Overview

Dataset statistics

Number of variables16
Number of observations9157
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory141.0 B

Variable types

Numeric10
Categorical6

Dataset

Description매년 공시되는 개별주택 공시가격(법정동코드, 기준년도, 기준월, 동코드, 동명, 기준년도, 토지대장면적, 산정대지면적 등) 입니다.
URLhttps://www.data.go.kr/data/15013512/fileData.do

Alerts

기준년도 has constant value ""Constant
기준월 has constant value ""Constant
데이터기준일자 has constant value ""Constant
특수지구분코드 is highly overall correlated with 특수지구분명High correlation
특수지구분명 is highly overall correlated with 특수지구분코드High correlation
법정동코드 is highly overall correlated with 법정동명High correlation
본번 is highly overall correlated with 법정동명High correlation
동코드 is highly overall correlated with 동명High correlation
동명 is highly overall correlated with 동코드High correlation
토지대장면적 is highly overall correlated with 산정대지면적 and 2 other fieldsHigh correlation
산정대지면적 is highly overall correlated with 토지대장면적 and 2 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 건물전체연면적 and 1 other fieldsHigh correlation
주택가격 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
법정동명 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (97.3%)Imbalance
특수지구분명 is highly imbalanced (97.3%)Imbalance
동코드 is highly skewed (γ1 = 67.63971218)Skewed
동명 is highly skewed (γ1 = 67.63971218)Skewed
토지대장면적 is highly skewed (γ1 = 31.14310281)Skewed
부번 has 325 (3.5%) zerosZeros
동코드 has 8511 (92.9%) zerosZeros
동명 has 8511 (92.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:32:01.669404
Analysis finished2023-12-12 20:32:16.395383
Duration14.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.43955
Minimum101
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:16.447992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile104
Q1109
median110
Q3115
95-th percentile122
Maximum122
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2930896
Coefficient of variation (CV)0.047074978
Kurtosis-0.51437883
Mean112.43955
Median Absolute Deviation (MAD)2
Skewness0.60995782
Sum1029609
Variance28.016797
MonotonicityIncreasing
2023-12-13T05:32:16.545210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
110 2336
25.5%
112 1407
15.4%
122 1347
14.7%
108 766
 
8.4%
109 733
 
8.0%
113 469
 
5.1%
103 302
 
3.3%
115 257
 
2.8%
119 210
 
2.3%
106 186
 
2.0%
Other values (11) 1144
12.5%
ValueCountFrequency (%)
101 10
 
0.1%
102 21
 
0.2%
103 302
 
3.3%
104 145
 
1.6%
105 56
 
0.6%
106 186
 
2.0%
107 182
 
2.0%
108 766
 
8.4%
109 733
 
8.0%
110 2336
25.5%
ValueCountFrequency (%)
122 1347
14.7%
121 150
 
1.6%
120 157
 
1.7%
119 210
 
2.3%
118 155
 
1.7%
117 117
 
1.3%
115 257
 
2.8%
114 142
 
1.6%
113 469
 
5.1%
112 1407
15.4%

법정동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
인천광역시 서구 석남동
2336 
인천광역시 서구 가좌동
1407 
인천광역시 서구 청라동
1347 
인천광역시 서구 가정동
766 
인천광역시 서구 신현동
733 
Other values (16)
2568 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 서구 백석동
2nd row인천광역시 서구 백석동
3rd row인천광역시 서구 백석동
4th row인천광역시 서구 백석동
5th row인천광역시 서구 백석동

Common Values

ValueCountFrequency (%)
인천광역시 서구 석남동 2336
25.5%
인천광역시 서구 가좌동 1407
15.4%
인천광역시 서구 청라동 1347
14.7%
인천광역시 서구 가정동 766
 
8.4%
인천광역시 서구 신현동 733
 
8.0%
인천광역시 서구 마전동 469
 
5.1%
인천광역시 서구 검암동 302
 
3.3%
인천광역시 서구 원당동 257
 
2.8%
인천광역시 서구 오류동 210
 
2.3%
인천광역시 서구 연희동 186
 
2.0%
Other values (11) 1144
12.5%

Length

2023-12-13T05:32:16.937277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 9157
33.3%
서구 9157
33.3%
석남동 2336
 
