Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액에 대한 물건지, 시가표준 금액, 연면적, 결정일자 정보를 제공합니다.
Author경상남도 통영시
URLhttps://www.data.go.kr/data/15080069/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 10 (0.1%) duplicate rowsDuplicates
기준일자 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 기준일자High correlation
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 (91.3%)Imbalance
시가표준액 is highly skewed (γ1 = 23.38248225)Skewed
법정리 has 5101 (51.0%) zerosZeros
부번 has 2586 (25.9%) zerosZeros

Reproduction

Analysis started2023-12-12 11:09:36.875261
Analysis finished2023-12-12 11:09:51.133904
Duration14.26 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
경상남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 10000
100.0%

Length

2023-12-12T20:09:51.259022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:51.441335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 10000
100.0%

Length

2023-12-12T20:09:51.658591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:51.851160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48220
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 10000
100.0%

Length

2023-12-12T20:09:52.037381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:52.218620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3393 
2020
3307 
2018
3300 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2020
3rd row2020
4th row2019
5th row2018

Common Values

ValueCountFrequency (%)
2019 3393
33.9%
2020 3307
33.1%
2018 3300
33.0%

Length

2023-12-12T20:09:52.400235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:52.587420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3393
33.9%
2020 3307
33.1%
2018 3300
33.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.9633
Minimum101
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:52.775331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1110
median117
Q3340
95-th percentile360
Maximum370
Range269
Interquartile range (IQR)230

Descriptive statistics

Standard deviation109.04516
Coefficient of variation (CV)0.51203735
Kurtosis-1.8277457
Mean212.9633
Median Absolute Deviation (MAD)16
Skewness0.18665967
Sum2129633
Variance11890.848
MonotonicityNot monotonic
2023-12-12T20:09:53.043565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
340 1600
16.0%
250 928
 
9.3%
111 920
 
9.2%
310 801
 
8.0%
109 574
 
5.7%
330 500
 
5.0%
110 480
 
4.8%
350 474
 
4.7%
104 448
 
4.5%
117 440
 
4.4%
Other values (14) 2835
28.3%
ValueCountFrequency (%)
101 239
2.4%
102 339
3.4%
103 77
 
0.8%
104 448
4.5%
105 221
 
2.2%
106 64
 
0.6%
107 191
 
1.9%
108 307
3.1%
109 574
5.7%
110 480
4.8%
ValueCountFrequency (%)
370 231
 
2.3%
360 365
 
3.6%
350 474
 
4.7%
340 1600
16.0%
330 500
 
5.0%
310 801
8.0%
250 928
9.3%
117 440
 
4.4%
116 251
 
2.5%
115 274
 
2.7%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.7439
Minimum0
Maximum31
Zeros5101
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:53.269235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324
95-th percentile27
Maximum31
Range31
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.077893
Coefficient of variation (CV)1.0284397
Kurtosis-1.9277382
Mean11.7439
Median Absolute Deviation (MAD)0
Skewness0.087736735
Sum117439
Variance145.8755
MonotonicityNot monotonic
2023-12-12T20:09:53.480187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5101
51.0%
24 1326
 
13.3%
21 820
 
8.2%
26 579
 
5.8%
23 569
 
5.7%
22 547
 
5.5%
27 441
 
4.4%
25 390
 
3.9%
28 115
 
1.1%
29 45
 
0.4%
Other values (2) 67
 
0.7%
ValueCountFrequency (%)
0 5101
51.0%
21 820
 
8.2%
22 547
 
5.5%
23 569
 
5.7%
24 1326
 
13.3%
25 390
 
3.9%
26 579
 
5.8%
27 441
 
4.4%
28 115
 
1.1%
29 45
 
0.4%
ValueCountFrequency (%)
31 28
 
0.3%
30 39
 
0.4%
29 45
 
0.4%
28 115
 
1.1%
27 441
 
4.4%
26 579
5.8%
25 390
 
3.9%
24 1326
13.3%
23 569
5.7%
22 547
5.5%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9891 
2
 
