Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description지방자치단체는 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하여 연도별 물건별 재산가액을 확인 할 수 있도록 함
Author대구광역시 동구
URLhttps://www.data.go.kr/data/15079904/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
기준일자 has constant value ""Constant
Dataset has 10 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (96.8%)Imbalance
동 is highly skewed (γ1 = 35.60391677)Skewed
시가표준액 is highly skewed (γ1 = 27.46389363)Skewed
부번 has 2081 (20.8%) zerosZeros
동 has 2853 (28.5%) zerosZeros

Reproduction

Analysis started2023-12-12 22:39:50.065226
Analysis finished2023-12-12 22:39:57.763484
Duration7.7 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

2023-12-13T07:39:57.824703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:57.899627image/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

2023-12-13T07:39:57.982538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:58.085915image/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

2023-12-13T07:39:58.191006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:58.278217image/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

2023-12-13T07:39:58.361949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:58.449777image/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%
Mean112.4823
Minimum101
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:39:58.540578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median110
Q3119
95-th percentile137
Maximum145
Range44
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.656649
Coefficient of variation (CV)0.10363097
Kurtosis0.26419048
Mean112.4823
Median Absolute Deviation (MAD)8
Skewness0.99805855
Sum1124823
Variance135.87747
MonotonicityNot monotonic
2023-12-13T07:39:58.658706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
102 1746
17.5%
101 1238
12.4%
103 773
 
7.7%
124 742
 
7.4%
111 574
 
5.7%
118 506
 
5.1%
105 412
 
4.1%
123 403
 
4.0%
117 353
 
3.5%
110 295
 
2.9%
Other values (35) 2958
29.6%
ValueCountFrequency (%)
101 1238
12.4%
102 1746
17.5%
103 773
7.7%
104 48
 
0.5%
105 412
 
4.1%
106 249
 
2.5%
107 109
 
1.1%
108 221
 
2.2%
109 168
 
1.7%
110 295
 
2.9%
ValueCountFrequency (%)
145 183
1.8%
144 57
 
0.6%
143 42
 
0.4%
142 101
1.0%
141 29
 
0.3%
140 16
 
0.2%
139 25
 
0.2%
138 33
 
0.3%
137 84
0.8%
136 32
 
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

2023-12-13T07:39:58.779667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:58.859153image/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
9967 
2
 
33

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 9967
99.7%
2 33
 
0.3%

Length

2023-12-13T07:39:58.947756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:59.039971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9967
99.7%
2 33
 
0.3%

본번
Real number (ℝ)

Distinct1272
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.2083
Minimum1
Maximum1846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:39:59.126803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70
Q1283
median534
Q3984
95-th percentile1508
Maximum1846
Range1845
Interquartile range (IQR)701

Descriptive statistics

Standard deviation433.56025
Coefficient of variation (CV)0.69346529
Kurtosis-0.67671559
Mean625.2083
Median Absolute Deviation (MAD)337
Skewness0.55708493
Sum6252083
Variance187974.49
MonotonicityNot monotonic
2023-12-13T07:39:59.248071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1188 236
 
2.4%
326 132
 
1.3%
174 128
 
1.3%
292 113
 
1.1%
1551 101
 
1.0%
331 76
 
0.8%
327 74
 
0.7%
286 73
 
0.7%
259 65
 
0.7%
1084 62
 
0.6%
Other values (1262) 8940
89.4%
ValueCountFrequency (%)
1 19
0.2%
2 1
 
< 0.1%
3 5
 
0.1%
5 9
0.1%
6 5
 
0.1%
7 10
0.1%
8 5
 
0.1%
9 6
 
0.1%
10 4
 
< 0.1%
11 9
0.1%
ValueCountFrequency (%)
1846 4
 
< 0.1%
1840 34
0.3%
1839 2
 
< 0.1%
1833 7
 
0.1%
1810 1
 
< 0.1%
1626 2
 
< 0.1%
1621 3
 
< 0.1%
1618 1
 
< 0.1%
1611 1
 
< 0.1%
1610 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct245
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5381
Minimum0
Maximum761
Zeros2081
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:39:59.367937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile48
Maximum761
Range761
Interquartile range (IQR)8

