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 memory140.0 B

Variable types

Categorical7
Numeric7
Text1

Dataset

Description부산광역시중구_일반건축물시가표준액_20190601
Author부산광역시 중구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15080138

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
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 (99.0%)Imbalance
부번 has 2000 (20.0%) zerosZeros
동 has 1756 (17.6%) zerosZeros

Reproduction

Analysis started2023-12-10 16:23:06.285410
Analysis finished2023-12-10 16:23:14.400458
Duration8.12 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

2023-12-11T01:23:14.496653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:14.638870image/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
중구
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 10000
100.0%

Length

2023-12-11T01:23:14.789884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:14.932683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
26110
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26110
2nd row26110
3rd row26110
4th row26110
5th row26110

Common Values

ValueCountFrequency (%)
26110 10000
100.0%

Length

2023-12-11T01:23:15.057053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:15.174377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26110 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 10000
100.0%

Length

2023-12-11T01:23:15.294511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:15.382298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 10000
100.0%

법정동
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.9625
Minimum101
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:15.488484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1108
median123
Q3132
95-th percentile141
Maximum141
Range40
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.546308
Coefficient of variation (CV)0.10287021
Kurtosis-1.1739954
Mean121.9625
Median Absolute Deviation (MAD)10
Skewness-0.11506708
Sum1219625
Variance157.40983
MonotonicityNot monotonic
2023-12-11T01:23:15.683233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
107 1193
 
11.9%
140 745
 
7.4%
124 712
 
7.1%
101 651
 
6.5%
141 528
 
5.3%
130 497
 
5.0%
123 440
 
4.4%
132 369
 
3.7%
122 325
 
3.2%
126 293
 
2.9%
Other values (31) 4247
42.5%
ValueCountFrequency (%)
101 651
6.5%
102 84
 
0.8%
103 100
 
1.0%
104 68
 
0.7%
105 141
 
1.4%
106 90
 
0.9%
107 1193
11.9%
108 205
 
2.1%
109 83
 
0.8%
110 32
 
0.3%
ValueCountFrequency (%)
141 528
5.3%
140 745
7.4%
139 190
 
1.9%
138 79
 
0.8%
137 212
 
2.1%
136 132
 
1.3%
135 57
 
0.6%
134 136
 
1.4%
133 180
 
1.8%
132 369
3.7%

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

2023-12-11T01:23:15.814771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:15.916997image/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
9991 
2
 
9

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 9991
99.9%
2 9
 
0.1%

Length

2023-12-11T01:23:16.028589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:16.123926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9991
99.9%
2 9
 
0.1%

본번
Real number (ℝ)

Distinct228
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.8453
Minimum1
Maximum747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:16.254954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115
median33
Q365
95-th percentile124
Maximum747
Range746
Interquartile range (IQR)50

Descriptive statistics

Standard deviation107.85891
Coefficient of variation (CV)1.8329231
Kurtosis21.647256
Mean58.8453
Median Absolute Deviation (MAD)21
Skewness4.5681763
Sum588453
Variance11633.545
MonotonicityNot monotonic
2023-12-11T01:23:16.396378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 465
 
4.7%
2 440
 
4.4%
3 429
 
4.3%
23 426
 
4.3%
1 250
 
2.5%
20 247
 
2.5%
37 229
 
2.3%
12 190
 
1.9%
18 189
 
1.9%
27 182
 
1.8%
Other values (218) 6953
69.5%
ValueCountFrequency (%)
1 250
2.5%
2 440
4.4%
3 429
4.3%
4 73
 
0.7%
5 141
 
1.4%
6 70
 
0.7%
7 103
 
1.0%
8 102
 
1.0%
9 88
 
0.9%
10 118
 
1.2%
ValueCountFrequency (%)
747 1
 
< 0.1%
746 2
 
< 0.1%
743 19
0.2%
742 10
0.1%
741 7
 
0.1%
728 6
 
0.1%
712 1
 
< 0.1%
702 4
 
< 0.1%
694 2
 
< 0.1%
681 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct131
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9151
Minimum0
Maximum508
Zeros2000
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:16.543787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile28
Maximum508
Range508
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.256899
Coefficient of variation (CV)3.0646358
Kurtosis134.42731
Mean7.9151
Median Absolute Deviation (MAD)2
Skewness10.24978
Sum79151
Variance588.39713
MonotonicityNot monotonic
2023-12-11T01:23:16.689764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2280
22.8%
0 2000
20.0%
2 928
9.3%
3 925
9.2%
5 454
 