109

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 9891
98.9%
2 109
 
1.1%

Length

2023-12-12T20:09:53.722614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:53.927918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9891
98.9%
2 109
 
1.1%

본번
Real number (ℝ)

Distinct1262
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628.268
Minimum1
Maximum2050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:54.162322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1185
median497
Q3994
95-th percentile1575
Maximum2050
Range2049
Interquartile range (IQR)809

Descriptive statistics

Standard deviation498.7479
Coefficient of variation (CV)0.79384578
Kurtosis-0.63597431
Mean628.268
Median Absolute Deviation (MAD)358
Skewness0.6579952
Sum6282680
Variance248749.47
MonotonicityNot monotonic
2023-12-12T20:09:54.449365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 229
 
2.3%
1158 125
 
1.2%
645 113
 
1.1%
1574 110
 
1.1%
1 93
 
0.9%
1580 78
 
0.8%
163 78
 
0.8%
1570 72
 
0.7%
1572 70
 
0.7%
409 67
 
0.7%
Other values (1252) 8965
89.6%
ValueCountFrequency (%)
1 93
0.9%
2 33
 
0.3%
3 5
 
0.1%
4 17
 
0.2%
5 30
 
0.3%
6 18
 
0.2%
7 13
 
0.1%
8 12
 
0.1%
9 9
 
0.1%
10 12
 
0.1%
ValueCountFrequency (%)
2050 48
0.5%
2035 2
 
< 0.1%
1987 1
 
< 0.1%
1940 1
 
< 0.1%
1921 1
 
< 0.1%
1916 2
 
< 0.1%
1914 1
 
< 0.1%
1902 4
 
< 0.1%
1901 2
 
< 0.1%
1899 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct156
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.3096
Minimum0
Maximum438
Zeros2586
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:54.731528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38.25
95-th percentile49
Maximum438
Range438
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation54.785035
Coefficient of variation (CV)3.5784759
Kurtosis43.634608
Mean15.3096
Median Absolute Deviation (MAD)2
Skewness6.5046779
Sum153096
Variance3001.4001
MonotonicityNot monotonic
2023-12-12T20:09:55.007520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2586
25.9%
1 1566
15.7%
2 1006
 
10.1%
3 548
 
5.5%
4 543
 
5.4%
5 424
 
4.2%
6 341
 
3.4%
7 269
 
2.7%
8 217
 
2.2%
9 151
 
1.5%
Other values (146) 2349
23.5%
ValueCountFrequency (%)
0 2586
25.9%
1 1566
15.7%
2 1006
 
10.1%
3 548
 
5.5%
4 543
 
5.4%
5 424
 
4.2%
6 341
 
3.4%
7 269
 
2.7%
8 217
 
2.2%
9 151
 
1.5%
ValueCountFrequency (%)
438 2
 
< 0.1%
435 1
 
< 0.1%
434 1
 
< 0.1%
433 2
 
< 0.1%
432 1
 
< 0.1%
430 1
 
< 0.1%
429 1
 
< 0.1%
428 1
 
< 0.1%
424 18
0.2%
423 16
0.2%


Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.5795
Minimum0
Maximum9002
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:55.682644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile10
Maximum9002
Range9002
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1405.4826
Coefficient of variation (CV)6.04302
Kurtosis34.014733
Mean232.5795
Median Absolute Deviation (MAD)0
Skewness5.9907222
Sum2325795
Variance1975381.2
MonotonicityNot monotonic
2023-12-12T20:09:55.936308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8424
84.2%
2 689
 
6.9%
9001 222
 
2.2%
3 164
 
1.6%
4 50
 
0.5%
10 46
 
0.5%
6 43
 
0.4%
5 40
 
0.4%
7 36
 
0.4%
9 25
 
0.2%
Other values (51) 261
 
2.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 8424
84.2%
2 689
 
6.9%
3 164
 
1.6%
4 50
 
0.5%
5 40
 
0.4%
6 43
 
0.4%
7 36
 
0.4%
8 23
 
0.2%
9 25
 
0.2%
ValueCountFrequency (%)
9002 9
 
0.1%
9001 222
2.2%
8004 3
 
< 0.1%
8003 4
 
< 0.1%
8002 4
 
< 0.1%
8001 2
 
< 0.1%
7002 13
 
0.1%
7001 1
 
< 0.1%
5001 2
 
< 0.1%
1005 1
 
< 0.1%


Text

Distinct416
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:09:56.661907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9622
Min length1

