Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical5
Numeric7
Text2

Dataset

Description지방세법에 의한 표준지방세시스템을 통해 작성된 일반건축물에 대한 지방세 부과기준인 시가표준액을 시군구명 , 자치단체코드, 과세년도, 법정도, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적 등의 항목으로 제공합니다.
Author전라북도 부안군
URLhttps://www.data.go.kr/data/15080118/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세연도 has constant value ""Constant
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (94.3%)Imbalance
시가표준액 is highly skewed (γ1 = 52.85173812)Skewed
연면적 is highly skewed (γ1 = 42.24422423)Skewed
물건지 has unique valuesUnique
부번 has 2233 (22.3%) zerosZeros
has 245 (2.5%) zerosZeros

Reproduction

Analysis started2024-03-14 19:47:01.921804
Analysis finished2024-03-14 19:47:16.526407
Duration14.6 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
전라북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 10000
100.0%

Length

2024-03-15T04:47:16.636529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:47:16.806808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부안군 10000
100.0%

Length

2024-03-15T04:47:17.083070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:47:17.380817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부안군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
45800
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45800
2nd row45800
3rd row45800
4th row45800
5th row45800

Common Values

ValueCountFrequency (%)
45800 10000
100.0%

Length

2024-03-15T04:47:17.704201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:47:17.891568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45800 10000
100.0%

과세연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 10000
100.0%

Length

2024-03-15T04:47:18.193653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:47:18.490255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 10000
100.0%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.605
Minimum250
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:18.771748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1310
median350
Q3380
95-th percentile410
Maximum420
Range170
Interquartile range (IQR)70

Descriptive statistics

Standard deviation54.275764
Coefficient of variation (CV)0.16124468
Kurtosis-0.97958752
Mean336.605
Median Absolute Deviation (MAD)30
Skewness-0.50754513
Sum3366050
Variance2945.8586
MonotonicityNot monotonic
2024-03-15T04:47:19.176371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 2303
23.0%
360 1362
13.6%
350 753
 
7.5%
370 708
 
7.1%
380 700
 
7.0%
410 637
 
6.4%
400 604
 
6.0%
330 593
 
5.9%
340 577
 
5.8%
320 541
 
5.4%
Other values (3) 1222
12.2%
ValueCountFrequency (%)
250 2303
23.0%
310 520
 
5.2%
320 541
 
5.4%
330 593
 
5.9%
340 577
 
5.8%
350 753
 
7.5%
360 1362
13.6%
370 708
 
7.1%
380 700
 
7.0%
390 511
 
5.1%
ValueCountFrequency (%)
420 191
 
1.9%
410 637
6.4%
400 604
6.0%
390 511
 
5.1%
380 700
7.0%
370 708
7.1%
360 1362
13.6%
350 753
7.5%
340 577
5.8%
330 593
5.9%

법정리
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.7554
Minimum21
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:19.545305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median23
Q325
95-th percentile29
Maximum32
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4887327
Coefficient of variation (CV)0.10476492
Kurtosis1.0692659
Mean23.7554
Median Absolute Deviation (MAD)1
Skewness1.1402667
Sum237554
Variance6.1937902
MonotonicityNot monotonic
2024-03-15T04:47:19.806489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22 2176
21.8%
24 1879
18.8%
21 1805
18.1%
23 1200
12.0%
25 938
9.4%
26 643
 
6.4%
27 460
 
4.6%
28 299
 
3.0%
29 232
 
2.3%
30 170
 
1.7%
Other values (2) 198
 
2.0%
ValueCountFrequency (%)
21 1805
18.1%
22 2176
21.8%
23 1200
12.0%
24 1879
18.8%
25 938
9.4%
26 643
 
6.4%
27 460
 
4.6%
28 299
 
3.0%
29 232
 
2.3%
30 170
 
1.7%
ValueCountFrequency (%)
32 155
 
1.6%
31 43
 
0.4%
30 170
 
1.7%
29 232
 
2.3%
28 299
 
3.0%
27 460
 
4.6%
26 643
 
6.4%
25 938
9.4%
24 1879
18.8%
23 1200
12.0%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9888 
2
 
110
7
 
2

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 9888
98.9%
2 110
 
1.1%
7 2
 
< 0.1%

Length

2024-03-15T04:47:20.014237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:47:20.327462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9888
98.9%
2 110
 
