Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory148.0 B

Variable types

Numeric8
Categorical7
Text1

Dataset

Description부산광역시금정구_일반건축물시가표준액_20230912
Author부산광역시 금정구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15080091

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
연번 is highly overall correlated with 법정동High correlation
법정동 is highly overall correlated with 연번High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (92.2%)Imbalance
동 is highly skewed (γ1 = 42.11534587)Skewed
연번 has unique valuesUnique
부번 has 797 (8.0%) zerosZeros
동 has 113 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:41:28.115566
Analysis finished2023-12-10 16:41:39.539077
Duration11.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19745.189
Minimum1
Maximum39097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:39.648340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1990.9
Q110041.75
median19842
Q329566.25
95-th percentile37197.05
Maximum39097
Range39096
Interquartile range (IQR)19524.5

Descriptive statistics

Standard deviation11243.997
Coefficient of variation (CV)0.569455
Kurtosis-1.1867366
Mean19745.189
Median Absolute Deviation (MAD)9771.5
Skewness-0.020353349
Sum1.9745189 × 108
Variance1.2642746 × 108
MonotonicityNot monotonic
2023-12-11T01:41:39.824367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37962 1
 
< 0.1%
25116 1
 
< 0.1%
6845 1
 
< 0.1%
21009 1
 
< 0.1%
2691 1
 
< 0.1%
18921 1
 
< 0.1%
8312 1
 
< 0.1%
29022 1
 
< 0.1%
5390 1
 
< 0.1%
18689 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
39097 1
< 0.1%
39088 1
< 0.1%
39082 1
< 0.1%
39069 1
< 0.1%
39068 1
< 0.1%
39067 1
< 0.1%
39057 1
< 0.1%
39055 1
< 0.1%
39052 1
< 0.1%
39050 1
< 0.1%

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

2023-12-11T01:41:40.013304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:40.129702image/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
금정구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금정구 10000
100.0%

Length

2023-12-11T01:41:40.236581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:40.374111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금정구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
26410
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26410
2nd row26410
3rd row26410
4th row26410
5th row26410

Common Values

ValueCountFrequency (%)
26410 10000
100.0%

Length

2023-12-11T01:41:40.505862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:40.648316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26410 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

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 10000
100.0%

Length

2023-12-11T01:41:40.778369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:40.874029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.8795
Minimum101
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:40.959564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1107
median108
Q3110
95-th percentile112
Maximum113
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5056751
Coefficient of variation (CV)0.02322661
Kurtosis0.2237352
Mean107.8795
Median Absolute Deviation (MAD)1
Skewness-0.61829822
Sum1078795
Variance6.2784076
MonotonicityNot monotonic
2023-12-11T01:41:41.076975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
108 2258
22.6%
107 1908
19.1%
109 1567
15.7%
110 1261
12.6%
104 1075
10.8%
111 716
 
7.2%
112 407
 
4.1%
103 287
 
2.9%
101 196
 
2.0%
113 144
 
1.4%
Other values (3) 181
 
1.8%
ValueCountFrequency (%)
101 196
 
2.0%
102 90
 
0.9%
103 287
 
2.9%
104 1075
10.8%
105 53
 
0.5%
106 38
 
0.4%
107 1908
19.1%
108 2258
22.6%
109 1567
15.7%
110 1261
12.6%
ValueCountFrequency (%)
113 144
 
1.4%
112 407
 
4.1%
111 716
 
7.2%
110 1261
12.6%
109 1567
15.7%
108 2258
22.6%
107 1908
19.1%
106 38
 
0.4%
105 53
 
0.5%
104 1075
10.8%

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

2023-12-11T01:41:41.220409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:41.320300image/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
9904 
2
 
96

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 9904
99.0%
2 96
 
1.0%

Length

2023-12-11T01:41:41.445583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:41.564358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9904
99.0%
2 96
 
1.0%

본번
Real number (ℝ)

Distinct803
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.008
Minimum1
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:41.694856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1126
median268
Q3482
95-th percentile1012.05
Maximum1641
Range1640
Interquartile range (IQR)356

Descriptive statistics

Standard deviation291.31662
Coefficient of variation (CV)0.83231417
Kurtosis0.488064
Mean350.008
Median Absolute Deviation (MAD)155
Skewness1.1159051
Sum3500080
Variance84865.372
MonotonicityNot monotonic
2023-12-11T01:41:41.845862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1027 313
 
