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 memory139.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공,물건별 재산가액 확인 가능 시가표준액이란 지방세 부과를 위하여 행정안전부·지방자치단체장이 일반건축물의 거래가격을 기준으로 종류·구조·용도·경과연수 등을 고려하여 평가한 금액
Author경상북도 경산시
URLhttps://www.data.go.kr/data/15080324/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 18 (0.2%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 과세년도High correlation
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 (95.0%)Imbalance
법정리 has 3168 (31.7%) zerosZeros
부번 has 3500 (35.0%) zerosZeros

Reproduction

Analysis started2023-12-12 20:26:51.796612
Analysis finished2023-12-12 20:26:58.811849
Duration7.02 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
경상북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 10000
100.0%

Length

2023-12-13T05:26:59.057583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:59.214843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경산시 10000
100.0%

Length

2023-12-13T05:26:59.371227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:59.520831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경산시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47290
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47290
2nd row47290
3rd row47290
4th row47290
5th row47290

Common Values

ValueCountFrequency (%)
47290 10000
100.0%

Length

2023-12-13T05:26:59.655341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:59.801795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47290 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
6758 
2020
3242 

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 6758
67.6%
2020 3242
32.4%

Length

2023-12-13T05:26:59.938216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:27:00.081620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 6758
67.6%
2020 3242
32.4%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.4915
Minimum101
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:00.244456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile109
Q1118
median253
Q3310
95-th percentile370
Maximum370
Range269
Interquartile range (IQR)192

Descriptive statistics

Standard deviation88.916877
Coefficient of variation (CV)0.37919019
Kurtosis-1.3012524
Mean234.4915
Median Absolute Deviation (MAD)77
Skewness-0.26679577
Sum2344915
Variance7906.2109
MonotonicityNot monotonic
2023-12-13T05:27:00.429375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
253 1787
17.9%
256 1029
10.3%
310 1000
10.0%
250 940
9.4%
330 717
 
7.2%
117 678
 
6.8%
370 562
 
5.6%
350 437
 
4.4%
340 360
 
3.6%
126 306
 
3.1%
Other values (26) 2184
21.8%
ValueCountFrequency (%)
101 22
 
0.2%
102 94
 
0.9%
103 60
 
0.6%
104 18
 
0.2%
105 24
 
0.2%
106 105
1.1%
107 63
 
0.6%
108 64
 
0.6%
109 254
2.5%
110 225
2.2%
ValueCountFrequency (%)
370 562
 
5.6%
350 437
 
4.4%
340 360
 
3.6%
330 717
7.2%
310 1000
10.0%
256 1029
10.3%
253 1787
17.9%
250 940
9.4%
128 89
 
0.9%
127 34
 
0.3%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5712
Minimum0
Maximum45
Zeros3168
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:00.601353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q329
95-th percentile36
Maximum45
Range45
Interquartile range (IQR)29

Descriptive statistics

Standard deviation13.577635
Coefficient of variation (CV)0.73111241
Kurtosis-1.2464401
Mean18.5712
Median Absolute Deviation (MAD)9
Skewness-0.3319823
Sum185712
Variance184.35217
MonotonicityNot monotonic
2023-12-13T05:27:00.736082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3168
31.7%
21 1426
14.3%
22 785
 
7.8%
24 433
 
4.3%
23 429
 
4.3%
30 369
 
3.7%
31 336
 
3.4%
29 328
 
3.3%
26 320
 
3.2%
32 317
 
3.2%
Other values (16) 2089
20.9%
ValueCountFrequency (%)
0 3168
31.7%
21 1426
14.3%
22 785
 
7.8%
23 429
 
4.3%
24 433
 
4.3%
25 296
 
3.0%
26 320
 
3.2%
27 256
 
2.6%
28 238
 
2.4%
29 328
 
3.3%
ValueCountFrequency (%)
45 94
0.9%
44 55
 
0.5%
43 30
 
0.3%
42 7
 
0.1%
41 25
 
0.2%
40 60
 
0.6%
39 109
1.1%
38 41
 
0.4%
37 78
 
0.8%
36 202
2.0%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9912 
2
 
71
7
 
17

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 9912
99.1%
2 71
 
0.7%
7 17
 
0.2%

Length

2023-12-13T05:27:01.211398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:27:01.336864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9912
99.1%
2 71
 
