Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows14
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://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15080069

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 14 (0.1%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 과세년도High 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 (93.3%)Imbalance
시가표준액 is highly skewed (γ1 = 80.07491197)Skewed
연면적 is highly skewed (γ1 = 76.62397116)Skewed
법정리 has 5038 (50.4%) zerosZeros
부번 has 2579 (25.8%) zerosZeros

Reproduction

Analysis started2023-12-11 00:02:00.206234
Analysis finished2023-12-11 00:02:07.064370
Duration6.86 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

2023-12-11T09:02:07.137446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:07.224543image/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

2023-12-11T09:02:07.312303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:07.411347image/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

2023-12-11T09:02:07.506980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:07.589319image/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
2020
3367 
2018
3336 
2019
3297 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 3367
33.7%
2018 3336
33.4%
2019 3297
33.0%

Length

2023-12-11T09:02:07.694732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:07.816030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 3367
33.7%
2018 3336
33.4%
2019 3297
33.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.1194
Minimum101
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:07.915849image/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 deviation109.06522
Coefficient of variation (CV)0.50936639
Kurtosis-1.8340668
Mean214.1194
Median Absolute Deviation (MAD)16
Skewness0.16368961
Sum2141194
Variance11895.223
MonotonicityNot monotonic
2023-12-11T09:02:08.031079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
340 1641
16.4%
250 950
 
9.5%
111 857
 
8.6%
310 794
 
7.9%
109 561
 
5.6%
330 527
 
5.3%
110 511
 
5.1%
350 472
 
4.7%
104 437
 
4.4%
117 383
 
3.8%
Other values (14) 2867
28.7%
ValueCountFrequency (%)
101 239
2.4%
102 366
3.7%
103 89
 
0.9%
104 437
4.4%
105 232
2.3%
106 67
 
0.7%
107 210
 
2.1%
108 269
2.7%
109 561
5.6%
110 511
5.1%
ValueCountFrequency (%)
370 241
 
2.4%
360 337
 
3.4%
350 472
 
4.7%
340 1641
16.4%
330 527
 
5.3%
310 794
7.9%
250 950
9.5%
117 383
 
3.8%
116 256
 
2.6%
115 300
 
3.0%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8713
Minimum0
Maximum31
Zeros5038
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:08.131599image/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 deviation12.056636
Coefficient of variation (CV)1.0156121
Kurtosis-1.9310625
Mean11.8713
Median Absolute Deviation (MAD)0
Skewness0.06283545
Sum118713
Variance145.36247
MonotonicityNot monotonic
2023-12-11T09:02:08.257780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5038
50.4%
24 1280
 
12.8%
21 855
 
8.6%
23 605
 
6.0%
26 592
 
5.9%
22 572
 
5.7%
27 451
 
4.5%
25 409
 
4.1%
28 92
 
0.9%
30 41
 
0.4%
Other values (2) 65
 
0.7%
ValueCountFrequency (%)
0 5038
50.4%
21 855
 
8.6%
22 572
 
5.7%
23 605
 
6.0%
24 1280
 
12.8%
25 409
 
4.1%
26 592
 
5.9%
27 451
 
4.5%
28 92
 
0.9%
29 38
 
0.4%
ValueCountFrequency (%)
31 27
 
0.3%
30 41
 
0.4%
29 38
 
0.4%
28 92
 
0.9%
27 451
 
4.5%
26 592
5.9%
25 409
 
4.1%
24 1280
12.8%
23 605
6.0%
22 572
5.7%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9920 
2
 
80

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 9920
99.2%
2 80
 
0.8%

Length

2023-12-11T09:02:08.376896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:08.479107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9920
99.2%
2 80
 
0.8%

본번
Real number (ℝ)

Distinct1261
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean629.9832
Minimum1
Maximum2050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:08.857722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1178
median512
Q3993
95-th percentile1575
Maximum2050
Range2049
Interquartile range (IQR)815

