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
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15080118

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 = 53.78764154)Skewed
연면적 is highly skewed (γ1 = 42.11509402)Skewed
물건지 has unique valuesUnique
부번 has 2216 (22.2%) zerosZeros
동 has 249 (2.5%) zerosZeros

Reproduction

Analysis started2024-03-14 03:26:57.695821
Analysis finished2024-03-14 03:27:03.722074
Duration6.03 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

2024-03-14T12:27:03.776990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:03.886948image/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

2024-03-14T12:27:03.970335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:04.039057image/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

2024-03-14T12:27:04.110332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:04.178557image/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

2024-03-14T12:27:04.249258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:04.318881image/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%
Mean336.538
Minimum250
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:04.380717image/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 deviation54.315648
Coefficient of variation (CV)0.16139529
Kurtosis-0.98435618
Mean336.538
Median Absolute Deviation (MAD)30
Skewness-0.5069017
Sum3365380
Variance2950.1896
MonotonicityNot monotonic
2024-03-14T12:27:04.471895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 2311
23.1%
360 1360
13.6%
350 763
 
7.6%
370 705
 
7.0%
380 702
 
7.0%
410 631
 
6.3%
400 603
 
6.0%
330 588
 
5.9%
340 571
 
5.7%
320 537
 
5.4%
Other values (3) 1229
12.3%
ValueCountFrequency (%)
250 2311
23.1%
310 520
 
5.2%
320 537
 
5.4%
330 588
 
5.9%
340 571
 
5.7%
350 763
 
7.6%
360 1360
13.6%
370 705
 
7.0%
380 702
 
7.0%
390 518
 
5.2%
ValueCountFrequency (%)
420 191
 
1.9%
410 631
6.3%
400 603
6.0%
390 518
 
5.2%
380 702
7.0%
370 705
7.0%
360 1360
13.6%
350 763
7.6%
340 571
5.7%
330 588
5.9%

법정리
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.7564
Minimum21
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:04.570121image/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 deviation2.4867001
Coefficient of variation (CV)0.10467496
Kurtosis1.051645
Mean23.7564
Median Absolute Deviation (MAD)1
Skewness1.137448
Sum237564
Variance6.1836774
MonotonicityNot monotonic
2024-03-14T12:27:04.877892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22 2199
22.0%
24 1877
18.8%
21 1788
17.9%
23 1199
12.0%
25 930
9.3%
26 641
 
6.4%
27 463
 
4.6%
28 302
 
3.0%
29 237
 
2.4%
30 169
 
1.7%
Other values (2) 195
 
1.9%
ValueCountFrequency (%)
21 1788
17.9%
22 2199
22.0%
23 1199
12.0%
24 1877
18.8%
25 930
9.3%
26 641
 
6.4%
27 463
 
4.6%
28 302
 
3.0%
29 237
 
2.4%
30 169
 
1.7%
ValueCountFrequency (%)
32 152
 
1.5%
31 43
 
0.4%
30 169
 
1.7%
29 237
 
2.4%
28 302
 
3.0%
27 463
 
4.6%
26 641
 
6.4%
25 930
9.3%
24 1877
18.8%
23 1199
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

2024-03-14T12:27:04.964066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:05.085171image/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%
Mean511.2082
Minimum1
Maximum4599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:05.236930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1200
median427.5
Q3676
95-th percentile1157.05
Maximum4599
Range4598
Interquartile range (IQR)476

Descriptive statistics

Standard deviation508.59204
Coefficient of variation (CV)0.99488241
Kurtosis20.970034
Mean511.2082
Median Absolute Deviation (MAD)234.5
Skewness3.6532815
Sum5112082
Variance258665.87
MonotonicityNot monotonic
2024-03-14T12:27:05.346035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574 187
 
1.9%
788 103
 
1.0%
769 101
 
1.0%
1 79
 
0.8%
374 71
 
0.7%
132 71
 
0.7%
455 60
 
0.6%
12 55
 
0.5%
257 51
 
0.5%
42 47
 
0.5%
Other values (1402) 9175
91.8%
ValueCountFrequency (%)
1 79
0.8%
2 19
 
0.2%
3 19
 
0.2%
4 17
 
0.2%
5 19
 
0.2%
6 9
 
0.1%
7 18
 
0.2%
8 13
 
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%
Mean11.4617
Minimum0
Maximum264
Zeros2216
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:05.452854image/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 deviation25.8558
Coefficient of variation (CV)2.2558434
Kurtosis23.428556
Mean11.4617
Median Absolute Deviation (MAD)3
Skewness4.3568137
Sum114617
Variance668.52239
MonotonicityNot monotonic
2024-03-14T12:27:05.559259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2216
22.2%
1 1818
18.2%
2 763
 
