Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows18
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description제공범위 : 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공. 관련 법령 : 지방세법. 소관기관 : 지방자치단체. 제공기관 : 시군구. 표준데이터 셋 제공시스템 : 표준지방세시스템. 자료기준일 : 매년 12월 31일.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080111

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 18 (0.2%) duplicate rowsDuplicates
특수지 is highly imbalanced (95.4%)Imbalance
동 is highly imbalanced (81.2%)Imbalance
연면적 is highly skewed (γ1 = 34.01796974)Skewed
부번 has 3702 (37.0%) zerosZeros

Reproduction

Analysis started2024-01-09 21:23:19.696168
Analysis finished2024-01-09 21:23:24.355990
Duration4.66 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

2024-01-10T06:23:24.404967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:24.471930image/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
홍성군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍성군 10000
100.0%

Length

2024-01-10T06:23:24.544230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:24.610563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44800
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44800
2nd row44800
3rd row44800
4th row44800
5th row44800

Common Values

ValueCountFrequency (%)
44800 10000
100.0%

Length

2024-01-10T06:23:24.681536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:24.748754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44800 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2024-01-10T06:23:24.823338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:24.894574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.8021
Minimum250
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:24.955309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median256
Q3350
95-th percentile390
Maximum390
Range140
Interquartile range (IQR)97

Descriptive statistics

Standard deviation53.239263
Coefficient of variation (CV)0.176991
Kurtosis-1.5820801
Mean300.8021
Median Absolute Deviation (MAD)6
Skewness0.35407302
Sum3008021
Variance2834.4192
MonotonicityNot monotonic
2024-01-10T06:23:25.039343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 2436
24.4%
256 1626
16.3%
253 1195
11.9%
330 728
 
7.3%
350 647
 
6.5%
340 619
 
6.2%
380 601
 
6.0%
390 582
 
5.8%
370 552
 
5.5%
320 534
 
5.3%
ValueCountFrequency (%)
250 2436
24.4%
253 1195
11.9%
256 1626
16.3%
320 534
 
5.3%
330 728
 
7.3%
340 619
 
6.2%
350 647
 
6.5%
360 480
 
4.8%
370 552
 
5.5%
380 601
 
6.0%
ValueCountFrequency (%)
390 582
 
5.8%
380 601
 
6.0%
370 552
 
5.5%
360 480
 
4.8%
350 647
 
6.5%
340 619
 
6.2%
330 728
7.3%
320 534
 
5.3%
256 1626
16.3%
253 1195
11.9%

법정리
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.6738
Minimum21
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:25.120925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median25
Q329
95-th percentile32
Maximum36
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.724108
Coefficient of variation (CV)0.1450548
Kurtosis-0.64921473
Mean25.6738
Median Absolute Deviation (MAD)3
Skewness0.54947687
Sum256738
Variance13.86898
MonotonicityNot monotonic
2024-01-10T06:23:25.204493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
25 1651
16.5%
21 1484
14.8%
22 1232
12.3%
24 829
8.3%
30 716
7.2%
26 630
 
6.3%
31 613
 
6.1%
23 592
 
5.9%
27 589
 
5.9%
29 495
 
5.0%
Other values (6) 1169
11.7%
ValueCountFrequency (%)
21 1484
14.8%
22 1232
12.3%
23 592
 
5.9%
24 829
8.3%
25 1651
16.5%
26 630
 
6.3%
27 589
 
5.9%
28 425
 
4.2%
29 495
 
5.0%
30 716
7.2%
ValueCountFrequency (%)
36 49
 
0.5%
35 79
 
0.8%
34 80
 
0.8%
33 261
 
2.6%
32 275
 
2.8%
31 613
6.1%
30 716
7.2%
29 495
5.0%
28 425
4.2%
27 589
5.9%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9913 
2
 
85
5
 
2

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 9913
99.1%
2 85
 
0.9%
5 2
 
< 0.1%

Length

2024-01-10T06:23:25.303491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:25.374630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9913
99.1%
2 85
 