0.7%
7 17
 
0.2%

본번
Real number (ℝ)

Distinct1107
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.3307
Minimum1
Maximum9303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:01.459897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1153
median360
Q3646.25
95-th percentile1130.2
Maximum9303
Range9302
Interquartile range (IQR)493.25

Descriptive statistics

Standard deviation372.34453
Coefficient of variation (CV)0.85335396
Kurtosis84.036732
Mean436.3307
Median Absolute Deviation (MAD)236
Skewness4.5399633
Sum4363307
Variance138640.45
MonotonicityNot monotonic
2023-12-13T05:27:01.617910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214 123
 
1.2%
15 94
 
0.9%
2 64
 
0.6%
637 57
 
0.6%
343 54
 
0.5%
300 49
 
0.5%
675 49
 
0.5%
230 48
 
0.5%
116 48
 
0.5%
1208 47
 
0.5%
Other values (1097) 9367
93.7%
ValueCountFrequency (%)
1 26
0.3%
2 64
0.6%
3 26
0.3%
4 37
0.4%
5 23
 
0.2%
6 18
 
0.2%
7 14
 
0.1%
8 17
 
0.2%
9 5
 
0.1%
10 20
 
0.2%
ValueCountFrequency (%)
9303 1
 
< 0.1%
8052 3
< 0.1%
2206 1
 
< 0.1%
2195 1
 
< 0.1%
2160 1
 
< 0.1%
2129 1
 
< 0.1%
2020 3
< 0.1%
2010 5
0.1%
1994 6
0.1%
1990 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1433
Minimum0
Maximum359
Zeros3500
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:01.797028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile21
Maximum359
Range359
Interquartile range (IQR)5

Descriptive statistics

Standard deviation20.629797
Coefficient of variation (CV)3.3580969
Kurtosis117.79324
Mean6.1433
Median Absolute Deviation (MAD)1
Skewness9.6240691
Sum61433
Variance425.58852
MonotonicityNot monotonic
2023-12-13T05:27:01.950424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3500
35.0%
1 1733
17.3%
2 894
 
8.9%
3 678
 
6.8%
4 423
 
4.2%
5 399
 
4.0%
6 346
 
3.5%
7 300
 
3.0%
8 203
 
2.0%
9 176
 
1.8%
Other values (137) 1348
 
13.5%
ValueCountFrequency (%)
0 3500
35.0%
1 1733
17.3%
2 894
 
8.9%
3 678
 
6.8%
4 423
 
4.2%
5 399
 
4.0%
6 346
 
3.5%
7 300
 
3.0%
8 203
 
2.0%
9 176
 
1.8%
ValueCountFrequency (%)
359 1
< 0.1%
358 1
< 0.1%
353 1
< 0.1%
352 1
< 0.1%
350 1
< 0.1%
344 1
< 0.1%
329 1
< 0.1%
305 1
< 0.1%
302 1
< 0.1%
296 1
< 0.1%


Real number (ℝ)

Distinct152
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.2128
Minimum0
Maximum9999
Zeros39
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:02.093636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile99
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1081.9628
Coefficient of variation (CV)7.202867
Kurtosis57.633357
Mean150.2128
Median Absolute Deviation (MAD)0
Skewness7.6699212
Sum1502128
Variance1170643.5
MonotonicityNot monotonic
2023-12-13T05:27:02.226144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8590
85.9%
2 318
 
