Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows15
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description부산광역시강서구_일반건축물시가표준액_20230519
Author부산광역시 강서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15080339

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 15 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (98.1%)Imbalance
시가표준액 is highly skewed (γ1 = 27.02256864)Skewed
연면적 is highly skewed (γ1 = 24.3510475)Skewed
부번 has 1762 (17.6%) zerosZeros
동 has 2397 (24.0%) zerosZeros

Reproduction

Analysis started2023-12-10 17:04:22.263963
Analysis finished2023-12-10 17:04:31.718618
Duration9.45 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

2023-12-11T02:04:31.840001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:31.999292image/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

2023-12-11T02:04:32.173112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:32.713558image/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

2023-12-11T02:04:32.894650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:33.077762image/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
4779 
2017
4583 
2019
 
363
2018
 
275

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 4779
47.8%
2017 4583
45.8%
2019 363
 
3.6%
2018 275
 
2.8%

Length

2023-12-11T02:04:33.292158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:33.502054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 4779
47.8%
2017 4583
45.8%
2019 363
 
3.6%
2018 275
 
2.8%

법정동
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.1461
Minimum101
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:33.702172image/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 deviation5.5731554
Coefficient of variation (CV)0.052014542
Kurtosis-0.70591987
Mean107.1461
Median Absolute Deviation (MAD)3
Skewness0.70043274
Sum1071461
Variance31.060061
MonotonicityNot monotonic
2023-12-11T02:04:33.908776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
102 2101
21.0%
109 1551
15.5%
104 1523
15.2%
101 1016
10.2%
117 767
 
7.7%
103 524
 
5.2%
110 505
 
5.1%
114 315
 
3.1%
115 296
 
3.0%
111 208
 
2.1%
Other values (12) 1194
11.9%
ValueCountFrequency (%)
101 1016
10.2%
102 2101
21.0%
103 524
 
5.2%
104 1523
15.2%
105 119
 
1.2%
106 49
 
0.5%
107 86
 
0.9%
108 132
 
1.3%
109 1551
15.5%
110 505
 
5.1%
ValueCountFrequency (%)
122 38
 
0.4%
121 77
 
0.8%
120 17
 
0.2%
119 159
 
1.6%
118 27
 
0.3%
117 767
7.7%
116 127
 
1.3%
115 296
 
3.0%
114 315
3.1%
113 158
 
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

2023-12-11T02:04:34.129637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:34.298945image/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
9973 
2
 
17
3
 
10

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 9973
99.7%
2 17
 
0.2%
3 10
 
0.1%

Length

2023-12-11T02:04:34.466373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:34.630450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9973
99.7%
2 17
 
0.2%
3 10
 
0.1%

본번
Real number (ℝ)

Distinct1998
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1904.4376
Minimum1
Maximum6530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:34.837788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile159
Q1596
median1581
Q33153
95-th percentile4367
Maximum6530
Range6529
Interquartile range (IQR)2557

Descriptive statistics

Standard deviation1418.4935
Coefficient of variation (CV)0.7448359
Kurtosis-0.018848881
Mean1904.4376
Median Absolute Deviation (MAD)1174
Skewness0.7075195
Sum19044376
Variance2012123.8
MonotonicityNot monotonic
2023-12-11T02:04:35.103176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 451
 
4.5%
3153 394
 
3.9%
3138 199
 
2.0%
3154 134
 
1.3%
3599 113
 
1.1%
1623 90
 
0.9%
3229 77
 
0.8%
3141 76
 
0.8%
29 67
 
0.7%
3603 65
 
0.7%
Other values (1988) 8334
83.3%
ValueCountFrequency (%)
1 36
0.4%
2 4
 
< 0.1%
3 2
 
< 0.1%
4 5
 
0.1%
5 12
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
6530 1
 
< 0.1%
6524 2
 
< 0.1%
6518 1
 
< 0.1%
6480 1
 
< 0.1%
6441 1
 
< 0.1%
6440 1
 
< 0.1%
6435 6
0.1%
6432 5
0.1%
6430 2
 
< 0.1%
6412 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct236
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.9667
Minimum0
Maximum2811
Zeros1762
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:35.381261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile27
Maximum2811
Range2811
Interquartile range (IQR)6

Descriptive statistics

Standard deviation104.89684
Coefficient of variation (CV)6.5697258
Kurtosis338.16227
Mean15.9667
Median Absolute Deviation (MAD)3
Skewness16.402835
Sum159667
Variance11003.347
MonotonicityNot monotonic
2023-12-11T02:04:35.683588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1888
18.9%
0 1762
17.6%
2 1251
12.5%
3 755
 