0.9%
5 2
 
< 0.1%

본번
Real number (ℝ)

Distinct1050
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.2669
Minimum1
Maximum2063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:25.462451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1185
median367
Q3572
95-th percentile942
Maximum2063
Range2062
Interquartile range (IQR)387

Descriptive statistics

Standard deviation282.23252
Coefficient of variation (CV)0.69986533
Kurtosis1.0468102
Mean403.2669
Median Absolute Deviation (MAD)193
Skewness0.93201548
Sum4032669
Variance79655.197
MonotonicityNot monotonic
2024-01-10T06:23:25.564403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
397 131
 
1.3%
587 98
 
1.0%
893 90
 
0.9%
230 86
 
0.9%
392 83
 
0.8%
589 62
 
0.6%
588 58
 
0.6%
584 48
 
0.5%
26 45
 
0.4%
113 44
 
0.4%
Other values (1040) 9255
92.5%
ValueCountFrequency (%)
1 15
0.1%
2 24
0.2%
3 25
0.2%
4 17
0.2%
5 14
0.1%
6 4
 
< 0.1%
7 16
0.2%
8 7
 
0.1%
9 20
0.2%
10 20
0.2%
ValueCountFrequency (%)
2063 1
 
< 0.1%
2059 3
< 0.1%
1711 3
< 0.1%
1710 1
 
< 0.1%
1670 2
< 0.1%
1663 1
 
< 0.1%
1656 1
 
< 0.1%
1652 2
< 0.1%
1639 2
< 0.1%
1638 4
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2077
Minimum0
Maximum155
Zeros3702
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:25.898235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile19
Maximum155
Range155
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.7379897
Coefficient of variation (CV)2.314326
Kurtosis62.260814
Mean4.2077
Median Absolute Deviation (MAD)1
Skewness6.5491801
Sum42077
Variance94.828444
MonotonicityNot monotonic
2024-01-10T06:23:26.007385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3702
37.0%
1 1822
18.2%
2 936
 
9.4%
3 733
 
7.3%
4 452
 
4.5%
5 389
 
3.9%
6 265
 
2.6%
7 248
 
2.5%
8 162
 
1.6%
9 142
 
1.4%
Other values (76) 1149
 
11.5%
ValueCountFrequency (%)
0 3702
37.0%
1 1822
18.2%
2 936
 
9.4%
3 733
 
7.3%
4 452
 
4.5%
5 389
 
3.9%
6 265
 
2.6%
7 248
 
2.5%
8 162
 
1.6%
9 142
 
1.4%
ValueCountFrequency (%)
155 1
 
< 0.1%
153 2
 
< 0.1%
119 1
 
< 0.1%
118 11
0.1%
109 1
 
< 0.1%
108 1
 
< 0.1%
107 2
 
< 0.1%
103 3
 
< 0.1%
102 1
 
< 0.1%
101 2
 
< 0.1%

동
Categorical

IMBALANCE 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
8250 
1
1283 
2
 
162
3
 
56
8001
 
43
Other values (32)
 
206

Length

Max length4
Median length1
Mean length1.0245
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8250
82.5%
1 1283
 
12.8%
2 162
 
1.6%
3 56
 
0.6%
8001 43
 
0.4%
4 38
 
0.4%
7 27
 
0.3%
5 23
 
0.2%
6 19
 
0.2%
8 11
 
0.1%
Other values (27) 88
 
0.9%

Length

2024-01-10T06:23:26.120985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 8250
82.5%
1 1283
 
12.8%
2 162
 
1.6%
3 56
 
0.6%
8001 43
 
0.4%
4 38
 
0.4%
7 27
 
0.3%
5 23
 
0.2%
6 19
 
0.2%
8 11
 
0.1%
Other values (27) 88
 
0.9%

호
Text

Distinct417
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:23:26.299009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0263
Min length1

Characters and Unicode

Total characters30263
Distinct characters20
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)2.8%

Sample

1st row201
2nd row105
3rd row102
4th row108
5th rowB101
ValueCountFrequency (%)
101 4311
43.1%
102 1677
 
