Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description이 데이터는 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하는 데이터로, 물건별 재산가액 확인이 가능합니다.
URLhttps://www.data.go.kr/data/15080339/fileData.do

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 11 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (98.4%)Imbalance
부번 has 1651 (16.5%) zerosZeros
동 has 2334 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-12 07:45:57.112224
Analysis finished2023-12-12 07:46:03.258120
Duration6.15 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

2023-12-12T16:46:03.323931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:03.440685image/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
강서구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 10000
100.0%

Length

2023-12-12T16:46:03.533595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:03.636330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
26440
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26440
2nd row26440
3rd row26440
4th row26440
5th row26440

Common Values

ValueCountFrequency (%)
26440 10000
100.0%

Length

2023-12-12T16:46:04.061738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:04.148961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26440 10000
100.0%

과세년도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
4819 
2017
4555 
2019
 
364
2018
 
262

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2017
3rd row2018
4th row2017
5th row2020

Common Values

ValueCountFrequency (%)
2020 4819
48.2%
2017 4555
45.6%
2019 364
 
3.6%
2018 262
 
2.6%

Length

2023-12-12T16:46:04.247321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:04.371850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 4819
48.2%
2017 4555
45.6%
2019 364
 
3.6%
2018 262
 
2.6%

법정동
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.1335
Minimum101
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:04.531625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median104
Q3110
95-th percentile117
Maximum122
Range21
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.5288457
Coefficient of variation (CV)0.051607067
Kurtosis-0.73534905
Mean107.1335
Median Absolute Deviation (MAD)3
Skewness0.68127171
Sum1071335
Variance30.568135
MonotonicityNot monotonic
2023-12-12T16:46:04.638969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
102 2022
20.2%
109 1571
15.7%
104 1533
15.3%
101 1053
10.5%
117 709
 
7.1%
103 555
 
5.5%
110 493
 
4.9%
114 335
 
3.4%
115 330
 
3.3%
111 214
 
2.1%
Other values (12) 1185
11.8%
ValueCountFrequency (%)
101 1053
10.5%
102 2022
20.2%
103 555
 
5.5%
104 1533
15.3%
105 89
 
0.9%
106 48
 
0.5%
107 84
 
0.8%
108 155
 
1.6%
109 1571
15.7%
110 493
 
4.9%
ValueCountFrequency (%)
122 28
 
0.3%
121 67
 
0.7%
120 13
 
0.1%
119 171
 
1.7%
118 26
 
0.3%
117 709
7.1%
116 140
 
1.4%
115 330
3.3%
114 335
3.4%
113 156
 
1.6%

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

2023-12-12T16:46:04.745667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:04.848288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9971 
2
 
14
3
 
14
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9971
99.7%
2 14
 
0.1%
3 14
 
0.1%
7 1
 
< 0.1%

Length

2023-12-12T16:46:04.940269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:46:05.048083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9971
99.7%
2 14
 
0.1%
3 14
 
0.1%
7 1
 
< 0.1%

본번
Real number (ℝ)

Distinct1973
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1909.6411
Minimum1
Maximum6525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:05.186591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile131
Q1599.75
median1581
Q33153
95-th percentile4367
Maximum6525
Range6524
Interquartile range (IQR)2553.25

Descriptive statistics

Standard deviation1423.9031
Coefficient of variation (CV)0.74563912
Kurtosis0.067970184
Mean1909.6411
Median Absolute Deviation (MAD)1150
Skewness0.73494726
Sum19096411
Variance2027500.1
MonotonicityNot monotonic
2023-12-12T16:46:05.365798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 392
 
3.9%
3153 365
 
3.6%
3138 211
 
2.1%
3599 131
 
1.3%
3154 109
 
1.1%
1623 101
 
1.0%
3141 86
 
0.9%
29 71
 
0.7%
3229 67
 
0.7%
3589 64
 
0.6%
Other values (1963) 8403
84.0%
ValueCountFrequency (%)
1 39
0.4%
2 9
 
0.1%
3 3
 
< 0.1%
4 5
 
0.1%
5 22
0.2%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
6525 3
< 0.1%
6518 1
 
< 0.1%
6441 1
 
< 0.1%
6435 4
< 0.1%
6432 4
< 0.1%
6425 1
 
< 0.1%
6419 1
 
< 0.1%
6418 1
 
< 0.1%
6413 1
 
< 0.1%
6408 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct257
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.754
Minimum0
Maximum2844
Zeros1651
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:05.499235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile29.05
Maximum2844
Range2844
Interquartile range (IQR)7