Descriptive statistics

Standard deviation53.18082
Coefficient of variation (CV)3.6580309
Kurtosis81.487583
Mean14.5381
Median Absolute Deviation (MAD)3
Skewness8.3128464
Sum145381
Variance2828.1996
MonotonicityNot monotonic
2023-12-13T07:39:59.490386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2081
20.8%
1 1715
17.2%
2 989
9.9%
3 634
 
6.3%
4 575
 
5.8%
6 490
 
4.9%
5 386
 
3.9%
7 333
 
3.3%
9 223
 
2.2%
8 206
 
2.1%
Other values (235) 2368
23.7%
ValueCountFrequency (%)
0 2081
20.8%
1 1715
17.2%
2 989
9.9%
3 634
 
6.3%
4 575
 
5.8%
5 386
 
3.9%
6 490
 
4.9%
7 333
 
3.3%
8 206
 
2.1%
9 223
 
2.2%
ValueCountFrequency (%)
761 4
< 0.1%
725 1
 
< 0.1%
716 1
 
< 0.1%
700 1
 
< 0.1%
698 1
 
< 0.1%
697 1
 
< 0.1%
694 3
< 0.1%
668 1
 
< 0.1%
667 1
 
< 0.1%
664 1
 
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0379
Minimum0
Maximum9001
Zeros2853
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:39:59.621144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile9.05
Maximum9001
Range9001
Interquartile range (IQR)1

Descriptive statistics

Standard deviation242.73783
Coefficient of variation (CV)16.141737
Kurtosis1312.8013
Mean15.0379
Median Absolute Deviation (MAD)0
Skewness35.603917
Sum150379
Variance58921.654
MonotonicityNot monotonic
2023-12-13T07:39:59.956678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6174
61.7%
0 2853
28.5%
2 308
 
3.1%
101 87
 
0.9%
105 68
 
0.7%
3 58
 
0.6%
7 33
 
0.3%
301 31
 
0.3%
110 30
 
0.3%
4 25
 
0.2%
Other values (58) 333
 
3.3%
ValueCountFrequency (%)
0 2853
28.5%
1 6174
61.7%
2 308
 
3.1%
3 58
 
0.6%
4 25
 
0.2%
5 17
 
0.2%
6 16
 
0.2%
7 33
 
0.3%
8 7
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
9001 7
0.1%
1100 1
 
< 0.1%
1000 4
< 0.1%
719 2
 
< 0.1%
702 1
 
< 0.1%
701 7
0.1%
601 1
 
< 0.1%
417 6
0.1%
401 6
0.1%
321 2
 
< 0.1%

호
Text

Distinct789
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:40:00.230006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0406
Min length1

Characters and Unicode

Total characters30406
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

Unique419 ?
Unique (%)4.2%

Sample

1st row201
2nd row101
3rd row0206-1
4th row701
5th row101
ValueCountFrequency (%)
101 2813
28.1%
201 1070
 
10.7%
102 916
 
9.2%
8101 561
 
5.6%
301 552
 
5.5%
103 330
 
3.3%
401 280
 
2.8%
202 170
 
1.7%
501 155
 
1.6%
0 155
 
1.6%
Other values (779) 2998
30.0%
2023-12-13T07:40:00.620662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12467
41.0%
0 8945
29.4%
2 3540
 
11.6%
3 1673
 
5.5%
8 1093
 
3.6%
4 917
 
3.0%
5 671
 
2.2%
6 437
 
1.4%
7 366
 
1.2%
9 292
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30401
> 99.9%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12467
41.0%
0 8945
29.4%
2 3540
 
11.6%
3 1673
 
5.5%
8 1093
 
3.6%
4 917
 
3.0%
5 671
 
2.2%
6 437
 
1.4%
7 366
 
1.2%
9 292
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12467
41.0%
0 8945
29.4%
2 3540
 
11.6%
3 1673
 
5.5%
8 1093
 
3.6%
4 917
 
3.0%
5 671
 
2.2%
6 437
 
1.4%
7 366
 
1.2%
9 292
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12467
41.0%
0 8945
29.4%
2 3540
 
