Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)0.1%
Total size in memory1.4 MiB
Average record size in memory146.0 B

Variable types

Categorical8
Numeric6
Text2

Dataset

Description대구광역시 중구 소재 일반건축물에 대한 지방세 부과기준인 시가표준액 데이터를 제공합니다.(과세년도 2021년 자료)- 제공내용 : 시도명,시군구명,자치단체코드,과세년도,법정동,법정리,특수지,본번,부번,동,호,물건지,시가표준액,연면적,결정일자,기준일자
Author대구광역시 중구
URLhttps://www.data.go.kr/data/15080002/fileData.do

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
특수지 has constant value ""Constant
결정일자 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 13 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
부번 has 2357 (23.6%) zerosZeros
동 has 357 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-12 04:52:54.337177
Analysis finished2023-12-12 04:53:01.249695
Duration6.91 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

2023-12-12T13:53:01.323470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:01.428132image/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
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

2023-12-12T13:53:01.543878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:01.682983image/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
27110
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27110
2nd row27110
3rd row27110
4th row27110
5th row27110

Common Values

ValueCountFrequency (%)
27110 10000
100.0%

Length

2023-12-12T13:53:01.821694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:01.985035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27110 10000
100.0%

과세년도
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2023-12-12T13:53:02.129485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:02.279964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.2712
Minimum101
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:02.459425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1112
median139
Q3154
95-th percentile157
Maximum157
Range56
Interquartile range (IQR)42

Descriptive statistics

Standard deviation20.570522
Coefficient of variation (CV)0.15435084
Kurtosis-1.5801143
Mean133.2712
Median Absolute Deviation (MAD)17
Skewness-0.21637575
Sum1332712
Variance423.14637
MonotonicityNot monotonic
2023-12-12T13:53:02.692908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156 1397
 
14.0%
154 1350
 
13.5%
157 658
 
6.6%
140 549
 
5.5%
108 432
 
4.3%
106 418
 
4.2%
112 366
 
3.7%
105 320
 
3.2%
122 292
 
2.9%
104 278
 
2.8%
Other values (47) 3940
39.4%
ValueCountFrequency (%)
101 155
 
1.6%
102 274
2.7%
103 169
 
1.7%
104 278
2.8%
105 320
3.2%
106 418
4.2%
107 146
 
1.5%
108 432
4.3%
109 35
 
0.4%
110 28
 
0.3%
ValueCountFrequency (%)
157 658
6.6%
156 1397
14.0%
155 88
 
0.9%
154 1350
13.5%
153 31
 
0.3%
152 160
 
1.6%
151 45
 
0.4%
150 82
 
0.8%
149 74
 
0.7%
148 47
 
0.5%

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

2023-12-12T13:53:02.876522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:03.016481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2023-12-12T13:53:03.151482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:03.277294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

본번
Real number (ℝ)

Distinct766
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.8385
Minimum1
Maximum3016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:03.427289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q141
median115
Q3341
95-th percentile2105
Maximum3016
Range3015
Interquartile range (IQR)300

Descriptive statistics

Standard deviation638.55002
Coefficient of variation (CV)1.6990011
Kurtosis4.6346761
Mean375.8385
Median Absolute Deviation (MAD)92
Skewness2.3774458
Sum3758385
Variance407746.12
MonotonicityNot monotonic
2023-12-12T13:53:03.657258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115 661
 
6.6%
2105 384
 
3.8%
562 306
 
3.1%
53 301
 
3.0%
1 190
 
1.9%
50 142
 
1.4%
51 139
 
1.4%
41 130
 
1.3%
16 110
 
1.1%
8 110
 
1.1%
Other values (756) 7527
75.3%
ValueCountFrequency (%)
1 190
1.9%
2 78
0.8%
3 41
 
0.4%
4 57
 
0.6%
5 87
0.9%
6 47
 
0.5%
7 41
 
0.4%
8 110
1.1%
9 70
 
0.7%
10 62
 
0.6%
ValueCountFrequency (%)
3016 7
0.1%
3012 5
 
0.1%
3006 5
 
0.1%
3000 10
0.1%
2991 7
0.1%
2990 2
 
< 0.1%
2971 5
 
0.1%
2967 7
0.1%
2951 13
0.1%
2937 9
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct228
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9356
Minimum0
Maximum546
Zeros2357
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:03.858256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q312
95-th percentile153
Maximum546
Range546
Interquartile range (IQR)11