Characters and Unicode

Total characters29622
Distinct characters15
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)2.1%

Sample

1st row101
2nd row8101
3rd row102
4th row101
5th row201
ValueCountFrequency (%)
101 3859
38.6%
102 1229
 
12.3%
201 1205
 
12.0%
301 448
 
4.5%
103 417
 
4.2%
8101 263
 
2.6%
202 213
 
2.1%
401 191
 
1.9%
104 171
 
1.7%
1 144
 
1.4%
Other values (406) 1860
18.6%
2023-12-12T20:09:57.264675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13228
44.7%
0 9194
31.0%
2 3572
 
12.1%
3 1350
 
4.6%
4 667
 
2.3%
8 510
 
1.7%
5 462
 
1.6%
6 266
 
0.9%
7 229
 
0.8%
9 128
 
0.4%
Other values (5) 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29606
99.9%
Uppercase Letter 13
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13228
44.7%
0 9194
31.1%
2 3572
 
12.1%
3 1350
 
4.6%
4 667
 
2.3%
8 510
 
1.7%
5 462
 
1.6%
6 266
 
0.9%
7 229
 
0.8%
9 128
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
C 6
46.2%
A 5
38.5%
H 2
 
15.4%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29608
> 99.9%
Latin 13
 
< 0.1%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13228
44.7%
0 9194
31.1%
2 3572
 
12.1%
3 1350
 
4.6%
4 667
 
2.3%
8 510
 
1.7%
5 462
 
1.6%
6 266
 
0.9%
7 229
 
0.8%
9 128
 
0.4%
Latin
ValueCountFrequency (%)
C 6
46.2%
A 5
38.5%
H 2
 
15.4%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29621
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13228
44.7%
0 9194
31.0%
2 3572
 
12.1%
3 1350
 
4.6%
4 667
 
2.3%
8 510
 
1.7%
5 462
 
1.6%
6 266
 
0.9%
7 229
 
0.8%
9 128
 
0.4%
Other values (4) 15
 
0.1%
Hangul
ValueCountFrequency (%)
1
100.0%
Distinct8732
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:09:57.745649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length26.324
Min length21

Characters and Unicode

Total characters263240
Distinct characters218
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

Unique7646 ?
Unique (%)76.5%

Sample

1st row경상남도 통영시 도산면 관덕리 65-3 1동 101호
2nd row경상남도 통영시 욕지면 연화리 42-13 1동 8101호
3rd row[ 죽림해안로 40 ] 0001동 0102호
4th row경상남도 통영시 정량동 279-6 1동 101호
5th row경상남도 통영시 광도면 죽림리 1580-4 1동 201호
ValueCountFrequency (%)
9108
 
14.4%
경상남도 5446
 
8.6%
통영시 5446
 
8.6%
1동 4423
 
7.0%
0001동 4001
 
6.3%
101호 2331
 
3.7%
0101호 1528
 
2.4%
광도면 824
 
1.3%
102호 767
 
1.2%
0201호 659
 
1.0%
Other values (4485) 28674
45.4%
2023-12-12T20:09:58.495383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53207
20.2%
0 30244
 
11.5%
1 29879
 
11.4%
13165
 
5.0%
10475
 
4.0%
2 9377
 
3.6%
7462
 
2.8%
6891
 
2.6%
5743
 
2.2%
5682
 
2.2%
Other values (208) 91115
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100276
38.1%
Decimal Number 95147
36.1%
Space Separator 53207
20.2%
Dash Punctuation 5489
 
2.1%
Close Punctuation 4554
 
1.7%
Open Punctuation 4554
 
1.7%
Uppercase Letter 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13165
13.1%
10475
 
