Overview

Dataset statistics

Number of variables16
Number of observations8969
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

Numeric11
Categorical5

Dataset

Description매년 공시되는 개별주택 공시가격(법정동코드, 기준년도, 기준월, 동코드, 동명, 기준년도, 토지대장면적, 산정대지면적 등) 입니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15013512&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
기준년도 is highly overall correlated with 법정동코드 and 13 other fieldsHigh correlation
법정동명 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
특수지구분명 is highly overall correlated with 본번 and 2 other fieldsHigh correlation
특수지구분코드 is highly overall correlated with 본번 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 법정동명 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 법정동명 and 3 other fieldsHigh correlation
부번 is highly overall correlated with 기준년도High correlation
기준월 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 기준월 and 1 other fieldsHigh correlation
동명 is highly overall correlated with 기준년도High correlation
토지대장면적 is highly overall correlated with 기준년도High correlation
산정대지면적 is highly overall correlated with 건물산정연면적 and 2 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 건물산정연면적 and 1 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 산정대지면적 and 3 other fieldsHigh correlation
주택가격 is highly overall correlated with 산정대지면적 and 2 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (92.3%)Imbalance
특수지구분명 is highly imbalanced (92.3%)Imbalance
기준년도 is highly imbalanced (95.4%)Imbalance
기준월 is highly skewed (γ1 = 66.94154956)Skewed
동코드 is highly skewed (γ1 = 66.94154956)Skewed
동명 is highly skewed (γ1 = 66.94153919)Skewed
부번 has 317 (3.5%) zerosZeros
기준월 has 8365 (93.3%) zerosZeros
동코드 has 8365 (93.3%) zerosZeros
동명 has 8331 (92.9%) zerosZeros

Reproduction

Analysis started2024-03-18 02:56:19.094228
Analysis finished2024-03-18 02:56:33.722243
Duration14.63 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%
Mean112.14093
Minimum101
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:33.776213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile104
Q1109
median110
Q3114
95-th percentile122
Maximum122
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.1082164
Coefficient of variation (CV)0.045551757
Kurtosis-0.25807829
Mean112.14093
Median Absolute Deviation (MAD)2
Skewness0.68285461
Sum1005792
Variance26.093874
MonotonicityIncreasing
2024-03-18T11:56:34.064088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
110 2395
26.7%
112 1423
15.9%
122 1109
12.4%
108 767
 
8.6%
109 733
 
8.2%
113 461
 
5.1%
103 309
 
3.4%
115 253
 
2.8%
106 187
 
2.1%
107 184
 
2.1%
Other values (11) 1148
12.8%
ValueCountFrequency (%)
101 11
 
0.1%
102 20
 
0.2%
103 309
 
3.4%
104 142
 
1.6%
105 59
 
0.7%
106 187
 
2.1%
107 184
 
2.1%
108 767
 
8.6%
109 733
 
8.2%
110 2395
26.7%
ValueCountFrequency (%)
122 1109
12.4%
121 148
 
1.7%
120 162
 
1.8%
119 183
 
2.0%
118 157
 
1.8%
117 115
 
1.3%
115 253
 
2.8%
114 143
 
1.6%
113 461
 
5.1%
112 1423
15.9%

법정동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
인천광역시 서구 석남동
2395 
인천광역시 서구 가좌동
1423 
인천광역시 서구 청라동
1109 
인천광역시 서구 가정동
767 
인천광역시 서구 신현동
733 
Other values (16)
2542 

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 2395
26.7%
인천광역시 서구 가좌동 1423
15.9%
인천광역시 서구 청라동 1109
12.4%
인천광역시 서구 가정동 767
 
