Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
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 7 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (96.8%)Imbalance
시가표준액 is highly skewed (γ1 = 20.82402993)Skewed
부번 has 959 (9.6%) zerosZeros
동 has 5637 (56.4%) zerosZeros

Reproduction

Analysis started2024-05-10 22:11:31.952379
Analysis finished2024-05-10 22:11:46.442424
Duration14.49 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

2024-05-10T22:11:46.637763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:46.844950image/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

2024-05-10T22:11:47.094904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:47.312804image/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

2024-05-10T22:11:47.588740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:47.870382image/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

2024-05-10T22:11:48.191592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:48.491023image/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%
Mean102.9522
Minimum101
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:48.776601image/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 deviation2.1431226
Coefficient of variation (CV)0.020816676
Kurtosis-0.5501201
Mean102.9522
Median Absolute Deviation (MAD)1
Skewness0.78119908
Sum1029522
Variance4.5929745
MonotonicityNot monotonic
2024-05-10T22:11:49.054240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
101 4179
41.8%
104 1478
 
14.8%
102 1137
 
11.4%
105 893
 
8.9%
103 817
 
8.2%
107 701
 
7.0%
106 540
 
5.4%
108 201
 
2.0%
109 54
 
0.5%
ValueCountFrequency (%)
101 4179
41.8%
102 1137
 
11.4%
103 817
 
8.2%
104 1478
 
14.8%
105 893
 
8.9%
106 540
 
5.4%
107 701
 
7.0%
108 201
 
2.0%
109 54
 
0.5%
ValueCountFrequency (%)
109 54
 
0.5%
108 201
 
2.0%
107 701
 
7.0%
106 540
 
5.4%
105 893
 
8.9%
104 1478
 
14.8%
103 817
 
8.2%
102 1137
 
11.4%
101 4179
41.8%

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

2024-05-10T22:11:49.503111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:49.908179image/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
9967 
2
 
33

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 9967
99.7%
2 33
 
0.3%

Length

2024-05-10T22:11:50.278738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:50.635093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9967
99.7%
2 33
 
0.3%

본번
Real number (ℝ)

Distinct648
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.9661
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:50.990338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1182
median331
Q3456
95-th percentile654
Maximum950
Range949
Interquartile range (IQR)274

Descriptive statistics

Standard deviation188.65536
Coefficient of variation (CV)0.58053859
Kurtosis-0.14563236
Mean324.9661
Median Absolute Deviation (MAD)133
Skewness0.40466934
Sum3249661
Variance35590.845
MonotonicityNot monotonic
2024-05-10T22:11:51.594993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 451
 
4.5%
539 203
 
2.0%
182 160
 
1.6%
201 153
 
1.5%
199 142
 
1.4%
431 114
 
1.1%
425 111
 
1.1%
10 109
 
1.1%
460 106
 
1.1%
378 103
 
1.0%
Other values (638) 8348
83.5%
ValueCountFrequency (%)
1 6
 
0.1%
2 11
 
0.1%
3 13
 
0.1%
4 1
 
< 0.1%
5 14
 
0.1%
6 9
 
0.1%
7 20
 
0.2%
8 9
 
0.1%
9 31
 
0.3%
10 109
1.1%
ValueCountFrequency (%)
950 21
0.2%
949 1
 
< 0.1%
947 3
 
< 0.1%
945 1
 
< 0.1%
938 2
 
< 0.1%
915 4
 
< 0.1%
914 1
 
< 0.1%
912 3
 
< 0.1%
911 2
 
< 0.1%
910 4
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct391
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.4245
Minimum0
Maximum1079
Zeros959
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:52.189348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile145
Maximum1079
Range1079
Interquartile range (IQR)22

Descriptive statistics

Standard deviation91.330497
Coefficient of variation (CV)2.7324417
Kurtosis44.729296
Mean33.4245
Median Absolute Deviation (MAD)6
Skewness6.0603373
Sum334245
Variance8341.2596
MonotonicityNot monotonic
2024-05-10T22:11:52.711060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1123
 
11.2%
0 959
 
9.6%
1 806
 
8.1%
3 715
 
7.1%
4 482
 
4.8%
5 406
 
4.1%
9 377
 
3.8%
7 289
 
2.9%
12 249
 
2.5%
8 240
 
2.4%
Other values (381) 4354
43.5%
ValueCountFrequency (%)
0 959
9.6%
1 806
8.1%
2 1123
11.2%
3 715
7.1%
4 482
4.8%
5 406
 
