Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
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_20180601
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 9 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (99.3%)Imbalance
부번 has 1949 (19.5%) zerosZeros
동 has 1775 (17.8%) zerosZeros

Reproduction

Analysis started2023-12-10 16:23:26.228383
Analysis finished2023-12-10 16:23:35.597440
Duration9.37 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

2023-12-11T01:23:36.005804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:36.140826image/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

2023-12-11T01:23:36.274982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:36.401884image/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

2023-12-11T01:23:36.549707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:36.685090image/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
2018
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2018 10000
100.0%

Length

2023-12-11T01:23:36.821747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:36.952451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 10000
100.0%

법정동
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.2437
Minimum101
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:37.084475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1111
median124
Q3132
95-th percentile141
Maximum141
Range40
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.482768
Coefficient of variation (CV)0.10211379
Kurtosis-1.148764
Mean122.2437
Median Absolute Deviation (MAD)10
Skewness-0.13537094
Sum1222437
Variance155.81949
MonotonicityNot monotonic
2023-12-11T01:23:37.303262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
107 1160
 
11.6%
140 774
 
7.7%
124 687
 
6.9%
101 607
 
6.1%
141 557
 
5.6%
130 502
 
5.0%
123 442
 
4.4%
132 379
 
3.8%
122 299
 
3.0%
126 288
 
2.9%
Other values (31) 4305
43.0%
ValueCountFrequency (%)
101 607
6.1%
102 88
 
0.9%
103 90
 
0.9%
104 73
 
0.7%
105 161
 
1.6%
106 101
 
1.0%
107 1160
11.6%
108 93
 
0.9%
109 91
 
0.9%
110 30
 
0.3%
ValueCountFrequency (%)
141 557
5.6%
140 774
7.7%
139 183
 
1.8%
138 71
 
0.7%
137 210
 
2.1%
136 151
 
1.5%
135 55
 
0.5%
134 125
 
1.2%
133 175
 
1.8%
132 379
3.8%

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

2023-12-11T01:23:37.525954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:37.661467image/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
9994 
2
 
6

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 9994
99.9%
2 6
 
0.1%

Length

2023-12-11T01:23:37.799906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:37.926445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9994
99.9%
2 6
 
0.1%

본번
Real number (ℝ)

Distinct233
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.5272
Minimum1
Maximum747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:38.103665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115
median34
Q366
95-th percentile117
Maximum747
Range746
Interquartile range (IQR)51

Descriptive statistics

Standard deviation102.94977
Coefficient of variation (CV)1.7895842
Kurtosis23.898954
Mean57.5272
Median Absolute Deviation (MAD)22
Skewness4.7523886
Sum575272
Variance10598.655
MonotonicityNot monotonic
2023-12-11T01:23:38.292205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 481
 
4.8%
3 432
 
4.3%
23 412
 
4.1%
2 401
 
4.0%
1 258
 
2.6%
20 237
 
2.4%
37 225
 
2.2%
12 223
 
2.2%
21 172
 
1.7%
46 167
 
1.7%
Other values (223) 6992
69.9%
ValueCountFrequency (%)
1 258
2.6%
2 401
4.0%
3 432
4.3%
4 72
 
0.7%
5 159
 
1.6%
6 80
 
0.8%
7 104
 
1.0%
8 117
 
1.2%
9 96
 
1.0%
10 122
 
1.2%
ValueCountFrequency (%)
747 1
 
< 0.1%
746 1
 
< 0.1%
743 14
0.1%
742 11
0.1%
741 5
 
0.1%
728 8
0.1%
712 1
 
< 0.1%
709 1
 
< 0.1%
702 5
 
0.1%
694 4
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct133
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9335
Minimum0
Maximum509
Zeros1949
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:38.526922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile28
Maximum509
Range509
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.95389
Coefficient of variation (CV)3.0193345
Kurtosis138.57123
Mean7.9335
Median Absolute Deviation (MAD)2
Skewness10.314906
Sum79335
Variance573.78886
MonotonicityNot monotonic
2023-12-11T01:23:38.720032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2213
22.1%
0 1949
19.5%
3 997
10.0%
2 913
9.1%
4 479
 
