Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory179.0 B

Variable types

Numeric8
Text4
Categorical6
Boolean1
DateTime1

Dataset

Description토지의 단위면적당 공시가격, 공동/개별 주택가격 등 전국의 부동산 관련 공부 정보를 제공
Author국토교통부
URLhttps://www.data.go.kr/data/15029187/standard.do

Alerts

기준월 has constant value ""Constant
제공기관명 is highly overall correlated with 고유번호 and 5 other fieldsHigh correlation
특수지구분명 is highly overall correlated with 특수지구분코드High correlation
특수지구분코드 is highly overall correlated with 특수지구분명High correlation
기준연도 is highly overall correlated with 건축물대장고유번호 and 3 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 고유번호 and 5 other fieldsHigh correlation
고유번호 is highly overall correlated with 법정동코드 and 5 other fieldsHigh correlation
법정동코드 is highly overall correlated with 고유번호 and 5 other fieldsHigh correlation
건축물대장고유번호 is highly overall correlated with 고유번호 and 6 other fieldsHigh correlation
토지대장면적 is highly overall correlated with 고유번호 and 3 other fieldsHigh correlation
산정대지면적 is highly overall correlated with 고유번호 and 3 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 건물산정연면적 and 4 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 건물전체연면적 and 1 other fieldsHigh correlation
주택가격 is highly overall correlated with 건물전체연면적 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (96.1%)Imbalance
특수지구분명 is highly imbalanced (96.1%)Imbalance
기준연도 is highly imbalanced (99.1%)Imbalance
표준지여부 is highly imbalanced (94.6%)Imbalance
토지대장면적 is highly skewed (γ1 = 99.74572944)Skewed
산정대지면적 is highly skewed (γ1 = 30.15324033)Skewed
건물전체연면적 is highly skewed (γ1 = 34.28997752)Skewed

Reproduction

Analysis started2023-12-12 19:36:28.423888
Analysis finished2023-12-12 19:36:40.928876
Duration12.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct6601
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4468116 × 1018
Minimum2.8140102 × 1018
Maximum4.313043 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:40.996300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8140102 × 1018
5-th percentile2.8140106 × 1018
Q12.82601 × 1018
median2.872036 × 1018
Q34.3130116 × 1018
95-th percentile4.313042 × 1018
Maximum4.313043 × 1018
Range1.4990328 × 1018
Interquartile range (IQR)1.4870016 × 1018

Descriptive statistics

Standard deviation7.2698798 × 1017
Coefficient of variation (CV)0.21091608
Kurtosis-1.8745817
Mean3.4468116 × 1018
Median Absolute Deviation (MAD)5.8025321 × 1016
Skewness0.35116305
Sum-8.8486546 × 1018
Variance5.2851152 × 1035
MonotonicityNot monotonic
2023-12-13T04:36:41.153956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2826010000000000000 2721
 
27.2%
2814010700100670010 48
 
0.5%
2814010700100500006 11
 
0.1%
2872037021113510000 11
 
0.1%
2872033021111180000 8
 
0.1%
2814010700100500023 7
 
0.1%
2872036023104720000 7
 
0.1%
2872038021104770000 7
 
0.1%
2872038021103250000 7
 
0.1%
2872038021101070000 7
 
0.1%
Other values (6591) 7166
71.7%
ValueCountFrequency (%)
2814010200100370121 1
< 0.1%
2814010200100370132 1
< 0.1%
2814010200100370164 1
< 0.1%
2814010200100370166 1
< 0.1%
2814010200100370167 1
< 0.1%
2814010200100370168 1
< 0.1%
2814010200100370177 1
< 0.1%
2814010200100370193 1
< 0.1%
2814010200100370194 1
< 0.1%
2814010200100370206 1
< 0.1%
ValueCountFrequency (%)
4313043029200530001 1
< 0.1%
4313043029105640003 1
< 0.1%
4313043029104430001 1
< 0.1%
4313043029104350001 1
< 0.1%
4313043029104290002 1
< 0.1%
4313043029103540002 1
< 0.1%
4313043029103210001 1
< 0.1%
4313043029103200000 1
< 0.1%
4313043029102580001 1
< 0.1%
4313043029102270004 1
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4468119 × 109
Minimum2.8140102 × 109
Maximum4.313043 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:41.356850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8140102 × 109
5-th percentile2.8140106 × 109
Q12.826011 × 109
median2.872036 × 109
Q34.3130116 × 109
95-th percentile4.313042 × 109
Maximum4.313043 × 109
Range1.4990328 × 109
Interquartile range (IQR)1.4870006 × 109

Descriptive statistics

Standard deviation7.2698773 × 108
Coefficient of variation (CV)0.21091599
Kurtosis-1.8745818
Mean3.4468119 × 109
Median Absolute Deviation (MAD)58025321
Skewness0.35116308
Sum3.4468119 × 1013
Variance5.2851116 × 1017
MonotonicityNot monotonic
2023-12-13T04:36:41.528143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2826011000 941
 