3.1%
302 197
 
2.0%
292 165
 
1.7%
87 146
 
1.5%
225 120
 
1.2%
420 106
 
1.1%
40 105
 
1.1%
207 90
 
0.9%
86 82
 
0.8%
414 74
 
0.7%
Other values (793) 8602
86.0%
ValueCountFrequency (%)
1 9
 
0.1%
2 15
 
0.1%
3 9
 
0.1%
4 7
 
0.1%
5 12
 
0.1%
7 22
0.2%
8 23
0.2%
9 45
0.4%
11 20
0.2%
13 9
 
0.1%
ValueCountFrequency (%)
1641 5
0.1%
1520 3
< 0.1%
1496 1
 
< 0.1%
1494 1
 
< 0.1%
1482 3
< 0.1%
1449 1
 
< 0.1%
1327 1
 
< 0.1%
1318 1
 
< 0.1%
1306 1
 
< 0.1%
1267 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct296
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.7896
Minimum0
Maximum1935
Zeros797
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:42.033669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile77
Maximum1935
Range1935
Interquartile range (IQR)18

Descriptive statistics

Standard deviation126.77926
Coefficient of variation (CV)4.2558228
Kurtosis119.19802
Mean29.7896
Median Absolute Deviation (MAD)7
Skewness10.284615
Sum297896
Variance16072.981
MonotonicityNot monotonic
2023-12-11T01:41:42.204878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1408
 
14.1%
0 797
 
8.0%
4 554
 
5.5%
2 546
 
5.5%
3 422
 
4.2%
6 364
 
3.6%
5 360
 
3.6%
7 347
 
3.5%
9 343
 
3.4%
11 337
 
3.4%
Other values (286) 4522
45.2%
ValueCountFrequency (%)
0 797
8.0%
1 1408
14.1%
2 546
 
5.5%
3 422
 
4.2%
4 554
 
5.5%
5 360
 
3.6%
6 364
 
3.6%
7 347
 
3.5%
8 304
 
3.0%
9 343
 
3.4%
ValueCountFrequency (%)
1935 1
< 0.1%
1916 1
< 0.1%
1907 1
< 0.1%
1885 1
< 0.1%
1877 1
< 0.1%
1862 2
< 0.1%
1840 1
< 0.1%
1833 1
< 0.1%
1830 1
< 0.1%
1828 1
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.9993
Minimum0
Maximum9001
Zeros113
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:42.347984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile101
Maximum9001
Range9001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation205.25867
Coefficient of variation (CV)15.789979
Kurtosis1837.1313
Mean12.9993
Median Absolute Deviation (MAD)0
Skewness42.115346
Sum129993
Variance42131.122
MonotonicityNot monotonic
2023-12-11T01:41:42.535408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8563
85.6%
2 320
 
3.2%
3 137
 
1.4%
101 119
 
1.2%
0 113
 
1.1%
4 54
 
0.5%
103 50
 
0.5%
5 45
 
0.4%
105 41
 
0.4%
112 35
 
0.4%
Other values (90) 523
 
5.2%
ValueCountFrequency (%)
0 113
 
1.1%
1 8563
85.6%
2 320
 
3.2%
3 137
 
1.4%
4 54
 
0.5%
5 45
 
0.4%
6 18
 
0.2%
7 18
 
0.2%
8 18
 
0.2%
9 18
 
0.2%
ValueCountFrequency (%)
9001 5
0.1%
1000 10
0.1%
401 2
 
< 0.1%
302 2
 
< 0.1%
301 6
0.1%
216 4
 
< 0.1%
212 3
 
< 0.1%
208 3
 
< 0.1%
207 8
0.1%
206 2
 
< 0.1%

호
Real number (ℝ)

Distinct598
Distinct (%)6.0%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1244.4523
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:42.721893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101
Q1101
median201
Q3501
95-th percentile8101
Maximum9999
Range9998
Interquartile range (IQR)400

Descriptive statistics

Standard deviation2584.621
Coefficient of variation (CV)2.0769144
Kurtosis3.1814296
Mean1244.4523
Median Absolute Deviation (MAD)100
Skewness2.249289
Sum12440790
Variance6680265.9
MonotonicityNot monotonic
2023-12-11T01:41:42.962327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 2435
24.3%
201 1223
12.2%
8101 881
 
8.8%
102 866
 
8.7%
301 627
 
6.3%
401 338
 
3.4%
103 303
 
3.0%
202 226
 
2.3%
501 214
 
2.1%
104 129
 
1.3%
Other values (588) 2755
27.6%
ValueCountFrequency (%)
1 33
0.3%
2 17
0.2%
3 10
 