8.6%
인천광역시 서구 신현동 733
 
8.2%
인천광역시 서구 마전동 461
 
5.1%
인천광역시 서구 검암동 309
 
3.4%
인천광역시 서구 원당동 253
 
2.8%
인천광역시 서구 연희동 187
 
2.1%
인천광역시 서구 심곡동 184
 
2.1%
Other values (11) 1148
12.8%

Length

2024-03-18T11:56:34.161067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 8969
33.3%
서구 8969
33.3%
석남동 2395
 
8.9%
가좌동 1423
 
5.3%
청라동 1109
 
4.1%
가정동 767
 
2.9%
신현동 733
 
2.7%
마전동 461
 
1.7%
검암동 309
 
1.1%
원당동 253
 
0.9%
Other values (13) 1519
 
5.6%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
1
8782 
6
 
96
5
 
62
2
 
25
7
 
4

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 8782
97.9%
6 96
 
1.1%
5 62
 
0.7%
2 25
 
0.3%
7 4
 
< 0.1%

Length

2024-03-18T11:56:34.252892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:56:34.338205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8782
97.9%
6 96
 
1.1%
5 62
 
0.7%
2 25
 
0.3%
7 4
 
< 0.1%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
일반
8782 
6
 
96
5
 
62
 
25
7
 
4

Length

Max length2
Median length2
Mean length1.9791504
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 8782
97.9%
6 96
 
1.1%
5 62
 
0.7%
25
 
0.3%
7 4
 
< 0.1%

Length

2024-03-18T11:56:34.436848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:56:34.527865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 8782
97.9%
6 96
 
1.1%
5 62
 
0.7%
25
 
0.3%
7 4
 
< 0.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct803
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.67488
Minimum1
Maximum2002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:34.630523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1168
median430
Q3563
95-th percentile903
Maximum2002
Range2001
Interquartile range (IQR)395

Descriptive statistics

Standard deviation282.72792
Coefficient of variation (CV)0.68844707
Kurtosis2.5484114
Mean410.67488
Median Absolute Deviation (MAD)218
Skewness1.0936824
Sum3683343
Variance79935.074
MonotonicityNot monotonic
2024-03-18T11:56:34.750504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 171
 
1.9%
143 165
 
1.8%
111 108
 
1.2%
105 95
 
1.1%
144 87
 
1.0%
174 82
 
0.9%
112 80
 
0.9%
587 80
 
0.9%
142 77
 
0.9%
183 71
 
0.8%
Other values (793) 7953
88.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 16
 
0.2%
5 4
 
< 0.1%
6 4
 
< 0.1%
7 8
 
0.1%
8 56
0.6%
9 50
0.6%
11 1
 
< 0.1%
ValueCountFrequency (%)
2002 4
< 0.1%
1763 1
 
< 0.1%
1758 1
 
< 0.1%
1754 2
< 0.1%
1753 1
 
< 0.1%
1752 1
 
< 0.1%
1741 1
 
< 0.1%
1739 3
< 0.1%
1738 3
< 0.1%
1732 3
< 0.1%

부번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct241
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.406623
Minimum0
Maximum1602
Zeros317
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:34.881981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median13
Q328
95-th percentile99
Maximum1602
Range1602
Interquartile range (IQR)23

Descriptive statistics

Standard deviation67.200359
Coefficient of variation (CV)2.4519752
Kurtosis214.9949
Mean27.406623
Median Absolute Deviation (MAD)10
Skewness12.392296
Sum245810
Variance4515.8883
MonotonicityNot monotonic
2024-03-18T11:56:35.010960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 465
 
5.2%
2 459
 
5.1%
3 437
 
4.9%
4 382
 
4.3%
5 364
 
4.1%
6 348
 
3.9%
7 335
 
3.7%
0 317
 
3.5%
8 308
 
3.4%
9 279
 
3.1%
Other values (231) 5275
58.8%
ValueCountFrequency (%)
0 317
3.5%
1 465
5.2%
2 459
5.1%
3 437
4.9%
4 382
4.3%
5 364
4.1%
6 348
3.9%
7 335
3.7%
8 308
3.4%
9 279
3.1%
ValueCountFrequency (%)
1602 2
< 0.1%
1601 1
< 0.1%
1306 1
< 0.1%
1305 1
< 0.1%
1302 1
< 0.1%
1212 1
< 0.1%
1211 1
< 0.1%
1202 1
< 0.1%
1002 1
< 0.1%
1001 1
< 0.1%

