Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows50
Duplicate rows (%)0.5%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical5
Numeric8
Text1
DateTime1

Dataset

Description지방세 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공함으로써 납세자가 물건별 재산가액을 쉽게 확인할 수 있는 데이터입니다.
Author전라남도 영암군
URLhttps://www.data.go.kr/data/15079993/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 50 (0.5%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (90.4%)Imbalance
부번 has 2576 (25.8%) zerosZeros
has 449 (4.5%) zerosZeros
has 1356 (13.6%) zerosZeros

Reproduction

Analysis started2024-04-29 22:46:13.073865
Analysis finished2024-04-29 22:46:23.420904
Duration10.35 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
전라남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 10000
100.0%

Length

2024-04-30T07:46:23.490711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:46:23.578624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영암군 10000
100.0%

Length

2024-04-30T07:46:23.675296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:46:23.760008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영암군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
46830
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46830
2nd row46830
3rd row46830
4th row46830
5th row46830

Common Values

ValueCountFrequency (%)
46830 10000
100.0%

Length

2024-04-30T07:46:23.857095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:46:23.949356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46830 10000
100.0%

과세년도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
2647 
2018
2477 
2017
2452 
2020
2424 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2020
3rd row2017
4th row2018
5th row2020

Common Values

ValueCountFrequency (%)
2019 2647
26.5%
2018 2477
24.8%
2017 2452
24.5%
2020 2424
24.2%

Length

2024-04-30T07:46:24.039011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:46:24.129517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 2647
26.5%
2018 2477
24.8%
2017 2452
24.5%
2020 2424
24.2%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.7224
Minimum250
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:24.220748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median310
Q3350
95-th percentile380
Maximum390
Range140
Interquartile range (IQR)97

Descriptive statistics

Standard deviation50.858278
Coefficient of variation (CV)0.16800302
Kurtosis-1.5653143
Mean302.7224
Median Absolute Deviation (MAD)57
Skewness0.23189665
Sum3027224
Variance2586.5644
MonotonicityNot monotonic
2024-04-30T07:46:24.320748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
253 3508
35.1%
250 1212
 
12.1%
330 950
 
9.5%
340 650
 
6.5%
350 622
 
6.2%
360 606
 
6.1%
380 604
 
6.0%
320 552
 
5.5%
310 455
 
4.5%
370 443
 
4.4%
ValueCountFrequency (%)
250 1212
 
12.1%
253 3508
35.1%
310 455
 
4.5%
320 552
 
5.5%
330 950
 
9.5%
340 650
 
6.5%
350 622
 
6.2%
360 606
 
6.1%
370 443
 
4.4%
380 604
 
6.0%
ValueCountFrequency (%)
390 398
 
4.0%
380 604
 
6.0%
370 443
 
4.4%
360 606
 
6.1%
350 622
 
6.2%
340 650
 
6.5%
330 950
 
9.5%
320 552
 
5.5%
310 455
 
4.5%
253 3508
35.1%

법정리
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.483
Minimum21
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:24.420439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median27
Q329
95-th percentile31
Maximum36
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9950639
Coefficient of variation (CV)0.11309383
Kurtosis-0.3894791
Mean26.483
Median Absolute Deviation (MAD)2
Skewness0.01561122
Sum264830
Variance8.970408
MonotonicityNot monotonic
2024-04-30T07:46:24.530291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
27 1573
15.7%
26 1386
13.9%
29 1100
11.0%
30 1099
11.0%
24 846
8.5%
28 821
8.2%
25 728
7.3%
21 674
6.7%
23 661
6.6%
22 513
 
5.1%
Other values (6) 599
 
6.0%
ValueCountFrequency (%)
21 674
6.7%
22 513
 
5.1%
23 661
6.6%
24 846
8.5%
25 728
7.3%
26 1386
13.9%
27 1573
15.7%
28 821
8.2%
29 1100
11.0%
30 1099
11.0%
ValueCountFrequency (%)
36 12
 
