27
 
0.3%
202 16
 
0.2%
Other values (33) 122
 
1.2%
ValueCountFrequency (%)
0 6264
62.6%
1 2864
28.6%
2 382
 
3.8%
3 111
 
1.1%
4 75
 
0.8%
5 9
 
0.1%
6 5
 
0.1%
7 7
 
0.1%
8 15
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
7003 1
 
< 0.1%
7001 4
 
< 0.1%
809 1
 
< 0.1%
301 35
0.4%
203 13
 
0.1%
202 16
 
0.2%
201 44
0.4%
178 1
 
< 0.1%
136 1
 
< 0.1%
117 1
 
< 0.1%

호
Text

Distinct1135
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T10:10:53.194496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0376
Min length1

Characters and Unicode

Total characters30376
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

Unique645 ?
Unique (%)6.5%

Sample

1st row401
2nd row101
3rd row210
4th row202
5th row106
ValueCountFrequency (%)
101 1380
 
13.8%
201 839
 
8.4%
102 561
 
5.6%
301 553
 
5.5%
8101 462
 
4.6%
1 395
 
4.0%
401 286
 
2.9%
103 244
 
2.4%
2 197
 
2.0%
202 190
 
1.9%
Other values (1125) 4893
48.9%
2023-12-12T10:10:53.822744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10207
33.6%
0 8449
27.8%
2 3746
 
12.3%
3 2159
 
7.1%
8 1445
 
4.8%
4 1395
 
4.6%
5 932
 
3.1%
6 773
 
2.5%
7 765
 
2.5%
9 497
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30368
> 99.9%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10207
33.6%
0 8449
27.8%
2 3746
 
12.3%
3 2159
 
7.1%
8 1445
 
4.8%
4 1395
 
4.6%
5 932
 
3.1%
6 773
 
2.5%
7 765
 
2.5%
9 497
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10207
33.6%
0 8449
27.8%
2 3746
 
12.3%
3 2159
 
7.1%
8 1445
 
4.8%
4 1395
 
4.6%
5 932
 
3.1%
6 773
 
2.5%
7 765
 
2.5%
9 497
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10207
33.6%
0 8449
27.8%
2 3746
 
12.3%
3 2159
 
7.1%
8 1445
 
4.8%
4 1395
 
4.6%
5 932
 
3.1%
6 773
 
2.5%
7 765
 
2.5%
9 497
 
1.6%

물건지
Text

Distinct9619
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T10:10:54.232268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length25.1626
Min length16

Characters and Unicode

Total characters251626
Distinct characters127
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

Unique9276 ?
Unique (%)92.8%

Sample

1st row[ 계산로 65-3 ] 0000동 0401호
2nd row[ 계양대로 209 ] 0001동 0101호
3rd row[ 계양대로 188 ] 0000동 0210호
4th row인천광역시 계양구 계산동 957-1 102동 202호
5th row[ 오조산로89번길 12 ] 0000동 0106호
ValueCountFrequency (%)
13372
22.8%
0000동 4776
 
8.2%
인천광역시 3314
 
5.7%
계양구 3314
 
5.7%
0001동 1716
 
2.9%
1동 1148
 
2.0%
계산동 1011
 
1.7%
0101호 971
 
1.7%
작전동 908
 
1.6%
장제로 899
 
1.5%
Other values (3444) 27118
46.3%
2023-12-12T10:10:54.791681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48547
19.3%
0 42101
16.7%
1 20071
 
8.0%
동 12171
 
4.8%
호 9951
 
4.0%
2 8764
 
3.5%
] 6686
 
2.7%
[ 6686
 
2.7%
로 6437
 
2.6%
계 5907
 
2.3%
Other values (117) 84305
33.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101003
40.1%
Other Letter 85297
33.9%
Space Separator 48547
19.3%
Close Punctuation 6686
 
2.7%
Open Punctuation 6686
 
2.7%
Dash Punctuation 3407
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12171
14.3%
호 9951
 
11.7%
로 6437
 
7.5%
계 5907
 
6.9%
양 4350
 
5.1%
천 3494
 
4.1%
시 3439
 
4.0%
역 3323
 
3.9%
인 3316
 
3.9%
구 3314
 
3.9%
Other values (103) 29595
34.7%
Decimal Number
ValueCountFrequency (%)
0 42101
41.7%
1 20071
19.9%
2 8764
 
8.7%
3 5803
 
5.7%
8 4674
 
4.6%
4 4665
 
4.6%
5 4185
 
4.1%
7 3983
 
3.9%
6 3842
 
3.8%
9 2915
 
2.9%
Space Separator
ValueCountFrequency (%)
48547
100.0%
Close Punctuation
ValueCountFrequency (%)
] 6686
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 6686
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166329
66.1%
Hangul 85297
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12171
14.3%
호 9951
 
