Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows32
Duplicate rows (%)0.3%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반 건축물에 대한 물건지 별 시가표준액 2017~2020년 정보 (시도명, 시군구명, 자치단체코드, 과세년도, 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적 등)
Author충청남도 공주시
URLhttps://www.data.go.kr/data/15080846/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 32 (0.3%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 과세년도High correlation
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (90.7%)Imbalance
법정리 has 3831 (38.3%) zerosZeros
부번 has 2799 (28.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:40:15.870597
Analysis finished2023-12-12 03:40:25.541288
Duration9.67 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
충청남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 10000
100.0%

Length

2023-12-12T12:40:25.629521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:25.779167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공주시 10000
100.0%

Length

2023-12-12T12:40:25.915407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:26.044983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공주시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44150
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44150
2nd row44150
3rd row44150
4th row44150
5th row44150

Common Values

ValueCountFrequency (%)
44150 10000
100.0%

Length

2023-12-12T12:40:26.183214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:26.326801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44150 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3439 
2018
3342 
2017
3208 
2020
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2019 3439
34.4%
2018 3342
33.4%
2017 3208
32.1%
2020 11
 
0.1%

Length

2023-12-12T12:40:26.554809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:26.718193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3439
34.4%
2018 3342
33.4%
2017 3208
32.1%
2020 11
 
0.1%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.8176
Minimum101
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:26.904550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile104
Q1120
median310
Q3360
95-th percentile390
Maximum400
Range299
Interquartile range (IQR)240

Descriptive statistics

Standard deviation115.20095
Coefficient of variation (CV)0.45387296
Kurtosis-1.7108303
Mean253.8176
Median Absolute Deviation (MAD)70
Skewness-0.254623
Sum2538176
Variance13271.258
MonotonicityNot monotonic
2023-12-12T12:40:27.126501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
120 1187
11.9%
340 818
 
8.2%
370 796
 
8.0%
380 792
 
7.9%
250 783
 
7.8%
330 707
 
7.1%
320 590
 
5.9%
310 475
 
4.8%
390 464
 
4.6%
360 422
 
4.2%
Other values (27) 2966
29.7%
ValueCountFrequency (%)
101 87
 
0.9%
102 71
 
0.7%
103 57
 
0.6%
104 308
3.1%
105 342
3.4%
106 94
 
0.9%
107 134
 
1.3%
108 187
1.9%
109 195
1.9%
110 132
 
1.3%
ValueCountFrequency (%)
400 322
 
3.2%
390 464
4.6%
380 792
7.9%
370 796
8.0%
360 422
4.2%
340 818
8.2%
330 707
7.1%
320 590
5.9%
310 475
4.8%
250 783
7.8%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.3685
Minimum0
Maximum43
Zeros3831
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:27.318575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q329
95-th percentile36
Maximum43
Range43
Interquartile range (IQR)29

Descriptive statistics

Standard deviation14.289567
Coefficient of variation (CV)0.82272891
Kurtosis-1.6206832
Mean17.3685
Median Absolute Deviation (MAD)10
Skewness-0.22390896
Sum173685
Variance204.19173
MonotonicityNot monotonic
2023-12-12T12:40:27.489025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3831
38.3%
21 716
 
7.2%
23 579
 
5.8%
33 485
 
4.9%
24 432
 
4.3%
29 422
 
4.2%
27 388
 
3.9%
25 373
 
3.7%
26 373
 
3.7%
28 345
 
3.5%
Other values (14) 2056
20.6%
ValueCountFrequency (%)
0 3831
38.3%
21 716
 
7.2%
22 210
 
2.1%
23 579
 
5.8%
24 432
 
4.3%
25 373
 
3.7%
26 373
 
3.7%
27 388
 
3.9%
28 345
 
3.5%
29 422
 
4.2%
ValueCountFrequency (%)
43 26
 
0.3%
42 11
 
0.1%
41 20
 
0.2%
40 23
 
0.2%
39 40
 
0.4%
38 159
1.6%
37 141
1.4%
36 171
1.7%
35 315
3.1%
34 174
1.7%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9882 
2
 
