Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)0.1%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical7
Numeric6
Text1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공<br/>인천광역시 부평구 일반건축물 시가표준액 데이터입니다.<br/>(시도명,시군구명,자치단체코드,과세년도,법정동,법정리,특수지,본번,부번,동,호,물건지,시가표준액,연면적,기준일자)<br/>예) 인천광역시,부평구,28237,2019,102,00,1,0251,0002,0001,0002,[ 백범로568번길 24 ] 0001동 0002호,51303000,209.4,20190601
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15080133&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 8 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (96.9%)Imbalance
동 is highly skewed (γ1 = 22.80672761)Skewed
연면적 is highly skewed (γ1 = 23.39156552)Skewed
부번 has 969 (9.7%) zerosZeros
동 has 5625 (56.2%) zerosZeros

Reproduction

Analysis started2024-05-10 22:10:57.964302
Analysis finished2024-05-10 22:11:11.910056
Duration13.95 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

2024-05-10T22:11:12.080453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:12.310574image/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
부평구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평구 10000
100.0%

Length

2024-05-10T22:11:12.600579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:12.961600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
28237
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28237
2nd row28237
3rd row28237
4th row28237
5th row28237

Common Values

ValueCountFrequency (%)
28237 10000
100.0%

Length

2024-05-10T22:11:13.282657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:13.582335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28237 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2017 10000
100.0%

Length

2024-05-10T22:11:13.897418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:14.200453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 10000
100.0%

법정동
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.9561
Minimum101
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:14.492033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median102
Q3104
95-th percentile107
Maximum109
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.168235
Coefficient of variation (CV)0.021059801
Kurtosis-0.54600591
Mean102.9561
Median Absolute Deviation (MAD)1
Skewness0.79544698
Sum1029561
Variance4.7012429
MonotonicityNot monotonic
2024-05-10T22:11:14.927868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
101 4222
42.2%
104 1383
 
13.8%
102 1124
 
11.2%
105 877
 
8.8%
103 842
 
8.4%
107 724
 
7.2%
106 554
 
5.5%
108 210
 
2.1%
109 64
 
0.6%
ValueCountFrequency (%)
101 4222
42.2%
102 1124
 
11.2%
103 842
 
8.4%
104 1383
 
13.8%
105 877
 
8.8%
106 554
 
5.5%
107 724
 
7.2%
108 210
 
2.1%
109 64
 
0.6%
ValueCountFrequency (%)
109 64
 
0.6%
108 210
 
2.1%
107 724
 
7.2%
106 554
 
5.5%
105 877
 
8.8%
104 1383
 
13.8%
103 842
 
8.4%
102 1124
 
11.2%
101 4222
42.2%

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

2024-05-10T22:11:15.377822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:15.691220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9968 
2
 
32

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 9968
99.7%
2 32
 
0.3%

Length

2024-05-10T22:11:16.058493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:16.353719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9968
99.7%
2 32
 
0.3%

본번
Real number (ℝ)

Distinct657
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.7306
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:16.665036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1180
median328
Q3456
95-th percentile654
Maximum950
Range949
Interquartile range (IQR)276

Descriptive statistics

Standard deviation191.11465
Coefficient of variation (CV)0.58853293
Kurtosis-0.10748458
Mean324.7306
Median Absolute Deviation (MAD)136
Skewness0.43751428
Sum3247306
Variance36524.81
MonotonicityNot monotonic
2024-05-10T22:11:17.177493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 472
 