16.8%
103 751
 
7.5%
201 620
 
6.2%
104 336
 
3.4%
301 237
 
2.4%
105 182
 
1.8%
8101 164
 
1.6%
106 124
 
1.2%
202 90
 
0.9%
Other values (407) 1508
 
15.1%
2024-01-10T06:23:26.618451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14073
46.5%
0 9560
31.6%
2 2979
 
9.8%
3 1373
 
4.5%
4 669
 
2.2%
5 441
 
1.5%
6 358
 
1.2%
8 332
 
1.1%
7 232
 
0.8%
9 134
 
0.4%
Other values (10) 112
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30151
99.6%
Dash Punctuation 61
 
0.2%
Uppercase Letter 36
 
0.1%
Other Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14073
46.7%
0 9560
31.7%
2 2979
 
9.9%
3 1373
 
4.6%
4 669
 
2.2%
5 441
 
1.5%
6 358
 
1.2%
8 332
 
1.1%
7 232
 
0.8%
9 134
 
0.4%
Other Letter
ValueCountFrequency (%)
가 4
26.7%
설 4
26.7%
부 3
20.0%
나 1
 
6.7%
화 1
 
6.7%
장 1
 
6.7%
실 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
A 29
80.6%
B 7
 
19.4%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30212
99.8%
Latin 36
 
0.1%
Hangul 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14073
46.6%
0 9560
31.6%
2 2979
 
9.9%
3 1373
 
4.5%
4 669
 
2.2%
5 441
 
1.5%
6 358
 
1.2%
8 332
 
1.1%
7 232
 
0.8%
9 134
 
0.4%
Hangul
ValueCountFrequency (%)
가 4
26.7%
설 4
26.7%
부 3
20.0%
나 1
 
6.7%
화 1
 
6.7%
장 1
 
6.7%
실 1
 
6.7%
Latin
ValueCountFrequency (%)
A 29
80.6%
B 7
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30248
> 99.9%
Hangul 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14073
46.5%
0 9560
31.6%
2 2979
 
9.8%
3 1373
 
4.5%
4 669
 
2.2%
5 441
 
1.5%
6 358
 
1.2%
8 332
 
1.1%
7 232
 
0.8%
9 134
 
0.4%
Other values (3) 97
 
0.3%
Hangul
ValueCountFrequency (%)
가 4
26.7%
설 4
26.7%
부 3
20.0%
나 1
 
6.7%
화 1
 
6.7%
장 1
 
6.7%
실 1
 
6.7%

물건지
Text

Distinct9370
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:23:26.890261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length26.4153
Min length20

Characters and Unicode

Total characters264153
Distinct characters165
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8909 ?
Unique (%)89.1%

Sample

1st row[ 남당항로435번길 15 ] 0000동 0201호
2nd row[ 광천로309번길 8 ] 0000동 0105호
3rd row[ 이응노로183번길 167-42 ] 0000동 0102호
4th row충청남도 홍성군 광천읍 벽계리 415-5 108호
5th row[ 청사로 152 ] 0000동 B101호
ValueCountFrequency (%)
7032
 
11.4%
충청남도 6484
 
10.5%
홍성군 6484
 
10.5%
0000동 3152
 
5.1%
101호 2970
 
4.8%
0101호 1341
 
2.2%
102호 1268
 
2.1%
홍성읍 1175
 
1.9%
1동 969
 
1.6%
광천읍 869
 
1.4%
Other values (4574) 29717
48.4%
2024-01-10T06:23:27.281821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51461
19.5%
0 29208
 
11.1%
1 22846
 
8.6%
호 10135
 
3.8%
홍 9508
 
3.6%
성 8425
 
3.2%
2 8331
 
3.2%
남 7298
 
2.8%
청 7019
 
2.7%
충 6968
 
2.6%
Other values (155) 102954
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113687
43.0%
Decimal Number 86684
32.8%
Space Separator 51461
19.5%
Dash Punctuation 5249
 
