Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공<br/>인천광역시 부평구 일반건축물 시가표준액 데이터입니다.<br/>(시도명,시군구명,자치단체코드,과세년도,법정동,법정리,특수지,본번,부번,동,호,물건지,시가표준액,연면적,기준일자)<br/>예) 인천광역시,부평구,28237,2019,102,00,1,0251,0002,0001,0002,[ 백범로568번길 24 ] 0001동 0002호,51303000,209.4,20190601
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15080133&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 14 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (97.7%)Imbalance
시가표준액 is highly skewed (γ1 = 21.81582368)Skewed
부번 has 914 (9.1%) zerosZeros
동 has 5986 (59.9%) zerosZeros

Reproduction

Analysis started2024-05-10 22:09:43.973230
Analysis finished2024-05-10 22:10:02.444486
Duration18.47 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
인천광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 10000
100.0%

Length

2024-05-10T22:10:02.654202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:02.984533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평구 10000
100.0%

Length

2024-05-10T22:10:03.307582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:03.607412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
28237
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28237
2nd row28237
3rd row28237
4th row28237
5th row28237

Common Values

ValueCountFrequency (%)
28237 10000
100.0%

Length

2024-05-10T22:10:03.928333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:04.229010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28237 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

2024-05-10T22:10:04.542967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:04.848532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.9858
Minimum101
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:05.176773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median102
Q3104
95-th percentile107
Maximum109
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1226985
Coefficient of variation (CV)0.020611565
Kurtosis-0.53440782
Mean102.9858
Median Absolute Deviation (MAD)1
Skewness0.74461032
Sum1029858
Variance4.5058489
MonotonicityNot monotonic
2024-05-10T22:10:05.511174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
101 4111
41.1%
104 1863
18.6%
102 979
 
9.8%
105 817
 
8.2%
103 799
 
8.0%
107 667
 
6.7%
106 486
 
4.9%
108 232
 
2.3%
109 46
 
0.5%
ValueCountFrequency (%)
101 4111
41.1%
102 979
 
9.8%
103 799
 
8.0%
104 1863
18.6%
105 817
 
8.2%
106 486
 
4.9%
107 667
 
6.7%
108 232
 
2.3%
109 46
 
0.5%
ValueCountFrequency (%)
109 46
 
0.5%
108 232
 
2.3%
107 667
 
6.7%
106 486
 
4.9%
105 817
 
8.2%
104 1863
18.6%
103 799
 
8.0%
102 979
 
9.8%
101 4111
41.1%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-05-10T22:10:05.908827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:06.207502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9978 
2
 
22

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 9978
99.8%
2 22
 
0.2%

Length

2024-05-10T22:10:06.663918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:07.026166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9978
99.8%
2 22
 
0.2%

본번
Real number (ℝ)

Distinct643
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337.9841
Minimum1
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:07.426591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1182
median374
Q3459
95-th percentile663.1
Maximum995
Range994
Interquartile range (IQR)277

Descriptive statistics

Standard deviation192.20649
Coefficient of variation (CV)0.56868501
Kurtosis-0.016066338
Mean337.9841
Median Absolute Deviation (MAD)163
Skewness0.38220755
Sum3379841
Variance36943.336
MonotonicityNot monotonic
2024-05-10T22:10:08.053497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 420
 
4.2%
426 324
 
3.2%
539 172
 
1.7%
425 147
 
1.5%
201 142
 
1.4%
182 128
 
1.3%
199 122
 
1.2%
431 119
 
1.2%
10 109
 
1.1%
408 107
 
1.1%
Other values (633) 8210
82.1%
ValueCountFrequency (%)
1 5
 
0.1%
2 8
 
0.1%
3 16
 
0.2%
5 13
 
0.1%
6 12
 
0.1%
7 27
 
0.3%
8 2
 
< 0.1%
9 22
 
0.2%
10 109
1.1%
11 10
 
0.1%
ValueCountFrequency (%)
995 3
 
< 0.1%
954 37
0.4%
950 24
0.2%
938 3
 
< 0.1%
915 4
 
< 0.1%
914 4
 
< 0.1%
912 4
 
< 0.1%
911 2
 
< 0.1%
909 1
 
< 0.1%
907 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct372
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.1031
Minimum0
Maximum1209
Zeros914
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:08.481353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q323
95-th percentile139
Maximum1209
Range1209
Interquartile range (IQR)21

Descriptive statistics

Standard deviation88.304984
Coefficient of variation (CV)2.7506684
Kurtosis51.410959
Mean32.1031
Median Absolute Deviation (MAD)6
Skewness6.4085829
Sum321031
Variance7797.7702
MonotonicityNot monotonic
2024-05-10T22:10:09.067137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1162
 
