Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows56
Duplicate rows (%)0.6%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical6
Numeric6
Text2

Dataset

Description울산광역시 동구 일반건축물 시가표준액으로써 과제년도, 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적 등 항목을 제공합니다.
Author울산광역시 동구
URLhttps://www.data.go.kr/data/15079850/fileData.do

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
Dataset has 56 (0.6%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (96.2%)Imbalance
시가표준액 is highly skewed (γ1 = 22.69716108)Skewed
연면적 is highly skewed (γ1 = 41.03112064)Skewed
부번 has 2088 (20.9%) zerosZeros
동 has 515 (5.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:35:39.073538
Analysis finished2024-04-06 08:35:50.877625
Duration11.8 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
울산광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 10000
100.0%

Length

2024-04-06T17:35:51.093383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:51.273001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 10000
100.0%

Length

2024-04-06T17:35:51.477757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:51.695989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
31170
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31170
2nd row31170
3rd row31170
4th row31170
5th row31170

Common Values

ValueCountFrequency (%)
31170 10000
100.0%

Length

2024-04-06T17:35:51.893299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:52.060920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31170 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2024-04-06T17:35:52.301610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:52.549981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.2487
Minimum101
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:35:52.754287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median103
Q3104
95-th percentile108
Maximum108
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2495676
Coefficient of variation (CV)0.021787854
Kurtosis-0.37414909
Mean103.2487
Median Absolute Deviation (MAD)1
Skewness0.85037502
Sum1032487
Variance5.0605544
MonotonicityNot monotonic
2024-04-06T17:35:52.969433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
101 3094
30.9%
104 2238
22.4%
102 1541
15.4%
103 1232
 
12.3%
108 852
 
8.5%
107 716
 
7.2%
106 200
 
2.0%
105 127
 
1.3%
ValueCountFrequency (%)
101 3094
30.9%
102 1541
15.4%
103 1232
 
12.3%
104 2238
22.4%
105 127
 
1.3%
106 200
 
2.0%
107 716
 
7.2%
108 852
 
8.5%
ValueCountFrequency (%)
108 852
 
8.5%
107 716
 
7.2%
106 200
 
2.0%
105 127
 
1.3%
104 2238
22.4%
103 1232
 
12.3%
102 1541
15.4%
101 3094
30.9%

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

2024-04-06T17:35:53.219103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:53.438960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9931 
2
 
67
9
 
2

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 9931
99.3%
2 67
 
0.7%
9 2
 
< 0.1%

Length

2024-04-06T17:35:53.612385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:35:54.246907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9931
99.3%
2 67
 
0.7%
9 2
 
< 0.1%

본번
Real number (ℝ)

Distinct744
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.0994
Minimum0
Maximum1529
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:35:54.466537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1207
median573
Q3867
95-th percentile1153
Maximum1529
Range1529
Interquartile range (IQR)660

Descriptive statistics

Standard deviation380.18056
Coefficient of variation (CV)0.68365577
Kurtosis-0.82460358
Mean556.0994
Median Absolute Deviation (MAD)327.5
Skewness0.29667444
Sum5560994
Variance144537.26
MonotonicityNot monotonic
2024-04-06T17:35:54.771824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 675
 
6.8%
1381 231
 
2.3%
300 181
 
1.8%
686 167
 
1.7%
303 163
 
1.6%
661 152
 
1.5%
544 137
 
1.4%
201 136
 
1.4%
866 136
 
1.4%
113 134
 
1.3%
Other values (734) 7888
78.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 675
6.8%
2 3
 
< 0.1%
3 23
 
0.2%
6 113
 
1.1%
8 73
 
0.7%
10 10
 
0.1%
11 4
 
< 0.1%
12 3
 
< 0.1%
15 4
 
< 0.1%
ValueCountFrequency (%)
1529 2
 
< 0.1%
1525 71
 
0.7%
1381 231
2.3%
1292 2
 
< 0.1%
1288 4
 
< 0.1%
1287 14
 
0.1%
1283 8
 
0.1%
1280 1
 
< 0.1%
1276 12
 
0.1%
1268 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct212
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.0741
Minimum0
Maximum481
Zeros2088
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:35:55.083428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q315
95-th percentile69
Maximum481
Range481
Interquartile range (IQR)14

