Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows16
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical7
Numeric5
Text3

Dataset

Description대구광역시 동구_일반건축물 시가표준액_20220601
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15079904&dataSetDetailId=1507990418c4fec8ee179&provdMethod=FILE

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 16 (0.2%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (96.5%)Imbalance
시가표준액 is highly skewed (γ1 = 23.75614069)Skewed
부번 has 2075 (20.8%) zerosZeros

Reproduction

Analysis started2023-09-29 01:07:00.105409
Analysis finished2023-09-29 01:07:31.319302
Duration31.21 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
대구광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 10000
100.0%

Length

2023-09-29T01:07:31.800702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:32.673476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 10000
100.0%

Length

2023-09-29T01:07:33.112466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:33.583537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
27140
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27140
2nd row27140
3rd row27140
4th row27140
5th row27140

Common Values

ValueCountFrequency (%)
27140 10000
100.0%

Length

2023-09-29T01:07:34.417075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:35.101387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27140 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-09-29T01:07:35.727606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:36.344918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.5634
Minimum101
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-09-29T01:07:37.353905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median110
Q3120
95-th percentile137
Maximum145
Range44
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.649504
Coefficient of variation (CV)0.10349283
Kurtosis0.23732128
Mean112.5634
Median Absolute Deviation (MAD)8
Skewness0.98349854
Sum1125634
Variance135.71095
MonotonicityNot monotonic
2023-09-29T01:07:38.728981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
102 1697
17.0%
101 1231
12.3%
124 792
 
7.9%
103 770
 
7.7%
111 629
 
6.3%
118 494
 
4.9%
105 427
 
4.3%
123 418
 
4.2%
117 308
 
3.1%
110 279
 
2.8%
Other values (35) 2955
29.5%
ValueCountFrequency (%)
101 1231
12.3%
102 1697
17.0%
103 770
7.7%
104 63
 
0.6%
105 427
 
4.3%
106 237
 
2.4%
107 103
 
1.0%
108 225
 
2.2%
109 155
 
1.6%
110 279
 
2.8%
ValueCountFrequency (%)
145 183
1.8%
144 61
 
0.6%
143 40
 
0.4%
142 103
1.0%
141 26
 
0.3%
140 16
 
0.2%
139 22
 
0.2%
138 29
 
0.3%
137 81
0.8%
136 28
 
0.3%

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

2023-09-29T01:07:39.731298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:40.211727image/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
9963 
2
 
37

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 9963
99.6%
2 37
 
0.4%

Length

2023-09-29T01:07:40.991681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:41.801451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9963
99.6%
2 37
 
0.4%

본번
Real number (ℝ)

Distinct1268
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean624.3235
Minimum1
Maximum1846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-09-29T01:07:42.482580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70.95
Q1279.75
median535
Q3984
95-th percentile1508
Maximum1846
Range1845
Interquartile range (IQR)704.25

Descriptive statistics

Standard deviation434.74928
Coefficient of variation (CV)0.69635258
Kurtosis-0.6648752
Mean624.3235
Median Absolute Deviation (MAD)342
Skewness0.56112439
Sum6243235
Variance189006.94
MonotonicityNot monotonic
2023-09-29T01:07:43.313970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1188 233
 
2.3%
174 133
 
1.3%
326 130
 
1.3%
331 89
 
0.9%
1551 87
 
0.9%
1084 84
 
0.8%
292 84
 
0.8%
286 82
 
0.8%
327 68
 
0.7%
1147 61
 
0.6%
Other values (1258) 8949
89.5%
ValueCountFrequency (%)
1 16
0.2%
2 1
 
< 0.1%
3 7
0.1%
5 10
0.1%
6 4
 
< 0.1%
7 8
0.1%
8 8
0.1%
9 5
 
0.1%
10 2
 
< 0.1%
11 11
0.1%
ValueCountFrequency (%)
1846 4
 
< 0.1%
1840 39
0.4%
1839 1
 
< 0.1%
1833 4
 
< 0.1%
1831 1
 
< 0.1%
1810 5
 
0.1%
1787 1
 
< 0.1%
1627 1
 
< 0.1%
1626 2
 
< 0.1%
1621 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct236
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.0011
Minimum0
Maximum754
Zeros2075
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-09-29T01:07:44.070406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile46
Maximum754
Range754
Interquartile range (IQR)8

