Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows58
Duplicate rows (%)0.6%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description2021년~2022년 서천군 일반건축물 시가표준액에 대한 과세자료로, 물건지 및 시가표준액, 연면적을 포함한 과세자료입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=346&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080610

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 58 (0.6%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (90.1%)Imbalance
부번 has 2666 (26.7%) zerosZeros
has 5138 (51.4%) zerosZeros

Reproduction

Analysis started2024-01-09 22:07:55.680794
Analysis finished2024-01-09 22:08:03.851433
Duration8.17 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

2024-01-10T07:08:03.916292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:04.003945image/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
서천군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 10000
100.0%

Length

2024-01-10T07:08:04.095155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:04.186169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44770
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 10000
100.0%

Length

2024-01-10T07:08:04.276221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:04.342359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 10000
100.0%

Length

2024-01-10T07:08:04.414980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:04.481611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.2699
Minimum250
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:04.548110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median320
Q3390
95-th percentile410
Maximum410
Range160
Interquartile range (IQR)137

Descriptive statistics

Standard deviation61.284256
Coefficient of variation (CV)0.18957613
Kurtosis-1.5183565
Mean323.2699
Median Absolute Deviation (MAD)67
Skewness0.056216899
Sum3232699
Variance3755.76
MonotonicityNot monotonic
2024-01-10T07:08:04.640581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 1844
18.4%
253 1703
17.0%
410 1281
12.8%
310 1129
11.3%
340 681
 
6.8%
400 664
 
6.6%
390 618
 
6.2%
320 472
 
4.7%
380 447
 
4.5%
330 358
 
3.6%
Other values (3) 803
8.0%
ValueCountFrequency (%)
250 1844
18.4%
253 1703
17.0%
310 1129
11.3%
320 472
 
4.7%
330 358
 
3.6%
340 681
 
6.8%
350 345
 
3.5%
360 177
 
1.8%
370 281
 
2.8%
380 447
 
4.5%
ValueCountFrequency (%)
410 1281
12.8%
400 664
6.6%
390 618
6.2%
380 447
 
4.5%
370 281
 
2.8%
360 177
 
1.8%
350 345
 
3.5%
340 681
6.8%
330 358
 
3.6%
320 472
 
4.7%

법정리
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9165
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:04.732861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median25
Q329
95-th percentile34
Maximum41
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4130347
Coefficient of variation (CV)0.17027896
Kurtosis-0.010067909
Mean25.9165
Median Absolute Deviation (MAD)3
Skewness0.76606787
Sum259165
Variance19.474875
MonotonicityNot monotonic
2024-01-10T07:08:04.833088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21 2035
20.3%
22 1142
11.4%
28 989
9.9%
24 892
8.9%
30 849
8.5%
23 668
 
6.7%
26 594
 
5.9%
29 592
 
5.9%
25 491
 
4.9%
27 381
 
3.8%
Other values (11) 1367
13.7%
ValueCountFrequency (%)
21 2035
20.3%
22 1142
11.4%
23 668
 
6.7%
24 892
8.9%
25 491
 
4.9%
26 594
 
5.9%
27 381
 
3.8%
28 989
9.9%
29 592
 
5.9%
30 849
8.5%
ValueCountFrequency (%)
41 9
 
0.1%
40 25
 
0.2%
39 76
 
0.8%
38 65
 
0.7%
37 61
 
0.6%
36 104
 
1.0%
35 95
 
0.9%
34 159
1.6%
33 263
2.6%
32 174
1.7%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9872 
2
 
128

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 9872
98.7%
2 128
 
1.3%

Length

2024-01-10T07:08:04.932477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:08:05.004421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9872
98.7%
2 128
 
1.3%

본번
Real number (ℝ)

Distinct868
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.3224
Minimum1
Maximum1471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:05.085574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1138
median276
Q3455
95-th percentile813
Maximum1471
Range1470
Interquartile range (IQR)317

Descriptive statistics

Standard deviation248.78679
Coefficient of variation (CV)0.76239569
Kurtosis1.7851645
Mean326.3224
Median Absolute Deviation (MAD)157
Skewness1.2069126
Sum3263224
Variance61894.868
MonotonicityNot monotonic
2024-01-10T07:08:05.190122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 156
 
1.6%
72 126
 
1.3%
313 121
 
1.2%
275 84
 
0.8%
73 74
 
0.7%
332 74
 
0.7%
78 59
 
0.6%
61 58
 
0.6%
303 52
 
0.5%
755 51
 
0.5%
Other values (858) 9145
91.5%
ValueCountFrequency (%)
1 46
0.5%
2 22
0.2%
3 29
0.3%
4 17
 
