Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical7
Numeric7
Text1

Dataset

Description인천광역시 남동구 일반건축물 시가표준액에 대한 데이터로(과세년도, 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적)등을 제공합니다.
Author인천광역시 남동구
URLhttps://www.data.go.kr/data/15080351/fileData.do

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 3 (< 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
호 is highly skewed (γ1 = 24.36999573)Skewed
부번 has 1533 (15.3%) zerosZeros
동 has 2448 (24.5%) zerosZeros

Reproduction

Analysis started2024-03-14 13:09:14.404299
Analysis finished2024-03-14 13:09:28.657376
Duration14.25 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

2024-03-14T22:09:28.847292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:29.134286image/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
남동구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 10000
100.0%

Length

2024-03-14T22:09:29.442923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:29.729936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
28200
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28200
2nd row28200
3rd row28200
4th row28200
5th row28200

Common Values

ValueCountFrequency (%)
28200 10000
100.0%

Length

2024-03-14T22:09:30.037920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:30.326311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2018 10000
100.0%

Length

2024-03-14T22:09:30.628732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:30.915116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 10000
100.0%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.7539
Minimum101
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:31.195228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median105
Q3110
95-th percentile111
Maximum111
Range10
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.2757646
Coefficient of variation (CV)0.040431271
Kurtosis-1.8202779
Mean105.7539
Median Absolute Deviation (MAD)4
Skewness0.1156641
Sum1057539
Variance18.282163
MonotonicityNot monotonic
2024-03-14T22:09:31.539149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
101 2546
25.5%
111 2124
21.2%
110 2030
20.3%
102 1634
16.3%
103 697
 
7.0%
107 369
 
3.7%
105 356
 
3.6%
104 71
 
0.7%
106 61
 
0.6%
109 61
 
0.6%
ValueCountFrequency (%)
101 2546
25.5%
102 1634
16.3%
103 697
 
7.0%
104 71
 
0.7%
105 356
 
3.6%
106 61
 
0.6%
107 369
 
3.7%
108 51
 
0.5%
109 61
 
0.6%
110 2030
20.3%
ValueCountFrequency (%)
111 2124
21.2%
110 2030
20.3%
109 61
 
0.6%
108 51
 
0.5%
107 369
 
3.7%
106 61
 
0.6%
105 356
 
3.6%
104 71
 
0.7%
103 697
 
7.0%
102 1634
16.3%

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

2024-03-14T22:09:31.909931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:32.201504image/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

2024-03-14T22:09:32.514826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:32.764798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9978
99.8%
2 22
 
0.2%

본번
Real number (ℝ)

Distinct1116
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean725.4909
Minimum1
Maximum1554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:32.952479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile92
Q1453
median671
Q31024
95-th percentile1464
Maximum1554
Range1553
Interquartile range (IQR)571

Descriptive statistics

Standard deviation399.7763
Coefficient of variation (CV)0.55104248
Kurtosis-0.53092341
Mean725.4909
Median Absolute Deviation (MAD)235
Skewness0.31100556
Sum7254909
Variance159821.09
MonotonicityNot monotonic
2024-03-14T22:09:33.215434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642 177
 
1.8%
448 157
 
1.6%
1527 135
 
1.4%
631 131
 
1.3%
647 126
 
1.3%
1456 125
 
1.2%
680 122
 
1.2%
645 120
 
1.2%
1 118
 
1.2%
1550 106
 
1.1%
Other values (1106) 8683
86.8%
ValueCountFrequency (%)
1 118
1.2%
2 9
 
0.1%
3 7
 
0.1%
4 7
 
0.1%
5 35
 
0.4%
6 9
 
0.1%
7 4
 
< 0.1%
10 7
 
0.1%
11 3
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
1554 1
 
< 0.1%
1550 106
1.1%
1546 5
 
0.1%
1543 13
 
0.1%
1542 23
 
0.2%
1541 3
 
< 0.1%
1540 4
 
< 0.1%
1538 4
 
< 0.1%
1537 5
 
0.1%
1536 6
 
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct237
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0798
Minimum0
Maximum800
Zeros1533
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:33.574150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile29
Maximum800
Range800
Interquartile range (IQR)8