Descriptive statistics

Standard deviation50.968658
Coefficient of variation (CV)3.6403324
Kurtosis95.564598
Mean14.0011
Median Absolute Deviation (MAD)3
Skewness8.9395291
Sum140011
Variance2597.8041
MonotonicityNot monotonic
2023-09-29T01:07:44.792757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2075
20.8%
1 1699
17.0%
2 952
9.5%
3 639
 
6.4%
4 605
 
6.0%
6 484
 
4.8%
5 412
 
4.1%
7 331
 
3.3%
9 231
 
2.3%
8 228
 
2.3%
Other values (226) 2344
23.4%
ValueCountFrequency (%)
0 2075
20.8%
1 1699
17.0%
2 952
9.5%
3 639
 
6.4%
4 605
 
6.0%
5 412
 
4.1%
6 484
 
4.8%
7 331
 
3.3%
8 228
 
2.3%
9 231
 
2.3%
ValueCountFrequency (%)
754 2
< 0.1%
705 2
< 0.1%
698 1
 
< 0.1%
697 2
< 0.1%
694 3
< 0.1%
668 4
< 0.1%
667 2
< 0.1%
666 1
 
< 0.1%
664 1
 
< 0.1%
663 2
< 0.1%

동
Text

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-09-29T01:07:46.154645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.0917
Min length1

Characters and Unicode

Total characters10917
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 6108
61.1%
0 2915
29.1%
2 340
 
3.4%
101 80
 
0.8%
105 59
 
0.6%
3 47
 
0.5%
110 30
 
0.3%
301 26
 
0.3%
4 25
 
0.2%
7 19
 
0.2%
Other values (52) 352
 
3.5%
2023-09-29T01:07:47.613146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6676
61.2%
0 3316
30.4%
2 431
 
3.9%
3 151
 
1.4%
5 88
 
0.8%
4 78
 
0.7%
7 56
 
0.5%
6 54
 
0.5%
9 35
 
0.3%
8 30
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10915
> 99.9%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6676
61.2%
0 3316
30.4%
2 431
 
3.9%
3 151
 
1.4%
5 88
 
0.8%
4 78
 
0.7%
7 56
 
0.5%
6 54
 
0.5%
9 35
 
0.3%
8 30
 
0.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10917
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6676
61.2%
0 3316
30.4%
2 431
 
3.9%
3 151
 
1.4%
5 88
 
0.8%
4 78
 
0.7%
7 56
 
0.5%
6 54
 
0.5%
9 35
 
0.3%
8 30
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6676
61.2%
0 3316
30.4%
2 431
 
3.9%
3 151
 
1.4%
5 88
 
0.8%
4 78
 
0.7%
7 56
 
0.5%
6 54
 
0.5%
9 35
 
0.3%
8 30
 
0.3%

호
Text

Distinct796
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-09-29T01:07:48.700637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0311
Min length1

Characters and Unicode

Total characters30311
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)4.3%

Sample

1st row301
2nd row8134
3rd row103
4th row121
5th row1122
ValueCountFrequency (%)
101 2863
28.6%
201 1072
 
10.7%
102 932
 
9.3%
8101 569
 
5.7%
301 505
 
5.1%
103 333
 
3.3%
401 242
 
2.4%
202 200
 
2.0%
0 167
 
1.7%
501 155
 
1.6%
Other values (786) 2962
29.6%
2023-09-29T01:07:51.903019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12518
41.3%
0 8879
29.3%
2 3605
 
11.9%
3 1647
 
5.4%
8 1099
 
3.6%
4 886
 
2.9%
5 668
 
2.2%
6 399
 
1.3%
7 324
 
1.1%
9 281
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30306
> 99.9%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12518
41.3%
0 8879
29.3%
2 3605
 
11.9%
3 1647
 
5.4%
8 1099
 
3.6%
4 886
 
2.9%
5 668
 
2.2%
6 399
 
1.3%
7 324
 
1.1%
9 281
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12518
41.3%
0 8879
29.3%
2 3605
 
11.9%
3 1647
 
5.4%
8 1099
 
3.6%
4 886
 
2.9%
5 668
 
2.2%
6 399
 
1.3%
7 324
 
1.1%
9 281
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12518
41.3%
0 8879
29.3%
2 3605
 
