Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description자치단체내 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하여물건별 재산가액 및 변동율 확인자료로 활용 가능합니다.
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/15080289/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 5 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (94.8%)Imbalance
동 is highly skewed (γ1 = 27.66185595)Skewed
시가표준액 is highly skewed (γ1 = 42.14720165)Skewed
연면적 is highly skewed (γ1 = 28.75606804)Skewed
부번 has 1413 (14.1%) zerosZeros
동 has 670 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-12 12:22:48.080238
Analysis finished2023-12-12 12:22:55.261257
Duration7.18 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

2023-12-12T21:22:55.336705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:55.430692image/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
사상구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 10000
100.0%

Length

2023-12-12T21:22:55.548887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:55.666729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
26530
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 10000
100.0%

Length

2023-12-12T21:22:55.766583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:55.866521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 10000
100.0%

과세년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
6019 
2021
3981 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 6019
60.2%
2021 3981
39.8%

Length

2023-12-12T21:22:55.989210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:56.109701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 6019
60.2%
2021 3981
39.8%

법정동
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.1815
Minimum101
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:56.224939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median104
Q3105
95-th percentile107
Maximum108
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7616095
Coefficient of variation (CV)0.016909043
Kurtosis-0.42305453
Mean104.1815
Median Absolute Deviation (MAD)1
Skewness0.033551127
Sum1041815
Variance3.1032681
MonotonicityNot monotonic
2023-12-12T21:22:56.359419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
104 3115
31.1%
105 1942
19.4%
102 1101
 
11.0%
106 940
 
9.4%
103 896
 
9.0%
101 876
 
8.8%
107 801
 
8.0%
108 329
 
3.3%
ValueCountFrequency (%)
101 876
 
8.8%
102 1101
 
11.0%
103 896
 
9.0%
104 3115
31.1%
105 1942
19.4%
106 940
 
9.4%
107 801
 
8.0%
108 329
 
3.3%
ValueCountFrequency (%)
108 329
 
3.3%
107 801
 
8.0%
106 940
 
9.4%
105 1942
19.4%
104 3115
31.1%
103 896
 
9.0%
102 1101
 
11.0%
101 876
 
8.8%

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

2023-12-12T21:22:56.511791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:56.604240image/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
9941 
2
 
59

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 9941
99.4%
2 59
 
0.6%

Length

2023-12-12T21:22:56.711407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:22:56.816701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9941
99.4%
2 59
 
0.6%

본번
Real number (ℝ)

Distinct733
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462.7387
Minimum1
Maximum1380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:56.927444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile79
Q1257
median505
Q3578
95-th percentile954
Maximum1380
Range1379
Interquartile range (IQR)321

Descriptive statistics

Standard deviation274.7589
Coefficient of variation (CV)0.59376686
Kurtosis1.6125066
Mean462.7387
Median Absolute Deviation (MAD)146
Skewness0.91937307
Sum4627387
Variance75492.456
MonotonicityNot monotonic
2023-12-12T21:22:57.097862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
529 983
 
9.8%
578 746
 
7.5%
152 414
 
4.1%
502 353
 
3.5%
143 88
 
0.9%
562 73
 
0.7%
132 72
 
0.7%
422 60
 
0.6%
271 59
 
0.6%
1375 52
 
0.5%
Other values (723) 7100
71.0%
ValueCountFrequency (%)
1 29
0.3%
2 1
 
< 0.1%
3 9
 
0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
10 18
0.2%
11 2
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1380 7
 
0.1%
1375 52
0.5%
1367 9
 
0.1%
1366 1
 
< 0.1%
1365 1
 
< 0.1%
1364 1
 
< 0.1%
1362 44
0.4%
1361 15
 
0.1%
1360 3
 
< 0.1%
1359 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8241
Minimum0
Maximum432
Zeros1413
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:57.234149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile37
Maximum432
Range432
Interquartile range (IQR)12

Descriptive statistics

Standard deviation24.023311
Coefficient of variation (CV)2.219428
Kurtosis92.437199
Mean10.8241
Median Absolute Deviation (MAD)4
Skewness7.9342021
Sum108241
Variance577.11947
MonotonicityNot monotonic
2023-12-12T21:22:57.356457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2151
21.5%
0 1413
14.1%
2 829
 
