Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.4%
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
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=345&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080846

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 35 (0.4%) 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 (89.0%)Imbalance
법정리 has 3814 (38.1%) zerosZeros
부번 has 2779 (27.8%) zerosZeros

Reproduction

Analysis started2024-01-09 23:09:07.910676
Analysis finished2024-01-09 23:09:14.498370
Duration6.59 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

2024-01-10T08:09:14.555571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:14.633102image/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

2024-01-10T08:09:14.715655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:14.791765image/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

2024-01-10T08:09:14.876743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:14.954130image/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
3509 
2018
3285 
2017
3199 
2020
 
7

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2019
4th row2018
5th row2019

Common Values

ValueCountFrequency (%)
2019 3509
35.1%
2018 3285
32.9%
2017 3199
32.0%
2020 7
 
0.1%

Length

2024-01-10T08:09:15.034798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:15.139153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3509
35.1%
2018 3285
32.9%
2017 3199
32.0%
2020 7
 
0.1%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.0914
Minimum101
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:15.248210image/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 deviation114.98054
Coefficient of variation (CV)0.45251646
Kurtosis-1.7062324
Mean254.0914
Median Absolute Deviation (MAD)70
Skewness-0.2621896
Sum2540914
Variance13220.524
MonotonicityNot monotonic
2024-01-10T08:09:15.383802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
120 1168
 
11.7%
340 867
 
8.7%
380 849
 
8.5%
250 781
 
7.8%
370 730
 
7.3%
330 720
 
7.2%
320 600
 
6.0%
390 470
 
4.7%
310 467
 
4.7%
360 406
 
4.1%
Other values (27) 2942
29.4%
ValueCountFrequency (%)
101 91
 
0.9%
102 88
 
0.9%
103 56
 
0.6%
104 293
2.9%
105 329
3.3%
106 80
 
0.8%
107 130
 
1.3%
108 187
1.9%
109 212
2.1%
110 136
1.4%
ValueCountFrequency (%)
400 296
 
3.0%
390 470
4.7%
380 849
8.5%
370 730
7.3%
360 406
4.1%
340 867
8.7%
330 720
7.2%
320 600
6.0%
310 467
4.7%
250 781
7.8%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.433
Minimum0
Maximum43
Zeros3814
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:15.526312image/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 deviation14.29336
Coefficient of variation (CV)0.81990249
Kurtosis-1.6198585
Mean17.433
Median Absolute Deviation (MAD)10
Skewness-0.23117888
Sum174330
Variance204.30014
MonotonicityNot monotonic
2024-01-10T08:09:15.655974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3814
38.1%
21 745
 
7.4%
23 580
 
5.8%
33 516
 
5.2%
29 427
 
4.3%
24 420
 
4.2%
28 392
 
3.9%
25 367
 
3.7%
26 362
 
3.6%
27 345
 
3.5%
Other values (14) 2032
20.3%
ValueCountFrequency (%)
0 3814
38.1%
21 745
 
7.4%
22 191
 
1.9%
23 580
 
5.8%
24 420
 
4.2%
25 367
 
3.7%
26 362
 
3.6%
27 345
 
3.5%
28 392
 
3.9%
29 427
 
4.3%
ValueCountFrequency (%)
43 14
 
0.1%
42 16
 
0.2%
41 14
 
0.1%
40 29
 
0.3%
39 50
 
0.5%
38 168
1.7%
37 149
1.5%
36 163
1.6%
35 300
3.0%
34 168
1.7%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9854 
2
 
146

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 9854
98.5%
2 146
 
1.5%

Length

2024-01-10T08:09:15.787978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:15.891905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9854
98.5%
2 146
 
1.5%

본번
Real number (ℝ)

Distinct837
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.18
Minimum1
Maximum1025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:16.014068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1136
median269
Q3490
95-th percentile726
Maximum1025
Range1024
Interquartile range (IQR)354

Descriptive statistics

Standard deviation228.12002
Coefficient of variation (CV)0.72608067
Kurtosis-0.48522741
Mean314.18
Median Absolute Deviation (MAD)161
Skewness0.62409086
Sum3141800
Variance52038.745
MonotonicityNot monotonic
2024-01-10T08:09:16.175714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 97
 
1.0%
182 85
 
0.9%
186 77
 
0.8%
360 76
 
0.8%
1 70
 
0.7%
725 62
 
0.6%
608 56
 
0.6%
231 51
 
0.5%
5 50
 
0.5%
20 50
 
0.5%
Other values (827) 9326
93.3%
ValueCountFrequency (%)
1 70
0.7%
2 31
0.3%
3 45
0.4%
4 20
 
