Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells832
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory147.0 B

Variable types

Numeric8
Text2
Categorical5
DateTime1

Dataset

Description전라북도 전주시 개별주택가격정보를 제공하며, 고유번호, 법정동명, 지번, 기준연도, 토지대장면적, 주택가격 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3069068

Alerts

기준년도 has constant value ""Constant
기준월 has constant value ""Constant
데이터기준일자 has constant value ""Constant
특수지구분코드 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
특수지구분명 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
토지대장면적 is highly overall correlated with 건물전체연면적 and 3 other fieldsHigh correlation
산정대지면적 is highly overall correlated with 건물산정연면적 and 1 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 산정대지면적 and 1 other fieldsHigh correlation
주택가격 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
동명 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (97.7%)Imbalance
특수지구분명 is highly imbalanced (97.7%)Imbalance
동명 is highly imbalanced (75.4%)Imbalance
부번 has 832 (8.3%) missing valuesMissing
동코드 is highly skewed (γ1 = 99.98837425)Skewed
토지대장면적 is highly skewed (γ1 = 28.12924448)Skewed
동코드 has 4584 (45.8%) zerosZeros

Reproduction

Analysis started2024-03-14 03:23:50.073322
Analysis finished2024-03-14 03:23:58.526685
Duration8.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.511211 × 109
Minimum4.5111101 × 109
Maximum4.5113139 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:58.588055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 109
5-th percentile4.5111118 × 109
Q14.5111129 × 109
median4.5111147 × 109
Q34.5113107 × 109
95-th percentile4.5113129 × 109
Maximum4.5113139 × 109
Range203800
Interquartile range (IQR)197800

Descriptive statistics

Standard deviation99090.221
Coefficient of variation (CV)2.1965326 × 10-5
Kurtosis-1.9995345
Mean4.511211 × 109
Median Absolute Deviation (MAD)4200
Skewness0.021204325
Sum4.511211 × 1013
Variance9.8188719 × 109
MonotonicityNot monotonic
2024-03-14T12:23:58.731019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511310300 713
 
7.1%
4511310700 656
 
6.6%
4511310200 526
 
5.3%
4511114200 508
 
5.1%
4511310400 425
 
4.2%
4511112000 401
 
4.0%
4511114000 384
 
3.8%
4511112900 384
 
3.8%
4511113700 372
 
3.7%
4511112800 319
 
3.2%
Other values (73) 5312
53.1%
ValueCountFrequency (%)
4511110100 10
 
0.1%
4511110200 4
 
< 0.1%
4511110300 9
 
0.1%
4511110400 29
0.3%
4511110500 10
 
0.1%
4511110600 8
 
0.1%
4511110700 41
0.4%
4511110800 15
 
0.1%
4511110900 17
0.2%
4511111000 42
0.4%
ValueCountFrequency (%)
4511313900 90
0.9%
4511313800 19
 
0.2%
4511313700 43
0.4%
4511313600 10
 
0.1%
4511313500 7
 
0.1%
4511313400 9
 
0.1%
4511313300 53
0.5%
4511313200 49
0.5%
4511313100 105
1.1%
4511313000 100
1.0%
Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:23:58.957789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.2872
Min length10

Characters and Unicode

Total characters122872
Distinct characters67
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전주시 완산구 다가동4가
2nd row전주시 완산구 효자동3가
3rd row전주시 완산구 전동
4th row전주시 덕진구 용정동
5th row전주시 덕진구 팔복동1가
ValueCountFrequency (%)
전주시 10000
33.3%
완산구 5053
16.8%
덕진구 4947
16.5%
인후동1가 713
 
2.4%
금암동 656
 
2.2%
진북동 526
 
1.8%
효자동3가 508
 
1.7%
인후동2가 425
 
1.4%
중노송동 401
 
1.3%
효자동1가 384
 
1.3%
Other values (76) 6387
21.3%
2024-03-14T12:23:59.336814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20000
16.3%
10275
 
8.4%
10187
 
8.3%
10039
 
8.2%
10000
 
8.1%
10000
 
8.1%
5985
 
4.9%
5821
 
4.7%
5727
 
4.7%
5508
 
4.5%
Other values (57) 29330
23.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97148
79.1%
Space Separator 20000
 
16.3%
Decimal Number 5724
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10275
10.6%
10187
10.5%
10039
10.3%
10000
10.3%
10000
10.3%
5985
 
