Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description부산광역시사상구_일반건축물시가표준액_20211231
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15080289

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 6 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (94.8%)Imbalance
동 is highly skewed (γ1 = 23.23470971)Skewed
시가표준액 is highly skewed (γ1 = 46.55327222)Skewed
연면적 is highly skewed (γ1 = 23.84473097)Skewed
부번 has 1429 (14.3%) zerosZeros
동 has 638 (6.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:29:21.719898
Analysis finished2023-12-10 16:29:28.193014
Duration6.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 10000
100.0%

Length

2023-12-11T01:29:28.261389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:28.342838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
사상구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 10000
100.0%

Length

2023-12-11T01:29:28.426342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:28.503877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
26530
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 10000
100.0%

Length

2023-12-11T01:29:28.584752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:28.664260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 10000
100.0%

과세년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
5973 
2021
4027 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 5973
59.7%
2021 4027
40.3%

Length

2023-12-11T01:29:28.752360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:28.839017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 5973
59.7%
2021 4027
40.3%

법정동
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.2021
Minimum101
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:28.913875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median104
Q3105
95-th percentile107
Maximum108
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7682105
Coefficient of variation (CV)0.016969048
Kurtosis-0.4284137
Mean104.2021
Median Absolute Deviation (MAD)1
Skewness0.033870869
Sum1042021
Variance3.1265682
MonotonicityNot monotonic
2023-12-11T01:29:29.013595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
104 3103
31.0%
105 1912
19.1%
102 1099
 
11.0%
106 1003
 
10.0%
103 886
 
8.9%
101 860
 
8.6%
107 781
 
7.8%
108 356
 
3.6%
ValueCountFrequency (%)
101 860
 
8.6%
102 1099
 
11.0%
103 886
 
8.9%
104 3103
31.0%
105 1912
19.1%
106 1003
 
10.0%
107 781
 
7.8%
108 356
 
3.6%
ValueCountFrequency (%)
108 356
 
3.6%
107 781
 
7.8%
106 1003
 
10.0%
105 1912
19.1%
104 3103
31.0%
103 886
 
8.9%
102 1099
 
11.0%
101 860
 
8.6%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-11T01:29:29.418184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:29.505541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9941 
2
 
59

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 9941
99.4%
2 59
 
0.6%

Length

2023-12-11T01:29:29.604113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:29.689014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9941
99.4%
2 59
 
0.6%

본번
Real number (ℝ)

Distinct754
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean468.52
Minimum1
Maximum1380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:29.783368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75
Q1257
median512
Q3578
95-th percentile956.05
Maximum1380
Range1379
Interquartile range (IQR)321

Descriptive statistics

Standard deviation279.06007
Coefficient of variation (CV)0.59562041
Kurtosis1.392381
Mean468.52
Median Absolute Deviation (MAD)148
Skewness0.85614793
Sum4685200
Variance77874.525
MonotonicityNot monotonic
2023-12-11T01:29:29.912165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
529 990
 
9.9%
578 761
 
7.6%
152 373
 
3.7%
502 322
 
3.2%
143 78
 
0.8%
132 56
 
0.6%
1375 54
 
0.5%
417 53
 
0.5%
271 51
 
0.5%
651 48
 
0.5%
Other values (744) 7214
72.1%
ValueCountFrequency (%)
1 27
0.3%
2 1
 
< 0.1%
3 13
0.1%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 6
 
0.1%
10 24
0.2%
11 4
 
< 0.1%
ValueCountFrequency (%)
1380 6
 
0.1%
1375 54
0.5%
1368 1
 
< 0.1%
1367 5
 
0.1%
1365 1
 
< 0.1%
1364 2
 
< 0.1%
1362 36
0.4%
1361 14
 
0.1%
1360 3
 
< 0.1%
1359 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.9327
Minimum0
Maximum425
Zeros1429
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:30.042531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile37
Maximum425
Range425
Interquartile range (IQR)12

Descriptive statistics

Standard deviation24.635699
Coefficient of variation (CV)2.2533957
Kurtosis90.641615
Mean10.9327
Median Absolute Deviation (MAD)4
Skewness7.9218065
Sum109327
Variance606.91766
MonotonicityNot monotonic
2023-12-11T01:29:30.169932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2218
22.2%
0 1429
14.3%
2 788
 