11.6%
3 1673
 
5.5%
8 1093
 
3.6%
4 917
 
3.0%
5 671
 
2.2%
6 437
 
1.4%
7 366
 
1.2%
9 292
 
1.0%

물건지
Text

Distinct9818
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:40:00.863980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length24.614
Min length15

Characters and Unicode

Total characters246140
Distinct characters135
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

Unique9689 ?
Unique (%)96.9%

Sample

1st row[ 안심로 323 ] 0001동 0201호
2nd row[ 효목로5길 40 ] 0001동 0101호
3rd row[ 팔공로49길 51 ] 0000동 0206-1호
4th row[ 아양로 46 ] 0001동 0701호
5th row[ 도평로 129 ] 0000동 0101호
ValueCountFrequency (%)
14890
25.0%
0001동 4698
 
7.9%
대구광역시 2555
 
4.3%
동구 2555
 
4.3%
0000동 2472
 
4.1%
0101호 2070
 
3.5%
1동 1476
 
2.5%
0201호 855
 
1.4%
101호 743
 
1.2%
0102호 698
 
1.2%
Other values (3722) 26591
44.6%
2023-12-13T07:40:01.206228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49603
20.2%
0 43356
17.6%
1 26345
10.7%
동 17340
 
7.0%
호 10149
 
4.1%
2 9329
 
3.8%
] 7445
 
3.0%
[ 7445
 
3.0%
로 7416
 
3.0%
3 5528
 
2.2%
Other values (125) 62184
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104969
42.6%
Other Letter 73628
29.9%
Space Separator 49603
20.2%
Close Punctuation 7445
 
3.0%
Open Punctuation 7445
 
3.0%
Dash Punctuation 3050
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17340
23.6%
호 10149
13.8%
로 7416
10.1%
구 5283
 
7.2%
길 3997
 
5.4%
대 2865
 
3.9%
역 2583
 
3.5%
광 2582
 
3.5%
시 2555
 
3.5%
신 1537
 
2.1%
Other values (111) 17321
23.5%
Decimal Number
ValueCountFrequency (%)
0 43356
41.3%
1 26345
25.1%
2 9329
 
8.9%
3 5528
 
5.3%
5 4280
 
4.1%
4 4177
 
4.0%
6 3450
 
3.3%
8 3240
 
3.1%
7 2927
 
2.8%
9 2337
 
2.2%
Space Separator
ValueCountFrequency (%)
49603
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7445
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7445
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 172512
70.1%
Hangul 73628
29.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17340
23.6%
호 10149
13.8%
로 7416
10.1%
구 5283
 
7.2%
길 3997
 
5.4%
대 2865
 
3.9%
역 2583
 
3.5%
광 2582
 
3.5%
시 2555
 
3.5%
신 1537
 
2.1%
Other values (111) 17321
23.5%
Common
ValueCountFrequency (%)
49603
28.8%
0 43356
25.1%
1 26345
15.3%
2 9329
 
5.4%
] 7445
 
4.3%
[ 7445
 
4.3%
3 5528
 
3.2%
5 4280
 
2.5%
4 4177
 
2.4%
6 3450
 
2.0%
Other values (4) 11554
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172512
70.1%
Hangul 73628
29.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49603
28.8%
0 43356
25.1%
1 26345
15.3%
2 9329
 
5.4%
] 7445
 
4.3%
[ 7445
 
4.3%
3 5528
 
3.2%
5 4280
 
2.5%
4 4177
 
2.4%
6 3450
 
2.0%
Other values (4) 11554
 
6.7%
Hangul
ValueCountFrequency (%)
동 17340
23.6%
호 10149
13.8%
로 7416
10.1%
구 5283
 
7.2%
길 3997
 
5.4%
대 2865
 
3.9%
역 2583
 
3.5%
광 2582
 
3.5%
시 2555
 
3.5%
신 1537
 
2.1%
Other values (111) 17321
23.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8471
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1615705 × 108
Minimum11880
Maximum2.9868855 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:40:01.324662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11880
5-th percentile1097856.5
Q18634400
median44617720
Q398860762
95-th percentile3.3299185 × 108
Maximum2.9868855 × 1010
Range2.9868843 × 1010
Interquartile range (IQR)90226362

