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 memory137.0 B

Variable types

Categorical6
Numeric6
Unsupported1
Text2

Dataset

Description제주특별자치도 제주시 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하는 데이터로, 일반건축물의 물건별 재산가액을 확인 할 수 있습니다.
Author제주특별자치도 제주시
URLhttps://www.data.go.kr/data/15080466/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 2 (< 0.1%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
본번 is highly overall correlated with 특수지High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly overall correlated with 본번High correlation
특수지 is highly imbalanced (95.3%)Imbalance
동 is an unsupported type, check if it needs cleaning or further analysisUnsupported
법정리 has 8924 (89.2%) zerosZeros
부번 has 1828 (18.3%) zerosZeros

Reproduction

Analysis started2023-12-12 16:18:30.725601
Analysis finished2023-12-12 16:18:36.378877
Duration5.65 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
제주특별자치도
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 10000
100.0%

Length

2023-12-13T01:18:36.456295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:36.556585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 10000
100.0%

Length

2023-12-13T01:18:36.658088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:36.749844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
50110
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 10000
100.0%

Length

2023-12-13T01:18:36.847675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:36.958160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 10000
100.0%

Length

2023-12-13T01:18:37.053020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:37.142260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.1606
Minimum101
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:37.258666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1106
median122
Q3137
95-th percentile250
Maximum330
Range229
Interquartile range (IQR)31

Descriptive statistics

Standard deviation43.29355
Coefficient of variation (CV)0.32512282
Kurtosis3.3768849
Mean133.1606
Median Absolute Deviation (MAD)15
Skewness2.1573429
Sum1331606
Variance1874.3314
MonotonicityNot monotonic
2023-12-13T01:18:37.409084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
137 1869
18.7%
122 1308
13.1%
104 864
 
8.6%
250 730
 
7.3%
102 467
 
4.7%
103 385
 
3.9%
106 378
 
3.8%
105 336
 
3.4%
117 274
 
2.7%
107 267
 
2.7%
Other values (36) 3122
31.2%
ValueCountFrequency (%)
101 208
 
2.1%
102 467
4.7%
103 385
3.9%
104 864
8.6%
105 336
 
3.4%
106 378
3.8%
107 267
 
2.7%
108 190
 
1.9%
109 222
 
2.2%
110 60
 
0.6%
ValueCountFrequency (%)
330 2
 
< 0.1%
310 2
 
< 0.1%
259 215
 
2.1%
256 14
 
0.1%
253 113
 
1.1%
250 730
 
7.3%
140 39
 
0.4%
139 73
 
0.7%
138 26
 
0.3%
137 1869
18.7%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9518
Minimum0
Maximum42
Zeros8924
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:37.548951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6578388
Coefficient of variation (CV)2.9330709
Kurtosis5.9398629
Mean2.9518
Median Absolute Deviation (MAD)0
Skewness2.7268538
Sum29518
Variance74.958173
MonotonicityNot monotonic
2023-12-13T01:18:37.677645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8924
89.2%
24 322
 
3.2%
25 172
 
1.7%
31 95
 
0.9%
33 72
 
0.7%
21 62
 
0.6%
30 47
 
0.5%
27 45
 
0.4%
29 44
 
0.4%
26 37
 
0.4%
Other values (13) 180
 
1.8%
ValueCountFrequency (%)
0 8924
89.2%
21 62
 
0.6%
22 14
 
0.1%
23 28
 
0.3%
24 322
 
3.2%
25 172
 
1.7%
26 37
 
0.4%
27 45
 
0.4%
28 20
 
0.2%
29 44
 
0.4%
ValueCountFrequency (%)
42 16
 
0.2%
41 14
 
0.1%
40 25
 
0.2%
39 18
 
0.2%
38 1
 
< 0.1%
37 7
 
0.1%
36 1
 
< 0.1%
35 5
 
0.1%
34 22
 
0.2%
33 72
0.7%

특수지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9915 
2
 
76
5
 
9

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 9915
99.2%
2 76
 
0.8%
5 9
 
0.1%

Length

2023-12-13T01:18:37.857698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:37.982613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9915
99.2%
2 76
 
0.8%
5 9
 
0.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct2117
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1136.0405
Minimum1
Maximum8005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:38.121470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile83
Q1289
median923
Q31487
95-th percentile2898.3
Maximum8005
Range8004
Interquartile range (IQR)1198