7.9%
7 410
 
4.1%
3 407
 
4.1%
4 388
 
3.9%
5 353
 
3.5%
6 307
 
3.1%
8 254
 
2.5%
9 247
 
2.5%
Other values (151) 3199
32.0%
ValueCountFrequency (%)
0 1429
14.3%
1 2218
22.2%
2 788
 
7.9%
3 407
 
4.1%
4 388
 
3.9%
5 353
 
3.5%
6 307
 
3.1%
7 410
 
4.1%
8 254
 
2.5%
9 247
 
2.5%
ValueCountFrequency (%)
425 1
< 0.1%
404 1
< 0.1%
400 2
< 0.1%
389 1
< 0.1%
386 1
< 0.1%
384 2
< 0.1%
378 1
< 0.1%
371 2
< 0.1%
345 1
< 0.1%
321 1
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6918
Minimum0
Maximum2000
Zeros638
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:30.303375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile17
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.564444
Coefficient of variation (CV)8.1809698
Kurtosis647.99966
Mean5.6918
Median Absolute Deviation (MAD)0
Skewness23.23471
Sum56918
Variance2168.2474
MonotonicityNot monotonic
2023-12-11T01:29:30.468040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7115
71.2%
0 638
 
6.4%
2 451
 
4.5%
3 241
 
2.4%
4 209
 
2.1%
5 137
 
1.4%
7 132
 
1.3%
6 113
 
1.1%
9 71
 
0.7%
10 62
 
0.6%
Other values (54) 831
 
8.3%
ValueCountFrequency (%)
0 638
 
6.4%
1 7115
71.2%
2 451
 
4.5%
3 241
 
2.4%
4 209
 
2.1%
5 137
 
1.4%
6 113
 
1.1%
7 132
 
1.3%
8 62
 
0.6%
9 71
 
0.7%
ValueCountFrequency (%)
2000 1
 
< 0.1%
1001 1
 
< 0.1%
1000 12
0.1%
802 2
 
< 0.1%
801 3
 
< 0.1%
403 1
 
< 0.1%
301 2
 
< 0.1%
116 8
0.1%
113 3
 
< 0.1%
112 2
 
< 0.1%

호
Text

Distinct1341
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:29:30.839027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0523
Min length1

Characters and Unicode

Total characters30523
Distinct characters18
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

Unique893 ?
Unique (%)8.9%

Sample

1st row3357
2nd row101
3rd row102
4th row101
5th row101
ValueCountFrequency (%)
101 1756
17.6%
201 1065
 
10.7%
102 734
 
7.3%
8101 471
 
4.7%
301 408
 
4.1%
103 319
 
3.2%
1 294
 
2.9%
202 234
 
2.3%
401 185
 
1.8%
104 162
 
1.6%
Other values (1331) 4372
43.7%
2023-12-11T01:29:31.343338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10511
34.4%
0 7600
24.9%
2 4721
15.5%
3 2367
 
7.8%
4 1391
 
4.6%
8 1110
 
3.6%
5 942
 
3.1%
6 754
 
2.5%
7 581
 
1.9%
9 492
 
1.6%
Other values (8) 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30469
99.8%
Dash Punctuation 43
 
0.1%
Other Letter 8
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10511
34.5%
0 7600
24.9%
2 4721
15.5%
3 2367
 
7.8%
4 1391
 
4.6%
8 1110
 
3.6%
5 942
 
3.1%
6 754
 
2.5%
7 581
 
1.9%
9 492
 
1.6%
Other Letter
ValueCountFrequency (%)
나 3
37.5%
다 2
25.0%
가 2
25.0%
라 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30513
> 99.9%
Hangul 8
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10511
34.4%
0 7600
24.9%
2 4721
15.5%
3 2367
 
7.8%
4 1391
 
4.6%
8 1110
 
3.6%
5 942
 
3.1%
6 754
 
2.5%
7 581
 
1.9%
9 492
 
1.6%
Other values (2) 44
 
0.1%
Hangul
ValueCountFrequency (%)
나 3
37.5%
다 2
25.0%
가 2
25.0%
라 1
 
12.5%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30515
> 99.9%
Hangul 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10511
34.4%
0 7600
24.9%
2 4721
15.5%
3 2367
 