6.2%
5821
 
6.0%
5727
 
5.9%
5508
 
5.7%
5326
 
5.5%
Other values (52) 18280
18.8%
Decimal Number
ValueCountFrequency (%)
1 2568
44.9%
2 1941
33.9%
3 1083
18.9%
4 132
 
2.3%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97148
79.1%
Common 25724
 
20.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10275
10.6%
10187
10.5%
10039
10.3%
10000
10.3%
10000
10.3%
5985
 
6.2%
5821
 
6.0%
5727
 
5.9%
5508
 
5.7%
5326
 
5.5%
Other values (52) 18280
18.8%
Common
ValueCountFrequency (%)
20000
77.7%
1 2568
 
10.0%
2 1941
 
7.5%
3 1083
 
4.2%
4 132
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97148
79.1%
ASCII 25724
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20000
77.7%
1 2568
 
10.0%
2 1941
 
7.5%
3 1083
 
4.2%
4 132
 
0.5%
Hangul
ValueCountFrequency (%)
10275
10.6%
10187
10.5%
10039
10.3%
10000
10.3%
10000
10.3%
5985
 
6.2%
5821
 
6.0%
5727
 
5.9%
5508
 
5.7%
5326
 
5.5%
Other values (52) 18280
18.8%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9978 
2
 
22

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 9978
99.8%
2 22
 
0.2%

Length

2024-03-14T12:23:59.460913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:23:59.540233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9978
99.8%
2 22
 
0.2%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9978 
 
22

Length

Max length2
Median length2
Mean length1.9978
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9978
99.8%
22
 
0.2%

Length

2024-03-14T12:23:59.619687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:23:59.700500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9978
99.8%
22
 
0.2%

본번
Text

Distinct1473
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:24:00.002339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0682
Min length1

Characters and Unicode

Total characters30682
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)1.9%

Sample

1st row149
2nd row1728
3rd row302
4th row159
5th row173
ValueCountFrequency (%)
167 56
 
0.6%
226 50
 
0.5%
171 50
 
0.5%
728 48
 
0.5%
253 47
 
0.5%
322 45
 
0.4%
509 43
 
0.4%
744 42
 
0.4%
63 42
 
0.4%
175 37
 
0.4%
Other values (1435) 9576
95.4%
2024-03-14T12:24:00.481555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5312
17.3%
5 3522
11.5%
2 3405
11.1%
6 3204
10.4%
7 2958
9.6%
4 2942
9.6%
3 2859
9.3%
8 2262
7.4%
9 2126
6.9%
0 2020
 
6.6%
Other values (2) 72
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30610
99.8%
Other Letter 36
 
0.1%
Space Separator 36
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5312
17.4%
5 3522
11.5%
2 3405
11.1%
6 3204
10.5%
7 2958
9.7%
4 2942
9.6%
3 2859
9.3%
8 2262
7.4%
9 2126
6.9%
0 2020
 
6.6%
Other Letter
ValueCountFrequency (%)
36
100.0%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30646
99.9%
Hangul 36
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5312
17.3%
5 3522
11.5%
2 3405
11.1%
6 3204
10.5%
7 2958
9.7%
4 2942
9.6%
3 2859
9.3%
8 2262
7.4%
9 2126
6.9%
0 2020
 
6.6%
Hangul
ValueCountFrequency (%)
36
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30646
99.9%
Hangul 36
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5312
17.3%
5 3522
11.5%
2 3405
11.1%
6 3204
10.5%
7 2958
9.7%
4 2942
9.6%
3 2859
9.3%
8 2262
7.4%
9 2126
6.9%
0 2020
 
6.6%
Hangul
ValueCountFrequency (%)
36
100.0%

부번
Real number (ℝ)

MISSING 

Distinct261
Distinct (%)2.8%
Missing832
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean21.440227
Minimum1
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:00.604984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q321
95-th percentile90.65
Maximum419
Range418
Interquartile range (IQR)18

Descriptive statistics

Standard deviation38.163493
Coefficient of variation (CV)1.7799948
Kurtosis23.900291
Mean21.440227
Median Absolute Deviation (MAD)6
Skewness4.2470932
Sum196564
Variance1456.4522
MonotonicityNot monotonic
2024-03-14T12:24:00.705683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1071
 