11.9%
3 1647
 
5.4%
8 1099
 
3.6%
4 886
 
2.9%
5 668
 
2.2%
6 399
 
1.3%
7 324
 
1.1%
9 281
 
0.9%

물건지
Text

Distinct9838
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-09-29T01:07:53.024285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length30
Mean length24.632
Min length15

Characters and Unicode

Total characters246320
Distinct characters132
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

Unique9714 ?
Unique (%)97.1%

Sample

1st row[ 화랑로37길 58-2 ] 0001동 0301호
2nd row[ 율하동로 143 ] 0001동 8134호
3rd row대구광역시 동구 불로동 968-7 1동 103호
4th row대구광역시 동구 봉무동 1551-2 1동 121호
5th row[ 율하동로 143 ] 0001동 1122호
ValueCountFrequency (%)
14994
25.1%
0001동 4655
 
7.8%
0000동 2561
 
4.3%
대구광역시 2503
 
4.2%
동구 2503
 
4.2%
0101호 2178
 
3.7%
1동 1453
 
2.4%
0201호 860
 
1.4%
0102호 710
 
1.2%
101호 684
 
1.1%
Other values (3745) 26523
44.5%
2023-09-29T01:07:54.700783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49628
20.1%
0 43578
17.7%
1 26310
10.7%
동 17269
 
7.0%
호 10181
 
4.1%
2 9213
 
3.7%
[ 7497
 
3.0%
] 7497
 
3.0%
로 7460
 
3.0%
3 5547
 
2.3%
Other values (122) 62140
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105273
42.7%
Other Letter 73349
29.8%
Space Separator 49628
20.1%
Open Punctuation 7497
 
3.0%
Close Punctuation 7497
 
3.0%
Dash Punctuation 3076
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17269
23.5%
호 10181
13.9%
로 7460
10.2%
구 5177
 
7.1%
길 4083
 
5.6%
대 2808
 
3.8%
광 2542
 
3.5%
역 2527
 
3.4%
시 2503
 
3.4%
신 1469
 
2.0%
Other values (108) 17330
23.6%
Decimal Number
ValueCountFrequency (%)
0 43578
41.4%
1 26310
25.0%
2 9213
 
8.8%
3 5547
 
5.3%
5 4374
 
4.2%
4 4264
 
4.1%
6 3466
 
3.3%
8 3259
 
3.1%
7 2924
 
2.8%
9 2338
 
2.2%
Space Separator
ValueCountFrequency (%)
49628
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7497
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7497
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3076
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 172971
70.2%
Hangul 73349
29.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17269
23.5%
호 10181
13.9%
로 7460
10.2%
구 5177
 
7.1%
길 4083
 
5.6%
대 2808
 
3.8%
광 2542
 
3.5%
역 2527
 
3.4%
시 2503
 
3.4%
신 1469
 
2.0%
Other values (108) 17330
23.6%
Common
ValueCountFrequency (%)
49628
28.7%
0 43578
25.2%
1 26310
15.2%
2 9213
 
5.3%
[ 7497
 
4.3%
] 7497
 
4.3%
3 5547
 
3.2%
5 4374
 
2.5%
4 4264
 
2.5%
6 3466
 
2.0%
Other values (4) 11597
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172971
70.2%
Hangul 73349
29.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49628
28.7%
0 43578
25.2%
1 26310
15.2%
2 9213
 
5.3%
[ 7497
 
4.3%
] 7497
 
4.3%
3 5547
 
3.2%
5 4374
 
2.5%
4 4264
 
2.5%
6 3466
 
2.0%
Other values (4) 11597
 
6.7%
Hangul
ValueCountFrequency (%)
동 17269
23.5%
호 10181
13.9%
로 7460
10.2%
구 5177
 
7.1%
길 4083
 
5.6%
대 2808
 
3.8%
광 2542
 
3.5%
역 2527
 
3.4%
시 2503
 
3.4%
신 1469
 
2.0%
Other values (108) 17330
23.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8583
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.091212 × 108
Minimum15730
Maximum1.9440122 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-09-29T01:07:55.419849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15730
5-th percentile1313339.5
Q19147337.5
median44371770
Q398348625
95-th percentile3.3229587 × 108
Maximum1.9440122 × 1010
Range1.9440107 × 1010
Interquartile range (IQR)89201288