11.7%
로 6437
 
7.5%
계 5907
 
6.9%
양 4350
 
5.1%
천 3494
 
4.1%
시 3439
 
4.0%
역 3323
 
3.9%
인 3316
 
3.9%
구 3314
 
3.9%
Other values (103) 29595
34.7%
Common
ValueCountFrequency (%)
48547
29.2%
0 42101
25.3%
1 20071
12.1%
2 8764
 
5.3%
] 6686
 
4.0%
[ 6686
 
4.0%
3 5803
 
3.5%
8 4674
 
2.8%
4 4665
 
2.8%
5 4185
 
2.5%
Other values (4) 14147
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166329
66.1%
Hangul 85297
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48547
29.2%
0 42101
25.3%
1 20071
12.1%
2 8764
 
5.3%
] 6686
 
4.0%
[ 6686
 
4.0%
3 5803
 
3.5%
8 4674
 
2.8%
4 4665
 
2.8%
5 4185
 
2.5%
Other values (4) 14147
 
8.5%
Hangul
ValueCountFrequency (%)
동 12171
14.3%
호 9951
 
11.7%
로 6437
 
7.5%
계 5907
 
6.9%
양 4350
 
5.1%
천 3494
 
4.1%
시 3439
 
4.0%
역 3323
 
3.9%
인 3316
 
3.9%
구 3314
 
3.9%
Other values (103) 29595
34.7%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7856
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88279655
Minimum19000
Maximum9.9655263 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:55.021566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19000
5-th percentile1665612.5
Q111791960
median35786290
Q386892825
95-th percentile3.0047563 × 108
Maximum9.9655263 × 109
Range9.9655073 × 109
Interquartile range (IQR)75100865

Descriptive statistics

Standard deviation2.3313025 × 108
Coefficient of variation (CV)2.6408151
Kurtosis451.44551
Mean88279655
Median Absolute Deviation (MAD)28938605
Skewness15.523849
Sum8.8279655 × 1011
Variance5.4349713 × 1016
MonotonicityNot monotonic
2023-12-12T10:10:55.167664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33389790 75
 
0.8%
2255870 56
 
0.6%
36065400 56
 
0.6%
34074000 56
 
0.6%
10588750 36
 
0.4%
106696890 35
 
0.4%
37412970 32
 
0.3%
2067390 24
 
0.2%
2391340 22
 
0.2%
10906410 22
 
0.2%
Other values (7846) 9586
95.9%
ValueCountFrequency (%)
19000 2
< 0.1%
22800 1
 
< 0.1%
34200 1
 
< 0.1%
35720 1
 
< 0.1%
38000 3
< 0.1%
39000 2
< 0.1%
40960 1
 
< 0.1%
41040 1
 
< 0.1%
53200 1
 
< 0.1%
56160 1
 
< 0.1%
ValueCountFrequency (%)
9965526290 1
< 0.1%
6165042900 1
< 0.1%
5573100480 1
< 0.1%
4756391210 1
< 0.1%
3772968340 1
< 0.1%
3770056000 1
< 0.1%
2981445720 1
< 0.1%
2924602420 1
< 0.1%
2922685060 1
< 0.1%
2790400000 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6333
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.28711
Minimum0.2601
Maximum23344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T10:10:55.314367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2601
5-th percentile6.5216
Q137.724575
median83.815
Q3169.695
95-th percentile566.5395
Maximum23344
Range23343.74
Interquartile range (IQR)131.97042

Descriptive statistics

Standard deviation448.60847
Coefficient of variation (CV)2.5447605
Kurtosis827.09434
Mean176.28711
Median Absolute Deviation (MAD)57.915
Skewness20.692387
Sum1762871.1
Variance201249.56
MonotonicityNot monotonic
2023-12-12T10:10:55.474274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.29 75
 