7.8%
4 1391
 
4.6%
8 1110
 
3.6%
5 942
 
3.1%
6 754
 
2.5%
7 581
 
1.9%
9 492
 
1.6%
Other values (4) 46
 
0.2%
Hangul
ValueCountFrequency (%)
나 3
37.5%
다 2
25.0%
가 2
25.0%
라 1
 
12.5%

물건지
Text

Distinct9170
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:29:31.640058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length26.425
Min length20

Characters and Unicode

Total characters264250
Distinct characters74
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8409 ?
Unique (%)84.1%

Sample

1st row부산광역시 사상구 괘법동 529-1 1동 3357호
2nd row부산광역시 사상구 학장동 557 1동 101호
3rd row부산광역시 사상구 삼락동 76-9 1동 102호
4th row[ 가야대로330번길 75 ] 0001동 0101호
5th row[ 모덕로 85 ] 0001동 0101호
ValueCountFrequency (%)
7462
 
12.5%
사상구 6269
 
10.5%
부산광역시 6269
 
10.5%
1동 3836
 
6.4%
0001동 3279
 
5.5%
괘법동 2146
 
3.6%
감전동 1438
 
2.4%
529-1 990
 
1.7%
0101호 882
 
1.5%
101호 874
 
1.5%
Other values (4485) 26317
44.0%
2023-12-11T01:29:32.154881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49762
18.8%
1 25815
 
9.8%
0 25357
 
9.6%
동 16554
 
6.3%
2 11711
 
4.4%
호 9998
 
3.8%
상 7233
 
2.7%
사 7227
 
2.7%
5 6672
 
2.5%
광 6622
 
2.5%
Other values (64) 97299
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105522
39.9%
Decimal Number 95835
36.3%
Space Separator 49762
18.8%
Dash Punctuation 5667
 
2.1%
Close Punctuation 3731
 
1.4%
Open Punctuation 3731
 
1.4%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16554
15.7%
호 9998
 
9.5%
상 7233
 
6.9%
사 7227
 
6.8%
광 6622
 
6.3%
산 6359
 
6.0%
시 6325
 
6.0%
역 6269
 
5.9%
구 6269
 
5.9%
부 6269
 
5.9%
Other values (48) 26397
25.0%
Decimal Number
ValueCountFrequency (%)
1 25815
26.9%
0 25357
26.5%
2 11711
12.2%
5 6672
 
7.0%
3 6271
 
6.5%
4 4448
 
4.6%
7 4162
 
4.3%
8 4106
 
4.3%
6 3687
 
3.8%
9 3606
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
49762
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5667
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3731
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 158726
60.1%
Hangul 105522
39.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16554
15.7%
호 9998
 
9.5%
상 7233
 
6.9%
사 7227
 
6.8%
광 6622
 
6.3%
산 6359
 
6.0%
시 6325
 
6.0%
역 6269
 
5.9%
구 6269
 
5.9%
부 6269
 
5.9%
Other values (48) 26397
25.0%
Common
ValueCountFrequency (%)
49762
31.4%
1 25815
16.3%
0 25357
16.0%
2 11711
 
7.4%
5 6672
 
4.2%
3 6271
 
4.0%
- 5667
 
3.6%
4 4448
 
2.8%
7 4162
 
2.6%
8 4106
 
2.6%
Other values (4) 14755
 
9.3%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158728
60.1%
Hangul 105522
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49762
31.4%
1 25815
16.3%
0 25357
16.0%
2 11711
 
7.4%
5 6672
 
4.2%
3 6271
 
4.0%
- 5667
 
3.6%
4 4448
 
2.8%
7 4162
 
2.6%
8 4106
 
2.6%
Other values (6) 14757
 
9.3%
Hangul
ValueCountFrequency (%)
동 16554
15.7%
호 9998
 
9.5%
상 7233
 
6.9%
사 7227
 
6.8%
광 6622
 
6.3%
산 6359
 
6.0%
시 6325
 
6.0%
역 6269
 
5.9%
구 6269
 
5.9%
부 6269
 
5.9%
Other values (48) 26397
25.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7596
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57813143
Minimum0
Maximum1.6879132 × 1010
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:32.313206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1444800
Q17822275
median18237600
Q352286825
95-th percentile2.0446043 × 108
Maximum1.6879132 × 1010
Range1.6879132 × 1010
Interquartile range (IQR)44464550