8.5%
가좌동 1407
 
5.1%
청라동 1347
 
4.9%
가정동 766
 
2.8%
신현동 733
 
2.7%
마전동 469
 
1.7%
검암동 302
 
1.1%
원당동 257
 
0.9%
Other values (13) 1540
 
5.6%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
1
9132 
2
 
25

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 9132
99.7%
2 25
 
0.3%

Length

2023-12-13T05:32:17.056278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:17.143199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9132
99.7%
2 25
 
0.3%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
일반
9132 
 
25

Length

Max length2
Median length2
Mean length1.9972698
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9132
99.7%
25
 
0.3%

Length

2023-12-13T05:32:17.245235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:17.328321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9132
99.7%
25
 
0.3%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct818
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.01463
Minimum1
Maximum1763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:17.435216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1168
median447
Q3570
95-th percentile1016.6
Maximum1763
Range1762
Interquartile range (IQR)402

Descriptive statistics

Standard deviation304.19807
Coefficient of variation (CV)0.71742352
Kurtosis2.0749718
Mean424.01463
Median Absolute Deviation (MAD)241
Skewness1.1238585
Sum3882702
Variance92536.465
MonotonicityNot monotonic
2023-12-13T05:32:17.570817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 172
 
1.9%
143 163
 
1.8%
111 107
 
1.2%
105 95
 
1.0%
8 85
 
0.9%
144 83
 
0.9%
174 82
 
0.9%
112 82
 
0.9%
587 79
 
0.9%
142 73
 
0.8%
Other values (808) 8136
88.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 9
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 85
0.9%
9 70
0.8%
11 1
 
< 0.1%
ValueCountFrequency (%)
1763 1
 
< 0.1%
1762 1
 
< 0.1%
1758 1
 
< 0.1%
1754 2
< 0.1%
1753 1
 
< 0.1%
1752 1
 
< 0.1%
1751 2
< 0.1%
1741 1
 
< 0.1%
1739 3
< 0.1%
1738 3
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct206
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.562084
Minimum0
Maximum481
Zeros325
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:17.709143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q327
95-th percentile88
Maximum481
Range481
Interquartile range (IQR)22

Descriptive statistics

Standard deviation30.545281
Coefficient of variation (CV)1.3538325
Kurtosis19.836831
Mean22.562084
Median Absolute Deviation (MAD)9
Skewness3.3487219
Sum206601
Variance933.01419
MonotonicityNot monotonic
2023-12-13T05:32:17.901516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 478
 
5.2%
1 471
 
5.1%
3 458
 
5.0%
4 401
 
4.4%
5 383
 
4.2%
6 360
 
3.9%
7 352
 
3.8%
0 325
 
3.5%
8 315
 
3.4%
9 294
 
3.2%
Other values (196) 5320
58.1%
ValueCountFrequency (%)
0 325
3.5%
1 471
5.1%
2 478
5.2%
3 458
5.0%
4 401
4.4%
5 383
4.2%
6 360
3.9%
7 352
3.8%
8 315
3.4%
9 294
3.2%
ValueCountFrequency (%)
481 1
< 0.1%
480 1
< 0.1%
345 1
< 0.1%
206 1
< 0.1%
205 1
< 0.1%
204 1
< 0.1%
201 2
< 0.1%
200 1
< 0.1%
199 2
< 0.1%
198 2
< 0.1%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
2023
9157 

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 9157
100.0%

Length

2023-12-13T05:32:18.018627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:18.105884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 9157
100.0%

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
1
9157 

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 9157
100.0%

Length

2023-12-13T05:32:18.194609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:18.294713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9157
100.0%

동코드
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3044665
Minimum0
Maximum9907
Zeros8511
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:18.390277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9907
Range9907
Interquartile range (IQR)0

Descriptive statistics

Standard deviation146.36892
Coefficient of variation (CV)63.515315
Kurtosis4574.747
Mean2.3044665
Median Absolute Deviation (MAD)0
Skewness67.639712
Sum21102
Variance21423.86
MonotonicityNot monotonic
2023-12-13T05:32:18.512559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8511
92.9%
1 476
 