Descriptive statistics

Standard deviation40.787964
Coefficient of variation (CV)2.3888793
Kurtosis37.291829
Mean17.0741
Median Absolute Deviation (MAD)4
Skewness5.439696
Sum170741
Variance1663.658
MonotonicityNot monotonic
2024-04-06T17:35:55.399071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2088
20.9%
1 1183
 
11.8%
3 705
 
7.0%
2 573
 
5.7%
4 460
 
4.6%
5 383
 
3.8%
6 341
 
3.4%
7 330
 
3.3%
10 251
 
2.5%
8 236
 
2.4%
Other values (202) 3450
34.5%
ValueCountFrequency (%)
0 2088
20.9%
1 1183
11.8%
2 573
 
5.7%
3 705
 
7.0%
4 460
 
4.6%
5 383
 
3.8%
6 341
 
3.4%
7 330
 
3.3%
8 236
 
2.4%
9 227
 
2.3%
ValueCountFrequency (%)
481 2
 
< 0.1%
418 2
 
< 0.1%
386 3
 
< 0.1%
385 31
0.3%
376 1
 
< 0.1%
346 1
 
< 0.1%
328 1
 
< 0.1%
314 4
 
< 0.1%
293 1
 
< 0.1%
286 2
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.8139
Minimum0
Maximum9011
Zeros515
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:35:55.750677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile104
Maximum9011
Range9011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1239.4039
Coefficient of variation (CV)6.3619892
Kurtosis43.021693
Mean194.8139
Median Absolute Deviation (MAD)0
Skewness6.6742576
Sum1948139
Variance1536122.1
MonotonicityNot monotonic
2024-04-06T17:35:56.011808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7453
74.5%
3 548
 
5.5%
0 515
 
5.1%
2 270
 
2.7%
10 263
 
2.6%
9001 140
 
1.4%
15 123
 
1.2%
101 75
 
0.8%
4 61
 
0.6%
401 44
 
0.4%
Other values (48) 508
 
5.1%
ValueCountFrequency (%)
0 515
 
5.1%
1 7453
74.5%
2 270
 
2.7%
3 548
 
5.5%
4 61
 
0.6%
5 29
 
0.3%
6 28
 
0.3%
7 9
 
0.1%
8 10
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9011 3
 
< 0.1%
9002 6
 
0.1%
9001 140
1.4%
8004 1
 
< 0.1%
8003 3
 
< 0.1%
8001 36
 
0.4%
7006 2
 
< 0.1%
7002 5
 
0.1%
7001 8
 
0.1%
6001 4
 
< 0.1%

호
Text

Distinct534
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:35:56.754132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9991
Min length1

Characters and Unicode

Total characters39991
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)2.3%

Sample

1st row0308
2nd row0101
3rd row0101
4th row0201
5th row0004
ValueCountFrequency (%)
0101 2392
23.9%
0102 988
 
9.9%
0201 971
 
9.7%
0000 509
 
5.1%
0301 469
 
4.7%
0001 439
 
4.4%
8101 407
 
4.1%
0103 396
 
4.0%
0401 248
 
2.5%
0202 188
 
1.9%
Other values (524) 2993
29.9%
2024-04-06T17:35:57.696974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20187
50.5%
1 11337
28.3%
2 3553
 
8.9%
3 1685
 
4.2%
4 958
 
2.4%
8 905
 
2.3%
5 565
 
1.4%
6 326
 
0.8%
7 258
 
0.6%
9 208
 
0.5%
Other values (5) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39982
> 99.9%
Dash Punctuation 3
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20187
50.5%
1 11337
28.4%
2 3553
 
8.9%
3 1685
 
4.2%
4 958
 
2.4%
8 905
 
2.3%
5 565
 
1.4%
6 326
 
0.8%
7 258
 
0.6%
9 208
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39989
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20187
50.5%
1 11337
28.4%
2 3553
 
8.9%
3 1685
 
4.2%
4 958
 
2.4%
8 905
 
2.3%
5 565
 
1.4%
6 326
 
0.8%
7 258
 
0.6%
9 208
 
0.5%
Other values (3) 7
 
< 0.1%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20187
50.5%
1 11337
28.3%
2 3553
 