4.7%
539 204
 
2.0%
182 181
 
1.8%
201 158
 
1.6%
199 151
 
1.5%
431 114
 
1.1%
425 113
 
1.1%
10 112
 
1.1%
378 95
 
0.9%
465 93
 
0.9%
Other values (647) 8307
83.1%
ValueCountFrequency (%)
1 7
 
0.1%
2 11
 
0.1%
3 8
 
0.1%
5 14
 
0.1%
6 13
 
0.1%
7 30
 
0.3%
8 8
 
0.1%
9 27
 
0.3%
10 112
1.1%
11 25
 
0.2%
ValueCountFrequency (%)
950 29
0.3%
949 1
 
< 0.1%
947 3
 
< 0.1%
938 3
 
< 0.1%
915 4
 
< 0.1%
914 1
 
< 0.1%
912 3
 
< 0.1%
911 2
 
< 0.1%
910 2
 
< 0.1%
909 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct374
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.6702
Minimum0
Maximum1131
Zeros969
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:17.581812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile139
Maximum1131
Range1131
Interquartile range (IQR)22

Descriptive statistics

Standard deviation89.656354
Coefficient of variation (CV)2.7442854
Kurtosis49.345647
Mean32.6702
Median Absolute Deviation (MAD)6
Skewness6.3446924
Sum326702
Variance8038.2619
MonotonicityNot monotonic
2024-05-10T22:11:18.014731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1156
 
11.6%
0 969
 
9.7%
1 849
 
8.5%
3 706
 
7.1%
4 482
 
4.8%
5 383
 
3.8%
9 377
 
3.8%
7 278
 
2.8%
6 242
 
2.4%
12 235
 
2.4%
Other values (364) 4323
43.2%
ValueCountFrequency (%)
0 969
9.7%
1 849
8.5%
2 1156
11.6%
3 706
7.1%
4 482
4.8%
5 383
 
3.8%
6 242
 
2.4%
7 278
 
2.8%
8 213
 
2.1%
9 377
 
3.8%
ValueCountFrequency (%)
1131 1
< 0.1%
1079 1
< 0.1%
1045 1
< 0.1%
1044 1
< 0.1%
1027 1
< 0.1%
1007 1
< 0.1%
990 1
< 0.1%
977 1
< 0.1%
975 1
< 0.1%
972 1
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.1665
Minimum0
Maximum9999
Zeros5625
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:18.693415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9999
Range9999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation435.76921
Coefficient of variation (CV)19.658909
Kurtosis519.32236
Mean22.1665
Median Absolute Deviation (MAD)0
Skewness22.806728
Sum221665
Variance189894.81
MonotonicityNot monotonic
2024-05-10T22:11:19.195236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 5625
56.2%
1 3508
35.1%
2 351
 
3.5%
3 154
 
1.5%
101 79
 
0.8%
7 39
 
0.4%
102 36
 
0.4%
4 35
 
0.4%
99 19
 
0.2%
6 18
 
0.2%
Other values (36) 136
 
1.4%
ValueCountFrequency (%)
0 5625
56.2%
1 3508
35.1%
2 351
 
3.5%
3 154
 
1.5%
4 35
 
0.4%
5 12
 
0.1%
6 18
 
0.2%
7 39
 
0.4%
8 7
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9999 18
0.2%
9992 1
 
< 0.1%
802 1
 
< 0.1%
401 1
 
< 0.1%
321 5
 
0.1%
307 1
 
< 0.1%
302 1
 
< 0.1%
228 3
 
< 0.1%
226 2
 
< 0.1%
209 2
 
< 0.1%

물건지
Text

Distinct9630
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:11:19.835262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length28.8779
Min length16

Characters and Unicode

Total characters288779
Distinct characters110
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9312 ?
Unique (%)93.1%

Sample

1st row인천광역시 부평구 체육관로 32 0000동 0804호
2nd row인천광역시 부평구 부평동 205-2 2238호
3rd row인천광역시 부평구 체육관로 32 0000동 0901호
4th row인천광역시 부평구 갈산동 416 108호
5th row인천광역시 부평구 영성중로 50 0000동 0113호
ValueCountFrequency (%)
인천광역시 10000
 
17.1%
부평구 10000
 
17.1%
0000동 4173
 
7.1%
0001동 1952
 
3.3%
1동 1556
 
2.7%
부평동 1192
 
2.0%
0001호 968
 
1.7%
청천동 841
 
1.4%
0002호 704
 
1.2%
1호 537
 
0.9%
Other values (4283) 26607
45.5%
2024-05-10T22:11:21.037670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48530
16.8%
0 42298
14.6%
1 16860
 