Descriptive statistics

Standard deviation4.4370279 × 108
Coefficient of variation (CV)4.0661466
Kurtosis757.81391
Mean1.091212 × 108
Median Absolute Deviation (MAD)38085885
Skewness23.756141
Sum1.091212 × 1012
Variance1.9687217 × 1017
MonotonicityNot monotonic
2023-09-29T01:07:56.369195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62988130 51
 
0.5%
94517550 36
 
0.4%
107830020 35
 
0.4%
57511620 35
 
0.4%
36333250 33
 
0.3%
137155240 33
 
0.3%
45598620 30
 
0.3%
53960130 27
 
0.3%
54557060 23
 
0.2%
61954850 22
 
0.2%
Other values (8573) 9675
96.8%
ValueCountFrequency (%)
15730 1
< 0.1%
28400 1
< 0.1%
28600 1
< 0.1%
37620 1
< 0.1%
47790 1
< 0.1%
54080 1
< 0.1%
54720 1
< 0.1%
66700 1
< 0.1%
67850 1
< 0.1%
68000 1
< 0.1%
ValueCountFrequency (%)
19440122400 1
< 0.1%
15547614540 1
< 0.1%
12612510060 1
< 0.1%
12165426870 1
< 0.1%
11551972430 1
< 0.1%
10657415930 1
< 0.1%
8733841110 1
< 0.1%
8400005330 1
< 0.1%
8091419680 1
< 0.1%
7993639350 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6876
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.78407
Minimum0.579
Maximum16474.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-09-29T01:07:56.953464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.579
5-th percentile10.3
Q142.9743
median86.74
Q3175.0111
95-th percentile564.503
Maximum16474.68
Range16474.101
Interquartile range (IQR)132.0368

Descriptive statistics

Standard deviation484.88506
Coefficient of variation (CV)2.6821227
Kurtosis372.843
Mean180.78407
Median Absolute Deviation (MAD)53.655
Skewness15.851956
Sum1807840.7
Variance235113.52
MonotonicityNot monotonic
2023-09-29T01:07:57.598017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 157
 
1.6%
65.0683 51
 
0.5%
51.9275 44
 
0.4%
20.0 38
 
0.4%
42.5448 37
 
0.4%
97.9422 36
 
0.4%
111.5027 35
 
0.4%
53.6026 35
 
0.4%
101.8984 33
 
0.3%
27.0 33
 
0.3%
Other values (6866) 9501
95.0%
ValueCountFrequency (%)
0.579 1
 
< 0.1%
0.81 2
 
< 0.1%
0.961 1
 
< 0.1%
0.99 2
 
< 0.1%
1.0 7
0.1%
1.14 1
 
< 0.1%
1.19 2
 
< 0.1%
1.2 4
< 0.1%
1.21 2
 
< 0.1%
1.42 1
 
< 0.1%
ValueCountFrequency (%)
16474.68 1
< 0.1%
13128.13 1
< 0.1%
12860.96 1
< 0.1%
12557.86 1
< 0.1%
12013.99 1
< 0.1%
10800.0 1
< 0.1%
9766.12 1
< 0.1%
7726.36 1
< 0.1%
7710.52 1
< 0.1%
7076.68 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-01
2nd row2022-06-01
3rd row2022-06-01
4th row2022-06-01
5th row2022-06-01

Common Values

ValueCountFrequency (%)
2022-06-01 10000
100.0%

Length

2023-09-29T01:07:58.530267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:07:58.835429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-01 10000
100.0%

Interactions

2023-09-29T01:07:23.799865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:10.952833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:13.880547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:17.315518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:20.627466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:24.479912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:11.642902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:14.347292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:17.973691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:21.151493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:25.003035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:12.200068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:14.946940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:18.621469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:21.925091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:25.530327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:12.831371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:15.663150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:19.462776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:22.554085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:26.223945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:13.297860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:16.620240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:20.096109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:07:23.309247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-29T01:07:59.214945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.1230.7340.3020.3710.0000.022
특수지0.1231.0000.1640.0000.0000.0000.000
본번0.7340.1641.0000.2440.6960.0820.092
부번0.3020.0000.2441.0000.0000.0000.000
동0.3710.0000.6960.0001.0000.0000.105
시가표준액0.0000.0000.0820.0000.0001.0000.950
연면적0.0220.0000.0920.0000.1050.9501.000
2023-09-29T01:07:59.602679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번시가표준액연면적특수지
법정동1.0000.184-0.2450.0750.0380.094
본번0.1841.000-0.1080.1840.0440.125
부번-0.245-0.1081.000-0.147-0.0880.000
시가표준액0.0750.184-0.1471.0000.8350.000
연면적0.0380.044-0.0880.8351.0000.000
특수지0.0940.1250.0000.0000.0001.000