8.3%
4 396
 
4.0%
3 396
 
4.0%
7 380
 
3.8%
5 362
 
3.6%
6 317
 
3.2%
8 295
 
2.9%
9 263
 
2.6%
Other values (150) 3198
32.0%
ValueCountFrequency (%)
0 1413
14.1%
1 2151
21.5%
2 829
 
8.3%
3 396
 
4.0%
4 396
 
4.0%
5 362
 
3.6%
6 317
 
3.2%
7 380
 
3.8%
8 295
 
2.9%
9 263
 
2.6%
ValueCountFrequency (%)
432 2
< 0.1%
425 1
< 0.1%
404 1
< 0.1%
400 1
< 0.1%
386 1
< 0.1%
384 1
< 0.1%
381 1
< 0.1%
360 1
< 0.1%
307 2
< 0.1%
274 1
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8723
Minimum0
Maximum3000
Zeros670
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:57.493226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile17
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation78.339227
Coefficient of variation (CV)11.399273
Kurtosis879.61012
Mean6.8723
Median Absolute Deviation (MAD)0
Skewness27.661856
Sum68723
Variance6137.0345
MonotonicityNot monotonic
2023-12-12T21:22:57.618390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7100
71.0%
0 670
 
6.7%
2 445
 
4.5%
3 263
 
2.6%
4 215
 
2.1%
7 139
 
1.4%
5 127
 
1.3%
6 96
 
1.0%
9 74
 
0.7%
8 66
 
0.7%
Other values (55) 805
 
8.1%
ValueCountFrequency (%)
0 670
 
6.7%
1 7100
71.0%
2 445
 
4.5%
3 263
 
2.6%
4 215
 
2.1%
5 127
 
1.3%
6 96
 
1.0%
7 139
 
1.4%
8 66
 
0.7%
9 74
 
0.7%
ValueCountFrequency (%)
3000 3
 
< 0.1%
2000 5
0.1%
1000 12
0.1%
801 2
 
< 0.1%
301 3
 
< 0.1%
116 6
0.1%
113 6
0.1%
112 3
 
< 0.1%
111 4
 
< 0.1%
109 6
0.1%

호
Text

Distinct1328
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:22:57.954332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0437
Min length1

Characters and Unicode

Total characters30437
Distinct characters16
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique892 ?
Unique (%)8.9%

Sample

1st row101
2nd row109
3rd row136
4th row201
5th row101
ValueCountFrequency (%)
101 1718
 
17.2%
201 1050
 
10.5%
102 802
 
8.0%
8101 440
 
4.4%
301 411
 
4.1%
1 287
 
2.9%
103 279
 
2.8%
202 253
 
2.5%
401 200
 
2.0%
104 165
 
1.7%
Other values (1318) 4395
44.0%
2023-12-12T21:22:58.429263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10399
34.2%
0 7536
24.8%
2 4851
15.9%
3 2391
 
7.9%
4 1403
 
4.6%
8 1128
 
3.7%
5 918
 
3.0%
6 710
 
2.3%
7 602
 
2.0%
9 467
 
1.5%
Other values (6) 32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30405
99.9%
Dash Punctuation 27
 
0.1%
Other Letter 4
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10399
34.2%
0 7536
24.8%
2 4851
16.0%
3 2391
 
7.9%
4 1403
 
4.6%
8 1128
 
3.7%
5 918
 
3.0%
6 710
 
2.3%
7 602
 
2.0%
9 467
 
1.5%
Other Letter
ValueCountFrequency (%)
라 1
25.0%
다 1
25.0%
나 1
25.0%
가 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30432
> 99.9%
Hangul 4
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10399
34.2%
0 7536
24.8%
2 4851
15.9%
3 2391
 
7.9%
4 1403
 
4.6%
8 1128
 
3.7%
5 918
 
3.0%
6 710
 
2.3%
7 602
 
2.0%
9 467
 
1.5%
Hangul
ValueCountFrequency (%)
라 1
25.0%
다 1
25.0%
나 1
25.0%
가 1
25.0%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30433
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10399
34.2%
0 7536
24.8%
2 4851
15.9%
3 2391
 