0.1%
35 40
 
0.4%
34 75
 
0.8%
33 92
 
0.9%
32 120
 
1.2%
31 260
 
2.6%
30 1099
11.0%
29 1100
11.0%
28 821
8.2%
27 1573
15.7%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9800 
2
 
174
3
 
26

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 9800
98.0%
2 174
 
1.7%
3 26
 
0.3%

Length

2024-04-30T07:46:24.658511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:46:24.749162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9800
98.0%
2 174
 
1.7%
3 26
 
0.3%

본번
Real number (ℝ)

Distinct1287
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean647.2494
Minimum1
Maximum2466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:24.875026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1200
median475.5
Q3895.25
95-th percentile1713
Maximum2466
Range2465
Interquartile range (IQR)695.25

Descriptive statistics

Standard deviation571.31093
Coefficient of variation (CV)0.88267511
Kurtosis0.11251365
Mean647.2494
Median Absolute Deviation (MAD)319.5
Skewness1.0487624
Sum6472494
Variance326396.18
MonotonicityNot monotonic
2024-04-30T07:46:25.014738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 172
 
1.7%
2172 141
 
1.4%
1685 96
 
1.0%
280 75
 
0.8%
1689 73
 
0.7%
1713 73
 
0.7%
41 62
 
0.6%
341 60
 
0.6%
342 51
 
0.5%
602 49
 
0.5%
Other values (1277) 9148
91.5%
ValueCountFrequency (%)
1 33
0.3%
2 25
0.2%
3 30
0.3%
4 33
0.3%
5 11
 
0.1%
6 22
0.2%
7 8
 
0.1%
8 12
 
0.1%
9 24
0.2%
10 12
 
0.1%
ValueCountFrequency (%)
2466 4
 
< 0.1%
2184 2
 
< 0.1%
2183 6
 
0.1%
2179 4
 
< 0.1%
2178 22
 
0.2%
2177 11
 
0.1%
2176 5
 
0.1%
2175 22
 
0.2%
2174 2
 
< 0.1%
2172 141
1.4%

부번
Real number (ℝ)

ZEROS 

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5948
Minimum0
Maximum146
Zeros2576
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:25.146832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile30
Maximum146
Range146
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.106777
Coefficient of variation (CV)1.9874411
Kurtosis24.45426
Mean6.5948
Median Absolute Deviation (MAD)2
Skewness4.2986086
Sum65948
Variance171.78759
MonotonicityNot monotonic
2024-04-30T07:46:25.294352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2576
25.8%
1 1964
19.6%
2 938
 
9.4%
3 614
 
6.1%
4 539
 
5.4%
5 427
 
4.3%
6 370
 
3.7%
7 298
 
3.0%
9 218
 
2.2%
8 217
 
2.2%
Other values (95) 1839
18.4%
ValueCountFrequency (%)
0 2576
25.8%
1 1964
19.6%
2 938
 
9.4%
3 614
 
6.1%
4 539
 
5.4%
5 427
 
4.3%
6 370
 
3.7%
7 298
 
3.0%
8 217
 
2.2%
9 218
 
2.2%
ValueCountFrequency (%)
146 4
< 0.1%
136 1
 
< 0.1%
134 2
< 0.1%
131 2
< 0.1%
126 1
 
< 0.1%
119 1
 
< 0.1%
109 3
< 0.1%
108 1
 
< 0.1%
106 2
< 0.1%
103 2
< 0.1%


Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681.4569
Minimum0
Maximum9999
Zeros449
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:25.428189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile9999
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2510.0062
Coefficient of variation (CV)3.6832941
Kurtosis9.7862904
Mean681.4569
Median Absolute Deviation (MAD)0
Skewness3.4315268
Sum6814569
Variance6300130.9
MonotonicityNot monotonic
2024-04-30T07:46:25.767172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7629
76.3%
2 685
 
