Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows55
Duplicate rows (%)0.5%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description강원도 강릉시의 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하는 데이터로 물건별 재산가액 확인이 가능합니다.
URLhttps://www.data.go.kr/data/15084155/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 55 (0.5%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (89.8%)Imbalance
법정리 has 6061 (60.6%) zerosZeros
부번 has 2589 (25.9%) zerosZeros

Reproduction

Analysis started2023-12-12 08:09:12.509538
Analysis finished2023-12-12 08:09:20.597456
Duration8.09 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
강원특별자치도
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원특별자치도 10000
100.0%

Length

2023-12-12T17:09:20.694748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:20.811519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원특별자치도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강릉시 10000
100.0%

Length

2023-12-12T17:09:20.933392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:21.048556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강릉시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
51150
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row51150
2nd row51150
3rd row51150
4th row51150
5th row51150

Common Values

ValueCountFrequency (%)
51150 10000
100.0%

Length

2023-12-12T17:09:21.146371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:21.264730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
51150 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-12T17:09:21.391613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:21.495431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.9061
Minimum101
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:21.595385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile105
Q1111
median125
Q3320
95-th percentile360
Maximum370
Range269
Interquartile range (IQR)209

Descriptive statistics

Standard deviation104.70698
Coefficient of variation (CV)0.53447535
Kurtosis-1.4443402
Mean195.9061
Median Absolute Deviation (MAD)16
Skewness0.6061926
Sum1959061
Variance10963.552
MonotonicityNot monotonic
2023-12-12T17:09:21.795201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
250 1005
 
10.1%
111 972
 
9.7%
110 965
 
9.7%
340 722
 
7.2%
360 554
 
5.5%
370 484
 
4.8%
113 459
 
4.6%
109 457
 
4.6%
350 416
 
4.2%
101 326
 
3.3%
Other values (37) 3640
36.4%
ValueCountFrequency (%)
101 326
 
3.3%
102 46
 
0.5%
103 29
 
0.3%
104 37
 
0.4%
105 197
 
2.0%
106 83
 
0.8%
107 84
 
0.8%
108 185
 
1.8%
109 457
4.6%
110 965
9.7%
ValueCountFrequency (%)
370 484
4.8%
360 554
5.5%
350 416
4.2%
340 722
7.2%
330 302
 
3.0%
320 171
 
1.7%
310 285
 
2.9%
250 1005
10.1%
139 20
 
0.2%
138 212
 
2.1%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.543
Minimum0
Maximum31
Zeros6061
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:21.965622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q322
95-th percentile28
Maximum31
Range31
Interquartile range (IQR)22

Descriptive statistics

Standard deviation11.968161
Coefficient of variation (CV)1.2541298
Kurtosis-1.6487806
Mean9.543
Median Absolute Deviation (MAD)0
Skewness0.50311986
Sum95430
Variance143.23687
MonotonicityNot monotonic
2023-12-12T17:09:22.132421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 6061
60.6%
22 925
 
9.2%
21 636
 
6.4%
23 476
 
4.8%
25 428
 
4.3%
28 299
 
3.0%
24 270
 
2.7%
26 261
 
2.6%
27 240
 
2.4%
29 192
 
1.9%
Other values (2) 212
 
2.1%
ValueCountFrequency (%)
0 6061
60.6%
21 636
 
6.4%
22 925
 
9.2%
23 476
 
4.8%
24 270
 
2.7%
25 428
 
4.3%
26 261
 
2.6%
27 240
 
2.4%
28 299
 
3.0%
29 192
 
1.9%
ValueCountFrequency (%)
31 30
 
0.3%
30 182
 
1.8%
29 192
 
1.9%
28 299
 
3.0%
27 240
 
2.4%
26 261
 
2.6%
25 428
4.3%
24 270
 
2.7%
23 476
4.8%
22 925
9.2%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9867 
2
 
133

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 9867
98.7%
2 133
 
1.3%

Length

2023-12-12T17:09:22.296704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:22.413243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9867
98.7%
2 133
 