7.5%
4 610
 
6.1%
5 491
 
4.9%
6 447
 
4.5%
7 423
 
4.2%
8 339
 
3.4%
9 218
 
2.2%
Other values (226) 1816
18.2%
ValueCountFrequency (%)
0 1762
17.6%
1 1888
18.9%
2 1251
12.5%
3 755
 
7.5%
4 610
 
6.1%
5 491
 
4.9%
6 447
 
4.5%
7 423
 
4.2%
8 339
 
3.4%
9 218
 
2.2%
ValueCountFrequency (%)
2811 2
< 0.1%
2771 2
< 0.1%
2570 1
< 0.1%
2017 1
< 0.1%
2004 1
< 0.1%
1964 1
< 0.1%
1871 1
< 0.1%
1827 1
< 0.1%
1744 1
< 0.1%
1714 2
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct127
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.1941
Minimum0
Maximum9051
Zeros2397
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:35.953716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile125
Maximum9051
Range9051
Interquartile range (IQR)0

Descriptive statistics

Standard deviation607.39098
Coefficient of variation (CV)9.1759081
Kurtosis177.9842
Mean66.1941
Median Absolute Deviation (MAD)0
Skewness13.231937
Sum661941
Variance368923.8
MonotonicityNot monotonic
2023-12-11T02:04:36.166367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6068
60.7%
0 2397
 
24.0%
2 160
 
1.6%
104 90
 
0.9%
106 75
 
0.8%
101 68
 
0.7%
102 52
 
0.5%
3 48
 
0.5%
103 48
 
0.5%
105 43
 
0.4%
Other values (117) 951
 
9.5%
ValueCountFrequency (%)
0 2397
 
24.0%
1 6068
60.7%
2 160
 
1.6%
3 48
 
0.5%
4 17
 
0.2%
5 16
 
0.2%
6 12
 
0.1%
7 24
 
0.2%
8 15
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
9051 1
 
< 0.1%
9040 1
 
< 0.1%
9035 1
 
< 0.1%
9032 1
 
< 0.1%
9031 3
< 0.1%
9030 1
 
< 0.1%
9028 6
0.1%
9023 2
 
< 0.1%
9014 1
 
< 0.1%
9013 1
 
< 0.1%

호
Text

Distinct797
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T02:04:36.577248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0926
Min length1

Characters and Unicode

Total characters30926
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

Unique550 ?
Unique (%)5.5%

Sample

1st row101
2nd row415
3rd row102
4th row102
5th row104
ValueCountFrequency (%)
101 3715
37.1%
201 1125
 
11.2%
102 956
 
9.6%
301 383
 
3.8%
103 278
 
2.8%
202 193
 
1.9%
104 150
 
1.5%
401 131
 
1.3%
8101 88
 
0.9%
100 87
 
0.9%
Other values (787) 2894
28.9%
2023-12-11T02:04:37.215986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13338
43.1%
0 9135
29.5%
2 3773
 
12.2%
3 1419
 
4.6%
4 752
 
2.4%
8 728
 
2.4%
5 517
 
1.7%
6 408
 
1.3%
9 406
 
1.3%
7 362
 
1.2%
Other values (5) 88
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30838
99.7%
Uppercase Letter 74
 
0.2%
Dash Punctuation 14
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13338
43.3%
0 9135
29.6%
2 3773
 
12.2%
3 1419
 
4.6%
4 752
 
2.4%
8 728
 
2.4%
5 517
 
1.7%
6 408
 
1.3%
9 406
 
1.3%
7 362
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 40
54.1%
D 21
28.4%
A 9
 
12.2%
C 4
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30852
99.8%
Latin 74
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13338
43.2%
0 9135
29.6%
2 3773
 
12.2%
3 1419
 
4.6%
4 752
 
2.4%
8 728
 
2.4%
5 517
 
1.7%
6 408
 
1.3%
9 406
 
1.3%
7 362
 
1.2%
Latin
ValueCountFrequency (%)
B 40
54.1%
D 21
28.4%
A 9
 
12.2%
C 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13338
43.1%
0 9135
29.5%
2 3773
 
12.2%
3 1419
 
4.6%
4 752
 
2.4%
8 728
 
2.4%
5 517
 
1.7%
6 408
 
1.3%
9 406
 
1.3%
7 362
 
1.2%
Other values (5) 88
 
0.3%

물건지
Text

Distinct8851
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T02:04:37.774657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length27.6008
Min length21