Descriptive statistics

Standard deviation500.00398
Coefficient of variation (CV)0.79367827
Kurtosis-0.63003179
Mean629.9832
Median Absolute Deviation (MAD)362
Skewness0.66021948
Sum6299832
Variance250003.98
MonotonicityNot monotonic
2023-12-11T09:02:08.995750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 246
 
2.5%
1158 122
 
1.2%
1574 98
 
1.0%
645 90
 
0.9%
163 85
 
0.9%
1 84
 
0.8%
1570 82
 
0.8%
1572 81
 
0.8%
1580 74
 
0.7%
986 73
 
0.7%
Other values (1251) 8965
89.6%
ValueCountFrequency (%)
1 84
0.8%
2 31
 
0.3%
3 8
 
0.1%
4 20
 
0.2%
5 21
 
0.2%
6 9
 
0.1%
7 10
 
0.1%
8 13
 
0.1%
9 5
 
0.1%
10 9
 
0.1%
ValueCountFrequency (%)
2050 47
0.5%
2048 1
 
< 0.1%
2036 1
 
< 0.1%
2035 1
 
< 0.1%
1994 1
 
< 0.1%
1987 2
 
< 0.1%
1985 2
 
< 0.1%
1954 1
 
< 0.1%
1941 1
 
< 0.1%
1940 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct164
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8508
Minimum0
Maximum447
Zeros2579
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:09.131766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile52
Maximum447
Range447
Interquartile range (IQR)9

Descriptive statistics

Standard deviation55.542251
Coefficient of variation (CV)3.5040661
Kurtosis40.847417
Mean15.8508
Median Absolute Deviation (MAD)2
Skewness6.2898396
Sum158508
Variance3084.9416
MonotonicityNot monotonic
2023-12-11T09:02:09.274541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2579
25.8%
1 1531
15.3%
2 1026
 
10.3%
4 545
 
5.5%
3 525
 
5.2%
5 381
 
3.8%
6 330
 
3.3%
7 278
 
2.8%
8 229
 
2.3%
9 160
 
1.6%
Other values (154) 2416
24.2%
ValueCountFrequency (%)
0 2579
25.8%
1 1531
15.3%
2 1026
 
10.3%
3 525
 
5.2%
4 545
 
5.5%
5 381
 
3.8%
6 330
 
3.3%
7 278
 
2.8%
8 229
 
2.3%
9 160
 
1.6%
ValueCountFrequency (%)
447 1
 
< 0.1%
436 1
 
< 0.1%
434 1
 
< 0.1%
428 1
 
< 0.1%
424 13
 
0.1%
423 10
 
0.1%
422 16
0.2%
418 33
0.3%
417 33
0.3%
415 9
 
0.1%


Real number (ℝ)

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.9557
Minimum0
Maximum9002
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:09.410397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile9
Maximum9002
Range9002
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1435.8652
Coefficient of variation (CV)5.9099879
Kurtosis32.41123
Mean242.9557
Median Absolute Deviation (MAD)0
Skewness5.8558068
Sum2429557
Variance2061709
MonotonicityNot monotonic
2023-12-11T09:02:09.547088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8379
83.8%
2 727
 
7.3%
9001 234
 
2.3%
3 164
 
1.6%
4 60
 
0.6%
6 56
 
0.6%
10 39
 
0.4%
5 37
 
0.4%
8 30
 
0.3%
101 28
 
0.3%
Other values (49) 246
 
2.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 8379
83.8%
2 727
 
7.3%
3 164
 
1.6%
4 60
 
0.6%
5 37
 
0.4%
6 56
 
0.6%
7 28
 
0.3%
8 30
 
0.3%
9 22
 
0.2%
ValueCountFrequency (%)
9002 10
 
0.1%
9001 234
2.3%
8004 2
 
< 0.1%
8003 2
 
< 0.1%
8002 2
 
< 0.1%
8001 4
 
< 0.1%
7002 15
 
0.1%
5001 2
 
< 0.1%
1007 1
 
< 0.1%
1005 1
 
< 0.1%


Text

Distinct387
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:02:09.835323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9574
Min length1