0.1%
4 8
 
0.1%
5 10
 
0.1%
6 5
 
0.1%
7 12
 
0.1%
8 4
 
< 0.1%
9 9
 
0.1%
10 8
 
0.1%
ValueCountFrequency (%)
9999 1
 
< 0.1%
9905 1
 
< 0.1%
9903 1
 
< 0.1%
9902 1
 
< 0.1%
9901 10
0.1%
9101 1
 
< 0.1%
9001 1
 
< 0.1%
8402 1
 
< 0.1%
8401 1
 
< 0.1%
8302 1
 
< 0.1%

물건지
Text

Distinct9801
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:41:43.338288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.7275
Min length21

Characters and Unicode

Total characters257275
Distinct characters122
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

Unique9627 ?
Unique (%)96.3%

Sample

1st row[ 두실로15번길 20 ] 0001동 0401호
2nd row[ 중앙대로 1829 ] 0001동 1301호
3rd row[ 장전온천천로73번길 9 ] 0001동 0301호
4th row부산광역시 금정구 부곡동 223-79 1동 101호
5th row[ 서동중심로 24-1 ] 0001동 0101호
ValueCountFrequency (%)
14192
23.6%
0001동 6852
 
11.4%
금정구 2904
 
4.8%
부산광역시 2904
 
4.8%
0101호 1750
 
2.9%
1동 1711
 
2.8%
0201호 935
 
1.6%
8101호 881
 
1.5%
101호 682
 
1.1%
0102호 652
 
1.1%
Other values (3482) 26577
44.3%
2023-12-11T01:41:44.427177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50040
19.5%
0 40018
15.6%
1 29803
 
11.6%
동 13893
 
5.4%
호 10000
 
3.9%
2 9135
 
3.6%
] 7096
 
2.8%
[ 7096
 
2.8%
로 6968
 
2.7%
3 5487
 
2.1%
Other values (112) 77739
30.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106636
41.4%
Other Letter 82644
32.1%
Space Separator 50040
19.5%
Close Punctuation 7096
 
2.8%
Open Punctuation 7096
 
2.8%
Dash Punctuation 3752
 
1.5%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 13893
16.8%
호 10000
 
12.1%
로 6968
 
8.4%
금 5256
 
6.4%
부 4191
 
5.1%
구 3845
 
4.7%
산 3528
 
4.3%
정 3406
 
4.1%
길 3209
 
3.9%
번 3081
 
3.7%
Other values (94) 25267
30.6%
Decimal Number
ValueCountFrequency (%)
0 40018
37.5%
1 29803
27.9%
2 9135
 
8.6%
3 5487
 
5.1%
5 4665
 
4.4%
4 4192
 
3.9%
8 3633
 
3.4%
7 3516
 
3.3%
6 3499
 
3.3%
9 2688
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
B 6
54.5%
D 3
27.3%
C 1
 
9.1%
A 1
 
9.1%
Space Separator
ValueCountFrequency (%)
50040
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7096
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7096
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174620
67.9%
Hangul 82644
32.1%
Latin 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 13893
16.8%
호 10000
 
12.1%
로 6968
 
8.4%
금 5256
 
6.4%
부 4191
 
5.1%
구 3845
 
4.7%
산 3528
 
4.3%
정 3406
 
4.1%
길 3209
 
3.9%
번 3081
 
3.7%
Other values (94) 25267
30.6%
Common
ValueCountFrequency (%)
50040
28.7%
0 40018
22.9%
1 29803
17.1%
2 9135
 
5.2%
] 7096
 
4.1%
[ 7096
 
4.1%
3 5487
 
3.1%
5 4665
 
2.7%
4 4192
 
2.4%
- 3752
 
2.1%
Other values (4) 13336
 
7.6%
Latin
ValueCountFrequency (%)
B 6
54.5%
D 3
27.3%
C 1
 
9.1%
A 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174631
67.9%
Hangul 82644
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50040
28.7%
0 40018
22.9%
1 29803
17.1%
2 9135
 
5.2%
] 7096
 
4.1%
[ 7096
 
4.1%
3 5487
 
3.1%
5 4665
 
2.7%
4 4192
 
2.4%
- 3752
 
2.1%
Other values (8) 13347
 
7.6%
Hangul
ValueCountFrequency (%)
동 13893
16.8%
호 10000
 
12.1%
로 6968
 
8.4%
금 5256
 
6.4%
부 4191
 
5.1%
구 3845
 
4.7%
산 3528
 
4.3%
정 3406
 
4.1%
길 3209
 
3.9%
번 3081
 
3.7%
Other values (94) 25267
30.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8520
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66024537
Minimum20700
Maximum5.3416906 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:44.657413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20700
5-th percentile616960
Q15357712.5
median28559700
Q363605160
95-th percentile2.5193782 × 108
Maximum5.3416906 × 109
Range5.3416699 × 109
Interquartile range (IQR)58247448