Characters and Unicode

Total characters276008
Distinct characters135
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

Unique7977 ?
Unique (%)79.8%

Sample

1st row부산광역시 강서구 미음동 216 101호
2nd row부산광역시 강서구 명지동 3599-3 105동 415호
3rd row부산광역시 강서구 송정동 1520 1동 102호
4th row[ 맥도길265번길 45 ] 0000동 0102호
5th row부산광역시 강서구 대저2동 3141-8 2동 104호
ValueCountFrequency (%)
8374
 
14.1%
부산광역시 5813
 
9.8%
강서구 5813
 
9.8%
1동 3760
 
6.3%
0001동 2308
 
3.9%
101호 1890
 
3.2%
0101호 1825
 
3.1%
0000동 1758
 
3.0%
송정동 1274
 
2.1%
대저2동 1260
 
2.1%
Other values (4878) 25303
42.6%
2023-12-11T02:04:38.621256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49378
17.9%
1 31764
 
11.5%
0 30551
 
11.1%
동 15871
 
5.8%
2 11507
 
4.2%
호 10893
 
3.9%
3 7599
 
2.8%
산 7010
 
2.5%
강 6165
 
2.2%
시 6041
 
2.2%
Other values (125) 99229
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106471
38.6%
Decimal Number 106150
38.5%
Space Separator 49378
17.9%
Dash Punctuation 5561
 
2.0%
Open Punctuation 4187
 
1.5%
Close Punctuation 4187
 
1.5%
Uppercase Letter 74
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15871
14.9%
호 10893
 
10.2%
산 7010
 
6.6%
강 6165
 
5.8%
시 6041
 
5.7%
서 5976
 
5.6%
구 5943
 
5.6%
부 5818
 
5.5%
역 5813
 
5.5%
광 5813
 
5.5%
Other values (107) 31128
29.2%
Decimal Number
ValueCountFrequency (%)
1 31764
29.9%
0 30551
28.8%
2 11507
 
10.8%
3 7599
 
7.2%
5 5663
 
5.3%
4 4531
 
4.3%
8 4304
 
4.1%
6 3842
 
3.6%
9 3221
 
3.0%
7 3168
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 40
54.1%
D 21
28.4%
A 9
 
12.2%
C 4
 
5.4%
Space Separator
ValueCountFrequency (%)
49378
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5561
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4187
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169463
61.4%
Hangul 106471
38.6%
Latin 74
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15871
14.9%
호 10893
 
10.2%
산 7010
 
6.6%
강 6165
 
5.8%
시 6041
 
5.7%
서 5976
 
5.6%
구 5943
 
5.6%
부 5818
 
5.5%
역 5813
 
5.5%
광 5813
 
5.5%
Other values (107) 31128
29.2%
Common
ValueCountFrequency (%)
49378
29.1%
1 31764
18.7%
0 30551
18.0%
2 11507
 
6.8%
3 7599
 
4.5%
5 5663
 
3.3%
- 5561
 
3.3%
4 4531
 
2.7%
8 4304
 
2.5%
[ 4187
 
2.5%
Other values (4) 14418
 
8.5%
Latin
ValueCountFrequency (%)
B 40
54.1%
D 21
28.4%
A 9
 
12.2%
C 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169537
61.4%
Hangul 106471
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49378
29.1%
1 31764
18.7%
0 30551
18.0%
2 11507
 
6.8%
3 7599
 
4.5%
5 5663
 
3.3%
- 5561
 
3.3%
4 4531
 
2.7%
8 4304
 
2.5%
[ 4187
 
2.5%
Other values (8) 14492
 
8.5%
Hangul
ValueCountFrequency (%)
동 15871
14.9%
호 10893
 
10.2%
산 7010
 
6.6%
강 6165
 
5.8%
시 6041
 
5.7%
서 5976
 
5.6%
구 5943
 
5.6%
부 5818
 
5.5%
역 5813
 
5.5%
광 5813
 
5.5%
Other values (107) 31128
29.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8343
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5202504 × 108
Minimum30000
Maximum2.8429344 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:38.906594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30000
5-th percentile2159582.5
Q122601340
median60110715
Q31.3719254 × 108
95-th percentile4.6656241 × 108
Maximum2.8429344 × 1010
Range2.8429314 × 1010
Interquartile range (IQR)1.145912 × 108

Descriptive statistics

Standard deviation5.9683105 × 108
Coefficient of variation (CV)3.9258734
Kurtosis1051.4736
Mean1.5202504 × 108
Median Absolute Deviation (MAD)48830985
Skewness27.022569
Sum1.5202504 × 1012
Variance3.562073 × 1017
MonotonicityNot monotonic
2023-12-11T02:04:39.172106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9123580 92
 