Descriptive statistics

Standard deviation1061.8271
Coefficient of variation (CV)0.93467366
Kurtosis6.0847699
Mean1136.0405
Median Absolute Deviation (MAD)631
Skewness2.0447741
Sum11360405
Variance1127476.9
MonotonicityNot monotonic
2023-12-13T01:18:38.320351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251 181
 
1.8%
260 165
 
1.7%
272 147
 
1.5%
274 123
 
1.2%
281 123
 
1.2%
1269 115
 
1.1%
261 113
 
1.1%
924 106
 
1.1%
923 105
 
1.1%
282 98
 
1.0%
Other values (2107) 8724
87.2%
ValueCountFrequency (%)
1 60
0.6%
2 3
 
< 0.1%
3 49
0.5%
4 10
 
0.1%
5 1
 
< 0.1%
6 7
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 17
 
0.2%
ValueCountFrequency (%)
8005 1
 
< 0.1%
7024 1
 
< 0.1%
7023 1
 
< 0.1%
7022 3
< 0.1%
7009 2
< 0.1%
7004 1
 
< 0.1%
6161 1
 
< 0.1%
6160 2
< 0.1%
6155 1
 
< 0.1%
6154 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5957
Minimum0
Maximum232
Zeros1828
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:38.538925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312
95-th percentile38
Maximum232
Range232
Interquartile range (IQR)11

Descriptive statistics

Standard deviation15.215229
Coefficient of variation (CV)1.5856299
Kurtosis22.523366
Mean9.5957
Median Absolute Deviation (MAD)4
Skewness3.6018942
Sum95957
Variance231.50319
MonotonicityNot monotonic
2023-12-13T01:18:38.703508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1828
18.3%
1 1531
15.3%
2 818
 
8.2%
3 722
 
7.2%
5 559
 
5.6%
4 377
 
3.8%
8 357
 
3.6%
9 323
 
3.2%
6 294
 
2.9%
7 277
 
2.8%
Other values (97) 2914
29.1%
ValueCountFrequency (%)
0 1828
18.3%
1 1531
15.3%
2 818
8.2%
3 722
 
7.2%
4 377
 
3.8%
5 559
 
5.6%
6 294
 
2.9%
7 277
 
2.8%
8 357
 
3.6%
9 323
 
3.2%
ValueCountFrequency (%)
232 2
< 0.1%
195 1
< 0.1%
167 1
< 0.1%
145 1
< 0.1%
142 1
< 0.1%
140 1
< 0.1%
130 1
< 0.1%
121 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%

동
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

호
Text

Distinct822
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:18:39.120710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.1522
Min length1

Characters and Unicode

Total characters31522
Distinct characters24
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique313 ?
Unique (%)3.1%

Sample

1st row901
2nd row1509
3rd row102
4th row102
5th row101
ValueCountFrequency (%)
101 2633
26.3%
201 879
 
8.8%
102 846
 
8.5%
8101 497
 
5.0%
301 435
 
4.3%
103 276
 
2.8%
202 189
 
1.9%
401 175
 
1.7%
302 108
 
1.1%
8102 103
 
1.0%
Other values (814) 3865
38.6%
2023-12-13T01:18:39.676067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11812
37.5%
0 8951
28.4%
2 3614
 
11.5%
3 1902
 
6.0%
8 1296
 
4.1%
4 1072
 
3.4%
5 938
 
3.0%
6 738
 
2.3%
7 643
 
2.0%
9 483
 
1.5%
Other values (14) 73
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31449
99.8%
Dash Punctuation 27
 
0.1%
Other Letter 26
 
0.1%
Lowercase Letter 8
 
< 0.1%
Space Separator 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11812
37.6%
0 8951
28.5%
2 3614
 
11.5%
3 1902
 
6.0%
8 1296
 
4.1%
4 1072
 
3.4%
5 938
 
3.0%
6 738
 
2.3%
7 643
 
2.0%
9 483
 
1.5%
Other Letter
ValueCountFrequency (%)
하 7
26.9%
지 7
26.9%
월 6
23.1%
일 6
23.1%
Uppercase Letter
ValueCountFrequency (%)
J 3
50.0%
B 1
 
16.7%
D 1
 
16.7%
F 1
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
37.5%
n 3
37.5%
e 1
 
12.5%
b 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31482
99.9%
Hangul 26
 
0.1%
Latin 14
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11812
37.5%
0 8951
28.4%
2 3614
 
