Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows23
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description경기도 이천시 일반건축물 시가표준액에 대한 시도명, 시군구명, 지방자치단체코드, 물건지 주소, 시가표준금액, 연면적, 결정일자에 대한 정보를 제공
URLhttps://www.data.go.kr/data/15080431/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 23 (0.2%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (87.3%)Imbalance
시가표준액 is highly skewed (γ1 = 24.0632339)Skewed
법정리 has 2254 (22.5%) zerosZeros
부번 has 2303 (23.0%) zerosZeros
has 3887 (38.9%) zerosZeros

Reproduction

Analysis started2023-12-12 15:25:22.817882
Analysis finished2023-12-12 15:25:31.909637
Duration9.09 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
경기도
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 10000
100.0%

Length

2023-12-13T00:25:32.003320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:32.146598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
이천시 10000
100.0%

Length

2023-12-13T00:25:32.263121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:32.367372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이천시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41500
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41500
2nd row41500
3rd row41500
4th row41500
5th row41500

Common Values

ValueCountFrequency (%)
41500 10000
100.0%

Length

2023-12-13T00:25:32.489110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:32.610222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41500 10000
100.0%

과세년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
5481 
2018
4519 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2017
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2017 5481
54.8%
2018 4519
45.2%

Length

2023-12-13T00:25:32.752432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:32.903854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 5481
54.8%
2018 4519
45.2%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.6665
Minimum101
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:33.042820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1250
median310
Q3350
95-th percentile370
Maximum380
Range279
Interquartile range (IQR)100

Descriptive statistics

Standard deviation97.548223
Coefficient of variation (CV)0.35907343
Kurtosis-0.83231255
Mean271.6665
Median Absolute Deviation (MAD)57
Skewness-0.83418708
Sum2716665
Variance9515.6558
MonotonicityNot monotonic
2023-12-13T00:25:33.216255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
253 994
9.9%
340 979
9.8%
310 926
9.3%
250 914
9.1%
360 817
8.2%
370 810
8.1%
350 765
7.6%
101 762
7.6%
320 706
 
7.1%
330 512
 
5.1%
Other values (15) 1815
18.1%
ValueCountFrequency (%)
101 762
7.6%
102 166
 
1.7%
103 308
3.1%
104 21
 
0.2%
105 19
 
0.2%
106 23
 
0.2%
107 48
 
0.5%
108 146
 
1.5%
109 208
 
2.1%
110 142
 
1.4%
ValueCountFrequency (%)
380 323
 
3.2%
370 810
8.1%
360 817
8.2%
350 765
7.6%
340 979
9.8%
330 512
5.1%
320 706
7.1%
310 926
9.3%
253 994
9.9%
250 914
9.1%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.444
Minimum0
Maximum34
Zeros2254
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:33.384966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median25
Q329
95-th percentile32
Maximum34
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.507263
Coefficient of variation (CV)0.5628675
Kurtosis-0.54757022
Mean20.444
Median Absolute Deviation (MAD)4
Skewness-1.0288817
Sum204440
Variance132.41711
MonotonicityNot monotonic
2023-12-13T00:25:33.524606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 2254
22.5%
21 1075
10.8%
28 754
 
7.5%
30 731
 
7.3%
26 680
 
6.8%
29 632
 
6.3%
23 619
 
6.2%
25 611
 
6.1%
22 556
 
5.6%
31 460
 
4.6%
Other values (5) 1628
16.3%
ValueCountFrequency (%)
0 2254
22.5%
21 1075
10.8%
22 556
 
5.6%
23 619
 
6.2%
24 446
 
4.5%
25 611
 
6.1%
26 680
 
6.8%
27 433
 
4.3%
28 754
 
7.5%
29 632
 
6.3%
ValueCountFrequency (%)
34 84
 
0.8%
33 280
 
2.8%
32 385
3.9%
31 460
4.6%
30 731
7.3%
29 632
6.3%
28 754
7.5%
27 433
4.3%
26 680
6.8%
25 611
6.1%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9826 
2
 