4.8%
5 424
 
4.2%
7 296
 
3.0%
10 296
 
3.0%
6 286
 
2.9%
9 211
 
2.1%
Other values (123) 1936
19.4%
ValueCountFrequency (%)
0 1949
19.5%
1 2213
22.1%
2 913
9.1%
3 997
10.0%
4 479
 
4.8%
5 424
 
4.2%
6 286
 
2.9%
7 296
 
3.0%
8 187
 
1.9%
9 211
 
2.1%
ValueCountFrequency (%)
509 2
 
< 0.1%
391 6
0.1%
375 1
 
< 0.1%
323 1
 
< 0.1%
314 1
 
< 0.1%
310 6
0.1%
306 1
 
< 0.1%
305 3
< 0.1%
299 1
 
< 0.1%
290 2
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.9798
Minimum0
Maximum6022
Zeros1775
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:38.860796image/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 deviation375.80075
Coefficient of variation (CV)13.928967
Kurtosis228.14609
Mean26.9798
Median Absolute Deviation (MAD)0
Skewness15.083389
Sum269798
Variance141226.21
MonotonicityNot monotonic
2023-12-11T01:23:38.978313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 7401
74.0%
0 1775
 
17.8%
2 272
 
2.7%
3 153
 
1.5%
6 102
 
1.0%
5 91
 
0.9%
4 85
 
0.9%
102 45
 
0.4%
6012 16
 
0.2%
6022 13
 
0.1%
Other values (17) 47
 
0.5%
ValueCountFrequency (%)
0 1775
 
17.8%
1 7401
74.0%
2 272
 
2.7%
3 153
 
1.5%
4 85
 
0.9%
5 91
 
0.9%
6 102
 
1.0%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
6022 13
 
0.1%
6012 16
 
0.2%
5022 8
 
0.1%
5012 6
 
0.1%
2022 1
 
< 0.1%
2012 3
 
< 0.1%
201 10
 
0.1%
109 3
 
< 0.1%
102 45
0.4%
101 1
 
< 0.1%

호
Real number (ℝ)

Distinct1225
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1443.5108
Minimum0
Maximum9002
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:39.121577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1102
median301
Q31004
95-th percentile8101
Maximum9002
Range9002
Interquartile range (IQR)902

Descriptive statistics

Standard deviation2608.2489
Coefficient of variation (CV)1.8068787
Kurtosis2.416787
Mean1443.5108
Median Absolute Deviation (MAD)200
Skewness2.0432717
Sum14435108
Variance6802962.5
MonotonicityNot monotonic
2023-12-11T01:23:39.299749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 1851
18.5%
201 1246
 
12.5%
301 791
 
7.9%
8101 755
 
7.5%
401 513
 
5.1%
102 377
 
3.8%
501 291
 
2.9%
202 270
 
2.7%
601 137
 
1.4%
302 100
 
1.0%
Other values (1215) 3669
36.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 19
0.2%
2 8
0.1%
3 11
0.1%
4 8
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 8
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9002 1
 
< 0.1%
9001 1
 
< 0.1%
8803 1
 
< 0.1%
8801 1
 
< 0.1%
8701 2
< 0.1%
8502 1
 
< 0.1%
8501 3
< 0.1%
8414 1
 
< 0.1%
8413 1
 
< 0.1%
8405 1
 
< 0.1%

물건지
Text

Distinct9416
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:23:39.549185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length24.5421
Min length17

Characters and Unicode

Total characters245421
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

Unique8984 ?
Unique (%)89.8%

Sample

1st row[ 광복로 40-1 ] 0001동 0201호
2nd row[ 중구로34번길 21-1 ] 0001동 0101호
3rd row[ 중구로 52 ] 0001동 8164호
4th row[ 국제시장2길 19 ] 0004동 8004호
5th row[ 대영로 221-1 ] 0001동 0201호
ValueCountFrequency (%)
17208
28.9%
0001동 6809
 
