Overview

Dataset statistics

Number of variables5
Number of observations1458
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.2 KiB
Average record size in memory45.1 B

Variable types

Categorical2
Numeric3

Dataset

Description밀양시 개별공시지가 입니다
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15002417

Alerts

본번 is highly overall correlated with 구분High correlation
공시지가 is highly overall correlated with 지명코드명High correlation
지명코드명 is highly overall correlated with 공시지가High correlation
구분 is highly overall correlated with 본번High correlation
지명코드명 is highly imbalanced (70.5%)Imbalance
부번 has 1160 (79.6%) zerosZeros

Reproduction

Analysis started2023-12-10 23:41:04.493920
Analysis finished2023-12-10 23:41:05.505626
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지명코드명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
39027
1382 
10100
 
76

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10100
2nd row10100
3rd row10100
4th row10100
5th row10100

Common Values

ValueCountFrequency (%)
39027 1382
94.8%
10100 76
 
5.2%

Length

2023-12-11T08:41:05.559485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:41:05.652599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
39027 1382
94.8%
10100 76
 
5.2%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
835 
1
623 

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 (%)
2 835
57.3%
1 623
42.7%

Length

2023-12-11T08:41:05.754770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:41:05.878652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 835
57.3%
1 623
42.7%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1182
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean889.2572
Minimum1
Maximum1968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:41:05.975025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q1284.25
median637.5
Q31678.75
95-th percentile1885.15
Maximum1968
Range1967
Interquartile range (IQR)1394.5

Descriptive statistics

Standard deviation696.83621
Coefficient of variation (CV)0.78361605
Kurtosis-1.5767158
Mean889.2572
Median Absolute Deviation (MAD)534
Skewness0.31690764
Sum1296537
Variance485580.71
MonotonicityNot monotonic
2023-12-11T08:41:06.097118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1840 62
 
4.3%
12 15
 
1.0%
30 7
 
0.5%
1561 7
 
0.5%
21 6
 
0.4%
1746 6
 
0.4%
1 5
 
0.3%
10 5
 
0.3%
29 5
 
0.3%
300 5
 
0.3%
Other values (1172) 1335
91.6%
ValueCountFrequency (%)
1 5
0.3%
2 2
 
0.1%
4 1
 
0.1%
5 2
 
0.1%
6 4
0.3%
7 3
0.2%
8 3
0.2%
9 3
0.2%
10 5
0.3%
11 2
 
0.1%
ValueCountFrequency (%)
1968 1
0.1%
1967 1
0.1%
1966 1
0.1%
1965 1
0.1%
1964 1
0.1%
1963 1
0.1%
1962 1
0.1%
1961 1
0.1%
1960 1
0.1%
1959 1
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.899177
Minimum0
Maximum122
Zeros1160
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:41:06.225479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum122
Range122
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.50492
Coefficient of variation (CV)5.0031166
Kurtosis41.640627
Mean2.899177
Median Absolute Deviation (MAD)0
Skewness6.3830191
Sum4227
Variance210.39271
MonotonicityNot monotonic
2023-12-11T08:41:06.351787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1160
79.6%
1 103
 
7.1%
2 68
 
4.7%
3 32
 
2.2%
4 15
 
1.0%
5 8
 
0.5%
6 3
 
0.2%
10 3
 
0.2%
12 2
 
0.1%
22 2
 
0.1%
Other values (57) 62
 
4.3%
ValueCountFrequency (%)
0 1160
79.6%
1 103
 
7.1%
2 68
 
4.7%
3 32
 
2.2%
4 15
 
1.0%
5 8
 
0.5%
6 3
 
0.2%
7 1
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
122 1
0.1%
121 1
0.1%
120 1
0.1%
119 1
0.1%
118 1
0.1%
117 1
0.1%
116 1
0.1%
115 1
0.1%
114 1
0.1%
102 1
0.1%

공시지가
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7134.3299
Minimum141
Maximum171000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:41:06.791152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile201
Q1216
median583
Q38925
95-th percentile30000
Maximum171000
Range170859
Interquartile range (IQR)8709

Descriptive statistics

Standard deviation17948.522
Coefficient of variation (CV)2.5157965
Kurtosis51.409094
Mean7134.3299
Median Absolute Deviation (MAD)378
Skewness6.3945802
Sum10401853
Variance3.2214946 × 108
MonotonicityNot monotonic
2023-12-11T08:41:06.945014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 54
 
3.7%
216 50
 
3.4%
2970 39
 
2.7%
330 38
 
2.6%
203 35
 
2.4%
205 30
 
2.1%
209 30
 
2.1%
211 29
 
2.0%
201 29
 
2.0%
6010 25
 
1.7%
Other values (266) 1099
75.4%
ValueCountFrequency (%)
141 1
 
0.1%
158 1
 
0.1%
165 21
 
1.4%
191 6
 
0.4%
197 5
 
0.3%
199 13
 
0.9%
201 29
2.0%
203 35
2.4%
205 30
2.1%
207 54
3.7%
ValueCountFrequency (%)
171000 2
 
0.1%
170000 8
0.5%
163000 1
 
0.1%
137000 1
 
0.1%
91000 1
 
0.1%
81700 1
 
0.1%
78700 1
 
0.1%
77900 1
 
0.1%
77300 1
 
0.1%
57700 1
 
0.1%

Interactions

2023-12-11T08:41:05.125886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:04.657880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:04.896902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:05.229866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:04.741578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:04.981077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:05.306132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:04.820431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:05.050193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:41:07.039997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지명코드명구분본번부번공시지가
지명코드명1.0000.4070.6360.0000.919
구분0.4071.0000.9920.2880.518
본번0.6360.9921.0000.2720.540
부번0.0000.2880.2721.0000.000
공시지가0.9190.5180.5400.0001.000
2023-12-11T08:41:07.122287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지명코드명
구분1.0000.267
지명코드명0.2671.000
2023-12-11T08:41:07.194645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번공시지가지명코드명구분
본번1.0000.0810.4890.4820.917
부번0.0811.0000.3760.0000.220
공시지가0.4890.3761.0000.7520.389
지명코드명0.4820.0000.7521.0000.267
구분0.9170.2200.3890.2671.000

Missing values

2023-12-11T08:41:05.401707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:41:05.475102image/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

지명코드명구분본번부번공시지가
010100110137000
1101001117260
21010011278700
31010011425900
41010012046100
51010012357700
61010015023700
71010016124400
81010016214200
91010016323700
지명코드명구분본번부번공시지가
14483902728050165
14493902728060165
14503902728070165
14513902728080330
145239027280902970
14533902728100165
14543902728110217
14553902728120165
14563902728130165
14573902728140165