1.3%

본번
Real number (ℝ)

Distinct1378
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.9155
Minimum1
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:22.535107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q1124
median312
Q3779.25
95-th percentile1307
Maximum2214
Range2213
Interquartile range (IQR)655.25

Descriptive statistics

Standard deviation472.15073
Coefficient of variation (CV)0.95593422
Kurtosis0.72114598
Mean493.9155
Median Absolute Deviation (MAD)244
Skewness1.172504
Sum4939155
Variance222926.31
MonotonicityNot monotonic
2023-12-12T17:09:22.687735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 234
 
2.3%
258 133
 
1.3%
8 102
 
1.0%
312 100
 
1.0%
86 95
 
0.9%
265 89
 
0.9%
190 78
 
0.8%
21 77
 
0.8%
123 75
 
0.8%
1278 63
 
0.6%
Other values (1368) 8954
89.5%
ValueCountFrequency (%)
1 234
2.3%
2 39
 
0.4%
3 31
 
0.3%
4 10
 
0.1%
5 8
 
0.1%
6 15
 
0.1%
7 19
 
0.2%
8 102
1.0%
9 16
 
0.2%
10 22
 
0.2%
ValueCountFrequency (%)
2214 8
0.1%
2131 1
 
< 0.1%
2112 1
 
< 0.1%
2111 1
 
< 0.1%
2058 1
 
< 0.1%
2049 1
 
< 0.1%
2047 2
 
< 0.1%
2024 1
 
< 0.1%
1984 2
 
< 0.1%
1948 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0854
Minimum0
Maximum685
Zeros2589
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:22.878852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile54
Maximum685
Range685
Interquartile range (IQR)8

Descriptive statistics

Standard deviation38.093346
Coefficient of variation (CV)3.1520137
Kurtosis77.177551
Mean12.0854
Median Absolute Deviation (MAD)2
Skewness7.4659827
Sum120854
Variance1451.103
MonotonicityNot monotonic
2023-12-12T17:09:23.081327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2589
25.9%
1 1612
16.1%
2 901
 
9.0%
4 688
 
6.9%
3 642
 
6.4%
5 428
 
4.3%
6 340
 
3.4%
7 264
 
2.6%
8 263
 
2.6%
10 190
 
1.9%
Other values (212) 2083
20.8%
ValueCountFrequency (%)
0 2589
25.9%
1 1612
16.1%
2 901
 
9.0%
3 642
 
6.4%
4 688
 
6.9%
5 428
 
4.3%
6 340
 
3.4%
7 264
 
2.6%
8 263
 
2.6%
9 180
 
1.8%
ValueCountFrequency (%)
685 1
 
< 0.1%
681 1
 
< 0.1%
662 1
 
< 0.1%
592 1
 
< 0.1%
512 3
< 0.1%
508 1
 
< 0.1%
483 1
 
< 0.1%
458 4
< 0.1%
444 1
 
< 0.1%
405 1
 
< 0.1%


Real number (ℝ)

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean757.7673
Minimum0
Maximum9207
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:23.292386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile7003
Maximum9207
Range9207
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2165.182
Coefficient of variation (CV)2.8573178
Kurtosis6.761573
Mean757.7673
Median Absolute Deviation (MAD)0
Skewness2.9075634
Sum7577673
Variance4688013.1
MonotonicityNot monotonic
2023-12-12T17:09:23.501029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7229
72.3%
1001 749
 
7.5%
2 591
 
5.9%
7001 283
 
2.8%
8001 187
 
1.9%
9000 116
 
1.2%
8002 108
 
1.1%
3 98
 
1.0%
7002 67
 
0.7%
1002 52
 
0.5%
Other values (95) 520
 
5.2%
ValueCountFrequency (%)
0 14
 
0.1%
1 7229
72.3%
2 591
 
5.9%
3 98
 
1.0%
4 45
 
0.4%
5 37
 
0.4%
6 19
 
0.2%
7 7
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
9207 4
 
< 0.1%
9105 1
 
< 0.1%
9002 2
 
< 0.1%
9001 16
 
0.2%
9000 116
1.2%
8019 5
 
0.1%
8009 2
 
< 0.1%
8008 4
 
< 0.1%
8007 1
 
< 0.1%
8006 2
 
< 0.1%


Text

Distinct647
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:09:23.791660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.012
Min length3