4.1%
6 231
 
2.3%
7 289
 
2.9%
8 240
 
2.4%
9 377
 
3.8%
ValueCountFrequency (%)
1079 1
< 0.1%
1074 1
< 0.1%
1007 2
< 0.1%
991 1
< 0.1%
977 1
< 0.1%
966 1
< 0.1%
965 2
< 0.1%
964 2
< 0.1%
930 1
< 0.1%
927 1
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9771
Minimum0
Maximum9999
Zeros5637
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:53.424903image/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 deviation518.91422
Coefficient of variation (CV)17.310354
Kurtosis364.53734
Mean29.9771
Median Absolute Deviation (MAD)0
Skewness19.130501
Sum299771
Variance269271.96
MonotonicityNot monotonic
2024-05-10T22:11:53.773807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 5637
56.4%
1 3503
35.0%
2 326
 
3.3%
3 164
 
1.6%
101 88
 
0.9%
4 57
 
0.6%
7 36
 
0.4%
102 31
 
0.3%
9999 23
 
0.2%
6 14
 
0.1%
Other values (32) 121
 
1.2%
ValueCountFrequency (%)
0 5637
56.4%
1 3503
35.0%
2 326
 
3.3%
3 164
 
1.6%
4 57
 
0.6%
5 12
 
0.1%
6 14
 
0.1%
7 36
 
0.4%
8 7
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
9999 23
0.2%
9998 2
 
< 0.1%
9990 1
 
< 0.1%
9917 1
 
< 0.1%
802 1
 
< 0.1%
321 5
 
0.1%
302 1
 
< 0.1%
226 1
 
< 0.1%
225 1
 
< 0.1%
209 1
 
< 0.1%

물건지
Text

Distinct9647
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:11:54.443803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length28.9193
Min length17

Characters and Unicode

Total characters289193
Distinct characters113
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9339 ?
Unique (%)93.4%

Sample

1st row인천광역시 부평구 부일로 85 0000동 0000호
2nd row인천광역시 부평구 시장로 33 0000동 8101-7호
3rd row인천광역시 부평구 백범로577번길 15-12 0000동 0002호
4th row인천광역시 부평구 경인로 1021 0001동 0004호
5th row인천광역시 부평구 삼산동 207-7 1동 2호
ValueCountFrequency (%)
인천광역시 10000
 
17.1%
부평구 10000
 
17.1%
0000동 4237
 
7.2%
0001동 1944
 
3.3%
1동 1559
 
2.7%
부평동 1148
 
2.0%
0001호 967
 
1.7%
청천동 880
 
1.5%
0002호 676
 
1.2%
1호 525
 
0.9%
Other values (4248) 26654
45.5%
2024-05-10T22:11:55.486542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48590
16.8%
0 42774
14.8%
1 16833
 
5.8%
부 13300
 
4.6%
동 12596
 
4.4%
평 12391
 
4.3%
천 11172
 
3.9%
인 10323
 
3.6%
시 10143
 
3.5%
광 10113
 
3.5%
Other values (103) 100958
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137577
47.6%
Decimal Number 99067
34.3%
Space Separator 48590
 
16.8%
Dash Punctuation 3939
 
1.4%
Uppercase Letter 19
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 13300
9.7%
동 12596
9.2%
평 12391
9.0%
천 11172
8.1%
인 10323
 
7.5%
시 10143
 
7.4%
광 10113
 
7.4%
구 10043
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (87) 27504
20.0%
Decimal Number
ValueCountFrequency (%)
0 42774
43.2%
1 16833
 
17.0%
2 9111
 
9.2%
3 7090
 
7.2%
4 5437
 
5.5%
5 4511
 
4.6%
6 3974
 
4.0%
8 3156
 
3.2%
9 3104
 
3.1%
7 3077
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 15
78.9%
C 3
 
15.8%
A 1
 
5.3%
Space Separator
ValueCountFrequency (%)
48590
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3939
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151596
52.4%
Hangul 137577
47.6%
Latin 20
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 13300
9.7%
동 12596
9.2%
평 12391
9.0%
천 11172
8.1%
인 10323
 
7.5%
시 10143
 
7.4%
광 10113
 
7.4%
구 10043
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (87) 27504
20.0%
Common
ValueCountFrequency (%)
48590
32.1%
0 42774
28.2%
1 16833
 
11.1%
2 9111
 
6.0%
3 7090
 
4.7%
4 5437
 
3.6%
5 4511
 
3.0%
6 3974
 
2.6%
- 3939
 
2.6%
8 3156
 
2.1%
Other values (2) 6181
 
4.1%
Latin
ValueCountFrequency (%)
B 15
75.0%
C 3
 
15.0%
c 1
 
5.0%
A 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151616
52.4%
Hangul 137577
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48590
32.0%
0 42774
28.2%
1 16833
 