0.2%
5 17
 
0.2%
6 10
 
0.1%
7 12
 
0.1%
8 12
 
0.1%
9 8
 
0.1%
10 24
0.2%
ValueCountFrequency (%)
1471 1
 
< 0.1%
1449 3
< 0.1%
1448 2
< 0.1%
1441 1
 
< 0.1%
1424 1
 
< 0.1%
1418 2
< 0.1%
1414 1
 
< 0.1%
1412 4
< 0.1%
1409 1
 
< 0.1%
1406 2
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct215
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0835
Minimum0
Maximum985
Zeros2666
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:05.306048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile47
Maximum985
Range985
Interquartile range (IQR)7

Descriptive statistics

Standard deviation67.432923
Coefficient of variation (CV)4.4706416
Kurtosis93.180534
Mean15.0835
Median Absolute Deviation (MAD)2
Skewness9.0978056
Sum150835
Variance4547.199
MonotonicityNot monotonic
2024-01-10T07:08:05.409178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2666
26.7%
1 1649
16.5%
2 1056
 
10.6%
3 680
 
6.8%
4 552
 
5.5%
5 467
 
4.7%
6 310
 
3.1%
7 236
 
2.4%
8 212
 
2.1%
9 208
 
2.1%
Other values (205) 1964
19.6%
ValueCountFrequency (%)
0 2666
26.7%
1 1649
16.5%
2 1056
 
10.6%
3 680
 
6.8%
4 552
 
5.5%
5 467
 
4.7%
6 310
 
3.1%
7 236
 
2.4%
8 212
 
2.1%
9 208
 
2.1%
ValueCountFrequency (%)
985 2
 
< 0.1%
975 1
 
< 0.1%
866 2
 
< 0.1%
825 1
 
< 0.1%
821 1
 
< 0.1%
808 1
 
< 0.1%
802 2
 
< 0.1%
800 1
 
< 0.1%
789 12
0.1%
786 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean948.4827
Minimum0
Maximum9999
Zeros5138
Zeros (%)51.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:05.512814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9000
Maximum9999
Range9999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2763.4047
Coefficient of variation (CV)2.9135003
Kurtosis4.6687674
Mean948.4827
Median Absolute Deviation (MAD)0
Skewness2.5810987
Sum9484827
Variance7636405.3
MonotonicityNot monotonic
2024-01-10T07:08:05.617393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5138
51.4%
1 3074
30.7%
9000 885
 
8.8%
2 396
 
4.0%
3 133
 
1.3%
4 55
 
0.5%
9001 47
 
0.5%
6 33
 
0.3%
9999 21
 
0.2%
5 21
 
0.2%
Other values (74) 197
 
2.0%
ValueCountFrequency (%)
0 5138
51.4%
1 3074
30.7%
2 396
 
4.0%
3 133
 
1.3%
4 55
 
0.5%
5 21
 
0.2%
6 33
 
0.3%
7 8
 
0.1%
8 6
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9999 21
0.2%
9064 1
 
< 0.1%
9063 2
 
< 0.1%
9060 3
 
< 0.1%
9059 3
 
< 0.1%
9058 5
 
0.1%
9052 1
 
< 0.1%
9051 1
 
< 0.1%
9047 1
 
< 0.1%
9046 1
 
< 0.1%


Text

Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:08:05.753788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9803
Min length1

Characters and Unicode

Total characters29803
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.5%

Sample

1st row104
2nd row101
3rd row101
4th row101
5th row106
ValueCountFrequency (%)
101 5067
50.7%
102 1664
 
16.6%
103 740
 
7.4%
201 737
 
7.4%
104 350
 
3.5%
301 194
 
1.9%
105 191
 
1.9%
8101 125
 
1.2%
202 113
 
1.1%
106 106
 
1.1%
Other values (103) 713
 
7.1%
2024-01-10T07:08:05.980544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14931
50.1%
0 9789
32.8%
2 2806
 
9.4%
3 1063
 
3.6%
4 472
 
1.6%
5 237
 
0.8%
8 215
 
0.7%
6 140
 
0.5%
7 87
 
0.3%
9 62
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29802
> 99.9%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14931
50.1%
0 9789
32.8%
2 2806
 
9.4%
3 1063
 
3.6%
4 472
 
1.6%
5 237
 
0.8%
8 215
 
0.7%
6 140
 
0.5%
7 87
 
0.3%
9 62
 
0.2%
Other Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29802
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14931
50.1%
0 9789
32.8%
2 2806
 