1.1%
7 2
 
< 0.1%

본번
Real number (ℝ)

Distinct1412
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511.8683
Minimum1
Maximum4599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:20.677747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1199
median427
Q3679
95-th percentile1159
Maximum4599
Range4598
Interquartile range (IQR)480

Descriptive statistics

Standard deviation511.65469
Coefficient of variation (CV)0.99958269
Kurtosis20.884326
Mean511.8683
Median Absolute Deviation (MAD)235
Skewness3.6565295
Sum5118683
Variance261790.53
MonotonicityNot monotonic
2024-03-15T04:47:21.121732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574 183
 
1.8%
788 105
 
1.1%
769 97
 
1.0%
1 81
 
0.8%
132 72
 
0.7%
374 71
 
0.7%
455 60
 
0.6%
257 54
 
0.5%
12 53
 
0.5%
42 46
 
0.5%
Other values (1402) 9178
91.8%
ValueCountFrequency (%)
1 81
0.8%
2 21
 
0.2%
3 18
 
0.2%
4 19
 
0.2%
5 19
 
0.2%
6 9
 
0.1%
7 18
 
0.2%
8 12
 
0.1%
9 13
 
0.1%
10 17
 
0.2%
ValueCountFrequency (%)
4599 1
< 0.1%
4557 2
< 0.1%
4518 1
< 0.1%
4515 1
< 0.1%
4514 1
< 0.1%
4513 1
< 0.1%
4511 1
< 0.1%
4510 1
< 0.1%
4466 2
< 0.1%
4455 2
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct189
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5096
Minimum0
Maximum264
Zeros2233
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:21.562581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile61
Maximum264
Range264
Interquartile range (IQR)8

Descriptive statistics

Standard deviation25.904015
Coefficient of variation (CV)2.2506443
Kurtosis23.272756
Mean11.5096
Median Absolute Deviation (MAD)3
Skewness4.3403499
Sum115096
Variance671.01801
MonotonicityNot monotonic
2024-03-15T04:47:21.920434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2233
22.3%
1 1810
18.1%
2 758
 
7.6%
3 682
 
6.8%
4 554
 
5.5%
5 447
 
4.5%
6 360
 
3.6%
7 275
 
2.8%
8 252
 
2.5%
9 226
 
2.3%
Other values (179) 2403
24.0%
ValueCountFrequency (%)
0 2233
22.3%
1 1810
18.1%
2 758
 
7.6%
3 682
 
6.8%
4 554
 
5.5%
5 447
 
4.5%
6 360
 
3.6%
7 275
 
2.8%
8 252
 
2.5%
9 226
 
2.3%
ValueCountFrequency (%)
264 2
< 0.1%
258 1
< 0.1%
257 1
< 0.1%
232 1
< 0.1%
230 1
< 0.1%
222 1
< 0.1%
220 2
< 0.1%
219 2
< 0.1%
218 2
< 0.1%
217 2
< 0.1%


Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.6849
Minimum0
Maximum7003
Zeros245
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:22.384897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum7003
Range7003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation549.70419
Coefficient of variation (CV)12.032514
Kurtosis156.08415
Mean45.6849
Median Absolute Deviation (MAD)0
Skewness12.566667
Sum456849
Variance302174.69
MonotonicityNot monotonic
2024-03-15T04:47:22.644838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8231
82.3%
2 1006
 
10.1%
0 245
 
2.5%
3 181
 
1.8%
4 67
 
0.7%
5 32
 
0.3%
7001 27
 
0.3%
7002 25
 
0.2%
6 17
 
0.2%
301 12
 
0.1%
Other values (74) 157
 
1.6%
ValueCountFrequency (%)
0 245
 
2.5%
1 8231
82.3%
2 1006
 
10.1%
3 181
 
1.8%
4 67
 
0.7%
5 32
 
0.3%
6 17
 
0.2%
7 8
 
0.1%
8 9
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
7003 10
 
0.1%
7002 25
0.2%
7001 27
0.3%
700 1
 
< 0.1%
600 1
 
< 0.1%
500 1
 
< 0.1%
400 1
 
< 0.1%
301 12
0.1%
300 1
 
< 0.1%
200 1
 
< 0.1%


Text

Distinct250
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T04:47:24.164049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters40000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)1.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 9129
91.3%
0101 67
 