3.2%
99 283
 
2.8%
9000 68
 
0.7%
3 49
 
0.5%
4 44
 
0.4%
0 39
 
0.4%
5 33
 
0.3%
8001 32
 
0.3%
7001 25
 
0.2%
Other values (142) 519
 
5.2%
ValueCountFrequency (%)
0 39
 
0.4%
1 8590
85.9%
2 318
 
3.2%
3 49
 
0.5%
4 44
 
0.4%
5 33
 
0.3%
6 16
 
0.2%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 18
 
0.2%
ValueCountFrequency (%)
9999 15
 
0.1%
9028 1
 
< 0.1%
9027 2
 
< 0.1%
9000 68
0.7%
8101 3
 
< 0.1%
8006 1
 
< 0.1%
8005 1
 
< 0.1%
8003 2
 
< 0.1%
8002 9
 
0.1%
8001 32
0.3%


Text

Distinct216
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:27:02.459000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0045
Min length3

Characters and Unicode

Total characters40045
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)0.8%

Sample

1st row0302
2nd row0202
3rd row0102
4th row0401
5th row0001
ValueCountFrequency (%)
0101 4147
41.5%
0102 1190
 
11.9%
0201 920
 
9.2%
0103 464
 
4.6%
0301 373
 
3.7%
0001 315
 
3.1%
0104 230
 
2.3%
8101 204
 
2.0%
0202 177
 
1.8%
0401 159
 
1.6%
Other values (206) 1821
18.2%
2023-12-13T05:27:02.842312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20274
50.6%
1 13414
33.5%
2 3098
 
7.7%
3 1288
 
3.2%
4 664
 
1.7%
5 406
 
1.0%
8 379
 
0.9%
6 240
 
0.6%
7 149
 
0.4%
9 109
 
0.3%
Other values (3) 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40021
99.9%
Dash Punctuation 21
 
0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20274
50.7%
1 13414
33.5%
2 3098
 
7.7%
3 1288
 
3.2%
4 664
 
1.7%
5 406
 
1.0%
8 379
 
0.9%
6 240
 
0.6%
7 149
 
0.4%
9 109
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40042
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20274
50.6%
1 13414
33.5%
2 3098
 
7.7%
3 1288
 
3.2%
4 664
 
1.7%
5 406
 
1.0%
8 379
 
0.9%
6 240
 
0.6%
7 149
 
0.4%
9 109
 
0.3%
Latin
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20274
50.6%
1 13414
33.5%
2 3098
 
7.7%
3 1288
 
3.2%
4 664
 
1.7%
5 406
 
1.0%
8 379
 
0.9%
6 240
 
0.6%
7 149
 
0.4%
9 109
 
0.3%
Other values (3) 24
 
0.1%
Distinct9098
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:27:03.287612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length26.8857
Min length20

Characters and Unicode

Total characters268857
Distinct characters213
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

Unique8349 ?
Unique (%)83.5%

Sample

1st row경상북도 경산시 중방동 848-2 1동 302호
2nd row경상북도 경산시 진량읍 봉회리 117 1동 202호
3rd row경상북도 경산시 용성면 미산리 871-90 1동 102호
4th row경상북도 경산시 삼남동 13-2 1동 401호
5th row[ 봉황길 8-16 ] 0001동 0001호
ValueCountFrequency (%)
7530
 
11.6%
경상북도 6235
 
9.6%
경산시 6235
 
9.6%
1동 4990
 
7.7%
0001동 3600
 
5.5%
101호 2600
 
4.0%
0101호 1547
 
2.4%
진량읍 1357
 
2.1%
와촌면 818
 
1.3%
102호 786
 
1.2%
Other values (4488) 29449
45.2%
2023-12-13T05:27:03.853987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55147
20.5%
1 30107
 
11.2%
0 27251
 
10.1%
12996
 
4.8%
11395
 
4.2%
10055
 
3.7%
2 8506
 
3.2%
7175
 
2.7%
6848
 
2.5%
6514
 
2.4%
Other values (203) 92863
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111046
41.3%
Decimal Number 90220
33.6%
Space Separator 55147
20.5%
Dash Punctuation 4911
 