5.8%
부 13301
 
4.6%
동 12583
 
4.4%
평 12405
 
4.3%
천 11110
 
3.8%
인 10338
 
3.6%
시 10129
 
3.5%
광 10117
 
3.5%
Other values (100) 101108
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137478
47.6%
Decimal Number 98786
34.2%
Space Separator 48530
 
16.8%
Dash Punctuation 3962
 
1.4%
Uppercase Letter 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 13301
9.7%
동 12583
9.2%
평 12405
9.0%
천 11110
8.1%
인 10338
 
7.5%
시 10129
 
7.4%
광 10117
 
7.4%
구 10053
 
7.3%
역 10022
 
7.3%
호 9953
 
7.2%
Other values (86) 27467
20.0%
Decimal Number
ValueCountFrequency (%)
0 42298
42.8%
1 16860
 
17.1%
2 9121
 
9.2%
3 6975
 
7.1%
4 5556
 
5.6%
5 4598
 
4.7%
6 4016
 
4.1%
8 3247
 
3.3%
9 3141
 
3.2%
7 2974
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 20
87.0%
C 3
 
13.0%
Space Separator
ValueCountFrequency (%)
48530
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151278
52.4%
Hangul 137478
47.6%
Latin 23
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 13301
9.7%
동 12583
9.2%
평 12405
9.0%
천 11110
8.1%
인 10338
 
7.5%
시 10129
 
7.4%
광 10117
 
7.4%
구 10053
 
7.3%
역 10022
 
7.3%
호 9953
 
7.2%
Other values (86) 27467
20.0%
Common
ValueCountFrequency (%)
48530
32.1%
0 42298
28.0%
1 16860
 
11.1%
2 9121
 
6.0%
3 6975
 
4.6%
4 5556
 
3.7%
5 4598
 
3.0%
6 4016
 
2.7%
- 3962
 
2.6%
8 3247
 
2.1%
Other values (2) 6115
 
4.0%
Latin
ValueCountFrequency (%)
B 20
87.0%
C 3
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151301
52.4%
Hangul 137478
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48530
32.1%
0 42298
28.0%
1 16860
 
11.1%
2 9121
 
6.0%
3 6975
 
4.6%
4 5556
 
3.7%
5 4598
 
3.0%
6 4016
 
2.7%
- 3962
 
2.6%
8 3247
 
2.1%
Other values (4) 6138
 
4.1%
Hangul
ValueCountFrequency (%)
부 13301
9.7%
동 12583
9.2%
평 12405
9.0%
천 11110
8.1%
인 10338
 
7.5%
시 10129
 
7.4%
광 10117
 
7.4%
구 10053
 
7.3%
역 10022
 
7.3%
호 9953
 
7.2%
Other values (86) 27467
20.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8252
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70681178
Minimum22440
Maximum8.9947658 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:21.470842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22440
5-th percentile1436392.5
Q19073830
median33293865
Q368589782
95-th percentile2.3174418 × 108
Maximum8.9947658 × 109
Range8.9947433 × 109
Interquartile range (IQR)59515952

Descriptive statistics

Standard deviation1.9375621 × 108
Coefficient of variation (CV)2.7412702
Kurtosis609.94398
Mean70681178
Median Absolute Deviation (MAD)27027840
Skewness18.460643
Sum7.0681178 × 1011
Variance3.7541467 × 1016
MonotonicityNot monotonic
2024-05-10T22:11:21.908810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1780200 157
 