Characters and Unicode

Total characters40120
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique408 ?
Unique (%)4.1%

Sample

1st row0101
2nd row0201
3rd row0101
4th row0501
5th row0102
ValueCountFrequency (%)
0101 4167
41.7%
0201 1028
 
10.3%
0102 985
 
9.8%
0103 429
 
4.3%
8101 427
 
4.3%
0301 392
 
3.9%
0202 229
 
2.3%
0401 192
 
1.9%
0104 173
 
1.7%
0501 104
 
1.0%
Other values (637) 1874
18.7%
2023-12-12T17:09:24.290516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18780
46.8%
1 13856
34.5%
2 3277
 
8.2%
3 1368
 
3.4%
8 842
 
2.1%
4 712
 
1.8%
5 441
 
1.1%
6 305
 
0.8%
7 251
 
0.6%
9 148
 
0.4%
Other values (3) 140
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39980
99.7%
Uppercase Letter 74
 
0.2%
Dash Punctuation 66
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18780
47.0%
1 13856
34.7%
2 3277
 
8.2%
3 1368
 
3.4%
8 842
 
2.1%
4 712
 
1.8%
5 441
 
1.1%
6 305
 
0.8%
7 251
 
0.6%
9 148
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
A 44
59.5%
B 30
40.5%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40046
99.8%
Latin 74
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18780
46.9%
1 13856
34.6%
2 3277
 
8.2%
3 1368
 
3.4%
8 842
 
2.1%
4 712
 
1.8%
5 441
 
1.1%
6 305
 
0.8%
7 251
 
0.6%
9 148
 
0.4%
Latin
ValueCountFrequency (%)
A 44
59.5%
B 30
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18780
46.8%
1 13856
34.5%
2 3277
 
8.2%
3 1368
 
3.4%
8 842
 
2.1%
4 712
 
1.8%
5 441
 
1.1%
6 305
 
0.8%
7 251
 
0.6%
9 148
 
0.4%
Other values (3) 140
 
0.3%
Distinct9421
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:09:24.685752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length28.109
Min length21

Characters and Unicode

Total characters281090
Distinct characters261
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9076 ?
Unique (%)90.8%

Sample

1st row[ 괴일길 110 ] 0001동 0101호
2nd row[ 남구길18번길 27 ] 0001동 0201호
3rd row[ 안현로 300 ] 0001동 0101호
4th row강원특별자치도 강릉시 지변동 123 1동 501호
5th row[ 샘물터길 8 ] 0001동 0102호
ValueCountFrequency (%)
10036
 
16.0%
강원특별자치도 4982
 
8.0%
강릉시 4982
 
8.0%
0001동 4420
 
7.1%
1동 2809
 
4.5%
101호 2319
 
3.7%
0101호 1848
 
3.0%
1001동 749
 
1.2%
주문진읍 652
 
1.0%
0201호 623
 
1.0%
Other values (5214) 29187
46.6%
2023-12-12T17:09:25.602371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52607
18.7%
0 34071
 
12.1%
1 31228
 
11.1%
13563
 
4.8%
11590
 
4.1%
10112
 
3.6%
2 9742
 
3.5%
5308
 
1.9%
3 5196
 
1.8%
5181
 
1.8%
Other values (251) 102492
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113501
40.4%
Decimal Number 100332
35.7%
Space Separator 52607
18.7%
Close Punctuation 5018
 
1.8%
Open Punctuation 5018
 
1.8%
Dash Punctuation 4540
 
1.6%
Uppercase Letter 74
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13563
 