0.9%
39249450 87
 
0.9%
42287180 81
 
0.8%
8903440 78
 
0.8%
48834840 76
 
0.8%
55006010 74
 
0.7%
9050200 64
 
0.6%
9505650 51
 
0.5%
9332820 50
 
0.5%
9480960 36
 
0.4%
Other values (8333) 9311
93.1%
ValueCountFrequency (%)
30000 1
< 0.1%
33000 1
< 0.1%
36120 1
< 0.1%
57420 1
< 0.1%
64500 1
< 0.1%
65250 1
< 0.1%
69160 1
< 0.1%
78000 1
< 0.1%
78400 1
< 0.1%
87000 1
< 0.1%
ValueCountFrequency (%)
28429343770 1
< 0.1%
26926617750 1
< 0.1%
16805908680 1
< 0.1%
14214539380 1
< 0.1%
11921166220 1
< 0.1%
11260077570 1
< 0.1%
10509302020 1
< 0.1%
8642260200 1
< 0.1%
7868503560 1
< 0.1%
6960897400 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5691
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.68303
Minimum1.04
Maximum61942.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:04:39.493463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile18
Q154
median126
Q3292.65
95-th percentile1169.348
Maximum61942.99
Range61941.95
Interquartile range (IQR)238.65

Descriptive statistics

Standard deviation1314.5556
Coefficient of variation (CV)3.7809025
Kurtosis901.26603
Mean347.68303
Median Absolute Deviation (MAD)84.545
Skewness24.351047
Sum3476830.3
Variance1728056.5
MonotonicityNot monotonic
2023-12-11T02:04:39.784419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.46 234
 
2.3%
48.218 184
 
1.8%
54.5397 181
 
1.8%
24.69 137
 
1.4%
49.2244 90
 
0.9%
55.6745 88
 
0.9%
39.3421 49
 
0.5%
198.0 44
 
0.4%
67.8 44
 
0.4%
300.0 43
 
0.4%
Other values (5681) 8906
89.1%
ValueCountFrequency (%)
1.04 1
 
< 0.1%
1.44 1
 
< 0.1%
1.5 1
 
< 0.1%
1.9 1
 
< 0.1%
2.0 4
< 0.1%
2.25 1
 
< 0.1%
2.35 4
< 0.1%
2.4 3
< 0.1%
2.47 1
 
< 0.1%
2.56 1
 
< 0.1%
ValueCountFrequency (%)
61942.99 1
< 0.1%
54935.93 1
< 0.1%
35470.47 1
< 0.1%
30480.0 1
< 0.1%
25903.1 1
< 0.1%
23036.07 1
< 0.1%
18517.95 1
< 0.1%
17622.0 1
< 0.1%
15705.49 1
< 0.1%
15213.06 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-05-19
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-19
2nd row2023-05-19
3rd row2023-05-19
4th row2023-05-19
5th row2023-05-19

Common Values

ValueCountFrequency (%)
2023-05-19 10000
100.0%

Length

2023-12-11T02:04:40.040956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:40.220618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-19 10000
100.0%

Interactions

2023-12-11T02:04:30.139853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:25.216118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.213616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.121476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.019644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.085340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:30.301905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:25.380569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.379307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.270374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.185533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.246217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:30.470436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:25.573032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.555844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.400996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.352937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.439992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:30.644623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:25.751904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.711733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.545728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.548177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.620191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:30.801248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:25.890088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.850390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.681902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.710248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.783740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:30.944087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.059370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:26.978708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:27.842785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:28.894873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:04:29.964589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:04:40.343360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동시가표준액연면적
과세년도1.0000.3870.0250.2670.0000.0580.0500.076
법정동0.3871.0000.1880.8350.0930.3030.0850.079
특수지0.0250.1881.0000.0900.0000.0000.0000.000
본번0.2670.8350.0901.0000.1410.3310.0000.000
부번0.0000.0930.0000.1411.0000.0000.0000.000
동0.0580.3030.0000.3310.0001.0000.0000.000
시가표준액0.0500.0850.0000.0000.0000.0001.0000.898
연면적0.0760.0790.0000.0000.0000.0000.8981.000
2023-12-11T02:04:40.667891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.023
과세년도0.0231.000
2023-12-11T02:04:40.859266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세년도특수지
법정동1.000-0.483-0.060-0.0840.039-0.0030.2410.114
본번-0.4831.000-0.091-0.0220.1100.0080.1620.054
부번-0.060-0.0911.000-0.1440.1110.1410.0000.000
동-0.084-0.022-0.1441.000-0.065-0.1420.0370.000
시가표준액0.0390.1100.111-0.0651.0000.8610.0340.000
연면적-0.0030.0080.141-0.1420.8611.0000.0340.000
과세년도0.2410.1620.0000.0370.0340.0341.0000.023
특수지0.1140.0540.0000.0000.0000.0000.0231.000