기준년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
2021
8923 
<NA>
 
46

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 8923
99.5%
<NA> 46
 
0.5%

Length

2024-03-18T11:56:35.147784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:56:35.254600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 8923
99.5%
na 46
 
0.5%

기준월
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3484223
Minimum0
Maximum9907
Zeros8365
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:35.341341image/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 deviation147.89483
Coefficient of variation (CV)62.976248
Kurtosis4480.774
Mean2.3484223
Median Absolute Deviation (MAD)0
Skewness66.94155
Sum21063
Variance21872.88
MonotonicityNot monotonic
2024-03-18T11:56:35.458667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8365
93.3%
1 431
 
4.8%
2 114
 
1.3%
3 20
 
0.2%
4 11
 
0.1%
10 6
 
0.1%
5 5
 
0.1%
11 2
 
< 0.1%
13 1
 
< 0.1%
20 1
 
< 0.1%
Other values (13) 13
 
0.1%
ValueCountFrequency (%)
0 8365
93.3%
1 431
 
4.8%
2 114
 
1.3%
3 20
 
0.2%
4 11
 
0.1%
5 5
 
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%

동코드
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3484223
Minimum0
Maximum9907
Zeros8365
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:35.577402image/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 deviation147.89483
Coefficient of variation (CV)62.976248
Kurtosis4480.774
Mean2.3484223
Median Absolute Deviation (MAD)0
Skewness66.94155
Sum21063
Variance21872.88
MonotonicityNot monotonic
2024-03-18T11:56:35.692738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8365
93.3%
1 431
 
4.8%
2 114
 
1.3%
3 20
 
0.2%
4 11
 
0.1%
10 6
 
0.1%
5 5
 
0.1%
11 2
 
< 0.1%
13 1
 
< 0.1%
20 1
 
< 0.1%
Other values (13) 13
 
0.1%
ValueCountFrequency (%)
0 8365
93.3%
1 431
 
4.8%
2 114
 
1.3%
3 20
 
0.2%
4 11
 
0.1%
5 5
 
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%
Mean2.3518787
Minimum0
Maximum9907
Zeros8331
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:35.792727image/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 deviation147.89478
Coefficient of variation (CV)62.883679
Kurtosis4480.7731
Mean2.3518787
Median Absolute Deviation (MAD)0
Skewness66.941539
Sum21094
Variance21872.867
MonotonicityNot monotonic
2024-03-18T11:56:35.886659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8331
92.9%
1 468
 
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 8331
92.9%
1 468
 
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 

Distinct1115
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.5955
Minimum33
Maximum15998.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:35.987248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile111.1
Q1178.6
median259.8
Q3358.9
95-th percentile1033
Maximum15998.8
Range15965.8
Interquartile range (IQR)180.3

Descriptive statistics

Standard deviation489.987
Coefficient of variation (CV)1.3896576
Kurtosis208.9405
Mean352.5955
Median Absolute Deviation (MAD)84.9
Skewness11.356883
Sum3162429
Variance240087.26
MonotonicityNot monotonic
2024-03-18T11:56:36.109520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 156
 
1.7%
2132.0 84
 
0.9%
160.1 80
 
0.9%
218.4 78
 
0.9%
200.0 71
 
0.8%
176.3 67
 
0.7%
237.5 65
 
0.7%
120.8 65
 
0.7%
192.9 64
 
0.7%
383.2 63
 
0.7%
Other values (1105) 8176
91.2%
ValueCountFrequency (%)
33.0 1
< 0.1%
37.0 1
< 0.1%
57.0 1
< 0.1%
63.0 1
< 0.1%
64.0 1
< 0.1%
70.0 1
< 0.1%
70.9 1
< 0.1%
71.0 1
< 0.1%
73.0 2
< 0.1%
76.0 1
< 0.1%
ValueCountFrequency (%)
15998.8 1
 