11.9%
11590
 
10.2%
10112
 
8.9%
5308
 
4.7%
5181
 
4.6%
5060
 
4.5%
5023
 
4.4%
5003
 
4.4%
4984
 
4.4%
4982
 
4.4%
Other values (235) 42695
37.6%
Decimal Number
ValueCountFrequency (%)
0 34071
34.0%
1 31228
31.1%
2 9742
 
9.7%
3 5196
 
5.2%
4 3959
 
3.9%
8 3540
 
3.5%
7 3389
 
3.4%
5 3367
 
3.4%
6 3326
 
3.3%
9 2514
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
A 44
59.5%
B 30
40.5%
Space Separator
ValueCountFrequency (%)
52607
100.0%
Close Punctuation
ValueCountFrequency (%)
] 5018
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 5018
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167515
59.6%
Hangul 113501
40.4%
Latin 74
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13563
 
11.9%
11590
 
10.2%
10112
 
8.9%
5308
 
4.7%
5181
 
4.6%
5060
 
4.5%
5023
 
4.4%
5003
 
4.4%
4984
 
4.4%
4982
 
4.4%
Other values (235) 42695
37.6%
Common
ValueCountFrequency (%)
52607
31.4%
0 34071
20.3%
1 31228
18.6%
2 9742
 
5.8%
3 5196
 
3.1%
] 5018
 
3.0%
[ 5018
 
3.0%
- 4540
 
2.7%
4 3959
 
2.4%
8 3540
 
2.1%
Other values (4) 12596
 
7.5%
Latin
ValueCountFrequency (%)
A 44
59.5%
B 30
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167589
59.6%
Hangul 113501
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52607
31.4%
0 34071
20.3%
1 31228
18.6%
2 9742
 
5.8%
3 5196
 
3.1%
] 5018
 
3.0%
[ 5018
 
3.0%
- 4540
 
2.7%
4 3959
 
2.4%
8 3540
 
2.1%
Other values (6) 12670
 
7.6%
Hangul
ValueCountFrequency (%)
13563
 
11.9%
11590
 
10.2%
10112
 
8.9%
5308
 
4.7%
5181
 
4.6%
5060
 
4.5%
5023
 
4.4%
5003
 
4.4%
4984
 
4.4%
4982
 
4.4%
Other values (235) 42695
37.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8275
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65518035
Minimum13500
Maximum7.9700419 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:25.789388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13500
5-th percentile671968.5
Q14785000
median23669810
Q366776622
95-th percentile2.2781956 × 108
Maximum7.9700419 × 109
Range7.9700284 × 109
Interquartile range (IQR)61991622

Descriptive statistics

Standard deviation1.873593 × 108
Coefficient of variation (CV)2.8596599
Kurtosis513.91687
Mean65518035
Median Absolute Deviation (MAD)21653810
Skewness17.223716
Sum6.5518035 × 1011
Variance3.5103507 × 1016
MonotonicityNot monotonic
2023-12-12T17:09:25.959820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65551310 176
 
1.8%
50733320 51
 
0.5%
45934770 38
 
0.4%
80982790 35
 
0.4%
31940110 30
 
0.3%
49983010 27
 
0.3%
4838400 26
 
0.3%
604800 22
 
0.2%
50904440 22
 
0.2%
127116380 20
 
0.2%
Other values (8265) 9553
95.5%
ValueCountFrequency (%)
13500 1
 
< 0.1%
38610 1
 
< 0.1%
54400 1
 
< 0.1%
58800 1
 
< 0.1%
59400 1
 
< 0.1%
64000 1
 
< 0.1%
70400 1
 
< 0.1%
72000 14
0.1%
78000 1
 
< 0.1%
78880 1
 
< 0.1%
ValueCountFrequency (%)
7970041870 1
< 0.1%
6171459870 1
< 0.1%
4154160120 1
< 0.1%
3903306600 1
< 0.1%
3239461360 1
< 0.1%
2774320740 1
< 0.1%
2629181790 1
< 0.1%
2593959410 1
< 0.1%
2587847550 1
< 0.1%
2523650400 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5926
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.26761
Minimum0.3
Maximum11401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:09:26.132249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile11.30803
Q130.8
median69.0767
Q3148.985
95-th percentile508.0375
Maximum11401
Range11400.7
Interquartile range (IQR)118.185