174

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 9826
98.3%
2 174
 
1.7%

Length

2023-12-13T00:25:33.702651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:33.836079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9826
98.3%
2 174
 
1.7%

본번
Real number (ℝ)

Distinct874
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.9844
Minimum1
Maximum1546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:33.988087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1132
median288
Q3463
95-th percentile756
Maximum1546
Range1545
Interquartile range (IQR)331

Descriptive statistics

Standard deviation245.21892
Coefficient of variation (CV)0.75224129
Kurtosis1.4726477
Mean325.9844
Median Absolute Deviation (MAD)166
Skewness1.0416157
Sum3259844
Variance60132.321
MonotonicityNot monotonic
2023-12-13T00:25:34.187957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 82
 
0.8%
1313 68
 
0.7%
345 59
 
0.6%
712 49
 
0.5%
427 48
 
0.5%
347 48
 
0.5%
213 44
 
0.4%
162 44
 
0.4%
165 43
 
0.4%
470 42
 
0.4%
Other values (864) 9473
94.7%
ValueCountFrequency (%)
1 82
0.8%
2 29
 
0.3%
3 30
 
0.3%
4 25
 
0.2%
5 33
0.3%
6 21
 
0.2%
7 26
 
0.3%
8 31
 
0.3%
9 37
0.4%
10 14
 
0.1%
ValueCountFrequency (%)
1546 2
 
< 0.1%
1419 2
 
< 0.1%
1355 4
 
< 0.1%
1316 1
 
< 0.1%
1315 1
 
< 0.1%
1313 68
0.7%
1306 1
 
< 0.1%
1304 1
 
< 0.1%
1303 2
 
< 0.1%
1281 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8704
Minimum0
Maximum303
Zeros2303
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:34.387951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile26
Maximum303
Range303
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.843795
Coefficient of variation (CV)2.160543
Kurtosis103.4593
Mean6.8704
Median Absolute Deviation (MAD)3
Skewness8.0476174
Sum68704
Variance220.33824
MonotonicityNot monotonic
2023-12-13T00:25:34.579821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2303
23.0%
1 1681
16.8%
2 951
9.5%
3 763
 
7.6%
4 544
 
5.4%
5 535
 
5.3%
7 380
 
3.8%
6 377
 
3.8%
8 283
 
2.8%
9 244
 
2.4%
Other values (102) 1939
19.4%
ValueCountFrequency (%)
0 2303
23.0%
1 1681
16.8%
2 951
9.5%
3 763
 
7.6%
4 544
 
5.4%
5 535
 
5.3%
6 377
 
3.8%
7 380
 
3.8%
8 283
 
2.8%
9 244
 
2.4%
ValueCountFrequency (%)
303 1
< 0.1%
296 1
< 0.1%
272 1
< 0.1%
267 1
< 0.1%
258 1
< 0.1%
236 2
< 0.1%
219 1
< 0.1%
210 1
< 0.1%
198 1
< 0.1%
194 1
< 0.1%


Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406.3192
Minimum0
Maximum9003
Zeros3887
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:34.804481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile201
Maximum9003
Range9003
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1835.9526
Coefficient of variation (CV)4.5184983
Kurtosis17.410627
Mean406.3192
Median Absolute Deviation (MAD)0
Skewness4.3867793
Sum4063192
Variance3370722
MonotonicityNot monotonic
2023-12-13T00:25:34.953195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 5135
51.3%
0 3887
38.9%
9000 409
 