9.4%
3 1063
 
3.6%
4 472
 
1.6%
5 237
 
0.8%
8 215
 
0.7%
6 140
 
0.5%
7 87
 
0.3%
9 62
 
0.2%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29802
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14931
50.1%
0 9789
32.8%
2 2806
 
9.4%
3 1063
 
3.6%
4 472
 
1.6%
5 237
 
0.8%
8 215
 
0.7%
6 140
 
0.5%
7 87
 
0.3%
9 62
 
0.2%
Hangul
ValueCountFrequency (%)
1
100.0%
Distinct9154
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:08:06.453228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length27.1348
Min length19

Characters and Unicode

Total characters271348
Distinct characters172
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

Unique8671 ?
Unique (%)86.7%

Sample

1st row충청남도 서천군 서천읍 군사리 184-21 104호
2nd row[ 주원로183번길 35 ] 0000동 0101호
3rd row[ 장산로1514번길 34 ] 0000동 0101호
4th row[ 화천길 1 ] 0003동 0101호
5th row충청남도 서천군 서면 신합리 483-1 106호
ValueCountFrequency (%)
8426
 
13.3%
충청남도 5787
 
9.1%
서천군 5787
 
9.1%
101호 2807
 
4.4%
0000동 2598
 
4.1%
0101호 2260
 
3.6%
1동 1965
 
3.1%
장항읍 1176
 
1.9%
0001동 1109
 
1.8%
102호 958
 
1.5%
Other values (4419) 30447
48.1%
2024-01-10T07:08:06.807004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53320
19.7%
0 33508
 
12.3%
1 25915
 
9.6%
10007
 
3.7%
8975
 
3.3%
2 8712
 
3.2%
7819
 
2.9%
7489
 
2.8%
6289
 
2.3%
6282
 
2.3%
Other values (162) 103032
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109091
40.2%
Decimal Number 94823
34.9%
Space Separator 53320
19.7%
Dash Punctuation 5688
 
2.1%
Close Punctuation 4213
 
1.6%
Open Punctuation 4213
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10007
 
9.2%
8975
 
8.2%
7819
 
7.2%
7489
 
6.9%
6289
 
5.8%
6282
 
5.8%
6165
 
5.7%
6061
 
5.6%
5865
 
5.4%
5802
 
5.3%
Other values (148) 38337
35.1%
Decimal Number
ValueCountFrequency (%)
0 33508
35.3%
1 25915
27.3%
2 8712
 
9.2%
3 5861
 
6.2%
4 4385
 
4.6%
5 3874
 
4.1%
9 3616
 
3.8%
7 3082
 
3.3%
6 3058
 
3.2%
8 2812
 
3.0%
Space Separator
ValueCountFrequency (%)
53320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5688
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4213
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162257
59.8%
Hangul 109091
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10007
 
9.2%
8975
 
8.2%
7819
 
7.2%
7489
 
6.9%
6289
 
5.8%
6282
 
5.8%
6165
 
5.7%
6061
 
5.6%
5865
 
5.4%
5802
 
5.3%
Other values (148) 38337
35.1%
Common
ValueCountFrequency (%)
53320
32.9%
0 33508
20.7%
1 25915
16.0%
2 8712
 
5.4%
3 5861
 
3.6%
- 5688
 
3.5%
4 4385
 
2.7%
] 4213
 
2.6%
[ 4213
 
2.6%
5 3874
 
2.4%
Other values (4) 12568
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162257
59.8%
Hangul 109091
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53320
32.9%
0 33508
20.7%
1 25915
16.0%
2 8712
 
5.4%
3 5861
 
3.6%
- 5688
 
3.5%
4 4385
 
2.7%
] 4213
 
2.6%
[ 4213
 
2.6%
5 3874
 
2.4%
Other values (4) 12568
 
7.7%
Hangul
ValueCountFrequency (%)
10007
 
9.2%
8975
 
8.2%
7819
 
7.2%
7489
 
6.9%
6289
 
5.8%
6282
 
5.8%
6165
 
5.7%
6061
 
5.6%
5865
 
5.4%
5802
 
5.3%
Other values (148) 38337
35.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8235
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41057083
Minimum29600
Maximum3.7776785 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:06.921256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29600
5-th percentile425028
Q11926000
median7228580
Q336598268
95-th percentile1.6750419 × 108
Maximum3.7776785 × 109
Range3.7776489 × 109
Interquartile range (IQR)34672268