< 0.1%
7041.0 23
 
0.3%
5130.0 4
 
< 0.1%
3240.0 4
 
< 0.1%
2336.0 7
 
0.1%
2191.0 1
 
< 0.1%
2132.0 84
0.9%
1661.0 5
 
0.1%
1606.0 1
 
< 0.1%
1597.5 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1289
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.08118
Minimum12.33
Maximum1643.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:36.234995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.33
5-th percentile42.75
Q168.13
median114.1
Q3221.78
95-th percentile381.656
Maximum1643.69
Range1631.36
Interquartile range (IQR)153.65

Descriptive statistics

Standard deviation128.98331
Coefficient of variation (CV)0.80073482
Kurtosis13.716663
Mean161.08118
Median Absolute Deviation (MAD)59.71
Skewness2.5706054
Sum1444737.1
Variance16636.693
MonotonicityNot monotonic
2024-03-18T11:56:36.355757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.26 86
 
1.0%
88.15 67
 
0.7%
330.0 67
 
0.7%
120.8 65
 
0.7%
77.02 64
 
0.7%
49.55 64
 
0.7%
82.56 63
 
0.7%
64.31 63
 
0.7%
277.0 59
 
0.7%
110.06 58
 
0.6%
Other values (1279) 8313
92.7%
ValueCountFrequency (%)
12.33 1
 
< 0.1%
15.13 1
 
< 0.1%
15.24 1
 
< 0.1%
15.99 1
 
< 0.1%
17.01 1
 
< 0.1%
17.08 1
 
< 0.1%
17.35 1
 
< 0.1%
17.41 1
 
< 0.1%
17.86 3
< 0.1%
18.2 1
 
< 0.1%
ValueCountFrequency (%)
1643.69 1
 
< 0.1%
1606.0 1
 
< 0.1%
1597.5 1
 
< 0.1%
1414.0 1
 
< 0.1%
1100.0 2
 
< 0.1%
1097.0 1
 
< 0.1%
1058.0 6
0.1%
1033.0 5
0.1%
973.0 4
< 0.1%
956.0 6
0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.27626
Minimum16.2
Maximum2959.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:36.464944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.2
5-th percentile93
Q1204.56
median383.41
Q3522.9
95-th percentile1188.8
Maximum2959.6
Range2943.4
Interquartile range (IQR)318.34

Descriptive statistics

Standard deviation372.62606
Coefficient of variation (CV)0.82026311
Kurtosis10.097411
Mean454.27626
Median Absolute Deviation (MAD)165.32
Skewness2.6184187
Sum4074403.8
Variance138850.18
MonotonicityNot monotonic
2024-03-18T11:56:36.612509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.02 67
 
0.7%
494.54 65
 
0.7%
191.16 65
 
0.7%
494.66 64
 
0.7%
154.6 63
 
0.7%
1618.95 63
 
0.7%
138.86 59
 
0.7%
198.38 58
 
0.6%
2360.87 57
 
0.6%
1532.3 56
 
0.6%
Other values (1339) 8352
93.1%
ValueCountFrequency (%)
16.2 37
0.4%
20.28 47
0.5%
22.14 2
 