8.9%
3 1685
 
4.2%
4 958
 
2.4%
8 905
 
2.3%
5 565
 
1.4%
6 326
 
0.8%
7 258
 
0.6%
9 208
 
0.5%
Other values (5) 9
 
< 0.1%

물건지
Text

Distinct8379
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:35:58.370774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length30
Mean length24.0915
Min length18

Characters and Unicode

Total characters240915
Distinct characters109
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

Unique7613 ?
Unique (%)76.1%

Sample

1st row울산광역시 동구 방어동 1078-3 1동 308호
2nd row[ 화진2가길 20 ] 0001동 0101호
3rd row울산광역시 동구 화정동 874-4 1동 101호
4th row[ 꽃바위로 348 ] 0001동 0201호
5th row울산광역시 동구 일산동 1-64 1동 4호
ValueCountFrequency (%)
13442
22.4%
0001동 5593
 
9.3%
울산광역시 3279
 
5.5%
동구 3279
 
5.5%
1동 1860
 
3.1%
0101호 1682
 
2.8%
방어동 969
 
1.6%
전하동 835
 
1.4%
방어진순환도로 792
 
1.3%
0102호 745
 
1.2%
Other values (2166) 27482
45.8%
2024-04-06T17:35:59.348099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49958
20.7%
0 39590
16.4%
1 26982
11.2%
동 17143
 
7.1%
호 9929
 
4.1%
2 7049
 
2.9%
[ 6721
 
2.8%
] 6721
 
2.8%
3 5556
 
2.3%
길 4436
 
1.8%
Other values (99) 66830
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97704
40.6%
Other Letter 77548
32.2%
Space Separator 49958
20.7%
Open Punctuation 6723
 
2.8%
Close Punctuation 6723
 
2.8%
Dash Punctuation 2257
 
0.9%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17143
22.1%
호 9929
12.8%
길 4436
 
5.7%
산 3818
 
4.9%
울 3279
 
4.2%
시 3279
 
4.2%
광 3279
 
4.2%
역 3279
 
4.2%
구 3279
 
4.2%
진 2286
 
2.9%
Other values (81) 23541
30.4%
Decimal Number
ValueCountFrequency (%)
0 39590
40.5%
1 26982
27.6%
2 7049
 
7.2%
3 5556
 
5.7%
4 4013
 
4.1%
5 3452
 
3.5%
6 3427
 
3.5%
8 3064
 
3.1%
7 2544
 
2.6%
9 2027
 
2.1%
Open Punctuation
ValueCountFrequency (%)
[ 6721
> 99.9%
( 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 6721
> 99.9%
) 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
49958
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 163365
67.8%
Hangul 77548
32.2%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17143
22.1%
호 9929
12.8%
길 4436
 
5.7%
산 3818
 
4.9%
울 3279
 
4.2%
시 3279
 
4.2%
광 3279
 
4.2%
역 3279
 
4.2%
구 3279
 
4.2%
진 2286
 
2.9%
Other values (81) 23541
30.4%
Common
ValueCountFrequency (%)
49958
30.6%
0 39590
24.2%
1 26982
16.5%
2 7049
 
4.3%
[ 6721
 
4.1%
] 6721
 
4.1%
3 5556
 
3.4%
4 4013
 
2.5%
5 3452
 
2.1%
6 3427
 
2.1%
Other values (6) 9896
 
6.1%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163367
67.8%
Hangul 77548
32.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49958
30.6%
0 39590
24.2%
1 26982
16.5%
2 7049
 
4.3%
[ 6721
 
4.1%
] 6721
 
4.1%
3 5556
 
3.4%
4 4013
 
2.5%
5 3452
 
2.1%
6 3427
 
2.1%
Other values (8) 9898
 
6.1%
Hangul
ValueCountFrequency (%)
동 17143
22.1%
호 9929
12.8%
길 4436
 
5.7%
산 3818
 
4.9%
울 3279
 
4.2%
시 3279
 
4.2%
광 3279
 
4.2%
역 3279
 
4.2%
구 3279
 
4.2%
진 2286
 
2.9%
Other values (81) 23541
30.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8370
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0132413 × 108
Minimum21420
Maximum2.053809 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:35:59.655725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21420
5-th percentile576000
Q15382175
median30003680
Q383187435
95-th percentile3.7844121 × 108
Maximum2.053809 × 1010
Range2.0538069 × 1010
Interquartile range (IQR)77805260