118

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 9882
98.8%
2 118
 
1.2%

Length

2023-12-12T12:40:27.664545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:27.814147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9882
98.8%
2 118
 
1.2%

본번
Real number (ℝ)

Distinct833
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.6561
Minimum1
Maximum1201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:27.986144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1137
median267
Q3487
95-th percentile728
Maximum1201
Range1200
Interquartile range (IQR)350

Descriptive statistics

Standard deviation229.40334
Coefficient of variation (CV)0.72675086
Kurtosis-0.39033882
Mean315.6561
Median Absolute Deviation (MAD)158
Skewness0.66206183
Sum3156561
Variance52625.894
MonotonicityNot monotonic
2023-12-12T12:40:28.221415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 88
 
0.9%
182 83
 
0.8%
1 83
 
0.8%
186 76
 
0.8%
360 66
 
0.7%
5 65
 
0.7%
725 63
 
0.6%
608 56
 
0.6%
22 53
 
0.5%
3 52
 
0.5%
Other values (823) 9315
93.2%
ValueCountFrequency (%)
1 83
0.8%
2 36
0.4%
3 52
0.5%
4 14
 
0.1%
5 65
0.7%
6 18
 
0.2%
7 15
 
0.1%
8 19
 
0.2%
9 13
 
0.1%
10 30
 
0.3%
ValueCountFrequency (%)
1201 1
 
< 0.1%
1117 1
 
< 0.1%
1025 4
< 0.1%
1016 3
 
< 0.1%
1007 1
 
< 0.1%
999 1
 
< 0.1%
992 2
 
< 0.1%
963 2
 
< 0.1%
962 7
0.1%
961 8
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6748
Minimum0
Maximum314
Zeros2799
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:28.446034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile24
Maximum314
Range314
Interquartile range (IQR)6

Descriptive statistics

Standard deviation18.325726
Coefficient of variation (CV)2.7455094
Kurtosis66.9481
Mean6.6748
Median Absolute Deviation (MAD)2
Skewness7.2517582
Sum66748
Variance335.83223
MonotonicityNot monotonic
2023-12-12T12:40:28.674991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2799
28.0%
1 1865
18.6%
2 1027
 
10.3%
3 766
 
7.7%
4 504
 
5.0%
5 390
 
3.9%
6 351
 
3.5%
7 274
 
2.7%
8 207
 
2.1%
10 199
 
2.0%
Other values (140) 1618
16.2%
ValueCountFrequency (%)
0 2799
28.0%
1 1865
18.6%
2 1027
 
10.3%
3 766
 
7.7%
4 504
 
5.0%
5 390
 
3.9%
6 351
 
3.5%
7 274
 
2.7%
8 207
 
2.1%
9 184
 
1.8%
ValueCountFrequency (%)
314 1
< 0.1%
279 1
< 0.1%
273 1
< 0.1%
222 1
< 0.1%
220 2
< 0.1%
210 1
< 0.1%
209 1
< 0.1%
208 1
< 0.1%
206 2
< 0.1%
203 1
< 0.1%


Real number (ℝ)

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9177
Minimum0
Maximum9301
Zeros94
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:28.912367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9301
Range9301
Interquartile range (IQR)0

Descriptive statistics

Standard deviation437.02839
Coefficient of variation (CV)14.607687
Kurtosis332.76866
Mean29.9177
Median Absolute Deviation (MAD)0
Skewness17.862184
Sum299177
Variance190993.81
MonotonicityNot monotonic
2023-12-12T12:40:29.109868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 8621
86.2%
2 706
 