0.2%
5 50
0.5%
6 31
0.3%
7 17
 
0.2%
8 23
 
0.2%
9 31
0.3%
10 33
0.3%
ValueCountFrequency (%)
1025 2
 
< 0.1%
1021 1
 
< 0.1%
993 1
 
< 0.1%
992 1
 
< 0.1%
963 1
 
< 0.1%
962 3
< 0.1%
961 7
0.1%
956 1
 
< 0.1%
955 4
< 0.1%
954 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct143
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4562
Minimum0
Maximum314
Zeros2779
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:16.332012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile23
Maximum314
Range314
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.680145
Coefficient of variation (CV)2.7384755
Kurtosis74.495696
Mean6.4562
Median Absolute Deviation (MAD)2
Skewness7.5428045
Sum64562
Variance312.58754
MonotonicityNot monotonic
2024-01-10T08:09:16.459121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2779
27.8%
1 1932
19.3%
2 1007
 
10.1%
3 769
 
7.7%
4 508
 
5.1%
5 407
 
4.1%
6 349
 
3.5%
7 252
 
2.5%
8 243
 
2.4%
10 203
 
2.0%
Other values (133) 1551
15.5%
ValueCountFrequency (%)
0 2779
27.8%
1 1932
19.3%
2 1007
 
10.1%
3 769
 
7.7%
4 508
 
5.1%
5 407
 
4.1%
6 349
 
3.5%
7 252
 
2.5%
8 243
 
2.4%
9 179
 
1.8%
ValueCountFrequency (%)
314 1
 
< 0.1%
281 1
 
< 0.1%
277 1
 
< 0.1%
273 1
 
< 0.1%
211 1
 
< 0.1%
209 2
< 0.1%
208 1
 
< 0.1%
206 4
< 0.1%
203 1
 
< 0.1%
198 2
< 0.1%


Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.4688
Minimum0
Maximum9301
Zeros94
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:16.584165image/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 deviation456.18063
Coefficient of variation (CV)14.049815
Kurtosis303.67781
Mean32.4688
Median Absolute Deviation (MAD)0
Skewness17.03909
Sum324688
Variance208100.76
MonotonicityNot monotonic
2024-01-10T08:09:16.698074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 8616
86.2%
2 698
 
7.0%
3 192
 
1.9%
0 94
 
0.9%
4 90
 
0.9%
5 52
 
0.5%
6 38
 
0.4%
7 31
 
0.3%
14 17
 
0.2%
11 16
 
0.2%
Other values (37) 156
 
1.6%
ValueCountFrequency (%)
0 94
 
0.9%
1 8616
86.2%
2 698
 
7.0%
3 192
 
1.9%
4 90
 
0.9%
5 52
 
0.5%
6 38
 
0.4%
7 31
 
0.3%
8 12
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
9301 2
 
< 0.1%
9002 1
 
< 0.1%
9001 6
0.1%
9000 5
0.1%
8000 3
 
< 0.1%
7000 10
0.1%
6011 1
 
< 0.1%
5002 1
 
< 0.1%
5000 3
 
< 0.1%
4001 2
 
< 0.1%


Text

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T08:09:16.837759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.6978
Min length1

Characters and Unicode

Total characters26978
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

Unique75 ?
Unique (%)0.8%

Sample

1st row101
2nd row101
3rd row102
4th row1
5th row401
ValueCountFrequency (%)
101 4655
46.6%
1 1012
 
10.1%
201 921
 
9.2%
102 879
 
8.8%
2 374
 
3.7%
301 359
 
3.6%
8101 320
 
3.2%
103 239
 
2.4%
202 143
 
1.4%
3 140
 
1.4%
Other values (140) 958
 
9.6%
2024-01-10T08:09:17.083366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13957
51.7%
0 8244
30.6%
2 2726
 
10.1%
3 902
 
3.3%
8 408
 
1.5%
4 368
 
1.4%
5 169
 
0.6%
6 105
 
0.4%
7 59
 
0.2%
9 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26975
> 99.9%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13957
51.7%
0 8244
30.6%
2 2726
 
10.1%
3 902
 
3.3%
8 408
 
1.5%
4 368
 
1.4%
5 169
 
0.6%
6 105
 
0.4%
7 59
 
0.2%
9 37
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26978
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13957
51.7%
0 8244
30.6%
2 2726
 
10.1%
3 902
 
3.3%
8 408
 
1.5%
4 368
 
1.4%
5 169
 
0.6%
6 105
 
0.4%
7 59
 
0.2%
9 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13957
51.7%
0 8244
30.6%
2 2726
 