9.4%
2814010700 825
 
8.2%
2826011200 536
 
5.4%
4313010500 416
 
4.2%
2826010800 299
 
3.0%
2872036022 284
 
2.8%
2826010900 274
 
2.7%
2872033021 228
 
2.3%
4313010900 186
 
1.9%
4313011600 177
 
1.8%
Other values (102) 5834
58.3%
ValueCountFrequency (%)
2814010200 152
 
1.5%
2814010300 64
 
0.6%
2814010400 66
 
0.7%
2814010500 116
 
1.2%
2814010600 148
 
1.5%
2814010700 825
8.2%
2817010600 8
 
0.1%
2826010400 23
 
0.2%
2826010500 27
 
0.3%
2826010600 70
 
0.7%
ValueCountFrequency (%)
4313043029 23
 
0.2%
4313043028 39
0.4%
4313043027 17
 
0.2%
4313043026 39
0.4%
4313043025 25
 
0.2%
4313043024 64
0.6%
4313043023 44
0.4%
4313043022 51
0.5%
4313043021 43
0.4%
4313042031 36
0.4%
Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:41.954857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length13.7743
Min length11

Characters and Unicode

Total characters137743
Distinct characters127
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row인천광역시 옹진군 덕적면 진리
2nd row충청북도 충주시 엄정면 신만리
3rd row충청북도 충주시 호암동
4th row인천광역시 서구 가좌동
5th row충청북도 충주시 엄정면 울능리
ValueCountFrequency (%)
인천광역시 5863
17.2%
충청북도 4131
 
12.1%
충주시 4131
 
12.1%
서구 2727
 
8.0%
옹진군 1763
 
5.2%
동구 1371
 
4.0%
석남동 941
 
2.8%
송림동 825
 
2.4%
동량면 556
 
1.6%
가좌동 536
 
1.6%
Other values (123) 11215
32.9%
2023-12-13T04:36:42.520405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24059
17.5%
10034
 
7.3%
8306
 
6.0%
8219
 
6.0%
6107
 
4.4%
5907
 
4.3%
5875
 
4.3%
5869
 
4.3%
4468
 
3.2%
4421
 
3.2%
Other values (117) 54478
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113684
82.5%
Space Separator 24059
 
17.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10034
 
8.8%
8306
 
7.3%
8219
 
7.2%
6107
 
5.4%
5907
 
5.2%
5875
 
5.2%
5869
 
5.2%
4468
 
3.9%
4421
 
3.9%
4346
 
3.8%
Other values (116) 50132
44.1%
Space Separator
ValueCountFrequency (%)
24059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113684
82.5%
Common 24059
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10034
 
8.8%
8306
 
7.3%
8219
 
7.2%
6107
 
5.4%
5907
 
5.2%
5875
 
5.2%
5869
 
5.2%
4468
 
3.9%
4421
 
3.9%
4346
 
3.8%
Other values (116) 50132
44.1%
Common
ValueCountFrequency (%)
24059
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113684
82.5%
ASCII 24059
 
17.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24059
100.0%
Hangul
ValueCountFrequency (%)
10034
 
8.8%
8306
 
7.3%
8219
 
7.2%
6107
 
5.4%
5907
 
5.2%
5875
 
5.2%
5869
 
5.2%
4468
 
3.9%
4421
 
3.9%
4346
 
3.8%
Other values (116) 50132
44.1%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9916 
2
 
59
6
 
23
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 9916
99.2%
2 59
 
0.6%
6 23
 
0.2%
7 2
 
< 0.1%

Length

2023-12-13T04:36:42.664809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:42.782960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9916
99.2%
2 59
 
0.6%
6 23
 
0.2%
7 2
 
< 0.1%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9916 
 
59
블럭지번(롯트세분)
 
23
블럭지번(지구)
 
2

Length

Max length10
Median length2
Mean length2.0137
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9916
99.2%
59
 
0.6%
블럭지번(롯트세분) 23
 
0.2%
블럭지번(지구) 2
 
< 0.1%

Length

2023-12-13T04:36:42.899115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:43.000583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9916
99.2%
59
 
0.6%
블럭지번(롯트세분 23
 
0.2%
블럭지번(지구 2
 
< 0.1%

지번
Text

Distinct8095
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:43.418667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.3229
Min length1

Characters and Unicode

Total characters53229
Distinct characters35
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

Unique6849 ?
Unique (%)68.5%

Sample

1st row49
2nd row771
3rd row641-3
4th row347-7
5th row390-3
ValueCountFrequency (%)
67-10 48
 
0.5%
111-1 11
 
0.1%
50-6 11
 
0.1%
166 9
 
0.1%
516 7
 
0.1%
50-23 7
 
0.1%
297 7
 
0.1%
345 7
 
0.1%
605 7
 
0.1%
242 7
 
0.1%
Other values (8087) 9905
98.8%
2023-12-13T04:36:44.037848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7888
14.8%
1 7561
14.2%
0 7227
13.6%
2 5109
9.6%
3 4414
8.3%
5 4395
8.3%
4 4098
7.7%
6 3366
6.3%
7 3296
6.2%
8 2930
 