7.6%
3 678
 
6.8%
4 564
 
5.6%
5 440
 
4.4%
6 360
 
3.6%
7 279
 
2.8%
8 257
 
2.6%
9 232
 
2.3%
Other values (179) 2393
23.9%
ValueCountFrequency (%)
0 2216
22.2%
1 1818
18.2%
2 763
 
7.6%
3 678
 
6.8%
4 564
 
5.6%
5 440
 
4.4%
6 360
 
3.6%
7 279
 
2.8%
8 257
 
2.6%
9 232
 
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 3
< 0.1%
219 2
< 0.1%
218 2
< 0.1%
217 2
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3803
Minimum0
Maximum7003
Zeros249
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:05.669468image/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 deviation554.08889
Coefficient of variation (CV)11.946643
Kurtosis153.53285
Mean46.3803
Median Absolute Deviation (MAD)0
Skewness12.464946
Sum463803
Variance307014.5
MonotonicityNot monotonic
2024-03-14T12:27:05.784121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8228
82.3%
2 1000
 
10.0%
0 249
 
2.5%
3 182
 
1.8%
4 70
 
0.7%
5 31
 
0.3%
7002 27
 
0.3%
7001 26
 
0.3%
6 18
 
0.2%
301 12
 
0.1%
Other values (74) 157
 
1.6%
ValueCountFrequency (%)
0 249
 
2.5%
1 8228
82.3%
2 1000
 
10.0%
3 182
 
1.8%
4 70
 
0.7%
5 31
 
0.3%
6 18
 
0.2%
7 8
 
0.1%
8 8
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
7003 10
 
0.1%
7002 27
0.3%
7001 26
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%

호
Text

Distinct250
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:27:06.133224image/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

Unique145 ?
Unique (%)1.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 9118
91.2%
0101 65
 
0.7%
0102 51
 
0.5%
0103 44
 
0.4%
0104 37
 
0.4%
0201 35
 
0.4%
0202 28
 
0.3%
0105 28
 
0.3%
0106 24
 
0.2%
0301 21
 
0.2%
Other values (240) 549
 
5.5%
2024-03-14T12:27:06.549121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 36518
91.3%
0 1508
 
3.8%
1 832
 
2.1%
2 381
 
1.0%
3 208
 
0.5%
4 155
 
0.4%
8 136
 
0.3%
5 112
 
0.3%
6 86
 
0.2%
7 61
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39997
> 99.9%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 36518
91.3%
0 1508
 
3.8%
1 832
 
2.1%
2 381
 
1.0%
3 208
 
0.5%
4 155
 
0.4%
8 136
 
0.3%
5 112
 
0.3%
6 86
 
0.2%
7 61
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39997
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 36518
91.3%
0 1508
 
3.8%
1 832
 
2.1%
2 381
 
1.0%
3 208
 
0.5%
4 155
 
0.4%
8 136
 
0.3%
5 112
 
0.3%
6 86
 
0.2%
7 61
 
0.2%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 36518
91.3%
0 1508
 
3.8%
1 832
 
2.1%
2 381
 
1.0%
3 208
 
0.5%
4 155
 
0.4%
8 136
 
0.3%
5 112
 
0.3%
6 86
 
0.2%
7 61
 
0.2%

물건지
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:27:06.885517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length31
Mean length30.9807
Min length30

Characters and Unicode

Total characters309807
Distinct characters116
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 0743-0006 0001동 9999호
2nd row줄포면 줄포리 1 0758-0003 0002동 9999호
3rd row행안면 진동리 1 0986-0017 0002동 9999호
4th row진서면 운호리 1 0400-0007 0001동 9999호
5th row부안읍 동중리 1 0164-0006 0001동 9999호
ValueCountFrequency (%)
1 9888
 
16.5%
9999호 9118
 
15.2%
0001동 8228
 
13.7%
부안읍 2311
 
3.9%
변산면 1360
 
2.3%
0002동 1000
 
1.7%
보안면 763
 
1.3%
봉덕리 713
 
1.2%
진서면 705
 
1.2%
백산면 702
 
1.2%
Other values (6835) 25212
42.0%
2024-03-14T12:27:07.304734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 74355
24.0%
50000
16.1%
9 39213
12.7%
1 26374
 
8.5%
동 11352
 
3.7%
호 10206
 
3.3%
리 10000
 
3.2%
- 10000
 
3.2%
면 7689
 
2.5%
2 6569
 
2.1%
Other values (106) 64049
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 169997
54.9%
Other Letter 79807
25.8%
Space Separator 50000
 