4.5%
4 438
 
4.4%
6 285
 
2.9%
7 280
 
2.8%
10 276
 
2.8%
9 190
 
1.9%
Other values (121) 1944
19.4%
ValueCountFrequency (%)
0 2000
20.0%
1 2280
22.8%
2 928
9.3%
3 925
9.2%
4 438
 
4.4%
5 454
 
4.5%
6 285
 
2.9%
7 280
 
2.8%
8 178
 
1.8%
9 190
 
1.9%
ValueCountFrequency (%)
508 1
 
< 0.1%
449 1
 
< 0.1%
418 1
 
< 0.1%
391 5
0.1%
384 2
 
< 0.1%
375 1
 
< 0.1%
345 1
 
< 0.1%
323 1
 
< 0.1%
310 4
< 0.1%
305 2
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9843
Minimum0
Maximum6022
Zeros1756
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:16.828612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum6022
Range6022
Interquartile range (IQR)0

Descriptive statistics

Standard deviation365.64006
Coefficient of variation (CV)14.071576
Kurtosis236.9623
Mean25.9843
Median Absolute Deviation (MAD)0
Skewness15.344029
Sum259843
Variance133692.66
MonotonicityNot monotonic
2023-12-11T01:23:16.950785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 7461
74.6%
0 1756
 
17.6%
2 258
 
2.6%
3 126
 
1.3%
6 113
 
1.1%
5 93
 
0.9%
4 79
 
0.8%
102 43
 
0.4%
6012 17
 
0.2%
6022 9
 
0.1%
Other values (19) 45
 
0.4%
ValueCountFrequency (%)
0 1756
 
17.6%
1 7461
74.6%
2 258
 
2.6%
3 126
 
1.3%
4 79
 
0.8%
5 93
 
0.9%
6 113
 
1.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
6022 9
 
0.1%
6012 17
 
0.2%
5022 7
 
0.1%
5012 8
 
0.1%
2022 4
 
< 0.1%
2012 2
 
< 0.1%
202 2
 
< 0.1%
201 4
 
< 0.1%
109 3
 
< 0.1%
102 43
0.4%

호
Real number (ℝ)

Distinct1223
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1453.0989
Minimum0
Maximum9002
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:17.080809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1103
median301
Q31005
95-th percentile8101
Maximum9002
Range9002
Interquartile range (IQR)902

Descriptive statistics

Standard deviation2611.235
Coefficient of variation (CV)1.7970112
Kurtosis2.3914263
Mean1453.0989
Median Absolute Deviation (MAD)200
Skewness2.0374853
Sum14530989
Variance6818548.4
MonotonicityNot monotonic
2023-12-11T01:23:17.229210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 1786
17.9%
201 1212
 
12.1%
301 790
 
7.9%
8101 735
 
7.3%
401 546
 
5.5%
102 368
 
3.7%
501 284
 
2.8%
202 271
 
2.7%
601 129
 
1.3%
302 102
 
1.0%
Other values (1213) 3777
37.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 25
0.2%
2 16
0.2%
3 10
 
0.1%
4 3
 
< 0.1%
5 6
 
0.1%
6 7
 
0.1%
7 6
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
9002 1
 
< 0.1%
8803 1
 
< 0.1%
8802 1
 
< 0.1%
8701 1
 
< 0.1%
8601 3
< 0.1%
8503 1
 
< 0.1%
8502 2
 
< 0.1%
8501 5
0.1%
8413 1
 
< 0.1%
8402 1
 
< 0.1%

물건지
Text

Distinct9402
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:23:17.534253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length24.4952
Min length20

Characters and Unicode

Total characters244952
Distinct characters86
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8968 ?
Unique (%)89.7%

Sample

1st row[ 용미길8번길 8-3 ] 0001동 0201호
2nd row[ 흑교로35번길 12 ] 0001동 0101호
3rd row[ 대영로226번안길 6 ] 0001동 0101호
4th row[ 국제시장2길 25 ] 0003동 0009호
5th row[ 중구로34번길 19-1 ] 0001동 0101호
ValueCountFrequency (%)
17368
29.2%
0001동 6892
 