5.5%
Other values (25) 2945
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45158
84.8%
Dash Punctuation 7888
 
14.8%
Lowercase Letter 70
 
0.1%
Other Letter 52
 
0.1%
Uppercase Letter 35
 
0.1%
Space Separator 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18
25.7%
r 11
15.7%
n 9
12.9%
e 7
 
10.0%
p 6
 
8.6%
c 5
 
7.1%
y 4
 
5.7%
u 3
 
4.3%
t 2
 
2.9%
b 2
 
2.9%
Other values (3) 3
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 7561
16.7%
0 7227
16.0%
2 5109
11.3%
3 4414
9.8%
5 4395
9.7%
4 4098
9.1%
6 3366
7.5%
7 3296
7.3%
8 2930
 
6.5%
9 2762
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
M 11
31.4%
J 9
25.7%
A 5
14.3%
D 3
 
8.6%
O 2
 
5.7%
F 2
 
5.7%
S 2
 
5.7%
N 1
 
2.9%
Other Letter
ValueCountFrequency (%)
26
50.0%
26
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7888
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53072
99.7%
Latin 105
 
0.2%
Hangul 52
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18
17.1%
M 11
10.5%
r 11
10.5%
J 9
8.6%
n 9
8.6%
e 7
 
6.7%
p 6
 
5.7%
c 5
 
4.8%
A 5
 
4.8%
y 4
 
3.8%
Other values (11) 20
19.0%
Common
ValueCountFrequency (%)
- 7888
14.9%
1 7561
14.2%
0 7227
13.6%
2 5109
9.6%
3 4414
8.3%
5 4395
8.3%
4 4098
7.7%
6 3366
6.3%
7 3296
6.2%
8 2930
 
5.5%
Other values (2) 2788
 
5.3%
Hangul
ValueCountFrequency (%)
26
50.0%
26
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53177
99.9%
Hangul 52
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7888
14.8%
1 7561
14.2%
0 7227
13.6%
2 5109
9.6%
3 4414
8.3%
5 4395
8.3%
4 4098
7.7%
6 3366
6.3%
7 3296
6.2%
8 2930
 
5.5%
Other values (23) 2893
 
5.4%
Hangul
ValueCountFrequency (%)
26
50.0%
26
50.0%

건축물대장고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct4585
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4465325 × 1018
Minimum1.1110167 × 1018
Maximum4.313043 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:44.234304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110167 × 1018
5-th percentile2.8140106 × 1018
Q12.83 × 1018
median2.872036 × 1018
Q34.3130116 × 1018
95-th percentile4.313042 × 1018
Maximum4.313043 × 1018
Range3.2020263 × 1018
Interquartile range (IQR)1.4830116 × 1018

Descriptive statistics

Standard deviation7.2884532 × 1017
Coefficient of variation (CV)0.21147206
Kurtosis-1.8051657
Mean3.4465325 × 1018
Median Absolute Deviation (MAD)5.8025321 × 1016
Skewness0.32703276
Sum6.8069282 × 1018
Variance5.3121549 × 1035
MonotonicityNot monotonic
2023-12-13T04:36:44.395414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2830000000000000000 2721
 
27.2%
2814010700100550144 73
 
0.7%
2814010700100499968 57
 
0.6%
2814010700100669952 52
 
0.5%
2814010700100320256 46
 
0.5%
2814010700100919808 41
 
0.4%
2814010700100910080 26
 
0.3%
2814010700100420096 23
 
0.2%
4313011800105030656 22
 
0.2%
2814010700100340224 22
 
0.2%
Other values (4575) 6917
69.2%
ValueCountFrequency (%)
1111016700100920064 8
0.1%
2814010200100369920 9
0.1%
2814010200100370432 10
0.1%
2814010200100380160 7
0.1%
2814010200100389888 2
 
< 0.1%
2814010200100440064 1
 
< 0.1%
2814010200100449792 1
 
< 0.1%
2814010200100579840 1
 
< 0.1%
2814010200100599808 3
 
< 0.1%
2814010200100610048 1
 
< 0.1%
ValueCountFrequency (%)
4313043029200529408 1
< 0.1%
4313043029105639936 1
< 0.1%
4313043029104430592 1
< 0.1%
4313043029104350208 1
< 0.1%
4313043029104289792 1
< 0.1%
4313043029103540224 1
< 0.1%
4313043029103209472 1
< 0.1%
4313043029103200256 1
< 0.1%
4313043029102580224 1
< 0.1%
4313043029102270464 1
< 0.1%

기준연도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
9992 
2017
 
8

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2019 9992
99.9%
2017 8
 
0.1%

Length

2023-12-13T04:36:44.596430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:44.714586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 9992
99.9%
2017 8
 
0.1%

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

2023-12-13T04:36:44.824028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:45.295380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%
Distinct88
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:45.497036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length100
Mean length100
Min length100