Missing values

2023-12-11T02:04:31.205938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:04:31.551074image/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
19702부산광역시강서구2644020171150121600101부산광역시 강서구 미음동 216 101호1621053085.772023-05-19
80409부산광역시강서구2644020201040135993105415부산광역시 강서구 명지동 3599-3 105동 415호1846128031.18462023-05-19
33444부산광역시강서구26440201710901152001102부산광역시 강서구 송정동 1520 1동 102호24535740138.622023-05-19
36329부산광역시강서구26440201710201581340102[ 맥도길265번길 45 ] 0000동 0102호2553070053.32023-05-19
63016부산광역시강서구26440202010201314182104부산광역시 강서구 대저2동 3141-8 2동 104호340425960337.542023-05-19
77522부산광역시강서구26440202010401324071115[ 명지오션시티1로 173 ] 0001동 0115호9058009081.78792023-05-19
81645부산광역시강서구264402020104013357408401[ 명지국제8로 237 ] 0000동 8401호143801190327.642023-05-19
31386부산광역시강서구264402017109011761111201[ 녹산산단262로59번길 11 ] 0001동 0201호1070190045.542023-05-19
59684부산광역시강서구26440202010201517811102[ 공항로339번길 83 ] 0001동 0102호67564800192.02023-05-19
45704부산광역시강서구2644020201020131531116116부산광역시 강서구 대저2동 3153-1 116동 116호5500601054.53972023-05-19
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
31912부산광역시강서구26440201711701185019531부산광역시 강서구 신호동 185 1동 9531호950565024.692023-05-19
75369부산광역시강서구26440202010201314841201[ 유통단지1로65번길 15 ] 0001동 0201호128451080135.642023-05-19
38889부산광역시강서구26440201710101410111102[ 대저동서로79번길 23 ] 0001동 0102호65232000144.02023-05-19
21390부산광역시강서구2644020171170121500204[ 신호산단2로 21 ] 0000동 0204호220184250266.892023-05-19
58912부산광역시강서구2644020201020131440327101부산광역시 강서구 대저2동 3144 327동 101호385398000540.02023-05-19
84039부산광역시강서구26440202011701185019447부산광역시 강서구 신호동 185 1동 9447호933282024.692023-05-19
74529부산광역시강서구264402020112011600191201부산광역시 강서구 생곡동 1600-19 1동 201호113115080150.022023-05-19
5341부산광역시강서구264402017109011500190102[ 녹산산단77로22번길 13 ] 0000동 0102호1400387066.0562023-05-19
40285부산광역시강서구264402018114011205018101부산광역시 강서구 지사동 1205 1동 8101호205105000323.02023-05-19
48245부산광역시강서구26440202010401358960102[ 명지국제8로10번길 16 ] 0000동 0102호9541822077.342023-05-19

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시강서구26440201710101323301101부산광역시 강서구 대저1동 323-30 1동 101호1476000036.02023-05-192
1부산광역시강서구2644020171040196771101부산광역시 강서구 명지동 967-7 1동 101호131577600498.42023-05-192
2부산광역시강서구26440201710901150621101부산광역시 강서구 송정동 1506-2 1동 101호1542000030.02023-05-192
3부산광역시강서구26440201711601183302101[ 가락대로 929 ] 0002동 0101호8366002.352023-05-192
4부산광역시강서구2644020171210183201101부산광역시 강서구 천성동 832 1동 101호22108806.722023-05-192
5부산광역시강서구26440202010101102821101[ 대저중앙로29번길 62 ] 0001동 0101호55482410332.232023-05-192
6부산광역시강서구26440202010101102821201[ 대저중앙로29번길 62 ] 0001동 0201호14412108.632023-05-192
7부산광역시강서구26440202010201157701101부산광역시 강서구 대저2동 1577 1동 101호4641390119.012023-05-192
8부산광역시강서구26440202010201213300101부산광역시 강서구 대저2동 2133 101호490800012.02023-05-192
9부산광역시강서구2644020201020131542103116부산광역시 강서구 대저2동 3154-2 103동 116호144218800160.62023-05-192