7.1%
3 189
 
1.9%
0 94
 
0.9%
4 89
 
0.9%
5 41
 
0.4%
7 36
 
0.4%
6 35
 
0.4%
1000 20
 
0.2%
14 18
 
0.2%
Other values (36) 151
 
1.5%
ValueCountFrequency (%)
0 94
 
0.9%
1 8621
86.2%
2 706
 
7.1%
3 189
 
1.9%
4 89
 
0.9%
5 41
 
0.4%
6 35
 
0.4%
7 36
 
0.4%
8 12
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
9301 4
 
< 0.1%
9300 1
 
< 0.1%
9001 2
 
< 0.1%
9000 5
0.1%
8000 2
 
< 0.1%
7000 12
0.1%
6000 1
 
< 0.1%
5002 1
 
< 0.1%
5001 2
 
< 0.1%
4001 2
 
< 0.1%


Text

Distinct156
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:40:29.352097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.7002
Min length1

Characters and Unicode

Total characters27002
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

Unique78 ?
Unique (%)0.8%

Sample

1st row8101
2nd row301
3rd row1
4th row4
5th row2
ValueCountFrequency (%)
101 4642
46.4%
1 986
 
9.9%
201 955
 
9.6%
102 922
 
9.2%
2 370
 
3.7%
301 340
 
3.4%
8101 286
 
2.9%
103 250
 
2.5%
202 140
 
1.4%
3 134
 
1.3%
Other values (146) 975
 
9.8%
2023-12-12T12:40:29.816147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13911
51.5%
0 8259
30.6%
2 2805
 
10.4%
3 883
 
3.3%
8 384
 
1.4%
4 353
 
1.3%
5 189
 
0.7%
6 112
 
0.4%
7 64
 
0.2%
9 40
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27000
> 99.9%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13911
51.5%
0 8259
30.6%
2 2805
 
10.4%
3 883
 
3.3%
8 384
 
1.4%
4 353
 
1.3%
5 189
 
0.7%
6 112
 
0.4%
7 64
 
0.2%
9 40
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13911
51.5%
0 8259
30.6%
2 2805
 
10.4%
3 883
 
3.3%
8 384
 
1.4%
4 353
 
1.3%
5 189
 
0.7%
6 112
 
0.4%
7 64
 
0.2%
9 40
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13911
51.5%
0 8259
30.6%
2 2805
 
10.4%
3 883
 
3.3%
8 384
 
1.4%
4 353
 
1.3%
5 189
 
0.7%
6 112
 
0.4%
7 64
 
0.2%
9 40
 
0.1%
Distinct7999
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:40:30.339696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length26.5563
Min length18

Characters and Unicode

Total characters265563
Distinct characters308
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

Unique6604 ?
Unique (%)66.0%

Sample

1st row충청남도 공주시 정안면 광정리 164 1동 8101호
2nd row[ 평목길 96 ] 0001동 0301호
3rd row[ 효부길 32 ] 0001동 0001호
4th row[ 용당길 12 ] 0001동 0004호
5th row충청남도 공주시 금흥동 125-2 1동 2호
ValueCountFrequency (%)
7044
 
10.9%
공주시 6478
 
10.0%
충청남도 6478
 
10.0%
1동 5399
 
8.4%
0001동 3222
 
5.0%
101호 3188
 
4.9%
0101호 1454
 
2.3%
1호 668
 
1.0%
우성면 622
 
1.0%
102호 588
 
0.9%
Other values (4271) 29401
45.6%
2023-12-12T12:40:31.067978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54545
20.5%
1 30143
 
11.4%
0 25388
 
9.6%
12371
 
4.7%
10061
 
3.8%
2 8797
 
3.3%
6696
 
2.5%
6647
 
2.5%
6605
 
2.5%
6596
 
2.5%
Other values (298) 97714
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111726
42.1%
Decimal Number 86446
32.6%
Space Separator 54545
20.5%
Dash Punctuation 5802
 
2.2%
Open Punctuation 3522
 
1.3%
Close Punctuation 3522
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12371
 
11.1%
10061
 
9.0%
6696
 
6.0%
6647
 
5.9%
6605
 
5.9%
6596
 
5.9%
6592
 
5.9%
6589
 
5.9%
6484
 
5.8%
4613
 
4.1%
Other values (284) 38472
34.4%
Decimal Number
ValueCountFrequency (%)
1 30143
34.9%
0 25388
29.4%
2 8797
 