Descriptive statistics

Standard deviation3.9966596 × 108
Coefficient of variation (CV)3.94443
Kurtosis847.49249
Mean1.0132413 × 108
Median Absolute Deviation (MAD)27276340
Skewness22.697161
Sum1.0132413 × 1012
Variance1.5973288 × 1017
MonotonicityNot monotonic
2024-04-06T17:35:59.960312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3705900 95
 
0.9%
3666300 78
 
0.8%
95007380 41
 
0.4%
306000 34
 
0.3%
98577840 30
 
0.3%
43502160 26
 
0.3%
142108490 20
 
0.2%
612000 19
 
0.2%
200100 18
 
0.2%
99901120 17
 
0.2%
Other values (8360) 9622
96.2%
ValueCountFrequency (%)
21420 1
< 0.1%
21840 1
< 0.1%
37570 1
< 0.1%
41970 1
< 0.1%
42840 1
< 0.1%
45730 1
< 0.1%
51200 1
< 0.1%
51480 1
< 0.1%
54400 1
< 0.1%
56000 2
< 0.1%
ValueCountFrequency (%)
20538090300 1
< 0.1%
10759399870 1
< 0.1%
8937371080 1
< 0.1%
7637623110 1
< 0.1%
7361569080 1
< 0.1%
7072470500 1
< 0.1%
7067150430 1
< 0.1%
6906354210 1
< 0.1%
6590323580 1
< 0.1%
6295444950 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6399
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.40742
Minimum0.26
Maximum130663.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:36:00.241527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile4.2495
Q127.384925
median77.99
Q3161.545
95-th percentile955.543
Maximum130663.32
Range130663.06
Interquartile range (IQR)134.16008

Descriptive statistics

Standard deviation1938.1538
Coefficient of variation (CV)6.9366584
Kurtosis2322.5733
Mean279.40742
Median Absolute Deviation (MAD)56.41
Skewness41.031121
Sum2794074.2
Variance3756440.3
MonotonicityNot monotonic
2024-04-06T17:36:00.527935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 125
 
1.2%
18.0 112
 
1.1%
22.46 95
 
0.9%
22.22 78
 
0.8%
3.0 49
 
0.5%
36.0 48
 
0.5%
128.2989 41
 
0.4%
1.0 35
 
0.4%
24.0 35
 
0.4%
134.0236 30
 
0.3%
Other values (6389) 9352
93.5%
ValueCountFrequency (%)
0.26 1
 
< 0.1%
0.37 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5056 1
 
< 0.1%
0.5259 1
 
< 0.1%
0.5781 1
 
< 0.1%
0.6245 1
 
< 0.1%
0.6542 3
< 0.1%
0.6887 1
 
< 0.1%
0.7089 1
 
< 0.1%
ValueCountFrequency (%)
130663.32 1
< 0.1%
68313.65 1
< 0.1%
47662.9 1
< 0.1%
44714.65 1
< 0.1%
43277.89 1
< 0.1%
41914.47 1
< 0.1%
30150.0 1
< 0.1%
23905.24 1
< 0.1%
22270.0 1
< 0.1%
19366.02 1
< 0.1%

Interactions

2024-04-06T17:35:48.295202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:41.850683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:43.070364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:44.354652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:45.579009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:46.806498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:48.581392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:42.053841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:43.363749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:44.541721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:45.769879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:47.035150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:48.816068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:42.285151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:43.576267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:44.763780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:46.065372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:47.428564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:49.111437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:42.480788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:43.758662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:45.026816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:46.281024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:47.707909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:49.401904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:42.656921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:43.940263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:45.212467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:46.456999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:47.930661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:49.723964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:42.841567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:44.144563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:45.382864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:46.622092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:48.103092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:36:00.694241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.1600.6690.2730.1670.0700.108
특수지0.1601.0000.1300.0000.2720.0000.000
본번0.6690.1301.0000.3810.1520.0890.038
부번0.2730.0000.3811.0000.0000.0000.000
동0.1670.2720.1520.0001.0000.0000.000
시가표준액0.0700.0000.0890.0000.0001.0000.858
연면적0.1080.0000.0380.0000.0000.8581.000
2024-04-06T17:36:00.942889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.4860.0550.112-0.0140.0270.102
본번-0.4861.000-0.080-0.185-0.013-0.0670.078
부번0.055-0.0801.000-0.236-0.042-0.0780.000
동0.112-0.185-0.2361.000-0.004-0.0080.189
시가표준액-0.014-0.013-0.042-0.0041.0000.8870.000
연면적0.027-0.067-0.078-0.0080.8871.0000.000
특수지0.1020.0780.0000.1890.0000.0001.000