Descriptive statistics

Standard deviation76.058928
Coefficient of variation (CV)2.9326072
Kurtosis16.313595
Mean25.9356
Median Absolute Deviation (MAD)3
Skewness4.1207548
Sum259356
Variance5784.9605
MonotonicityNot monotonic
2023-12-12T13:53:04.078461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2357
23.6%
1 1514
15.1%
3 837
 
8.4%
2 659
 
6.6%
6 394
 
3.9%
4 356
 
3.6%
5 304
 
3.0%
9 217
 
2.2%
30 214
 
2.1%
7 213
 
2.1%
Other values (218) 2935
29.3%
ValueCountFrequency (%)
0 2357
23.6%
1 1514
15.1%
2 659
 
6.6%
3 837
 
8.4%
4 356
 
3.6%
5 304
 
3.0%
6 394
 
3.9%
7 213
 
2.1%
8 174
 
1.7%
9 217
 
2.2%
ValueCountFrequency (%)
546 1
 
< 0.1%
458 6
 
0.1%
455 2
 
< 0.1%
426 3
 
< 0.1%
425 2
 
< 0.1%
403 1
 
< 0.1%
400 126
1.3%
397 1
 
< 0.1%
391 1
 
< 0.1%
390 3
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.4437
Minimum0
Maximum8001
Zeros357
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:04.300578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile101
Maximum8001
Range8001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation594.83871
Coefficient of variation (CV)5.4351115
Kurtosis42.422298
Mean109.4437
Median Absolute Deviation (MAD)0
Skewness6.325482
Sum1094437
Variance353833.1
MonotonicityNot monotonic
2023-12-12T13:53:04.516763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 8808
88.1%
0 357
 
3.6%
101 136
 
1.4%
2 102
 
1.0%
2000 98
 
1.0%
3000 89
 
0.9%
1000 75
 
0.8%
5000 57
 
0.6%
4000 56
 
0.6%
3 36
 
0.4%
Other values (35) 186
 
1.9%
ValueCountFrequency (%)
0 357
 
3.6%
1 8808
88.1%
2 102
 
1.0%
3 36
 
0.4%
4 15
 
0.1%
5 23
 
0.2%
6 9
 
0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
8001 1
 
< 0.1%
5000 57
0.6%
4000 56
0.6%
3000 89
0.9%
2000 98
1.0%
1000 75
0.8%
402 2
 
< 0.1%
401 1
 
< 0.1%
304 5
 
0.1%
303 1
 
< 0.1%

호
Text

Distinct1544
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:53:04.960819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.2036
Min length1

Characters and Unicode

Total characters32036
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique961 ?
Unique (%)9.6%

Sample

1st row104
2nd row201
3rd row501
4th row201
5th row71
ValueCountFrequency (%)
101 2182
21.8%
201 1111
 
11.1%
102 724
 
7.2%
301 673
 
6.7%
8101 537
 
5.4%
401 383
 
3.8%
103 264
 
2.6%
501 155
 
1.6%
202 152
 
1.5%
104 103
 
1.0%
Other values (1534) 3716
37.2%
2023-12-12T13:53:05.695990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11684
36.5%
0 8790
27.4%
2 3915
 
12.2%
3 2060
 
6.4%
4 1260
 
3.9%
8 1223
 
3.8%
5 994
 
3.1%
6 780
 
2.4%
7 562
 
1.8%
9 446
 
1.4%
Other values (10) 322
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31714
99.0%
Dash Punctuation 169
 
0.5%
Lowercase Letter 102
 
0.3%
Uppercase Letter 51
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11684
36.8%
0 8790
27.7%
2 3915
 
12.3%
3 2060
 
6.5%
4 1260
 
4.0%
8 1223
 
3.9%
5 994
 
3.1%
6 780
 
2.5%
7 562
 
1.8%
9 446
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
a 38
37.3%
n 36
35.3%
e 12
 
11.8%
b 12
 
11.8%
r 3
 
2.9%
u 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
J 36
70.6%
F 12
 
23.5%
M 3
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31883
99.5%
Latin 153
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11684
36.6%
0 8790
27.6%
2 3915
 
12.3%
3 2060
 
6.5%
4 1260
 
4.0%
8 1223
 
3.8%
5 994
 
3.1%
6 780
 
2.4%
7 562
 
1.8%
9 446
 
1.4%
Latin
ValueCountFrequency (%)
a 38
24.8%
J 36
23.5%
n 36
23.5%
F 12
 
7.8%
e 12
 
7.8%
b 12
 
7.8%
M 3
 
2.0%
r 3
 
2.0%
u 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11684
36.5%
0 8790
27.4%
2 3915
 