10.7%
2 711
 
7.1%
3 579
 
5.8%
4 517
 
5.2%
5 498
 
5.0%
6 428
 
4.3%
7 409
 
4.1%
8 375
 
3.8%
9 314
 
3.1%
10 303
 
3.0%
Other values (251) 3963
39.6%
(Missing) 832
 
8.3%
ValueCountFrequency (%)
1 1071
10.7%
2 711
7.1%
3 579
5.8%
4 517
5.2%
5 498
5.0%
6 428
 
4.3%
7 409
 
4.1%
8 375
 
3.8%
9 314
 
3.1%
10 303
 
3.0%
ValueCountFrequency (%)
419 1
< 0.1%
404 1
< 0.1%
395 1
< 0.1%
389 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
355 1
< 0.1%
350 1
< 0.1%
348 1
< 0.1%
347 1
< 0.1%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2024-03-14T12:24:00.804260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:00.903427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2024-03-14T12:24:00.986716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:01.053434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

동코드
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.7609
Minimum0
Maximum99999
Zeros4584
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:01.150242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum99999
Range99999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1000.0213
Coefficient of variation (CV)92.931008
Kurtosis9998.4439
Mean10.7609
Median Absolute Deviation (MAD)0
Skewness99.988374
Sum107609
Variance1000042.6
MonotonicityNot monotonic
2024-03-14T12:24:01.263067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 5169
51.7%
0 4584
45.8%
2 161
 
1.6%
3 30
 
0.3%
4 14
 
0.1%
8 5
 
0.1%
7 4
 
< 0.1%
6 3
 
< 0.1%
5 3
 
< 0.1%
11 3
 
< 0.1%
Other values (19) 24
 
0.2%
ValueCountFrequency (%)
0 4584
45.8%
1 5169
51.7%
2 161
 
1.6%
3 30
 
0.3%
4 14
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
99999 1
< 0.1%
602 1
< 0.1%
601 1
< 0.1%
201 1
< 0.1%
45 1
< 0.1%
42 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
34 1
< 0.1%
30 1
< 0.1%

동명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1동
5169 
0동
4584 
2동
 
161
3동
 
30
4동
 
14
Other values (24)
 
42

Length

Max length6
Median length2
Mean length2.0031
Min length2

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row1동
2nd row1동
3rd row1동
4th row0동
5th row0동

Common Values

ValueCountFrequency (%)
1동 5169
51.7%
0동 4584
45.8%
2동 161
 
1.6%
3동 30
 
0.3%
4동 14
 
0.1%
8동 5
 
0.1%
7동 4
 
< 0.1%
11동 3
 
< 0.1%
5동 3
 
< 0.1%
13동 3
 
< 0.1%
Other values (19) 24
 
0.2%

Length

2024-03-14T12:24:01.381022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1동 5169
51.7%
0동 4584
45.8%
2동 161
 
1.6%
3동 30
 
0.3%
4동 14
 
0.1%
8동 5
 
< 0.1%
7동 4
 
< 0.1%
13동 3
 
< 0.1%
6동 3
 
< 0.1%
5동 3
 
< 0.1%
Other values (19) 24
 
0.2%

토지대장면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2694
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.66432
Minimum3
Maximum177518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:01.529005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile96
Q1165
median218
Q3307
95-th percentile664
Maximum177518
Range177515
Interquartile range (IQR)142

Descriptive statistics

Standard deviation3885.9685
Coefficient of variation (CV)7.9849053
Kurtosis945.35634
Mean486.66432
Median Absolute Deviation (MAD)64
Skewness28.129244
Sum4866643.2
Variance15100751
MonotonicityNot monotonic
2024-03-14T12:24:02.424627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165.0 102
 
1.0%
162.0 82
 
0.8%
145.0 71
 
0.7%
142.0 66
 
0.7%
132.0 66
 
0.7%
159.0 64
 
0.6%
152.0 64
 
0.6%
188.0 64
 
0.6%
185.0 63
 
0.6%
139.0 62
 
0.6%
Other values (2684) 9296
93.0%
ValueCountFrequency (%)
3.0 1
< 0.1%
4.0 1
< 0.1%
6.0 1
< 0.1%
7.0 1
< 0.1%
9.9 1
< 0.1%
12.0 1
< 0.1%
13.0 1
< 0.1%
13.2 2
< 0.1%
14.0 1
< 0.1%
16.5 1
< 0.1%
ValueCountFrequency (%)
177518.0 1
 
