Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 확인할 수 있으며, 각 연도별 · 물건지 별로 데이터를 비교해서 볼 수 있습니다. 자료 기간(2017~2021)
URLhttps://www.data.go.kr/data/15079974/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
법정리 has constant value ""Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (98.9%)Imbalance
동 is highly skewed (γ1 = 33.27709521)Skewed
부번 has 634 (6.3%) zerosZeros
동 has 791 (7.9%) zerosZeros

Reproduction

Analysis started2023-12-12 07:23:23.940349
Analysis finished2023-12-12 07:23:31.652037
Duration7.71 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
대전광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 10000
100.0%

Length

2023-12-12T16:23:31.733490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:31.845317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 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

2023-12-12T16:23:31.961395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:32.061415image/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
30140
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30140
2nd row30140
3rd row30140
4th row30140
5th row30140

Common Values

ValueCountFrequency (%)
30140 10000
100.0%

Length

2023-12-12T16:23:32.150419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:32.255670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30140 10000
100.0%

과세연도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018
4718 
2017
4675 
2019
607 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2018
4th row2018
5th row2017

Common Values

ValueCountFrequency (%)
2018 4718
47.2%
2017 4675
46.8%
2019 607
 
6.1%

Length

2023-12-12T16:23:32.384239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:32.502255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 4718
47.2%
2017 4675
46.8%
2019 607
 
6.1%

법정동
Real number (ℝ)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.1485
Minimum101
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:32.633237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median110
Q3115
95-th percentile117
Maximum126
Range25
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.0017872
Coefficient of variation (CV)0.054987354
Kurtosis-1.3028236
Mean109.1485
Median Absolute Deviation (MAD)5
Skewness0.12689353
Sum1091485
Variance36.02145
MonotonicityNot monotonic
2023-12-12T16:23:32.798584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
102 1386
13.9%
105 1184
11.8%
115 1044
10.4%
101 846
 
8.5%
116 703
 
7.0%
113 602
 
6.0%
114 593
 
5.9%
117 542
 
5.4%
104 427
 
4.3%
111 408
 
4.1%
Other values (16) 2265
22.7%
ValueCountFrequency (%)
101 846
8.5%
102 1386
13.9%
103 309
 
3.1%
104 427
 
4.3%
105 1184
11.8%
106 360
 
3.6%
107 182
 
1.8%
108 107
 
1.1%
109 137
 
1.4%
110 310
 
3.1%
ValueCountFrequency (%)
126 14
 
0.1%
125 31
 
0.3%
124 21
 
0.2%
123 22
 
0.2%
122 12
 
0.1%
121 3
 
< 0.1%
120 7
 
0.1%
119 187
 
1.9%
118 171
 
1.7%
117 542
5.4%

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

2023-12-12T16:23:32.942416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:33.038559image/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
9990 
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9990
99.9%
2 10
 
0.1%

Length

2023-12-12T16:23:33.195801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:33.320252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9990
99.9%
2 10
 
0.1%

본번
Real number (ℝ)

Distinct604
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.4133
Minimum1
Maximum913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:33.437741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q185
median161
Q3326
95-th percentile509
Maximum913
Range912
Interquartile range (IQR)241

Descriptive statistics

Standard deviation164.34387
Coefficient of variation (CV)0.7885479
Kurtosis0.75703904
Mean208.4133
Median Absolute Deviation (MAD)113
Skewness0.9853153
Sum2084133
Variance27008.908
MonotonicityNot monotonic
2023-12-12T16:23:33.590528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 234
 