Descriptive statistics

Standard deviation342.6797
Coefficient of variation (CV)2.2505094
Kurtosis243.10392
Mean152.26761
Median Absolute Deviation (MAD)48.0767
Skewness11.898634
Sum1522676.1
Variance117429.38
MonotonicityNot monotonic
2023-12-12T17:09:26.302178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 597
 
6.0%
60.3159 178
 
1.8%
27.0 144
 
1.4%
54.4349 51
 
0.5%
30.0 48
 
0.5%
15.0 43
 
0.4%
36.0 39
 
0.4%
50.92 38
 
0.4%
60.9948 35
 
0.4%
42.3609 30
 
0.3%
Other values (5916) 8797
88.0%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.46 1
 
< 0.1%
0.81 3
< 0.1%
0.9 1
 
< 0.1%
0.99 1
 
< 0.1%
1.0 1
 
< 0.1%
1.04 1
 
< 0.1%
1.08 1
 
< 0.1%
1.24 1
 
< 0.1%
1.35 2
< 0.1%
ValueCountFrequency (%)
11401.0 1
< 0.1%
8052.4 1
< 0.1%
7647.41 1
< 0.1%
7535.4 1
< 0.1%
6386.22 1
< 0.1%
5626.81 1
< 0.1%
5553.69 1
< 0.1%
5508.0 1
< 0.1%
5152.88 1
< 0.1%
4606.99 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20220601
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220601
2nd row20220601
3rd row20220601
4th row20220601
5th row20220601

Common Values

ValueCountFrequency (%)
20220601 10000
100.0%

Length

2023-12-12T17:09:26.454922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:09:26.568475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220601 10000
100.0%

Interactions

2023-12-12T17:09:19.313957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.298719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.074693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.852262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.631055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.710092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.517334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.443938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.402781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.175421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.945757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.727398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.845907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.619071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.574192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.520468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.286188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.088095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.832365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.976417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.720533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.680889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.614521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.380435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.193915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.933560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.110061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.817445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.785561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.737685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.503729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.306218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.048119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.230897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.964652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.910565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.837086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.616660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.420732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.157085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.313787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.075767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:20.035604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:14.962554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:15.728632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:16.527336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:17.293192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:18.412201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:09:19.208293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:09:26.634814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.6840.1580.2950.1510.2390.0290.072
법정리0.6841.0000.1050.3900.2510.3570.0260.041
특수지0.1580.1051.0000.1200.0000.0290.0000.000
본번0.2950.3900.1201.0000.2970.2960.0000.030
부번0.1510.2510.0000.2971.0000.0580.0000.000
0.2390.3570.0290.2960.0581.0000.0000.038
시가표준액0.0290.0260.0000.0000.0000.0001.0000.793
연면적0.0720.0410.0000.0300.0000.0380.7931.000
2023-12-12T17:09:26.772202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.842-0.040-0.1180.200-0.175-0.0790.113
법정리0.8421.000-0.046-0.0980.156-0.211-0.0800.128
본번-0.040-0.0461.000-0.019-0.087-0.0140.0250.092
부번-0.118-0.098-0.0191.000-0.2170.0310.0050.000
0.2000.156-0.087-0.2171.000-0.194-0.2150.035
시가표준액-0.175-0.211-0.0140.031-0.1941.0000.7990.000
연면적-0.079-0.0800.0250.005-0.2150.7991.0000.000
특수지0.1130.1280.0920.0000.0350.0000.0001.000