5.2%
2 112
 
1.2%
3 19
 
0.2%
4 10
 
0.1%
10 6
 
0.1%
5 6
 
0.1%
11 2
 
< 0.1%
9907 1
 
< 0.1%
20 1
 
< 0.1%
Other values (13) 13
 
0.1%
ValueCountFrequency (%)
0 8511
92.9%
1 476
 
5.2%
2 112
 
1.2%
3 19
 
0.2%
4 10
 
0.1%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9907 1
< 0.1%
9901 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
41 1
< 0.1%
30 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
15 1
< 0.1%
13 1
< 0.1%

동명
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3044665
Minimum0
Maximum9907
Zeros8511
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:18.649525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9907
Range9907
Interquartile range (IQR)0

Descriptive statistics

Standard deviation146.36892
Coefficient of variation (CV)63.515315
Kurtosis4574.747
Mean2.3044665
Median Absolute Deviation (MAD)0
Skewness67.639712
Sum21102
Variance21423.86
MonotonicityNot monotonic
2023-12-13T05:32:18.768888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8511
92.9%
1 476
 
5.2%
2 112
 
1.2%
3 19
 
0.2%
4 10
 
0.1%
10 6
 
0.1%
5 6
 
0.1%
11 2
 
< 0.1%
9907 1
 
< 0.1%
20 1
 
< 0.1%
Other values (13) 13
 
0.1%
ValueCountFrequency (%)
0 8511
92.9%
1 476
 
5.2%
2 112
 
1.2%
3 19
 
0.2%
4 10
 
0.1%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9907 1
< 0.1%
9901 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
41 1
< 0.1%
30 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
15 1
< 0.1%
13 1
< 0.1%

토지대장면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3001
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.35145
Minimum37
Maximum33025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:18.889216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile114.38
Q1138.9
median194.5
Q3298.5
95-th percentile538.4
Maximum33025
Range32988
Interquartile range (IQR)159.6

Descriptive statistics

Standard deviation554.55466
Coefficient of variation (CV)2.036173
Kurtosis1501.852
Mean272.35145
Median Absolute Deviation (MAD)66.1
Skewness31.143103
Sum2493922.2
Variance307530.87
MonotonicityNot monotonic
2023-12-13T05:32:19.018842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.0 39
 
0.4%
136.0 34
 
0.4%
132.0 30
 
0.3%
147.0 30
 
0.3%
141.0 30
 
0.3%
145.0 28
 
0.3%
274.0 28
 
0.3%
135.0 27
 
0.3%
142.0 27
 
0.3%
165.0 27
 
0.3%
Other values (2991) 8857
96.7%
ValueCountFrequency (%)
37.0 1
< 0.1%
45.0 1
< 0.1%
57.0 1
< 0.1%
63.0 1
< 0.1%
64.0 1
< 0.1%
69.0 1
< 0.1%
70.0 2
< 0.1%
70.9 1
< 0.1%
73.0 1
< 0.1%
76.0 1
< 0.1%
ValueCountFrequency (%)
33025.0 1
 
< 0.1%
15998.8 1
 
< 0.1%
15273.0 1
 
< 0.1%
8117.0 1
 
< 0.1%
7041.0 16
0.2%
5130.0 1
 
< 0.1%
4484.0 1
 
< 0.1%
4235.0 1
 
< 0.1%
3897.0 1
 
< 0.1%
3570.0 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3008
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.54217
Minimum37
Maximum4484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:19.143252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile114
Q1138.5
median193
Q3297.6
95-th percentile517
Maximum4484
Range4447
Interquartile range (IQR)159.1

Descriptive statistics

Standard deviation203.71466
Coefficient of variation (CV)0.81963822
Kurtosis86.126514
Mean248.54217
Median Absolute Deviation (MAD)65.3
Skewness6.8024547
Sum2275900.6
Variance41499.664
MonotonicityNot monotonic
2023-12-13T05:32:19.276760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.0 39
 