Characters and Unicode

Total characters29574
Distinct characters16
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

Unique171 ?
Unique (%)1.7%

Sample

1st row201
2nd row301
3rd row202
4th row102
5th row107
ValueCountFrequency (%)
101 3756
37.6%
102 1265
 
12.7%
201 1224
 
12.2%
301 441
 
4.4%
103 425
 
4.2%
8101 256
 
2.6%
202 208
 
2.1%
401 182
 
1.8%
104 164
 
1.6%
1 151
 
1.5%
Other values (377) 1928
19.3%
2023-12-11T09:02:10.269385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13146
44.5%
0 9144
30.9%
2 3635
 
12.3%
3 1358
 
4.6%
4 688
 
2.3%
8 505
 
1.7%
5 449
 
1.5%
6 284
 
1.0%
7 220
 
0.7%
9 133
 
0.4%
Other values (6) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29562
> 99.9%
Uppercase Letter 8
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13146
44.5%
0 9144
30.9%
2 3635
 
12.3%
3 1358
 
4.6%
4 688
 
2.3%
8 505
 
1.7%
5 449
 
1.5%
6 284
 
1.0%
7 220
 
0.7%
9 133
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
H 2
25.0%
C 2
25.0%
A 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Letter
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29564
> 99.9%
Latin 8
 
< 0.1%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13146
44.5%
0 9144
30.9%
2 3635
 
12.3%
3 1358
 
4.6%
4 688
 
2.3%
8 505
 
1.7%
5 449
 
1.5%
6 284
 
1.0%
7 220
 
0.7%
9 133
 
0.4%
Latin
ValueCountFrequency (%)
B 3
37.5%
H 2
25.0%
C 2
25.0%
A 1
 
12.5%
Hangul
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29572
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13146
44.5%
0 9144
30.9%
2 3635
 
12.3%
3 1358
 
4.6%
4 688
 
2.3%
8 505
 
1.7%
5 449
 
1.5%
6 284
 
1.0%
7 220
 
0.7%
9 133
 
0.4%
Other values (5) 10
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
100.0%
Distinct8705
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:02:10.604576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length26.3013
Min length21

Characters and Unicode

Total characters263013
Distinct characters216
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

Unique7603 ?
Unique (%)76.0%

Sample

1st row경상남도 통영시 무전동 1055-9 1동 201호
2nd row경상남도 통영시 중앙동 120-2 1동 301호
3rd row[ 도동길 117 ] 0001동 0202호
4th row경상남도 통영시 동호동 356 1동 102호
5th row경상남도 통영시 광도면 죽림리 1573-2 522동 107호
ValueCountFrequency (%)
9234
 
14.6%
통영시 5383
 
8.5%
경상남도 5383
 
8.5%
1동 4338
 
6.9%
0001동 4041
 
6.4%
101호 2256
 
3.6%
0101호 1500
 
2.4%
광도면 837
 
1.3%
102호 807
 
1.3%
0201호 694
 
1.1%
Other values (4464) 28711
45.4%
2023-12-11T09:02:11.056098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53184
20.2%
0 30460
 
11.6%
1 29678
 
11.3%
13085
 
5.0%
10465
 
4.0%
2 9438
 
3.6%
7378
 
2.8%
6774
 
2.6%
5662
 
2.2%
5597
 
2.1%
Other values (206) 91292
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99996
38.0%
Decimal Number 95216
36.2%
Space Separator 53184
20.2%
Dash Punctuation 5375
 
2.0%
Open Punctuation 4617
 
1.8%
Close Punctuation 4617
 
1.8%
Uppercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13085
13.1%
10465
 