Missing values

2024-04-06T17:35:50.130329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:35:50.699531image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적
15399울산광역시동구311702022101011078310308울산광역시 동구 방어동 1078-3 1동 308호370590022.46
811울산광역시동구311702022101019091110101[ 화진2가길 20 ] 0001동 0101호1227732093.72
8811울산광역시동구31170202210201874410101울산광역시 동구 화정동 874-4 1동 101호84671220329.46
217울산광역시동구311702022101011261910201[ 꽃바위로 348 ] 0001동 0201호2623137082.23
15796울산광역시동구3117020221030116410004울산광역시 동구 일산동 1-64 1동 4호98770058.1
1263울산광역시동구31170202210101340310000[ 서진길 8-1 ] 0001동 0000호337680053.6
12755울산광역시동구31170202210701303310109울산광역시 동구 동부동 303-3 1동 109호15038103.069
12640울산광역시동구311702022102018711810301[ 화진길 87 ] 0001동 0301호140630000160.72
5608울산광역시동구31170202210201844110000[ 대송9길 8 ] 0001동 0000호33106630109.77
8415울산광역시동구311702022104012830410001울산광역시 동구 전하동 283 41동 1호3387075601376.86
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적
2109울산광역시동구311702022102018501110000[ 양지2길 19 ] 0001동 0000호1436925035.0
8219울산광역시동구311702022104013034418103[ 녹수8길 60 ] 0001동 8103호211328020.8
19796울산광역시동구311702022101011047310101[ 화암9길 29 ] 0001동 0101호3269900037.12
2704울산광역시동구3117020221010110211010001[ 꽃바위로 205 ] 0001동 0001호109341160158.73
8609울산광역시동구311702022103015331210101[ 번덕8길 123 ] 0001동 0101호64670940171.45
19047울산광역시동구311702022104011030156울산광역시 동구 전하동 1 3동 156호216000045.0
3752울산광역시동구311702022101011234010101울산광역시 동구 방어동 1234 1동 101호11313237608551.2
17900울산광역시동구311702022108011814010301[ 명덕5길 12 ] 0001동 0301호80773120174.08
13611울산광역시동구31170202210401651120103울산광역시 동구 전하동 651-1 2동 103호541270054.1
17691울산광역시동구31170202210201138110102[ 학문로 89 ] 0001동 0102호31866508.5

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적# duplicates
43울산광역시동구311702022104012830410001울산광역시 동구 전하동 283 41동 1호3387075601376.865
37울산광역시동구31170202210301964520101[ 해수욕장1길 45 ] 0002동 0101호12614405.764
5울산광역시동구3117020221010110401110001[ 꽃바위로 165 ] 0001동 0001호168538680297.693
26울산광역시동구311702022101011381010101울산광역시 동구 방어동 1381 1동 101호33602006.343
29울산광역시동구311702022102016542010000[ 방어진순환도로 567 ] 0001동 0000호48705800144.13
30울산광역시동구311702022102018493010001[ 대학길 40 ] 0001동 0001호107601000165.543
41울산광역시동구311702022104011030101울산광역시 동구 전하동 1 3동 101호1020006.03
0울산광역시동구31170202210101686060010001울산광역시 동구 방어동 686 6001동 1호30600018.02
1울산광역시동구31170202210101686060010001울산광역시 동구 방어동 686 6001동 1호45900027.02
2울산광역시동구31170202210101922110000[ 화진1길 32 ] 0001동 0000호3126505087.72