Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows15
Duplicate rows (%)0.1%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical6
Numeric6
Text1
DateTime1

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 15 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (97.1%)Imbalance
동 is highly skewed (γ1 = 21.24175589)Skewed
연면적 is highly skewed (γ1 = 28.47569527)Skewed
부번 has 940 (9.4%) zerosZeros
동 has 5702 (57.0%) zerosZeros

Reproduction

Analysis started2024-05-10 22:10:24.795433
Analysis finished2024-05-10 22:10:39.046494
Duration14.25 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

2024-05-10T22:10:39.238472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:39.538680image/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

2024-05-10T22:10:39.878432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:40.287377image/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

2024-05-10T22:10:40.538950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:40.879529image/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

2024-05-10T22:10:41.168572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:41.463647image/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%
Mean102.9644
Minimum101
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:41.754801image/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 deviation2.1666107
Coefficient of variation (CV)0.021042329
Kurtosis-0.55289073
Mean102.9644
Median Absolute Deviation (MAD)1
Skewness0.79189273
Sum1029644
Variance4.6942021
MonotonicityNot monotonic
2024-05-10T22:10:42.190946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
101 4172
41.7%
104 1378
 
13.8%
102 1159
 
11.6%
105 873
 
8.7%
103 858
 
8.6%
107 731
 
7.3%
106 553
 
5.5%
108 216
 
2.2%
109 60
 
0.6%
ValueCountFrequency (%)
101 4172
41.7%
102 1159
 
11.6%
103 858
 
8.6%
104 1378
 
13.8%
105 873
 
8.7%
106 553
 
5.5%
107 731
 
7.3%
108 216
 
2.2%
109 60
 
0.6%
ValueCountFrequency (%)
109 60
 
0.6%
108 216
 
2.2%
107 731
 
7.3%
106 553
 
5.5%
105 873
 
8.7%
104 1378
 
13.8%
103 858
 
8.6%
102 1159
 
11.6%
101 4172
41.7%

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

2024-05-10T22:10:42.791387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:43.100505image/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
9970 
2
 
30

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 9970
99.7%
2 30
 
0.3%

Length

2024-05-10T22:10:43.477463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:43.711258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9970
99.7%
2 30
 
0.3%

본번
Real number (ℝ)

Distinct658
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.1429
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:44.127682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1182
median341
Q3459
95-th percentile654
Maximum950
Range949
Interquartile range (IQR)277

Descriptive statistics

Standard deviation190.65096
Coefficient of variation (CV)0.58099981
Kurtosis-0.073044297
Mean328.1429
Median Absolute Deviation (MAD)141
Skewness0.42394209
Sum3281429
Variance36347.79
MonotonicityNot monotonic
2024-05-10T22:10:44.568484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 445
 
4.5%
539 189
 
1.9%
182 161
 
1.6%
201 154
 
1.5%
431 121
 
1.2%
10 115
 
1.1%
199 110
 
1.1%
378 105
 
1.1%
425 102
 
1.0%
460 101
 
1.0%
Other values (648) 8397
84.0%
ValueCountFrequency (%)
1 5
 
0.1%
2 10
 
0.1%
3 15
 
0.1%
4 1
 
< 0.1%
5 17
 
0.2%
6 14
 
0.1%
7 19
 
0.2%
8 7
 
0.1%
9 27
 
0.3%
10 115
1.1%
ValueCountFrequency (%)
950 39
0.4%
947 5
 
0.1%
938 2
 
< 0.1%
915 5
 
0.1%
914 3
 
< 0.1%
912 4
 
< 0.1%
911 1
 
< 0.1%
910 3
 
< 0.1%
908 1
 
< 0.1%
907 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct382
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.1111
Minimum0
Maximum1133
Zeros940
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:45.012372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q323
95-th percentile141.05
Maximum1133
Range1133
Interquartile range (IQR)21

Descriptive statistics

Standard deviation92.403285
Coefficient of variation (CV)2.7907042
Kurtosis47.565156
Mean33.1111
Median Absolute Deviation (MAD)6
Skewness6.2691165
Sum331111
Variance8538.367
MonotonicityNot monotonic
2024-05-10T22:10:45.461052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1114
 
11.1%
0 940
 
9.4%
1 837
 
8.4%
3 697
 
7.0%
4 465
 
4.7%
9 396
 
4.0%
5 365
 
3.6%
7 327
 
3.3%
6 264
 
2.6%
12 244
 
2.4%
Other values (372) 4351
43.5%
ValueCountFrequency (%)
0 940
9.4%
1 837
8.4%
2 1114
11.1%
3 697
7.0%
4 465
4.7%
5 365
 