Descriptive statistics

Standard deviation43.640984
Coefficient of variation (CV)3.6127241
Kurtosis105.62991
Mean12.0798
Median Absolute Deviation (MAD)3
Skewness9.1533517
Sum120798
Variance1904.5355
MonotonicityNot monotonic
2024-03-14T22:09:34.025700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1698
17.0%
0 1533
15.3%
2 1031
10.3%
4 756
 
7.6%
3 752
 
7.5%
5 502
 
5.0%
6 462
 
4.6%
8 361
 
3.6%
7 343
 
3.4%
9 262
 
2.6%
Other values (227) 2300
23.0%
ValueCountFrequency (%)
0 1533
15.3%
1 1698
17.0%
2 1031
10.3%
3 752
7.5%
4 756
7.6%
5 502
 
5.0%
6 462
 
4.6%
7 343
 
3.4%
8 361
 
3.6%
9 262
 
2.6%
ValueCountFrequency (%)
800 1
< 0.1%
762 1
< 0.1%
755 1
< 0.1%
744 1
< 0.1%
714 1
< 0.1%
668 1
< 0.1%
658 1
< 0.1%
652 1
< 0.1%
589 1
< 0.1%
571 1
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.1999
Minimum0
Maximum9100
Zeros2448
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:34.346616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9100
Range9100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1350.3259
Coefficient of variation (CV)6.1045502
Kurtosis35.516489
Mean221.1999
Median Absolute Deviation (MAD)0
Skewness6.0995706
Sum2211999
Variance1823380
MonotonicityNot monotonic
2024-03-14T22:09:34.751646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 6940
69.4%
0 2448
 
24.5%
2 206
 
2.1%
9000 119
 
1.2%
3 38
 
0.4%
8001 35
 
0.4%
9001 30
 
0.3%
8002 29
 
0.3%
102 24
 
0.2%
5001 15
 
0.1%
Other values (39) 116
 
1.2%
ValueCountFrequency (%)
0 2448
 
24.5%
1 6940
69.4%
2 206
 
2.1%
3 38
 
0.4%
4 7
 
0.1%
5 8
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9100 1
 
< 0.1%
9006 1
 
< 0.1%
9005 2
 
< 0.1%
9004 3
 
< 0.1%
9003 1
 
< 0.1%
9002 15
 
0.1%
9001 30
 
0.3%
9000 119
1.2%
8004 3
 
< 0.1%
8002 29
 
0.3%

호
Real number (ℝ)

SKEWED 

Distinct1091
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2051.7261
Minimum0
Maximum810301
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:35.115489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1101
median202
Q3604
95-th percentile8101
Maximum810301
Range810301
Interquartile range (IQR)503

Descriptive statistics

Standard deviation20456.39
Coefficient of variation (CV)9.9703318
Kurtosis666.58746
Mean2051.7261
Median Absolute Deviation (MAD)198
Skewness24.369996
Sum20517261
Variance4.1846389 × 108
MonotonicityNot monotonic
2024-03-14T22:09:35.508777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 1105
 
11.1%
1 707
 
7.1%
201 685
 
6.9%
102 416
 
4.2%
301 402
 
4.0%
2 383
 
3.8%
8101 329
 
3.3%
3 218
 
2.2%
401 212
 
2.1%
202 205
 
2.1%
Other values (1081) 5338
53.4%
ValueCountFrequency (%)
0 17
 
0.2%
1 707
7.1%
2 383
3.8%
3 218
 
2.2%
4 148
 
1.5%
5 83
 
0.8%
6 57
 
0.6%
7 33
 
0.3%
8 23
 
0.2%
9 14
 
0.1%
ValueCountFrequency (%)
810301 1
< 0.1%
502104 1
< 0.1%
502101 1
< 0.1%
502093 1
< 0.1%
502089 1
< 0.1%
502082 1
< 0.1%
502060 1
< 0.1%
502035 1
< 0.1%
502023 1
< 0.1%
502014 1
< 0.1%

물건지
Text

Distinct9686
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T22:09:36.710003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.6679
Min length18

Characters and Unicode

Total characters256679
Distinct characters136
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

