Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells10000
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.8 KiB
Average record size in memory86.0 B

Variable types

Numeric5
Boolean1
Categorical2
Unsupported1

Dataset

Description부산광역시_연제구_개별공시지가정보_20200916
Author부산광역시 연제구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15039887

Alerts

No is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
행정동 is highly overall correlated with No and 1 other fieldsHigh correlation
본번 is highly overall correlated with NoHigh correlation
법정동 is highly overall correlated with No and 1 other fieldsHigh correlation
표준지여부 is highly imbalanced (81.8%)Imbalance
구분 is highly imbalanced (85.0%)Imbalance
Unnamed: 8 has 10000 (100.0%) missing valuesMissing
No has unique valuesUnique
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 241 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:11:30.490056
Analysis finished2023-12-10 16:11:33.697699
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13180.83
Minimum1
Maximum26405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:11:33.785690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1302.9
Q16537.25
median13188.5
Q319860.75
95-th percentile25118.05
Maximum26405
Range26404
Interquartile range (IQR)13323.5

Descriptive statistics

Standard deviation7653.0807
Coefficient of variation (CV)0.58062207
Kurtosis-1.2067778
Mean13180.83
Median Absolute Deviation (MAD)6662
Skewness0.0036694457
Sum1.318083 × 108
Variance58569644
MonotonicityNot monotonic
2023-12-11T01:11:33.925907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10176 1
 
< 0.1%
12889 1
 
< 0.1%
21360 1
 
< 0.1%
3771 1
 
< 0.1%
3700 1
 
< 0.1%
16968 1
 
< 0.1%
4598 1
 
< 0.1%
22032 1
 
< 0.1%
7386 1
 
< 0.1%
22422 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
18 1
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
26405 1
< 0.1%
26401 1
< 0.1%
26399 1
< 0.1%
26396 1
< 0.1%
26393 1
< 0.1%
26392 1
< 0.1%
26391 1
< 0.1%
26389 1
< 0.1%
26388 1
< 0.1%
26384 1
< 0.1%

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9724 
True
 
276
ValueCountFrequency (%)
False 9724
97.2%
True 276
 
2.8%
2023-12-11T01:11:34.029354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

법정동
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연산동
6812 
거제동
3188 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연산동
2nd row연산동
3rd row거제동
4th row연산동
5th row거제동

Common Values

ValueCountFrequency (%)
연산동 6812
68.1%
거제동 3188
31.9%

Length

2023-12-11T01:11:34.117324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:11:34.213048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연산동 6812
68.1%
거제동 3188
31.9%

행정동
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean668.357
Minimum610
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:11:34.288338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile610
Q1630
median670
Q3700
95-th percentile730
Maximum730
Range120
Interquartile range (IQR)70

Descriptive statistics

Standard deviation37.864415
Coefficient of variation (CV)0.056652979
Kurtosis-1.1491839
Mean668.357
Median Absolute Deviation (MAD)30
Skewness0.06785284
Sum6683570
Variance1433.7139
MonotonicityNot monotonic
2023-12-11T01:11:34.389204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
700 1148
11.5%
620 1051
10.5%
730 1001
10.0%
660 929
9.3%
680 888
8.9%
670 862
8.6%
610 814
8.1%
630 742
7.4%
720 706
7.1%
690 706
7.1%
Other values (2) 1153
11.5%
ValueCountFrequency (%)
610 814
8.1%
620 1051
10.5%
630 742
7.4%
640 581
5.8%
650 572
5.7%
660 929
9.3%
670 862
8.6%
680 888
8.9%
690 706
7.1%
700 1148
11.5%
ValueCountFrequency (%)
730 1001
10.0%
720 706
7.1%
700 1148
11.5%
690 706
7.1%
680 888
8.9%
670 862
8.6%
660 929
9.3%
650 572
5.7%
640 581
5.8%
630 742
7.4%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9784 
산
 