< 0.1%
153620.0 1
 
< 0.1%
113058.0 1
 
< 0.1%
95405.0 1
 
< 0.1%
81525.0 6
0.1%
76085.0 3
< 0.1%
70747.0 1
 
< 0.1%
36668.0 1
 
< 0.1%
34480.0 1
 
< 0.1%
31252.0 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7716
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.01564
Minimum5.2
Maximum4952.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:02.533760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile42.31
Q180.32
median127.47
Q3316.2675
95-th percentile721.4505
Maximum4952.76
Range4947.56
Interquartile range (IQR)235.9475

Descriptive statistics

Standard deviation253.03996
Coefficient of variation (CV)1.0859355
Kurtosis29.835653
Mean233.01564
Median Absolute Deviation (MAD)64.77
Skewness3.5173587
Sum2330156.4
Variance64029.221
MonotonicityNot monotonic
2024-03-14T12:24:02.645976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 18
 
0.2%
49.5 14
 
0.1%
84.15 14
 
0.1%
97.45 12
 
0.1%
37.0 10
 
0.1%
49.8 10
 
0.1%
83.5 9
 
0.1%
59.4 9
 
0.1%
46.08 9
 
0.1%
33.0 8
 
0.1%
Other values (7706) 9887
98.9%
ValueCountFrequency (%)
5.2 1
< 0.1%
6.2 1
< 0.1%
7.93 1
< 0.1%
9.25 1
< 0.1%
10.5 1
< 0.1%
11.2 2
< 0.1%
11.9 2
< 0.1%
12.2 2
< 0.1%
12.5 1
< 0.1%
13.2 1
< 0.1%
ValueCountFrequency (%)
4952.76 1
< 0.1%
3826.43 1
< 0.1%
3447.65 1
< 0.1%
3143.9 1
< 0.1%
2855.85 1
< 0.1%
2763.51 1
< 0.1%
2657.23 1
< 0.1%
2632.41 1
< 0.1%
2590.13 1
< 0.1%
2478.81 1
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4795
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.07158
Minimum0
Maximum3981.23
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:02.752527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.587
Q1132
median185
Q3260.1175
95-th percentile520
Maximum3981.23
Range3981.23
Interquartile range (IQR)128.1175

Descriptive statistics

Standard deviation175.19898
Coefficient of variation (CV)0.78893024
Kurtosis58.616561
Mean222.07158
Median Absolute Deviation (MAD)62.375
Skewness4.9066439
Sum2220715.8
Variance30694.683
MonotonicityNot monotonic
2024-03-14T12:24:02.863765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165.0 89
 
0.9%
162.0 70
 
0.7%
132.0 64
 
0.6%
145.0 61
 
0.6%
159.0 59
 
0.6%
185.0 57
 
0.6%
142.0 57
 
0.6%
139.0 57
 
0.6%
152.0 56
 
0.6%
172.0 52
 
0.5%
Other values (4785) 9378
93.8%
ValueCountFrequency (%)
0.0 8
0.1%
0.01 15
0.1%
0.02 4
 
< 0.1%
3.0 1
 
< 0.1%
3.5 1
 
< 0.1%
4.33 1
 
< 0.1%
4.67 1
 
< 0.1%
6.0 1
 
< 0.1%
7.0 2
 
< 0.1%
7.18 1
 
< 0.1%
ValueCountFrequency (%)
3981.23 1
< 0.1%
3406.0 1
< 0.1%
3207.0 1
< 0.1%
2884.35 1
< 0.1%
2609.0 1
< 0.1%
1817.0 1
< 0.1%
1759.0 1
< 0.1%
1668.0 1
< 0.1%
1653.0 1
< 0.1%
1626.0 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7388
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.36599
Minimum4.44
Maximum3447.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:02.976864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.44
5-th percentile39.2
Q174.74
median107.625
Q3198.245
95-th percentile591.743
Maximum3447.65
Range3443.21
Interquartile range (IQR)123.505

Descriptive statistics

Standard deviation177.16993
Coefficient of variation (CV)0.9988946
Kurtosis15.094246
Mean177.36599
Median Absolute Deviation (MAD)45.925
Skewness2.5975167
Sum1773659.9
Variance31389.184
MonotonicityNot monotonic
2024-03-14T12:24:03.099938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 19
 