12.2%
3 2060
 
6.4%
4 1260
 
3.9%
8 1223
 
3.8%
5 994
 
3.1%
6 780
 
2.4%
7 562
 
1.8%
9 446
 
1.4%
Other values (10) 322
 
1.0%

물건지
Text

Distinct9567
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:53:06.024351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length30
Mean length25.0442
Min length21

Characters and Unicode

Total characters250442
Distinct characters89
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9265 ?
Unique (%)92.7%

Sample

1st row[ 국채보상로143길 83 ] 0001동 0104호
2nd row[ 중앙대로 446 ] 0001동 0201호
3rd row[ 동성로 30 ] 0001동 0501호
4th row[ 봉산문화1길 3 ] 0001동 0201호
5th row[ 명륜로 155 ] 0002동 0071호
ValueCountFrequency (%)
17410
29.1%
0001동 7867
 
13.1%
0101호 1890
 
3.2%
대구광역시 1295
 
2.2%
중구 1295
 
2.2%
0201호 1001
 
1.7%
1동 941
 
1.6%
국채보상로 719
 
1.2%
큰장로26길 707
 
1.2%
달구벌대로 661
 
1.1%
Other values (4008) 26141
43.6%
2023-12-12T13:53:06.519307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49927
19.9%
0 45063
18.0%
1 28179
11.3%
동 12891
 
5.1%
2 10469
 
4.2%
호 9993
 
4.0%
] 8705
 
3.5%
[ 8705
 
3.5%
로 7993
 
3.2%
3 5397
 
2.2%
Other values (79) 63120
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111208
44.4%
Other Letter 68618
27.4%
Space Separator 49927
19.9%
Close Punctuation 8705
 
3.5%
Open Punctuation 8705
 
3.5%
Dash Punctuation 3247
 
1.3%
Other Punctuation 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12891
18.8%
호 9993
14.6%
로 7993
11.6%
길 4520
 
6.6%
구 3457
 
5.0%
대 3089
 
4.5%
성 2019
 
2.9%
중 1911
 
2.8%
상 1477
 
2.2%
달 1399
 
2.0%
Other values (63) 19869
29.0%
Decimal Number
ValueCountFrequency (%)
0 45063
40.5%
1 28179
25.3%
2 10469
 
9.4%
3 5397
 
4.9%
5 4740
 
4.3%
6 4448
 
4.0%
4 4236
 
3.8%
8 3797
 
3.4%
7 2718
 
2.4%
9 2161
 
1.9%
Other Punctuation
ValueCountFrequency (%)
· 30
93.8%
？ 2
 
6.2%
Space Separator
ValueCountFrequency (%)
49927
100.0%
Close Punctuation
ValueCountFrequency (%)
] 8705
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 8705
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 181824
72.6%
Hangul 68618
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12891
18.8%
호 9993
14.6%
로 7993
11.6%
길 4520
 
6.6%
구 3457
 
5.0%
대 3089
 
4.5%
성 2019
 
2.9%
중 1911
 
2.8%
상 1477
 
2.2%
달 1399
 
2.0%
Other values (63) 19869
29.0%
Common
ValueCountFrequency (%)
49927
27.5%
0 45063
24.8%
1 28179
15.5%
2 10469
 
5.8%
] 8705
 
4.8%
[ 8705
 
4.8%
3 5397
 
3.0%
5 4740
 
2.6%
6 4448
 
2.4%
4 4236
 
2.3%
Other values (6) 11955
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 181792
72.6%
Hangul 68618
 
27.4%
None 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49927
27.5%
0 45063
24.8%
1 28179
15.5%
2 10469
 
5.8%
] 8705
 
4.8%
[ 8705
 
4.8%
3 5397
 
3.0%
5 4740
 
2.6%
6 4448
 
2.4%
4 4236
 
2.3%
Other values (4) 11923
 
6.6%
Hangul
ValueCountFrequency (%)
동 12891
18.8%
호 9993
14.6%
로 7993
11.6%
길 4520
 
6.6%
구 3457
 
5.0%
대 3089
 
4.5%
성 2019
 
2.9%
중 1911
 
2.8%
상 1477
 
2.2%
달 1399
 
2.0%
Other values (63) 19869
29.0%
None
ValueCountFrequency (%)
· 30
93.8%
？ 2
 
6.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8586
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68339854
Minimum15350
Maximum8.0355722 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:06.683209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15350
5-th percentile1048574
Q13910645
median18051595
Q353199662
95-th percentile2.636263 × 108
Maximum8.0355722 × 109
Range8.0355569 × 109
Interquartile range (IQR)49289018