Descriptive statistics

Standard deviation2.2788185 × 108
Coefficient of variation (CV)3.9416964
Kurtosis3142.4476
Mean57813143
Median Absolute Deviation (MAD)15179260
Skewness46.553272
Sum5.7813143 × 1011
Variance5.193014 × 1016
MonotonicityNot monotonic
2023-12-11T01:29:32.515068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13411840 156
 
1.6%
13134080 125
 
1.2%
16985310 81
 
0.8%
17365060 73
 
0.7%
14769840 72
 
0.7%
15100060 69
 
0.7%
1921480 49
 
0.5%
26827060 49
 
0.5%
26271470 42
 
0.4%
9373260 41
 
0.4%
Other values (7586) 9243
92.4%
ValueCountFrequency (%)
0 2
< 0.1%
30000 1
< 0.1%
73800 1
< 0.1%
74000 1
< 0.1%
88000 1
< 0.1%
102000 1
< 0.1%
114000 1
< 0.1%
115500 1
< 0.1%
120000 1
< 0.1%
122100 1
< 0.1%
ValueCountFrequency (%)
16879131860 1
< 0.1%
8053128000 1
< 0.1%
4317376440 1
< 0.1%
3029080250 1
< 0.1%
2856777000 1
< 0.1%
2292742610 1
< 0.1%
2076396000 1
< 0.1%
1932800000 1
< 0.1%
1740528000 1
< 0.1%
1665200000 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5507
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.48926
Minimum0
Maximum19159.06
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:29:32.730033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.39
Q128.242
median60.02
Q3150.1077
95-th percentile492.48
Maximum19159.06
Range19159.06
Interquartile range (IQR)121.8657

Descriptive statistics

Standard deviation367.37526
Coefficient of variation (CV)2.5425784
Kurtosis1002.9458
Mean144.48926
Median Absolute Deviation (MAD)42.935
Skewness23.844731
Sum1444892.6
Variance134964.58
MonotonicityNot monotonic
2023-12-11T01:29:32.946265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.02 295
 
2.9%
39.68 281
 
2.8%
60.02 92
 
0.9%
79.37 91
 
0.9%
4.84 84
 
0.8%
15.86 74
 
0.7%
16.72 61
 
0.6%
20.406 59
 
0.6%
5.733 54
 
0.5%
5.812 48
 
0.5%
Other values (5497) 8861
88.6%
ValueCountFrequency (%)
0.0 2
< 0.1%
0.75 1
 
< 0.1%
1.0 2
< 0.1%
1.2 1
 
< 0.1%
1.87 1
 
< 0.1%
1.95 1
 
< 0.1%
2.0 4
< 0.1%
2.05 1
 
< 0.1%
2.16 2
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
19159.06 1
< 0.1%
14589.0 1
< 0.1%
7299.2 1
< 0.1%
6511.88 1
< 0.1%
6391.0 1
< 0.1%
5204.0 1
< 0.1%
4832.0 1
< 0.1%
4163.0 1
< 0.1%
3977.0 1
< 0.1%
3626.1 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-12-31 00:00:00
Maximum2021-12-31 00:00:00
2023-12-11T01:29:33.099302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:33.184886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T01:29:27.258210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.005231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.693256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.359618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.939175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.633832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.377900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.120806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.808783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.451569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.043318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.742650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.473886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.219073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.916212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.543462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.160190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.844987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.561335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.325555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.016899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.647464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.268799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.932710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.656783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.468176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.118724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.735885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.427180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.040465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.755269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:24.589139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.244148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:25.843094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:26.536731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:27.155124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:29:33.270691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동시가표준액연면적
과세년도1.0000.3610.0440.1670.0520.0000.0000.008
법정동0.3611.0000.0910.7120.2560.0560.0360.047
특수지0.0440.0911.0000.2330.0000.0000.0000.163
본번0.1670.7120.2331.0000.2770.0890.0000.052
부번0.0520.2560.0000.2771.0000.0000.0000.000
동0.0000.0560.0000.0890.0001.0000.0000.000
시가표준액0.0000.0360.0000.0000.0000.0001.0000.879
연면적0.0080.0470.1630.0520.0000.0000.8791.000
2023-12-11T01:29:33.383529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.028
과세년도0.0281.000
2023-12-11T01:29:33.482602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세년도특수지
법정동1.000-0.1410.0800.0170.0470.0050.2710.068
본번-0.1411.000-0.2380.0600.042-0.0180.1280.178
부번0.080-0.2381.000-0.3900.1430.2740.0400.000
동0.0170.060-0.3901.000-0.154-0.2100.0000.000
시가표준액0.0470.0420.143-0.1541.0000.8450.0000.000
연면적0.005-0.0180.274-0.2100.8451.0000.0060.117
과세년도0.2710.1280.0400.0000.0000.0061.0000.028
특수지0.0680.1780.0000.0000.0000.1170.0281.000