0.4%
136.0 34
 
0.4%
132.0 31
 
0.3%
147.0 30
 
0.3%
141.0 29
 
0.3%
145.0 28
 
0.3%
274.0 28
 
0.3%
135.0 27
 
0.3%
142.0 27
 
0.3%
165.0 27
 
0.3%
Other values (2998) 8857
96.7%
ValueCountFrequency (%)
37.0 1
< 0.1%
45.0 1
< 0.1%
49.88 1
< 0.1%
52.68 1
< 0.1%
57.0 1
< 0.1%
63.0 1
< 0.1%
64.0 1
< 0.1%
69.0 1
< 0.1%
70.0 2
< 0.1%
70.9 1
< 0.1%
ValueCountFrequency (%)
4484.0 1
< 0.1%
4235.0 1
< 0.1%
3897.0 1
< 0.1%
3570.0 1
< 0.1%
3240.0 1
< 0.1%
3183.0 1
< 0.1%
3042.0 1
< 0.1%
2336.0 2
< 0.1%
2303.0 2
< 0.1%
2132.0 2
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7511
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.57445
Minimum16.2
Maximum5797.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:19.403173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.2
5-th percentile71.464
Q1149.01
median229.42
Q3413.52
95-th percentile719.648
Maximum5797.87
Range5781.67
Interquartile range (IQR)264.51

Descriptive statistics

Standard deviation239.20006
Coefficient of variation (CV)0.79580971
Kurtosis49.730037
Mean300.57445
Median Absolute Deviation (MAD)107.33
Skewness4.0780715
Sum2752360.2
Variance57216.671
MonotonicityNot monotonic
2023-12-13T05:32:19.531628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.97 28
 
0.3%
48.6 24
 
0.3%
98.11 21
 
0.2%
72.09 17
 
0.2%
65.37 13
 
0.1%
143.16 13
 
0.1%
81.59 12
 
0.1%
162.96 11
 
0.1%
65.0 10
 
0.1%
144.96 10
 
0.1%
Other values (7501) 8998
98.3%
ValueCountFrequency (%)
16.2 1
< 0.1%
20.28 1
< 0.1%
22.14 1
< 0.1%
22.2 1
< 0.1%
23.96 1
< 0.1%
24.48 1
< 0.1%
25.18 1
< 0.1%
26.52 1
< 0.1%
27.15 1
< 0.1%
27.43 1
< 0.1%
ValueCountFrequency (%)
5797.87 1
< 0.1%
4124.8 1
< 0.1%
3076.27 1
< 0.1%
2959.6 1
< 0.1%
2661.32 1
< 0.1%
2588.1 1
< 0.1%
2581.87 1
< 0.1%
2436.77 1
< 0.1%
2373.24 1
< 0.1%
2360.87 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7256
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.4959
Minimum8.35
Maximum1337.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:19.672029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.35
5-th percentile64.648
Q1108.19
median185.27
Q3269.49
95-th percentile596.344
Maximum1337.95
Range1329.6
Interquartile range (IQR)161.3

Descriptive statistics

Standard deviation160.79367
Coefficient of variation (CV)0.72923658
Kurtosis4.122099
Mean220.4959
Median Absolute Deviation (MAD)80.6
Skewness1.8917394
Sum2019080.9
Variance25854.606
MonotonicityNot monotonic
2023-12-13T05:32:19.824193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.97 28
 
0.3%
48.6 24
 
0.3%
98.11 20
 
0.2%
72.09 18
 
0.2%
65.37 13
 
0.1%
144.96 11
 
0.1%
81.59 11
 
0.1%
99.07 11
 
0.1%
143.16 11
 
0.1%
162.96 11
 
0.1%
Other values (7246) 8999
98.3%
ValueCountFrequency (%)
8.35 1
< 0.1%
16.2 1
< 0.1%
17.46 1
< 0.1%
20.28 1
< 0.1%
22.14 1
< 0.1%
22.2 1
< 0.1%
23.96 1
< 0.1%
24.48 1
< 0.1%
24.9 1
< 0.1%
25.18 1
< 0.1%
ValueCountFrequency (%)
1337.95 1
< 0.1%
1104.56 1
< 0.1%
1086.94 1
< 0.1%
1021.19 1
< 0.1%
1004.26 1
< 0.1%
991.87 1
< 0.1%
935.4 1
< 0.1%
927.4 1
< 0.1%
907.11 1
< 0.1%
904.68 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1328
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0666652 × 108
Minimum14900000
Maximum2.249 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2023-12-13T05:32:20.031007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14900000
5-th percentile84980000
Q11.45 × 108
median1.94 × 108
Q34.71 × 108
95-th percentile7.86 × 108
Maximum2.249 × 109
Range2.2341 × 109
Interquartile range (IQR)3.26 × 108