Characters and Unicode

Total characters1000000
Distinct characters15
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

Unique70 ?
Unique (%)0.7%

Sample

1st row1
2nd row3
3rd row2
4th row0
5th row1
ValueCountFrequency (%)
1 6373
63.7%
0 2665
26.7%
2 546
 
5.5%
3 114
 
1.1%
4 64
 
0.6%
10 47
 
0.5%
5 35
 
0.4%
6 20
 
0.2%
11 14
 
0.1%
9 11
 
0.1%
Other values (78) 111
 
1.1%
2023-12-13T04:36:45.904458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
989743
99.0%
1 6516
 
0.7%
0 2752
 
0.3%
2 577
 
0.1%
3 125
 
< 0.1%
4 80
 
< 0.1%
5 51
 
< 0.1%
6 36
 
< 0.1%
9 33
 
< 0.1%
8 32
 
< 0.1%
Other values (5) 55
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 989743
99.0%
Decimal Number 10225
 
1.0%
Other Letter 16
 
< 0.1%
Uppercase Letter 8
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6516
63.7%
0 2752
26.9%
2 577
 
5.6%
3 125
 
1.2%
4 80
 
0.8%
5 51
 
0.5%
6 36
 
0.4%
9 33
 
0.3%
8 32
 
0.3%
7 23
 
0.2%
Other Letter
ValueCountFrequency (%)
8
50.0%
8
50.0%
Space Separator
ValueCountFrequency (%)
989743
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 999976
> 99.9%
Hangul 16
 
< 0.1%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
989743
99.0%
1 6516
 
0.7%
0 2752
 
0.3%
2 577
 
0.1%
3 125
 
< 0.1%
4 80
 
< 0.1%
5 51
 
< 0.1%
6 36
 
< 0.1%
9 33
 
< 0.1%
8 32
 
< 0.1%
Other values (2) 31
 
< 0.1%
Hangul
ValueCountFrequency (%)
8
50.0%
8
50.0%
Latin
ValueCountFrequency (%)
A 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 999984
> 99.9%
Hangul 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
989743
99.0%
1 6516
 
0.7%
0 2752
 
0.3%
2 577
 
0.1%
3 125
 
< 0.1%
4 80
 
< 0.1%
5 51
 
< 0.1%
6 36
 
< 0.1%
9 33
 
< 0.1%
8 32
 
< 0.1%
Other values (3) 39
 
< 0.1%
Hangul
ValueCountFrequency (%)
8
50.0%
8
50.0%

동명
Text

Distinct95
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:36:46.168504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length100
Mean length100
Min length100

Characters and Unicode

Total characters1000000
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

Unique71 ?
Unique (%)0.7%

Sample

1st row1
2nd row3동
3rd row2동
4th row0
5th row1동
ValueCountFrequency (%)
1동 4819
48.2%
0 2478
24.8%
1 1793
 
17.9%
2동 378
 
3.8%
2 170
 
1.7%
3동 74
 
0.7%
10동 42
 
0.4%
4동 37
 
0.4%
5동 25
 
0.2%
0동 18
 
0.2%
Other values (85) 166
 
1.7%
2023-12-13T04:36:46.518121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
984288
98.4%
1 6735
 
0.7%
5516
 
0.6%
0 2562
 
0.3%
2 578
 
0.1%
3 102
 
< 0.1%
4 64
 
< 0.1%
5 42
 
< 0.1%
6 32
 
< 0.1%
9 31
 
< 0.1%
Other values (2) 50
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 984288
98.4%
Decimal Number 10196
 
1.0%
Other Letter 5516
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6735
66.1%
0 2562
 
25.1%
2 578
 
5.7%
3 102
 
1.0%
4 64
 
0.6%
5 42
 
0.4%
6 32
 
0.3%
9 31
 
0.3%
8 29
 
0.3%
7 21
 
0.2%
Space Separator
ValueCountFrequency (%)
984288
100.0%
Other Letter
ValueCountFrequency (%)
5516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 994484
99.4%
Hangul 5516
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
984288
99.0%
1 6735
 
0.7%
0 2562
 
0.3%
2 578
 
0.1%
3 102
 
< 0.1%
4 64
 
< 0.1%
5 42
 
< 0.1%
6 32
 
< 0.1%
9 31
 
< 0.1%
8 29
 
< 0.1%
Hangul
ValueCountFrequency (%)
5516
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 994484
99.4%
Hangul 5516
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
984288
99.0%
1 6735
 
0.7%
0 2562
 
0.3%
2 578
 
0.1%
3 102
 
< 0.1%
4 64
 
< 0.1%
5 42
 
< 0.1%
6 32
 
< 0.1%
9 31
 
< 0.1%
8 29
 
< 0.1%
Hangul
ValueCountFrequency (%)
5516
100.0%

토지대장면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2962
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1282.8029
Minimum7
Maximum7166553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:46.670066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile77.38
Q1144
median258
Q3525.25
95-th percentile1252
Maximum7166553
Range7166546
Interquartile range (IQR)381.25