7.9%
4 1403
 
4.6%
8 1128
 
3.7%
5 918
 
3.0%
6 710
 
2.3%
7 602
 
2.0%
9 467
 
1.5%
Other values (2) 28
 
0.1%
Hangul
ValueCountFrequency (%)
라 1
25.0%
다 1
25.0%
나 1
25.0%
가 1
25.0%

물건지
Text

Distinct9101
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:22:59.021809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length26.4324
Min length21

Characters and Unicode

Total characters264324
Distinct characters73
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

Unique8285 ?
Unique (%)82.8%

Sample

1st row[ 새벽시장로64번길 34 ] 0001동 0101호
2nd row부산광역시 사상구 괘법동 578 12동 109호
3rd row부산광역시 사상구 괘법동 578 9동 136호
4th row[ 새벽로202번길 14 ] 0001동 0201호
5th row[ 사상로342번길 30 ] 0011동 0101호
ValueCountFrequency (%)
7514
 
12.6%
부산광역시 6243
 
10.4%
사상구 6243
 
10.4%
1동 3797
 
6.4%
0001동 3303
 
5.5%
괘법동 2145
 
3.6%
감전동 1465
 
2.5%
529-1 983
 
1.6%
0101호 919
 
1.5%
101호 798
 
1.3%
Other values (4430) 26333
44.1%
2023-12-12T21:22:59.443417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49743
18.8%
1 25606
 
9.7%
0 25446
 
9.6%
동 16530
 
6.3%
2 11947
 
4.5%
호 9999
 
3.8%
사 7200
 
2.7%
상 7192
 
2.7%
5 6739
 
2.5%
광 6616
 
2.5%
Other values (63) 97306
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105368
39.9%
Decimal Number 96026
36.3%
Space Separator 49743
18.8%
Dash Punctuation 5672
 
2.1%
Open Punctuation 3757
 
1.4%
Close Punctuation 3757
 
1.4%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16530
15.7%
호 9999
 
9.5%
사 7200
 
6.8%
상 7192
 
6.8%
광 6616
 
6.3%
산 6328
 
6.0%
시 6317
 
6.0%
부 6243
 
5.9%
역 6243
 
5.9%
구 6243
 
5.9%
Other values (48) 26457
25.1%
Decimal Number
ValueCountFrequency (%)
1 25606
26.7%
0 25446
26.5%
2 11947
12.4%
5 6739
 
7.0%
3 6412
 
6.7%
4 4468
 
4.7%
7 4097
 
4.3%
8 4003
 
4.2%
6 3692
 
3.8%
9 3616
 
3.8%
Space Separator
ValueCountFrequency (%)
49743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5672
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3757
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3757
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 158955
60.1%
Hangul 105368
39.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16530
15.7%
호 9999
 
9.5%
사 7200
 
6.8%
상 7192
 
6.8%
광 6616
 
6.3%
산 6328
 
6.0%
시 6317
 
6.0%
부 6243
 
5.9%
역 6243
 
5.9%
구 6243
 
5.9%
Other values (48) 26457
25.1%
Common
ValueCountFrequency (%)
49743
31.3%
1 25606
16.1%
0 25446
16.0%
2 11947
 
7.5%
5 6739
 
4.2%
3 6412
 
4.0%
- 5672
 
3.6%
4 4468
 
2.8%
7 4097
 
2.6%
8 4003
 
2.5%
Other values (4) 14822
 
9.3%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158956
60.1%
Hangul 105368
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49743
31.3%
1 25606
16.1%
0 25446
16.0%
2 11947
 
7.5%
5 6739
 
4.2%
3 6412
 
4.0%
- 5672
 
3.6%
4 4468
 
2.8%
7 4097
 
2.6%
8 4003
 
2.5%
Other values (5) 14823
 
9.3%
Hangul
ValueCountFrequency (%)
동 16530
15.7%
호 9999
 
9.5%
사 7200
 
6.8%
상 7192
 
6.8%
광 6616
 
6.3%
산 6328
 
6.0%
시 6317
 
6.0%
부 6243
 
5.9%
역 6243
 
5.9%
구 6243
 
5.9%
Other values (48) 26457
25.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7587
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59653911
Minimum0
Maximum1.739686 × 1010
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:59.588826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1527254
Q18143400
median18077000
Q351461050
95-th percentile2.0273484 × 108
Maximum1.739686 × 1010
Range1.739686 × 1010
Interquartile range (IQR)43317650