10.2%
3 4903
 
5.7%
4 3642
 
4.2%
5 3346
 
3.9%
6 2902
 
3.4%
8 2725
 
3.2%
7 2653
 
3.1%
9 1947
 
2.3%
Space Separator
ValueCountFrequency (%)
54545
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5802
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3522
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153837
57.9%
Hangul 111726
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12371
 
11.1%
10061
 
9.0%
6696
 
6.0%
6647
 
5.9%
6605
 
5.9%
6596
 
5.9%
6592
 
5.9%
6589
 
5.9%
6484
 
5.8%
4613
 
4.1%
Other values (284) 38472
34.4%
Common
ValueCountFrequency (%)
54545
35.5%
1 30143
19.6%
0 25388
16.5%
2 8797
 
5.7%
- 5802
 
3.8%
3 4903
 
3.2%
4 3642
 
2.4%
[ 3522
 
2.3%
] 3522
 
2.3%
5 3346
 
2.2%
Other values (4) 10227
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153837
57.9%
Hangul 111726
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54545
35.5%
1 30143
19.6%
0 25388
16.5%
2 8797
 
5.7%
- 5802
 
3.8%
3 4903
 
3.2%
4 3642
 
2.4%
[ 3522
 
2.3%
] 3522
 
2.3%
5 3346
 
2.2%
Other values (4) 10227
 
6.6%
Hangul
ValueCountFrequency (%)
12371
 
11.1%
10061
 
9.0%
6696
 
6.0%
6647
 
5.9%
6605
 
5.9%
6596
 
5.9%
6592
 
5.9%
6589
 
5.9%
6484
 
5.8%
4613
 
4.1%
Other values (284) 38472
34.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8811
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58466719
Minimum19200
Maximum4.2523372 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:31.260455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19200
5-th percentile470352
Q13276000
median14885325
Q354997920
95-th percentile2.4797173 × 108
Maximum4.2523372 × 109
Range4.252318 × 109
Interquartile range (IQR)51721920

Descriptive statistics

Standard deviation1.436782 × 108
Coefficient of variation (CV)2.4574357
Kurtosis149.4944
Mean58466719
Median Absolute Deviation (MAD)13765885
Skewness9.0696113
Sum5.8466719 × 1011
Variance2.0643425 × 1016
MonotonicityNot monotonic
2023-12-12T12:40:31.420518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980000 22
 
0.2%
936000 17
 
0.2%
738000 14
 
0.1%
900000 12
 
0.1%
756000 11
 
0.1%
720000 10
 
0.1%
1320000 10
 
0.1%
828000 9
 
0.1%
324000 9
 
0.1%
846000 9
 
0.1%
Other values (8801) 9877
98.8%
ValueCountFrequency (%)
19200 1
< 0.1%
21600 2
< 0.1%
27600 1
< 0.1%
33600 1
< 0.1%
36400 1
< 0.1%
39600 1
< 0.1%
43200 1
< 0.1%
44640 1
< 0.1%
51030 1
< 0.1%
51480 1
< 0.1%
ValueCountFrequency (%)
4252337150 1
< 0.1%
3228929510 1
< 0.1%
2629197600 1
< 0.1%
2344890990 1
< 0.1%
2065080040 1
< 0.1%
1965578770 1
< 0.1%
1853118690 1
< 0.1%
1843342530 1
< 0.1%
1789042500 1
< 0.1%
1750239030 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5281
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.25783
Minimum1
Maximum10382.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:40:31.586736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.9795
Q143.545
median102.24
Q3199.26
95-th percentile757.044
Maximum10382.41
Range10381.41
Interquartile range (IQR)155.715

Descriptive statistics

Standard deviation346.04593
Coefficient of variation (CV)1.7366742
Kurtosis119.13965
Mean199.25783
Median Absolute Deviation (MAD)71.145
Skewness7.5852294
Sum1992578.3
Variance119747.79
MonotonicityNot monotonic
2023-12-12T12:40:31.753591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 270
 