1.6%
34802500 57
 
0.6%
5224280 42
 
0.4%
35535290 41
 
0.4%
35937270 41
 
0.4%
5004200 37
 
0.4%
28412280 34
 
0.3%
4805080 33
 
0.3%
61231390 32
 
0.3%
5371000 30
 
0.3%
Other values (8242) 9496
95.0%
ValueCountFrequency (%)
22440 1
< 0.1%
24250 1
< 0.1%
32640 1
< 0.1%
33000 1
< 0.1%
34000 2
< 0.1%
34650 1
< 0.1%
44800 1
< 0.1%
53900 1
< 0.1%
54450 1
< 0.1%
56100 1
< 0.1%
ValueCountFrequency (%)
8994765780 1
< 0.1%
5200963170 1
< 0.1%
5192903200 1
< 0.1%
4062559500 1
< 0.1%
3999014460 1
< 0.1%
3085447000 1
< 0.1%
2713868850 1
< 0.1%
2702212620 1
< 0.1%
2295093600 1
< 0.1%
2264768730 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6541
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.88252
Minimum0.4
Maximum23496.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:22.372385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile6.687
Q131.95
median72.74
Q3145.8
95-th percentile499.8145
Maximum23496.7
Range23496.3
Interquartile range (IQR)113.85

Descriptive statistics

Standard deviation464.4811
Coefficient of variation (CV)2.9796869
Kurtosis912.68437
Mean155.88252
Median Absolute Deviation (MAD)48.74
Skewness23.391566
Sum1558825.2
Variance215742.7
MonotonicityNot monotonic
2024-05-10T22:11:22.787558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 157
 
1.6%
51.483 57
 
0.6%
18.0 53
 
0.5%
9.97 42
 
0.4%
55.89 41
 
0.4%
52.567 41
 
0.4%
9.55 37
 
0.4%
42.03 36
 
0.4%
9.17 33
 
0.3%
149.71 32
 
0.3%
Other values (6531) 9471
94.7%
ValueCountFrequency (%)
0.4 1
 
< 0.1%
0.45 1
 
< 0.1%
0.64 1
 
< 0.1%
0.8 1
 
< 0.1%
0.81 1
 
< 0.1%
0.88 1
 
< 0.1%
0.99 5
0.1%
1.0 7
0.1%
1.13 1
 
< 0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
23496.7 1
< 0.1%
17687.0 1
< 0.1%
10258.31 1
< 0.1%
9068.06 1
< 0.1%
7489.75 1
< 0.1%
7488.15 1
< 0.1%
7357.89 1
< 0.1%
6229.39 1
< 0.1%
5986.68 1
< 0.1%
5495.6 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-06-01
2nd row2017-06-01
3rd row2017-06-01
4th row2017-06-01
5th row2017-06-01

Common Values

ValueCountFrequency (%)
2017-06-01 10000
100.0%

Length

2024-05-10T22:11:23.263857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:23.575133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-06-01 10000
100.0%

Interactions

2024-05-10T22:11:09.324445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:00.545015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:02.421486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:04.110775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:05.636182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:07.481403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:09.645858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:00.845014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:02.841328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:04.410160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:05.977854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:07.812412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:09.864523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:01.305815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:03.085492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:04.642158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:06.274718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:08.095438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:10.085778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:01.593398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:03.344869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:04.862912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:06.589425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:08.415659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:10.328053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:01.877013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:03.608435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:05.093812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:06.888558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:08.706166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:10.586278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:02.144291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:03.853740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:05.362823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:07.192863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:09.025729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:11:23.765633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.0840.5110.1920.0720.0610.074
특수지0.0841.0000.1660.0000.0000.0000.010
본번0.5110.1661.0000.3640.0510.0000.000
부번0.1920.0000.3641.0000.0000.0000.000
동0.0720.0000.0510.0001.0000.0000.000
시가표준액0.0610.0000.0000.0000.0001.0000.936
연면적0.0740.0100.0000.0000.0000.9361.000
2024-05-10T22:11:24.092236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.194-0.1830.0330.0540.1500.084
본번-0.1941.000-0.1360.0590.1150.0380.127
부번-0.183-0.1361.000-0.0630.0380.0710.000
동0.0330.059-0.0631.0000.0110.1340.000
시가표준액0.0540.1150.0380.0111.0000.8770.000
연면적0.1500.0380.0710.1340.8771.0000.011
특수지0.0840.1270.0000.0000.0000.0111.000