Descriptive statistics

Standard deviation1.35286 × 108
Coefficient of variation (CV)3.295071
Kurtosis323.02448
Mean41057083
Median Absolute Deviation (MAD)6530400
Skewness15.022048
Sum4.1057083 × 1011
Variance1.8302302 × 1016
MonotonicityNot monotonic
2024-01-10T07:08:07.030891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2106000 34
 
0.3%
1872000 33
 
0.3%
2340000 33
 
0.3%
2574000 27
 
0.3%
1638000 22
 
0.2%
3780000 19
 
0.2%
270000 18
 
0.2%
1440000 17
 
0.2%
43872190 16
 
0.2%
252000 15
 
0.1%
Other values (8225) 9766
97.7%
ValueCountFrequency (%)
29600 1
< 0.1%
45400 1
< 0.1%
52890 1
< 0.1%
56000 1
< 0.1%
59220 1
< 0.1%
60160 1
< 0.1%
60480 1
< 0.1%
62000 1
< 0.1%
66100 1
< 0.1%
69120 1
< 0.1%
ValueCountFrequency (%)
3777678500 1
< 0.1%
3686396910 1
< 0.1%
3686143250 1
< 0.1%
3557943210 1
< 0.1%
3150625490 1
< 0.1%
3112401400 1
< 0.1%
2778273100 1
< 0.1%
2089881900 1
< 0.1%
2081924490 1
< 0.1%
2032476090 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5345
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.21974
Minimum0.95
Maximum14622.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:08:07.138266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile12.92
Q131.915
median81.2
Q3178.5525
95-th percentile496.77
Maximum14622.49
Range14621.54
Interquartile range (IQR)146.6375

Descriptive statistics

Standard deviation440.99543
Coefficient of variation (CV)2.6372212
Kurtosis439.24179
Mean167.21974
Median Absolute Deviation (MAD)59.32
Skewness17.306487
Sum1672197.4
Variance194476.97
MonotonicityNot monotonic
2024-01-10T07:08:07.276255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 549
 
5.5%
27.0 123
 
1.2%
16.5 62
 
0.6%
36.0 53
 
0.5%
15.0 47
 
0.5%
198.0 44
 
0.4%
24.0 39
 
0.4%
50.0 38
 
0.4%
66.0 37
 
0.4%
99.0 37
 
0.4%
Other values (5335) 8971
89.7%
ValueCountFrequency (%)
0.95 1
< 0.1%
1.35 1
< 0.1%
1.6 1
< 0.1%
1.7 1
< 0.1%
1.88 1
< 0.1%
1.98 1
< 0.1%
2.0 2
< 0.1%
2.08 1
< 0.1%
2.11 1
< 0.1%
2.16 1
< 0.1%
ValueCountFrequency (%)
14622.49 1
< 0.1%
13806.73 1
< 0.1%
13325.63 1
< 0.1%
13056.0 1
< 0.1%
9973.49 1
< 0.1%
7797.47 1
< 0.1%
7623.05 1
< 0.1%
7612.27 1
< 0.1%
7485.0 1
< 0.1%
6882.0 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-01 00:00:00
Maximum2020-06-01 00:00:00
2024-01-10T07:08:07.395940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:07.488111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T07:08:02.823218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.096124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.830680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.590871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.351004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.303385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.042180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.903997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.174738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.910511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.668210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.651837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.502466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.127551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.987446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.255433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.997194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.751596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.731227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.701116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.216639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:03.068870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.334226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.078641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.826442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.802806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.891957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.296610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:03.143885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.406491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.153966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.900665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.872257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:01.077717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.374411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:03.417099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.672560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.426449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.187010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.149771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:01.441406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.646044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:03.497675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:57.754343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:58.508154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:07:59.268013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:00.227529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:01.637726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:08:02.741573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:08:07.548441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.5400.0810.4060.1710.1520.0420.000
법정리0.5401.0000.0310.4650.2650.0930.0410.067
특수지0.0810.0311.0000.2410.0000.0000.0000.000
본번0.4060.4650.2411.0000.1710.0840.0830.018
부번0.1710.2650.0000.1711.0000.0000.0000.000
0.1520.0930.0000.0840.0001.0000.0720.084
시가표준액0.0420.0410.0000.0830.0000.0721.0000.919
연면적0.0000.0670.0000.0180.0000.0840.9191.000
2024-01-10T07:08:07.636701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.129-0.104-0.0610.065-0.114-0.0370.066
법정리0.1291.000-0.049-0.100-0.004-0.0970.0140.021
본번-0.104-0.0491.0000.0050.0040.1070.0130.185
부번-0.061-0.1000.0051.000-0.0300.1130.0240.000
0.065-0.0040.004-0.0301.000-0.028-0.1490.000
시가표준액-0.114-0.0970.1070.113-0.0281.0000.6950.000
연면적-0.0370.0140.0130.024-0.1490.6951.0000.000
특수지0.0660.0210.1850.0000.0000.0000.0001.000

