Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory140.0 B

Variable types

Categorical7
Numeric7
Text1

Dataset

Description일반건축물 시가표준액에 대한 데이터로
Author강원도 고성군
URLhttps://www.data.go.kr/data/15080157/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
기준일자 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 기준일자High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (82.5%)Imbalance
동 is highly skewed (γ1 = 21.76426126)Skewed
부번 has 2531 (25.3%) zerosZeros

Reproduction

Analysis started2023-12-12 12:40:21.677671
Analysis finished2023-12-12 12:40:29.571602
Duration7.89 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
강원도
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 10000
100.0%

Length

2023-12-12T21:40:29.627626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:29.719882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고성군 10000
100.0%

Length

2023-12-12T21:40:29.826447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:29.928624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고성군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
42820
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42820
2nd row42820
3rd row42820
4th row42820
5th row42820

Common Values

ValueCountFrequency (%)
42820 10000
100.0%

Length

2023-12-12T21:40:30.032381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:30.140788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42820 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
2615 
2018
2505 
2019
2460 
2017
2420 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2018
3rd row2019
4th row2018
5th row2019

Common Values

ValueCountFrequency (%)
2020 2615
26.2%
2018 2505
25.1%
2019 2460
24.6%
2017 2420
24.2%

Length

2023-12-12T21:40:30.244953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:30.368849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 2615
26.2%
2018 2505
25.1%
2019 2460
24.6%
2017 2420
24.2%

법정동
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
330
3612 
320
1898 
253
1796 
250
1748 
310
946 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250
2nd row253
3rd row250
4th row320
5th row330

Common Values

ValueCountFrequency (%)
330 3612
36.1%
320 1898
19.0%
253 1796
18.0%
250 1748
17.5%
310 946
 
9.5%

Length

2023-12-12T21:40:30.498914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:30.626340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
330 3612
36.1%
320 1898
19.0%
253 1796
18.0%
250 1748
17.5%
310 946
 
9.5%

법정리
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.4086
Minimum21
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:30.729612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median28
Q332
95-th percentile36
Maximum37
Range16
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.2000337
Coefficient of variation (CV)0.1897227
Kurtosis-1.3468163
Mean27.4086
Median Absolute Deviation (MAD)5
Skewness0.16949052
Sum274086
Variance27.04035
MonotonicityNot monotonic
2023-12-12T21:40:30.827891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
21 2020
20.2%
33 1097
11.0%
22 957
9.6%
30 843
8.4%
29 814
8.1%
23 546
 
5.5%
31 519
 
5.2%
35 493
 
4.9%
25 429
 
4.3%
24 414
 
4.1%
Other values (7) 1868
18.7%
ValueCountFrequency (%)
21 2020
20.2%
22 957
9.6%
23 546
 
5.5%
24 414
 
4.1%
25 429
 
4.3%
26 157
 
1.6%
27 319
 
3.2%
28 356
 
3.6%
29 814
8.1%
30 843
8.4%
ValueCountFrequency (%)
37 393
 
3.9%
36 135
 
1.4%
35 493
4.9%
34 316
 
3.2%
33 1097
11.0%
32 192
 
1.9%
31 519
5.2%
30 843
8.4%
29 814
8.1%
28 356
 
3.6%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9738 
2
 
262

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 9738
97.4%
2 262
 
2.6%

Length

2023-12-12T21:40:30.953074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:31.034993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9738
97.4%
2 262
 
2.6%

본번
Real number (ℝ)

Distinct726
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.0766
Minimum1
Maximum1792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:31.127250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1117
median239
Q3396
95-th percentile760.05
Maximum1792
Range1791
Interquartile range (IQR)279

Descriptive statistics

Standard deviation230.42089
Coefficient of variation (CV)0.83161441
Kurtosis4.3547511
Mean277.0766
Median Absolute Deviation (MAD)133
Skewness1.6855929
Sum2770766
Variance53093.788
MonotonicityNot monotonic
2023-12-12T21:40:31.280048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
403 388
 
3.9%
474 287
 
2.9%
243 189
 
1.9%
988 182
 
1.8%
331 165
 
1.7%
40 145
 
1.5%
72 144
 
1.4%
239 137
 
1.4%
202 131
 
1.3%
287 110
 
1.1%
Other values (716) 8122
81.2%
ValueCountFrequency (%)
1 109
1.1%
2 60
0.6%
3 27
 