10.4%
7462
 
7.4%
6891
 
6.9%
5743
 
5.7%
5682
 
5.7%
5615
 
5.6%
5501
 
5.5%
5446
 
5.4%
3191
 
3.2%
Other values (191) 31105
31.0%
Decimal Number
ValueCountFrequency (%)
0 30244
31.8%
1 29879
31.4%
2 9377
 
9.9%
3 5460
 
5.7%
4 4279
 
4.5%
5 4158
 
4.4%
6 3191
 
3.4%
8 2903
 
3.1%
7 2864
 
3.0%
9 2792
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
C 6
46.2%
A 5
38.5%
H 2
 
15.4%
Space Separator
ValueCountFrequency (%)
53207
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5489
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4554
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162951
61.9%
Hangul 100276
38.1%
Latin 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13165
13.1%
10475
 
10.4%
7462
 
7.4%
6891
 
6.9%
5743
 
5.7%
5682
 
5.7%
5615
 
5.6%
5501
 
5.5%
5446
 
5.4%
3191
 
3.2%
Other values (191) 31105
31.0%
Common
ValueCountFrequency (%)
53207
32.7%
0 30244
18.6%
1 29879
18.3%
2 9377
 
5.8%
- 5489
 
3.4%
3 5460
 
3.4%
] 4554
 
2.8%
[ 4554
 
2.8%
4 4279
 
2.6%
5 4158
 
2.6%
Other values (4) 11750
 
7.2%
Latin
ValueCountFrequency (%)
C 6
46.2%
A 5
38.5%
H 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162964
61.9%
Hangul 100276
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53207
32.6%
0 30244
18.6%
1 29879
18.3%
2 9377
 
5.8%
- 5489
 
3.4%
3 5460
 
3.4%
] 4554
 
2.8%
[ 4554
 
2.8%
4 4279
 
2.6%
5 4158
 
2.6%
Other values (7) 11763
 
7.2%
Hangul
ValueCountFrequency (%)
13165
13.1%
10475
 
10.4%
7462
 
7.4%
6891
 
6.9%
5743
 
5.7%
5682
 
5.7%
5615
 
5.6%
5501
 
5.5%
5446
 
5.4%
3191
 
3.2%
Other values (191) 31105
31.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9016
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58102464
Minimum43200
Maximum8.7365558 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:58.727913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43200
5-th percentile549726
Q13381675
median20199400
Q359216075
95-th percentile2.0087683 × 108
Maximum8.7365558 × 109
Range8.7365126 × 109
Interquartile range (IQR)55834400

Descriptive statistics

Standard deviation1.861093 × 108
Coefficient of variation (CV)3.2031224
Kurtosis857.14395
Mean58102464
Median Absolute Deviation (MAD)18751060
Skewness23.382482
Sum5.8102464 × 1011
Variance3.4636672 × 1016
MonotonicityNot monotonic
2023-12-12T20:09:58.959038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61165440 62
 
0.6%
60981760 18
 
0.2%
4392990 11
 
0.1%
4297230 11
 
0.1%
576000 10
 
0.1%
16747940 10
 
0.1%
39982230 9
 
0.1%
25333740 9
 
0.1%
882000 8
 
0.1%
475200 8
 
0.1%
Other values (9006) 9844
98.4%
ValueCountFrequency (%)
43200 1
 
< 0.1%
47000 2
< 0.1%
47520 1
 
< 0.1%
52800 1
 
< 0.1%
54400 1
 
< 0.1%
57000 1
 
< 0.1%
60130 1
 
< 0.1%
66000 1
 
< 0.1%
66700 1
 
< 0.1%
72000 4
< 0.1%
ValueCountFrequency (%)
8736555820 1
< 0.1%
7305292200 1
< 0.1%
5718258200 1
< 0.1%
4722119640 1
< 0.1%
3641251510 1
< 0.1%
2363576360 1
< 0.1%
2272824150 1
< 0.1%
2238774350 1
< 0.1%
2040693560 1
< 0.1%
2038758000 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5658
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.39795
Minimum0.9
Maximum11527.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:09:59.176006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile9.75
Q130.7
median69.585
Q3139.2175
95-th percentile442.032
Maximum11527.32
Range11526.42
Interquartile range (IQR)108.5175