Missing values

2023-12-11T01:29:27.915536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:29:28.097706image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
49976부산광역시사상구26530202010401529113357부산광역시 사상구 괘법동 529-1 1동 3357호1028560017.22021-12-31
16068부산광역시사상구2653020201070155701101부산광역시 사상구 학장동 557 1동 101호62740300133.492021-12-31
75079부산광역시사상구265302021101017691102부산광역시 사상구 삼락동 76-9 1동 102호53592000348.02021-12-31
84706부산광역시사상구26530202110601921591101[ 가야대로330번길 75 ] 0001동 0101호783619020.162021-12-31
71610부산광역시사상구26530202110201728101101[ 모덕로 85 ] 0001동 0101호223227670427.232021-12-31
664부산광역시사상구265302020106016891101[ 가야대로360번길 1 ] 0001동 0101호1499011070.082021-12-31
50875부산광역시사상구26530202010301417181101[ 사상로319번길 12 ] 0001동 0101호99286720263.362021-12-31
67587부산광역시사상구26530202110501157140501[ 학감대로 232 ] 0000동 0501호70547940126.432021-12-31
6811부산광역시사상구265302020103017651918101[ 백양대로778번길 5-12 ] 0001동 8101호2051456075.22021-12-31
3389부산광역시사상구2653020201060117801301[ 주례로 15 ] 0001동 0301호2063600030.82021-12-31
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
47418부산광역시사상구26530202010501150221201[ 새벽로121번길 104 ] 0001동 0201호465186025.012021-12-31
38205부산광역시사상구26530202010101365121203부산광역시 사상구 삼락동 365-12 1동 203호7465860196.472021-12-31
63587부산광역시사상구26530202110601571711104[ 가야대로378번길 1 ] 0001동 1104호92430980102.35992021-12-31
55410부산광역시사상구26530202110401578016130부산광역시 사상구 괘법동 578 16동 130호1698531030.022021-12-31
20331부산광역시사상구2653020201040155941301[ 새벽로223번길 26 ] 0001동 0301호261795600264.442021-12-31
3100부산광역시사상구265302020105015081603부산광역시 사상구 감전동 508-16 3호26658320192.342021-12-31
83339부산광역시사상구2653020211030141922102부산광역시 사상구 덕포동 419-2 2동 102호355465056.22021-12-31
4796부산광역시사상구26530202010101398131101[ 낙동대로1302번길 47 ] 0001동 0101호129486950252.192021-12-31
75199부산광역시사상구26530202110101342231103[ 낙동대로1420번길 25 ] 0001동 0103호75880000280.02021-12-31
4843부산광역시사상구2653020201080114171101[ 하신번영로 452 ] 0001동 0101호233207360443.362021-12-31

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시사상구26530202010301365311부산광역시 사상구 덕포동 365-3 1동 1호49343000133.02021-12-312
1부산광역시사상구2653020201040155761715부산광역시 사상구 괘법동 557-6 17동 15호24535860128.462021-12-312
2부산광역시사상구2653020201070127671101부산광역시 사상구 학장동 276-7 1동 101호3663009.92021-12-312
3부산광역시사상구2653020201070128991101부산광역시 사상구 학장동 289-9 1동 101호228781580492.482021-12-312
4부산광역시사상구26530202110201390011[ 백양대로907번길 19 ] 0001동 0001호171820930301.972021-12-312
5부산광역시사상구265302021104015251117[ 광장로37번길 50 ] 0001동 0007호52166400148.22021-12-312