0.3%
4 18
 
0.2%
5 50
0.5%
6 32
 
0.3%
7 32
 
0.3%
8 13
 
0.1%
9 32
 
0.3%
10 15
 
0.1%
ValueCountFrequency (%)
1792 2
 
< 0.1%
1688 1
 
< 0.1%
1679 3
< 0.1%
1672 5
0.1%
1668 2
 
< 0.1%
1527 1
 
< 0.1%
1503 1
 
< 0.1%
1484 1
 
< 0.1%
1479 1
 
< 0.1%
1458 3
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct152
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0258
Minimum0
Maximum390
Zeros2531
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:31.418845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile72
Maximum390
Range390
Interquartile range (IQR)9

Descriptive statistics

Standard deviation28.11432
Coefficient of variation (CV)2.3378336
Kurtosis19.564528
Mean12.0258
Median Absolute Deviation (MAD)2
Skewness3.9653313
Sum120258
Variance790.41498
MonotonicityNot monotonic
2023-12-12T21:40:31.578703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2531
25.3%
1 1699
17.0%
2 1245
12.4%
3 551
 
5.5%
5 384
 
3.8%
4 337
 
3.4%
9 326
 
3.3%
13 267
 
2.7%
6 229
 
2.3%
7 213
 
2.1%
Other values (142) 2218
22.2%
ValueCountFrequency (%)
0 2531
25.3%
1 1699
17.0%
2 1245
12.4%
3 551
 
5.5%
4 337
 
3.4%
5 384
 
3.8%
6 229
 
2.3%
7 213
 
2.1%
8 181
 
1.8%
9 326
 
3.3%
ValueCountFrequency (%)
390 1
 
< 0.1%
322 1
 
< 0.1%
310 2
 
< 0.1%
231 3
 
< 0.1%
214 3
 
< 0.1%
188 4
< 0.1%
186 1
 
< 0.1%
185 8
0.1%
184 1
 
< 0.1%
182 2
 
< 0.1%

동
Real number (ℝ)

SKEWED 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.004
Minimum1
Maximum2036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:31.717453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile10
Maximum2036
Range2035
Interquartile range (IQR)2

Descriptive statistics

Standard deviation68.585428
Coefficient of variation (CV)7.6172177
Kurtosis599.54822
Mean9.004
Median Absolute Deviation (MAD)0
Skewness21.764261
Sum90040
Variance4703.961
MonotonicityNot monotonic
2023-12-12T21:40:31.868963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5784
57.8%
2 1654
 
16.5%
3 849
 
8.5%
4 400
 
4.0%
5 280
 
2.8%
6 158
 
1.6%
7 128
 
1.3%
8 116
 
1.2%
9 97
 
1.0%
10 59
 
0.6%
Other values (48) 475
 
4.8%
ValueCountFrequency (%)
1 5784
57.8%
2 1654
 
16.5%
3 849
 
8.5%
4 400
 
4.0%
5 280
 
2.8%
6 158
 
1.6%
7 128
 
1.3%
8 116
 
1.2%
9 97
 
1.0%
10 59
 
0.6%
ValueCountFrequency (%)
2036 3
< 0.1%
2031 1
 
< 0.1%
2023 1
 
< 0.1%
2005 1
 
< 0.1%
2004 1
 
< 0.1%
2003 1
 
< 0.1%
502 3
< 0.1%
501 3
< 0.1%
412 4
< 0.1%
411 5
0.1%

호
Real number (ℝ)

Distinct594
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484.3799
Minimum1
Maximum8401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:32.044985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101
Q1101
median101
Q3201
95-th percentile1714.05
Maximum8401
Range8400
Interquartile range (IQR)100

Descriptive statistics

Standard deviation1486.4555
Coefficient of variation (CV)3.0687802
Kurtosis20.648614
Mean484.3799
Median Absolute Deviation (MAD)0
Skewness4.6617358
Sum4843799
Variance2209549.8
MonotonicityNot monotonic
2023-12-12T21:40:32.210912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 5210
52.1%
201 879
 
8.8%
102 871
 
8.7%
301 286
 
2.9%
103 259
 
2.6%
8101 212
 
2.1%
202 128
 
1.3%
104 99
 
1.0%
401 94
 
0.9%
302 60
 
0.6%
Other values (584) 1902
 
19.0%
ValueCountFrequency (%)
1 52
0.5%
2 29
0.3%
3 14
 
0.1%
4 9
 
0.1%
5 5
 
0.1%
6 7
 
0.1%
7 9
 
0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
10 7
 
0.1%
ValueCountFrequency (%)
8401 1
 
< 0.1%
8303 3
 
< 0.1%
8302 1
 
< 0.1%
8301 8
0.1%
8211 1
 
< 0.1%
8210 1
 
< 0.1%
8209 1
 
< 0.1%
8208 1
 
< 0.1%
8207 2
 
< 0.1%
8206 3
 
< 0.1%

물건지
Text

Distinct7472
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:40:32.601823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length27.625
Min length21