11.5%
3 1902
 
6.0%
8 1296
 
4.1%
4 1072
 
3.4%
5 938
 
3.0%
6 738
 
2.3%
7 643
 
2.0%
9 483
 
1.5%
Other values (2) 33
 
0.1%
Latin
ValueCountFrequency (%)
J 3
21.4%
a 3
21.4%
n 3
21.4%
B 1
 
7.1%
D 1
 
7.1%
F 1
 
7.1%
e 1
 
7.1%
b 1
 
7.1%
Hangul
ValueCountFrequency (%)
하 7
26.9%
지 7
26.9%
월 6
23.1%
일 6
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31496
99.9%
Hangul 26
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11812
37.5%
0 8951
28.4%
2 3614
 
11.5%
3 1902
 
6.0%
8 1296
 
4.1%
4 1072
 
3.4%
5 938
 
3.0%
6 738
 
2.3%
7 643
 
2.0%
9 483
 
1.5%
Other values (10) 47
 
0.1%
Hangul
ValueCountFrequency (%)
하 7
26.9%
지 7
26.9%
월 6
23.1%
일 6
23.1%

물건지
Text

Distinct9904
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:18:39.997564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length36
Mean length25.4202
Min length21

Characters and Unicode

Total characters254202
Distinct characters226
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

Unique9822 ?
Unique (%)98.2%

Sample

1st row[ 비월길 2 ] 0000동 0901호
2nd row[ 노연로 49 ] 0001동 1509호
3rd row제주특별자치도 제주시 노형동 3074 90동 102호
4th row[ 도남서길 13 ] 0001동 0102호
5th row제주특별자치도 제주시 용담2동 1963-5 2동 101호
ValueCountFrequency (%)
14472
23.9%
0001동 6248
 
10.3%
제주특별자치도 2764
 
4.6%
제주시 2764
 
4.6%
0101호 1592
 
2.6%
1동 1431
 
2.4%
101호 1041
 
1.7%
0201호 651
 
1.1%
0000동 508
 
0.8%
한림읍 502
 
0.8%
Other values (4643) 28495
47.1%
2023-12-13T01:18:40.589872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50468
19.9%
0 39646
15.6%
1 28222
 
11.1%
동 12811
 
5.0%
2 9988
 
3.9%
호 9948
 
3.9%
] 7236
 
2.8%
[ 7236
 
2.8%
제 5706
 
2.2%
주 5654
 
2.2%
Other values (216) 77287
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103635
40.8%
Other Letter 82696
32.5%
Space Separator 50468
19.9%
Close Punctuation 7236
 
2.8%
Open Punctuation 7236
 
2.8%
Dash Punctuation 2911
 
1.1%
Uppercase Letter 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12811
15.5%
호 9948
 
12.0%
제 5706
 
6.9%
주 5654
 
6.8%
로 5078
 
6.1%
도 3818
 
4.6%
길 3812
 
4.6%
자 2768
 
3.3%
시 2766
 
3.3%
치 2764
 
3.3%
Other values (199) 27571
33.3%
Decimal Number
ValueCountFrequency (%)
0 39646
38.3%
1 28222
27.2%
2 9988
 
9.6%
3 5595
 
5.4%
4 4348
 
4.2%
5 3767
 
3.6%
8 3642
 
3.5%
6 3162
 
3.1%
7 2769
 
2.7%
9 2496
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 10
50.0%
L 9
45.0%
D 1
 
5.0%
Space Separator
ValueCountFrequency (%)
50468
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7236
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7236
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171486
67.5%
Hangul 82696
32.5%
Latin 20
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12811
15.5%
호 9948
 
12.0%
제 5706
 
6.9%
주 5654
 
6.8%
로 5078
 
6.1%
도 3818
 
4.6%
길 3812
 
4.6%
자 2768
 
3.3%
시 2766
 
3.3%
치 2764
 
3.3%
Other values (199) 27571
33.3%
Common
ValueCountFrequency (%)
50468
29.4%
0 39646
23.1%
1 28222
16.5%
2 9988
 
5.8%
] 7236
 
4.2%
[ 7236
 
4.2%
3 5595
 
3.3%
4 4348
 
2.5%
5 3767
 
2.2%
8 3642
 
2.1%
Other values (4) 11338
 
6.6%
Latin
ValueCountFrequency (%)
B 10
50.0%
L 9
45.0%
D 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171506
67.5%
Hangul 82696
32.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50468
29.4%
0 39646
23.1%
1 28222
16.5%
2 9988
 