6.9%
9999 656
 
6.6%
0 449
 
4.5%
3 205
 
2.1%
4 72
 
0.7%
6 43
 
0.4%
5 38
 
0.4%
101 16
 
0.2%
107 15
 
0.1%
Other values (50) 192
 
1.9%
ValueCountFrequency (%)
0 449
 
4.5%
1 7629
76.3%
2 685
 
6.9%
3 205
 
2.1%
4 72
 
0.7%
5 38
 
0.4%
6 43
 
0.4%
7 15
 
0.1%
8 13
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
9999 656
6.6%
9998 6
 
0.1%
9901 1
 
< 0.1%
9026 1
 
< 0.1%
9025 2
 
< 0.1%
9024 1
 
< 0.1%
9021 1
 
< 0.1%
9020 1
 
< 0.1%
9014 1
 
< 0.1%
9001 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct139
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.7645
Minimum0
Maximum9011
Zeros1356
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:25.893238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median101
Q3102
95-th percentile301
Maximum9011
Range9011
Interquartile range (IQR)100

Descriptive statistics

Standard deviation888.92458
Coefficient of variation (CV)4.5876545
Kurtosis76.081203
Mean193.7645
Median Absolute Deviation (MAD)1
Skewness8.7649894
Sum1937645
Variance790186.9
MonotonicityNot monotonic
2024-04-30T07:46:26.024358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 3933
39.3%
0 1356
 
13.6%
1 1110
 
11.1%
102 1074
 
10.7%
201 602
 
6.0%
103 426
 
4.3%
104 186
 
1.9%
301 161
 
1.6%
2 147
 
1.5%
105 111
 
1.1%
Other values (129) 894
 
8.9%
ValueCountFrequency (%)
0 1356
13.6%
1 1110
11.1%
2 147
 
1.5%
3 64
 
0.6%
4 36
 
0.4%
5 13
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 9
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9011 2
< 0.1%
9009 1
 
< 0.1%
9008 1
 
< 0.1%
9004 2
< 0.1%
9003 2
< 0.1%
9002 2
< 0.1%
9001 1
 
< 0.1%
8202 1
 
< 0.1%
8201 4
< 0.1%
8105 2
< 0.1%
Distinct7298
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T07:46:26.336634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length27.5951
Min length19

Characters and Unicode

Total characters275951
Distinct characters246
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

Unique5534 ?
Unique (%)55.3%

Sample

1st row전라남도 영암군 신북면 갈곡리 38-65 1동 8101호
2nd row전라남도 영암군 삼호읍 용당리 710 1동 901호
3rd row전라남도 영암군 삼호읍 용앙리 392-19 2동 101호
4th row전라남도 영암군 학산면 학계리 391-2 1동
5th row전라남도 영암군 삼호읍 용당리 2172-1 1동 104호
ValueCountFrequency (%)
전라남도 7358
 
11.1%
영암군 7358
 
11.1%
1동 5438
 
8.2%
5284
 
8.0%
101호 2716
 
4.1%
삼호읍 2609
 
3.9%
0001동 2191
 
3.3%
0101호 1217
 
1.8%
1호 764
 
1.2%
102호 764
 
1.2%
Other values (4231) 30379
46.0%
2024-04-30T07:46:26.798430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56078
20.3%
1 28658
 
10.4%
0 21411
 
7.8%
12529
 
4.5%
10537
 
3.8%
8519
 
3.1%
8417
 
3.1%
8100
 
2.9%
7921
 
2.9%
7859
 
2.8%
Other values (236) 105922
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124230
45.0%
Decimal Number 84022
30.4%
Space Separator 56078
20.3%
Dash Punctuation 6337
 
2.3%
Open Punctuation 2642
 
1.0%
Close Punctuation 2642
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12529
 
10.1%
10537
 
8.5%
8519
 
6.9%
8417
 
6.8%
8100
 
6.5%
7921
 
6.4%
7859
 
6.3%
7580
 
6.1%
7418
 
6.0%
7365
 
5.9%
Other values (222) 37985
30.6%
Decimal Number
ValueCountFrequency (%)
1 28658
34.1%
0 21411
25.5%
2 7724
 