Descriptive statistics

Standard deviation114.34268
Coefficient of variation (CV)6.4403898
Kurtosis275.33049
Mean17.754
Median Absolute Deviation (MAD)3
Skewness14.917428
Sum177540
Variance13074.249
MonotonicityNot monotonic
2023-12-12T16:46:05.629060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1917
19.2%
0 1651
16.5%
2 1185
11.8%
3 758
 
7.6%
4 623
 
6.2%
5 484
 
4.8%
6 466
 
4.7%
7 400
 
4.0%
8 366
 
3.7%
9 278
 
2.8%
Other values (247) 1872
18.7%
ValueCountFrequency (%)
0 1651
16.5%
1 1917
19.2%
2 1185
11.8%
3 758
 
7.6%
4 623
 
6.2%
5 484
 
4.8%
6 466
 
4.7%
7 400
 
4.0%
8 366
 
3.7%
9 278
 
2.8%
ValueCountFrequency (%)
2844 1
< 0.1%
2813 1
< 0.1%
2706 1
< 0.1%
2622 2
< 0.1%
2504 1
< 0.1%
2186 2
< 0.1%
2005 1
< 0.1%
1964 1
< 0.1%
1871 1
< 0.1%
1854 1
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct124
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.8376
Minimum0
Maximum9051
Zeros2334
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:05.786848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile128
Maximum9051
Range9051
Interquartile range (IQR)0

Descriptive statistics

Standard deviation618.6157
Coefficient of variation (CV)9.2555044
Kurtosis170.4882
Mean66.8376
Median Absolute Deviation (MAD)0
Skewness12.980962
Sum668376
Variance382685.38
MonotonicityNot monotonic
2023-12-12T16:46:05.931627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6199
62.0%
0 2334
 
23.3%
2 139
 
1.4%
104 96
 
1.0%
106 79
 
0.8%
102 45
 
0.4%
101 45
 
0.4%
105 39
 
0.4%
3 37
 
0.4%
110 34
 
0.3%
Other values (114) 953
 
9.5%
ValueCountFrequency (%)
0 2334
 
23.3%
1 6199
62.0%
2 139
 
1.4%
3 37
 
0.4%
4 25
 
0.2%
5 21
 
0.2%
6 16
 
0.2%
7 20
 
0.2%
8 12
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9051 1
 
< 0.1%
9042 1
 
< 0.1%
9040 2
< 0.1%
9039 2
< 0.1%
9035 1
 
< 0.1%
9032 1
 
< 0.1%
9031 2
< 0.1%
9028 4
< 0.1%
9023 1
 
< 0.1%
9016 1
 
< 0.1%

호
Text

Distinct778
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:46:06.228911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0863
Min length1

Characters and Unicode

Total characters30863
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique548 ?
Unique (%)5.5%

Sample

1st row301
2nd row101
3rd row301
4th row101
5th row102
ValueCountFrequency (%)
101 3779
37.8%
201 1223
 
12.2%
102 949
 
9.5%
301 369
 
3.7%
103 311
 
3.1%
202 174
 
1.7%
104 145
 
1.5%
401 112
 
1.1%
105 109
 
1.1%
100 87
 
0.9%
Other values (768) 2742
27.4%
2023-12-12T16:46:06.733225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13384
43.4%
0 9250
30.0%
2 3765
 
12.2%
3 1327
 
4.3%
4 743
 
2.4%
8 646
 
2.1%
5 534
 
1.7%
6 396
 
1.3%
7 366
 
1.2%
9 358
 
1.2%
Other values (5) 94
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30769
99.7%
Uppercase Letter 66
 
0.2%
Dash Punctuation 28
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13384
43.5%
0 9250
30.1%
2 3765
 
12.2%
3 1327
 
4.3%
4 743
 
2.4%
8 646
 
2.1%
5 534
 
1.7%
6 396
 
1.3%
7 366
 
1.2%
9 358
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 25
37.9%
D 24
36.4%
C 10
 
15.2%
A 7
 
10.6%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30797
99.8%
Latin 66
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13384
43.5%
0 9250
30.0%
2 3765
 
12.2%
3 1327
 
4.3%
4 743
 
2.4%
8 646
 
2.1%
5 534
 
1.7%
6 396
 
1.3%
7 366
 
1.2%
9 358
 
1.2%
Latin
ValueCountFrequency (%)
B 25
37.9%
D 24
36.4%
C 10
 
15.2%
A 7
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13384
43.4%
0 9250
30.0%
2 3765
 