Descriptive statistics

Standard deviation3.0617772 × 108
Coefficient of variation (CV)5.1325674
Kurtosis2178.8147
Mean59653911
Median Absolute Deviation (MAD)14800020
Skewness42.147202
Sum5.9653911 × 1011
Variance9.3744798 × 1016
MonotonicityNot monotonic
2023-12-12T21:22:59.735826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13411840 150
 
1.5%
13134080 133
 
1.3%
17365060 88
 
0.9%
16985310 73
 
0.7%
14769840 72
 
0.7%
15100060 70
 
0.7%
9373260 46
 
0.5%
9484280 42
 
0.4%
1921480 37
 
0.4%
6303440 37
 
0.4%
Other values (7577) 9252
92.5%
ValueCountFrequency (%)
0 1
< 0.1%
102200 1
< 0.1%
102600 1
< 0.1%
114000 2
< 0.1%
142080 1
< 0.1%
146520 1
< 0.1%
151800 2
< 0.1%
156800 1
< 0.1%
159100 1
< 0.1%
178200 1
< 0.1%
ValueCountFrequency (%)
17396860320 1
< 0.1%
17199393120 1
< 0.1%
9708592900 1
< 0.1%
7849285110 1
< 0.1%
7016080310 1
< 0.1%
2905551180 1
< 0.1%
2854854540 1
< 0.1%
2803435440 1
< 0.1%
2238913950 1
< 0.1%
1992518640 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5499
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.04855
Minimum0
Maximum19746.72
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:22:59.887084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.733
Q128.242
median59.77
Q3150.0175
95-th percentile491.318
Maximum19746.72
Range19746.72
Interquartile range (IQR)121.7755

Descriptive statistics

Standard deviation392.26303
Coefficient of variation (CV)2.7421671
Kurtosis1301.7845
Mean143.04855
Median Absolute Deviation (MAD)42.81
Skewness28.756068
Sum1430485.5
Variance153870.28
MonotonicityNot monotonic
2023-12-12T21:23:00.052300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.02 303
 
3.0%
39.68 283
 
2.8%
15.86 88
 
0.9%
60.02 86
 
0.9%
4.84 73
 
0.7%
79.37 69
 
0.7%
20.4 67
 
0.7%
5.733 59
 
0.6%
20.406 59
 
0.6%
16.72 57
 
0.6%
Other values (5489) 8856
88.6%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.9 1
< 0.1%
1.0 1
< 0.1%
1.4 1
< 0.1%
1.5 1
< 0.1%
1.95 1
< 0.1%
2.16 2
< 0.1%
2.2 1
< 0.1%
2.4 1
< 0.1%
2.55 1
< 0.1%
ValueCountFrequency (%)
19746.72 2
< 0.1%
8527.53 1
< 0.1%
8001.31 1
< 0.1%
6886.95 1
< 0.1%
5444.04 1
< 0.1%
3703.09 1
< 0.1%
3668.79 1
< 0.1%
3530.3 1
< 0.1%
3486.86 1
< 0.1%
3346.65 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-12-31
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2021-12-31
4th row2021-12-31
5th row2021-12-31

Common Values

ValueCountFrequency (%)
2021-12-31 10000
100.0%

Length

2023-12-12T21:23:00.189353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:23:00.276250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 10000
100.0%

Interactions

2023-12-12T21:22:53.751668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:49.787245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.661844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.711229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.371514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.015677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.935075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:49.946137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.790066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.827251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.474728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.122495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:54.090149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.094275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.910244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.940105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.590866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.236019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:54.231627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.224280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.020079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.039176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.689553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.345082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:54.386295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.353670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.123899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.140741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.794600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.481572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:54.592233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:50.479554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:51.264533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.252677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:52.913071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:22:53.603848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:23:00.338866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동시가표준액연면적
과세년도1.0000.3370.0250.1710.0500.0000.0000.000
법정동0.3371.0000.0850.7100.2620.0950.0210.042
특수지0.0250.0851.0000.2330.0000.0000.0000.000
본번0.1710.7100.2331.0000.2880.1210.0000.000
부번0.0500.2620.0000.2881.0000.0000.0000.000
동0.0000.0950.0000.1210.0001.0000.0000.000
시가표준액0.0000.0210.0000.0000.0000.0001.0000.878
연면적0.0000.0420.0000.0000.0000.0000.8781.000
2023-12-12T21:23:00.455334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.016
과세년도0.0161.000
2023-12-12T21:23:00.552106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세년도특수지
법정동1.000-0.1560.0580.0320.026-0.0300.2530.064
본번-0.1561.000-0.2460.0520.014-0.0390.1310.179
부번0.058-0.2461.000-0.3770.1510.2860.0380.000
동0.0320.052-0.3771.000-0.171-0.2400.0000.000
시가표준액0.0260.0140.151-0.1711.0000.8380.0000.000
연면적-0.030-0.0390.286-0.2400.8381.0000.0000.000
과세년도0.2530.1310.0380.0000.0000.0001.0000.016
특수지0.0640.1790.0000.0000.0000.0000.0161.000