11.6%
1 1036
 
10.4%
0 914
 
9.1%
3 663
 
6.6%
4 521
 
5.2%
9 386
 
3.9%
5 348
 
3.5%
7 272
 
2.7%
6 235
 
2.4%
8 231
 
2.3%
Other values (362) 4232
42.3%
ValueCountFrequency (%)
0 914
9.1%
1 1036
10.4%
2 1162
11.6%
3 663
6.6%
4 521
5.2%
5 348
 
3.5%
6 235
 
2.4%
7 272
 
2.7%
8 231
 
2.3%
9 386
 
3.9%
ValueCountFrequency (%)
1209 1
 
< 0.1%
1105 1
 
< 0.1%
1079 2
< 0.1%
1069 1
 
< 0.1%
1065 1
 
< 0.1%
1030 1
 
< 0.1%
1004 1
 
< 0.1%
977 2
< 0.1%
964 1
 
< 0.1%
900 3
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct56
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.2735
Minimum0
Maximum9999
Zeros5986
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:09.621214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9999
Range9999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation793.0528
Coefficient of variation (CV)11.615822
Kurtosis151.12463
Mean68.2735
Median Absolute Deviation (MAD)0
Skewness12.353444
Sum682735
Variance628932.74
MonotonicityNot monotonic
2024-05-10T22:10:10.129746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5986
59.9%
1 3050
30.5%
2 356
 
3.6%
3 135
 
1.4%
101 81
 
0.8%
102 65
 
0.7%
4 49
 
0.5%
9999 38
 
0.4%
7 30
 
0.3%
6 21
 
0.2%
Other values (46) 189
 
1.9%
ValueCountFrequency (%)
0 5986
59.9%
1 3050
30.5%
2 356
 
3.6%
3 135
 
1.4%
4 49
 
0.5%
5 14
 
0.1%
6 21
 
0.2%
7 30
 
0.3%
8 8
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9999 38
0.4%
9998 10
 
0.1%
9997 3
 
< 0.1%
9994 2
 
< 0.1%
9991 2
 
< 0.1%
9988 1
 
< 0.1%
9982 1
 
< 0.1%
9980 1
 
< 0.1%
9917 2
 
< 0.1%
9909 1
 
< 0.1%

호
Text

Distinct1384
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:10:10.921017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.3481
Min length1

Characters and Unicode

Total characters23481
Distinct characters30
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique862 ?
Unique (%)8.6%

Sample

1st row4
2nd row2118
3rd row101
4th row3
5th row105
ValueCountFrequency (%)
1 1398
 
14.0%
2 897
 
9.0%
3 633
 
6.3%
4 422
 
4.2%
5 291
 
2.9%
101 290
 
2.9%
201 195
 
1.9%
102 187
 
1.9%
6 178
 
1.8%
301 145
 
1.4%
Other values (1376) 5378
53.7%
2024-05-10T22:10:12.066603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6311
26.9%
0 4914
20.9%
2 3377
14.4%
3 2373
 
10.1%
4 1726
 
7.4%
5 1275
 
5.4%
6 951
 
4.1%
8 932
 
4.0%
7 792
 
3.4%
9 537
 
2.3%
Other values (20) 293
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23188
98.8%
Dash Punctuation 182
 
0.8%
Uppercase Letter 47
 
0.2%
Other Letter 28
 
0.1%
Lowercase Letter 22
 
0.1%
Space Separator 14
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6311
27.2%
0 4914
21.2%
2 3377
14.6%
3 2373
 
10.2%
4 1726
 
7.4%
5 1275
 
5.5%
6 951
 
4.1%
8 932
 
4.0%
7 792
 
3.4%
9 537
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 8
36.4%
n 7
31.8%
r 1
 
4.5%
o 1
 
4.5%
v 1
 
4.5%
e 1
 
4.5%
b 1
 
4.5%
u 1
 
4.5%
g 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 27
57.4%
A 8
 
17.0%
J 7
 
14.9%
C 2
 
4.3%
M 1
 
2.1%
N 1
 
2.1%
F 1
 
2.1%
Other Letter
ValueCountFrequency (%)
월 14
50.0%
일 14
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23384
99.6%
Latin 69
 
0.3%
Hangul 28
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 27
39.1%
A 8
 
11.6%
a 8
 
11.6%
J 7
 
10.1%
n 7
 
10.1%
C 2
 
2.9%
M 1
 
1.4%
r 1
 
1.4%
N 1
 
1.4%
o 1
 
1.4%
Other values (6) 6
 
8.7%
Common
ValueCountFrequency (%)
1 6311
27.0%
0 4914
21.0%
2 3377
14.4%
3 2373
 