Descriptive statistics

Standard deviation2.4723845 × 108
Coefficient of variation (CV)3.6177784
Kurtosis403.54883
Mean68339854
Median Absolute Deviation (MAD)15741040
Skewness16.758887
Sum6.8339854 × 1011
Variance6.112685 × 1016
MonotonicityNot monotonic
2023-12-12T13:53:06.855976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3581950 116
 
1.2%
3645850 87
 
0.9%
55505500 70
 
0.7%
39453410 52
 
0.5%
3830450 42
 
0.4%
46640450 22
 
0.2%
48184430 22
 
0.2%
6861700 22
 
0.2%
27417030 18
 
0.2%
28362480 15
 
0.1%
Other values (8576) 9534
95.3%
ValueCountFrequency (%)
15350 1
< 0.1%
25200 1
< 0.1%
25340 1
< 0.1%
26880 1
< 0.1%
27880 1
< 0.1%
30710 1
< 0.1%
30740 1
< 0.1%
30750 1
< 0.1%
32200 1
< 0.1%
32920 1
< 0.1%
ValueCountFrequency (%)
8035572220 1
< 0.1%
7715247210 1
< 0.1%
6402373560 1
< 0.1%
6362720000 1
< 0.1%
6179470800 1
< 0.1%
5299119960 1
< 0.1%
5171267500 1
< 0.1%
5056675560 1
< 0.1%
3987485620 1
< 0.1%
3724504000 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6647
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.1494
Minimum0.08
Maximum11960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:53:07.018617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile5.6295
Q120.145
median54.9773
Q3125.2525
95-th percentile484.146
Maximum11960
Range11959.92
Interquartile range (IQR)105.1075

Descriptive statistics

Standard deviation349.70555
Coefficient of variation (CV)2.6462893
Kurtosis408.38017
Mean132.1494
Median Absolute Deviation (MAD)41.9427
Skewness15.859396
Sum1321494
Variance122293.97
MonotonicityNot monotonic
2023-12-12T13:53:07.169378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.09 117
 
1.2%
10.27 87
 
0.9%
51.8743 70
 
0.7%
36.6327 52
 
0.5%
18.0 49
 
0.5%
10.79 43
 
0.4%
43.3059 22
 
0.2%
42.7546 22
 
0.2%
9.2977 22
 
0.2%
13.22 20
 
0.2%
Other values (6637) 9496
95.0%
ValueCountFrequency (%)
0.08 1
< 0.1%
0.12 1
< 0.1%
0.14 1
< 0.1%
0.17 1
< 0.1%
0.23 1
< 0.1%
0.24 1
< 0.1%
0.27 1
< 0.1%
0.2844 1
< 0.1%
0.32 1
< 0.1%
0.34 2
< 0.1%
ValueCountFrequency (%)
11960.0 1
< 0.1%
11525.0 1
< 0.1%
10654.26 1
< 0.1%
8293.23 1
< 0.1%
7345.13 1
< 0.1%
5807.01 1
< 0.1%
5475.69 1
< 0.1%
5158.4 1
< 0.1%
5008.62 1
< 0.1%
4655.63 1
< 0.1%

결정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 10000
100.0%

Length

2023-12-12T13:53:07.297447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:07.391118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

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

2023-12-12T13:53:07.493539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:53:07.592976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 10000
100.0%

Interactions

2023-12-12T13:53:00.004867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:55.855905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.661280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.413172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.474340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.207284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:53:00.131034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:55.976845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.770083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.535764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.579569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.327350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:53:00.274541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.085441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.890577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.947054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.691918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.454098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:53:00.382023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.221133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.005492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.053703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.792969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.588178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:53:00.517417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.385299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.158516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.210427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.944716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.737741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:53:00.690760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:56.513803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:57.299437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:58.348711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.087792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:59.885162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:53:07.672937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적
법정동1.0000.6140.3860.2020.0660.053
본번0.6141.0000.2980.5910.0000.000
부번0.3860.2981.0000.0000.0590.080
동0.2020.5910.0001.0000.0000.000
시가표준액0.0660.0000.0590.0001.0000.918
연면적0.0530.0000.0800.0000.9181.000
2023-12-12T13:53:07.804506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적
법정동1.0000.3510.2030.125-0.105-0.087
본번0.3511.000-0.0700.2180.019-0.049
부번0.203-0.0701.000-0.255-0.138-0.050
동0.1250.218-0.2551.0000.014-0.139
시가표준액-0.1050.019-0.1380.0141.0000.852
연면적-0.087-0.049-0.050-0.1390.8521.000