Characters and Unicode

Total characters276250
Distinct characters181
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

Unique5315 ?
Unique (%)53.1%

Sample

1st row강원도 고성군 간성읍 봉호리 105-1 1동 101호
2nd row강원도 고성군 거진읍 반암리 228-6 1동 101호
3rd row[ 간성로39번길 8 ] 0001동 0201호
4th row강원도 고성군 죽왕면 공현진리 102 1동 101호
5th row강원도 고성군 토성면 용암리 211-2 2동 101호
ValueCountFrequency (%)
강원도 7759
 
11.4%
고성군 7759
 
11.4%
4482
 
6.6%
101호 4112
 
6.0%
1동 3877
 
5.7%
토성면 2995
 
4.4%
0001동 1907
 
2.8%
2동 1421
 
2.1%
거진읍 1384
 
2.0%
죽왕면 1308
 
1.9%
Other values (3355) 31002
45.6%
2023-12-12T21:40:33.138985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58006
21.0%
1 27820
 
10.1%
0 20861
 
7.6%
성 12697
 
4.6%
2 10419
 
3.8%
호 10374
 
3.8%
동 10296
 
3.7%
원 8861
 
3.2%
도 7967
 
2.9%
강 7899
 
2.9%
Other values (171) 101050
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122335
44.3%
Decimal Number 85102
30.8%
Space Separator 58006
21.0%
Dash Punctuation 6325
 
2.3%
Close Punctuation 2241
 
0.8%
Open Punctuation 2241
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 12697
 
10.4%
호 10374
 
8.5%
동 10296
 
8.4%
원 8861
 
7.2%
도 7967
 
6.5%
강 7899
 
6.5%
리 7850
 
6.4%
고 7778
 
6.4%
군 7759
 
6.3%
면 5098
 
4.2%
Other values (157) 35756
29.2%
Decimal Number
ValueCountFrequency (%)
1 27820
32.7%
0 20861
24.5%
2 10419
 
12.2%
3 6271
 
7.4%
4 4954
 
5.8%
5 3362
 
4.0%
7 3299
 
3.9%
8 2947
 
3.5%
6 2645
 
3.1%
9 2524
 
3.0%
Space Separator
ValueCountFrequency (%)
58006
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6325
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2241
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153915
55.7%
Hangul 122335
44.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 12697
 
10.4%
호 10374
 
8.5%
동 10296
 
8.4%
원 8861
 
7.2%
도 7967
 
6.5%
강 7899
 
6.5%
리 7850
 
6.4%
고 7778
 
6.4%
군 7759
 
6.3%
면 5098
 
4.2%
Other values (157) 35756
29.2%
Common
ValueCountFrequency (%)
58006
37.7%
1 27820
18.1%
0 20861
 
13.6%
2 10419
 
6.8%
- 6325
 
4.1%
3 6271
 
4.1%
4 4954
 
3.2%
5 3362
 
2.2%
7 3299
 
2.1%
8 2947
 
1.9%
Other values (4) 9651
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153915
55.7%
Hangul 122335
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58006
37.7%
1 27820
18.1%
0 20861
 
13.6%
2 10419
 
6.8%
- 6325
 
4.1%
3 6271
 
4.1%
4 4954
 
3.2%
5 3362
 
2.2%
7 3299
 
2.1%
8 2947
 
1.9%
Other values (4) 9651
 
6.3%
Hangul
ValueCountFrequency (%)
성 12697
 
10.4%
호 10374
 
8.5%
동 10296
 
8.4%
원 8861
 
7.2%
도 7967
 
6.5%
강 7899
 
6.5%
리 7850
 
6.4%
고 7778
 
6.4%
군 7759
 
6.3%
면 5098
 
4.2%
Other values (157) 35756
29.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7504
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53858757
Minimum18000
Maximum3.5692568 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:33.296300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18000
5-th percentile438652.5
Q13823980
median20449500
Q354182040
95-th percentile1.8659882 × 108
Maximum3.5692568 × 109
Range3.5692388 × 109
Interquartile range (IQR)50358060

Descriptive statistics

Standard deviation1.3564451 × 108
Coefficient of variation (CV)2.5185229
Kurtosis155.14276
Mean53858757
Median Absolute Deviation (MAD)18340000
Skewness9.8243932
Sum5.3858757 × 1011
Variance1.8399433 × 1016
MonotonicityNot monotonic
2023-12-12T21:40:33.468028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33534840 70
 