4.1%
2 294
 
2.9%
3 52
 
0.5%
101 36
 
0.4%
5000 35
 
0.4%
201 27
 
0.3%
301 14
 
0.1%
8001 7
 
0.1%
Other values (31) 104
 
1.0%
ValueCountFrequency (%)
0 3887
38.9%
1 5135
51.3%
2 294
 
2.9%
3 52
 
0.5%
4 6
 
0.1%
5 7
 
0.1%
6 4
 
< 0.1%
7 5
 
0.1%
8 4
 
< 0.1%
9 7
 
0.1%
ValueCountFrequency (%)
9003 1
 
< 0.1%
9002 1
 
< 0.1%
9001 6
 
0.1%
9000 409
4.1%
8001 7
 
0.1%
5001 6
 
0.1%
5000 35
 
0.4%
4444 5
 
0.1%
1000 2
 
< 0.1%
401 2
 
< 0.1%


Text

Distinct339
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:25:35.298009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length2.086
Min length1

Characters and Unicode

Total characters20860
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)1.7%

Sample

1st row202
2nd row1
3rd row8
4th row1
5th row8102
ValueCountFrequency (%)
1 1861
18.6%
101 1070
 
10.7%
2 845
 
8.4%
0 598
 
6.0%
3 594
 
5.9%
9000 568
 
5.7%
102 442
 
4.4%
4 373
 
3.7%
5 251
 
2.5%
202 213
 
2.1%
Other values (330) 3187
31.9%
2023-12-13T00:25:35.747329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6639
31.8%
0 6610
31.7%
2 2535
 
12.2%
3 1466
 
7.0%
9 1061
 
5.1%
4 877
 
4.2%
5 573
 
2.7%
8 438
 
2.1%
6 392
 
1.9%
7 262
 
1.3%
Other values (4) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20853
> 99.9%
Other Letter 4
 
< 0.1%
Space Separator 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6639
31.8%
0 6610
31.7%
2 2535
 
12.2%
3 1466
 
7.0%
9 1061
 
5.1%
4 877
 
4.2%
5 573
 
2.7%
8 438
 
2.1%
6 392
 
1.9%
7 262
 
1.3%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20856
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6639
31.8%
0 6610
31.7%
2 2535
 
12.2%
3 1466
 
7.0%
9 1061
 
5.1%
4 877
 
4.2%
5 573
 
2.7%
8 438
 
2.1%
6 392
 
1.9%
7 262
 
1.3%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20856
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6639
31.8%
0 6610
31.7%
2 2535
 
12.2%
3 1466
 
7.0%
9 1061
 
5.1%
4 877
 
4.2%
5 573
 
2.7%
8 438
 
2.1%
6 392
 
1.9%
7 262
 
1.3%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%
Distinct8887
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:25:36.101264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length25.8768
Min length15

Characters and Unicode

Total characters258768
Distinct characters179
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

Unique8008 ?
Unique (%)80.1%

Sample

1st row[ 증신로291번길 133 ] 0000동 0202호
2nd row[ 대월로 832 ] 0000동 0001호
3rd row경기도 이천시 대월면 부필리 385-2 1동 8호
4th row경기도 이천시 사음동 164-17 1호
5th row경기도 이천시 마장면 해월리 33 1동 8102호
ValueCountFrequency (%)
경기도 7087
 
11.2%
이천시 7087
 
11.2%
5826
 
9.2%
1동 3631
 
5.8%
0001동 1504
 
2.4%
1호 1242
 
2.0%
0000동 1174
 
1.9%
모가면 721
 
1.1%
2호 707
 
1.1%
설성면 702
 
1.1%
Other values (4904) 33359
52.9%
2023-12-13T00:25:36.662856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53040
20.5%
0 24491
 
9.5%
1 19749
 
7.6%
10798
 
4.2%
8680
 
3.4%
2 8186
 
3.2%
7693
 
3.0%
7668
 
3.0%
7556
 
2.9%
7381
 
2.9%
Other values (169) 103526
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112339
43.4%
Decimal Number 81331
31.4%
Space Separator 53040
20.5%
Dash Punctuation 6232
 
2.4%
Open Punctuation 2913
 
1.1%
Close Punctuation 2913
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10798
 