Unique9381 ?
Unique (%)93.8%

Sample

1st row[ 경인로644번길 40 ] 0001동 0303호
2nd row[ 앵고개로 442 ] 0001동 0201호
3rd row인천광역시 남동구 고잔동 720-9 1동 303호
4th row[ 선수촌공원로 36 ] 0000동 0918호
5th row[ 은봉로312번길 29-12 ] 0001동 0101호
ValueCountFrequency (%)
14088
23.6%
0001동 4665
 
7.8%
인천광역시 2956
 
4.9%
남동구 2956
 
4.9%
1동 2275
 
3.8%
0000동 2156
 
3.6%
고잔동 1517
 
2.5%
0101호 680
 
1.1%
0201호 434
 
0.7%
101호 424
 
0.7%
Other values (4382) 27573
46.2%
2024-03-14T22:09:38.226519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49724
19.4%
0 41271
16.1%
1 22825
 
8.9%
동 16401
 
6.4%
호 10421
 
4.1%
2 8982
 
3.5%
[ 7044
 
2.7%
] 7044
 
2.7%
로 7043
 
2.7%
3 6259
 
2.4%
Other values (126) 79665
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104595
40.7%
Other Letter 84952
33.1%
Space Separator 49724
19.4%
Open Punctuation 7044
 
2.7%
Close Punctuation 7044
 
2.7%
Dash Punctuation 3320
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16401
19.3%
호 10421
 
12.3%
로 7043
 
8.3%
남 4478
 
5.3%
구 4032
 
4.7%
인 3685
 
4.3%
역 3238
 
3.8%
길 3175
 
3.7%
번 3174
 
3.7%
천 3110
 
3.7%
Other values (112) 26195
30.8%
Decimal Number
ValueCountFrequency (%)
0 41271
39.5%
1 22825
21.8%
2 8982
 
8.6%
3 6259
 
6.0%
6 5610
 
5.4%
4 5232
 
5.0%
5 4168
 
4.0%
7 3801
 
3.6%
8 3659
 
3.5%
9 2788
 
2.7%
Space Separator
ValueCountFrequency (%)
49724
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 7044
100.0%
Close Punctuation
ValueCountFrequency (%)
] 7044
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171727
66.9%
Hangul 84952
33.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16401
19.3%
호 10421
 
12.3%
로 7043
 
8.3%
남 4478
 
5.3%
구 4032
 
4.7%
인 3685
 
4.3%
역 3238
 
3.8%
길 3175
 
3.7%
번 3174
 
3.7%
천 3110
 
3.7%
Other values (112) 26195
30.8%
Common
ValueCountFrequency (%)
49724
29.0%
0 41271
24.0%
1 22825
13.3%
2 8982
 
5.2%
[ 7044
 
4.1%
] 7044
 
4.1%
3 6259
 
3.6%
6 5610
 
3.3%
4 5232
 
3.0%
5 4168
 
2.4%
Other values (4) 13568
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171727
66.9%
Hangul 84952
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49724
29.0%
0 41271
24.0%
1 22825
13.3%
2 8982
 
5.2%
[ 7044
 
4.1%
] 7044
 
4.1%
3 6259
 
3.6%
6 5610
 
3.3%
4 5232
 
3.0%
5 4168
 
2.4%
Other values (4) 13568
 
7.9%
Hangul
ValueCountFrequency (%)
동 16401
19.3%
호 10421
 
12.3%
로 7043
 
8.3%
남 4478
 
5.3%
구 4032
 
4.7%
인 3685
 
4.3%
역 3238
 
3.8%
길 3175
 
3.7%
번 3174
 
3.7%
천 3110
 
3.7%
Other values (112) 26195
30.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7991
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81199890
Minimum12540
Maximum8.394955 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:38.646982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12540
5-th percentile1253789
Q110922400
median34623360
Q381475200
95-th percentile2.9820031 × 108
Maximum8.394955 × 109
Range8.3949424 × 109
Interquartile range (IQR)70552800

Descriptive statistics

Standard deviation1.9136818 × 108
Coefficient of variation (CV)2.3567541
Kurtosis429.11633
Mean81199890
Median Absolute Deviation (MAD)28233335
Skewness14.22394
Sum8.119989 × 1011
Variance3.6621778 × 1016
MonotonicityNot monotonic
2024-03-14T22:09:39.067567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3708180 44
 