2.7%
198.0 65
 
0.7%
50.0 47
 
0.5%
100.0 46
 
0.5%
330.0 37
 
0.4%
66.0 37
 
0.4%
27.0 36
 
0.4%
48.0 35
 
0.4%
200.0 35
 
0.4%
33.0 32
 
0.3%
Other values (5271) 9360
93.6%
ValueCountFrequency (%)
1.0 3
< 0.1%
1.3 1
 
< 0.1%
1.32 1
 
< 0.1%
1.38 2
< 0.1%
1.4 1
 
< 0.1%
1.8 1
 
< 0.1%
2.0 2
< 0.1%
2.04 1
 
< 0.1%
2.08 2
< 0.1%
2.4 2
< 0.1%
ValueCountFrequency (%)
10382.41 1
< 0.1%
6219.87 1
< 0.1%
5416.99 1
< 0.1%
4880.0 1
< 0.1%
4337.64 1
< 0.1%
4158.42 1
< 0.1%
4095.16 1
< 0.1%
4087.1 1
< 0.1%
4028.86 1
< 0.1%
3941.0 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-06-01
3439 
2018-06-01
3342 
2017-06-01
3208 
2020-06-01
 
11

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-06-01
2nd row2017-06-01
3rd row2018-06-01
4th row2018-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 3439
34.4%
2018-06-01 3342
33.4%
2017-06-01 3208
32.1%
2020-06-01 11
 
0.1%

Length

2023-12-12T12:40:32.282295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:40:32.443347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 3439
34.4%
2018-06-01 3342
33.4%
2017-06-01 3208
32.1%
2020-06-01 11
 
0.1%

Interactions

2023-12-12T12:40:24.247577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.265075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.099223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.124803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.162220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.138981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.300979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.380112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.385717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.212325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.271904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.303857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.277028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.458246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.522326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.506531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.323696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.393726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.436759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.389216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.591472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.663879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.639667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.444333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.544957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.602890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.513999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.706103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.789144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.752334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.586875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.710648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.747635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.969665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.860889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.903698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.847922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.706101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:20.857036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.873994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.080376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.980592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:25.024541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:18.982586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:19.941923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:21.008486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:22.002093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:23.187660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:40:24.113716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:40:32.557405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.0550.0410.0000.1210.0130.0000.0620.0001.000
법정동0.0551.0000.6700.0780.2540.1340.0780.0380.0000.055
법정리0.0410.6701.0000.0380.2430.1260.0430.0370.0440.041
특수지0.0000.0780.0381.0000.2670.0000.0000.0000.0220.000
본번0.1210.2540.2430.2671.0000.1680.0450.0660.0490.121
부번0.0130.1340.1260.0000.1681.0000.2460.0000.0000.013
0.0000.0780.0430.0000.0450.2461.0000.0000.0000.000
시가표준액0.0620.0380.0370.0000.0660.0000.0001.0000.8520.062
연면적0.0000.0000.0440.0220.0490.0000.0000.8521.0000.000
기준일자1.0000.0550.0410.0000.1210.0130.0000.0620.0001.000
2023-12-12T12:40:32.713261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도기준일자특수지
과세년도1.0001.0000.000
기준일자1.0001.0000.000
특수지0.0000.0001.000
2023-12-12T12:40:32.865103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.7540.091-0.2100.045-0.2260.0220.0360.0560.036
법정리0.7541.0000.060-0.2260.040-0.2510.0140.0280.0400.028
본번0.0910.0601.000-0.069-0.0230.0850.0420.0730.2040.073
부번-0.210-0.226-0.0691.000-0.0530.085-0.0640.0080.0000.008
0.0450.040-0.023-0.0531.000-0.074-0.0450.0000.0000.000
시가표준액-0.226-0.2510.0850.085-0.0741.0000.6220.0390.0000.039
연면적0.0220.0140.042-0.064-0.0450.6221.0000.0000.0230.000
과세년도0.0360.0280.0730.0080.0000.0390.0001.0000.0001.000
특수지0.0560.0400.2040.0000.0000.0000.0230.0001.0000.000
기준일자0.0360.0280.0730.0080.0000.0390.0001.0000.0001.000