9.6%
8680
 
7.7%
7693
 
6.8%
7668
 
6.8%
7556
 
6.7%
7381
 
6.6%
7136
 
6.4%
7087
 
6.3%
6442
 
5.7%
4697
 
4.2%
Other values (155) 37201
33.1%
Decimal Number
ValueCountFrequency (%)
0 24491
30.1%
1 19749
24.3%
2 8186
 
10.1%
3 6067
 
7.5%
4 4905
 
6.0%
5 4022
 
4.9%
9 3995
 
4.9%
6 3435
 
4.2%
7 3395
 
4.2%
8 3086
 
3.8%
Space Separator
ValueCountFrequency (%)
53040
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6232
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2913
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 146429
56.6%
Hangul 112339
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10798
 
9.6%
8680
 
7.7%
7693
 
6.8%
7668
 
6.8%
7556
 
6.7%
7381
 
6.6%
7136
 
6.4%
7087
 
6.3%
6442
 
5.7%
4697
 
4.2%
Other values (155) 37201
33.1%
Common
ValueCountFrequency (%)
53040
36.2%
0 24491
16.7%
1 19749
 
13.5%
2 8186
 
5.6%
- 6232
 
4.3%
3 6067
 
4.1%
4 4905
 
3.3%
5 4022
 
2.7%
9 3995
 
2.7%
6 3435
 
2.3%
Other values (4) 12307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146429
56.6%
Hangul 112339
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53040
36.2%
0 24491
16.7%
1 19749
 
13.5%
2 8186
 
5.6%
- 6232
 
4.3%
3 6067
 
4.1%
4 4905
 
3.3%
5 4022
 
2.7%
9 3995
 
2.7%
6 3435
 
2.3%
Other values (4) 12307
 
8.4%
Hangul
ValueCountFrequency (%)
10798
 
9.6%
8680
 
7.7%
7693
 
6.8%
7668
 
6.8%
7556
 
6.7%
7381
 
6.6%
7136
 
6.4%
7087
 
6.3%
6442
 
5.7%
4697
 
4.2%
Other values (155) 37201
33.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8698
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89821693
Minimum7200
Maximum2.1945098 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:36.851437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7200
5-th percentile453876
Q12934150
median16557505
Q357820215
95-th percentile2.9810587 × 108
Maximum2.1945098 × 1010
Range2.1945091 × 1010
Interquartile range (IQR)54886065

Descriptive statistics

Standard deviation4.9877502 × 108
Coefficient of variation (CV)5.5529461
Kurtosis781.16257
Mean89821693
Median Absolute Deviation (MAD)15440505
Skewness24.063234
Sum8.9821693 × 1011
Variance2.4877652 × 1017
MonotonicityNot monotonic
2023-12-13T00:25:36.997833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
594000 17
 
0.2%
792000 16
 
0.2%
1188000 16
 
0.2%
576000 12
 
0.1%
1584000 12
 
0.1%
540000 12
 
0.1%
1080000 11
 
0.1%
720000 11
 
0.1%
768000 10
 
0.1%
1134000 10
 
0.1%
Other values (8688) 9873
98.7%
ValueCountFrequency (%)
7200 1
< 0.1%
18000 1
< 0.1%
21240 1
< 0.1%
29000 1
< 0.1%
33000 2
< 0.1%
37920 1
< 0.1%
38880 1
< 0.1%
40600 1
< 0.1%
42570 1
< 0.1%
45000 1
< 0.1%
ValueCountFrequency (%)
21945098470 1
< 0.1%
17821042880 1
< 0.1%
14422575130 1
< 0.1%
14409439940 1
< 0.1%
13139223930 1
< 0.1%
12396593840 1
< 0.1%
9118120560 1
< 0.1%
7351436520 1
< 0.1%
6450722300 1
< 0.1%
6089989530 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5780
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.89614
Minimum0.86
Maximum36148.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:37.145127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile13.199
Q148
median109.72
Q3227.75125
95-th percentile892.8615
Maximum36148.16
Range36147.3
Interquartile range (IQR)179.75125