< 0.1%
22.2 1
 
< 0.1%
23.96 1
 
< 0.1%
29.75 8
 
0.1%
31.4 2
 
< 0.1%
32.9 1
 
< 0.1%
33.56 1
 
< 0.1%
34.0 2
 
< 0.1%
ValueCountFrequency (%)
2959.6 23
 
0.3%
2581.87 3
 
< 0.1%
2360.87 57
0.6%
1988.68 1
 
< 0.1%
1912.32 5
 
0.1%
1747.81 6
 
0.1%
1655.51 1
 
< 0.1%
1625.67 1
 
< 0.1%
1618.95 63
0.7%
1602.18 8
 
0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1341
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.47147
Minimum8.35
Maximum1337.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:36.978928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.35
5-th percentile62.004
Q199.42
median148.98
Q3300.87
95-th percentile611.94
Maximum1337.95
Range1329.6
Interquartile range (IQR)201.45

Descriptive statistics

Standard deviation180.74317
Coefficient of variation (CV)0.80519439
Kurtosis1.9798423
Mean224.47147
Median Absolute Deviation (MAD)62.25
Skewness1.5486161
Sum2013284.6
Variance32668.092
MonotonicityNot monotonic
2024-03-18T11:56:37.101817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.42 76
 
0.8%
87.51 67
 
0.7%
191.16 65
 
0.7%
79.72 65
 
0.7%
197.54 64
 
0.7%
209.25 63
 
0.7%
117.78 61
 
0.7%
138.86 59
 
0.7%
103.48 58
 
0.6%
227.24 57
 
0.6%
Other values (1331) 8334
92.9%
ValueCountFrequency (%)
8.35 1
 
< 0.1%
16.2 37
0.4%
17.46 1
 
< 0.1%
20.28 47
0.5%
22.14 2
 
< 0.1%
22.2 1
 
< 0.1%
23.96 1
 
< 0.1%
24.9 1
 
< 0.1%
28.69 1
 
< 0.1%
28.92 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%
927.4 1
 
< 0.1%
904.68 1
 
< 0.1%
903.76 3
< 0.1%
901.67 1
 
< 0.1%
891.1 5
0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1373
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9871496 × 108
Minimum14500000
Maximum2.056 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2024-03-18T11:56:37.233799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14500000
5-th percentile82840000
Q11.41 × 108
median1.86 × 108
Q34.59 × 108
95-th percentile7.886 × 108
Maximum2.056 × 109
Range2.0415 × 109
Interquartile range (IQR)3.18 × 108

Descriptive statistics

Standard deviation2.3599239 × 108
Coefficient of variation (CV)0.79002535
Kurtosis2.024809
Mean2.9871496 × 108
Median Absolute Deviation (MAD)69000000
Skewness1.4847931
Sum2.6791745 × 1012
Variance5.569241 × 1016
MonotonicityNot monotonic
2024-03-18T11:56:37.399675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170000000 67
 
0.7%
151000000 65
 
0.7%
157000000 64
 
0.7%
142000000 63
 
0.7%
166000000 63
 
0.7%
149000000 61
 
0.7%
160000000 59
 
0.7%
141000000 58
 
0.6%
165000000 57
 
0.6%
163000000 56
 
0.6%
Other values (1363) 8356
93.2%
ValueCountFrequency (%)
14500000 1
< 0.1%
20700000 1
< 0.1%
21400000 1
< 0.1%
21500000 1
< 0.1%
21900000 1
< 0.1%
22000000 1
< 0.1%
23700000 1
< 0.1%
28200000 1
< 0.1%
31100000 1
< 0.1%
31500000 2
< 0.1%
ValueCountFrequency (%)
2056000000 1
< 0.1%
1919000000 1
< 0.1%
1680000000 1
< 0.1%
1436000000 1
< 0.1%
1369000000 1
< 0.1%
1323000000 1
< 0.1%
1315000000 1
< 0.1%
1308000000 1
< 0.1%
1300000000 1
< 0.1%
1295000000 1
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
2021-05-17
8969 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-17
2nd row2021-05-17
3rd row2021-05-17
4th row2021-05-17
5th row2021-05-17

Common Values

ValueCountFrequency (%)
2021-05-17 8969
100.0%

Length

2024-03-18T11:56:37.534757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:56:37.615628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-17 8969
100.0%