11.5%
0101호 1667
 
2.8%
부산광역시 1396
 
2.3%
중구 1396
 
2.3%
0000동 1223
 
2.1%
0201호 1135
 
1.9%
중앙대로 877
 
1.5%
8101호 755
 
1.3%
0301호 731
 
1.2%
Other values (2386) 26256
44.2%
2023-12-11T01:23:39.966860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49456
20.2%
0 44639
18.2%
1 25318
 
10.3%
동 11193
 
4.6%
호 9999
 
4.1%
2 9059
 
3.7%
[ 8604
 
3.5%
] 8604
 
3.5%
로 7440
 
3.0%
3 6073
 
2.5%
Other values (76) 65036
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104775
42.7%
Other Letter 70974
28.9%
Space Separator 49456
20.2%
Open Punctuation 8604
 
3.5%
Close Punctuation 8604
 
3.5%
Dash Punctuation 3008
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11193
15.8%
호 9999
14.1%
로 7440
 
10.5%
길 4245
 
6.0%
중 3942
 
5.6%
대 3202
 
4.5%
번 3081
 
4.3%
광 2997
 
4.2%
구 2794
 
3.9%
시 1754
 
2.5%
Other values (62) 20327
28.6%
Decimal Number
ValueCountFrequency (%)
0 44639
42.6%
1 25318
24.2%
2 9059
 
8.6%
3 6073
 
5.8%
4 4439
 
4.2%
5 3897
 
3.7%
8 3406
 
3.3%
9 2906
 
2.8%
6 2829
 
2.7%
7 2209
 
2.1%
Space Separator
ValueCountFrequency (%)
49456
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 8604
100.0%
Close Punctuation
ValueCountFrequency (%)
] 8604
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3008
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174447
71.1%
Hangul 70974
28.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11193
15.8%
호 9999
14.1%
로 7440
 
10.5%
길 4245
 
6.0%
중 3942
 
5.6%
대 3202
 
4.5%
번 3081
 
4.3%
광 2997
 
4.2%
구 2794
 
3.9%
시 1754
 
2.5%
Other values (62) 20327
28.6%
Common
ValueCountFrequency (%)
49456
28.4%
0 44639
25.6%
1 25318
14.5%
2 9059
 
5.2%
[ 8604
 
4.9%
] 8604
 
4.9%
3 6073
 
3.5%
4 4439
 
2.5%
5 3897
 
2.2%
8 3406
 
2.0%
Other values (4) 10952
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174447
71.1%
Hangul 70974
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49456
28.4%
0 44639
25.6%
1 25318
14.5%
2 9059
 
5.2%
[ 8604
 
4.9%
] 8604
 
4.9%
3 6073
 
3.5%
4 4439
 
2.5%
5 3897
 
2.2%
8 3406
 
2.0%
Other values (4) 10952
 
6.3%
Hangul
ValueCountFrequency (%)
동 11193
15.8%
호 9999
14.1%
로 7440
 
10.5%
길 4245
 
6.0%
중 3942
 
5.6%
대 3202
 
4.5%
번 3081
 
4.3%
광 2997
 
4.2%
구 2794
 
3.9%
시 1754
 
2.5%
Other values (62) 20327
28.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7662
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68925383
Minimum74640
Maximum1.0549092 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:40.161344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74640
5-th percentile1123200
Q14509010
median17683810
Q344381600
95-th percentile2.2604757 × 108
Maximum1.0549092 × 1010
Range1.0549018 × 1010
Interquartile range (IQR)39872590

Descriptive statistics

Standard deviation3.4315216 × 108
Coefficient of variation (CV)4.9786037
Kurtosis443.5433
Mean68925383
Median Absolute Deviation (MAD)14889770
Skewness19.071098
Sum6.8925383 × 1011
Variance1.1775341 × 1017
MonotonicityNot monotonic
2023-12-11T01:23:40.338857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29176320 85
 