5.8%
] 7236
 
4.2%
[ 7236
 
4.2%
3 5595
 
3.3%
4 4348
 
2.5%
5 3767
 
2.2%
8 3642
 
2.1%
Other values (7) 11358
 
6.6%
Hangul
ValueCountFrequency (%)
동 12811
15.5%
호 9948
 
12.0%
제 5706
 
6.9%
주 5654
 
6.8%
로 5078
 
6.1%
도 3818
 
4.6%
길 3812
 
4.6%
자 2768
 
3.3%
시 2766
 
3.3%
치 2764
 
3.3%
Other values (199) 27571
33.3%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7619
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69355115
Minimum31000
Maximum6.4408165 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:40.767343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31000
5-th percentile1010352
Q110145250
median37750980
Q369508965
95-th percentile2.1969997 × 108
Maximum6.4408165 × 109
Range6.4407855 × 109
Interquartile range (IQR)59363715

Descriptive statistics

Standard deviation1.6707772 × 108
Coefficient of variation (CV)2.4090179
Kurtosis375.28643
Mean69355115
Median Absolute Deviation (MAD)28524485
Skewness14.474863
Sum6.9355115 × 1011
Variance2.7914964 × 1016
MonotonicityNot monotonic
2023-12-13T01:18:40.917085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23228280 50
 
0.5%
5904080 39
 
0.4%
34907990 33
 
0.3%
40738770 32
 
0.3%
22489260 31
 
0.3%
51927400 29
 
0.3%
52829760 29
 
0.3%
36131880 27
 
0.3%
726000 27
 
0.3%
33471370 27
 
0.3%
Other values (7609) 9676
96.8%
ValueCountFrequency (%)
31000 1
< 0.1%
32000 1
< 0.1%
48600 1
< 0.1%
52800 1
< 0.1%
55800 1
< 0.1%
60000 1
< 0.1%
60860 1
< 0.1%
64000 1
< 0.1%
69300 1
< 0.1%
70000 1
< 0.1%
ValueCountFrequency (%)
6440816480 1
< 0.1%
4896070400 1
< 0.1%
4378256640 1
< 0.1%
3510369000 1
< 0.1%
2415409840 1
< 0.1%
2402730310 1
< 0.1%
2153365920 1
< 0.1%
2057392520 1
< 0.1%
1948641090 1
< 0.1%
1857934860 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6138
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.04379
Minimum1
Maximum9915.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:18:41.093929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.8722
Q136.4455
median65.775
Q3134.87787
95-th percentile410.91
Maximum9915.05
Range9914.05
Interquartile range (IQR)98.432375

Descriptive statistics

Standard deviation302.06889
Coefficient of variation (CV)2.2368218
Kurtosis322.72069
Mean135.04379
Median Absolute Deviation (MAD)37.1634
Skewness13.759588
Sum1350437.9
Variance91245.616
MonotonicityNot monotonic
2023-12-13T01:18:41.252623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 59
 
0.6%
34.26 50
 
0.5%
8.11 39
 
0.4%
18.0 37
 
0.4%
33.1825 35
 
0.4%
38.91 33
 
0.3%
33.17 31
 
0.3%
49.4198 29
 
0.3%
40.6 29
 
0.3%
31.8169 28
 
0.3%
Other values (6128) 9630
96.3%
ValueCountFrequency (%)
1.0 3
< 0.1%
1.2 1
 
< 0.1%
1.4 1
 
< 0.1%
1.54 1
 
< 0.1%
1.56 1
 
< 0.1%
1.58 1
 
< 0.1%
1.63 1
 
< 0.1%
1.65 1
 
< 0.1%
1.68 1
 
< 0.1%
1.71 1
 
< 0.1%
ValueCountFrequency (%)
9915.05 1
< 0.1%
9415.52 1
< 0.1%
8344.34 1
< 0.1%
6593.76 1
< 0.1%
6565.14 1
< 0.1%
5229.6 1
< 0.1%
3763.55 1
< 0.1%
3736.7501 1
< 0.1%
3491.225 1
< 0.1%
3355.84 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020-06-01 10000
100.0%

Length

2023-12-13T01:18:41.414433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:18:41.510249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-01 10000
100.0%