Descriptive statistics

Standard deviation1.6251425 × 108
Coefficient of variation (CV)2.4614219
Kurtosis209.59318
Mean66024537
Median Absolute Deviation (MAD)24911815
Skewness10.941529
Sum6.6024537 × 1011
Variance2.641088 × 1016
MonotonicityNot monotonic
2023-12-11T01:41:44.919042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
616960 308
 
3.1%
47630 32
 
0.3%
1336320 22
 
0.2%
3133950 21
 
0.2%
3873400 18
 
0.2%
45183010 17
 
0.2%
47330 15
 
0.1%
2025600 14
 
0.1%
25258560 13
 
0.1%
40627980 13
 
0.1%
Other values (8510) 9527
95.3%
ValueCountFrequency (%)
20700 1
 
< 0.1%
29400 1
 
< 0.1%
44270 1
 
< 0.1%
47330 15
0.1%
47630 32
0.3%
49740 1
 
< 0.1%
51850 1
 
< 0.1%
54870 2
 
< 0.1%
55470 1
 
< 0.1%
57580 2
 
< 0.1%
ValueCountFrequency (%)
5341690570 1
< 0.1%
3313033920 1
< 0.1%
3220406420 1
< 0.1%
3150084560 1
< 0.1%
2926187880 1
< 0.1%
2924532070 1
< 0.1%
2447928260 1
< 0.1%
2424621650 1
< 0.1%
2419665860 1
< 0.1%
2169298650 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6538
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.85462
Minimum0.157
Maximum8216.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:41:45.181391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.157
5-th percentile1.8566
Q129.7
median73.919
Q3141.11
95-th percentile522.203
Maximum8216.85
Range8216.693
Interquartile range (IQR)111.41

Descriptive statistics

Standard deviation309.89294
Coefficient of variation (CV)2.09593
Kurtosis108.37148
Mean147.85462
Median Absolute Deviation (MAD)50.52
Skewness8.0279009
Sum1478546.2
Variance96033.636
MonotonicityNot monotonic
2023-12-11T01:41:45.415998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.558 308
 
3.1%
18.0 43
 
0.4%
0.158 32
 
0.3%
12.29 23
 
0.2%
8.7 23
 
0.2%
18.1 18
 
0.2%
27.0 18
 
0.2%
51.9345 17
 
0.2%
9.0 16
 
0.2%
36.0 15
 
0.1%
Other values (6528) 9487
94.9%
ValueCountFrequency (%)
0.157 15
0.1%
0.158 32
0.3%
0.165 1
 
< 0.1%
0.172 1
 
< 0.1%
0.182 2
 
< 0.1%
0.184 1
 
< 0.1%
0.191 2
 
< 0.1%
0.196 1
 
< 0.1%
0.199 1
 
< 0.1%
0.201 1
 
< 0.1%
ValueCountFrequency (%)
8216.85 1
< 0.1%
6132.825 1
< 0.1%
4694.47 1
< 0.1%
4591.96 1
< 0.1%
4538.38 1
< 0.1%
4507.77 1
< 0.1%
4265.58 1
< 0.1%
3929.9 1
< 0.1%
3888.8 1
< 0.1%
3884.89 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-09-12
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-12
2nd row2023-09-12
3rd row2023-09-12
4th row2023-09-12
5th row2023-09-12

Common Values

ValueCountFrequency (%)
2023-09-12 10000
100.0%

Length

2023-12-11T01:41:45.634337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:45.786181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-12 10000
100.0%