Descriptive statistics

Standard deviation1004.7337
Coefficient of variation (CV)3.6155008
Kurtosis476.29528
Mean277.89614
Median Absolute Deviation (MAD)77.72
Skewness18.686302
Sum2778961.4
Variance1009489.8
MonotonicityNot monotonic
2023-12-13T00:25:37.295582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 223
 
2.2%
198.0 118
 
1.2%
36.0 56
 
0.6%
396.0 56
 
0.6%
180.0 40
 
0.4%
48.0 38
 
0.4%
27.0 35
 
0.4%
60.0 35
 
0.4%
99.0 32
 
0.3%
12.0 32
 
0.3%
Other values (5770) 9335
93.3%
ValueCountFrequency (%)
0.86 1
 
< 0.1%
1.0 6
0.1%
1.18 2
 
< 0.1%
1.2 2
 
< 0.1%
1.29 2
 
< 0.1%
1.32 1
 
< 0.1%
1.43 1
 
< 0.1%
1.44 5
0.1%
1.65 1
 
< 0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
36148.16 1
< 0.1%
28625.7602 1
< 0.1%
28438.48 1
< 0.1%
28412.58 1
< 0.1%
24635.88 1
< 0.1%
24270.1819 1
< 0.1%
20524.162 1
< 0.1%
20398.48 1
< 0.1%
14525.66 1
< 0.1%
13725.3 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-06-01 00:00:00
Maximum2018-06-01 00:00:00
2023-12-13T00:25:37.421175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:37.557760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-13T00:25:30.469971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:24.998059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.840450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.747999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.732256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.621196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.301838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.580696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.101344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.935540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.887652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.843259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.717957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.414429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.737748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.215771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.048943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.034371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.962056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.819571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.530132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.882236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.349746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.184604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.170907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.141279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.922071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.951567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:31.018075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.495933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.301508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.297534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.284217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.010523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.067344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:31.152236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.622524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.473546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.421164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.416829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.103681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.187062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:31.313016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:25.727962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:26.612445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:27.588411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:28.518444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:29.196139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:30.337967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:25:37.685059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적결정일자
과세년도1.0000.0790.0340.0000.0640.0000.0000.0180.0171.000
법정동0.0791.0000.8670.0600.3170.1340.0740.0630.0750.079
법정리0.0340.8671.0000.0870.3230.1080.0790.0310.0310.034
특수지0.0000.0600.0871.0000.2580.0000.1360.0000.0000.000
본번0.0640.3170.3230.2581.0000.0810.0310.0750.0720.064
부번0.0000.1340.1080.0000.0811.0000.0000.0060.0000.000
0.0000.0740.0790.1360.0310.0001.0000.0000.0000.000
시가표준액0.0180.0630.0310.0000.0750.0060.0001.0000.9850.018
연면적0.0170.0750.0310.0000.0720.0000.0000.9851.0000.017
결정일자1.0000.0790.0340.0000.0640.0000.0000.0180.0171.000
2023-12-13T00:25:37.904871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.000
과세년도0.0001.000
2023-12-13T00:25:38.420401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.5640.018-0.170-0.117-0.1960.1200.0970.073
법정리0.5641.0000.065-0.113-0.048-0.1430.1170.0410.106
본번0.0180.0651.000-0.083-0.0220.0220.0010.0490.198
부번-0.170-0.113-0.0831.000-0.0260.068-0.0470.0000.000
-0.117-0.048-0.022-0.0261.0000.027-0.0850.0000.098
시가표준액-0.196-0.1430.0220.0680.0271.0000.5780.0180.000
연면적0.1200.1170.001-0.047-0.0850.5781.0000.0170.000
과세년도0.0970.0410.0490.0000.0000.0180.0171.0000.000
특수지0.0730.1060.1980.0000.0980.0000.0000.0001.000