9.2%
9 5148
 
6.1%
3 4844
 
5.8%
4 3603
 
4.3%
6 3593
 
4.3%
7 3205
 
3.8%
5 3049
 
3.6%
8 2787
 
3.3%
Space Separator
ValueCountFrequency (%)
56078
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6337
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2642
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151721
55.0%
Hangul 124230
45.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12529
 
10.1%
10537
 
8.5%
8519
 
6.9%
8417
 
6.8%
8100
 
6.5%
7921
 
6.4%
7859
 
6.3%
7580
 
6.1%
7418
 
6.0%
7365
 
5.9%
Other values (222) 37985
30.6%
Common
ValueCountFrequency (%)
56078
37.0%
1 28658
18.9%
0 21411
 
14.1%
2 7724
 
5.1%
- 6337
 
4.2%
9 5148
 
3.4%
3 4844
 
3.2%
4 3603
 
2.4%
6 3593
 
2.4%
7 3205
 
2.1%
Other values (4) 11120
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151721
55.0%
Hangul 124230
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56078
37.0%
1 28658
18.9%
0 21411
 
14.1%
2 7724
 
5.1%
- 6337
 
4.2%
9 5148
 
3.4%
3 4844
 
3.2%
4 3603
 
2.4%
6 3593
 
2.4%
7 3205
 
2.1%
Other values (4) 11120
 
7.3%
Hangul
ValueCountFrequency (%)
12529
 
10.1%
10537
 
8.5%
8519
 
6.9%
8417
 
6.8%
8100
 
6.5%
7921
 
6.4%
7859
 
6.3%
7580
 
6.1%
7418
 
6.0%
7365
 
5.9%
Other values (222) 37985
30.6%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8019
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78815956
Minimum17820
Maximum1.3116094 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:26.951849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17820
5-th percentile474240
Q11907795
median9573000
Q348921928
95-th percentile2.8714347 × 108
Maximum1.3116094 × 1010
Range1.3116076 × 1010
Interquartile range (IQR)47014132

Descriptive statistics

Standard deviation3.3672063 × 108
Coefficient of variation (CV)4.2722394
Kurtosis346.90847
Mean78815956
Median Absolute Deviation (MAD)8828805
Skewness14.726128
Sum7.8815956 × 1011
Variance1.1338078 × 1017
MonotonicityNot monotonic
2024-04-30T07:46:27.122406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
830000 25
 
0.2%
850000 21
 
0.2%
740000 20
 
0.2%
720000 20
 
0.2%
800000 19
 
0.2%
700000 19
 
0.2%
790000 18
 
0.2%
1584000 17
 
0.2%
810000 17
 
0.2%
760000 16
 
0.2%
Other values (8009) 9808
98.1%
ValueCountFrequency (%)
17820 1
< 0.1%
36000 1
< 0.1%
50000 1
< 0.1%
51480 1
< 0.1%
52200 1
< 0.1%
54000 1
< 0.1%
55080 1
< 0.1%
56700 1
< 0.1%
58000 1
< 0.1%
59130 1
< 0.1%
ValueCountFrequency (%)
13116093600 1
< 0.1%
6637724300 1
< 0.1%
6604416000 1
< 0.1%
6466267970 1
< 0.1%
6282676950 1
< 0.1%
6042134900 1
< 0.1%
5553930560 1
< 0.1%
5140003550 1
< 0.1%
5073278540 1
< 0.1%
4952085210 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4514
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.272
Minimum0.73
Maximum33411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T07:46:27.249198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.73
5-th percentile10
Q136.4837
median100
Q3246.2
95-th percentile1169.411
Maximum33411
Range33410.27
Interquartile range (IQR)209.7163

Descriptive statistics

Standard deviation1074.2559
Coefficient of variation (CV)3.3437582
Kurtosis239.61538
Mean321.272
Median Absolute Deviation (MAD)79.2
Skewness12.74738
Sum3212720
Variance1154025.7
MonotonicityNot monotonic
2024-04-30T07:46:27.369189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 390
 