Missing values

2023-12-12T12:40:25.179202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:40:25.445483image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
10644충청남도공주시441502017370211164018101충청남도 공주시 정안면 광정리 164 1동 8101호235156810966.92772017-06-01
5140충청남도공주시4415020173802511701301[ 평목길 96 ] 0001동 0301호959040016.22017-06-01
48189충청남도공주시44150201811501652011[ 효부길 32 ] 0001동 0001호51480039.62018-06-01
47253충청남도공주시441502018105011872214[ 용당길 12 ] 0001동 0004호6844505.852018-06-01
58126충청남도공주시44150201812101125212충청남도 공주시 금흥동 125-2 1동 2호1173600072.02018-06-01
30724충청남도공주시4415020183303216011충청남도 공주시 계룡면 금대리 6 1동 1호15820000140.02018-06-01
83755충청남도공주시44150201932024179301201충청남도 공주시 탄천면 안영리 793 1동 201호233230000562.02019-06-01
89065충청남도공주시44150202034033193001401[ 임금봉길 78-18 ] 0001동 0401호629103620817.01772020-06-01
70546충청남도공주시4415020191170181711101[ 왕촌길 40 ] 0001동 0101호1227660033.182019-06-01
71371충청남도공주시44150201911001318121101충청남도 공주시 금학동 318-12 1동 101호6061708078.792019-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
24402충청남도공주시44150201712001588162101충청남도 공주시 신관동 588-16 2동 101호22928034.742017-06-01
21629충청남도공주시4415020172502313560110[ 중앙1길 62 ] 0001동 0010호20161050103.392017-06-01
47919충청남도공주시4415020181090129211101[ 무령로 277 ] 0001동 0101호1510850027.472018-06-01
14364충청남도공주시44150201740021175192101[ 산성길 4-7 ] 0002동 0101호3410035084.242017-06-01
61149충청남도공주시4415020191040124435101충청남도 공주시 중동 244-3 5동 101호867042001494.92019-06-01
35443충청남도공주시44150201839031146411101충청남도 공주시 사곡면 월가리 464-1 1동 101호792000330.02018-06-01
6870충청남도공주시44150201737035152221101충청남도 공주시 정안면 사현리 522-2 1동 101호57623040236.162017-06-01
59367충청남도공주시441502019390301271211충청남도 공주시 사곡면 회학리 271-2 1동 1호1027000102.72019-06-01
58884충청남도공주시44150201939022157411102충청남도 공주시 사곡면 신영리 574-1 1동 102호733680183.422019-06-01
38240충청남도공주시441502018310261202011충청남도 공주시 이인면 목동리 202 1동 1호4097720061.162018-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
27충청남도공주시44150201934033133501102충청남도 공주시 반포면 학봉리 335 1동 102호23087402.82019-06-017
28충청남도공주시441502019340331794171101충청남도 공주시 반포면 학봉리 794-17 1동 101호20538003.152019-06-017
4충청남도공주시44150201734029136001101충청남도 공주시 반포면 봉곡리 360 1동 101호1149120043.22017-06-016
13충청남도공주시44150201834029136001101충청남도 공주시 반포면 봉곡리 360 1동 101호533400021.02018-06-014
16충청남도공주시441502018340331794171101충청남도 공주시 반포면 학봉리 794-17 1동 101호20569503.152018-06-014
23충청남도공주시44150201931037157501101충청남도 공주시 이인면 신영리 575 1동 101호780160195.042019-06-013
25충청남도공주시44150201934029136001101충청남도 공주시 반포면 봉곡리 360 1동 101호508200021.02019-06-013
0충청남도공주시441502017250301101101충청남도 공주시 유구읍 구계리 1 1동 101호5184000129.62017-06-012
1충청남도공주시441502017320311168141101충청남도 공주시 탄천면 송학리 168-14 1동 101호70950000330.02017-06-012
2충청남도공주시4415020173203121571101충청남도 공주시 탄천면 송학리 산 15-7 1동 101호576000192.02017-06-012