Interactions

2024-03-18T11:56:32.318043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.621660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.586144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.499727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.733070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.752923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.894924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.062035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.146577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.088484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.397787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.397323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.689425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.667777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.576782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.840888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.849705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.973930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.142653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.225749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.180341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.479556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.489876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.771408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.750221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.813711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.947275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.950366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.081638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.241162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.312843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.290878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.563683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.576477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.857784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.841714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.890985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.027638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.064200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.167511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.328981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.399892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.387408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.643719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.661099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.925487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.921167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.964730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.100042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.142607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.248135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.402508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.476030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.477811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.717677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.739026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:21.991765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.996642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.039791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.185955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.225677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.333088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.485097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.557060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.558450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.797260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.819118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.088165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.074645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.113367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.283003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.294702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.619743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.591329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.644299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.631534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.876353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.930859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.190745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.163058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.210434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.369795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.391555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.697192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.692746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.731673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:30.958324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.960523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:33.047190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.275461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.257318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.323986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.480143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.518077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.782187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.805098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.821910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.056065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.043562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:33.148265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.373102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.334144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.435855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.572539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.622641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.866488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:28.905872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.907432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.175098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.129112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:33.254122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:22.481586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:23.416394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:24.553982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:25.656071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:26.758519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:27.968765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.028287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:29.997659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:31.298946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:56:32.219401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:56:37.693010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명특수지구분코드특수지구분명본번부번기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격
법정동코드1.0001.0000.3430.3430.7450.1800.0000.0000.0000.0720.4380.3690.6340.549
법정동명1.0001.0000.5430.5430.9080.2860.0000.0000.0000.1950.5890.3680.6220.670
특수지구분코드0.3430.5431.0001.0000.6830.7270.0000.0000.0000.0310.2010.1960.3940.319
특수지구분명0.3430.5431.0001.0000.6830.7270.0000.0000.0000.0310.2010.1960.3940.319
본번0.7450.9080.6830.6831.0000.1230.0000.0000.0000.0180.3480.2350.4280.444
부번0.1800.2860.7270.7270.1231.0000.0000.0000.0000.0000.1600.0930.2900.240
기준월0.0000.0000.0000.0000.0000.0001.0000.9240.0000.2020.0000.0000.0000.000
동코드0.0000.0000.0000.0000.0000.0000.9241.0000.0000.2020.0000.0000.0000.000
동명0.0000.0000.0000.0000.0000.0000.0000.0001.0000.2020.0000.0000.0000.000
토지대장면적0.0720.1950.0310.0310.0180.0000.2020.2020.2021.0000.4800.1100.0800.091
산정대지면적0.4380.5890.2010.2010.3480.1600.0000.0000.0000.4801.0000.3470.6770.830
건물전체연면적0.3690.3680.1960.1960.2350.0930.0000.0000.0000.1100.3471.0000.7100.461
건물산정연면적0.6340.6220.3940.3940.4280.2900.0000.0000.0000.0800.6770.7101.0000.835
주택가격0.5490.6700.3190.3190.4440.2400.0000.0000.0000.0910.8300.4610.8351.000
2024-03-18T11:56:37.849206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도법정동명특수지구분명특수지구분코드
기준년도1.0001.0001.0001.000
법정동명1.0001.0000.3020.302
특수지구분명1.0000.3021.0001.000
특수지구분코드1.0000.3021.0001.000
2024-03-18T11:56:37.942081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드본번부번기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격법정동명특수지구분코드특수지구분명기준년도
법정동코드1.000-0.290-0.0030.0190.0190.0370.0500.2830.1020.3090.3110.9990.1490.1491.000
본번-0.2901.000-0.264-0.009-0.009-0.0020.0050.0610.0270.0750.0920.6810.5090.5091.000
부번-0.003-0.2641.000-0.108-0.108-0.028-0.026-0.167-0.017-0.161-0.1920.1090.3860.3861.000
기준월0.019-0.009-0.1081.0001.0000.1050.012-0.038-0.057-0.063-0.0560.0000.0000.0001.000
동코드0.019-0.009-0.1081.0001.0000.1050.012-0.038-0.057-0.063-0.0560.0000.0000.0001.000
동명0.037-0.002-0.0280.1050.1051.0000.0230.0280.0080.0220.0240.0000.0000.0001.000
토지대장면적0.0500.005-0.0260.0120.0120.0231.0000.3420.4980.1640.2720.0880.0210.0211.000
산정대지면적0.2830.061-0.167-0.038-0.0380.0280.3421.000-0.0500.6040.8380.2720.1170.1171.000
건물전체연면적0.1020.027-0.017-0.057-0.0570.0080.498-0.0501.0000.5300.2610.1440.0830.0831.000
건물산정연면적0.3090.075-0.161-0.063-0.0630.0220.1640.6040.5301.0000.8310.2840.1740.1741.000
주택가격0.3110.092-0.192-0.056-0.0560.0240.2720.8380.2610.8311.0000.3320.1900.1901.000
법정동명0.9990.6810.1090.0000.0000.0000.0880.2720.1440.2840.3321.0000.3020.3021.000
특수지구분코드0.1490.5090.3860.0000.0000.0000.0210.1170.0830.1740.1900.3021.0001.0001.000
특수지구분명0.1490.5090.3860.0000.0000.0000.0210.1170.0830.1740.1900.3021.0001.0001.000
기준년도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-18T11:56:33.449418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:56:33.641445image/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 백석동1일반302021000422.0422.0262.77262.773540000002021-05-17
1101인천광역시 서구 백석동1일반602021000330.0330.0179.65179.653070000002021-05-17
2101인천광역시 서구 백석동1일반1622021111165.0165.078.9678.961870000002021-05-17
3101인천광역시 서구 백석동1일반1822021000702.0702.0203.67203.675720000002021-05-17
4101인천광역시 서구 백석동1일반19132021001330.0129.53298.26117.061740000002021-05-17
5101인천광역시 서구 백석동1일반2102021111361.0148.1577.6977.69985000002021-05-17
6101인천광역시 서구 백석동1일반2112021110586.0295.05197.71197.712470000002021-05-17
7101인천광역시 서구 백석동1일반2252021000590.0590.095.8595.854270000002021-05-17
8101인천광역시 서구 백석동1일반2262021000553.0553.0260.88260.886170000002021-05-17
9101인천광역시 서구 백석동1일반38420210001323.0206.17118.7641.231590000002021-05-17
법정동코드법정동명특수지구분코드특수지구분명본번부번기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격데이터기준일자
8959122인천광역시 서구 청라동1일반1901<NA>000256.0161.82397.92251.524530000002021-05-17
8960122인천광역시 서구 청라동1일반1902<NA>000214.6214.6397.31397.314680000002021-05-17
8961122인천광역시 서구 청라동1일반1903<NA>000206.1206.1365.88365.884760000002021-05-17
8962122인천광역시 서구 청라동1일반1905<NA>0003240.0818.05502.65202.654490000002021-05-17
8963122인천광역시 서구 청라동1일반1906<NA>000218.2218.2338.52338.524740000002021-05-17
8964122인천광역시 서구 청라동1일반1911<NA>000277.6139.11673.44337.474410000002021-05-17
8965122인천광역시 서구 청라동1일반1912<NA>000274.0274.0403.56403.564480000002021-05-17
8966122인천광역시 서구 청라동1일반1914<NA>000336.4336.4121.68121.684580000002021-05-17
8967122인천광역시 서구 청라동1일반1915<NA>000277.6139.11673.44337.474410000002021-05-17
8968122인천광역시 서구 청라동1일반1916<NA>000222.7222.7501.04501.044690000002021-05-17