0.7%
0102 50
 
0.5%
0103 40
 
0.4%
0104 38
 
0.4%
0201 37
 
0.4%
0202 26
 
0.3%
0105 26
 
0.3%
0106 22
 
0.2%
0301 21
 
0.2%
Other values (240) 544
 
5.4%
2024-03-15T04:47:25.859490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 36564
91.4%
0 1485
 
3.7%
1 827
 
2.1%
2 370
 
0.9%
3 207
 
0.5%
4 156
 
0.4%
8 140
 
0.4%
5 106
 
0.3%
6 83
 
0.2%
7 58
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39996
> 99.9%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 36564
91.4%
0 1485
 
3.7%
1 827
 
2.1%
2 370
 
0.9%
3 207
 
0.5%
4 156
 
0.4%
8 140
 
0.4%
5 106
 
0.3%
6 83
 
0.2%
7 58
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39996
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 36564
91.4%
0 1485
 
3.7%
1 827
 
2.1%
2 370
 
0.9%
3 207
 
0.5%
4 156
 
0.4%
8 140
 
0.4%
5 106
 
0.3%
6 83
 
0.2%
7 58
 
0.1%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 36564
91.4%
0 1485
 
3.7%
1 827
 
2.1%
2 370
 
0.9%
3 207
 
0.5%
4 156
 
0.4%
8 140
 
0.4%
5 106
 
0.3%
6 83
 
0.2%
7 58
 
0.1%

물건지
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T04:47:27.270175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length31
Mean length30.9817
Min length30

Characters and Unicode

Total characters309817
Distinct characters117
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row계화면 창북리 1 0889-0011 0001동 9999호
2nd row위도면 진리 1 0252-0001 0002동 9999호
3rd row부안읍 내요리 1 0381-0011 0001동 9999호
4th row부안읍 봉덕리 1 0786-0010 0001동 9999호
5th row부안읍 모산리 1 0339-0001 0001동 9999호
ValueCountFrequency (%)
1 9888
 
16.5%
9999호 9129
 
15.2%
0001동 8231
 
13.7%
부안읍 2303
 
3.8%
변산면 1362
 
2.3%
0002동 1006
 
1.7%
보안면 753
 
1.3%
진서면 708
 
1.2%
봉덕리 706
 
1.2%
백산면 700
 
1.2%
Other values (6853) 25214
42.0%
2024-03-15T04:47:29.049654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 74356
24.0%
50000
16.1%
9 39264
12.7%
1 26379
 
8.5%
11345
 
3.7%
10208
 
3.3%
10000
 
3.2%
- 10000
 
3.2%
7697
 
2.5%
2 6564
 
2.1%
Other values (107) 64004
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 169996
54.9%
Other Letter 79817
25.8%
Space Separator 50000
 
16.1%
Dash Punctuation 10000
 
3.2%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11345
14.2%
10208
12.8%
10000
12.5%
7697
 
9.6%
3797
 
4.8%
3180
 
4.0%
2735
 
3.4%
2346
 
2.9%
2303
 
2.9%
1971
 
2.5%
Other values (94) 24235
30.4%
Decimal Number
ValueCountFrequency (%)
0 74356
43.7%
9 39264
23.1%
1 26379
 
15.5%
2 6564
 
3.9%
3 4748
 
2.8%
4 4576
 
2.7%
5 4134
 
2.4%
7 3630
 
2.1%
6 3295
 
1.9%
8 3050
 
1.8%
Space Separator
ValueCountFrequency (%)
50000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229996
74.2%
Hangul 79817
 
25.8%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11345
14.2%
10208
12.8%
10000
12.5%
7697
 
9.6%
3797
 
4.8%
3180
 
4.0%
2735
 
3.4%
2346
 
2.9%
2303
 
2.9%
1971
 
2.5%
Other values (94) 24235
30.4%
Common
ValueCountFrequency (%)
0 74356
32.3%
50000
21.7%
9 39264
17.1%
1 26379
 
11.5%
- 10000
 
4.3%
2 6564
 
2.9%
3 4748
 
2.1%
4 4576
 
2.0%
5 4134
 
1.8%
7 3630
 
1.6%
Other values (2) 6345
 
2.8%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230000
74.2%
Hangul 79817
 