Interactions

2023-12-13T01:18:35.374815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.153279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.688531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.217998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.883203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.467689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.474463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.230284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.771317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.347856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.966174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.836363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.574267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.330629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.857785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.482455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.066637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.940526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.681687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.429784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.956065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.592123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.170068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.074066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.787758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.514400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.032063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.684909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.253449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.177636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.910248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:32.601768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.126512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:33.786594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:34.353591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:35.281928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:18:41.578249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.7410.1240.4080.2890.1320.117
법정리0.7411.0000.3220.1940.0910.0000.000
특수지0.1240.3221.0000.8130.0000.0000.000
본번0.4080.1940.8131.0000.2670.0000.000
부번0.2890.0910.0000.2671.0000.0000.000
시가표준액0.1320.0000.0000.0000.0001.0000.960
연면적0.1170.0000.0000.0000.0000.9601.000
2023-12-13T01:18:41.702763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.538-0.1700.015-0.009-0.0760.117
법정리0.5381.0000.098-0.201-0.0590.0520.149
본번-0.1700.0981.000-0.2910.0160.0950.710
부번0.015-0.201-0.2911.0000.016-0.1110.000
시가표준액-0.009-0.0590.0160.0161.0000.7430.000
연면적-0.0760.0520.095-0.1110.7431.0000.000
특수지0.1170.1490.7100.0000.0000.0001.000

Missing values

2023-12-13T01:18:36.062096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:18:36.277121image/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
40293제주특별자치도제주시50110202013701304190901[ 비월길 2 ] 0000동 0901호2391304044.122020-06-01
32881제주특별자치도제주시50110202012201917611509[ 노연로 49 ] 0001동 1509호3883856036.12892020-06-01
83830제주특별자치도제주시501102020122013074090102제주특별자치도 제주시 노형동 3074 90동 102호547525019.912020-06-01
81899제주특별자치도제주시5011020201290183241102[ 도남서길 13 ] 0001동 0102호399414013.132020-06-01
69649제주특별자치도제주시50110202010901196352101제주특별자치도 제주시 용담2동 1963-5 2동 101호115980096.652020-06-01
6261제주특별자치도제주시50110202010601125900303[ 탑동로 38 ] 0000동 0303호794682025.972020-06-01
37186제주특별자치도제주시50110202013701274371633[ 신대로18길 47 ] 0001동 0633호6060559057.8852020-06-01
11495제주특별자치도제주시50110202010401103071301[ 고산동산1길 9-12 ] 0001동 0301호88743360152.482020-06-01
4299제주특별자치도제주시5011020201050153051605[ 서사로 94 ] 0001동 0605호2643237045.812020-06-01
23361제주특별자치도제주시50110202025340111711524[ 하귀9길 2 ] 0001동 0524호4089123050.17332020-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
4562제주특별자치도제주시50110202010501537101201[ 서사로 113 ] 0001동 0201호43396860145.142020-06-01
74648제주특별자치도제주시5011020201130174033101제주특별자치도 제주시 삼양1동 740-3 3동 101호85656000172.02020-06-01
25258제주특별자치도제주시501102020259241306101540[ 신북로 470 ] 0001동 0540호3704905044.26412020-06-01
69254제주특별자치도제주시5011020201080116341102[ 서문로4길 21 ] 0001동 0102호326196010.7022020-06-01
11101제주특별자치도제주시501102020104011768171101[ 광양10길 5 ] 0001동 0101호89604090184.22020-06-01
40192제주특별자치도제주시501102020137012832301006[ 은남4길 27 ] 0000동 1006호3156090047.462020-06-01
2758제주특별자치도제주시501102020104011011011004[ 중앙로 262 ] 0001동 1004호4023292041.39192020-06-01
71130제주특별자치도제주시50110202010801284118101제주특별자치도 제주시 용담1동 284-1 1동 8101호360640025.762020-06-01
34212제주특별자치도제주시50110202012201928011508[ 원노형4길 1 ] 0001동 1508호3499597033.8782020-06-01
10393제주특별자치도제주시50110202010101132911102[ 관덕로15길 12-1 ] 0001동 0102호1470937030.45422020-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번호물건지시가표준액연면적기준일자# duplicates
0제주특별자치도제주시501102020137012793101제주특별자치도 제주시 연동 279-3 1동 101호838451640786.542020-06-013
1제주특별자치도제주시50110202025029122402101제주특별자치도 제주시 한림읍 상대리 2240-2 101동 101호94668000196.02020-06-012