0.7%
34485570 67
 
0.7%
34053420 63
 
0.6%
32843400 55
 
0.5%
76515920 52
 
0.5%
77226280 50
 
0.5%
30828600 48
 
0.5%
76921840 44
 
0.4%
64746600 39
 
0.4%
75907040 39
 
0.4%
Other values (7494) 9473
94.7%
ValueCountFrequency (%)
18000 1
< 0.1%
28800 2
< 0.1%
46200 1
< 0.1%
46980 1
< 0.1%
47840 1
< 0.1%
49920 1
< 0.1%
50220 1
< 0.1%
51840 1
< 0.1%
54000 1
< 0.1%
59800 1
< 0.1%
ValueCountFrequency (%)
3569256850 1
< 0.1%
3505998930 1
< 0.1%
2315322300 1
< 0.1%
2313530100 1
< 0.1%
2297574720 1
< 0.1%
2179596510 1
< 0.1%
1883968800 1
< 0.1%
1873876110 1
< 0.1%
1808153610 1
< 0.1%
1790642700 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3775
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.18582
Minimum0.91
Maximum6013.7203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:40:33.646507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.91
5-th percentile12
Q138.4
median82.4
Q3145.87
95-th percentile511.99
Maximum6013.7203
Range6012.8103
Interquartile range (IQR)107.47

Descriptive statistics

Standard deviation290.17319
Coefficient of variation (CV)1.8942562
Kurtosis75.576991
Mean153.18582
Median Absolute Deviation (MAD)49.235
Skewness7.0477416
Sum1531858.2
Variance84200.479
MonotonicityNot monotonic
2023-12-12T21:40:33.783041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 369
 
3.7%
86.43 255
 
2.5%
101.48 192
 
1.9%
76.12 164
 
1.6%
104.43 106
 
1.1%
57.62 91
 
0.9%
71.48 70
 
0.7%
36.0 68
 
0.7%
66.4 67
 
0.7%
98.48 65
 
0.7%
Other values (3765) 8553
85.5%
ValueCountFrequency (%)
0.91 1
 
< 0.1%
1.0 3
< 0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.4 1
 
< 0.1%
1.56 1
 
< 0.1%
1.59 2
< 0.1%
1.6 1
 
< 0.1%
1.62 3
< 0.1%
1.68 1
 
< 0.1%
ValueCountFrequency (%)
6013.7203 1
< 0.1%
5165.35 1
< 0.1%
4745.81 2
< 0.1%
3882.69 2
< 0.1%
3695.64 1
< 0.1%
3600.0 1
< 0.1%
3414.685 1
< 0.1%
3386.885 2
< 0.1%
3364.23 2
< 0.1%
3265.08 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20200601
2615 
20180601
2505 
20190601
2460 
20170601
2420 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20190601
2nd row20180601
3rd row20190601
4th row20180601
5th row20190601

Common Values

ValueCountFrequency (%)
20200601 2615
26.2%
20180601 2505
25.1%
20190601 2460
24.6%
20170601 2420
24.2%

Length

2023-12-12T21:40:33.920119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:40:34.014453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200601 2615
26.2%
20180601 2505
25.1%
20190601 2460
24.6%
20170601 2420
24.2%