Missing values

2023-12-12T17:09:20.211452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:09:20.470516image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
14564강원특별자치도강릉시51150202231022130210101[ 괴일길 110 ] 0001동 0101호25847500122.520220601
35722강원특별자치도강릉시511502022111011156810201[ 남구길18번길 27 ] 0001동 0201호55944000199.820220601
22528강원특별자치도강릉시51150202213701493010101[ 안현로 300 ] 0001동 0101호7605407083.8220220601
24176강원특별자치도강릉시51150202213201123010501강원특별자치도 강릉시 지변동 123 1동 501호6989014601345.85320220601
42658강원특별자치도강릉시5115020221100118310310102[ 샘물터길 8 ] 0001동 0102호3518448063.7420220601
36759강원특별자치도강릉시511502022111011125410102[ 남구길17번길 19 ] 0001동 0102호2967230067.920220601
54191강원특별자치도강릉시511502022113011180010601강원특별자치도 강릉시 강문동 1-1 8001동 601호6555131060.315920220601
8100강원특별자치도강릉시51150202234028111910203강원특별자치도 강릉시 강동면 정동진리 11-9 1동 203호1412802027.5420220601
55655강원특별자치도강릉시511502022360231489210101강원특별자치도 강릉시 사천면 석교리 489-2 1동 101호167066250445.5120220601
36950강원특별자치도강릉시511502022111011081110101[ 강릉대로419번길 3 ] 0001동 0101호60063120129.620220601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
5837강원특별자치도강릉시511502022360271415010010101강원특별자치도 강릉시 사천면 방동리 415 1001동 101호24705008.120220601
45480강원특별자치도강릉시5115020221100116211810201[ 강릉대로 209 ] 0001동 0201호33480000111.620220601
10114강원특별자치도강릉시511502022350211368120201강원특별자치도 강릉시 옥계면 현내리 368-1 2동 201호702960087.8720220601
9132강원특별자치도강릉시5115020223502511212710101[ 동해대로 290-36 ] 0001동 0101호184715760564.8820220601
43026강원특별자치도강릉시511502022110011371210201[ 강릉대로 272 ] 0001동 0201호54598500330.920220601
23914강원특별자치도강릉시51150202213801730080010202강원특별자치도 강릉시 안현동 730 8001동 202호2690100063.020220601
27957강원특별자치도강릉시511502022125016392710103[ 강변로350번길 1 ] 0001동 0103호213759024.1420220601
11585강원특별자치도강릉시511502022330271601010010101강원특별자치도 강릉시 구정면 제비리 601 1001동 101호82080018.020220601
6336강원특별자치도강릉시5115020223503018020101강원특별자치도 강릉시 옥계면 산계리 8 2동 101호104965120632.3220220601
38537강원특별자치도강릉시511502022111011191210201[ 경강로 2333 ] 0001동 0201호128278260192.6120220601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
2강원특별자치도강릉시5115020221100161210010101강원특별자치도 강릉시 교동 61-2 1001동 101호483840027.0202206014
16강원특별자치도강릉시51150202212901220290101강원특별자치도 강릉시 병산동 22 29동 101호3340896079.735202206014
50강원특별자치도강릉시511502022360231394410101강원특별자치도 강릉시 사천면 석교리 394-4 1동 101호787320018.0202206014
54강원특별자치도강릉시5115020223702311652010101[ 해안로 1177-32 ] 0001동 0101호13349100138.62202206014
7강원특별자치도강릉시511502022111016521610010101강원특별자치도 강릉시 포남동 652-16 1001동 101호505440027.0202206013
12강원특별자치도강릉시51150202212301698010101강원특별자치도 강릉시 월호평동 698 1동 101호12176170126.44202206013
17강원특별자치도강릉시51150202212901220290101강원특별자치도 강릉시 병산동 22 29동 101호148896000792.0202206013
20강원특별자치도강릉시5115020221320254010000강원특별자치도 강릉시 지변동 산 54 1동2263388059.72202206013
32강원특별자치도강릉시511502022340231659110180101강원특별자치도 강릉시 강동면 안인리 659-1 1018동 101호397440036.0202206013
36강원특별자치도강릉시511502022340231723110010101강원특별자치도 강릉시 강동면 안인리 723-1 1001동 101호362880027.0202206013