2.0%
Close Punctuation 3516
 
1.3%
Open Punctuation 3516
 
1.3%
Uppercase Letter 40
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 10135
 
8.9%
홍 9508
 
8.4%
성 8425
 
7.4%
남 7298
 
6.4%
청 7019
 
6.2%
충 6968
 
6.1%
도 6577
 
5.8%
군 6484
 
5.7%
리 6484
 
5.7%
동 5722
 
5.0%
Other values (138) 39067
34.4%
Decimal Number
ValueCountFrequency (%)
0 29208
33.7%
1 22846
26.4%
2 8331
 
9.6%
3 6126
 
7.1%
4 4659
 
5.4%
5 3839
 
4.4%
6 3455
 
4.0%
7 2896
 
3.3%
8 2678
 
3.1%
9 2646
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 29
72.5%
B 9
 
22.5%
L 2
 
5.0%
Space Separator
ValueCountFrequency (%)
51461
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5249
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3516
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150426
56.9%
Hangul 113687
43.0%
Latin 40
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 10135
 
8.9%
홍 9508
 
8.4%
성 8425
 
7.4%
남 7298
 
6.4%
청 7019
 
6.2%
충 6968
 
6.1%
도 6577
 
5.8%
군 6484
 
5.7%
리 6484
 
5.7%
동 5722
 
5.0%
Other values (138) 39067
34.4%
Common
ValueCountFrequency (%)
51461
34.2%
0 29208
19.4%
1 22846
15.2%
2 8331
 
5.5%
3 6126
 
4.1%
- 5249
 
3.5%
4 4659
 
3.1%
5 3839
 
2.6%
] 3516
 
2.3%
[ 3516
 
2.3%
Other values (4) 11675
 
7.8%
Latin
ValueCountFrequency (%)
A 29
72.5%
B 9
 
22.5%
L 2
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150466
57.0%
Hangul 113687
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51461
34.2%
0 29208
19.4%
1 22846
15.2%
2 8331
 
5.5%
3 6126
 
4.1%
- 5249
 
3.5%
4 4659
 
3.1%
5 3839
 
2.6%
] 3516
 
2.3%
[ 3516
 
2.3%
Other values (7) 11715
 
7.8%
Hangul
ValueCountFrequency (%)
호 10135
 
8.9%
홍 9508
 
8.4%
성 8425
 
7.4%
남 7298
 
6.4%
청 7019
 
6.2%
충 6968
 
6.1%
도 6577
 
5.8%
군 6484
 
5.7%
리 6484
 
5.7%
동 5722
 
5.0%
Other values (138) 39067
34.4%

시가표준액
Real number (ℝ)

Distinct8268
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54145442
Minimum6050
Maximum3.4142855 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:27.396062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6050
5-th percentile408000
Q11597035
median9462525
Q351325500
95-th percentile2.1484996 × 108
Maximum3.4142855 × 109
Range3.4142795 × 109
Interquartile range (IQR)49728465

Descriptive statistics

Standard deviation1.5062977 × 108
Coefficient of variation (CV)2.7819474
Kurtosis138.59018
Mean54145442
Median Absolute Deviation (MAD)8900925
Skewness9.7397292
Sum5.4145442 × 1011
Variance2.2689328 × 1016
MonotonicityNot monotonic
2024-01-10T06:23:27.507497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990000 26
 
0.3%
38123020 26
 
0.3%
41584150 26
 
0.3%
882000 24
 
0.2%
972000 23
 
0.2%
633600 23
 
0.2%
39312050 21
 
0.2%
489600 20
 
0.2%
960000 19
 
0.2%
561600 17
 
0.2%
Other values (8258) 9775
97.8%
ValueCountFrequency (%)
6050 1
< 0.1%
6400 1
< 0.1%
13500 1
< 0.1%
14000 1
< 0.1%
16500 1
< 0.1%
17850 1
< 0.1%
19800 1
< 0.1%
23250 1
< 0.1%
25600 1
< 0.1%
26350 1
< 0.1%
ValueCountFrequency (%)
3414285510 1
< 0.1%
2985219330 1
< 0.1%
2916711640 1
< 0.1%
2746872000 1
< 0.1%
2743748000 1
< 0.1%
2682431850 1
< 0.1%
2396137400 1
< 0.1%
2317644300 1
< 0.1%
2275310100 1
< 0.1%
2271911880 1
< 0.1%