2.3%
48 207
 
2.1%
154 196
 
2.0%
335 150
 
1.5%
248 97
 
1.0%
452 81
 
0.8%
187 80
 
0.8%
79 79
 
0.8%
12 76
 
0.8%
101 72
 
0.7%
Other values (594) 8728
87.3%
ValueCountFrequency (%)
1 234
2.3%
2 19
 
0.2%
3 40
 
0.4%
4 46
 
0.5%
5 32
 
0.3%
6 47
 
0.5%
7 30
 
0.3%
8 9
 
0.1%
9 56
 
0.6%
10 71
 
0.7%
ValueCountFrequency (%)
913 6
0.1%
912 5
0.1%
910 2
 
< 0.1%
884 1
 
< 0.1%
877 3
 
< 0.1%
793 3
 
< 0.1%
782 9
0.1%
780 2
 
< 0.1%
778 7
0.1%
774 4
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.017
Minimum0
Maximum722
Zeros634
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:33.742275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q315
95-th percentile54
Maximum722
Range722
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.266828
Coefficient of variation (CV)2.3891383
Kurtosis77.985405
Mean16.017
Median Absolute Deviation (MAD)6
Skewness7.3816014
Sum160170
Variance1464.3501
MonotonicityNot monotonic
2023-12-12T16:23:33.971515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1509
 
15.1%
2 731
 
7.3%
0 634
 
6.3%
4 622
 
6.2%
3 618
 
6.2%
5 475
 
4.8%
10 405
 
4.0%
6 393
 
3.9%
7 393
 
3.9%
8 378
 
3.8%
Other values (212) 3842
38.4%
ValueCountFrequency (%)
0 634
6.3%
1 1509
15.1%
2 731
7.3%
3 618
6.2%
4 622
6.2%
5 475
 
4.8%
6 393
 
3.9%
7 393
 
3.9%
8 378
 
3.8%
9 305
 
3.0%
ValueCountFrequency (%)
722 1
 
< 0.1%
705 1
 
< 0.1%
640 2
< 0.1%
589 3
< 0.1%
412 1
 
< 0.1%
401 1
 
< 0.1%
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9239
Minimum0
Maximum2000
Zeros791
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:34.144329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50.203958
Coefficient of variation (CV)12.794403
Kurtosis1270.4942
Mean3.9239
Median Absolute Deviation (MAD)0
Skewness33.277095
Sum39239
Variance2520.4374
MonotonicityNot monotonic
2023-12-12T16:23:34.309469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 8357
83.6%
0 791
 
7.9%
2 469
 
4.7%
3 92
 
0.9%
4 53
 
0.5%
5 39
 
0.4%
101 22
 
0.2%
201 20
 
0.2%
6 19
 
0.2%
7 15
 
0.1%
Other values (30) 123
 
1.2%
ValueCountFrequency (%)
0 791
 
7.9%
1 8357
83.6%
2 469
 
4.7%
3 92
 
0.9%
4 53
 
0.5%
5 39
 
0.4%
6 19
 
0.2%
7 15
 
0.1%
8 7
 
0.1%
9 12
 
0.1%
ValueCountFrequency (%)
2000 5
 
0.1%
1000 1
 
< 0.1%
402 5
 
0.1%
401 13
0.1%
201 20
0.2%
116 1
 
< 0.1%
115 3
 
< 0.1%
114 9
0.1%
113 11
0.1%
105 4
 
< 0.1%

호
Text

Distinct554
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:23:34.672377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1816
Min length1

Characters and Unicode

Total characters31816
Distinct characters14
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

Unique295 ?
Unique (%)2.9%

Sample

1st row8102
2nd row7101
3rd row101
4th row301
5th row101
ValueCountFrequency (%)
101 2144
21.4%
201 1170
11.7%
102 1131
11.3%
8101 805
 
8.1%
301 647
 
6.5%
103 460
 
4.6%
401 332
 
3.3%
7101 271
 
2.7%
202 234
 
2.3%
501 195
 
1.9%
Other values (544) 2611
26.1%
2023-12-12T16:23:35.210994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12346
38.8%
0 9427
29.6%
2 3984
 
12.5%
3 1808
 
5.7%
8 1406
 
4.4%
4 965
 
3.0%
7 613
 
1.9%
5 590
 
1.9%
6 351
 
1.1%
9 314
 
1.0%
Other values (4) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31804
> 99.9%
Dash Punctuation 9
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12346
38.8%
0 9427
29.6%
2 3984
 
12.5%
3 1808
 
5.7%
8 1406
 
4.4%
4 965
 
3.0%
7 613
 
1.9%
5 590
 
1.9%
6 351
 
1.1%
9 314
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
n 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31813
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12346
38.8%
0 9427
29.6%
2 3984
 