Descriptive statistics

Standard deviation359.05519
Coefficient of variation (CV)2.5393238
Kurtosis361.3635
Mean141.39795
Median Absolute Deviation (MAD)45.585
Skewness15.579175
Sum1413979.5
Variance128920.63
MonotonicityNot monotonic
2023-12-12T20:09:59.426199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 227
 
2.3%
91.84 81
 
0.8%
27.0 44
 
0.4%
30.0 40
 
0.4%
36.0 40
 
0.4%
15.0 37
 
0.4%
12.0 30
 
0.3%
11.97 26
 
0.3%
24.0 25
 
0.2%
19.8 23
 
0.2%
Other values (5648) 9427
94.3%
ValueCountFrequency (%)
0.9 1
< 0.1%
0.98 1
< 0.1%
1.0 1
< 0.1%
1.2 1
< 0.1%
1.32 1
< 0.1%
1.44 2
< 0.1%
1.56 1
< 0.1%
1.6 1
< 0.1%
1.65 1
< 0.1%
1.8 2
< 0.1%
ValueCountFrequency (%)
11527.32 1
< 0.1%
10587.38 1
< 0.1%
10149.83 1
< 0.1%
9567.0 1
< 0.1%
7616.322 1
< 0.1%
7503.71 1
< 0.1%
6256.3 1
< 0.1%
5791.68 1
< 0.1%
4984.42 1
< 0.1%
4962.89 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-06-01
3393 
2020-06-01
3307 
2018-06-01
3300 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-06-01
2nd row2020-06-01
3rd row2020-06-01
4th row2019-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 3393
33.9%
2020-06-01 3307
33.1%
2018-06-01 3300
33.0%

Length

2023-12-12T20:09:59.652389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:09:59.837935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 3393
33.9%
2020-06-01 3307
33.1%
2018-06-01 3300
33.0%

Interactions

2023-12-12T20:09:49.061715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:40.601069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:41.951999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:43.782616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.968973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:46.534359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.710515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:49.263607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:40.786427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:42.173178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:43.974204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:45.164888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:46.709175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.895463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:49.465909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:40.990085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:42.362183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.149565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:45.462844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:46.896760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:48.090203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:49.682455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:41.161676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:42.530396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.303593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:45.757162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.062032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:48.269929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:49.881480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:41.339433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:42.721849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.452889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:45.946057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.225088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:48.440505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:50.065965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:41.528957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:42.884336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.623411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:46.140283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.379631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:48.638218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:50.253599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:41.745153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:43.592983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:44.805902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:46.335160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:47.541620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:09:48.860184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:09:59.986381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.0490.0370.0000.0210.0000.0000.0000.0001.000
법정동0.0491.0000.8960.0780.6570.1590.1090.0460.0800.049
법정리0.0370.8961.0000.1390.5950.1540.1170.0280.0830.037
특수지0.0000.0780.1391.0000.1680.0000.0000.1810.1080.000
본번0.0210.6570.5950.1681.0000.2280.1150.0900.0860.021
부번0.0000.1590.1540.0000.2281.0000.0340.0000.0000.000
0.0000.1090.1170.0000.1150.0341.0000.0000.1500.000
시가표준액0.0000.0460.0280.1810.0900.0000.0001.0000.8690.000
연면적0.0000.0800.0830.1080.0860.0000.1500.8691.0000.000
기준일자1.0000.0490.0370.0000.0210.0000.0000.0000.0001.000
2023-12-12T20:10:00.237396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자특수지과세년도
기준일자1.0000.0001.000
특수지0.0001.0000.000
과세년도1.0000.0001.000
2023-12-12T20:10:00.423702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.7840.258-0.164-0.102-0.0660.0450.0360.0960.036
법정리0.7841.0000.227-0.154-0.102-0.120-0.0060.0270.1700.027
본번0.2580.2271.000-0.0230.0130.2120.1510.0130.1290.013
부번-0.164-0.154-0.0231.0000.0130.062-0.0100.0000.0000.000
-0.102-0.1020.0130.0131.000-0.055-0.1290.0000.0000.000
시가표준액-0.066-0.1200.2120.062-0.0551.0000.8040.0000.1360.000
연면적0.045-0.0060.151-0.010-0.1290.8041.0000.0000.1080.000
과세년도0.0360.0270.0130.0000.0000.0000.0001.0000.0001.000
특수지0.0960.1700.1290.0000.0000.1360.1080.0001.0000.000
기준일자0.0360.0270.0130.0000.0000.0000.0001.0000.0001.000