Interactions

2023-12-12T21:40:28.553630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:23.993471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.814788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.607896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.307881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.976612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.651312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.652893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.104998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.920379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.709996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.414607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.070493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.745977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.757739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.225774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.025970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.808991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.508941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.166900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.836690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.862622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.354290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.139430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.917761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.603065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.271792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.934636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.964498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.465856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.282101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.032402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.704490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.374279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.026593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:29.058145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.562398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.393240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.133398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.784341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.473609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.129397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:29.151597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:24.678907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:25.513934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.221744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:26.883542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:27.572269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:40:28.232964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:40:34.090790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번동호시가표준액연면적기준일자
과세년도1.0000.0000.0000.0470.0000.0000.0720.0090.0000.0001.000
법정동0.0001.0000.6970.0410.4590.2530.0890.1630.0710.1070.000
법정리0.0000.6971.0000.1870.5930.2400.2040.2790.1170.1630.000
특수지0.0470.0410.1871.0000.2340.0800.0000.0170.0000.0000.047
본번0.0000.4590.5930.2341.0000.4590.2270.1160.0960.1430.000
부번0.0000.2530.2400.0800.4591.0000.0000.0000.0200.0000.000
동0.0720.0890.2040.0000.2270.0001.0000.1740.0630.0000.072
호0.0090.1630.2790.0170.1160.0000.1741.0000.1040.0820.009
시가표준액0.0000.0710.1170.0000.0960.0200.0630.1041.0000.8570.000
연면적0.0000.1070.1630.0000.1430.0000.0000.0820.8571.0000.000
기준일자1.0000.0000.0000.0470.0000.0000.0720.0090.0000.0001.000
2023-12-12T21:40:34.560161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자법정동과세년도특수지
기준일자1.0000.0001.0000.031
법정동0.0001.0000.0000.051
과세년도1.0000.0001.0000.031
특수지0.0310.0510.0311.000
2023-12-12T21:40:34.659792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정리본번부번동호시가표준액연면적과세년도법정동특수지기준일자
법정리1.0000.161-0.1210.1550.0440.1510.1050.0000.3690.1440.000
본번0.1611.0000.0060.0630.0050.1430.1280.0000.2070.1790.000
부번-0.1210.0061.000-0.1260.1680.074-0.0300.0000.1520.0790.000
동0.1550.063-0.1261.000-0.194-0.0010.0020.0290.0720.0000.029
호0.0440.0050.168-0.1941.0000.2740.0610.0060.1040.0180.006
시가표준액0.1510.1430.074-0.0010.2741.0000.7600.0000.0430.0000.000
연면적0.1050.128-0.0300.0020.0610.7601.0000.0000.0450.0000.000
과세년도0.0000.0000.0000.0290.0060.0000.0001.0000.0000.0311.000
법정동0.3690.2070.1520.0720.1040.0430.0450.0001.0000.0510.000
특수지0.1440.1790.0790.0000.0180.0000.0000.0310.0511.0000.031
기준일자0.0000.0000.0000.0290.0060.0000.0001.0000.0000.0311.000

Missing values

2023-12-12T21:40:29.274877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:40:29.488943image/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
25908강원도고성군42820201925024110511101강원도 고성군 간성읍 봉호리 105-1 1동 101호43509310549.3620190601
14098강원도고성군42820201825332122861101강원도 고성군 거진읍 반암리 228-6 1동 101호443610016.7420180601
27734강원도고성군42820201925022122911201[ 간성로39번길 8 ] 0001동 0201호61348620246.3820190601
11974강원도고성군42820201832024110201101강원도 고성군 죽왕면 공현진리 102 1동 101호4010806098.2820180601
33494강원도고성군42820201933029121122101강원도 고성군 토성면 용암리 211-2 2동 101호4681600167.220190601
29296강원도고성군42820201925330114602102강원도 고성군 거진읍 대대리 146 2동 102호1057008028.8820190601
6846강원도고성군428202017250371722107507강원도 고성군 간성읍 흘리 72-2 107동 507호2787720071.4820170601
40727강원도고성군42820202033028187401101[ 진등3길 87 ] 0001동 0101호35692568505165.3520200601
43155강원도고성군428202020250371722107905강원도 고성군 간성읍 흘리 72-2 107동 905호1829888071.4820200601
35967강원도고성군42820201925326113212102강원도 고성군 거진읍 산북리 132-1 2동 102호44550029.720190601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
2647강원도고성군42820201732026156221103강원도 고성군 죽왕면 인정리 562-2 1동 103호1696240065.2420170601
4195강원도고성군42820201725030181771101강원도 고성군 간성읍 어천리 817-7 1동 101호38637042.9320170601
5615강원도고성군42820201733033147423210강원도 고성군 토성면 원암리 474-2 3동 210호75907040101.4820170601
23605강원도고성군4282020182532112551303[ 거진항1길 62 ] 0001동 0303호3545694045.8120180601
17383강원도고성군42820201832021129361102[ 심층수길 43 ] 0001동 0102호431745017.6820180601
7111강원도고성군4282020172503721928101강원도 고성군 간성읍 흘리 산 1-92 8동 101호1620000180.020170601
34746강원도고성군4282020193303416701102강원도 고성군 토성면 용촌리 67 1동 102호1738800045.020190601
17225강원도고성군42820201833034140632101[ 동해대로 4567 ] 0002동 0101호1558000041.020180601
13879강원도고성군4282020182533212301201강원도 고성군 거진읍 반암리 23 1동 201호848106066.7820180601
37631강원도고성군4282020202533212104101강원도 고성군 거진읍 반암리 21 4동 101호35650011.520200601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0강원도고성군4282020173103716301101강원도 고성군 현내면 제진리 63 1동 101호180000018.0201706012
1강원도고성군42820201833035130311101강원도 고성군 토성면 봉포리 303-1 1동 101호70140058.45201806012