3.6%
6 264
 
2.6%
7 327
 
3.3%
8 236
 
2.4%
9 396
 
4.0%
ValueCountFrequency (%)
1133 1
< 0.1%
1057 1
< 0.1%
1054 1
< 0.1%
1044 1
< 0.1%
1030 2
< 0.1%
991 2
< 0.1%
990 1
< 0.1%
966 2
< 0.1%
965 1
< 0.1%
947 1
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.6063
Minimum0
Maximum9999
Zeros5702
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:45.843506image/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 deviation462.59945
Coefficient of variation (CV)18.800041
Kurtosis450.43326
Mean24.6063
Median Absolute Deviation (MAD)0
Skewness21.241756
Sum246063
Variance213998.25
MonotonicityNot monotonic
2024-05-10T22:10:46.213277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5702
57.0%
1 3453
34.5%
2 338
 
3.4%
3 159
 
1.6%
101 77
 
0.8%
4 41
 
0.4%
102 36
 
0.4%
7 30
 
0.3%
9999 17
 
0.2%
99 15
 
0.1%
Other values (34) 132
 
1.3%
ValueCountFrequency (%)
0 5702
57.0%
1 3453
34.5%
2 338
 
3.4%
3 159
 
1.6%
4 41
 
0.4%
5 15
 
0.1%
6 14
 
0.1%
7 30
 
0.3%
8 10
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9999 17
0.2%
9998 1
 
< 0.1%
9991 1
 
< 0.1%
9002 1
 
< 0.1%
9001 2
 
< 0.1%
401 1
 
< 0.1%
321 7
0.1%
227 1
 
< 0.1%
225 1
 
< 0.1%
209 2
 
< 0.1%

물건지
Text

Distinct9612
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:10:46.853119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length28.9379
Min length17

Characters and Unicode

Total characters289379
Distinct characters111
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

Unique9280 ?
Unique (%)92.8%

Sample

1st row인천광역시 부평구 체육관로 30 0000동 0304호
2nd row인천광역시 부평구 갈산동 369 106호
3rd row인천광역시 부평구 충선로209번길 41 0000동 0107호
4th row인천광역시 부평구 부흥로327번길 20 0001동 0004호
5th row인천광역시 부평구 청천동 259 101호
ValueCountFrequency (%)
인천광역시 10000
 
17.1%
부평구 10000
 
17.1%
0000동 4326
 
7.4%
0001동 1928
 
3.3%
1동 1525
 
2.6%
부평동 1133
 
1.9%
0001호 964
 
1.6%
청천동 788
 
1.3%
0002호 687
 
1.2%
1호 513
 
0.9%
Other values (4178) 26749
45.6%
2024-05-10T22:10:47.956402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48613
16.8%
0 43067
14.9%
1 16659
 
5.8%
부 13297
 
4.6%
동 12582
 
4.3%
평 12353
 
4.3%
천 11095
 
3.8%
인 10336
 
3.6%
광 10123
 
3.5%
시 10121
 
3.5%
Other values (101) 101133
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137646
47.6%
Decimal Number 99299
34.3%
Space Separator 48613
 
16.8%
Dash Punctuation 3802
 
1.3%
Uppercase Letter 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 13297
9.7%
동 12582
9.1%
평 12353
9.0%
천 11095
8.1%
인 10336
 
7.5%
광 10123
 
7.4%
시 10121
 
7.4%
구 10051
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (86) 27696
20.1%
Decimal Number
ValueCountFrequency (%)
0 43067
43.4%
1 16659
 
16.8%
2 9141
 
9.2%
3 7014
 
7.1%
4 5317
 
5.4%
5 4745
 
4.8%
6 4000
 
4.0%
8 3211
 
3.2%
9 3102
 
3.1%
7 3043
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 17
89.5%
A 1
 
5.3%
C 1
 
5.3%
Space Separator
ValueCountFrequency (%)
48613
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151714
52.4%
Hangul 137646
47.6%
Latin 19
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 13297
9.7%
동 12582
9.1%
평 12353
9.0%
천 11095
8.1%
인 10336
 
7.5%
광 10123
 
7.4%
시 10121
 
7.4%
구 10051
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (86) 27696
20.1%
Common
ValueCountFrequency (%)
48613
32.0%
0 43067
28.4%
1 16659
 
11.0%
2 9141
 
6.0%
3 7014
 
4.6%
4 5317
 
3.5%
5 4745
 
3.1%
6 4000
 
2.6%
- 3802
 
2.5%
8 3211
 
2.1%
Other values (2) 6145
 
4.1%
Latin
ValueCountFrequency (%)
B 17
89.5%
A 1
 
5.3%
C 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151733
52.4%
Hangul 137646
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48613
32.0%
0 43067
28.4%
1 16659
 