Missing values

2023-12-12T13:53:00.871299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:53:01.120750image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자기준일자
22597대구광역시중구27110202110301271211104[ 국채보상로143길 83 ] 0001동 0104호806284039.142021-06-012021-12-31
37700대구광역시중구271102021151012151201[ 중앙대로 446 ] 0001동 0201호10124520120.532021-06-012021-12-31
32083대구광역시중구2711020211230116611501[ 동성로 30 ] 0001동 0501호3400954201218.982021-06-012021-12-31
20737대구광역시중구27110202110801223181201[ 봉산문화1길 3 ] 0001동 0201호6031260091.82021-06-012021-12-31
12522대구광역시중구2711020211570155149271[ 명륜로 155 ] 0002동 0071호2394888099.272021-06-012021-12-31
14204대구광역시중구2711020211560159561101[ 명덕로35길 73 ] 0001동 0101호525262052.52021-06-012021-12-31
33351대구광역시중구27110202111201110011601[ 달구벌대로 2095 ] 0001동 1601호16904905901978.22021-06-012021-12-31
21102대구광역시중구271102021104015501901[ 국채보상로 719 ] 0001동 0901호159917220382.122021-06-012021-12-31
11253대구광역시중구2711020211570121231401[ 동덕로 84 ] 0001동 0401호3828672093.842021-06-012021-12-31
28700대구광역시중구2711020211320125701201[ 북성로4길 64 ] 0001동 0201호4358640099.062021-06-012021-12-31
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자기준일자
23790대구광역시중구2711020211020156101102[ 국채보상로131길 18 ] 0001동 0102호2996076065.792021-06-012021-12-31
13528대구광역시중구271102021157011172019501[ 동덕로 32 ] 0001동 9501호357918054.232021-06-012021-12-31
20726대구광역시중구2711020211080122361201[ 봉산문화길 43 ] 0001동 0201호30754500106.052021-06-012021-12-31
40270대구광역시중구271102021154011151851101[ 큰장로28길 9 ] 0001동 0101호134247930212.672021-06-012021-12-31
25640대구광역시중구271102021154012105050005008[ 큰장로26길 25 ] 5000동 5008호2348099026.3952021-06-012021-12-31
35490대구광역시중구27110202115701735161101[ 명륜로26길 49 ] 0001동 0101호225112029.622021-06-012021-12-31
19823대구광역시중구271102021105012931104[ 동성로4길 25 ] 0001동 0104호152591120128.722021-06-012021-12-31
1042대구광역시중구271102021143012131122[ 경상감영길 101 ] 0001동 0122호872929039.332021-06-012021-12-31
15603대구광역시중구271102021154011153018145[ 큰장로26길 65 ] 0001동 8145호187229010.312021-06-012021-12-31
30355대구광역시중구271102021113011371301[ 종로 56 ] 0001동 0301호373883063.372021-06-012021-12-31

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자기준일자# duplicates
0대구광역시중구2711020211040152751101[ 동덕로30길 139-11 ] 0001동 0101호24480018.02021-06-012021-12-312
1대구광역시중구27110202110801230111대구광역시 중구 봉산동 230-1 1동 1호169391250967.952021-06-012021-12-312
2대구광역시중구271102021112015331149[ 달구벌대로 2085 ] 0001동 0149호20930403.062021-06-012021-12-312
3대구광역시중구271102021112015331173[ 달구벌대로 2085 ] 0001동 0173호9893170801446.372021-06-012021-12-312
4대구광역시중구271102021117013611301[ 국채보상로 582 ] 0001동 0301호508926021.032021-06-012021-12-312
5대구광역시중구271102021117013611301[ 국채보상로 582 ] 0001동 0301호532158021.992021-06-012021-12-312
6대구광역시중구271102021129013111101대구광역시 중구 도원동 3-11 1동 101호1311552043.22021-06-012021-12-312
7대구광역시중구2711020211540111540011090[ 큰장로26길 6 ] 0001동 1090호282696012.232021-06-012021-12-312
8대구광역시중구2711020211540111540011138[ 큰장로26길 6 ] 0001동 1138호323147013.982021-06-012021-12-312
9대구광역시중구27110202115601925211[ 중앙대로 359 ] 0001동 0001호436985620399.382021-06-012021-12-312