10.5%
7378
 
7.4%
6774
 
6.8%
5662
 
5.7%
5597
 
5.6%
5565
 
5.6%
5435
 
5.4%
5383
 
5.4%
3180
 
3.2%
Other values (188) 31472
31.5%
Decimal Number
ValueCountFrequency (%)
0 30460
32.0%
1 29678
31.2%
2 9438
 
9.9%
3 5446
 
5.7%
4 4225
 
4.4%
5 4105
 
4.3%
6 3200
 
3.4%
7 3032
 
3.2%
8 2934
 
3.1%
9 2698
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
C 2
25.0%
H 2
25.0%
A 1
 
12.5%
Space Separator
ValueCountFrequency (%)
53184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5375
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4617
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 163009
62.0%
Hangul 99996
38.0%
Latin 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13085
13.1%
10465
 
10.5%
7378
 
7.4%
6774
 
6.8%
5662
 
5.7%
5597
 
5.6%
5565
 
5.6%
5435
 
5.4%
5383
 
5.4%
3180
 
3.2%
Other values (188) 31472
31.5%
Common
ValueCountFrequency (%)
53184
32.6%
0 30460
18.7%
1 29678
18.2%
2 9438
 
5.8%
3 5446
 
3.3%
- 5375
 
3.3%
[ 4617
 
2.8%
] 4617
 
2.8%
4 4225
 
2.6%
5 4105
 
2.5%
Other values (4) 11864
 
7.3%
Latin
ValueCountFrequency (%)
B 3
37.5%
C 2
25.0%
H 2
25.0%
A 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163017
62.0%
Hangul 99996
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53184
32.6%
0 30460
18.7%
1 29678
18.2%
2 9438
 
5.8%
3 5446
 
3.3%
- 5375
 
3.3%
[ 4617
 
2.8%
] 4617
 
2.8%
4 4225
 
2.6%
5 4105
 
2.5%
Other values (8) 11872
 
7.3%
Hangul
ValueCountFrequency (%)
13085
13.1%
10465
 
10.5%
7378
 
7.4%
6774
 
6.8%
5662
 
5.7%
5597
 
5.6%
5565
 
5.6%
5435
 
5.4%
5383
 
5.4%
3180
 
3.2%
Other values (188) 31472
31.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8974
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62680309
Minimum19000
Maximum4.4919914 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:11.193765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19000
5-th percentile540000
Q13404310
median20102580
Q359019712
95-th percentile2.0621068 × 108
Maximum4.4919914 × 1010
Range4.4919895 × 1010
Interquartile range (IQR)55615402

Descriptive statistics

Standard deviation4.8557278 × 108
Coefficient of variation (CV)7.7468153
Kurtosis7300.2601
Mean62680309
Median Absolute Deviation (MAD)18608610
Skewness80.074912
Sum6.2680309 × 1011
Variance2.3578092 × 1017
MonotonicityNot monotonic
2023-12-11T09:02:11.319509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61165440 40
 
0.4%
60981760 18
 
0.2%
4476780 14
 
0.1%
41358770 13
 
0.1%
16779070 12
 
0.1%
16747940 12
 
0.1%
25333740 11
 
0.1%
4392990 10
 
0.1%
540000 9
 
0.1%
720000 9
 
0.1%
Other values (8964) 9852
98.5%
ValueCountFrequency (%)
19000 1
< 0.1%
35400 1
< 0.1%
46000 1
< 0.1%
48720 1
< 0.1%
51610 1
< 0.1%
59360 1
< 0.1%
60480 1
< 0.1%
63000 1
< 0.1%
66700 1
< 0.1%
69000 1
< 0.1%
ValueCountFrequency (%)
44919913710 1
< 0.1%
7305292200 1
< 0.1%
7294704820 1
< 0.1%
5914028400 1
< 0.1%
5655695200 1
< 0.1%
4875705380 1
< 0.1%
2718156570 1
< 0.1%
2290397760 1
< 0.1%
2229801580 1
< 0.1%
2132419090 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5600
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.70681
Minimum0.82
Maximum87333.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:02:11.455418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.82
5-th percentile9.739
Q130.84
median68.78
Q3138.16
95-th percentile430.36
Maximum87333.36
Range87332.54
Interquartile range (IQR)107.32