Descriptive statistics

Standard deviation71721.285
Coefficient of variation (CV)55.909825
Kurtosis9965.7849
Mean1282.8029
Median Absolute Deviation (MAD)137.1
Skewness99.745729
Sum12828029
Variance5.1439428 × 109
MonotonicityNot monotonic
2023-12-13T04:36:46.814582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660.0 111
 
1.1%
4236.1 48
 
0.5%
330.0 43
 
0.4%
165.0 41
 
0.4%
496.0 33
 
0.3%
155.0 32
 
0.3%
500.0 31
 
0.3%
136.0 30
 
0.3%
149.0 30
 
0.3%
132.0 29
 
0.3%
Other values (2952) 9572
95.7%
ValueCountFrequency (%)
7.0 2
< 0.1%
10.0 1
 
< 0.1%
17.5 4
< 0.1%
18.0 1
 
< 0.1%
18.8 2
< 0.1%
20.0 1
 
< 0.1%
20.8 3
< 0.1%
21.5 1
 
< 0.1%
22.0 2
< 0.1%
22.1 1
 
< 0.1%
ValueCountFrequency (%)
7166553.0 1
 
< 0.1%
175727.0 1
 
< 0.1%
100685.0 2
 
< 0.1%
89653.0 1
 
< 0.1%
59875.0 2
 
< 0.1%
48324.0 1
 
< 0.1%
37686.0 1
 
< 0.1%
35573.0 1
 
< 0.1%
33423.0 1
 
< 0.1%
32978.0 5
0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4267
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.00552
Minimum0
Maximum32978
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:46.961608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.3855
Q1119.3
median208
Q3455.1575
95-th percentile934.02
Maximum32978
Range32978
Interquartile range (IQR)335.8575

Descriptive statistics

Standard deviation840.27475
Coefficient of variation (CV)2.3602857
Kurtosis1140.8733
Mean356.00552
Median Absolute Deviation (MAD)125
Skewness30.15324
Sum3560055.2
Variance706061.65
MonotonicityNot monotonic
2023-12-13T04:36:47.094131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660.0 109
 
1.1%
165.0 38
 
0.4%
330.0 36
 
0.4%
496.0 30
 
0.3%
0.0 27
 
0.3%
155.0 27
 
0.3%
132.0 26
 
0.3%
109.0 26
 
0.3%
500.0 26
 
0.3%
175.0 24
 
0.2%
Other values (4257) 9631
96.3%
ValueCountFrequency (%)
0.0 27
0.3%
5.93 1
 
< 0.1%
6.78 1
 
< 0.1%
6.91 1
 
< 0.1%
7.13 2
 
< 0.1%
7.41 1
 
< 0.1%
7.62 1
 
< 0.1%
7.96 1
 
< 0.1%
8.36 1
 
< 0.1%
8.69 1
 
< 0.1%
ValueCountFrequency (%)
32978.0 5
0.1%
11657.55 1
 
< 0.1%
9004.0 1
 
< 0.1%
8028.0 1
 
< 0.1%
7029.5 1
 
< 0.1%
5712.0 1
 
< 0.1%
5329.0 1
 
< 0.1%
5133.0 4
< 0.1%
5051.0 1
 
< 0.1%
4832.0 5
0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5180
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.41511
Minimum6.38
Maximum47855.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:47.291144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.38
5-th percentile39.66
Q172
median111
Q3199.1325
95-th percentile534.03
Maximum47855.03
Range47848.65
Interquartile range (IQR)127.1325

Descriptive statistics

Standard deviation1361.7025
Coefficient of variation (CV)6.440895
Kurtosis1196.7628
Mean211.41511
Median Absolute Deviation (MAD)51
Skewness34.289978
Sum2114151.1
Variance1854233.8
MonotonicityNot monotonic
2023-12-13T04:36:47.424148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 122
 
1.2%
100.0 63
 
0.6%
85.0 56
 
0.6%
84.0 56
 
0.6%
96.0 54
 
0.5%
97.0 53
 
0.5%
81.0 52
 
0.5%
98.0 51
 
0.5%
83.0 50
 
0.5%
77.0 50
 
0.5%
Other values (5170) 9393
93.9%
ValueCountFrequency (%)
6.38 1
 
< 0.1%
12.4 1
 
< 0.1%
13.0 1
 
< 0.1%
14.0 3
< 0.1%
14.35 1
 
< 0.1%
14.46 1
 
< 0.1%
14.8 1
 
< 0.1%
15.0 1
 
< 0.1%
15.88 1
 
< 0.1%
16.0 2
< 0.1%
ValueCountFrequency (%)
47855.03 8
0.1%
5322.22 1
 
< 0.1%
4124.8 1
 
< 0.1%
2542.69 1
 
< 0.1%
2332.0 1
 
< 0.1%
2025.0 1
 
< 0.1%
1963.99 1
 
< 0.1%
1890.49 1
 
< 0.1%
1787.06 1
 
< 0.1%
1747.81 1
 
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4830
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.95702
Minimum0
Maximum956
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:47.564002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.06
Q167
median97.475
Q3153
95-th percentile332.5405
Maximum956
Range956
Interquartile range (IQR)86