Descriptive statistics

Standard deviation2.3495065 × 108
Coefficient of variation (CV)0.7661438
Kurtosis2.2525458
Mean3.0666652 × 108
Median Absolute Deviation (MAD)77000000
Skewness1.4360743
Sum2.8081453 × 1012
Variance5.5201809 × 1016
MonotonicityNot monotonic
2023-12-13T05:32:20.225153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173000000 60
 
0.7%
174000000 59
 
0.6%
165000000 58
 
0.6%
166000000 57
 
0.6%
159000000 57
 
0.6%
171000000 56
 
0.6%
161000000 56
 
0.6%
139000000 56
 
0.6%
183000000 55
 
0.6%
176000000 54
 
0.6%
Other values (1318) 8589
93.8%
ValueCountFrequency (%)
14900000 1
< 0.1%
20700000 2
< 0.1%
20800000 1
< 0.1%
21400000 2
< 0.1%
24500000 1
< 0.1%
29300000 1
< 0.1%
31200000 1
< 0.1%
31600000 1
< 0.1%
33000000 1
< 0.1%
33400000 1
< 0.1%
ValueCountFrequency (%)
2249000000 1
< 0.1%
2088000000 1
< 0.1%
1944000000 1
< 0.1%
1820000000 1
< 0.1%
1604000000 1
< 0.1%
1353000000 1
< 0.1%
1323000000 1
< 0.1%
1285000000 1
< 0.1%
1268000000 1
< 0.1%
1256000000 1
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
2023-05-09
9157 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-09
2nd row2023-05-09
3rd row2023-05-09
4th row2023-05-09
5th row2023-05-09

Common Values

ValueCountFrequency (%)
2023-05-09 9157
100.0%

Length

2023-12-13T05:32:20.395537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:32:20.504535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-09 9157
100.0%

Interactions

2023-12-13T05:32:15.032159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.094111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.217136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.191996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.976298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.816857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.374781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.657113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.786272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.916023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.148648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.193745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.329697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.263431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.049997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.905648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.494784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.759820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.915596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.018791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.253870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.318105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.437351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.339821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.141325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.033460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.627081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.862423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.031865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.146890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.359357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.444876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.523070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.424909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.214034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.195786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.745677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.987368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.133771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.248797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.446627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.560487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.615530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.511804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.292117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.658900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.880712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.096905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.228340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.350837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.535787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.654083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.730989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.595104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.373971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.793497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.029885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.205055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.320921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.458806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.649422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.781601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.825873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.672416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.459274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.901675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.158717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.329250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.422091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.567633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.749296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:05.896913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.924016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.743972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.533007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:09.996793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.286172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.453485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.545377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.660248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.856470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.000594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.012321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.823678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.611145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.133055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.403131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.566430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.659596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.788083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:15.961746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:06.106154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.109998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:07.903793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:08.706148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:10.247349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:11.541458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:12.673479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:13.785855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:32:14.914303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:32:20.582395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명특수지구분코드특수지구분명본번부번동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격
법정동코드1.0001.0000.0930.0930.7340.2040.0000.0000.1150.3870.2640.6120.539
법정동명1.0001.0000.1350.1350.9100.3590.0000.0000.2110.3930.3940.6250.658
특수지구분코드0.0930.1351.0001.0000.0810.0000.0000.0000.2030.1500.0000.0620.014
특수지구분명0.0930.1351.0001.0000.0810.0000.0000.0000.2030.1500.0000.0620.014
본번0.7340.9100.0810.0811.0000.2350.0000.0000.0360.0810.4460.4970.452
부번0.2040.3590.0000.0000.2351.0000.0000.0000.0000.0970.0350.1050.140
동코드0.0000.0000.0000.0000.0000.0001.0000.9240.2800.0000.0000.0000.000
동명0.0000.0000.0000.0000.0000.0000.9241.0000.2800.0000.0000.0000.000
토지대장면적0.1150.2110.2030.2030.0360.0000.2800.2801.0000.7110.0670.0190.000
산정대지면적0.3870.3930.1500.1500.0810.0970.0000.0000.7111.0000.3240.2830.260
건물전체연면적0.2640.3940.0000.0000.4460.0350.0000.0000.0670.3241.0000.5240.409
건물산정연면적0.6120.6250.0620.0620.4970.1050.0000.0000.0190.2830.5241.0000.780
주택가격0.5390.6580.0140.0140.4520.1400.0000.0000.0000.2600.4090.7801.000
2023-12-13T05:32:20.753735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지구분코드특수지구분명법정동명
특수지구분코드1.0000.9800.118
특수지구분명0.9801.0000.118
법정동명0.1180.1181.000
2023-12-13T05:32:20.878480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드본번부번동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격법정동명특수지구분코드특수지구분명
법정동코드1.000-0.290-0.0170.0350.0350.4250.4270.0860.1770.3560.9990.0710.071
본번-0.2901.000-0.265-0.008-0.0080.0030.0130.1910.1750.1250.6850.0610.061
부번-0.017-0.2651.000-0.108-0.108-0.375-0.362-0.198-0.118-0.2140.1420.0000.000
동코드0.035-0.008-0.1081.0001.0000.1830.155-0.093-0.142-0.0530.0000.0000.000
동명0.035-0.008-0.1081.0001.0000.1830.155-0.093-0.142-0.0530.0000.0000.000
토지대장면적0.4250.003-0.3750.1830.1831.0000.9870.5180.4230.6720.1040.2480.248
산정대지면적0.4270.013-0.3620.1550.1550.9871.0000.5340.4370.6850.1560.1150.115
건물전체연면적0.0860.191-0.198-0.093-0.0930.5180.5341.0000.7520.5230.1740.0000.000
건물산정연면적0.1770.175-0.118-0.142-0.1420.4230.4370.7521.0000.7970.2850.0470.047
주택가격0.3560.125-0.214-0.053-0.0530.6720.6850.5230.7971.0000.3220.0140.014
법정동명0.9990.6850.1420.0000.0000.1040.1560.1740.2850.3221.0000.1180.118
특수지구분코드0.0710.0610.0000.0000.0000.2480.1150.0000.0470.0140.1181.0000.980
특수지구분명0.0710.0610.0000.0000.0000.2480.1150.0000.0470.0140.1180.9801.000