0.2%
84.15 14
 
0.1%
97.45 12
 
0.1%
49.5 12
 
0.1%
49.8 11
 
0.1%
46.08 11
 
0.1%
59.4 9
 
0.1%
29.7 9
 
0.1%
37.0 9
 
0.1%
84.24 9
 
0.1%
Other values (7378) 9885
98.9%
ValueCountFrequency (%)
4.44 1
< 0.1%
5.2 1
< 0.1%
6.2 1
< 0.1%
6.84 1
< 0.1%
7.51 1
< 0.1%
7.93 1
< 0.1%
8.2 1
< 0.1%
9.2 1
< 0.1%
9.25 2
< 0.1%
10.5 1
< 0.1%
ValueCountFrequency (%)
3447.65 1
< 0.1%
1193.88 1
< 0.1%
1046.63 1
< 0.1%
1032.39 1
< 0.1%
940.56 1
< 0.1%
907.41 2
< 0.1%
898.46 1
< 0.1%
893.93 1
< 0.1%
890.25 1
< 0.1%
889.39 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1731
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4839238 × 108
Minimum433000
Maximum6.152 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:03.209472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum433000
5-th percentile21595000
Q149300000
median79900000
Q31.58 × 108
95-th percentile5.3705 × 108
Maximum6.152 × 109
Range6.151567 × 109
Interquartile range (IQR)1.087 × 108

Descriptive statistics

Standard deviation1.7710445 × 108
Coefficient of variation (CV)1.1934875
Kurtosis135.60153
Mean1.4839238 × 108
Median Absolute Deviation (MAD)39900000
Skewness5.678099
Sum1.4839238 × 1012
Variance3.1365987 × 1016
MonotonicityNot monotonic
2024-03-14T12:24:03.326837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 49
 
0.5%
110000000 46
 
0.5%
109000000 43
 
0.4%
104000000 42
 
0.4%
102000000 42
 
0.4%
101000000 41
 
0.4%
121000000 40
 
0.4%
106000000 35
 
0.4%
131000000 35
 
0.4%
119000000 34
 
0.3%
Other values (1721) 9593
95.9%
ValueCountFrequency (%)
433000 1
< 0.1%
513000 1
< 0.1%
613000 1
< 0.1%
663000 1
< 0.1%
750000 1
< 0.1%
886000 1
< 0.1%
1000000 1
< 0.1%
1040000 1
< 0.1%
1080000 1
< 0.1%
1190000 1
< 0.1%
ValueCountFrequency (%)
6152000000 1
< 0.1%
1888000000 1
< 0.1%
1483000000 1
< 0.1%
1445000000 1
< 0.1%
1267000000 1
< 0.1%
1005000000 1
< 0.1%
998000000 1
< 0.1%
932000000 1
< 0.1%
915000000 1
< 0.1%
909000000 1
< 0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-07-26 00:00:00
Maximum2021-07-26 00:00:00
2024-03-14T12:24:03.421909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:03.497450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T12:23:57.539857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:51.852035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.572945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.236470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.952513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.713447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.688377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.726160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.646216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:51.934699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.674782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.347206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.043773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.869671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.814381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.827334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.724235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.008134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.739898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.430286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.131745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.958437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.925654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.911423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.805245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.087865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.815740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.514290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.234083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.068945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.059797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.064787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.890397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.176086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.906745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.600149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.315291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.187252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.190519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.188226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.969258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.254030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.991035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.671965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.387949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.298261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.313311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.273833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:58.062162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.367864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.079766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.780752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.479186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.444203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.482387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.364206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:58.146904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:52.462861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.157435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:53.877280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:54.601911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:55.559987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:56.609654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:57.443660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:24:03.570163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명특수지구분코드특수지구분명부번동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격
법정동코드1.0001.0000.0410.0410.1790.0000.9710.0290.0300.0290.0730.011
법정동명1.0001.0000.1510.1510.2800.0840.6690.2450.4240.4900.4950.215
특수지구분코드0.0410.1511.0000.9990.0000.0000.4550.6330.0000.0290.0000.000
특수지구분명0.0410.1510.9991.0000.0000.0000.4550.6330.0000.0290.0000.000
부번0.1790.2800.0000.0001.0000.0000.0280.0000.0000.0170.0690.000
동코드0.0000.0840.0000.0000.0001.0001.0000.0000.0000.0000.0180.000
동명0.9710.6690.4550.4550.0281.0001.0000.4610.0000.0470.0400.532
토지대장면적0.0290.2450.6330.6330.0000.0000.4611.0000.2730.3880.3140.314
산정대지면적0.0300.4240.0000.0000.0000.0000.0000.2731.0000.6340.7220.611
건물전체연면적0.0290.4900.0290.0290.0170.0000.0470.3880.6341.0000.7030.716
건물산정연면적0.0730.4950.0000.0000.0690.0180.0400.3140.7220.7031.0000.926
주택가격0.0110.2150.0000.0000.0000.0000.5320.3140.6110.7160.9261.000
2024-03-14T12:24:03.758635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지구분코드동명특수지구분명
특수지구분코드1.0000.3900.977
동명0.3901.0000.390
특수지구분명0.9770.3901.000
2024-03-14T12:24:03.862306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드부번동코드토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격특수지구분코드특수지구분명동명
법정동코드1.0000.089-0.7580.2460.0780.2490.1190.0300.0260.0260.942
부번0.0891.000-0.197-0.254-0.107-0.134-0.082-0.1060.0000.0000.010
동코드-0.758-0.1971.0000.047-0.012-0.074-0.0250.0560.0000.0000.999
토지대장면적0.246-0.2540.0471.0000.4450.7210.4500.5270.6390.6390.192
산정대지면적0.078-0.107-0.0120.4451.0000.1900.8950.7230.0000.0000.000
건물전체연면적0.249-0.134-0.0740.7210.1901.0000.4240.5170.0290.0290.017
건물산정연면적0.119-0.082-0.0250.4500.8950.4241.0000.8040.0000.0000.019
주택가격0.030-0.1060.0560.5270.7230.5170.8041.0000.0000.0000.284
특수지구분코드0.0260.0000.0000.6390.0000.0290.0000.0001.0000.9770.390
특수지구분명0.0260.0000.0000.6390.0000.0290.0000.0000.9771.0000.390
동명0.9420.0100.9990.1920.0000.0170.0190.2840.3900.3901.000