연면적
Real number (ℝ)

SKEWED 

Distinct6210
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.68072
Minimum0.538
Maximum33912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:27.618642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.538
5-th percentile17.0685
Q151.72
median119.19235
Q3235.05
95-th percentile750
Maximum33912
Range33911.462
Interquartile range (IQR)183.33

Descriptive statistics

Standard deviation497.27482
Coefficient of variation (CV)2.2533677
Kurtosis2134.8234
Mean220.68072
Median Absolute Deviation (MAD)78.23735
Skewness34.01797
Sum2206807.2
Variance247282.24
MonotonicityNot monotonic
2024-01-10T06:23:27.723072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 306
 
3.1%
198.0 64
 
0.6%
66.0 46
 
0.5%
27.0 43
 
0.4%
36.0 36
 
0.4%
180.0 31
 
0.3%
196.0 29
 
0.3%
60.0 29
 
0.3%
50.0 26
 
0.3%
56.1948 26
 
0.3%
Other values (6200) 9364
93.6%
ValueCountFrequency (%)
0.538 1
< 0.1%
0.593 1
< 0.1%
0.598 1
< 0.1%
0.9 1
< 0.1%
1.0 1
< 0.1%
1.21 1
< 0.1%
1.26 1
< 0.1%
1.28 1
< 0.1%
2.21 1
< 0.1%
2.222 1
< 0.1%
ValueCountFrequency (%)
33912.0 1
< 0.1%
8850.8 1
< 0.1%
7026.03 1
< 0.1%
6979.76 1
< 0.1%
6235.07 1
< 0.1%
5916.2508 1
< 0.1%
5910.0964 1
< 0.1%
5591.26 1
< 0.1%
4740.15 1
< 0.1%
4300.0 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20210601
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 10000
100.0%

Length

2024-01-10T06:23:27.826044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:27.898074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 10000
100.0%

Interactions

2024-01-10T06:23:23.595061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:20.875376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.564703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.033851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.563156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.112498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.673514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.177178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.643953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.103413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.665756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.190864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.764494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.256517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.725170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.184320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.778021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.273367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.841757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.323672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.794876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.267733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.864313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.346453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.929494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.405239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.876454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.370112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.943625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.431176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:24.014534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.484530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:21.955293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:22.468419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.028922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:23.515649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:23:27.947002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.4050.0320.2560.1080.1870.0540.027
법정리0.4051.0000.0620.4590.1700.1610.0590.043
특수지0.0320.0621.0000.1910.0000.0000.0000.000
본번0.2560.4590.1911.0000.0680.4330.2600.015
부번0.1080.1700.0000.0681.0000.2840.0000.000
동0.1870.1610.0000.4330.2841.0000.0000.000
시가표준액0.0540.0590.0000.2600.0000.0001.0000.686
연면적0.0270.0430.0000.0150.0000.0000.6861.000
2024-01-10T06:23:28.031733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동특수지
동1.0000.000
특수지0.0001.000
2024-01-10T06:23:28.097127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지동
법정동1.0000.228-0.148-0.055-0.2690.0680.0170.070
법정리0.2281.000-0.037-0.071-0.1220.0690.0600.061
본번-0.148-0.0371.000-0.2230.2600.0160.1160.166
부번-0.055-0.071-0.2231.000-0.038-0.0180.0000.107
시가표준액-0.269-0.1220.260-0.0381.0000.4580.0000.000
연면적0.0680.0690.016-0.0180.4581.0000.0000.000
특수지0.0170.0600.1160.0000.0000.0001.0000.000
동0.0700.0610.1660.1070.0000.0000.0001.000