Missing values

2023-12-13T05:32:16.116545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:32:16.317753image/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
0101인천광역시 서구 백석동1일반302023100422.0422.0262.77262.773360000002023-05-09
1101인천광역시 서구 백석동1일반602023100330.0330.0179.65179.652940000002023-05-09
2101인천광역시 서구 백석동1일반1622023111165.0165.0135.25135.252290000002023-05-09
3101인천광역시 서구 백석동1일반19132023100330.0330.0298.26117.061710000002023-05-09
4101인천광역시 서구 백석동1일반2102023111361.0361.077.6977.69924000002023-05-09
5101인천광역시 서구 백석동1일반2112023111586.0586.0197.71197.712340000002023-05-09
6101인천광역시 서구 백석동1일반2252023100590.0590.095.8595.853970000002023-05-09
7101인천광역시 서구 백석동1일반2262023100553.0553.0260.88260.886050000002023-05-09
8101인천광역시 서구 백석동1일반38420231001323.0593.8118.7641.231520000002023-05-09
9101인천광역시 서구 백석동1일반188120231001097.01097.0490.5490.59640000002023-05-09
법정동코드법정동명특수지구분코드특수지구분명본번부번기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격데이터기준일자
9147122인천광역시 서구 청라동1일반19012023100274.0274.0409.66265.164590000002023-05-09
9148122인천광역시 서구 청라동1일반19022023100274.0274.0423.08275.274820000002023-05-09
9149122인천광역시 서구 청라동1일반19032023100274.0274.0427.56274.524850000002023-05-09
9150122인천광역시 서구 청라동1일반19052023100274.0274.0399.84250.984620000002023-05-09
9151122인천광역시 서구 청라동1일반19062023100274.0274.0408.93265.934850000002023-05-09
9152122인천광역시 서구 청라동1일반19112023100274.0274.0398.48248.774510000002023-05-09
9153122인천광역시 서구 청라동1일반19122023100274.0274.0419.18259.394610000002023-05-09
9154122인천광역시 서구 청라동1일반19142023100274.0274.0409.59260.564670000002023-05-09
9155122인천광역시 서구 청라동1일반19152023100274.0274.0410.46263.264480000002023-05-09
9156122인천광역시 서구 청라동1일반19162023100274.0274.0410.72269.34800000002023-05-09