0.4%
11565010 40
 
0.4%
32509120 27
 
0.3%
31130400 26
 
0.3%
322330 24
 
0.2%
14273400 24
 
0.2%
3775760 21
 
0.2%
1008800 19
 
0.2%
24534040 19
 
0.2%
34377660 19
 
0.2%
Other values (7981) 9737
97.4%
ValueCountFrequency (%)
12540 2
< 0.1%
15640 1
< 0.1%
17160 1
< 0.1%
18000 1
< 0.1%
18900 1
< 0.1%
21170 1
< 0.1%
22360 1
< 0.1%
23150 1
< 0.1%
26520 1
< 0.1%
28600 1
< 0.1%
ValueCountFrequency (%)
8394954960 1
< 0.1%
3787045000 1
< 0.1%
3767865600 1
< 0.1%
3620659200 1
< 0.1%
3356016480 1
< 0.1%
2951354700 1
< 0.1%
2361091680 1
< 0.1%
2195186990 1
< 0.1%
2135621710 1
< 0.1%
2116478410 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6677
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.02164
Minimum0.0618
Maximum11774.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:09:39.469241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0618
5-th percentile5.848
Q132.101
median74.28
Q3169.5025
95-th percentile676.81
Maximum11774.58
Range11774.518
Interquartile range (IQR)137.4015

Descriptive statistics

Standard deviation416.08655
Coefficient of variation (CV)2.2985459
Kurtosis227.68544
Mean181.02164
Median Absolute Deviation (MAD)54.4647
Skewness11.320685
Sum1810216.4
Variance173128.01
MonotonicityNot monotonic
2024-03-14T22:09:40.082904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 48
 
0.5%
7.56 44
 
0.4%
10.85 41
 
0.4%
50.48 27
 
0.3%
33.8374 26
 
0.3%
0.8693 24
 
0.2%
19.2364 24
 
0.2%
12.0 22
 
0.2%
8.72 22
 
0.2%
36.0 21
 
0.2%
Other values (6667) 9701
97.0%
ValueCountFrequency (%)
0.0618 1
 
< 0.1%
0.0676 1
 
< 0.1%
0.1318 1
 
< 0.1%
0.132 1
 
< 0.1%
0.1328 3
< 0.1%
0.1364 1
 
< 0.1%
0.1378 1
 
< 0.1%
0.143 2
< 0.1%
0.2 1
 
< 0.1%
0.22 2
< 0.1%
ValueCountFrequency (%)
11774.58 1
< 0.1%
11757.64 1
< 0.1%
11314.56 1
< 0.1%
7773.89 1
< 0.1%
7574.09 1
< 0.1%
7568.32 1
< 0.1%
7560.0 1
< 0.1%
4421.52 1
< 0.1%
4412.1 1
< 0.1%
4167.88 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-15
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-15
2nd row2024-01-15
3rd row2024-01-15
4th row2024-01-15
5th row2024-01-15

Common Values

ValueCountFrequency (%)
2024-01-15 10000
100.0%

Length

2024-03-14T22:09:40.494019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:09:40.779503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-15 10000
100.0%