Descriptive statistics

Standard deviation117.47063
Coefficient of variation (CV)0.8970167
Kurtosis12.763751
Mean130.95702
Median Absolute Deviation (MAD)37.525
Skewness3.1629264
Sum1309570.2
Variance13799.349
MonotonicityNot monotonic
2023-12-13T04:36:47.705559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 129
 
1.3%
100.0 72
 
0.7%
85.0 68
 
0.7%
84.0 66
 
0.7%
97.0 61
 
0.6%
77.0 58
 
0.6%
96.0 57
 
0.6%
66.0 57
 
0.6%
98.0 56
 
0.6%
81.0 56
 
0.6%
Other values (4820) 9320
93.2%
ValueCountFrequency (%)
0.0 27
0.3%
6.0 1
 
< 0.1%
6.38 1
 
< 0.1%
9.26 1
 
< 0.1%
9.91 1
 
< 0.1%
10.45 1
 
< 0.1%
11.01 2
 
< 0.1%
11.57 1
 
< 0.1%
11.9 1
 
< 0.1%
13.0 1
 
< 0.1%
ValueCountFrequency (%)
956.0 1
< 0.1%
938.0 1
< 0.1%
936.0 1
< 0.1%
907.11 1
< 0.1%
903.76 1
< 0.1%
897.0 1
< 0.1%
886.0 1
< 0.1%
883.18 1
< 0.1%
883.0 1
< 0.1%
881.39 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1690
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1317011 × 108
Minimum0
Maximum1.13 × 109
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:36:47.849093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12900000
Q139600000
median79300000
Q31.41 × 108
95-th percentile3.49 × 108
Maximum1.13 × 109
Range1.13 × 109
Interquartile range (IQR)1.014 × 108

Descriptive statistics

Standard deviation1.2346221 × 108
Coefficient of variation (CV)1.0909436
Kurtosis12.392752
Mean1.1317011 × 108
Median Absolute Deviation (MAD)46700000
Skewness3.0694906
Sum1.1317011 × 1012
Variance1.5242918 × 1016
MonotonicityNot monotonic
2023-12-13T04:36:48.020655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111000000 50
 
0.5%
109000000 50
 
0.5%
112000000 50
 
0.5%
103000000 46
 
0.5%
147000000 45
 
0.4%
106000000 45
 
0.4%
104000000 44
 
0.4%
100000000 44
 
0.4%
136000000 43
 
0.4%
138000000 42
 
0.4%
Other values (1680) 9541
95.4%
ValueCountFrequency (%)
0 27
0.3%
357000 1
 
< 0.1%
601000 1
 
< 0.1%
789000 1
 
< 0.1%
907000 1
 
< 0.1%
1060000 1
 
< 0.1%
1230000 1
 
< 0.1%
1260000 1
 
< 0.1%
1300000 1
 
< 0.1%
1340000 1
 
< 0.1%
ValueCountFrequency (%)
1130000000 1
< 0.1%
1110000000 1
< 0.1%
1070000000 1
< 0.1%
1050000000 1
< 0.1%
1010000000 2
< 0.1%
1000000000 1
< 0.1%
992000000 1
< 0.1%
980000000 1
< 0.1%
952000000 1
< 0.1%
949000000 1
< 0.1%

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9939 
True
 
61
ValueCountFrequency (%)
False 9939
99.4%
True 61
 
0.6%
2023-12-13T04:36:48.133055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-01-11 00:00:00
Maximum2020-02-28 00:00:00
2023-12-13T04:36:48.213386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:48.345367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

제공기관코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4390000
4131 
3560000
2721 
3580000
1763 
3500000
1371 
AAAAAAA
 
8

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3580000
2nd row4390000
3rd row4390000
4th row3560000
5th row4390000

Common Values

ValueCountFrequency (%)
4390000 4131
41.3%
3560000 2721
27.2%
3580000 1763
17.6%
3500000 1371
 
13.7%
AAAAAAA 8
 
0.1%
3600000 6
 
0.1%

Length

2023-12-13T04:36:48.513322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:48.638701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4390000 4131
41.3%
3560000 2721
27.2%
3580000 1763
17.6%
3500000 1371
 
13.7%
aaaaaaa 8
 
0.1%
3600000 6
 
0.1%

제공기관명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
충청북도 충주시
4131 
인천광역시 서구
2721 
인천광역시 옹진군
1763 
인천광역시 동구
1371 
공공데이터활용지원센터
 
8

Length

Max length11
Median length8
Mean length8.1787
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 옹진군
2nd row충청북도 충주시
3rd row충청북도 충주시
4th row인천광역시 서구
5th row충청북도 충주시

Common Values

ValueCountFrequency (%)
충청북도 충주시 4131
41.3%
인천광역시 서구 2721
27.2%
인천광역시 옹진군 1763
17.6%
인천광역시 동구 1371
 