11.0%
2 9141
 
6.0%
3 7014
 
4.6%
4 5317
 
3.5%
5 4745
 
3.1%
6 4000
 
2.6%
- 3802
 
2.5%
8 3211
 
2.1%
Other values (5) 6164
 
4.1%
Hangul
ValueCountFrequency (%)
부 13297
9.7%
동 12582
9.1%
평 12353
9.0%
천 11095
8.1%
인 10336
 
7.5%
광 10123
 
7.4%
시 10121
 
7.4%
구 10051
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (86) 27696
20.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8261
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73392404
Minimum22440
Maximum6.6324807 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:48.379311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22440
5-th percentile1427880
Q19041175
median33429420
Q369849360
95-th percentile2.4521599 × 108
Maximum6.6324807 × 109
Range6.6324582 × 109
Interquartile range (IQR)60808185

Descriptive statistics

Standard deviation1.99047 × 108
Coefficient of variation (CV)2.7120927
Kurtosis324.07287
Mean73392404
Median Absolute Deviation (MAD)27054080
Skewness14.245329
Sum7.3392404 × 1011
Variance3.961971 × 1016
MonotonicityNot monotonic
2024-05-10T22:10:48.824026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1780200 143
 
1.4%
34802500 62
 
0.6%
28412280 42
 
0.4%
4805080 37
 
0.4%
35535290 36
 
0.4%
5224280 36
 
0.4%
5004200 33
 
0.3%
5371000 31
 
0.3%
35937270 28
 
0.3%
28121000 26
 
0.3%
Other values (8251) 9526
95.3%
ValueCountFrequency (%)
22440 1
< 0.1%
32670 1
< 0.1%
33000 1
< 0.1%
33660 1
< 0.1%
34000 1
< 0.1%
43650 1
< 0.1%
45900 1
< 0.1%
52200 1
< 0.1%
53900 1
< 0.1%
56000 1
< 0.1%
ValueCountFrequency (%)
6632480660 1
< 0.1%
6175257760 1
< 0.1%
4662762110 1
< 0.1%
4323009760 1
< 0.1%
3999014460 1
< 0.1%
3661442350 1
< 0.1%
3620308440 1
< 0.1%
3085447000 1
< 0.1%
3009876520 1
< 0.1%
2990290800 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6561
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.45114
Minimum0.4
Maximum30735.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:49.201977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile6.927
Q132.1975
median72.055
Q3148.6125
95-th percentile523.514
Maximum30735.45
Range30735.05
Interquartile range (IQR)116.415

Descriptive statistics

Standard deviation502.51407
Coefficient of variation (CV)3.1124839
Kurtosis1452.4124
Mean161.45114
Median Absolute Deviation (MAD)48.255
Skewness28.475695
Sum1614511.4
Variance252520.39
MonotonicityNot monotonic
2024-05-10T22:10:49.591309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 143
 
1.4%
51.483 62
 
0.6%
18.0 62
 
0.6%
42.03 45
 
0.4%
9.17 37
 
0.4%
9.97 36
 
0.4%
52.567 36
 
0.4%
9.55 33
 
0.3%
10.25 31
 
0.3%
55.89 28
 
0.3%
Other values (6551) 9487
94.9%
ValueCountFrequency (%)
0.4 1
 
< 0.1%
0.56 1
 
< 0.1%
0.81 1
 
< 0.1%
0.9 2
 
< 0.1%
0.99 8
0.1%
1.0 7
0.1%
1.0269 1
 
< 0.1%
1.2 3
 
< 0.1%
1.25 1
 
< 0.1%
1.27 1
 
< 0.1%
ValueCountFrequency (%)
30735.45 1
< 0.1%
10333.43 1
< 0.1%
9365.75 1
< 0.1%
9068.06 1
< 0.1%
8974.94 1
< 0.1%
7605.69 1
< 0.1%
7489.75 1
< 0.1%
7378.9 1
< 0.1%
7162.461 1
< 0.1%
6794.0 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-06-01 00:00:00
Maximum2017-06-01 00:00:00
2024-05-10T22:10:49.953707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:50.328146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-05-10T22:10:36.126460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:27.339106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:29.025165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:30.750246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:32.553285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:34.317863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:36.420380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:27.622184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:29.318003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:31.043242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:32.867001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:34.640156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:36.686079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:27.905679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:29.574711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:31.282424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:33.144325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:34.937757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:36.954018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:28.176876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:29.844562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:31.549950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:33.417496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:35.214088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:37.241733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:28.424004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:30.202968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:31.813948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:33.738748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:35.506258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:37.543706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:28.720463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:30.474572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:32.102530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:34.038238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:35.795898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:10:50.579802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.0860.5170.2170.0650.0890.067
특수지0.0861.0000.1600.0000.0000.0000.000
본번0.5170.1601.0000.3810.0420.0000.000
부번0.2170.0000.3811.0000.0000.0000.000
동0.0650.0000.0420.0001.0000.0000.000
시가표준액0.0890.0000.0000.0000.0001.0000.770
연면적0.0670.0000.0000.0000.0000.7701.000
2024-05-10T22:10:50.873586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.220-0.1760.0280.0410.1460.086
본번-0.2201.000-0.1430.0490.1320.0440.123
부번-0.176-0.1431.000-0.0600.0270.0590.000
동0.0280.049-0.0601.0000.0120.1300.000
시가표준액0.0410.1320.0270.0121.0000.8780.000
연면적0.1460.0440.0590.1300.8781.0000.000
특수지0.0860.1230.0000.0000.0000.0001.000