25.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74356
32.3%
50000
21.7%
9 39264
17.1%
1 26379
 
11.5%
- 10000
 
4.3%
2 6564
 
2.9%
3 4748
 
2.1%
4 4576
 
2.0%
5 4134
 
1.8%
7 3630
 
1.6%
Other values (3) 6349
 
2.8%
Hangul
ValueCountFrequency (%)
11345
14.2%
10208
12.8%
10000
12.5%
7697
 
9.6%
3797
 
4.8%
3180
 
4.0%
2735
 
3.4%
2346
 
2.9%
2303
 
2.9%
1971
 
2.5%
Other values (94) 24235
30.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8385
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0403815 × 108
Minimum42000
Maximum5.2089539 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:29.524890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42000
5-th percentile646730
Q12995950
median12487215
Q359076777
95-th percentile4.1503392 × 108
Maximum5.2089539 × 1010
Range5.2089497 × 1010
Interquartile range (IQR)56080827

Descriptive statistics

Standard deviation6.5669876 × 108
Coefficient of variation (CV)6.3120958
Kurtosis3966.9279
Mean1.0403815 × 108
Median Absolute Deviation (MAD)11143490
Skewness52.851738
Sum1.0403815 × 1012
Variance4.3125327 × 1017
MonotonicityNot monotonic
2024-03-15T04:47:30.019991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11985243 32
 
0.3%
22910220 28
 
0.3%
20323591 24
 
0.2%
700000 24
 
0.2%
560000 19
 
0.2%
633600 18
 
0.2%
76923000 18
 
0.2%
420000 16
 
0.2%
980000 16
 
0.2%
705600 16
 
0.2%
Other values (8375) 9789
97.9%
ValueCountFrequency (%)
42000 1
 
< 0.1%
48000 1
 
< 0.1%
72000 4
< 0.1%
90000 2
< 0.1%
126000 1
 
< 0.1%
134400 1
 
< 0.1%
138500 1
 
< 0.1%
140000 2
< 0.1%
142800 1
 
< 0.1%
144000 1
 
< 0.1%
ValueCountFrequency (%)
52089539139 1
< 0.1%
12632566820 1
< 0.1%
9819808320 1
< 0.1%
9743268000 1
< 0.1%
9465042017 1
< 0.1%
8435772953 1
< 0.1%
7390809380 1
< 0.1%
7068080681 1
< 0.1%
7032607250 1
< 0.1%
6486889590 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6128
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.31473
Minimum0.8772
Maximum96468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T04:47:30.436199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8772
5-th percentile18
Q154
median121.895
Q3318.9475
95-th percentile1558.2105
Maximum96468
Range96467.123
Interquartile range (IQR)264.9475

Descriptive statistics

Standard deviation1549.6521
Coefficient of variation (CV)4.0533413
Kurtosis2423.9654
Mean382.31473
Median Absolute Deviation (MAD)85.895
Skewness42.244224
Sum3823147.3
Variance2401421.6
MonotonicityNot monotonic
2024-03-15T04:47:30.917481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 251
 
2.5%
16.5 60
 
0.6%
10.0 57
 
0.6%
100.0 49
 
0.5%
36.0 49
 
0.5%
70.0 48
 
0.5%
30.0 44
 
0.4%
40.0 44
 
0.4%
60.0 42
 
0.4%
330.0 41
 
0.4%
Other values (6118) 9315
93.2%
ValueCountFrequency (%)
0.8772 1
 
< 0.1%
2.0 3
< 0.1%
2.5 1
 
< 0.1%
3.0 1
 
< 0.1%
3.24 1
 
< 0.1%
3.6 2
< 0.1%
3.75 1
 
< 0.1%
4.136 1
 
< 0.1%
4.2 1
 
< 0.1%
4.39 1
 
< 0.1%
ValueCountFrequency (%)
96468.0 1
< 0.1%
86071.35 1
< 0.1%
20506.92 1
< 0.1%
19841.0 1
< 0.1%
17088.16 1
< 0.1%
16570.72 1
< 0.1%
15054.74 1
< 0.1%
13030.31 1
< 0.1%
12335.2402 1
< 0.1%
12281.37 1
< 0.1%