10.1%
4 1726
 
7.4%
5 1275
 
5.5%
6 951
 
4.1%
8 932
 
4.0%
7 792
 
3.4%
9 537
 
2.3%
Other values (2) 196
 
0.8%
Hangul
ValueCountFrequency (%)
월 14
50.0%
일 14
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23453
99.9%
Hangul 28
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6311
26.9%
0 4914
21.0%
2 3377
14.4%
3 2373
 
10.1%
4 1726
 
7.4%
5 1275
 
5.4%
6 951
 
4.1%
8 932
 
4.0%
7 792
 
3.4%
9 537
 
2.3%
Other values (18) 265
 
1.1%
Hangul
ValueCountFrequency (%)
월 14
50.0%
일 14
50.0%

물건지
Text

Distinct9619
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T22:10:12.783884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length25.0526
Min length16

Characters and Unicode

Total characters250526
Distinct characters113
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

Unique9290 ?
Unique (%)92.9%

Sample

1st row[ 경인로 737 ] 0001동 0004호
2nd row인천광역시 부평구 청천동 386 1동 2118호
3rd row인천광역시 부평구 청천동 399-3 101호
4th row[ 수변로64번길 17 ] 0000동 0003호
5th row[ 백범로578번길 65 ] 0000동 0105호
ValueCountFrequency (%)
13476
23.0%
0000동 4650
 
7.9%
인천광역시 3262
 
5.6%
부평구 3262
 
5.6%
0001동 1803
 
3.1%
1동 1247
 
2.1%
부평동 1098
 
1.9%
0001호 928
 
1.6%
부평대로 831
 
1.4%
청천동 762
 
1.3%
Other values (4234) 27329
46.6%
2024-05-10T22:10:13.851316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48648
19.4%
0 43745
17.5%
1 16373
 
6.5%
동 12293
 
4.9%
호 9965
 
4.0%
2 9004
 
3.6%
3 7591
 
3.0%
부 6910
 
2.8%
[ 6738
 
2.7%
] 6738
 
2.7%
Other values (103) 82521
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100563
40.1%
Other Letter 84096
33.6%
Space Separator 48648
19.4%
Open Punctuation 6738
 
2.7%
Close Punctuation 6738
 
2.7%
Dash Punctuation 3707
 
1.5%
Uppercase Letter 36
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12293
14.6%
호 9965
11.8%
부 6910
 
8.2%
로 6738
 
8.0%
평 6078
 
7.2%
천 4342
 
5.2%
인 3575
 
4.3%
광 3392
 
4.0%
시 3370
 
4.0%
구 3298
 
3.9%
Other values (86) 24135
28.7%
Decimal Number
ValueCountFrequency (%)
0 43745
43.5%
1 16373
 
16.3%
2 9004
 
9.0%
3 7591
 
7.5%
4 5437
 
5.4%
5 4730
 
4.7%
6 4004
 
4.0%
7 3293
 
3.3%
9 3246
 
3.2%
8 3140
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 27
75.0%
A 7
 
19.4%
C 2
 
5.6%
Space Separator
ValueCountFrequency (%)
48648
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 6738
100.0%
Close Punctuation
ValueCountFrequency (%)
] 6738
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166394
66.4%
Hangul 84096
33.6%
Latin 36
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12293
14.6%
호 9965
11.8%
부 6910
 
8.2%
로 6738
 
8.0%
평 6078
 
7.2%
천 4342
 
5.2%
인 3575
 
4.3%
광 3392
 
4.0%
시 3370
 
4.0%
구 3298
 
3.9%
Other values (86) 24135
28.7%
Common
ValueCountFrequency (%)
48648
29.2%
0 43745
26.3%
1 16373
 
9.8%
2 9004
 
5.4%
3 7591
 
4.6%
[ 6738
 
4.0%
] 6738
 
4.0%
4 5437
 
3.3%
5 4730
 
2.8%
6 4004
 
2.4%
Other values (4) 13386
 
8.0%
Latin
ValueCountFrequency (%)
B 27
75.0%
A 7
 
19.4%
C 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166430
66.4%
Hangul 84096
33.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48648
29.2%
0 43745
26.3%
1 16373
 