1.8%
Close Punctuation 3765
 
1.4%
Open Punctuation 3765
 
1.4%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12996
 
11.7%
11395
 
10.3%
10055
 
9.1%
7175
 
6.5%
6848
 
6.2%
6514
 
5.9%
6308
 
5.7%
6307
 
5.7%
5324
 
4.8%
2957
 
2.7%
Other values (187) 35167
31.7%
Decimal Number
ValueCountFrequency (%)
1 30107
33.4%
0 27251
30.2%
2 8506
 
9.4%
3 5089
 
5.6%
4 3989
 
4.4%
5 3571
 
4.0%
6 3053
 
3.4%
8 2983
 
3.3%
9 2889
 
3.2%
7 2782
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
55147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4911
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3765
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 157808
58.7%
Hangul 111046
41.3%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12996
 
11.7%
11395
 
10.3%
10055
 
9.1%
7175
 
6.5%
6848
 
6.2%
6514
 
5.9%
6308
 
5.7%
6307
 
5.7%
5324
 
4.8%
2957
 
2.7%
Other values (187) 35167
31.7%
Common
ValueCountFrequency (%)
55147
34.9%
1 30107
19.1%
0 27251
17.3%
2 8506
 
5.4%
3 5089
 
3.2%
- 4911
 
3.1%
4 3989
 
2.5%
] 3765
 
2.4%
[ 3765
 
2.4%
5 3571
 
2.3%
Other values (4) 11707
 
7.4%
Latin
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157811
58.7%
Hangul 111046
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55147
34.9%
1 30107
19.1%
0 27251
17.3%
2 8506
 
5.4%
3 5089
 
3.2%
- 4911
 
3.1%
4 3989
 
2.5%
] 3765
 
2.4%
[ 3765
 
2.4%
5 3571
 
2.3%
Other values (6) 11710
 
7.4%
Hangul
ValueCountFrequency (%)
12996
 
11.7%
11395
 
10.3%
10055
 
9.1%
7175
 
6.5%
6848
 
6.2%
6514
 
5.9%
6308
 
5.7%
6307
 
5.7%
5324
 
4.8%
2957
 
2.7%
Other values (187) 35167
31.7%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8939
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80351491
Minimum14080
Maximum8.2165411 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:04.044964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14080
5-th percentile518352
Q14245337.5
median23328000
Q381047925
95-th percentile3.0679251 × 108
Maximum8.2165411 × 109
Range8.216527 × 109
Interquartile range (IQR)76802588

Descriptive statistics

Standard deviation2.2907126 × 108
Coefficient of variation (CV)2.8508651
Kurtosis419.67069
Mean80351491
Median Absolute Deviation (MAD)22023300
Skewness16.016537
Sum8.0351491 × 1011
Variance5.2473642 × 1016
MonotonicityNot monotonic
2023-12-13T05:27:04.212290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990000 19
 
0.2%
792000 17
 
0.2%
3644680 15
 
0.1%
3890640 14
 
0.1%
590400 11
 
0.1%
748800 10
 
0.1%
648000 10
 
0.1%
360000 10
 
0.1%
2178000 9
 
0.1%
960000 9
 
0.1%
Other values (8929) 9876
98.8%
ValueCountFrequency (%)
14080 1
< 0.1%
15000 1
< 0.1%
17000 1
< 0.1%
18960 1
< 0.1%
19800 1
< 0.1%
20160 1
< 0.1%
24000 1
< 0.1%
24750 2
< 0.1%
28000 1
< 0.1%
36960 1
< 0.1%
ValueCountFrequency (%)
8216541090 1
< 0.1%
7174379940 1
< 0.1%
6713120700 1
< 0.1%
6150323200 1
< 0.1%
4868831830 1
< 0.1%
4258367560 1
< 0.1%
2973358940 1
< 0.1%
2495147760 1
< 0.1%
2460819200 1
< 0.1%
2430626850 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6155
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.04351
Minimum0.64
Maximum17371.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:27:04.365422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile13.7545
Q144.8
median102.695
Q3224.605
95-th percentile867.385
Maximum17371.38
Range17370.74
Interquartile range (IQR)179.805