11.6%
0101호 1601
 
2.7%
중구 1316
 
2.2%
부산광역시 1316
 
2.2%
0000동 1231
 
2.1%
0201호 1118
 
1.9%
중앙대로 834
 
1.4%
8101호 735
 
1.2%
0301호 732
 
1.2%
Other values (2382) 26341
44.3%
2023-12-11T01:23:18.061891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49484
20.2%
0 44851
18.3%
1 25276
 
10.3%
동 11121
 
4.5%
호 9999
 
4.1%
2 8869
 
3.6%
] 8684
 
3.5%
[ 8684
 
3.5%
로 7549
 
3.1%
3 6010
 
2.5%
Other values (76) 64425
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104921
42.8%
Other Letter 70243
28.7%
Space Separator 49484
20.2%
Close Punctuation 8684
 
3.5%
Open Punctuation 8684
 
3.5%
Dash Punctuation 2936
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11121
15.8%
호 9999
14.2%
로 7549
 
10.7%
길 4187
 
6.0%
중 3924
 
5.6%
대 3231
 
4.6%
번 3052
 
4.3%
광 2875
 
4.1%
구 2807
 
4.0%
시 1669
 
2.4%
Other values (62) 19829
28.2%
Decimal Number
ValueCountFrequency (%)
0 44851
42.7%
1 25276
24.1%
2 8869
 
8.5%
3 6010
 
5.7%
4 4549
 
4.3%
5 3838
 
3.7%
8 3456
 
3.3%
6 2910
 
2.8%
9 2908
 
2.8%
7 2254
 
2.1%
Space Separator
ValueCountFrequency (%)
49484
100.0%
Close Punctuation
ValueCountFrequency (%)
] 8684
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 8684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174709
71.3%
Hangul 70243
28.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11121
15.8%
호 9999
14.2%
로 7549
 
10.7%
길 4187
 
6.0%
중 3924
 
5.6%
대 3231
 
4.6%
번 3052
 
4.3%
광 2875
 
4.1%
구 2807
 
4.0%
시 1669
 
2.4%
Other values (62) 19829
28.2%
Common
ValueCountFrequency (%)
49484
28.3%
0 44851
25.7%
1 25276
14.5%
2 8869
 
5.1%
] 8684
 
5.0%
[ 8684
 
5.0%
3 6010
 
3.4%
4 4549
 
2.6%
5 3838
 
2.2%
8 3456
 
2.0%
Other values (4) 11008
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174709
71.3%
Hangul 70243
28.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49484
28.3%
0 44851
25.7%
1 25276
14.5%
2 8869
 
5.1%
] 8684
 
5.0%
[ 8684
 
5.0%
3 6010
 
3.4%
4 4549
 
2.6%
5 3838
 
2.2%
8 3456
 
2.0%
Other values (4) 11008
 
6.3%
Hangul
ValueCountFrequency (%)
동 11121
15.8%
호 9999
14.2%
로 7549
 
10.7%
길 4187
 
6.0%
중 3924
 
5.6%
대 3231
 
4.6%
번 3052
 
4.3%
광 2875
 
4.1%
구 2807
 
4.0%
시 1669
 
2.4%
Other values (62) 19829
28.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7588
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71981622
Minimum41740
Maximum1.1389757 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:18.263712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41740
5-th percentile1085270
Q14435657.5
median17447170
Q343521512
95-th percentile2.2250345 × 108
Maximum1.1389757 × 1010
Range1.1389715 × 1010
Interquartile range (IQR)39085855

Descriptive statistics

Standard deviation3.822829 × 108
Coefficient of variation (CV)5.3108403
Kurtosis398.20122
Mean71981622
Median Absolute Deviation (MAD)14727970
Skewness18.299341
Sum7.1981622 × 1011
Variance1.4614021 × 1017
MonotonicityNot monotonic
2023-12-11T01:23:18.497009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444600 91
 