Missing values

2024-05-10T22:10:37.934819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:10:38.757589image/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
60395인천광역시부평구2823720171050145910인천광역시 부평구 체육관로 30 0000동 0304호193606920262.342017-06-01
794인천광역시부평구2823720171060136900인천광역시 부평구 갈산동 369 106호1803172036.922017-06-01
62090인천광역시부평구2823720171050146330인천광역시 부평구 충선로209번길 41 0000동 0107호1277493026.672017-06-01
47709인천광역시부평구28237201710101397311인천광역시 부평구 부흥로327번길 20 0001동 0004호64976590152.172017-06-01
19099인천광역시부평구2823720171040125900인천광역시 부평구 청천동 259 101호1130338026.92017-06-01
8132인천광역시부평구2823720171010153921인천광역시 부평구 부평동 539-2 1동 1223호3480250051.4832017-06-01
20685인천광역시부평구28237201710401396191인천광역시 부평구 청천동 396-19 1동 1호2860800096.02017-06-01
63028인천광역시부평구28237201710401391220인천광역시 부평구 평천로141번길 55 0000동 0001호170340890236.062017-06-01
10074인천광역시부평구2823720171010166581인천광역시 부평구 부평동 665-8 1동 1호332083200601.62017-06-01
56701인천광역시부평구28237201710101180241인천광역시 부평구 장제로 49 0001동 0408호1580403027.392017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자
41305인천광역시부평구28237201710101543271인천광역시 부평구 부평대로 43 0001동 0002호19737270104.432017-06-01
34217인천광역시부평구2823720171020148871인천광역시 부평구 동암남로 23 0001동 0004호67107400159.42017-06-01
30746인천광역시부평구2823720171050146430인천광역시 부평구 길주로 639 0000동 0111호6356070067.262017-06-01
39048인천광역시부평구28237201710101439120인천광역시 부평구 부평대로 102 0000동 0105호5713965066.832017-06-01
54056인천광역시부평구28237201710401236250인천광역시 부평구 원길로6번길 1 0000동 0005호67875000181.02017-06-01
30494인천광역시부평구282372017105014651102인천광역시 부평구 길주로 623 0102동 0814호194506400202.42017-06-01
52506인천광역시부평구28237201710201408480인천광역시 부평구 열우물로 18 0000동 1108호2812100046.12017-06-01
22600인천광역시부평구282372017104015031인천광역시 부평구 청천동 50-3 1동 3호129150000315.02017-06-01
43398인천광역시부평구282372017101012111770인천광역시 부평구 부평문화로 75 0000동 0002호1175616047.12017-06-01
3189인천광역시부평구2823720171070149921인천광역시 부평구 부개동 499-2 1동 1호108946560202.882017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자# duplicates
4인천광역시부평구2823720171010123930인천광역시 부평구 시장로61번길 11 0000동 0001호2017451701054.052017-06-013
7인천광역시부평구28237201710101751580인천광역시 부평구 경인로911번길 44 0000동 0001호81977580102.85772017-06-013
12인천광역시부평구2823720171060113301인천광역시 부평구 부평대로 332 0001동 0001호36032805.842017-06-013
0인천광역시부평구28237201710101101870인천광역시 부평구 길주남로102번길 5 0000동 0000호144299180196.59292017-06-012
1인천광역시부평구28237201710101109660인천광역시 부평구 길주남로 80 0000동 0000호218216460244.092017-06-012
2인천광역시부평구28237201710101146290인천광역시 부평구 부평문화로115번길 50 0000동 0001호54685800155.82017-06-012
3인천광역시부평구28237201710101224112인천광역시 부평구 부평동 224-1 12동 1호9382502.252017-06-012
5인천광역시부평구2823720171010134120인천광역시 부평구 경원대로1367번길 37 0000동 0000호4205377049.24332017-06-012
6인천광역시부평구28237201710101747530인천광역시 부평구 남부역로17번길 21 0000동 0000호108535290129.67182017-06-012
8인천광역시부평구28237201710201174190인천광역시 부평구 마장로72번길 8 0000동 0001호3266750089.52017-06-012