11.1%
2 9111
 
6.0%
3 7090
 
4.7%
4 5437
 
3.6%
5 4511
 
3.0%
6 3974
 
2.6%
- 3939
 
2.6%
8 3156
 
2.1%
Other values (6) 6201
 
4.1%
Hangul
ValueCountFrequency (%)
부 13300
9.7%
동 12596
9.2%
평 12391
9.0%
천 11172
8.1%
인 10323
 
7.5%
시 10143
 
7.4%
광 10113
 
7.4%
구 10043
 
7.3%
역 10028
 
7.3%
호 9964
 
7.2%
Other values (87) 27504
20.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8279
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74063424
Minimum32300
Maximum9.6887216 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:55.927804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32300
5-th percentile1398572
Q19196392.5
median33462375
Q369618150
95-th percentile2.4373099 × 108
Maximum9.6887216 × 109
Range9.6886893 × 109
Interquartile range (IQR)60421758

Descriptive statistics

Standard deviation2.4282648 × 108
Coefficient of variation (CV)3.2786288
Kurtosis627.67651
Mean74063424
Median Absolute Deviation (MAD)27110755
Skewness20.82403
Sum7.4063424 × 1011
Variance5.8964698 × 1016
MonotonicityNot monotonic
2024-05-10T22:11:56.295293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1780200 150
 
1.5%
34802500 58
 
0.6%
28412280 47
 
0.5%
35535290 37
 
0.4%
5224280 34
 
0.3%
61231390 34
 
0.3%
5004200 33
 
0.3%
4805080 31
 
0.3%
5371000 29
 
0.3%
35937270 27
 
0.3%
Other values (8269) 9520
95.2%
ValueCountFrequency (%)
32300 1
 
< 0.1%
32530 1
 
< 0.1%
32640 1
 
< 0.1%
34000 3
< 0.1%
41080 1
 
< 0.1%
45180 1
 
< 0.1%
45900 1
 
< 0.1%
48960 1
 
< 0.1%
49000 1
 
< 0.1%
51000 1
 
< 0.1%
ValueCountFrequency (%)
9688721580 1
< 0.1%
9118067080 1
< 0.1%
6300465720 1
< 0.1%
6286770000 1
< 0.1%
6175257760 1
< 0.1%
5200963170 1
< 0.1%
4662762110 1
< 0.1%
4459321110 1
< 0.1%
4348667040 1
< 0.1%
3092762400 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6651
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.34359
Minimum0.64
Maximum15205.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:11:56.667966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile6.40415
Q132.28525
median74.128
Q3147.86025
95-th percentile521.786
Maximum15205.15
Range15204.51
Interquartile range (IQR)115.575

Descriptive statistics

Standard deviation493.70284
Coefficient of variation (CV)3.0040893
Kurtosis369.91512
Mean164.34359
Median Absolute Deviation (MAD)49.428
Skewness16.171549
Sum1643435.9
Variance243742.49
MonotonicityNot monotonic
2024-05-10T22:11:57.130278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 152
 
1.5%
51.483 58
 
0.6%
42.03 48
 
0.5%
18.0 44
 
0.4%
52.567 37
 
0.4%
9.97 34
 
0.3%
149.71 34
 
0.3%
9.55 33
 
0.3%
9.17 31
 
0.3%
10.25 29
 
0.3%
Other values (6641) 9500
95.0%
ValueCountFrequency (%)
0.64 3
< 0.1%
0.79 2
 
< 0.1%
0.83 1
 
< 0.1%
0.9 2
 
< 0.1%
0.95 1
 
< 0.1%
0.99 3
< 0.1%
1.0 7
0.1%
1.06 1
 
< 0.1%
1.2 2
 
< 0.1%
1.33 1
 
< 0.1%
ValueCountFrequency (%)
15205.15 1
< 0.1%
14875.0 1
< 0.1%
13439.56 1
< 0.1%
13207.5 1
< 0.1%
10871.09 1
< 0.1%
10333.43 1
< 0.1%
10258.31 1
< 0.1%
9179.67 1
< 0.1%
7880.784 1
< 0.1%
7878.02 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

2024-05-10T22:11:57.565478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:11:57.841309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-06-01 10000
100.0%