12.5%
3 1808
 
5.7%
8 1406
 
4.4%
4 965
 
3.0%
7 613
 
1.9%
5 590
 
1.9%
6 351
 
1.1%
9 314
 
1.0%
Latin
ValueCountFrequency (%)
J 1
33.3%
a 1
33.3%
n 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12346
38.8%
0 9427
29.6%
2 3984
 
12.5%
3 1808
 
5.7%
8 1406
 
4.4%
4 965
 
3.0%
7 613
 
1.9%
5 590
 
1.9%
6 351
 
1.1%
9 314
 
1.0%
Other values (4) 12
 
< 0.1%

물건지
Text

Distinct9226
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T16:23:35.555180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length25.2357
Min length20

Characters and Unicode

Total characters252357
Distinct characters116
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

Unique8504 ?
Unique (%)85.0%

Sample

1st row대전광역시 중구 중촌동 17-2 1동 8102호
2nd row대전광역시 중구 안영동 280-4 1동 7101호
3rd row[ 계백로 1574 ] 0001동 0101호
4th row[ 보문로337번길 15 ] 0001동 0301호
5th row[ 계룡로815번길 6 ] 0001동 0101호
ValueCountFrequency (%)
15180
25.4%
0001동 6717
 
11.2%
중구 2410
 
4.0%
대전광역시 2410
 
4.0%
0101호 1666
 
2.8%
1동 1640
 
2.7%
0201호 942
 
1.6%
0102호 863
 
1.4%
8101호 805
 
1.3%
0000동 628
 
1.0%
Other values (3367) 26585
44.4%
2023-12-12T16:23:35.990132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49846
19.8%
0 41683
16.5%
1 29460
11.7%
동 12748
 
5.1%
호 10036
 
4.0%
2 8999
 
3.6%
] 7590
 
3.0%
[ 7590
 
3.0%
로 7419
 
2.9%
3 5777
 
2.3%
Other values (106) 71209
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 108942
43.2%
Other Letter 75342
29.9%
Space Separator 49846
19.8%
Close Punctuation 7590
 
3.0%
Open Punctuation 7590
 
3.0%
Dash Punctuation 3047
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12748
16.9%
호 10036
13.3%
로 7419
 
9.8%
대 5126
 
6.8%
길 3566
 
4.7%
중 3553
 
4.7%
번 3395
 
4.5%
전 2581
 
3.4%
구 2411
 
3.2%
광 2410
 
3.2%
Other values (92) 22097
29.3%
Decimal Number
ValueCountFrequency (%)
0 41683
38.3%
1 29460
27.0%
2 8999
 
8.3%
3 5777
 
5.3%
4 4627
 
4.2%
5 4383
 
4.0%
8 4017
 
3.7%
6 3678
 
3.4%
7 3548
 
3.3%
9 2770
 
2.5%
Space Separator
ValueCountFrequency (%)
49846
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7590
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7590
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177015
70.1%
Hangul 75342
29.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12748
16.9%
호 10036
13.3%
로 7419
 
9.8%
대 5126
 
6.8%
길 3566
 
4.7%
중 3553
 
4.7%
번 3395
 
4.5%
전 2581
 
3.4%
구 2411
 
3.2%
광 2410
 
3.2%
Other values (92) 22097
29.3%
Common
ValueCountFrequency (%)
49846
28.2%
0 41683
23.5%
1 29460
16.6%
2 8999
 
5.1%
] 7590
 
4.3%
[ 7590
 
4.3%
3 5777
 
3.3%
4 4627
 
2.6%
5 4383
 
2.5%
8 4017
 
2.3%
Other values (4) 13043
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177015
70.1%
Hangul 75342
29.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49846
28.2%
0 41683
23.5%
1 29460
16.6%
2 8999
 
5.1%
] 7590
 
4.3%
[ 7590
 
4.3%
3 5777
 
3.3%
4 4627
 
2.6%
5 4383
 
2.5%
8 4017
 
2.3%
Other values (4) 13043
 
7.4%
Hangul
ValueCountFrequency (%)
동 12748
16.9%
호 10036
13.3%
로 7419
 
9.8%
대 5126
 
6.8%
길 3566
 
4.7%
중 3553
 
4.7%
번 3395
 
4.5%
전 2581
 
3.4%
구 2411
 
3.2%
광 2410
 
3.2%
Other values (92) 22097
29.3%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct9359
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56889765
Minimum13460
Maximum6.3885504 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:36.134539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13460
5-th percentile526236.5
Q14643955
median19969940
Q355898498
95-th percentile2.006144 × 108
Maximum6.3885504 × 109
Range6.3885369 × 109
Interquartile range (IQR)51254542