216

Length

Max length2
Median length2
Mean length1.9784
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9784
97.8%
산 216
 
2.2%

Length

2023-12-11T01:11:34.499988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:11:34.588904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9784
97.8%
산 216
 
2.2%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1310
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.7118
Minimum1
Maximum2360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:11:34.701143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile69
Q1467.75
median785
Q31429
95-th percentile2063
Maximum2360
Range2359
Interquartile range (IQR)961.25

Descriptive statistics

Standard deviation623.99352
Coefficient of variation (CV)0.65634351
Kurtosis-0.90212155
Mean950.7118
Median Absolute Deviation (MAD)428
Skewness0.48947771
Sum9507118
Variance389367.92
MonotonicityNot monotonic
2023-12-11T01:11:34.818733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1811 308
 
3.1%
2022 195
 
1.9%
676 145
 
1.5%
643 88
 
0.9%
1941 80
 
0.8%
1876 79
 
0.8%
649 62
 
0.6%
766 60
 
0.6%
1824 59
 
0.6%
815 59
 
0.6%
Other values (1300) 8865
88.6%
ValueCountFrequency (%)
1 37
0.4%
2 30
0.3%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 15
0.1%
11 8
 
0.1%
ValueCountFrequency (%)
2360 1
< 0.1%
2359 1
< 0.1%
2356 1
< 0.1%
2355 1
< 0.1%
2351 1
< 0.1%
2350 1
< 0.1%
2342 1
< 0.1%
2338 1
< 0.1%
2336 1
< 0.1%
2334 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct559
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.8932
Minimum0
Maximum900
Zeros241
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:11:34.936387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median20
Q344
95-th percentile213
Maximum900
Range900
Interquartile range (IQR)37

Descriptive statistics

Standard deviation100.96593
Coefficient of variation (CV)2.023641
Kurtosis22.86708
Mean49.8932
Median Absolute Deviation (MAD)15
Skewness4.4414324
Sum498932
Variance10194.118
MonotonicityNot monotonic
2023-12-11T01:11:35.052886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 433
 
4.3%
2 404
 
4.0%
3 352
 
3.5%
6 307
 
3.1%
4 305
 
3.0%
5 277
 
2.8%
7 257
 
2.6%
0 241
 
2.4%
9 232
 
2.3%
8 225
 
2.2%
Other values (549) 6967
69.7%
ValueCountFrequency (%)
0 241
2.4%
1 433
4.3%
2 404
4.0%
3 352
3.5%
4 305
3.0%
5 277
2.8%
6 307
3.1%
7 257
2.6%
8 225
2.2%
9 232
2.3%
ValueCountFrequency (%)
900 1
< 0.1%
897 1
< 0.1%
896 1
< 0.1%
871 1
< 0.1%
870 1
< 0.1%
867 1
< 0.1%
864 1
< 0.1%
859 1
< 0.1%
856 1
< 0.1%
855 1
< 0.1%

결정지가
Real number (ℝ)

Distinct2474
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1403703.9
Minimum1350
Maximum14700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:11:35.165217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1350
5-th percentile360000
Q1861000
median1202000
Q31654250
95-th percentile3227000
Maximum14700000
Range14698650
Interquartile range (IQR)793250

Descriptive statistics

Standard deviation1020326
Coefficient of variation (CV)0.72688123
Kurtosis26.644404
Mean1403703.9
Median Absolute Deviation (MAD)383000
Skewness3.5302093
Sum1.4037039 × 1010
Variance1.0410652 × 1012
MonotonicityNot monotonic
2023-12-11T01:11:35.300365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1240000 270
 