Interactions

2024-03-15T04:47:13.978346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:03.168701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:04.875560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:06.694996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:08.230849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:10.246226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:12.365873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:14.255210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:03.448516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:05.100907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:06.967616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:08.408716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:10.536422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:12.656157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:14.415584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:03.723696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:05.360918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:07.228345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:08.577561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:10.853387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:12.852753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:14.646687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:03.998212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:05.566037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:07.476325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:08.830675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:11.161919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:13.120784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:14.933079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:04.202247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:05.887583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:07.730619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:09.292181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:11.451931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:13.395686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:15.165056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:04.381882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:06.161077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:07.914086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:09.671641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:11.740215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:13.607139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:15.327826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:04.622719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:06.427967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:08.072086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:09.949454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:12.038531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:47:13.783318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:47:31.242665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.3830.0740.3100.1600.1760.0400.020
법정리0.3831.0000.1000.3530.2280.2110.0000.000
특수지0.0740.1001.0000.1060.0000.0000.0000.000
본번0.3100.3530.1061.0000.1170.1300.0420.098
부번0.1600.2280.0000.1171.0000.0000.0000.000
0.1760.2110.0000.1300.0001.0000.0000.000
시가표준액0.0400.0000.0000.0420.0000.0001.0000.680
연면적0.0200.0000.0000.0980.0000.0000.6801.000
2024-03-15T04:47:31.555855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.000-0.0700.074-0.0420.063-0.114-0.0020.053
법정리-0.0701.0000.000-0.103-0.028-0.0960.0310.059
본번0.0740.0001.000-0.082-0.0390.1310.1190.063
부번-0.042-0.103-0.0821.000-0.1260.008-0.0710.000
0.063-0.028-0.039-0.1261.000-0.065-0.0560.000
시가표준액-0.114-0.0960.1310.008-0.0651.0000.6650.000
연면적-0.0020.0310.119-0.071-0.0560.6651.0000.000
특수지0.0530.0590.0630.0000.0000.0000.0001.000

Missing values

2024-03-15T04:47:15.610961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:47:16.288813image/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

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적
4374전라북도부안군4580020233402218891119999계화면 창북리 1 0889-0011 0001동 9999호1058000084.64
10310전라북도부안군458002023420211252129999위도면 진리 1 0252-0001 0002동 9999호816480077.76
2235전라북도부안군4580020232503013811119999부안읍 내요리 1 0381-0011 0001동 9999호2566642331908.45
1740전라북도부안군4580020232502417861019999부안읍 봉덕리 1 0786-0010 0001동 9999호88847771283.42
2029전라북도부안군458002023250261339119999부안읍 모산리 1 0339-0001 0001동 9999호1314000131.4
5705전라북도부안군45800202336022120229999변산면 격포리 1 0020-0002 0002동 9999호140160012.0
2725전라북도부안군4580020233102614861819999주산면 사산리 1 0486-0018 0001동 9999호1034187085.47
1982전라북도부안군458002023250251200419999부안읍 신운리 1 0200-0004 0001동 9999호207993450456.73
6233전라북도부안군4580020233602217691310189변산면 격포리 1 0769-0013 0001동 0189호1206677918.3514
860전라북도부안군458002023250221506519999부안읍 서외리 1 0506-0005 0001동 9999호263810460530.55
시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적
3814전라북도부안군458002023330231765019999행안면 진동리 1 0765-0000 0001동 9999호437346049.14
6181전라북도부안군4580020233602217691310137변산면 격포리 1 0769-0013 0001동 0137호1198524318.2274
8158전라북도부안군458002023380281257419999백산면 평교리 1 0257-0004 0001동 9999호91000065.0
7919전라북도부안군4580020233802419571119999백산면 대수리 1 0957-0011 0001동 9999호919600002090.0
9829전라북도부안군458002023410211839319999줄포면 줄포리 1 0839-0003 0001동 9999호298742042.56
2113전라북도부안군4580020232502817319999부안읍 연곡리 1 0007-0003 0001동 9999호868445271258.7
9028전라북도부안군458002023400211657519999하서면 청호리 1 0657-0005 0001동 9999호224172057.48
3088전라북도부안군458002023320221616119999동진면 내기리 1 0616-0001 0001동 9999호27725760134.08
981전라북도부안군45800202325023111219999부안읍 선은리 1 0011-0002 0001동 9999호323340063.4
8625전라북도부안군4580020233902217341519999상서면 가오리 1 0734-0015 0001동 9999호597890001133.9