Missing values

2024-01-10T06:23:24.129806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:23:24.284942image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
29894충청남도홍성군448002021370261464260201[ 남당항로435번길 15 ] 0000동 0201호108844540220.7820210601
7626충청남도홍성군44800202125322119110105[ 광천로309번길 8 ] 0000동 0105호30741100181.920210601
20726충청남도홍성군4480020212562119460102[ 이응노로183번길 167-42 ] 0000동 0102호2265000453.020210601
7077충청남도홍성군44800202125328141550108충청남도 홍성군 광천읍 벽계리 415-5 108호2644600264.4620210601
5708충청남도홍성군44800202125625156000B101[ 청사로 152 ] 0000동 B101호8516140075.280820210601
2536충청남도홍성군448002021250301552900101[ 조양로 220-10 ] 0000동 0101호4050736099.0420210601
25145충청남도홍성군44800202134027133000101충청남도 홍성군 장곡면 가송리 330 101호1680000105.020210601
1658충청남도홍성군448002021250261392108101충청남도 홍성군 홍성읍 남장리 392-1 8101호14169600108.020210601
15246충청남도홍성군44800202132025126730103충청남도 홍성군 금마면 부평리 267-3 103호32562000402.020210601
24144충청남도홍성군448002021370301928308101[ 임해로 293 ] 0000동 8101호660600018.020210601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
11557충청남도홍성군448002021250231255720101충청남도 홍성군 홍성읍 소향리 255-72 101호226000010.020210601
5291충청남도홍성군44800202125021124711101[ 아문길29번길 49 ] 0001동 0101호2201707047.0420210601
6698충청남도홍성군44800202125331128160101충청남도 홍성군 광천읍 운용리 281-6 101호2485800497.1620210601
8505충청남도홍성군448002021253301509508101충청남도 홍성군 광천읍 옹암리 509-5 8101호224046900671.020210601
4753충청남도홍성군448002021250301104208002B-301[ 조양로247번길 9 ] 8002동 B-301호683001180854.8220210601
9405충청남도홍성군44800202125021133821201[ 조양로170번길 31 ] 0001동 0201호7733000104.520210601
19603충청남도홍성군44800202125633160900101충청남도 홍성군 홍북읍 대인리 609 101호960000192.020210601
17981충청남도홍성군44800202125625191800501[ 상하천로 24-1 ] 0000동 0501호305805310435.620120210601
4214충청남도홍성군44800202132032118110101충청남도 홍성군 금마면 화양리 181-1 101호15655440105.7820210601
28370충청남도홍성군44800202135029130110104충청남도 홍성군 은하면 덕실리 301-1 104호427500057.020210601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
17충청남도홍성군44800202139025137921101충청남도 홍성군 구항면 장양리 379-2 1동 101호22148000196.0202106014
9충청남도홍성군4480020213502316831101충청남도 홍성군 은하면 장곡리 68-3 1동 101호27374160622.14202106013
0충청남도홍성군44800202125025130500127충청남도 홍성군 홍성읍 옥암리 305 127호3784625055.25202106012
1충청남도홍성군44800202125026122011201충청남도 홍성군 홍성읍 남장리 220-1 1동 201호105378000675.5202106012
2충청남도홍성군448002021256241161130101충청남도 홍성군 홍북읍 내덕리 161-13 101호354375001012.5202106012
3충청남도홍성군44800202125633135551101충청남도 홍성군 홍북읍 대인리 355-5 1동 101호28120056.24202106012
4충청남도홍성군448002021320311119131101충청남도 홍성군 금마면 죽림리 119-13 1동 101호191029800434.06202106012
5충청남도홍성군44800202133024145751101충청남도 홍성군 홍동면 홍원리 457-5 1동 101호98683200623.0202106012
6충청남도홍성군44800202133024186711101충청남도 홍성군 홍동면 홍원리 867-1 1동 101호10890000247.5202106012
7충청남도홍성군44800202134027142911101충청남도 홍성군 장곡면 가송리 429-1 1동 101호2520000504.0202106012