2.7%
1400000 190
 
1.9%
402600 83
 
0.8%
653400 79
 
0.8%
633600 72
 
0.7%
504900 65
 
0.7%
1600000 64
 
0.6%
990000 56
 
0.6%
3461000 51
 
0.5%
534600 43
 
0.4%
Other values (2464) 9027
90.3%
ValueCountFrequency (%)
1350 3
 
< 0.1%
1650 2
 
< 0.1%
1700 1
 
< 0.1%
1710 1
 
< 0.1%
1730 2
 
< 0.1%
1750 2
 
< 0.1%
3000 11
0.1%
3500 1
 
< 0.1%
3950 2
 
< 0.1%
3960 1
 
< 0.1%
ValueCountFrequency (%)
14700000 1
 
< 0.1%
14200000 3
< 0.1%
12920000 2
< 0.1%
12830000 1
 
< 0.1%
12800000 1
 
< 0.1%
12300000 1
 
< 0.1%
11930000 1
 
< 0.1%
11500000 1
 
< 0.1%
10580000 1
 
< 0.1%
9720000 1
 
< 0.1%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2023-12-11T01:11:32.823454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.285890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.644136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.037223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.444619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.911967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.355706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.715962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.107873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.515464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:33.043045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.439398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.809632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.187738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.589159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:33.206864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.504020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.888331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.278360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.659033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:33.325228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.570200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:31.956645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.351582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:11:32.732665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:11:35.642516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No표준지여부법정동행정동구분본번부번결정지가
No1.0000.0680.9990.8600.3990.9550.4740.372
표준지여부0.0681.0000.0230.0340.0040.0510.0000.145
법정동0.9990.0231.0001.0000.0270.5590.1010.089
행정동0.8600.0341.0001.0000.1220.7440.3390.282
구분0.3990.0040.0270.1221.0000.5570.0580.122
본번0.9550.0510.5590.7440.5571.0000.5000.327
부번0.4740.0000.1010.3390.0580.5001.0000.180
결정지가0.3720.1450.0890.2820.1220.3270.1801.000
2023-12-11T01:11:35.737130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동구분표준지여부
법정동1.0000.0170.015
구분0.0171.0000.003
표준지여부0.0150.0031.000
2023-12-11T01:11:35.820814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No행정동본번부번결정지가표준지여부법정동구분
No1.0000.5850.6870.053-0.0690.0520.9700.306
행정동0.5851.0000.0850.030-0.0230.0271.0000.080
본번0.6870.0851.0000.047-0.0590.0390.4320.430
부번0.0530.0300.0471.000-0.1610.0000.0780.044
결정지가-0.069-0.023-0.059-0.1611.0000.1110.0680.095
표준지여부0.0520.0270.0390.0000.1111.0000.0150.003
법정동0.9701.0000.4320.0780.0680.0151.0000.017
구분0.3060.0800.4300.0440.0950.0030.0171.000

Missing values

2023-12-11T01:11:33.468145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:11:33.641828image/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

No표준지여부법정동행정동구분본번부번결정지가Unnamed: 8
1017510176N연산동720일반33948916500<NA>
1983019831N연산동690일반1371111940000<NA>
42854286N거제동630일반747331907000<NA>
1096610967N연산동720일반378261019000<NA>
28822883N거제동630일반61551029000<NA>
64306431N거제동620일반101331193000<NA>
2526825269N연산동700일반2129181294000<NA>
1833418335N연산동680일반1136111769000<NA>
32723273N거제동640일반6491191322000<NA>
2621226213N연산동680산13440873800<NA>
No표준지여부법정동행정동구분본번부번결정지가Unnamed: 8
74327433N거제동620일반13124889000<NA>
2004920050N연산동660일반146113461000<NA>
239240N거제동610일반1833633600<NA>
18981899N거제동610일반386333524000<NA>
2378823789N연산동700일반2018271138000<NA>
1686316864N연산동660일반822100965000<NA>
2446324464N연산동660일반2027101400000<NA>
2380823809N연산동700일반201850911800<NA>
2465424655N연산동660일반2063431710000<NA>
2560125602N연산동700일반2139221860000<NA>