9.8%
2 9004
 
5.4%
3 7591
 
4.6%
[ 6738
 
4.0%
] 6738
 
4.0%
4 5437
 
3.3%
5 4730
 
2.8%
6 4004
 
2.4%
Other values (7) 13422
 
8.1%
Hangul
ValueCountFrequency (%)
동 12293
14.6%
호 9965
11.8%
부 6910
 
8.2%
로 6738
 
8.0%
평 6078
 
7.2%
천 4342
 
5.2%
인 3575
 
4.3%
광 3392
 
4.0%
시 3370
 
4.0%
구 3298
 
3.9%
Other values (86) 24135
28.7%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8042
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72409297
Minimum28460
Maximum1.0276677 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:14.306535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28460
5-th percentile1741532
Q110042470
median35099485
Q374275005
95-th percentile2.3314395 × 108
Maximum1.0276677 × 1010
Range1.0276648 × 1010
Interquartile range (IQR)64232535

Descriptive statistics

Standard deviation2.009708 × 108
Coefficient of variation (CV)2.7754834
Kurtosis847.10814
Mean72409297
Median Absolute Deviation (MAD)27762525
Skewness21.815824
Sum7.2409297 × 1011
Variance4.0389263 × 1016
MonotonicityNot monotonic
2024-05-10T22:10:14.782368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1840400 146
 
1.5%
41596040 57
 
0.6%
78198460 52
 
0.5%
37582590 50
 
0.5%
16677610 49
 
0.5%
8871360 48
 
0.5%
30681900 39
 
0.4%
4832590 38
 
0.4%
38228760 36
 
0.4%
5032850 33
 
0.3%
Other values (8032) 9452
94.5%
ValueCountFrequency (%)
28460 1
< 0.1%
33350 1
< 0.1%
33480 1
< 0.1%
37000 1
< 0.1%
37950 1
< 0.1%
38000 1
< 0.1%
38640 1
< 0.1%
39000 1
< 0.1%
41040 1
< 0.1%
42000 1
< 0.1%
ValueCountFrequency (%)
10276676510 1
< 0.1%
6366015000 1
< 0.1%
5065556800 1
< 0.1%
4133282280 1
< 0.1%
4007581490 1
< 0.1%
2682809090 1
< 0.1%
2326881600 1
< 0.1%
2302817540 1
< 0.1%
2154364500 1
< 0.1%
2055454060 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6575
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.24035
Minimum0.45
Maximum17687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T22:10:15.194784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile7.04
Q132
median71.2261
Q3137.22
95-th percentile482.08761
Maximum17687
Range17686.55
Interquartile range (IQR)105.22

Descriptive statistics

Standard deviation417.60012
Coefficient of variation (CV)2.7981716
Kurtosis600.30221
Mean149.24035
Median Absolute Deviation (MAD)46.065
Skewness19.457284
Sum1492403.5
Variance174389.86
MonotonicityNot monotonic
2024-05-10T22:10:15.820225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 146
 
1.5%
71.2261 57
 
0.6%
98.9854 52
 
0.5%
18.0 51
 
0.5%
51.483 50
 
0.5%
21.1109 49
 
0.5%
15.1907 48
 
0.5%
42.03 41
 
0.4%
9.17 39
 
0.4%
55.89 36
 
0.4%
Other values (6565) 9431
94.3%
ValueCountFrequency (%)
0.45 1
 
< 0.1%
0.5 1
 
< 0.1%
0.52 1
 
< 0.1%
0.6 2
 
< 0.1%
0.79 1
 
< 0.1%
0.9 1
 
< 0.1%
0.91 1
 
< 0.1%
0.99 2
 
< 0.1%
1.0 9
0.1%
1.08 1
 
< 0.1%
ValueCountFrequency (%)
17687.0 1
< 0.1%
13778.61 1
< 0.1%
13207.5 1
< 0.1%
8885.99 1
< 0.1%
8500.146 1
< 0.1%
8452.52 1
< 0.1%
8189.06 1
< 0.1%
5986.68 1
< 0.1%
5141.51 1
< 0.1%
4524.13 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 10000
100.0%

Length

2024-05-10T22:10:16.237897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:10:16.529520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2024-05-10T22:09:59.098438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:50.052015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:52.058232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:53.799657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:55.423855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:57.021623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:59.555527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:50.389368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:52.352405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:54.114585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:55.704607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:57.334569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:59.893560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:50.677629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:52.623414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:54.386780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:55.979291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:57.610881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:00.222260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:50.978279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:52.902356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:54.668133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:56.255491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:58.056368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:00.520256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:51.265652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:53.185242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:54.911561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:56.487319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:58.431535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:10:00.822855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:51.604459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:53.457238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:55.170394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:56.737746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:09:58.771912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:10:16.717972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동시가표준액연면적
법정동1.0000.0630.4960.2210.0950.0000.071
특수지0.0631.0000.1650.0000.0110.0000.000
본번0.4960.1651.0000.3180.0790.0580.035
부번0.2210.0000.3181.0000.0000.0190.000
동0.0950.0110.0790.0001.0000.0000.000
시가표준액0.0000.0000.0580.0190.0001.0000.873
연면적0.0710.0000.0350.0000.0000.8731.000
2024-05-10T22:10:17.024809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적특수지
법정동1.000-0.205-0.2290.0040.0370.1460.063
본번-0.2051.000-0.1380.0090.1190.0210.126
부번-0.229-0.1381.000-0.0290.0150.0600.000
동0.0040.009-0.0291.000-0.0220.1260.019
시가표준액0.0370.1190.015-0.0221.0000.8540.000
연면적0.1460.0210.0600.1260.8541.0000.000
특수지0.0630.1260.0000.0190.0000.0001.000