Missing values

2024-01-10T07:08:03.609098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:08:03.759415image/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
20383충청남도서천군447702020253211184210104충청남도 서천군 서천읍 군사리 184-21 104호126336022.422020-06-01
5882충청남도서천군44770202041025129310101[ 주원로183번길 35 ] 0000동 0101호58560048.82020-06-01
15224충청남도서천군4477020203203318000101[ 장산로1514번길 34 ] 0000동 0101호75394032.782020-06-01
16509충청남도서천군44770202025026127503101[ 화천길 1 ] 0003동 0101호73477750189.8652020-06-01
7364충청남도서천군44770202041026148310106충청남도 서천군 서면 신합리 483-1 106호172000040.02020-06-01
17755충청남도서천군44770202025030199631106충청남도 서천군 장항읍 원수리 996-3 1동 106호546000065.02020-06-01
14801충청남도서천군4477020203203511711101충청남도 서천군 화양면 창외리 17-1 1동 101호19980009.02020-06-01
14758충청남도서천군44770202032029113201101충청남도 서천군 화양면 대등리 132 1동 101호3360000336.02020-06-01
15341충청남도서천군44770202034040132191101충청남도 서천군 한산면 신성리 32-19 1동 101호373500045.02020-06-01
6930충청남도서천군44770202040026122860201충청남도 서천군 비인면 남당리 228-6 201호61538400199.82020-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
4169충청남도서천군4477020203803011304106[ 종판로901번길 57 ] 0004동 0106호45941500167.062020-06-01
4877충청남도서천군44770202041021139930108충청남도 서천군 서면 개야리 399-3 108호40800024.02020-06-01
9364충청남도서천군4477020203102816110101충청남도 서천군 마서면 남전리 61-1 101호91616400199.62020-06-01
2119충청남도서천군44770202039021122322101[ 성검로613번길 6 ] 0002동 0101호850000050.02020-06-01
5938충청남도서천군44770202040024115903102충청남도 서천군 비인면 칠지리 159 3동 102호62666109.82020-06-01
10914충청남도서천군44770202025322120741101충청남도 서천군 서천읍 사곡리 207-4 1동 101호95455360333.762020-06-01
3897충청남도서천군44770202038030136420102[ 저산길33번길 67-6 ] 0000동 0102호616930032.32020-06-01
18832충청남도서천군44770202025021118610101[ 장서로29번길 11 ] 0000동 0101호505328061.032020-06-01
4991충청남도서천군44770202039024138080101[ 갯벌체험로 1186-11 ] 0000동 0101호116979200166.42020-06-01
2498충청남도서천군44770202038029125651105[ 종판로887번길 2 ] 0001동 0105호148740049.582020-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
43충청남도서천군44770202041026130641101충청남도 서천군 서면 신합리 306-4 1동 101호28800012.02020-06-016
14충청남도서천군44770202033022121121101충청남도 서천군 기산면 신산리 211-2 1동 101호43428000184.82020-06-015
48충청남도서천군447702020410281436141101[ 춘장대길7번길 15 ] 0001동 0101호46800018.02020-06-015
36충청남도서천군44770202041023134131101충청남도 서천군 서면 월리 341-3 1동 101호28804800282.42020-06-014
49충청남도서천군447702020410281436141102[ 춘장대길7번길 15 ] 0001동 0102호9360012.02020-06-014
39충청남도서천군447702020410251485871101충청남도 서천군 서면 원두리 485-87 1동 101호36381070340.012020-06-013
44충청남도서천군4477020204102811201101충청남도 서천군 서면 도둔리 1-20 1동 101호211792018.912020-06-013
45충청남도서천군447702020410281724331102충청남도 서천군 서면 도둔리 72-433 1동 102호970200099.02020-06-013
51충청남도서천군447702020410281453241101충청남도 서천군 서면 도둔리 453-24 1동 101호72000018.02020-06-013
57충청남도서천군44770202041028268591101충청남도 서천군 서면 도둔리 산 68-59 1동 101호20400012.02020-06-013