0.9%
29176320 78
 
0.8%
2039400 54
 
0.5%
3447600 54
 
0.5%
2719200 53
 
0.5%
2703750 50
 
0.5%
4042750 48
 
0.5%
205300 30
 
0.3%
36730190 30
 
0.3%
1245000 28
 
0.3%
Other values (7578) 9484
94.8%
ValueCountFrequency (%)
41740 1
< 0.1%
49000 1
< 0.1%
78030 1
< 0.1%
96870 1
< 0.1%
138320 1
< 0.1%
157870 1
< 0.1%
164220 1
< 0.1%
167670 1
< 0.1%
168630 1
< 0.1%
172000 1
< 0.1%
ValueCountFrequency (%)
11389756750 1
< 0.1%
10396853540 1
< 0.1%
9449222750 1
< 0.1%
9117955000 1
< 0.1%
8441540510 1
< 0.1%
7970832600 1
< 0.1%
7913379870 1
< 0.1%
7752945110 1
< 0.1%
7748482540 1
< 0.1%
7734680360 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5326
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.1562
Minimum0.2785
Maximum13377.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:18.732612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2785
5-th percentile4
Q119
median53.105
Q3122.31
95-th percentile467.7505
Maximum13377.68
Range13377.401
Interquartile range (IQR)103.31

Descriptive statistics

Standard deviation407.29065
Coefficient of variation (CV)3.013481
Kurtosis357.55995
Mean135.1562
Median Absolute Deviation (MAD)40.055
Skewness15.404271
Sum1351562
Variance165885.68
MonotonicityNot monotonic
2023-12-11T01:23:18.990945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95 91
 
0.9%
41.92 79
 
0.8%
9.9 68
 
0.7%
6.63 55
 
0.5%
13.2 55
 
0.5%
10.5 51
 
0.5%
16.5 49
 
0.5%
15.7 49
 
0.5%
9.6 35
 
0.4%
4.0 32
 
0.3%
Other values (5316) 9436
94.4%
ValueCountFrequency (%)
0.2785 4
 
< 0.1%
0.2953 3
 
< 0.1%
0.3209 5
 
0.1%
0.3228 30
0.3%
0.3235 6
 
0.1%
0.3285 1
 
< 0.1%
0.33 1
 
< 0.1%
0.407 1
 
< 0.1%
0.4491 1
 
< 0.1%
0.4612 3
 
< 0.1%
ValueCountFrequency (%)
13377.68 1
< 0.1%
13368.72 1
< 0.1%
10523.0 1
< 0.1%
9294.55 1
< 0.1%
6867.67 1
< 0.1%
6455.0 1
< 0.1%
6247.42 1
< 0.1%
6028.4 1
< 0.1%
6028.38 1
< 0.1%
5616.11 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20190601
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20190601
2nd row20190601
3rd row20190601
4th row20190601
5th row20190601

Common Values

ValueCountFrequency (%)
20190601 10000
100.0%

Length

2023-12-11T01:23:19.192050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:19.327424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20190601 10000
100.0%

Interactions

2023-12-11T01:23:12.497770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.184683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.813213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.402240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.006566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.858065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.536355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:12.653069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.274185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.903236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.487873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.103065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.987210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.633374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:12.801864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.370139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.989483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.574977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.217532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.084734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.729875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:12.948982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.447620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.064944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.658912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.321481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.165930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.847629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:13.077372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.552137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.148605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.742035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.451727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.259941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.968177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:13.268372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.644263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.226837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.816556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.592563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.349972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:12.110235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:13.420977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:08.728746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.309140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:09.916103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:10.721859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:11.443884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:12.274692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:23:19.426466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동호시가표준액연면적
법정동1.0000.0810.6910.2740.1710.3790.2350.149
특수지0.0811.0000.0000.1500.0000.0000.0000.087
본번0.6910.0001.0000.0520.0000.1430.0000.000
부번0.2740.1500.0521.0000.0000.0000.0000.000
동0.1710.0000.0000.0001.0000.0500.0000.000
호0.3790.0000.1430.0000.0501.0000.0890.160
시가표준액0.2350.0000.0000.0000.0000.0891.0000.891
연면적0.1490.0870.0000.0000.0000.1600.8911.000
2023-12-11T01:23:19.621113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동호시가표준액연면적특수지
법정동1.000-0.156-0.325-0.1140.061-0.197-0.2200.058
본번-0.1561.0000.074-0.117-0.0530.0790.0760.000
부번-0.3250.0741.0000.102-0.1980.1010.1940.115
동-0.114-0.1170.1021.000-0.075-0.054-0.0060.000
호0.061-0.053-0.198-0.0751.000-0.051-0.1030.000
시가표준액-0.1970.0790.101-0.054-0.0511.0000.8930.000
연면적-0.2200.0760.194-0.006-0.1030.8931.0000.087
특수지0.0580.0000.1150.0000.0000.0000.0871.000