3.9%
18.0 224
 
2.2%
16.5 67
 
0.7%
192.0 59
 
0.6%
27.0 49
 
0.5%
66.0 48
 
0.5%
36.0 42
 
0.4%
160.0 41
 
0.4%
60.0 40
 
0.4%
24.0 40
 
0.4%
Other values (4504) 9000
90.0%
ValueCountFrequency (%)
0.73 1
 
< 0.1%
1.17 1
 
< 0.1%
1.5 1
 
< 0.1%
2.0 1
 
< 0.1%
2.2 1
 
< 0.1%
2.28 1
 
< 0.1%
2.3 1
 
< 0.1%
2.4 2
< 0.1%
2.6 1
 
< 0.1%
2.64 4
< 0.1%
ValueCountFrequency (%)
33411.0 1
 
< 0.1%
28650.27 1
 
< 0.1%
22474.0 1
 
< 0.1%
19260.0 1
 
< 0.1%
17640.0 1
 
< 0.1%
16953.39 3
< 0.1%
16801.23 1
 
< 0.1%
15495.49 1
 
< 0.1%
15480.475 1
 
< 0.1%
15317.87 1
 
< 0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2024-04-30T07:46:27.467132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:27.543187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-30T07:46:22.300619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:16.773669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.605236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.388822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.137431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.913062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.850207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.548941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.391531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:16.922999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.696885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.489832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.230028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.000690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.948402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.640195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.495580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.018955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.803383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.606575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.320638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.087384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.046391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.746460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.588399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.120477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.895623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.696307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.410749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.170875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.125456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.833784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.692259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.227290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.001386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.777647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.508516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.265908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.210843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.929532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.794851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.312048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.089609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.859540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.605908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.348791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.290299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.013467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.876368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.400989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.173572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.945696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.702307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.439624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.370959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.108306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.969425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:17.501068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:18.286809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.047379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:19.814051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:20.547783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:21.459117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:46:22.201302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:46:27.620527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적
과세년도1.0000.0780.0290.0130.0230.0000.0280.0120.0000.000
법정동0.0781.0000.4290.1080.4760.1410.0600.0780.1180.065
법정리0.0290.4291.0000.1560.6260.2000.1140.0920.0580.067
특수지0.0130.1080.1561.0000.4140.0000.0160.0000.0000.000
본번0.0230.4760.6260.4141.0000.2440.0840.0760.1040.098
부번0.0000.1410.2000.0000.2441.0000.1440.0000.0000.000
0.0280.0600.1140.0160.0840.1441.0000.0390.0000.000
0.0120.0780.0920.0000.0760.0000.0391.0000.0000.000
시가표준액0.0000.1180.0580.0000.1040.0000.0000.0001.0000.822
연면적0.0000.0650.0670.0000.0980.0000.0000.0000.8221.000
2024-04-30T07:46:27.781440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지
과세년도1.0000.012
특수지0.0121.000
2024-04-30T07:46:27.908258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.000-0.118-0.108-0.1070.000-0.209-0.289-0.0320.0530.072
법정리-0.1181.0000.1840.089-0.0340.0690.1140.0400.0180.075
본번-0.1080.1841.0000.052-0.0110.0240.1320.0860.0140.274
부번-0.1070.0890.0521.000-0.0970.0440.1140.0610.0000.000
0.000-0.034-0.011-0.0971.0000.006-0.131-0.2190.0220.012
-0.2090.0690.0240.0440.0061.0000.2870.0010.0100.000
시가표준액-0.2890.1140.1320.114-0.1310.2871.0000.6540.0000.000
연면적-0.0320.0400.0860.061-0.2190.0010.6541.0000.0000.000
과세년도0.0530.0180.0140.0000.0220.0100.0000.0001.0000.012
특수지0.0720.0750.2740.0000.0120.0000.0000.0000.0121.000