Interactions

2024-05-10T22:11:43.834550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:34.599117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:36.284723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:37.930279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:39.577010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:41.772574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:44.143777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:34.895973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:36.581460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:38.218168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:40.023618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:42.081581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:44.409022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:35.162416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:36.831923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:38.478302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:40.339183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:42.591191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:44.679901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:35.443483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:37.095989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:38.748297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:40.685594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:42.888216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:44.943252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:35.692015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:37.373448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:39.022201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:40.968993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:43.266096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:45.233680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:35.981063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:37.639513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:39.295604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:41.400921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:11:43.531147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:11:58.064436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.0950.5110.2050.1030.0560.108
특수지0.0951.0000.1910.0000.0000.0000.077
본번0.5110.1911.0000.3690.0250.0000.000
부번0.2050.0000.3691.0000.0000.0000.000
동0.1030.0000.0250.0001.0000.0000.000
시가표준액0.0560.0000.0000.0000.0001.0000.870
연면적0.1080.0770.0000.0000.0000.8701.000
2024-05-10T22:11:58.363008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.196-0.1830.0150.0550.1620.095
본번-0.1961.000-0.1290.0590.1250.0410.146
부번-0.183-0.1291.000-0.0570.0110.0410.000
동0.0150.059-0.0571.000-0.0180.1060.000
시가표준액0.0550.1250.011-0.0181.0000.8720.000
연면적0.1620.0410.0410.1060.8721.0000.059
특수지0.0950.1460.0000.0000.0000.0591.000

Missing values

2024-05-10T22:11:45.641847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:11:46.203746image/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
38773인천광역시부평구2823720171070120900인천광역시 부평구 부일로 85 0000동 0000호21698206.662017-06-01
50573인천광역시부평구2823720171010119260인천광역시 부평구 시장로 33 0000동 8101-7호407762011.392017-06-01
37993인천광역시부평구28237201710201557510인천광역시 부평구 백범로577번길 15-12 0000동 0002호123032800248.052017-06-01
26616인천광역시부평구28237201710701229121인천광역시 부평구 경인로 1021 0001동 0004호3818400086.02017-06-01
13587인천광역시부평구2823720171050120771인천광역시 부평구 삼산동 207-7 1동 2호2547450038.252017-06-01
32825인천광역시부평구28237201710101654391인천광역시 부평구 동수로 57-1 0001동 0002호82727520178.62017-06-01
34937인천광역시부평구282372017103025401인천광역시 부평구 마장로 207 0001동 0001호56160000360.02017-06-01
26251인천광역시부평구28237201710101153180인천광역시 부평구 경원대로1403번길 13 0000동 8101호6327000085.52017-06-01
57428인천광역시부평구28237201710101102900인천광역시 부평구 장제로199번길 47 0000동 0204호4001737050.212017-06-01
39527인천광역시부평구28237201710101451300인천광역시 부평구 부평대로 115 0000동 0303호100530880117.9942017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자
19563인천광역시부평구2823720171040137651인천광역시 부평구 청천동 376-5 1동 4호117576016.562017-06-01
51958인천광역시부평구282372017101017603561인천광역시 부평구 안남로16번길 16 0001동 0001호1669773065.432017-06-01
55138인천광역시부평구282372017108013380인천광역시 부평구 일신로 83 0000동 0000호102600000180.02017-06-01
52180인천광역시부평구2823720171040139710인천광역시 부평구 안남로417번길 19 0000동 0413호2260269040.292017-06-01
32279인천광역시부평구2823720171010157120인천광역시 부평구 대정로 81 0000동 0201호3212460099.152017-06-01
27145인천광역시부평구28237201710101756890인천광역시 부평구 경인로 868 0000동 0007호72529000176.92017-06-01
16242인천광역시부평구2823720171020156022인천광역시 부평구 십정동 560-2 2동 1호2848500067.52017-06-01
18600인천광역시부평구2823720171040119901인천광역시 부평구 청천동 199 1동 16호30378090301.372017-06-01
13511인천광역시부평구28237201710501161인천광역시 부평구 삼산동 1-6 1동 1호12733870122.52017-06-01
22804인천광역시부평구2823720171040162681인천광역시 부평구 청천동 62-68 1동 9호106517630265.632017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동물건지시가표준액연면적기준일자# duplicates
0인천광역시부평구28237201710101224112인천광역시 부평구 부평동 224-1 12동 1호9382502.252017-06-012
1인천광역시부평구28237201710101529270인천광역시 부평구 부평대로63번길 10-5 0000동 0000호6860700099.02017-06-012
2인천광역시부평구28237201710101534390인천광역시 부평구 광장로4번길 29 0000동 0000호8731245099.55812017-06-012
3인천광역시부평구2823720171010174311인천광역시 부평구 부평동 743-1 1동 1호408650200833.982017-06-012
4인천광역시부평구2823720171010187851인천광역시 부평구 부평동 878-5 1동 1호5220002.252017-06-012
5인천광역시부평구28237201710201563101인천광역시 부평구 십정동 563-10 1동 102호1930110063.72017-06-012
6인천광역시부평구2823720171040119901인천광역시 부평구 청천동 199 1동 101호39679380131.282017-06-012