Missing values

2023-09-29T01:07:27.612805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-29T01:07:30.264189image/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
22732대구광역시동구27140202210301190171301[ 화랑로37길 58-2 ] 0001동 0301호40292980119.212022-06-01
37570대구광역시동구27140202211701174118134[ 율하동로 143 ] 0001동 8134호341546015.992022-06-01
46455대구광역시동구2714020221060196871103대구광역시 동구 불로동 968-7 1동 103호3445821067.172022-06-01
24312대구광역시동구27140202210501155121121대구광역시 동구 봉무동 1551-2 1동 121호8100977067.36782022-06-01
37333대구광역시동구27140202211701174111122[ 율하동로 143 ] 0001동 1122호566707016.982022-06-01
24067대구광역시동구27140202210501155111203[ 팔공로 249 ] 0001동 0203호75083790102.2942022-06-01
38105대구광역시동구27140202211801143401301[ 안심로22길 54 ] 0001동 0301호225892860283.7852022-06-01
3081대구광역시동구2714020221150119131101[ 경안로105길 53 ] 0001동 0101호70642950106.232022-06-01
1612대구광역시동구27140202211501201151301[ 경안로 719 ] 0001동 0301호465014550464.552022-06-01
451대구광역시동구2714020221110110723113[ 동촌로42길 25 ] 0001동 0003호159172015.212022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
29173대구광역시동구2714020221010118400110302대구광역시 동구 신암동 1840 110동 302호9305587098.46622022-06-01
18549대구광역시동구2714020221020145118003[ 동부로 75 ] 0001동 8003호48815610166.722022-06-01
14630대구광역시동구271402022110011019170401[ 동촌로 54 ] 0000동 0401호126042960199.122022-06-01
17440대구광역시동구2714020221010161551101[ 평화로 83 ] 0001동 0101호399799039.962022-06-01
23241대구광역시동구27140202210101576311501대구광역시 동구 신암동 576-31 1동 501호106972600402.912022-06-01
49033대구광역시동구27140202212301374240101[ 반야월북로 144 ] 0000동 0101호66615480200.772022-06-01
44268대구광역시동구2714020221400120221101대구광역시 동구 내동 202-2 1동 101호68640019.52022-06-01
21866대구광역시동구2714020221020178761201[ 송라로 5 ] 0001동 0201호41034600201.152022-06-01
47173대구광역시동구27140202211801111101203[ 안심로 58 ] 0001동 0203호265076070307.872022-06-01
41347대구광역시동구271402022101015971181대구광역시 동구 신암동 597-1 1동 81호377347017.092022-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
4대구광역시동구271402022110012101101대구광역시 동구 검사동 21 1동 101호16872000148.02022-06-013
13대구광역시동구2714020221430119321101[ 서촌로 106 ] 0001동 0101호34408406.762022-06-013
0대구광역시동구2714020221020148111101대구광역시 동구 신천동 481-1 1동 101호791100027.02022-06-012
1대구광역시동구271402022105011546110[ 팔공로53길 70 ] 0001동 0000호10604965401276.632022-06-012
2대구광역시동구27140202210901982110[ 아양로 324 ] 0001동 0000호182096700339.12022-06-012
3대구광역시동구27140202211001311101대구광역시 동구 검사동 3-1 1동 101호16872000148.02022-06-012
5대구광역시동구271402022110019912800[ 해동로 191 ] 0000동 0000호44474220105.142022-06-012
6대구광역시동구27140202211101108475400대구광역시 동구 방촌동 1084-754119349450190.352022-06-012
7대구광역시동구2714020221110111185701[ 동촌로58길 69 ] 0000동 0001호74434360110.112022-06-012
8대구광역시동구2714020221120148601101대구광역시 동구 둔산동 486 1동 101호33177600256.02022-06-012