Descriptive statistics

Standard deviation1.6144408 × 108
Coefficient of variation (CV)2.8378405
Kurtosis448.28615
Mean56889765
Median Absolute Deviation (MAD)17743400
Skewness16.447412
Sum5.6889765 × 1011
Variance2.6064191 × 1016
MonotonicityNot monotonic
2023-12-12T16:23:36.272690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68680 18
 
0.2%
68040 11
 
0.1%
79380 9
 
0.1%
3453630 9
 
0.1%
3437860 9
 
0.1%
28720 8
 
0.1%
5865060 7
 
0.1%
13787200 7
 
0.1%
13852830 7
 
0.1%
33480 7
 
0.1%
Other values (9349) 9908
99.1%
ValueCountFrequency (%)
13460 1
 
< 0.1%
14390 1
 
< 0.1%
15460 1
 
< 0.1%
16090 1
 
< 0.1%
28720 8
0.1%
31120 3
 
< 0.1%
33480 7
0.1%
35910 1
 
< 0.1%
36000 1
 
< 0.1%
41850 1
 
< 0.1%
ValueCountFrequency (%)
6388550400 1
< 0.1%
4871177890 1
< 0.1%
3849402900 1
< 0.1%
3803765760 1
< 0.1%
3439629440 1
< 0.1%
3423092760 1
< 0.1%
3117615310 1
< 0.1%
2884117230 1
< 0.1%
2142541630 1
< 0.1%
1878407090 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6686
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.91988
Minimum0.09
Maximum6654.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:23:36.424014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile5.9795
Q128.2275
median70
Q3148.5
95-th percentile438.8885
Maximum6654.74
Range6654.65
Interquartile range (IQR)120.2725

Descriptive statistics

Standard deviation275.55515
Coefficient of variation (CV)1.9979364
Kurtosis133.34066
Mean137.91988
Median Absolute Deviation (MAD)50.975
Skewness9.0686916
Sum1379198.8
Variance75930.639
MonotonicityNot monotonic
2023-12-12T16:23:36.823822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 102
 
1.0%
0.12 44
 
0.4%
27.0 37
 
0.4%
1.0 36
 
0.4%
12.0 25
 
0.2%
15.0 23
 
0.2%
12.27 21
 
0.2%
24.0 21
 
0.2%
9.0 20
 
0.2%
15.77 20
 
0.2%
Other values (6676) 9651
96.5%
ValueCountFrequency (%)
0.09 1
 
< 0.1%
0.12 44
0.4%
0.13 12
 
0.1%
0.14 15
 
0.1%
0.15 4
 
< 0.1%
0.16 4
 
< 0.1%
0.17 3
 
< 0.1%
0.18 1
 
< 0.1%
0.19 3
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
6654.74 1
< 0.1%
5615.4 1
< 0.1%
5544.88 1
< 0.1%
5518.88 1
< 0.1%
5030.13 1
< 0.1%
4593.92 1
< 0.1%
4588.45 1
< 0.1%
4537.25 1
< 0.1%
4134.17 2
< 0.1%
3699.19 1
< 0.1%