0.9%
444600 75
 
0.8%
4144800 56
 
0.6%
2178000 52
 
0.5%
2772000 48
 
0.5%
2904000 48
 
0.5%
3348150 47
 
0.5%
36503740 31
 
0.3%
1123200 29
 
0.3%
12060140 27
 
0.3%
Other values (7652) 9502
95.0%
ValueCountFrequency (%)
74640 1
< 0.1%
134550 1
< 0.1%
140000 1
< 0.1%
156150 1
< 0.1%
173250 1
< 0.1%
175030 1
< 0.1%
183000 1
< 0.1%
183740 1
< 0.1%
212800 1
< 0.1%
216920 1
< 0.1%
ValueCountFrequency (%)
10549092400 1
< 0.1%
10086471450 1
< 0.1%
9018306880 1
< 0.1%
9009256080 1
< 0.1%
7996080400 1
< 0.1%
7654623950 1
< 0.1%
7637929220 1
< 0.1%
7446943270 1
< 0.1%
6918521910 1
< 0.1%
6916082940 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5380
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.54768
Minimum0.407
Maximum13700.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:23:40.511414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.407
5-th percentile4.624795
Q120.5
median56.54
Q3126.21
95-th percentile459.706
Maximum13700.12
Range13699.713
Interquartile range (IQR)105.71

Descriptive statistics

Standard deviation399.31455
Coefficient of variation (CV)2.9678294
Kurtosis421.87521
Mean134.54768
Median Absolute Deviation (MAD)42.745
Skewness16.914044
Sum1345476.8
Variance159452.11
MonotonicityNot monotonic
2023-12-11T01:23:40.709821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.92 86
 
0.9%
0.95 75
 
0.8%
9.9 71
 
0.7%
15.7 58
 
0.6%
13.2 57
 
0.6%
10.5 49
 
0.5%
6.63 47
 
0.5%
9.6 38
 
0.4%
16.5 34
 
0.3%
4.0 34
 
0.3%
Other values (5370) 9451
94.5%
ValueCountFrequency (%)
0.407 2
 
< 0.1%
0.4491 1
 
< 0.1%
0.4612 3
< 0.1%
0.4798 6
0.1%
0.4824 1
 
< 0.1%
0.4848 1
 
< 0.1%
0.4921 1
 
< 0.1%
0.66 2
 
< 0.1%
0.7 1
 
< 0.1%
0.72 1
 
< 0.1%
ValueCountFrequency (%)
13700.12 1
< 0.1%
12631.77 1
< 0.1%
11986.3 1
< 0.1%
10384.52 1
< 0.1%
7764.04 1
< 0.1%
6994.51 1
< 0.1%
6867.67 1
< 0.1%
6198.45 1
< 0.1%
6028.28 1
< 0.1%
6022.23 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20180601
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20180601
2nd row20180601
3rd row20180601
4th row20180601
5th row20180601

Common Values

ValueCountFrequency (%)
20180601 10000
100.0%

Length

2023-12-11T01:23:40.882164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:23:40.987850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20180601 10000
100.0%

Interactions

2023-12-11T01:23:33.960386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:28.674182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.674329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.546082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.338314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.034078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.858313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.121956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:28.809744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.808572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.681136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.447208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.130400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.011874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.293078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:28.985472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.945152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.801731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.558245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.244762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.154392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.433386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.134203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.057474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.909246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.651085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.341012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.296152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.605426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.303742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.198147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.033983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.759232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.456078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.471078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.737601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.417516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.311094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.134100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.846155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.581791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.634438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:34.886327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:29.530136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:30.414207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.240471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:31.940006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:32.707463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:23:33.782793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:23:41.068983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동호시가표준액연면적
법정동1.0000.0650.6940.1940.1690.3790.1600.125
특수지0.0651.0000.0000.0740.0000.0000.0000.054
본번0.6940.0001.0000.1080.0000.1550.0000.071
부번0.1940.0740.1081.0000.0000.0000.0000.000
동0.1690.0000.0000.0001.0000.0450.0000.000
호0.3790.0000.1550.0000.0451.0000.1180.147
시가표준액0.1600.0000.0000.0000.0000.1181.0000.945
연면적0.1250.0540.0710.0000.0000.1470.9451.000
2023-12-11T01:23:41.216266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동호시가표준액연면적특수지
법정동1.000-0.143-0.338-0.1270.058-0.201-0.2360.044
본번-0.1431.0000.054-0.131-0.0600.0620.0560.000
부번-0.3380.0541.0000.104-0.1830.0900.1770.074
동-0.127-0.1310.1041.000-0.066-0.066-0.0110.000
호0.058-0.060-0.183-0.0661.000-0.018-0.0750.000
시가표준액-0.2010.0620.090-0.066-0.0181.0000.8940.000
연면적-0.2360.0560.177-0.011-0.0750.8941.0000.054
특수지0.0440.0000.0740.0000.0000.0000.0541.000