16.1%
Dash Punctuation 10000
 
3.2%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11352
14.2%
호 10206
12.8%
리 10000
12.5%
면 7689
 
9.6%
안 3808
 
4.8%
산 3180
 
4.0%
서 2761
 
3.5%
부 2355
 
3.0%
읍 2311
 
2.9%
포 1958
 
2.5%
Other values (93) 24187
30.3%
Decimal Number
ValueCountFrequency (%)
0 74355
43.7%
9 39213
23.1%
1 26374
 
15.5%
2 6569
 
3.9%
3 4774
 
2.8%
4 4559
 
2.7%
5 4157
 
2.4%
7 3650
 
2.1%
6 3281
 
1.9%
8 3065
 
1.8%
Space Separator
ValueCountFrequency (%)
50000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229997
74.2%
Hangul 79807
 
25.8%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11352
14.2%
호 10206
12.8%
리 10000
12.5%
면 7689
 
9.6%
안 3808
 
4.8%
산 3180
 
4.0%
서 2761
 
3.5%
부 2355
 
3.0%
읍 2311
 
2.9%
포 1958
 
2.5%
Other values (93) 24187
30.3%
Common
ValueCountFrequency (%)
0 74355
32.3%
50000
21.7%
9 39213
17.0%
1 26374
 
11.5%
- 10000
 
4.3%
2 6569
 
2.9%
3 4774
 
2.1%
4 4559
 
2.0%
5 4157
 
1.8%
7 3650
 
1.6%
Other values (2) 6346
 
2.8%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230000
74.2%
Hangul 79807
 
25.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74355
32.3%
50000
21.7%
9 39213
17.0%
1 26374
 
11.5%
- 10000
 
4.3%
2 6569
 
2.9%
3 4774
 
2.1%
4 4559
 
2.0%
5 4157
 
1.8%
7 3650
 
1.6%
Other values (3) 6349
 
2.8%
Hangul
ValueCountFrequency (%)
동 11352
14.2%
호 10206
12.8%
리 10000
12.5%
면 7689
 
9.6%
안 3808
 
4.8%
산 3180
 
4.0%
서 2761
 
3.5%
부 2355
 
3.0%
읍 2311
 
2.9%
포 1958
 
2.5%
Other values (93) 24187
30.3%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8368
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0316703 × 108
Minimum42000
Maximum5.2089539 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:07.510655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42000
5-th percentile633600
Q12995950
median12584150
Q359238870
95-th percentile4.1503392 × 108
Maximum5.2089539 × 1010
Range5.2089497 × 1010
Interquartile range (IQR)56242920

Descriptive statistics

Standard deviation6.5246804 × 108
Coefficient of variation (CV)6.3243853
Kurtosis4070.4094
Mean1.0316703 × 108
Median Absolute Deviation (MAD)11226850
Skewness53.787642
Sum1.0316703 × 1012
Variance4.2571454 × 1017
MonotonicityNot monotonic
2024-03-14T12:27:07.624913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11985243 35
 
0.4%
22910220 27
 
0.3%
20323591 26
 
0.3%
700000 23
 
0.2%
560000 19
 
0.2%
633600 19
 
0.2%
76923000 18
 
0.2%
420000 16
 
0.2%
2442000 16
 
0.2%
840000 15
 
0.1%
Other values (8358) 9786
97.9%
ValueCountFrequency (%)
42000 1
 
< 0.1%
48000 1
 
< 0.1%
72000 4
< 0.1%
90000 2
< 0.1%
126000 1
 
< 0.1%
133120 1
 
< 0.1%
134400 1
 
< 0.1%
138500 1
 
< 0.1%
140000 2
< 0.1%
142800 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 

Distinct6153
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384.1936
Minimum0.8772
Maximum96468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:07.735600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8772
5-th percentile18
Q153.7575
median122.71
Q3320.51
95-th percentile1580.672
Maximum96468
Range96467.123
Interquartile range (IQR)266.7525

Descriptive statistics

Standard deviation1551.2436
Coefficient of variation (CV)4.0376612
Kurtosis2413.8268
Mean384.1936
Median Absolute Deviation (MAD)86.92
Skewness42.115094
Sum3841936
Variance2406356.7
MonotonicityNot monotonic
2024-03-14T12:27:07.849850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 247
 
2.5%
16.5 63
 
0.6%
10.0 55
 
0.5%
36.0 53
 
0.5%
100.0 49
 
0.5%
70.0 48
 
0.5%
30.0 46
 
0.5%
40.0 45
 
0.4%
50.0 43
 
0.4%
330.0 42
 
0.4%
Other values (6143) 9309
93.1%
ValueCountFrequency (%)
0.8772 1
 
< 0.1%
2.0 4
< 0.1%
2.5 1
 
< 0.1%
3.0 1
 
< 0.1%
3.2 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%
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