10.1%
3 902
 
3.3%
8 408
 
1.5%
4 368
 
1.4%
5 169
 
0.6%
6 105
 
0.4%
7 59
 
0.2%
9 37
 
0.1%
Distinct8045
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T08:09:17.377854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length26.5493
Min length18

Characters and Unicode

Total characters265493
Distinct characters316
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

Unique6675 ?
Unique (%)66.8%

Sample

1st row충청남도 공주시 이인면 오룡리 212 1동 101호
2nd row충청남도 공주시 반포면 마암리 204-15 1동 101호
3rd row[ 전막2길 16-5 ] 0001동 0102호
4th row충청남도 공주시 신풍면 산정리 489 1동 1호
5th row충청남도 공주시 정안면 대산리 771-4 1동 401호
ValueCountFrequency (%)
7034
 
10.9%
충청남도 6483
 
10.0%
공주시 6483
 
10.0%
1동 5376
 
8.3%
0001동 3240
 
5.0%
101호 3164
 
4.9%
0101호 1491
 
2.3%
1호 698
 
1.1%
우성면 689
 
1.1%
102호 577
 
0.9%
Other values (4281) 29373
45.5%
2024-01-10T08:09:17.811166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54608
20.6%
1 30228
 
11.4%
0 25367
 
9.6%
12304
 
4.6%
10057
 
3.8%
2 8529
 
3.2%
6710
 
2.5%
6661
 
2.5%
6610
 
2.5%
6607
 
2.5%
Other values (306) 97812
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111812
42.1%
Decimal Number 86234
32.5%
Space Separator 54608
20.6%
Dash Punctuation 5805
 
2.2%
Close Punctuation 3517
 
1.3%
Open Punctuation 3517
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12304
 
11.0%
10057
 
9.0%
6710
 
6.0%
6661
 
6.0%
6610
 
5.9%
6607
 
5.9%
6592
 
5.9%
6582
 
5.9%
6489
 
5.8%
4645
 
4.2%
Other values (292) 38555
34.5%
Decimal Number
ValueCountFrequency (%)
1 30228
35.1%
0 25367
29.4%
2 8529
 
9.9%
3 4927
 
5.7%
4 3582
 
4.2%
5 3403
 
3.9%
6 2913
 
3.4%
8 2719
 
3.2%
7 2617
 
3.0%
9 1949
 
2.3%
Space Separator
ValueCountFrequency (%)
54608
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5805
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3517
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3517
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153681
57.9%
Hangul 111812
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12304
 
11.0%
10057
 
9.0%
6710
 
6.0%
6661
 
6.0%
6610
 
5.9%
6607
 
5.9%
6592
 
5.9%
6582
 
5.9%
6489
 
5.8%
4645
 
4.2%
Other values (292) 38555
34.5%
Common
ValueCountFrequency (%)
54608
35.5%
1 30228
19.7%
0 25367
16.5%
2 8529
 
5.5%
- 5805
 
3.8%
3 4927
 
3.2%
4 3582
 
2.3%
] 3517
 
2.3%
[ 3517
 
2.3%
5 3403
 
2.2%
Other values (4) 10198
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153681
57.9%
Hangul 111812
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54608
35.5%
1 30228
19.7%
0 25367
16.5%
2 8529
 
5.5%
- 5805
 
3.8%
3 4927
 
3.2%
4 3582
 
2.3%
] 3517
 
2.3%
[ 3517
 
2.3%
5 3403
 
2.2%
Other values (4) 10198
 
6.6%
Hangul
ValueCountFrequency (%)
12304
 
11.0%
10057
 
9.0%
6710
 
6.0%
6661
 
6.0%
6610
 
5.9%
6607
 
5.9%
6592
 
5.9%
6582
 
5.9%
6489
 
5.8%
4645
 
4.2%
Other values (292) 38555
34.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8773
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58476000
Minimum2390
Maximum4.8770194 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:17.942237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2390
5-th percentile504000
Q13491235
median15169580
Q355087575
95-th percentile2.4483836 × 108
Maximum4.8770194 × 109
Range4.877017 × 109
Interquartile range (IQR)51596340

Descriptive statistics

Standard deviation1.4307579 × 108
Coefficient of variation (CV)2.4467438
Kurtosis176.71907
Mean58476000
Median Absolute Deviation (MAD)14035320
Skewness9.2491797
Sum5.8476 × 1011
Variance2.0470683 × 1016
MonotonicityNot monotonic
2024-01-10T08:09:18.362666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756000 21
 