Missing values

2023-12-11T01:23:35.137508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:23:35.434673image/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
5988부산광역시중구261102018132014831201[ 광복로 40-1 ] 0001동 0201호362007016.5320180601
13286부산광역시중구261102018127011921101[ 중구로34번길 21-1 ] 0001동 0101호1752527076.2320180601
5407부산광역시중구2611020181300150218164[ 중구로 52 ] 0001동 8164호16588007.5420180601
3601부산광역시중구2611020181300134148004[ 국제시장2길 19 ] 0004동 8004호270600012.020180601
2027부산광역시중구2611020181010158111201[ 대영로 221-1 ] 0001동 0201호3153659083.2120180601
13830부산광역시중구261102018127012300620[ 광복로49번길 25 ] 0000동 0620호7538185069.66920180601
1560부산광역시중구2611020181010161911801[ 동광길 173 ] 0001동 0801호2763684039.8820180601
10552부산광역시중구261102018117011641201[ 대청로 101 ] 0001동 0201호25342000126.7120180601
9348부산광역시중구26110201811601371102419부산광역시 중구 대청동1가 37-1 102동 419호5988580074.320180601
10954부산광역시중구261102018122012731802[ 보수대로106번길 14-1 ] 0001동 0802호1970124028.28220180601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
7680부산광역시중구261102018120011161161701부산광역시 중구 보수동1가 116-116 1동 701호1437030091.2420180601
10156부산광역시중구2611020181200126121101[ 흑교로 80 ] 0001동 0101호3426957052.6920180601
19213부산광역시중구2611020181400192001124부산광역시 중구 남포동5가 92 1124호708876010.1520180601
15092부산광역시중구261102018107015381101[ 중앙대로 85 ] 0001동 0101호59684500227.1120180601
3609부산광역시중구2611020181300134148025[ 국제시장2길 19 ] 0004동 8025호324720014.420180601
3433부산광역시중구2611020181320122208101[ 광복로 31 ] 0000동 8101호1591095073.3920180601
818부산광역시중구261102018107012310714[ 중앙대로 80 ] 0000동 0714호7300801.5620180601
21801부산광역시중구261102018140013302101[ 비프광장로 40 ] 0002동 0101호42589403.8520180601
13566부산광역시중구261102018127012300332[ 광복로49번길 25 ] 0000동 0332호12910802.1220180601
17643부산광역시중구26110201810801501907[ 중앙대로 56 ] 0001동 0907호3924827042.293420180601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시중구2611020181070181201201[ 중앙대로116번길 5 ] 0001동 0201호228153400254.92201806012
1부산광역시중구261102018111011101304[ 중앙대로 21 ] 0001동 0304호21996009.4201806012
2부산광역시중구2611020181110111012203[ 중앙대로 21 ] 0001동 2203호18720009.6201806012
3부산광역시중구2611020181110111012229[ 중앙대로 21 ] 0001동 2229호18720009.6201806012
4부산광역시중구2611020181110111018203[ 중앙대로 21 ] 0001동 8203호196560012.6201806012
5부산광역시중구261102018121013811101[ 흑교로87번길 3-1 ] 0001동 0101호415800019.8201806012
6부산광역시중구2611020181240149211[ 광복로 4-5 ] 0001동 0001호5487390064.18201806012
7부산광역시중구2611020181300121361068[ 중구로 28 ] 0006동 1068호6380003.19201806012
8부산광역시중구261102018141019881101[ 비프광장로 12-1 ] 0001동 0101호6224790072.55201806012