2024-03-14T12:27:02.846110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.071081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.915510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.508800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.126563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.682313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.247925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.981632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.178563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.991776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.616382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.204128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.795759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.323039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:03.078574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.264901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.080865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.736998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.281601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.873126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.397665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:03.146876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.612492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.154985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.829865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.352205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.943945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.485880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:03.214006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.684624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.232049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.911011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.437344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.013740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.561826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:03.290712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.767157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.350420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.987069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.521542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.096355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.648593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:03.366060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:26:59.840859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:00.435876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.057653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:01.597117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.172222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:02.737949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:27:07.924477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.3820.0690.3090.1590.1780.0360.020
법정리0.3821.0000.0960.3470.2320.2130.0000.000
특수지0.0690.0961.0000.1070.0000.0000.0000.000
본번0.3090.3470.1071.0000.1180.1300.0460.100
부번0.1590.2320.0000.1181.0000.0000.0000.000
동0.1780.2130.0000.1300.0001.0000.0000.000
시가표준액0.0360.0000.0000.0460.0000.0001.0000.682
연면적0.0200.0000.0000.1000.0000.0000.6821.000
2024-03-14T12:27:08.016238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번동시가표준액연면적특수지
법정동1.000-0.0650.075-0.0440.064-0.119-0.0030.050
법정리-0.0651.0000.003-0.102-0.032-0.0920.0370.057
본번0.0750.0031.000-0.076-0.0390.1310.1230.063
부번-0.044-0.102-0.0761.000-0.1310.006-0.0740.000
동0.064-0.032-0.039-0.1311.000-0.066-0.0570.000
시가표준액-0.119-0.0920.1310.006-0.0661.0000.6640.000
연면적-0.0030.0370.123-0.074-0.0570.6641.0000.000
특수지0.0500.0570.0630.0000.0000.0000.0001.000

Missing values

2024-03-14T12:27:03.468630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:27:03.640219image/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

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번동호물건지시가표준액연면적
4306전라북도부안군458002023340221743619999계화면 창북리 1 0743-0006 0001동 9999호1875900096.2
9817전라북도부안군458002023410211758329999줄포면 줄포리 1 0758-0003 0002동 9999호49332800126.5
3843전라북도부안군4580020233302319861729999행안면 진동리 1 0986-0017 0002동 9999호296382240653.4
7324전라북도부안군458002023370231400719999진서면 운호리 1 0400-0007 0001동 9999호689400068.94
223전라북도부안군458002023250211164619999부안읍 동중리 1 0164-0006 0001동 9999호146069280188.72
4462전라북도부안군45800202334022142321719999계화면 창북리 1 4232-0017 0001동 9999호3168009.0
1786전라북도부안군458002023250241793010105부안읍 봉덕리 1 0793-0000 0001동 0105호67146016107.68
6578전라북도부안군4580020233602414034219999변산면 운산리 1 0403-0042 0001동 9999호90000060.0
3070전라북도부안군4580020233202213461519999동진면 내기리 1 0346-0015 0001동 9999호3198580088.0
9498전라북도부안군458002023400251600019999하서면 장신리 1 0600-0000 0001동 9999호21000030.0
시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번동호물건지시가표준액연면적
4190전라북도부안군4580020233402115551229999계화면 계화리 1 0555-0012 0002동 9999호200000010.0
2382전라북도부안군4580020232503213651029999부안읍 신흥리 1 0365-0010 0002동 9999호1338875094.6
9932전라북도부안군458002023410221860119999줄포면 장동리 1 0860-0001 0001동 9999호37203900347.7
3558전라북도부안군458002023330211318229999행안면 역리 1 0318-0002 0002동 9999호177448433492.93
2997전라북도부안군458002023320211355619999동진면 봉황리 1 0355-0006 0001동 9999호1919960038.5
1142전라북도부안군458002023250231322619999부안읍 선은리 1 0322-0006 0001동 9999호342000068.4
3790전라북도부안군458002023330231473019999행안면 진동리 1 0473-0000 0001동 9999호1536000040.0
9356전라북도부안군458002023400241780129999하서면 백련리 1 0780-0001 0002동 9999호1150800096.0
803전라북도부안군4580020232502214553329999부안읍 서외리 1 0455-0033 0002동 9999호241584797378.72
1842전라북도부안군458002023250241808000104부안읍 봉덕리 1 0808-0000 0000동 0104호36027200142.4