Descriptive statistics

Standard deviation524.62006
Coefficient of variation (CV)2.2225566
Kurtosis225.64688
Mean236.04351
Median Absolute Deviation (MAD)72.28
Skewness11.144492
Sum2360435.1
Variance275226.21
MonotonicityNot monotonic
2023-12-13T05:27:04.565592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 241
 
2.4%
198.0 62
 
0.6%
60.0 38
 
0.4%
36.0 38
 
0.4%
27.0 34
 
0.3%
50.0 33
 
0.3%
72.0 32
 
0.3%
96.0 30
 
0.3%
90.0 30
 
0.3%
22.36 29
 
0.3%
Other values (6145) 9433
94.3%
ValueCountFrequency (%)
0.64 1
 
< 0.1%
0.7 1
 
< 0.1%
0.72 1
 
< 0.1%
0.79 1
 
< 0.1%
0.93 1
 
< 0.1%
1.0 18
0.2%
1.13 1
 
< 0.1%
1.2 3
 
< 0.1%
1.21 3
 
< 0.1%
1.26 2
 
< 0.1%
ValueCountFrequency (%)
17371.38 1
< 0.1%
13728.4 1
< 0.1%
11475.42 1
< 0.1%
8750.31 1
< 0.1%
8178.1 1
< 0.1%
7776.0 1
< 0.1%
7284.48 1
< 0.1%
7235.58 1
< 0.1%
6608.0 1
< 0.1%
6557.0 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20210601
6758 
20200601
3242 

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 6758
67.6%
20200601 3242
32.4%

Length

2023-12-13T05:27:04.717381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:27:04.814438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 6758
67.6%
20200601 3242
32.4%

Interactions

2023-12-13T05:26:57.625712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:53.923959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.557620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.230609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.814266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.426308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.006448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.722723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:53.999034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.645546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.308493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.913708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.503594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.079371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.832239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.088005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.735748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.381654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.997443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.588373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.155620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.921615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.173725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.844784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.458462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.075060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.662109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.227615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:58.024095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.265980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.946606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.544675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.163548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.741567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.345077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:58.134678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.359386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.057697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.635822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.247510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.814057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.434175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:58.246114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:54.459082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.144529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:55.711678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.339097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:56.904079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:57.525434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:27:04.912542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.1510.0270.0220.0350.0430.0040.0000.0001.000
법정동0.1511.0000.6660.1300.1930.0710.1110.0140.0560.151
법정리0.0270.6661.0000.0870.2510.1010.1160.0880.1040.027
특수지0.0220.1300.0871.0000.5170.0000.0000.1400.1790.022
본번0.0350.1930.2510.5171.0000.0000.0010.1110.0190.035
부번0.0430.0710.1010.0000.0001.0000.0170.0000.0000.043
0.0040.1110.1160.0000.0010.0171.0000.0000.0000.004
시가표준액0.0000.0140.0880.1400.1110.0000.0001.0000.9100.000
연면적0.0000.0560.1040.1790.0190.0000.0000.9101.0000.000
기준일자1.0000.1510.0270.0220.0350.0430.0040.0000.0001.000
2023-12-13T05:27:05.050262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지기준일자
과세년도1.0000.0361.000
특수지0.0361.0000.036
기준일자1.0000.0361.000
2023-12-13T05:27:05.178543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.703-0.147-0.232-0.090-0.2990.0040.1080.0540.108
법정리0.7031.000-0.188-0.280-0.069-0.2300.0170.0360.0550.036
본번-0.147-0.1881.0000.059-0.0680.0800.0060.0420.4550.042
부번-0.232-0.2800.0591.000-0.0970.030-0.0680.0330.0000.033
-0.090-0.069-0.068-0.0971.000-0.064-0.0940.0050.0000.005
시가표준액-0.299-0.2300.0800.030-0.0641.0000.7240.0000.0890.000
연면적0.0040.0170.006-0.068-0.0940.7241.0000.0000.0790.000
과세년도0.1080.0360.0420.0330.0050.0000.0001.0000.0361.000
특수지0.0540.0550.4550.0000.0000.0890.0790.0361.0000.036
기준일자0.1080.0360.0420.0330.0050.0000.0001.0000.0361.000