Descriptive statistics

Standard deviation6.0734195 × 108
Coefficient of variation (CV)5.2286276
Kurtosis969.28519
Mean1.1615705 × 108
Median Absolute Deviation (MAD)38677015
Skewness27.463894
Sum1.1615705 × 1012
Variance3.6886424 × 1017
MonotonicityNot monotonic
2023-12-13T07:40:01.441371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62988130 50
 
0.5%
94517550 46
 
0.5%
137155240 36
 
0.4%
36333250 32
 
0.3%
45598620 30
 
0.3%
57511620 29
 
0.3%
53960130 25
 
0.2%
61954850 24
 
0.2%
107830020 23
 
0.2%
54557060 22
 
0.2%
Other values (8461) 9683
96.8%
ValueCountFrequency (%)
11880 1
< 0.1%
28400 1
< 0.1%
48970 1
< 0.1%
51150 1
< 0.1%
53280 1
< 0.1%
57600 1
< 0.1%
57960 1
< 0.1%
59000 1
< 0.1%
60950 1
< 0.1%
61080 1
< 0.1%
ValueCountFrequency (%)
29868855320 1
< 0.1%
19440122400 1
< 0.1%
18622125360 1
< 0.1%
17525427180 1
< 0.1%
14654777840 1
< 0.1%
12901949530 1
< 0.1%
12584587180 1
< 0.1%
12513970800 1
< 0.1%
12165426870 1
< 0.1%
11551972430 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6854
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.70724
Minimum0.81
Maximum19596.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:40:01.547979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.81
5-th percentile9.9
Q142.9743
median87.285
Q3175.65852
95-th percentile561.186
Maximum19596.81
Range19596
Interquartile range (IQR)132.68422

Descriptive statistics

Standard deviation628.39393
Coefficient of variation (CV)3.2950712
Kurtosis435.54963
Mean190.70724
Median Absolute Deviation (MAD)55.20485
Skewness18.194994
Sum1907072.4
Variance394878.93
MonotonicityNot monotonic
2023-12-13T07:40:01.669344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 213
 
2.1%
51.9275 50
 
0.5%
65.0683 50
 
0.5%
97.9422 46
 
0.5%
42.5448 42
 
0.4%
101.8984 36
 
0.4%
27.0 32
 
0.3%
55.513 32
 
0.3%
20.0 30
 
0.3%
53.6026 29
 
0.3%
Other values (6844) 9440
94.4%
ValueCountFrequency (%)
0.81 1
 
< 0.1%
0.82 1
 
< 0.1%
0.83 3
 
< 0.1%
0.9 1
 
< 0.1%
0.99 2
 
< 0.1%
1.0 15
0.1%
1.09 1
 
< 0.1%
1.16 2
 
< 0.1%
1.17 1
 
< 0.1%
1.19 3
 
< 0.1%
ValueCountFrequency (%)
19596.81 1
< 0.1%
19012.67 1
< 0.1%
18297.51 1
< 0.1%
16474.68 1
< 0.1%
16386.87 1
< 0.1%
14217.156 1
< 0.1%
13128.13 1
< 0.1%
12557.86 1
< 0.1%
11407.82 1
< 0.1%
10842.16 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-06-01 00:00:00
Maximum2022-06-01 00:00:00
2023-12-13T07:40:01.758858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:01.841711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T07:39:56.694026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:51.592920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.330937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.154330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.955359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:55.931639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.787182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:51.677130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.455104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.239005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:54.156901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.030563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.886749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:51.768678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.572244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.346335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:54.392238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.124713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.990216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:51.860893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.676490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.436376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:54.606715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.222189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:57.240557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.136224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.951144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.751343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:55.035472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.499487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:57.342338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:52.229316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.046925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:53.846583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:55.674096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:56.588384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:40:01.896646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.0910.7380.2820.0320.0000.039
특수지0.0911.0000.1570.0000.0000.0000.000
본번0.7380.1571.0000.2390.0960.0810.130
부번0.2820.0000.2391.0000.0000.0000.000
동0.0320.0000.0960.0001.0000.0000.000
시가표준액0.0000.0000.0810.0000.0001.0000.849
연면적0.0390.0000.1300.0000.0000.8491.000
2023-12-13T07:40:01.979315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.0000.184-0.244-0.0460.0410.0190.070
본번0.1841.000-0.1320.0310.1810.0380.120
부번-0.244-0.1321.000-0.109-0.154-0.0900.000
동-0.0460.031-0.1091.000-0.085-0.0640.059
시가표준액0.0410.181-0.154-0.0851.0000.8380.000
연면적0.0190.038-0.090-0.0640.8381.0000.000
특수지0.0700.1200.0000.0590.0000.0001.000