Missing values

2023-12-12T21:22:54.841338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:22:55.146727image/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
47655부산광역시사상구26530202010501151111101[ 새벽시장로64번길 34 ] 0001동 0101호230034024.12021-12-31
34708부산광역시사상구26530202010401578012109부산광역시 사상구 괘법동 578 12동 109호1341184039.682021-12-31
33324부산광역시사상구2653020201040157809136부산광역시 사상구 괘법동 578 9동 136호1736506030.022021-12-31
21302부산광역시사상구26530202010401581121201[ 새벽로202번길 14 ] 0001동 0201호49358400118.652021-12-31
6746부산광역시사상구26530202010301408911101[ 사상로342번길 30 ] 0011동 0101호118583019.832021-12-31
53538부산광역시사상구26530202110101357211201[ 낙동대로1396번길 59 ] 0001동 0201호35429660110.032021-12-31
39216부산광역시사상구265302020101017911201[ 낙동대로 1468 ] 0001동 0201호2350232097.522021-12-31
19646부산광역시사상구26530202010401520251101부산광역시 사상구 괘법동 520-25 1동 101호73732500101.72021-12-31
48566부산광역시사상구26530202010401529114198부산광역시 사상구 괘법동 529-1 1동 4198호1208558020.212021-12-31
74612부산광역시사상구26530202110101400111301부산광역시 사상구 삼락동 400-11 1동 301호7207800175.82021-12-31
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
82422부산광역시사상구2653020211030176981101[ 백양대로768번길 11 ] 0001동 0101호2537268063.42021-12-31
43508부산광역시사상구26530202010201647115101부산광역시 사상구 모라동 647-1 15동 101호533197084.32021-12-31
29723부산광역시사상구2653020201070124591101부산광역시 사상구 학장동 245-9 1동 101호28446080108.162021-12-31
25873부산광역시사상구2653020201080156771808[ 엄궁로 204 ] 0001동 0808호5253277067.95962021-12-31
52251부산광역시사상구2653020211030142216188[ 사상로285번길 8-3 ] 0001동 0088호216763010.32021-12-31
22546부산광역시사상구26530202010401928181301[ 운산로 72 ] 0001동 0301호2869836093.482021-12-31
13036부산광역시사상구2653020201050115226229부산광역시 사상구 감전동 152-2 6동 229호12902605.8122021-12-31
11668부산광역시사상구2653020201050150211189부산광역시 사상구 감전동 502-1 1동 189호684480018.42021-12-31
83019부산광역시사상구2653020211030137311501[ 사상로367번길 73 ] 0001동 0501호483888160974.012021-12-31
54825부산광역시사상구2653020211040157806223부산광역시 사상구 괘법동 578 6동 223호1313408039.682021-12-31

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0부산광역시사상구2653020201010139241102부산광역시 사상구 삼락동 392-4 1동 102호1681719033.172021-12-312
1부산광역시사상구26530202010301365311부산광역시 사상구 덕포동 365-3 1동 1호49343000133.02021-12-312
2부산광역시사상구265302020104015847119[ 새벽로202번길 32 ] 0001동 0019호242562011.342021-12-312
3부산광역시사상구265302020106014392611부산광역시 사상구 주례동 439-26 1동 1호71581160173.322021-12-312
4부산광역시사상구26530202010601666012부산광역시 사상구 주례동 666 1동 2호23244000309.922021-12-312