13.7%
공공데이터활용지원센터 8
 
0.1%
광주광역시 서구 6
 
0.1%

Length

2023-12-13T04:36:48.765620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:48.892188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 5855
29.3%
충청북도 4131
20.7%
충주시 4131
20.7%
서구 2727
13.6%
옹진군 1763
 
8.8%
동구 1371
 
6.9%
공공데이터활용지원센터 8
 
< 0.1%
광주광역시 6
 
< 0.1%

Interactions

2023-12-13T04:36:39.638997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:32.431454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.462244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.566803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.744417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.884571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.124110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.903262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.750017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:32.554218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.571706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.719112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.900164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.041333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.230411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.997157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.854828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:32.697615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.690741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.864647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.054712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.168827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.338408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.088633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.956918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:32.837041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.824161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.000313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.180287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.284627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.438263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.177678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:40.056542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:32.974117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.978227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.166081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.329019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.432430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.539781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.281176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:40.165273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.112795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.159744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.299495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.490648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.532730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.633136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.373461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:40.257314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.240390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.307499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.466219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.623168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:37.930529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.728558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.459852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:40.352287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:33.361261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:34.440229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:35.611198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:36.753012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.025941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:38.815783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:39.543716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:36:49.024228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드특수지구분코드특수지구분명건축물대장고유번호기준연도동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자제공기관코드제공기관명
고유번호1.0001.0000.0650.0651.0000.0280.6360.8850.0000.0470.0130.1930.4210.1001.0001.0001.000
법정동코드1.0001.0000.0660.0661.0000.0280.6350.9150.0000.0470.0280.1970.4300.1021.0001.0001.000
특수지구분코드0.0650.0661.0001.0000.0650.0000.0000.0000.1940.0000.0000.2110.3030.0000.0710.0710.071
특수지구분명0.0650.0661.0001.0000.0650.0000.0000.0000.1940.0000.0000.2110.3030.0000.0710.0710.071
건축물대장고유번호1.0001.0000.0650.0651.000NaN0.6210.8860.0000.0470.0000.1930.4200.0931.0001.0001.000
기준연도0.0280.0280.0000.000NaN1.0001.0000.0000.0000.0001.0000.0000.0000.5071.0001.0001.000
동코드0.6360.6350.0000.0000.6211.0001.0001.0000.0000.2740.8970.2280.4010.4410.8830.8830.883
동명0.8850.9150.0000.0000.8860.0001.0001.0000.0000.3470.0000.2350.4300.1120.8860.8860.886
토지대장면적0.0000.0000.1940.1940.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
산정대지면적0.0470.0470.0000.0000.0470.0000.2740.3470.0001.0000.0000.0000.0000.0000.0310.0310.031
건물전체연면적0.0130.0280.0000.0000.0001.0000.8970.0000.0000.0001.0000.0000.0000.2210.9410.9410.941
건물산정연면적0.1930.1970.2110.2110.1930.0000.2280.2350.0000.0000.0001.0000.8360.0000.3040.3040.304
주택가격0.4210.4300.3030.3030.4200.0000.4010.4300.0000.0000.0000.8361.0000.0000.4230.4230.423
표준지여부0.1000.1020.0000.0000.0930.5070.4410.1120.0000.0000.2210.0000.0001.0000.5380.5380.538
데이터기준일자1.0001.0000.0710.0711.0001.0000.8830.8860.0000.0310.9410.3040.4230.5381.0001.0001.000
제공기관코드1.0001.0000.0710.0711.0001.0000.8830.8860.0000.0310.9410.3040.4230.5381.0001.0001.000
제공기관명1.0001.0000.0710.0711.0001.0000.8830.8860.0000.0310.9410.3040.4230.5381.0001.0001.000
2023-12-13T04:36:49.225719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제공기관명특수지구분명특수지구분코드기준연도제공기관코드표준지여부
제공기관명1.0000.0460.0461.0001.0000.389
특수지구분명0.0461.0001.0000.0000.0460.000
특수지구분코드0.0461.0001.0000.0000.0460.000
기준연도1.0000.0000.0001.0001.0000.338
제공기관코드1.0000.0460.0461.0001.0000.389
표준지여부0.3890.0000.0000.3380.3891.000
2023-12-13T04:36:49.396111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드건축물대장고유번호토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격특수지구분코드특수지구분명기준연도표준지여부제공기관코드제공기관명
고유번호1.0000.9901.0000.5890.709-0.0720.015-0.3800.0430.0430.0170.0641.0001.000
법정동코드0.9901.0000.9900.5990.718-0.0810.007-0.3750.0430.0430.0170.0641.0001.000
건축물대장고유번호1.0000.9901.0000.5880.708-0.0730.015-0.3800.0280.0281.0000.3651.0001.000
토지대장면적0.5890.5990.5881.0000.8040.0680.121-0.0730.1290.1290.0000.0000.0000.000
산정대지면적0.7090.7180.7080.8041.0000.0480.234-0.0010.0000.0000.0000.0000.0210.021
건물전체연면적-0.072-0.081-0.0730.0680.0481.0000.8630.6130.0000.0001.0000.3610.7070.707
건물산정연면적0.0150.0070.0150.1210.2340.8631.0000.6880.1280.1280.0000.0000.1650.165
주택가격-0.380-0.375-0.380-0.073-0.0010.6130.6881.0000.1850.1850.0000.0000.2390.239
특수지구분코드0.0430.0430.0280.1290.0000.0000.1280.1851.0001.0000.0000.0000.0460.046
특수지구분명0.0430.0430.0280.1290.0000.0000.1280.1851.0001.0000.0000.0000.0460.046
기준연도0.0170.0171.0000.0000.0001.0000.0000.0000.0000.0001.0000.3381.0001.000
표준지여부0.0640.0640.3650.0000.0000.3610.0000.0000.0000.0000.3381.0000.3890.389
제공기관코드1.0001.0001.0000.0000.0210.7070.1650.2390.0460.0461.0000.3891.0001.000
제공기관명1.0001.0001.0000.0000.0210.7070.1650.2390.0460.0461.0000.3891.0001.000