기준일자
Date

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-06-01 00:00:00
Maximum2019-06-01 00:00:00
2023-12-12T16:23:36.926766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:37.011276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2023-12-12T16:23:30.417897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.513674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.338168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.194976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.937304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.580000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.542489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.621495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.469679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.327408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.151291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.674692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.654616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.747088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.558352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.426178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.326907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.777734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.787261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.876784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.687996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.525881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.512323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.910912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:31.002026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.084586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.950043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.732093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.785265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.142105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:31.130912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.192781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.079318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.834723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.029334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.287477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:23:37.090150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도법정동특수지본번부번동시가표준액연면적기준일자
과세연도1.0000.3520.0000.1050.0420.0000.0000.0001.000
법정동0.3521.0000.0970.7600.2990.1340.0000.0310.352
특수지0.0000.0971.0000.0550.0000.0000.0000.0000.000
본번0.1050.7600.0551.0000.1820.1400.0270.0830.105
부번0.0420.2990.0000.1821.0000.0000.0000.0000.042
동0.0000.1340.0000.1400.0001.0000.0000.0000.000
시가표준액0.0000.0000.0000.0270.0000.0001.0000.9030.000
연면적0.0000.0310.0000.0830.0000.0000.9031.0000.000
기준일자1.0000.3520.0000.1050.0420.0000.0000.0001.000
2023-12-12T16:23:37.223876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세연도
특수지1.0000.000
과세연도0.0001.000
2023-12-12T16:23:37.403217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세연도특수지
법정동1.0000.3590.1220.069-0.028-0.0530.2260.067
본번0.3591.000-0.0180.0230.0310.0070.0620.042
부번0.122-0.0181.000-0.096-0.059-0.0450.0270.000
동0.0690.023-0.0961.000-0.015-0.0090.0220.027
시가표준액-0.0280.031-0.059-0.0151.0000.8800.0000.000
연면적-0.0530.007-0.045-0.0090.8801.0000.0000.000
과세연도0.2260.0620.0270.0220.0000.0001.0000.000
특수지0.0670.0420.0000.0270.0000.0000.0001.000

Missing values

2023-12-12T16:23:31.306000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:23:31.550267image/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

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
10612대전광역시중구3014020171040117218102대전광역시 중구 중촌동 17-2 1동 8102호30694460100.442017-06-01
41984대전광역시중구30140201711901280417101대전광역시 중구 안영동 280-4 1동 7101호571200084.02017-06-01
51240대전광역시중구3014020181150130571101[ 계백로 1574 ] 0001동 0101호582143066.992018-06-01
44831대전광역시중구30140201810201192221301[ 보문로337번길 15 ] 0001동 0301호30031560100.442018-06-01
17689대전광역시중구301402017112013511101[ 계룡로815번길 6 ] 0001동 0101호4640288095.012017-06-01
82842대전광역시중구30140201811701165151101[ 대둔산로 453 ] 0001동 0101호49470030202.582018-06-01
59722대전광역시중구3014020181050131611101[ 중교로 48 ] 0001동 0101호30567240154.382018-06-01
36525대전광역시중구30140201711601117518101[ 중앙로16번길 27-7 ] 0001동 8101호902192049.92017-06-01
48632대전광역시중구3014020181020111541102대전광역시 중구 선화동 115-4 1동 102호440818059.572018-06-01
64334대전광역시중구30140201811501335101145[ 대둔산로 518 ] 0001동 0145호735820026.132018-06-01
시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
74745대전광역시중구301402018107016051102대전광역시 중구 석교동 60-5 1동 102호118090024.12018-06-01
44212대전광역시중구3014020181040120141102[ 어덕마을로 146 ] 0001동 0102호50669190114.32018-06-01
29151대전광역시중구30140201710601368161101[ 보문로 17 ] 0001동 0101호307116014.032017-06-01
60691대전광역시중구3014020181050119611601[ 중앙로138번길 29 ] 0001동 0601호68487450133.272018-06-01
66031대전광역시중구3014020181150110018102대전광역시 중구 유천동 10 1동 8102호1158152040.782018-06-01
25554대전광역시중구3014020171110112621101대전광역시 중구 부사동 126-2 1동 101호359380051.342017-06-01
28411대전광역시중구30140201710601361191201[ 문창로 4 ] 0001동 0201호8655120120.212017-06-01
21037대전광역시중구3014020171150127211101[ 문화로105번길 30 ] 0001동 0101호5777046097.82017-06-01
27011대전광역시중구3014020171110114681301[ 보문로 81 ] 0001동 0301호274284360362.812017-06-01
61547대전광역시중구30140201810401413331104대전광역시 중구 중촌동 413-33 1동 104호243277020.292018-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
1대전광역시중구3014020171180110001101대전광역시 중구 사정동 100 1동 101호4060002.92017-06-013
0대전광역시중구30140201710301126011대전광역시 중구 목동 126 1동 1호500192032.482017-06-012