Missing values

2023-12-13T05:26:58.414653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:26:58.682869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
29050경상북도경산시47290202111701848210302경상북도 경산시 중방동 848-2 1동 302호174238820390.6720210601
4595경상북도경산시472902021253241117010202경상북도 경산시 진량읍 봉회리 117 1동 202호78010450131.1120210601
22595경상북도경산시4729020213403318719010102경상북도 경산시 용성면 미산리 871-90 1동 102호34937500279.520210601
21027경상북도경산시4729020211010113210401경상북도 경산시 삼남동 13-2 1동 401호1936116001241.120210601
34570경상북도경산시47290202125324184010001[ 봉황길 8-16 ] 0001동 0001호960000192.020210601
14026경상북도경산시4729020213303311981990101경상북도 경산시 자인면 단북리 198-1 99동 101호84800020.020210601
48713경상북도경산시472902021253441377010101경상북도 경산시 진량읍 당곡리 377 1동 101호9796080199.9220210601
54167경상북도경산시472902021256231169010203경상북도 경산시 압량읍 압량리 169 1동 203호8533830167.3320210601
2713경상북도경산시472902021102012731410003경상북도 경산시 삼북동 273-14 1동 3호903579052.2320210601
30363경상북도경산시4729020211060157213010101경상북도 경산시 백천동 572-1 301동 101호2860774041.0520210601
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
39567경상북도경산시472902021250351470110101경상북도 경산시 하양읍 환상리 470-1 1동 101호243733430397.6420210601
24727경상북도경산시472902021250211390910104[ 대학로298길 19-6 ] 0001동 0104호1607713022.4420210601
56383경상북도경산시472902021350311464710201경상북도 경산시 남산면 사월리 464-7 1동 201호706640048.420210601
72999경상북도경산시4729020203102917891410101경상북도 경산시 와촌면 박사리 789-14 1동 101호883200220.820200601
21583경상북도경산시47290202111001638110504[ 펜타힐즈2로 25 ] 0001동 0504호268016210302.63820210601
50536경상북도경산시472902021253401498010105경상북도 경산시 진량읍 대원리 498 1동 105호22472918304778.9320210601
30988경상북도경산시47290202110901873010101[ 성암로12길 19 ] 0001동 0101호1102751057.4220210601
80433경상북도경산시472902020256231268010102경상북도 경산시 압량읍 압량리 268 1동 102호193370031.720200601
14587경상북도경산시47290202135034155010004경상북도 경산시 남산면 전지리 55 1동 4호900000180.020210601
76157경상북도경산시472902020253251548210102경상북도 경산시 진량읍 북리 548-2 1동 102호24968580244.7920200601

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
11경상북도경산시472902021253391520010101경상북도 경산시 진량읍 신제리 520 1동 101호2068226029.42202106016
5경상북도경산시472902020253391520010101경상북도 경산시 진량읍 신제리 520 1동 101호2085878029.42202006014
2경상북도경산시4729020202502472010010101경상북도 경산시 하양읍 서사리 2010 1동 101호482310018.0202006013
17경상북도경산시472902021350251297010101경상북도 경산시 남산면 평기리 297 1동 101호874800174.96202106013
0경상북도경산시47290202011601112010101경상북도 경산시 계양동 112 1동 101호4670812501245.55202006012
1경상북도경산시472902020125024135910101경상북도 경산시 평산동 산 41-35 91동 101호289800018.0202006012
3경상북도경산시4729020202502472010010201경상북도 경산시 하양읍 서사리 2010 1동 201호419400018.0202006012
4경상북도경산시4729020202502472020010201경상북도 경산시 하양읍 서사리 2020 1동 201호629100027.0202006012
6경상북도경산시472902020310271330010101경상북도 경산시 와촌면 계전리 330 1동 101호700000175.0202006012
7경상북도경산시47290202110301143159910101[ 장산로20길 38 ] 0091동 0101호223200014.4202106012