Missing values

2023-12-13T04:36:40.507772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:36:40.797309image/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

고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자제공기관코드제공기관명
2473928720350221004900002872035022인천광역시 옹진군 덕적면 진리1일반4928720350221004902402019111516.0516.049.049.050900000N2020-02-283580000인천광역시 옹진군
891343130420261077100004313042026충청북도 충주시 엄정면 신만리1일반77143130420261077104642019133동1789.01789.077.077.029200000N2019-05-014390000충청북도 충주시
196143130107001064100034313010700충청북도 충주시 호암동1일반641-343130107001064094722019122동625.0625.043.043.013900000N2019-05-014390000충청북도 충주시
1567928260100000000000002826011200인천광역시 서구 가좌동1일반347-728300000000000000002019100198.896.22266.58129.04125000000N2020-02-273560000인천광역시 서구
748043130420221039000034313042022충청북도 충주시 엄정면 울능리1일반390-343130420221039001602019111동2991.0224.021309.098.021300000N2019-05-014390000충청북도 충주시
835443130390301013200014313039030충청북도 충주시 금가면 도촌리1일반132-143130390301013201922019111동363.0363.0182.0118.060000000N2019-05-014390000충청북도 충주시
220543130115001004400154313011500충청북도 충주시 봉방동1일반44-1543130115001004400642019111동182.0182.090.090.059000000N2019-05-014390000충청북도 충주시
1559228260100000000000002826011200인천광역시 서구 가좌동1일반361-428300000000000000002019100197.685.8262.64114.04104000000N2020-02-273560000인천광역시 서구
941943130400261006000064313040026충청북도 충주시 동량면 용교리1일반60-643130400261005998082019111동243.0243.093.093.021800000N2019-05-014390000충청북도 충주시
1690628260100000000000002826011000인천광역시 서구 석남동1일반449-128300000000000000002019100121.5117.33109.0982.84119000000N2020-02-273560000인천광역시 서구
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자제공기관코드제공기관명
660943130410211123200024313041021충청북도 충주시 산척면 송강리1일반1232-243130410211123200002019111동468.0468.0121.0121.034600000N2019-05-014390000충청북도 충주시
2697128140107001005500902814010700인천광역시 동구 송림동1일반55-9028140107001005501442019111동31.421.1460.6640.8322500000N2019-06-303500000인천광역시 동구
1972828720330211119500002872033021인천광역시 옹진군 백령면 진촌리1일반1195-0001287203302111194982420191121023.0911.8773.4692.95440000000N2020-02-283580000인천광역시 옹진군
1870228260100000000000002826010800인천광역시 서구 가정동1일반527-1528300000000000000002019100138.2138.2121.8121.8184000000N2020-02-273560000인천광역시 서구
400143130104001024100004313010400충청북도 충주시 충인동1일반24143130104001024097282019111동179.0371.01113.067.031800000N2019-05-014390000충청북도 충주시
986543130400211132700024313040021충청북도 충주시 동량면 조동리1일반1327-243130400211132702722019111동489.0489.0115.0115.086200000N2019-05-014390000충청북도 충주시
2247328720370221032700002872037022인천광역시 옹진군 자월면 이작리1일반327287203702210326988820191311142.071.2618.018.011500000N2020-02-283580000인천광역시 옹진군
985343130400211137400004313040021충청북도 충주시 동량면 조동리1일반137443130400211137397762019122동837.0837.091.091.087000000N2019-05-014390000충청북도 충주시
2552028140107001003500632814010700인천광역시 동구 송림동1일반35-6328140107001003499522019111동206.3175.71238.25202.91203000000N2019-06-303500000인천광역시 동구
601343130410221073800024313041022충청북도 충주시 산척면 영덕리1일반738-243130410221073802242019111동220.0220.084.084.029300000N2019-05-014390000충청북도 충주시