Missing values

2024-05-10T22:10:01.340596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:10:02.128358image/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
64574인천광역시부평구28237202110201578314[ 경인로 737 ] 0001동 0004호86707600242.22021-06-01
60088인천광역시부평구28237202110401386012118인천광역시 부평구 청천동 386 1동 2118호61376310233.372021-06-01
27743인천광역시부평구2823720211040139930101인천광역시 부평구 청천동 399-3 101호663890015.762021-06-01
53335인천광역시부평구282372021107014891303[ 수변로64번길 17 ] 0000동 0003호615148013.442021-06-01
40878인천광역시부평구2823720211020112000105[ 백범로578번길 65 ] 0000동 0105호52933530183.7972021-06-01
3021인천광역시부평구282372021101015461400410[ 경원대로1367번길 10-8 ] 0000동 0410호2283210033.092021-06-01
4083인천광역시부평구2823720211010175613312인천광역시 부평구 부평동 756-133 1동 2호1089430095.692021-06-01
44998인천광역시부평구2823720211010137831103[ 장제로 145 ] 0001동 0103호5916556062.092021-06-01
63906인천광역시부평구28237202110501456611인천광역시 부평구 삼산동 456-6 1동 1호63853070102.462021-06-01
47713인천광역시부평구2823720211010153430204[ 광장로4번길 14 ] 0000동 0204호3005655027.4742021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
9008인천광역시부평구28237202110201233047인천광역시 부평구 십정동 233 4동 7호1394960074.22021-06-01
59545인천광역시부평구28237202110401234218103[ 원길로 19 ] 0001동 8103호690900028.22021-06-01
27466인천광역시부평구2823720211040139660102인천광역시 부평구 청천동 396-6 102호1737171044.562021-06-01
8148인천광역시부평구28237202110201403712[ 열우물로 53 ] 0001동 0002호49910880105.63152021-06-01
31872인천광역시부평구2823720211050139090104[ 부평북로 467 ] 0000동 0104호1006168029.082021-06-01
47776인천광역시부평구2823720211010141570103[ 주부토로 52 ] 0000동 0103호6801666056.9322021-06-01
40110인천광역시부평구2823720211030128151108인천광역시 부평구 산곡동 281-5 1동 108호1184469027.612021-06-01
59998인천광역시부평구2823720211040125930305[ 원길로 34 ] 0000동 0305호3695138066.342021-06-01
44777인천광역시부평구28237202110101241101103[ 부흥로334번길 4 ] 0000동 1103호7840266090.1182021-06-01
58102인천광역시부평구2823720211040119901101인천광역시 부평구 청천동 199 1동 101호300000024.02021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
7인천광역시부평구28237202110101776722인천광역시 부평구 부평동 776-7 2동 2호237000015.02021-06-014
6인천광역시부평구28237202110101776721인천광역시 부평구 부평동 776-7 2동 1호237000015.02021-06-013
0인천광역시부평구282372021101011822200[ 경원대로1418번길 15 ] 0000동 0000호124866560166.22282021-06-012
1인천광역시부평구282372021101012076200[ 부평대로40번길 17 ] 0000동 0000호103492660101.76272021-06-012
2인천광역시부평구282372021101014421511[ 부평대로 88 ] 0001동 0001호637166290657.532021-06-012
3인천광역시부평구282372021101015292700[ 부평대로63번길 10-5 ] 0000동 0000호93118350140.452021-06-012
4인천광역시부평구28237202110101563110[ 대정로 92 ] 0001동 0000호276813810373.71922021-06-012
5인천광역시부평구2823720211010166300101인천광역시 부평구 부평동 663 101호9617870157.672021-06-012
8인천광역시부평구2823720211020137211[ 함봉로42번길 46 ] 0001동 0001호479817960563.82842021-06-012
9인천광역시부평구282372021102014071301[ 열우물로 37 ] 0000동 0001호2578400088.02021-06-012