12.2%
3 1327
 
4.3%
4 743
 
2.4%
8 646
 
2.1%
5 534
 
1.7%
6 396
 
1.3%
7 366
 
1.2%
9 358
 
1.2%
Other values (5) 94
 
0.3%

물건지
Text

Distinct8791
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:46:07.161431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length27.6256
Min length18

Characters and Unicode

Total characters276256
Distinct characters138
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

Unique7864 ?
Unique (%)78.6%

Sample

1st row부산광역시 강서구 화전동 585-8 1동 301호
2nd row부산광역시 강서구 구랑동 1194-3 1동 101호
3rd row부산광역시 강서구 지사동 1202-3 1동 301호
4th row부산광역시 강서구 녹산동 146-4 1동 101호
5th row[ 대저로89번길 64 ] 0000동 0102호
ValueCountFrequency (%)
8336
 
14.0%
부산광역시 5832
 
9.8%
강서구 5832
 
9.8%
1동 3838
 
6.5%
0001동 2361
 
4.0%
101호 1937
 
3.3%
0101호 1842
 
3.1%
0000동 1716
 
2.9%
송정동 1292
 
2.2%
대저2동 1202
 
2.0%
Other values (4895) 25211
42.4%
2023-12-12T16:46:07.788117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49399
17.9%
1 32025
 
11.6%
0 30553
 
11.1%
동 15952
 
5.8%
2 11422
 
4.1%
호 10814
 
3.9%
3 7660
 
2.8%
산 7019
 
2.5%
강 6235
 
2.3%
시 6074
 
2.2%
Other values (128) 99103
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106554
38.6%
Decimal Number 106198
38.4%
Space Separator 49399
17.9%
Dash Punctuation 5703
 
2.1%
Close Punctuation 4168
 
1.5%
Open Punctuation 4168
 
1.5%
Uppercase Letter 66
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15952
15.0%
호 10814
 
10.1%
산 7019
 
6.6%
강 6235
 
5.9%
시 6074
 
5.7%
서 5982
 
5.6%
구 5962
 
5.6%
부 5835
 
5.5%
광 5832
 
5.5%
역 5832
 
5.5%
Other values (110) 31017
29.1%
Decimal Number
ValueCountFrequency (%)
1 32025
30.2%
0 30553
28.8%
2 11422
 
10.8%
3 7660
 
7.2%
5 5592
 
5.3%
4 4577
 
4.3%
8 4127
 
3.9%
6 3756
 
3.5%
9 3301
 
3.1%
7 3185
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 25
37.9%
D 24
36.4%
C 10
 
15.2%
A 7
 
10.6%
Space Separator
ValueCountFrequency (%)
49399
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5703
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4168
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169636
61.4%
Hangul 106554
38.6%
Latin 66
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15952
15.0%
호 10814
 
10.1%
산 7019
 
6.6%
강 6235
 
5.9%
시 6074
 
5.7%
서 5982
 
5.6%
구 5962
 
5.6%
부 5835
 
5.5%
광 5832
 
5.5%
역 5832
 
5.5%
Other values (110) 31017
29.1%
Common
ValueCountFrequency (%)
49399
29.1%
1 32025
18.9%
0 30553
18.0%
2 11422
 
6.7%
3 7660
 
4.5%
- 5703
 
3.4%
5 5592
 
3.3%
4 4577
 
2.7%
] 4168
 
2.5%
[ 4168
 
2.5%
Other values (4) 14369
 
8.5%
Latin
ValueCountFrequency (%)
B 25
37.9%
D 24
36.4%
C 10
 
15.2%
A 7
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169702
61.4%
Hangul 106554
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49399
29.1%
1 32025
18.9%
0 30553
18.0%
2 11422
 
6.7%
3 7660
 
4.5%
- 5703
 
3.4%
5 5592
 
3.3%
4 4577
 
2.7%
] 4168
 
2.5%
[ 4168
 
2.5%
Other values (8) 14435
 
8.5%
Hangul
ValueCountFrequency (%)
동 15952
15.0%
호 10814
 
10.1%
산 7019
 
6.6%
강 6235
 
5.9%
시 6074
 
5.7%
서 5982
 
5.6%
구 5962
 
5.6%
부 5835
 
5.5%
광 5832
 
5.5%
역 5832
 
5.5%
Other values (110) 31017
29.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8421
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4274419 × 108
Minimum36000
Maximum1.5774984 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:07.958903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36000
5-th percentile2099652
Q122832332
median60519760
Q31.364062 × 108
95-th percentile4.6276584 × 108
Maximum1.5774984 × 1010
Range1.5774948 × 1010
Interquartile range (IQR)1.1357387 × 108