Missing values

2023-12-11T01:23:13.650776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:23:14.282705image/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
20886부산광역시중구2611020191360119101201[ 용미길8번길 8-3 ] 0001동 0201호250110039.720190601
8989부산광역시중구261102019125017781101[ 흑교로35번길 12 ] 0001동 0101호1936149036.7620190601
11889부산광역시중구2611020191010166001101[ 대영로226번안길 6 ] 0001동 0101호304114200990.620190601
7412부산광역시중구2611020191300138139[ 국제시장2길 25 ] 0003동 0009호15540007.420190601
8818부산광역시중구2611020191270119151101[ 중구로34번길 19-1 ] 0001동 0101호33977090137.4220190601
4247부산광역시중구2611020191240124201203[ 중구로29번길 35 ] 0001동 0203호781621034.58520190601
14850부산광역시중구2611020191150113461101[ 동영로 17 ] 0001동 0101호196263032.8220190601
15151부산광역시중구261102019118017161301[ 대청로 77-1 ] 0001동 0301호1028345047.8320190601
22823부산광역시중구261102019141013011427[ 자갈치로 33 ] 0001동 1427호270375010.520190601
17670부산광역시중구261102019139011931201[ 자갈치해안로 75-1 ] 0001동 0201호22131900105.3920190601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
8322부산광역시중구2611020191270112141202[ 중구로24번길 21-8 ] 0001동 0202호1444800068.820190601
21249부산광역시중구2611020191400192001166부산광역시 중구 남포동5가 92 1166호7218001.820190601
19559부산광역시중구2611020191320130121102부산광역시 중구 창선동2가 30-12 1동 102호523181021.1620190601
6893부산광역시중구2611020191300134143[ 국제시장2길 19 ] 0004동 0003호17415008.120190601
20232부산광역시중구2611020191400171218101[ 자갈치로 55 ] 0001동 8101호628712038.1520190601
3257부산광역시중구261102019107019011101부산광역시 중구 중앙동4가 90-1 1동 101호422330570645.4220190601
20912부산광역시중구2611020191360122418102[ 용미길 4 ] 0001동 8102호2228688069.320190601
10445부산광역시중구261102019107012310315[ 중앙대로 80 ] 0000동 0315호2959392042.5220190601
856부산광역시중구2611020191070179118101부산광역시 중구 중앙동4가 79-1 1동 8101호12391405.3420190601
22190부산광역시중구2611020191400192003028부산광역시 중구 남포동5가 92 3028호540147013.4720190601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시중구261102019107015281101[ 해관로 79-1 ] 0001동 0101호179179690170.81201906013
4부산광역시중구2611020191110111012229[ 중앙대로 21 ] 0001동 2229호19296009.6201906013
5부산광역시중구2611020191110111012420[ 중앙대로 21 ] 0001동 2420호223110011.1201906013
1부산광역시중구2611020191070181201201[ 중앙대로116번길 5 ] 0001동 0201호235546080254.92201906012
2부산광역시중구261102019111011101303[ 중앙대로 21 ] 0001동 0303호23868709.5201906012
3부산광역시중구2611020191110111012226[ 중앙대로 21 ] 0001동 2226호17487008.7201906012
6부산광역시중구26110201912101861211[ 흑교로 63-1 ] 0001동 0001호2069870034.1201906012
7부산광역시중구261102019123012601201[ 중구로33번길 18 ] 0001동 0201호121701840123.18201906012
8부산광역시중구26110201912401472601[ 보수대로24번길 4-1 ] 0000동 0001호975502030.58201906012
9부산광역시중구261102019127013520101[ 광복중앙로33번길 11 ] 0000동 0101호1479232015.94201906012