0.8%
18.0 67
 
0.7%
54.0 61
 
0.6%
9.85 61
 
0.6%
163.2 59
 
0.6%
3.83 56
 
0.6%
55.4 56
 
0.6%
57.47 32
 
0.3%
12.0 27
 
0.3%
27.0 25
 
0.2%
Other values (6323) 9481
94.8%
ValueCountFrequency (%)
0.2601 1
 
< 0.1%
0.3068 6
0.1%
0.4193 6
0.1%
0.45 1
 
< 0.1%
0.47 1
 
< 0.1%
0.5 2
 
< 0.1%
0.51 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 2
 
< 0.1%
0.71 1
 
< 0.1%
ValueCountFrequency (%)
23344.0 1
< 0.1%
12394.9332 1
< 0.1%
10007.8736 1
< 0.1%
7971.78 1
< 0.1%
7425.48 1
< 0.1%
7164.0579 1
< 0.1%
5663.72 1
< 0.1%
5052.92 1
< 0.1%
5020.01 1
< 0.1%
5012.23 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-04-18 00:00:00
Maximum2023-04-18 00:00:00
2023-12-12T10:10:55.590003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:55.693904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T10:10:48.642893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:44.829825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.564098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.316192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.028013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.023032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.780118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:44.933214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.691520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.446080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.153173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.115522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.890308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.062014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.800105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.578678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.536553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.222551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:49.008201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.176185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.905451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.695546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.694679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.331892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:49.143853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.305848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.063869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.820361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.826149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.444077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:49.264253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:45.446364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.205521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:46.929691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:47.921479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:10:48.546540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:10:56.071812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.2730.7250.1680.0000.0000.000
특수지0.2731.0000.2460.0000.0000.0500.000
본번0.7250.2461.0000.2920.0790.0500.067
부번0.1680.0000.2921.0000.0570.0000.000
동0.0000.0000.0790.0571.0000.0000.000
시가표준액0.0000.0500.0500.0000.0001.0000.892
연면적0.0000.0000.0670.0000.0000.8921.000
2023-12-12T10:10:56.182889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.447-0.1220.0570.0230.0250.169
본번-0.4471.0000.013-0.0080.000-0.1060.151
부번-0.1220.0131.000-0.073-0.0260.0670.000
동0.057-0.008-0.0731.0000.0380.1040.000
시가표준액0.0230.000-0.0260.0381.0000.8710.031
연면적0.025-0.1060.0670.1040.8711.0000.000
특수지0.1690.1510.0000.0000.0310.0001.000

Missing values

2023-12-12T10:10:49.414054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:10:49.682944image/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
4267인천광역시계양구28245202110201970110401[ 계산로 65-3 ] 0000동 0401호2424897041.312023-04-18
17702인천광역시계양구2824520211020194001101[ 계양대로 209 ] 0001동 0101호103449370238.52023-04-18
23297인천광역시계양구2824520211020195660210[ 계양대로 188 ] 0000동 0210호124528730135.542023-04-18
19921인천광역시계양구282452021102019571102202인천광역시 계양구 계산동 957-1 102동 202호92210030107.7222023-04-18
15214인천광역시계양구2824520211060120960106[ 오조산로89번길 12 ] 0000동 0106호3359600045.42023-04-18
21927인천광역시계양구28245202110201107650103[ 계양문화로59번길 1 ] 0000동 0103호2565593037.252023-04-18
14927인천광역시계양구2824520211060120920613[ 계산새로87번길 15 ] 0000동 0613호2503878040.982023-04-18
30901인천광역시계양구2824520211100164040201[ 당미1길 20 ] 0000동 0201호287459780411.83352023-04-18
12202인천광역시계양구282452021101011531101[ 봉오대로569번길 10 ] 0001동 0101호11385005.52023-04-18
6964인천광역시계양구28245202110201106202428인천광역시 계양구 계산동 1062 2동 428호3741297057.472023-04-18
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
7949인천광역시계양구28245202110301899104133[ 장제로 738 ] 0000동 4133호102447509.532023-04-18
4986인천광역시계양구2824520211020198331101[ 계산로41번길 10 ] 0001동 0101호3302110098.02023-04-18
12007인천광역시계양구2824520211030191250205인천광역시 계양구 작전동 912-5 205호2796920057.082023-04-18
31738인천광역시계양구28245202110401140250101인천광역시 계양구 서운동 140-25 101호166968000240.02023-04-18
12941인천광역시계양구2824520211010133330403[ 안남로573번길 30-1 ] 0000동 0403호40376008.242023-04-18
21435인천광역시계양구28245202110201106940102[ 장제로767번길 4 ] 0000동 0102호35162730205.632023-04-18
25311인천광역시계양구2824520211030191052102인천광역시 계양구 작전동 910-5 2동 102호1185696010.742023-04-18
16328인천광역시계양구28245202110101520611인천광역시 계양구 효성동 520-6 1동 1호67989600319.22023-04-18
5884인천광역시계양구28245202110201108520206인천광역시 계양구 계산동 1085-2 206호2136890043.612023-04-18
4602인천광역시계양구2824520211020110761018101[ 계양문화로53번길 8 ] 0001동 8101호106184540294.142023-04-18

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0인천광역시계양구28245202110101519301인천광역시 계양구 효성동 519-3 1호61272000306.362023-04-182
1인천광역시계양구28245202110101519602인천광역시 계양구 효성동 519-6 2호142848023.042023-04-182
2인천광역시계양구28245202110101519702[ 마장로511번길 12 ] 0000동 0002호308016049.682023-04-182
3인천광역시계양구2824520211010165518410인천광역시 계양구 효성동 655-184 1동1576800027.02023-04-182
4인천광역시계양구28245202110301906108104[ 계양문화로29번길 11 ] 0000동 8104호30105607.43352023-04-182
5인천광역시계양구2824520211040155221101인천광역시 계양구 서운동 55-22 1동 101호37065600132.02023-04-182
6인천광역시계양구282452021119014311인천광역시 계양구 갈현동 4-3 1동 1호59251500247.52023-04-182
7인천광역시계양구28245202111901383011인천광역시 계양구 갈현동 38-30 1동 1호36352800396.02023-04-182
8인천광역시계양구2824520211230113400201인천광역시 계양구 장기동 134 201호170100000945.02023-04-182