Missing values

2024-03-14T12:23:58.282821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:23:58.445867image/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

법정동코드법정동명특수지구분코드특수지구분명본번부번기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격데이터기준일자
15074511111600전주시 완산구 다가동4가1일반14912021111동181.8217.73105.64126.53812000002021-07-26
202154511114200전주시 완산구 효자동3가1일반172822021111동250.4448.95183.99329.883570000002021-07-26
9854511111100전주시 완산구 전동1일반30292021111동79.3214.9251.96140.81662000002021-07-26
397354511312500전주시 덕진구 용정동1일반159702021100동496.099.0518.099.0603000002021-07-26
334434511310800전주시 덕진구 팔복동1가1일반17322021100동410.0174.6410.0174.6734000002021-07-26
110214511112900전주시 완산구 서신동1일반848162021111동202.8325.77202.8325.772060000002021-07-26
222854511310200전주시 덕진구 진북동1일반417852021100동212.9151.61193.99138.14788000002021-07-26
122124511113200전주시 완산구 평화동1가1일반583282021111동179.0100.81179.0100.81817000002021-07-26
94204511112800전주시 완산구 중화산동2가1일반60932021111동209.9483.11159.52367.173110000002021-07-26
410524511313100전주시 덕진구 만성동1일반1185152021100동263.3401.52263.3401.524730000002021-07-26
법정동코드법정동명특수지구분코드특수지구분명본번부번기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격데이터기준일자
184344511114200전주시 완산구 효자동3가1일반1354762021111동138.065.58138.065.58811000002021-07-26
310594511310700전주시 덕진구 금암동1일반17832021100동149.0148.5149.0148.5389000002021-07-26
201074511114200전주시 완산구 효자동3가1일반1716162021111동322.5559.86239.04414.964430000002021-07-26
354304511311400전주시 덕진구 우아동2가1일반89542021100동211.5476.58211.5476.583900000002021-07-26
369784511311700전주시 덕진구 호성동2가1일반545<NA>2021100동202.075.82190.075.82748000002021-07-26
407674511313000전주시 덕진구 여의동1일반103642021100동337.066.0337.066.0745000002021-07-26
252404511310300전주시 덕진구 인후동1가1일반6591322021100동203.0104.64203.0104.64768000002021-07-26
390154511312200전주시 덕진구 송천동2가1일반244252021100동327.0492.8378.94118.971450000002021-07-26
330394511310700전주시 덕진구 금암동1일반159032021100동190.2307.07190.2307.072320000002021-07-26
416164511313300전주시 덕진구 팔복동4가1일반147<NA>2021100동299.0144.0299.0144.0624000002021-07-26