Missing values

2023-12-12T20:09:50.546087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:09:50.960898image/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
15990경상남도통영시4822020183302416531101경상남도 통영시 도산면 관덕리 65-3 1동 101호75600018.02018-06-01
60282경상남도통영시482202020350231421318101경상남도 통영시 욕지면 연화리 42-13 1동 8101호1777305047.882020-06-01
69151경상남도통영시482202020340241157121102[ 죽림해안로 40 ] 0001동 0102호48470400201.962020-06-01
36326경상남도통영시4822020191090127961101경상남도 통영시 정량동 279-6 1동 101호620894073.962019-06-01
24271경상남도통영시482202018340241158041201경상남도 통영시 광도면 죽림리 1580-4 1동 201호204796110325.592018-06-01
40751경상남도통영시48220201934025137201105경상남도 통영시 광도면 용호리 372 1동 105호1373484055.162019-06-01
31340경상남도통영시4822020191040179181101[ 통영해안로 315-2 ] 0001동 0101호342295038.162019-06-01
50450경상남도통영시482202019340241157771502[ 죽림1로 17-43 ] 0001동 0502호111347960215.542019-06-01
86963경상남도통영시4822020201170164501310경상남도 통영시 도남동 645 1동 310호3557772053.422020-06-01
42138경상남도통영시4822020193402411571341101[ 죽림5로 31-5 ] 0001동 0101호312676880477.882019-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
61362경상남도통영시48220202010401249261201경상남도 통영시 항남동 249-26 1동 201호457600052.02020-06-01
33903경상남도통영시48220201911001660141101[ 북신시장1길 31-3 ] 0001동 0101호935410098.02019-06-01
15516경상남도통영시48220201811001664202101경상남도 통영시 북신동 664-20 2동 101호5805195083.052018-06-01
17169경상남도통영시48220201833026150401101[ 저산서촌길 70 ] 0001동 0101호993250072.52018-06-01
59589경상남도통영시48220202033021115661101경상남도 통영시 도산면 원산리 156-6 1동 101호820800072.02020-06-01
83659경상남도통영시48220202011701359101102경상남도 통영시 도남동 359-10 1동 102호1815005.52020-06-01
83066경상남도통영시4822020201110198603271[ 안개로 37 ] 0003동 0271호73684030107.72522020-06-01
12529경상남도통영시48220201837022229611101경상남도 통영시 사량면 돈지리 산 296-1 1동 101호124740046.22018-06-01
37404경상남도통영시4822020191130153122101경상남도 통영시 인평동 531-2 2동 101호749952016.742019-06-01
44412경상남도통영시48220201933025193421101경상남도 통영시 도산면 오륜리 934-2 1동 101호83795970273.042019-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
0경상남도통영시482202018113025101101경상남도 통영시 인평동 산 51 1동 101호656500065.02018-06-012
1경상남도통영시4822020182502112610101101경상남도 통영시 산양읍 영운리 261 101동 101호3285225042.392018-06-012
2경상남도통영시4822020182502316911101경상남도 통영시 산양읍 미남리 69-1 1동 101호427500015.02018-06-012
3경상남도통영시482202019109011403025경상남도 통영시 정량동 1403 2동 5호2003063087.472019-06-012
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