Descriptive statistics

Standard deviation3.9002102 × 108
Coefficient of variation (CV)2.7323075
Kurtosis406.95937
Mean1.4274419 × 108
Median Absolute Deviation (MAD)48216760
Skewness15.110484
Sum1.4274419 × 1012
Variance1.521164 × 1017
MonotonicityNot monotonic
2023-12-12T16:46:08.109020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55006010 79
 
0.8%
42287180 72
 
0.7%
48834840 69
 
0.7%
9123580 68
 
0.7%
39249450 62
 
0.6%
9332820 58
 
0.6%
8903440 56
 
0.6%
9505650 55
 
0.5%
9050200 54
 
0.5%
40068660 37
 
0.4%
Other values (8411) 9390
93.9%
ValueCountFrequency (%)
36000 1
< 0.1%
36120 1
< 0.1%
53850 1
< 0.1%
72000 1
< 0.1%
83600 1
< 0.1%
92400 1
< 0.1%
95040 1
< 0.1%
95700 1
< 0.1%
108000 1
< 0.1%
108680 1
< 0.1%
ValueCountFrequency (%)
15774983840 1
< 0.1%
11921166220 1
< 0.1%
7575840720 1
< 0.1%
6683077800 1
< 0.1%
5678632270 1
< 0.1%
5546437470 1
< 0.1%
5546072700 1
< 0.1%
5174726480 1
< 0.1%
5173961700 1
< 0.1%
5143929280 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5772
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.277
Minimum0.4
Maximum23036.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:46:08.267877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile19.771
Q154.5397
median126
Q3291.855
95-th percentile1156.1785
Maximum23036.07
Range23035.67
Interquartile range (IQR)237.3153

Descriptive statistics

Standard deviation898.14742
Coefficient of variation (CV)2.7359438
Kurtosis164.06132
Mean328.277
Median Absolute Deviation (MAD)82.33
Skewness10.584049
Sum3282770
Variance806668.78
MonotonicityNot monotonic
2023-12-12T16:46:08.443179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.46 178
 