Missing values

2024-04-30T07:46:23.108301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:46:23.301956image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터기준일자
5567전라남도영암군468302017330241386518101전라남도 영암군 신북면 갈곡리 38-65 1동 8101호2754400088.02022-12-31
69832전라남도영암군46830202025329171001901전라남도 영암군 삼호읍 용당리 710 1동 901호243969170412.80742022-12-31
13079전라남도영암군468302017253261392192101전라남도 영암군 삼호읍 용앙리 392-19 2동 101호3313350099.52022-12-31
33364전라남도영암군468302018380241391210전라남도 영암군 학산면 학계리 391-2 1동1080000120.02022-12-31
77465전라남도영암군468302020253291217211104전라남도 영암군 삼호읍 용당리 2172-1 1동 104호4693500003129.02022-12-31
17312전라남도영암군468302017390261209101101[ 흑석로 1875 ] 0001동 0101호2016300085.82022-12-31
72570전라남도영암군468302020310281254571201전라남도 영암군 덕진면 금강리 254-57 1동 201호1134600113.462022-12-31
36594전라남도영암군468302018370211312110전라남도 영암군 서호면 몽해리 312-1 1동1296000324.02022-12-31
73446전라남도영암군4683020202532611801111[ 삼호중앙로 204-10 ] 0001동 0001호6773527091.462022-12-31
77905전라남도영암군468302020253271168990102전라남도 영암군 삼호읍 난전리 1689-9 102호107065530305.032022-12-31
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터기준일자
38424전라남도영암군4683020183502111921102[ 토말로 118 ] 0001동 0102호165510030.652022-12-31
35249전라남도영암군46830201839024123911101전라남도 영암군 미암면 남산리 239-1 1동 101호12224960120.82022-12-31
56837전라남도영암군468302019253231150110[ 매자리길 18-8 ] 0001동 0000호529875033.752022-12-31
80185전라남도영암군46830202025023177012전라남도 영암군 영암읍 서남리 77 1동 2호1423642015.752022-12-31
47430전라남도영암군4683020192502317221011[ 서남역로 28 ] 0101동 0001호101571340143.262022-12-31
60355전라남도영암군46830201925328199831201전라남도 영암군 삼호읍 삼포리 998-3 1동 201호33384000156.02022-12-31
48082전라남도영암군468302019250361246012전라남도 영암군 영암읍 한대리 246 1동 2호230400096.02022-12-31
38896전라남도영암군468302018350251157611[ 덕화만수로 100 ] 0001동 0001호19998000198.02022-12-31
85741전라남도영암군468302020350261834310전라남도 영암군 도포면 영호리 834-3 1동702000175.52022-12-31
30901전라남도영암군46830201825026134811102[ 낭주로 199 ] 0001동 0102호3192320110.082022-12-31

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적데이터기준일자# duplicates
42전라남도영암군4683020202502413141101전라남도 영암군 영암읍 동무리 31-4 1동 101호286200018.02022-12-3110
1전라남도영암군4683020172502413141101전라남도 영암군 영암읍 동무리 31-4 1동 101호333000018.02022-12-316
28전라남도영암군4683020192502413141101전라남도 영암군 영암읍 동무리 31-4 1동 101호295200018.02022-12-315
43전라남도영암군4683020202532711685101101전라남도 영암군 삼호읍 난전리 1685-10 1동 101호98627100344.852022-12-315
19전라남도영암군4683020182502413141101전라남도 영암군 영암읍 동무리 31-4 1동 101호311400018.02022-12-314
21전라남도영암군4683020182532711685101101전라남도 영암군 삼호읍 난전리 1685-10 1동 101호100351350344.852022-12-314
34전라남도영암군4683020192532711685101101전라남도 영암군 삼호읍 난전리 1685-10 1동 101호98627100344.852022-12-314
15전라남도영암군468302017350231405110전라남도 영암군 도포면 덕화리 405-1 1동2082240115.682022-12-313
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