Missing values

2023-12-13T00:25:31.512892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:25:31.794529image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
89706경기도이천시4150020181100137520202[ 증신로291번길 133 ] 0000동 0202호92420200168.652018-06-01
37681경기도이천시415002017350251228601[ 대월로 832 ] 0000동 0001호161562100364.72017-06-01
63452경기도이천시415002018350341385218경기도 이천시 대월면 부필리 385-2 1동 8호13036800134.42018-06-01
86853경기도이천시415002018111011641701경기도 이천시 사음동 164-17 1호56477400119.02018-06-01
71877경기도이천시41500201834032133018102경기도 이천시 마장면 해월리 33 1동 8102호883008025.22018-06-01
51079경기도이천시415002017310261358010경기도 이천시 신둔면 도암리 358 1동154249690265.492017-06-01
83086경기도이천시4150020181090114511101경기도 이천시 증포동 145-1 1동 101호1687075094.252018-06-01
77580경기도이천시415002018250281855103경기도 이천시 장호원읍 선읍리 855-1 3호6732000396.02018-06-01
76172경기도이천시415002018310241731114경기도 이천시 신둔면 장동리 731-1 1동 4호7380006.02018-06-01
24729경기도이천시41500201735034192711[ 대평로 405-53 ] 0001동 0001호525948032.072017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
30812경기도이천시415002017253301737130201[ 경충대로2110번길 14-1 ] 0000동 0201호105769120145.68752017-06-01
56806경기도이천시4150020183702911712703경기도 이천시 설성면 송계리 171-27 3호1950000195.02018-06-01
37932경기도이천시415002017350261332801경기도 이천시 대월면 사동리 332-8 1호73700640198.122017-06-01
11091경기도이천시41500201710801602011[ 안흥로 70 ] 0001동 0001호554484480656.352017-06-01
67909경기도이천시415002018320241115004경기도 이천시 백사면 송말리 115 4호292714077.032018-06-01
75034경기도이천시415002018310311207111경기도 이천시 신둔면 인후리 207-1 1동 1호112349280294.882018-06-01
75642경기도이천시415002018310231345211경기도 이천시 신둔면 지석리 345-2 1동 1호1080003.62018-06-01
80626경기도이천시41500201825023147311[ 서동대로8880번길 47-47 ] 0001동 0001호356820920470.742018-06-01
10130경기도이천시41500201710901381204[ 이섭대천로 1330 ] 0000동 0004호127320620171.732017-06-01
47976경기도이천시415002017253221917012경기도 이천시 부발읍 죽당리 917 1동 2호162582038.712017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자# duplicates
3경기도이천시415002017250291693011경기도 이천시 장호원읍 이황리 693 1동 1호739431018.152017-06-013
20경기도이천시415002018360291360011경기도 이천시 모가면 신갈리 360 1동 1호42204804.842018-06-013
0경기도이천시415002017101011541311[ 중리천로31번길 2 ] 0001동 0001호257685180333.53162017-06-012
1경기도이천시41500201711201332100경기도 이천시 단월동 332-154885600198.02017-06-012
2경기도이천시415002017250281419207경기도 이천시 장호원읍 선읍리 419-2 7호480000160.02017-06-012
4경기도이천시415002017250291693011경기도 이천시 장호원읍 이황리 693 1동 1호461485260792.932017-06-012
5경기도이천시415002017253241599311경기도 이천시 부발읍 고백리 599-3 1동 1호118761120802.442017-06-012
6경기도이천시415002017253241599311경기도 이천시 부발읍 고백리 599-3 1동 1호2681582401811.882017-06-012
7경기도이천시415002017253281407211[ 가좌로118번길 73 ] 0001동 0001호118265000542.52017-06-012
8경기도이천시415002017253331340000경기도 이천시 부발읍 가산리 34025490643207891.842017-06-012