Missing values

2024-05-10T22:11:11.041713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:11:11.650090image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자
60525인천광역시부평구2823720171050145920인천광역시 부평구 체육관로 32 0000동 0804호109006210154.792017-06-01
4983인천광역시부평구2823720171010120520인천광역시 부평구 부평동 205-2 2238호17802004.32017-06-01
60527인천광역시부평구2823720171050145920인천광역시 부평구 체육관로 32 0000동 0901호123857500175.8792017-06-01
1168인천광역시부평구2823720171060141600인천광역시 부평구 갈산동 416 108호581996011.812017-06-01
53638인천광역시부평구2823720171050139130인천광역시 부평구 영성중로 50 0000동 0113호839270020.472017-06-01
43006인천광역시부평구28237201710701124350인천광역시 부평구 부평문화로 190 0000동 0004호48986800168.922017-06-01
62452인천광역시부평구2823720171040139980인천광역시 부평구 평천로 149 0000동 0402호2768880083.42017-06-01
3061인천광역시부평구28237201710701496150인천광역시 부평구 부개동 496-15 126호803913014.332017-06-01
8404인천광역시부평구2823720171010153921인천광역시 부평구 부평동 539-2 1동 426호5011052074.1282017-06-01
24524인천광역시부평구2823720171010115910인천광역시 부평구 경원대로 1415 0000동 0606호2625012056.092017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자
19373인천광역시부평구28237201710401303165인천광역시 부평구 청천동 303-16 5동520096317010258.312017-06-01
62666인천광역시부평구2823720171060174151인천광역시 부평구 평천로 336-1 0001동 0001호36275800119.922017-06-01
17299인천광역시부평구28237201710801109171인천광역시 부평구 일신동 109-17 1동 104호1727121078.92017-06-01
53774인천광역시부평구2823720171040123431인천광역시 부평구 원길로 17 0001동 0401호129631680189.522017-06-01
52259인천광역시부평구2823720171010128550인천광역시 부평구 안남로63번길 21 0000동 0301호5914296091.272017-06-01
1625인천광역시부평구28237201710901260인천광역시 부평구 구산동 2-6 6호2765942071.882017-06-01
29546인천광역시부평구28237201710701496210인천광역시 부평구 길주남로 159 0000동 0302호1067736026.172017-06-01
26744인천광역시부평구2823720171070132240인천광역시 부평구 경인로 1092 0000동 0001호1590050082.62017-06-01
34946인천광역시부평구2823720171030120020인천광역시 부평구 마장로 237 0000동 0002호5377644088.742017-06-01
5113인천광역시부평구2823720171010120520인천광역시 부평구 부평동 205-2 2305호17802004.32017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자# duplicates
5인천광역시부평구2823720171060113301인천광역시 부평구 부평대로 332 0001동 0001호36032805.842017-06-013
0인천광역시부평구28237201710101101870인천광역시 부평구 길주남로102번길 5 0000동 0000호144299180196.59292017-06-012
1인천광역시부평구2823720171010134120인천광역시 부평구 경원대로1367번길 37 0000동 0000호4205377049.24332017-06-012
2인천광역시부평구28237201710101442151인천광역시 부평구 부평대로 88 0001동 0001호609740710657.532017-06-012
3인천광역시부평구28237201710401396181인천광역시 부평구 청천동 396-18 1동 1호43659000189.02017-06-012
4인천광역시부평구2823720171050146450인천광역시 부평구 길주로 643 0000동 0001호742715130673.42632017-06-012
6인천광역시부평구282372017108017951인천광역시 부평구 일신동 79-5 1동179951200314.62017-06-012
7인천광역시부평구2823720171080179510인천광역시 부평구 일신동 79-51316800032.02017-06-012