0.2%
846000 17
 
0.2%
1980000 15
 
0.1%
720000 11
 
0.1%
594000 11
 
0.1%
2400000 10
 
0.1%
540000 10
 
0.1%
900000 10
 
0.1%
828000 9
 
0.1%
2160000 9
 
0.1%
Other values (8763) 9877
98.8%
ValueCountFrequency (%)
2390 1
< 0.1%
14400 1
< 0.1%
27000 1
< 0.1%
46000 1
< 0.1%
48000 1
< 0.1%
49000 1
< 0.1%
52000 1
< 0.1%
56680 1
< 0.1%
59400 1
< 0.1%
59520 1
< 0.1%
ValueCountFrequency (%)
4877019360 1
< 0.1%
2306347920 1
< 0.1%
2203923640 1
< 0.1%
2134235730 1
< 0.1%
1948897760 1
< 0.1%
1928259030 1
< 0.1%
1789042500 1
< 0.1%
1782713470 1
< 0.1%
1551776160 1
< 0.1%
1545264000 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5282
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.37701
Minimum0.01
Maximum11780.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T08:09:18.481819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile14.85
Q145.855
median101.88895
Q3198.4
95-th percentile701.7875
Maximum11780.24
Range11780.23
Interquartile range (IQR)152.545

Descriptive statistics

Standard deviation352.85666
Coefficient of variation (CV)1.7787175
Kurtosis162.563
Mean198.37701
Median Absolute Deviation (MAD)69.11105
Skewness8.6259322
Sum1983770.1
Variance124507.82
MonotonicityNot monotonic
2024-01-10T08:09:18.612786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 265
 
2.6%
198.0 60
 
0.6%
50.0 48
 
0.5%
180.0 44
 
0.4%
100.0 43
 
0.4%
27.0 42
 
0.4%
66.0 41
 
0.4%
99.0 41
 
0.4%
36.0 37
 
0.4%
24.0 30
 
0.3%
Other values (5272) 9349
93.5%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.13 1
 
< 0.1%
1.0 2
< 0.1%
1.32 1
 
< 0.1%
1.38 1
 
< 0.1%
1.46 1
 
< 0.1%
1.7 1
 
< 0.1%
1.95 1
 
< 0.1%
2.0 4
< 0.1%
2.08 1
 
< 0.1%
ValueCountFrequency (%)
11780.24 1
< 0.1%
7056.0 1
< 0.1%
5204.07 1
< 0.1%
4337.64 1
< 0.1%
4158.42 1
< 0.1%
4144.12 1
< 0.1%
4102.0 1
< 0.1%
4095.16 1
< 0.1%
4087.1 1
< 0.1%
4028.86 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-06-01
3509 
2018-06-01
3285 
2017-06-01
3199 
2020-06-01
 
7

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-06-01
2nd row2018-06-01
3rd row2019-06-01
4th row2018-06-01
5th row2019-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 3509
35.1%
2018-06-01 3285
32.9%
2017-06-01 3199
32.0%
2020-06-01 7
 
0.1%

Length

2024-01-10T08:09:18.762614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:18.873431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 3509
35.1%
2018-06-01 3285
32.9%
2017-06-01 3199
32.0%
2020-06-01 7
 
0.1%

Interactions

2024-01-10T08:09:13.467652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.535659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.109508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.684040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.256761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.083370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.707177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.589813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.617913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.192713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.767550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.342540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.164988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.822595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.698582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.697305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.275860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.847724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.422936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.242548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.917736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.805928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.778994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.352561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.926454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.508414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.331830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.022256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.900183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.860033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.429429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.009571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.591578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.420650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.138024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.988445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:09.940210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.511517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.089217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.916182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.496096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.242237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:14.089939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.022650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:10.598185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.170173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:11.998294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:12.599561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:13.355243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:09:18.963363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.0400.0320.0200.0510.0000.0000.0180.0001.000
법정동0.0401.0000.6730.0770.2600.1450.0940.0530.0300.040
법정리0.0320.6731.0000.0460.2730.1240.0400.0360.0740.032
특수지0.0200.0770.0461.0000.3150.0000.0000.0000.0350.020
본번0.0510.2600.2730.3151.0000.1430.0450.0900.0530.051
부번0.0000.1450.1240.0000.1431.0000.2410.0000.0000.000
0.0000.0940.0400.0000.0450.2411.0000.0000.0000.000
시가표준액0.0180.0530.0360.0000.0900.0000.0001.0000.8320.018
연면적0.0000.0300.0740.0350.0530.0000.0000.8321.0000.000
기준일자1.0000.0400.0320.0200.0510.0000.0000.0180.0001.000
2024-01-10T08:09:19.069890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지기준일자
과세년도1.0000.0131.000
특수지0.0131.0000.013
기준일자1.0000.0131.000
2024-01-10T08:09:19.154436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.7540.096-0.1920.043-0.2330.0100.0260.0550.026
법정리0.7541.0000.054-0.2150.034-0.261-0.0100.0220.0500.022
본번0.0960.0541.000-0.019-0.0330.0840.0320.0300.2420.030
부번-0.192-0.215-0.0191.000-0.0580.088-0.0570.0000.0000.000
0.0430.034-0.033-0.0581.000-0.084-0.0520.0000.0000.000
시가표준액-0.233-0.2610.0840.088-0.0841.0000.6220.0120.0000.012
연면적0.010-0.0100.032-0.057-0.0520.6221.0000.0000.0370.000
과세년도0.0260.0220.0300.0000.0000.0120.0001.0000.0131.000
특수지0.0550.0500.2420.0000.0000.0000.0370.0131.0000.013
기준일자0.0260.0220.0300.0000.0000.0120.0001.0000.0131.000