Interactions

2023-12-11T01:41:38.136680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.163335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.289463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.064029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.805933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.755189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.605207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:36.934522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.252664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.318840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.409834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.169854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.926664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.856774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.737736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.174757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.385896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.462248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.502136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.248251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.013187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.952526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.832011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.302582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.516651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.613876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.607775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.333008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.121282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.041739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.947128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.455119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.649292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.749110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.697731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.427726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.235240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.152164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:36.071706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.627439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.762641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:31.892557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.771496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.508544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.356299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.252716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:36.210634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.738316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.869173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.033370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.855564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.600157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.484797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.369983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:36.370674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:37.872086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.970554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.183745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:32.966847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:33.714310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:34.604742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:35.492187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:36.499764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:38.004534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:41:45.896581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동특수지본번부번동호시가표준액연면적
연번1.0000.9340.1430.6500.3050.0930.2190.0580.073
법정동0.9341.0000.1130.7230.3290.1140.2630.0480.060
특수지0.1430.1131.0000.1810.0000.0530.0000.0000.057
본번0.6500.7230.1811.0000.1800.0370.2240.2220.257
부번0.3050.3290.0000.1801.0000.0000.0000.0000.000
동0.0930.1140.0530.0370.0001.0000.0000.0000.000
호0.2190.2630.0000.2240.0000.0001.0000.0000.000
시가표준액0.0580.0480.0000.2220.0000.0000.0001.0000.962
연면적0.0730.0600.0570.2570.0000.0000.0000.9621.000
2023-12-11T01:41:46.104191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동본번부번동호시가표준액연면적특수지
연번1.0000.864-0.0710.076-0.080-0.0940.0030.0630.109
법정동0.8641.000-0.1920.077-0.099-0.075-0.0360.0490.087
본번-0.071-0.1921.000-0.0190.1000.050-0.104-0.1630.139
부번0.0760.077-0.0191.000-0.281-0.040-0.005-0.0130.000
동-0.080-0.0990.100-0.2811.0000.089-0.104-0.1300.088
호-0.094-0.0750.050-0.0400.0891.0000.051-0.0100.000
시가표준액0.003-0.036-0.104-0.005-0.1040.0511.0000.8670.000
연면적0.0630.049-0.163-0.013-0.130-0.0100.8671.0000.043
특수지0.1090.0870.1390.0000.0880.0000.0000.0431.000

Missing values

2023-12-11T01:41:39.146753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:41:39.433891image/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

연번시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
3796137962부산광역시금정구26410202110701159201401[ 두실로15번길 20 ] 0001동 0401호1182060059.42023-09-12
91329133부산광역시금정구2641020211070185911301[ 중앙대로 1829 ] 0001동 1301호6784026058.972023-09-12
1931819319부산광역시금정구26410202110801413331301[ 장전온천천로73번길 9 ] 0001동 0301호5569352071.772023-09-12
2262722628부산광역시금정구26410202110901223791101부산광역시 금정구 부곡동 223-79 1동 101호14691810140.392023-09-12
3025330254부산광역시금정구2641020211100121361101[ 서동중심로 24-1 ] 0001동 0101호1256300054.352023-09-12
38233824부산광역시금정구2641020211040111541203[ 중앙대로2035번길 37 ] 0001동 0203호63324360159.912023-09-12
1743517436부산광역시금정구26410202110801417391201부산광역시 금정구 장전동 417-39 1동 201호64263760155.982023-09-12
1564615647부산광역시금정구264102021108012325718101[ 금정로119번길 20 ] 0001동 8101호31854810105.342023-09-12
1922619227부산광역시금정구26410202110801538111[ 금강로 209 ] 0001동 0402-1호91074780178.5782023-09-12
46134614부산광역시금정구26410202110401335441101[ 남산로 30 ] 0001동 0101호82961110155.142023-09-12
연번시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
69666967부산광역시금정구2641020211050183411109부산광역시 금정구 선동 834-1 1동 109호2125340065.82023-09-12
3676336764부산광역시금정구26410202110101181101101[ 체육공원로 651-1 ] 0001동 0101호171365600198.82023-09-12
3255832559부산광역시금정구2641020211100130213141101[ 삼차로 59-1 ] 0001동 0101호253000040.02023-09-12
1846118462부산광역시금정구264102021108015037711301부산광역시 금정구 장전동 503-77 11동 301호205335690575.172023-09-12
3017830179부산광역시금정구26410202111001196113201부산광역시 금정구 서동 196-11 3동 201호362440044.22023-09-12
1225912260부산광역시금정구2641020211070110271105202부산광역시 금정구 구서동 1027-1 105동 202호6169601.5582023-09-12
1986819869부산광역시금정구2641020211080142170156부산광역시 금정구 장전동 421-7 156호120057300597.32023-09-12
2475924760부산광역시금정구2641020211090122631101[ 무학송로 59 ] 0001동 0101호156616200232.82023-09-12
3214732148부산광역시금정구264102021110011632218101[ 서부로52번길 1 ] 0001동 8101호2874248095.32023-09-12
3220632207부산광역시금정구26410202111001204111504[ 금사로 10 ] 0001동 0504호2921562031.862023-09-12