Missing values

2023-12-13T07:39:57.481084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:39:57.681164image/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
6425대구광역시동구2714020221230130081201[ 안심로 323 ] 0001동 0201호83342500314.52022-06-01
10184대구광역시동구2714020221030143951101[ 효목로5길 40 ] 0001동 0101호472135047.192022-06-01
27502대구광역시동구271402022105011548000206-1[ 팔공로49길 51 ] 0000동 0206-1호144454480122.62692022-06-01
28784대구광역시동구2714020221010121441701[ 아양로 46 ] 0001동 0701호148260370439.292022-06-01
15708대구광역시동구27140202210701978340101[ 도평로 129 ] 0000동 0101호6620462080.182022-06-01
38725대구광역시동구27140202212401118811543[ 첨복로 7 ] 0001동 0543호6298813065.06832022-06-01
16909대구광역시동구271402022102012832118101대구광역시 동구 신천동 283-21 1동 8101호39168000240.02022-06-01
43290대구광역시동구2714020221240154830501[ 안심로 442 ] 0000동 0501호154542510271.272022-06-01
9407대구광역시동구2714020221030138921101[ 동부로 198 ] 0001동 0101호13761980134.462022-06-01
26689대구광역시동구2714020221280189021406[ 메디밸리로 13 ] 0001동 0406호248942050262.87442022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
27907대구광역시동구27140202214501137821301[ 동화천로77길 8 ] 0001동 0301호121711590121.592022-06-01
6865대구광역시동구27140202211901878711101대구광역시 동구 용계동 878-71 1동 101호48797200131.922022-06-01
38706대구광역시동구2714020221450152231119대구광역시 동구 지묘동 522-3 1동 119호1571102029.962022-06-01
34428대구광역시동구2714020221030153220109[ 화랑로 123 ] 0000동 0109호704820027.642022-06-01
49576대구광역시동구2714020221450124821401[ 팔공로101길 47 ] 0001동 0401호123192000212.42022-06-01
19602대구광역시동구2714020221020118521101[ 송라로 44 ] 0001동 0101호75706200230.182022-06-01
4400대구광역시동구27140202211901392101101[ 신평로16길 52 ] 0001동 0101호2709717058.762022-06-01
25784대구광역시동구27140202210501154371304[ 팔공로51길 41 ] 0001동 0304호163210700193.6072022-06-01
38803대구광역시동구27140202211701174418286대구광역시 동구 신기동 174-4 1동 8286호469920022.02022-06-01
9606대구광역시동구271402022106011156518101[ 팔공로31길 10-5 ] 0001동 8101호148149025.112022-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
1대구광역시동구2714020221010129400101대구광역시 동구 신암동 294 101호333293000989.02022-06-013
0대구광역시동구271402022101018113101대구광역시 동구 신암동 81-1 3동 101호502200018.02022-06-012
2대구광역시동구27140202210901998510대구광역시 동구 입석동 998-5 1동205118160544.082022-06-012
3대구광역시동구271402022110012101101대구광역시 동구 검사동 21 1동 101호16872000148.02022-06-012
4대구광역시동구271402022110026101101대구광역시 동구 검사동 산 61 1동 101호61860960542.642022-06-012
5대구광역시동구2714020221110111185701[ 동촌로58길 69 ] 0000동 0001호74434360110.112022-06-012
6대구광역시동구27140202211701160821101대구광역시 동구 신기동 160-82 1동 101호522000018.02022-06-012
7대구광역시동구2714020221190194700101[ 화랑로 574 ] 0000동 0101호348800032.02022-06-012
8대구광역시동구2714020221430119321101[ 서촌로 106 ] 0001동 0101호34408406.762022-06-012
9대구광역시동구271402022144013401101대구광역시 동구 덕곡동 34 1동 101호25143008.672022-06-012