Interactions

2024-03-14T22:09:25.777852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:15.777121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.019482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:18.388921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:19.997742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:21.821764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:23.761344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:26.033285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:15.982811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.219787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:18.552392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:20.244233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:22.085079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:24.013020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:26.313187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:16.157001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.403944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:18.777282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:20.518026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:22.374973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:24.288537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:26.596823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:16.328542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.590472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:18.995346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:20.790992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:22.664921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:24.560148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:26.850521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:16.479055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.768712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:19.198108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:21.037292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:22.925594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:24.807724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:27.132849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:16.655661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:17.961888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:19.456709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:21.310090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:23.215362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:25.084394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:27.389003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:16.808153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:18.152215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:19.722627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:21.558126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:23.481802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:09:25.334865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:09:40.960165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동특수지본번부번동호시가표준액연면적
법정동1.0000.2660.9050.2160.1720.0490.1440.102
특수지0.2661.0000.1960.0000.0000.0000.0000.074
본번0.9050.1961.0000.3850.1520.0750.0330.043
부번0.2160.0000.3851.0000.0000.0000.0000.000
동0.1720.0000.1520.0001.0000.0000.0000.000
호0.0490.0000.0750.0000.0001.0000.0000.000
시가표준액0.1440.0000.0330.0000.0000.0001.0000.902
연면적0.1020.0740.0430.0000.0000.0000.9021.000
2024-03-14T22:09:41.256737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동호시가표준액연면적특수지
법정동1.000-0.3560.0600.405-0.1450.0790.1690.200
본번-0.3561.000-0.144-0.2750.1150.083-0.0090.151
부번0.060-0.1441.0000.143-0.210-0.0070.1200.000
동0.405-0.2750.1431.000-0.2030.0110.1230.000
호-0.1450.115-0.210-0.2031.0000.037-0.1820.000
시가표준액0.0790.083-0.0070.0110.0371.0000.8600.000
연면적0.169-0.0090.1200.123-0.1820.8601.0000.053
특수지0.2000.1510.0000.0000.0000.0000.0531.000

Missing values

2024-03-14T22:09:27.785568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:09:28.393294image/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
69236인천광역시남동구2820020181020111341303[ 경인로644번길 40 ] 0001동 0303호15744904.78862024-01-15
18959인천광역시남동구28200201811101690161201[ 앵고개로 442 ] 0001동 0201호12381863302318.7012024-01-15
53178인천광역시남동구2820020181110172091303인천광역시 남동구 고잔동 720-9 1동 303호122691090223.422024-01-15
28645인천광역시남동구28200201810101152700918[ 선수촌공원로 36 ] 0000동 0918호3223300.86932024-01-15
66065인천광역시남동구28200201811001605101101[ 은봉로312번길 29-12 ] 0001동 0101호112176060141.442024-01-15
35200인천광역시남동구282002018101011248111101인천광역시 남동구 구월동 1248-11 1동 101호46980000162.02024-01-15
41010인천광역시남동구2820020181050150261101인천광역시 남동구 서창동 502-6 1동 101호2056864051.682024-01-15
22066인천광역시남동구28200201810301977371101[ 구월로372번길 46 ] 0001동 0101호1189166028.32024-01-15
19841인천광역시남동구282002018103019081590001[ 구월말로 79-8 ] 9000동 0001호22950013.52024-01-15
46763인천광역시남동구2820020181050170840201[ 서창방산로 51 ] 0000동 0201호96280000103.752024-01-15
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
54225인천광역시남동구28200201811101704513인천광역시 남동구 고잔동 704-5 1동 3호29561000287.02024-01-15
36530인천광역시남동구28200201810101116951101인천광역시 남동구 구월동 1169-5 1동 101호111302530295.862024-01-15
76849인천광역시남동구282002018101011169308101[ 성말로44번길 17 ] 0000동 8101호15750005.02024-01-15
19657인천광역시남동구28200201810301955815[ 하촌로71번길 15 ] 0001동 0005호72445620184.342024-01-15
60196인천광역시남동구2820020181100156221201[ 남동대로370번길 131 ] 0001동 0201호263318620615.952024-01-15
72118인천광역시남동구28200201810201394411003[ 석산로 3-2 ] 0001동 1003호17840405.69622024-01-15
85163인천광역시남동구28200201811001642112329[ 논현로46번길 51 ] 0001동 2329호4430272047.742024-01-15
64310인천광역시남동구28200201811101246531101[ 청능대로468번길 86 ] 0001동 0101호144552870368.12024-01-15
20524인천광역시남동구2820020181030191281101[ 구월말로 65-1 ] 0001동 0101호875160093.62024-01-15
86232인천광역시남동구2820020181100163141205[ 논현역로 6 ] 0001동 0205호4154520047.3182024-01-15

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
0인천광역시남동구282002018103012921[ 수현로119번길 11 ] 0002동 0001호2611875041.792024-01-152
1인천광역시남동구28200201811101644121101인천광역시 남동구 고잔동 644-12 1동 101호107325000675.02024-01-152
2인천광역시남동구282002018111016791021인천광역시 남동구 고잔동 679-10 2동 1호82968750281.252024-01-152