Missing values

2024-01-10T08:09:14.225760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:09:14.401581image/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
32962충청남도공주시44150201831023121201101충청남도 공주시 이인면 오룡리 212 1동 101호14250000190.02018-06-01
29383충청남도공주시441502018340281204151101충청남도 공주시 반포면 마암리 204-15 1동 101호117593880161.732018-06-01
62745충청남도공주시44150201912001595131102[ 전막2길 16-5 ] 0001동 0102호311640063.62019-06-01
50739충청남도공주시441502018400211489011충청남도 공주시 신풍면 산정리 489 1동 1호50292055.882018-06-01
68084충청남도공주시44150201937038177141401충청남도 공주시 정안면 대산리 771-4 1동 401호11857133002658.552019-06-01
65798충청남도공주시4415020191200165131105[ 번영2로 67 ] 0001동 0105호1770822033.332019-06-01
86410충청남도공주시44150201932022157801101충청남도 공주시 탄천면 광명리 578 1동 101호160500015.02019-06-01
68326충청남도공주시44150201937035142911104[ 차령고개로 17-99 ] 0001동 0104호2274800047.02019-06-01
41959충청남도공주시441502018250211661111충청남도 공주시 유구읍 백교리 66-11 1동 1호1443420048.62018-06-01
35382충청남도공주시44150201838022127101101충청남도 공주시 우성면 단지리 271 1동 101호1238400096.02018-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
61101충청남도공주시44150201940024115011101[ 용수봉갑길 416-53 ] 0001동 0101호15983040332.982019-06-01
426충청남도공주시44150201710501181511충청남도 공주시 산성동 181-5 1동 1호26293350148.552017-06-01
75564충청남도공주시441502019250302611101충청남도 공주시 유구읍 구계리 산 6-1 1동 101호73800018.02019-06-01
20526충청남도공주시4415020171270112531101충청남도 공주시 동현동 125-3 1동 101호21000025.02017-06-01
57856충청남도공주시4415020181210119601101[ 장기로 87-3 ] 0001동 0101호121005760164.412018-06-01
30689충청남도공주시44150201834029124711101[ 반포초교길 137 ] 0001동 0101호6509468095.3072018-06-01
14964충청남도공주시4415020172503418101101충청남도 공주시 유구읍 덕곡리 81 1동 101호201600018.02017-06-01
31982충청남도공주시441502018330221383011[ 영규대사로 499 ] 0001동 0001호75524760195.662018-06-01
40253충청남도공주시441502018250211271101충청남도 공주시 유구읍 백교리 2-7 1동 101호5799200131.82018-06-01
48192충청남도공주시44150201810901257212[ 국고개길 44 ] 0001동 0002호2580678082.982018-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
17충청남도공주시44150201834033133501102충청남도 공주시 반포면 학봉리 335 1동 102호21929602.82018-06-015
28충청남도공주시44150201934029136001101충청남도 공주시 반포면 봉곡리 360 1동 101호1045440043.22019-06-015
29충청남도공주시44150201934033133501102충청남도 공주시 반포면 학봉리 335 1동 102호23087402.82019-06-015
18충청남도공주시441502018340331794171101충청남도 공주시 반포면 학봉리 794-17 1동 101호20569503.152018-06-014
30충청남도공주시441502019340331794171101충청남도 공주시 반포면 학봉리 794-17 1동 101호20538003.152019-06-014
0충청남도공주시44150201731037157501101충청남도 공주시 이인면 신영리 575 1동 101호585120195.042017-06-013
5충청남도공주시44150201734029136001101충청남도 공주시 반포면 봉곡리 360 1동 101호558600021.02017-06-013
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