1.8%
54.5397 174
 
1.7%
48.218 155
 
1.6%
24.69 142
 
1.4%
49.2244 99
 
1.0%
55.6745 91
 
0.9%
18.0 48
 
0.5%
39.3421 44
 
0.4%
67.8 43
 
0.4%
198.0 38
 
0.4%
Other values (5762) 8988
89.9%
ValueCountFrequency (%)
0.4 1
< 0.1%
1.0 1
< 0.1%
1.2 1
< 0.1%
1.44 1
< 0.1%
1.5 1
< 0.1%
1.52 1
< 0.1%
1.59 1
< 0.1%
2.0 1
< 0.1%
2.11 1
< 0.1%
2.31 1
< 0.1%
ValueCountFrequency (%)
23036.07 1
< 0.1%
19730.98 1
< 0.1%
18343.44 1
< 0.1%
17658.19 1
< 0.1%
16672.33 1
< 0.1%
15374.0 1
< 0.1%
15213.06 1
< 0.1%
14098.11 1
< 0.1%
13518.26 1
< 0.1%
12399.0 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-05-19 00:00:00
Maximum2023-05-19 00:00:00
2023-12-12T16:46:08.563912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:08.645311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:46:02.194235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:58.812284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.457557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.191857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.943753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.567530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.303120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:58.897891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.555099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.302357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.057547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.655100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.441604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:58.999009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.681708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.430114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.183326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.756583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.551274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.105608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.818927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.552091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.269454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.865665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.671812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.210967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.931241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.664803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.348609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.972456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.833975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:45:59.342131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.089499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:00.799016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:01.468850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:46:02.093814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:46:08.717342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동시가표준액연면적
과세년도1.0000.3630.0000.2530.0000.0360.0000.000
법정동0.3631.0000.1370.8330.1420.3920.0780.157
특수지0.0000.1371.0000.0570.0000.0000.0000.000
본번0.2530.8330.0571.0000.2080.3530.0310.085
부번0.0000.1420.0000.2081.0000.0000.0000.000
동0.0360.3920.0000.3530.0001.0000.0000.000
시가표준액0.0000.0780.0000.0310.0000.0001.0000.861
연면적0.0000.1570.0000.0850.0000.0000.8611.000
2023-12-12T16:46:08.821982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.000
과세년도0.0001.000
2023-12-12T16:46:08.909683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세년도특수지
법정동1.000-0.468-0.064-0.0780.0610.0170.2250.082
본번-0.4681.000-0.111-0.0160.084-0.0110.1540.034
부번-0.064-0.1111.000-0.1410.0880.1200.0000.000
동-0.078-0.016-0.1411.000-0.044-0.1300.0300.000
시가표준액0.0610.0840.088-0.0441.0000.8640.0000.000
연면적0.017-0.0110.120-0.1300.8641.0000.0000.000
과세년도0.2250.1540.0000.0300.0000.0001.0000.000
특수지0.0820.0340.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T16:46:02.973272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:46:03.168304image/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
55305부산광역시강서구2644020201100158581301부산광역시 강서구 화전동 585-8 1동 301호70561050150.452023-05-19
16806부산광역시강서구26440201711301119431101부산광역시 강서구 구랑동 1194-3 1동 101호89768000196.02023-05-19
40052부산광역시강서구26440201811401120231301부산광역시 강서구 지사동 1202-3 1동 301호60078000102.02023-05-19
19390부산광역시강서구2644020171110114641101부산광역시 강서구 녹산동 146-4 1동 101호225225057.752023-05-19
63201부산광역시강서구264402020101011288170102[ 대저로89번길 64 ] 0000동 0102호188535600198.02023-05-19
9985부산광역시강서구2644020171030125011101[ 낙동북로21번길 26 ] 0001동 0101호4663360098.82023-05-19
3465부산광역시강서구264402017102014344700부산광역시 강서구 대저2동 4344-71030000103.02023-05-19
19035부산광역시강서구2644020171170124720102[ 신호산단1로 179 ] 0000동 0102호5841792069.152023-05-19
69842부산광역시강서구26440202010401324701101부산광역시 강서구 명지동 3247 1동 101호20402258403535.922023-05-19
7550부산광역시강서구264402017104013238121502[ 명지오션시티4로 70 ] 0001동 0502호212094540272.442023-05-19
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
55875부산광역시강서구2644020201020131531114222부산광역시 강서구 대저2동 3153-1 114동 222호4228718048.2182023-05-19
61859부산광역시강서구26440202011501163841101부산광역시 강서구 미음동 1638-4 1동 101호2687850055.02023-05-19
70812부산광역시강서구2644020201080129761101[ 가락대로1397번길 93 ] 0001동 0101호16212904.322023-05-19
57138부산광역시강서구2644020201170121500101[ 신호산단2로 21 ] 0000동 0101호82135480154.392023-05-19
85300부산광역시강서구2644020201180183211102부산광역시 강서구 동선동 832-1 1동 102호2408160083.042023-05-19
43500부산광역시강서구26440201911001584131201부산광역시 강서구 화전동 584-13 1동 201호68639400102.62023-05-19
35255부산광역시강서구26440201710901171575114부산광역시 강서구 송정동 1715-7 5동 114호3448390054.052023-05-19
338부산광역시강서구26440201710901176081301부산광역시 강서구 송정동 1760-8 1동 301호91796000212.02023-05-19
25983부산광역시강서구264402017101012428441101[ 공항로1309번길 33 ] 0001동 0101호129960000228.02023-05-19
32084부산광역시강서구26440201710901177141201부산광역시 강서구 송정동 1771-4 1동 201호57100000100.02023-05-19

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시강서구26440201710101102821101[ 대저중앙로29번길 62 ] 0001동 0101호46881000312.542023-05-193
6부산광역시강서구26440201711601183302101[ 가락대로 929 ] 0002동 0101호185627250538.052023-05-193
1부산광역시강서구26440201710101132551101부산광역시 강서구 대저1동 1325-5 1동 101호23095000149.02023-05-192
2부산광역시강서구2644020171040190091101부산광역시 강서구 명지동 900-9 1동 101호130284000493.52023-05-192
3부산광역시강서구26440201710401158231101부산광역시 강서구 명지동 1582-3 1동 101호117589500373.32023-05-192
4부산광역시강서구26440201711001579101201부산광역시 강서구 화전동 579-10 1동 201호371144940548.222023-05-192
5부산광역시강서구26440201711101133811101부산광역시 강서구 녹산동 1338-1 1동 101호342542200826.02023-05-192
7부산광역시강서구26440202010201203311101부산광역시 강서구 대저2동 2033-1 1동 101호410400027.02023-05-192
8부산광역시강서구2644020201020154081010[ 신노전로 192-3 ] 0001동 0000호648000027.02023-05-192
9부산광역시강서구26440202010901147131101부산광역시 강서구 송정동 1471-3 1동 101호591500022.752023-05-192