Descriptive statistics

Standard deviation959.08602
Coefficient of variation (CV)6.3639195
Kurtosis6850.2883
Mean150.70681
Median Absolute Deviation (MAD)44.78
Skewness76.623971
Sum1507068.1
Variance919845.99
MonotonicityNot monotonic
2023-12-11T09:02:11.585599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 218
 
2.2%
91.84 60
 
0.6%
12.0 45
 
0.4%
27.0 45
 
0.4%
15.0 44
 
0.4%
24.0 38
 
0.4%
36.0 36
 
0.4%
11.97 33
 
0.3%
62.57 30
 
0.3%
9.0 29
 
0.3%
Other values (5590) 9422
94.2%
ValueCountFrequency (%)
0.82 1
 
< 0.1%
1.0 3
< 0.1%
1.1013 1
 
< 0.1%
1.28 1
 
< 0.1%
1.37 1
 
< 0.1%
1.44 1
 
< 0.1%
1.61 1
 
< 0.1%
1.68 3
< 0.1%
1.74 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
87333.36 1
< 0.1%
18739.0 1
< 0.1%
10587.38 2
< 0.1%
10480.65 1
< 0.1%
9567.0 1
< 0.1%
6816.66 1
< 0.1%
6509.62 1
< 0.1%
6256.3 1
< 0.1%
5791.68 1
< 0.1%
5283.89 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-06-01
3367 
2018-06-01
3336 
2019-06-01
3297 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020-06-01 3367
33.7%
2018-06-01 3336
33.4%
2019-06-01 3297
33.0%

Length

2023-12-11T09:02:11.729955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:11.827137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-01 3367
33.7%
2018-06-01 3336
33.4%
2019-06-01 3297
33.0%

Interactions

2023-12-11T09:02:06.082959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.261224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.876692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.484368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.085294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.817852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.433597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.176417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.349416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.963455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.562098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.168075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.911554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.523254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.277447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.456550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.085725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.647917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.254016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.005480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.617978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.359643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.538708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.168085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.747070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.335072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.085995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.701922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.439623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.623598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.246878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.839840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.463921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.166215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.785672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.521386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.705081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.328198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.916493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.605742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.246811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.868326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:06.611973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:02.794307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:03.407013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.002237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:04.721985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.339589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:05.969578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:02:11.899715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.0370.0390.0000.0000.0000.0250.0230.0001.000
법정동0.0371.0000.8980.0760.6520.1760.1110.0070.0150.037
법정리0.0390.8981.0000.1820.6050.1670.1190.0000.0180.039
특수지0.0000.0760.1821.0000.1380.0000.0000.0000.0000.000
본번0.0000.6520.6050.1381.0000.2380.1080.1340.1120.000
부번0.0000.1760.1670.0000.2381.0000.0360.0000.0000.000
0.0250.1110.1190.0000.1080.0361.0000.0000.0000.025
시가표준액0.0230.0070.0000.0000.1340.0000.0001.0000.7790.023
연면적0.0000.0150.0180.0000.1120.0000.0000.7791.0000.000
기준일자1.0000.0370.0390.0000.0000.0000.0250.0230.0001.000
2023-12-11T09:02:12.031961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도기준일자특수지
과세년도1.0001.0000.000
기준일자1.0001.0000.000
특수지0.0000.0001.000
2023-12-11T09:02:12.143730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.7780.274-0.162-0.109-0.0520.0560.0280.0930.028
법정리0.7781.0000.244-0.159-0.102-0.0860.0170.0290.2230.029
본번0.2740.2441.000-0.0420.0120.2090.1450.0000.1060.000
부번-0.162-0.159-0.0421.0000.0050.065-0.0060.0000.0000.000
-0.109-0.1020.0120.0051.000-0.055-0.1200.0100.0000.010
시가표준액-0.052-0.0860.2090.065-0.0551.0000.8070.0070.0000.007
연면적0.0560.0170.145-0.006-0.1200.8071.0000.0000.0000.000
과세년도0.0280.0290.0000.0000.0100.0070.0001.0000.0001.000
특수지0.0930.2230.1060.0000.0000.0000.0000.0001.0000.000
기준일자0.0280.0290.0000.0000.0100.0070.0001.0000.0001.000

Missing values

2023-12-11T09:02:06.763255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:02:06.961258image/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
71216경상남도통영시48220202011101105591201경상남도 통영시 무전동 1055-9 1동 201호54551670126.572020-06-01
72051경상남도통영시4822020201050112021301경상남도 통영시 중앙동 120-2 1동 301호317580015.82020-06-01
25748경상남도통영시48220201835022163541202[ 도동길 117 ] 0001동 0202호1192320029.442018-06-01
35534경상남도통영시4822020191080135601102경상남도 통영시 동호동 356 1동 102호134000020.02019-06-01
22594경상남도통영시48220201834024115732522107경상남도 통영시 광도면 죽림리 1573-2 522동 107호6027878074.69492018-06-01
37753경상남도통영시48220201911001664202101경상남도 통영시 북신동 664-20 2동 101호5846720083.052019-06-01
41362경상남도통영시48220201934024140221101[ 죽림1로 12 ] 0001동 0101호38152400131.562019-06-01
67990경상남도통영시482202020340241158441101[ 신죽1길 32 ] 0001동 0101호258951000438.92020-06-01
23418경상남도통영시482202018250261128441102경상남도 통영시 산양읍 남평리 1284-4 1동 102호544896012.82018-06-01
42710경상남도통영시482202019330231136521101경상남도 통영시 도산면 법송리 1365-2 1동 101호90305280257.282019-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
65911경상남도통영시4822020203402411571281201[ 죽림5로 25-42 ] 0001동 0201호511839000639.02020-06-01
12529경상남도통영시48220201837022229611101경상남도 통영시 사량면 돈지리 산 296-1 1동 101호124740046.22018-06-01
51769경상남도통영시482202019340241157081415[ 죽림4로 23-96 ] 0001동 0415호4135877062.572019-06-01
79690경상남도통영시48220202010101102111211경상남도 통영시 도천동 1021-1 1동 211호51473760113.882020-06-01
38389경상남도통영시4822020191070137221101경상남도 통영시 태평동 372-2 1동 101호30657120150.282019-06-01
45997경상남도통영시482202019370211100741101경상남도 통영시 사량면 금평리 1007-4 1동 101호274380010.762019-06-01
40448경상남도통영시4822020193402417032101경상남도 통영시 광도면 죽림리 70-3 2동 101호75600050.42019-06-01
82713경상남도통영시482202020105013841016경상남도 통영시 중앙동 38-4 10동 16호299625011.752020-06-01
30557경상남도통영시4822020191080116031701경상남도 통영시 동호동 160-3 1동 701호96556320208.322019-06-01
60114경상남도통영시48220202010101110337101경상남도 통영시 도천동 110-33 7동 101호432000060.02020-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
4경상남도통영시482202019310251117301201경상남도 통영시 용남면 원평리 1173 1동 201호1031250016.52019-06-013
11경상남도통영시48220202031024129631101경상남도 통영시 용남면 삼화리 296-3 1동 101호4659632074.082020-06-013
0경상남도통영시482202018109011398021[ 멘데산업길 89 ] 0002동 0001호2000700087.752018-06-012
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