Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description파주시 2017년부터 2020년까지의 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공함으로써 물건별 재산가액 확인 가능
Author경기도 파주시
URLhttps://www.data.go.kr/data/15079996/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
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
과세년도 is highly imbalanced (83.8%)Imbalance
특수지 is highly imbalanced (92.5%)Imbalance
시가표준액 is highly skewed (γ1 = 31.07589126)Skewed
연면적 is highly skewed (γ1 = 42.52419094)Skewed
법정리 has 3808 (38.1%) zerosZeros
부번 has 1949 (19.5%) zerosZeros

Reproduction

Analysis started2023-12-12 06:45:06.367806
Analysis finished2023-12-12 06:45:19.773662
Duration13.41 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

2023-12-12T15:45:19.840141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:19.945586image/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
파주시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파주시 10000
100.0%

Length

2023-12-12T15:45:20.057481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:20.168033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41480
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41480
2nd row41480
3rd row41480
4th row41480
5th row41480

Common Values

ValueCountFrequency (%)
41480 10000
100.0%

Length

2023-12-12T15:45:20.264516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:20.359693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41480 10000
100.0%

과세년도
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
9763 
2018
 
237

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2017 9763
97.6%
2018 237
 
2.4%

Length

2023-12-12T15:45:20.452013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:20.545211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 9763
97.6%
2018 237
 
2.4%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.8865
Minimum101
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:20.700866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1113
median253
Q3320
95-th percentile360
Maximum400
Range299
Interquartile range (IQR)207

Descriptive statistics

Standard deviation98.347709
Coefficient of variation (CV)0.43346655
Kurtosis-1.5584205
Mean226.8865
Median Absolute Deviation (MAD)97
Skewness-0.12151489
Sum2268865
Variance9672.2718
MonotonicityNot monotonic
2023-12-12T15:45:20.903809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
101 1027
 
10.3%
350 960
 
9.6%
250 951
 
9.5%
320 934
 
9.3%
262 760
 
7.6%
310 712
 
7.1%
253 581
 
5.8%
256 571
 
5.7%
115 415
 
4.2%
113 393
 
3.9%
Other values (24) 2696
27.0%
ValueCountFrequency (%)
101 1027
10.3%
102 162
 
1.6%
104 105
 
1.1%
105 90
 
0.9%
106 71
 
0.7%
107 43
 
0.4%
108 305
 
3.0%
109 61
 
0.6%
110 60
 
0.6%
111 174
 
1.7%
ValueCountFrequency (%)
400 12
 
0.1%
390 12
 
0.1%
380 38
 
0.4%
370 371
 
3.7%
360 290
 
2.9%
350 960
9.6%
320 934
9.3%
310 712
7.1%
262 760
7.6%
256 571
5.7%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0551
Minimum0
Maximum36
Zeros3808
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:21.051424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q325
95-th percentile29
Maximum36
Range36
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.026202
Coefficient of variation (CV)0.79881247
Kurtosis-1.6803773
Mean15.0551
Median Absolute Deviation (MAD)5
Skewness-0.37171125
Sum150551
Variance144.62953
MonotonicityNot monotonic
2023-12-12T15:45:21.203519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3808
38.1%
21 1085
 
10.8%
23 1083
 
10.8%
22 896
 
9.0%
25 774
 
7.7%
27 534
 
5.3%
26 516
 
5.2%
24 506
 
5.1%
29 254
 
2.5%
28 206
 
2.1%
Other values (7) 338
 
3.4%
ValueCountFrequency (%)
0 3808
38.1%
21 1085
 
10.8%
22 896
 
9.0%
23 1083
 
10.8%
24 506
 
5.1%
25 774
 
7.7%
26 516
 
5.2%
27 534
 
5.3%
28 206
 
2.1%
29 254
 
2.5%
ValueCountFrequency (%)
36 10
 
0.1%
35 73
 
0.7%
34 3
 
< 0.1%
33 16
 
0.2%
32 7
 
0.1%
31 44
 
0.4%
30 185
 
1.8%
29 254
2.5%
28 206
 
2.1%
27 534
5.3%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9844 
2
 
152
4
 
4

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 9844
98.4%
2 152
 
1.5%
4 4
 
< 0.1%

Length

2023-12-12T15:45:21.351539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:21.469196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9844
98.4%
2 152
 
1.5%
4 4
 
< 0.1%

본번
Real number (ℝ)

Distinct1161
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.2566
Minimum0
Maximum1981
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:21.589999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q1136
median354
Q3685.25
95-th percentile1652
Maximum1981
Range1981
Interquartile range (IQR)549.25

Descriptive statistics

Standard deviation453.49237
Coefficient of variation (CV)0.92879926
Kurtosis0.95642793
Mean488.2566
Median Absolute Deviation (MAD)247.5
Skewness1.2697933
Sum4882566
Variance205655.33
MonotonicityNot monotonic
2023-12-12T15:45:21.734119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1652 159
 
1.6%
52 123
 
1.2%
10 114
 
1.1%
329 99
 
1.0%
17 83
 
0.8%
688 80
 
0.8%
263 69
 
0.7%
502 65
 
0.7%
986 60
 
0.6%
1695 47
 
0.5%
Other values (1151) 9101
91.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 40
0.4%
2 25
0.2%
3 29
0.3%
4 13
 
0.1%
5 24
0.2%
6 13
 
0.1%
7 28
0.3%
8 22
0.2%
9 17
0.2%
ValueCountFrequency (%)
1981 1
 
< 0.1%
1976 1
 
< 0.1%
1938 1
 
< 0.1%
1936 1
 
< 0.1%
1933 1
 
< 0.1%
1913 1
 
< 0.1%
1906 1
 
< 0.1%
1901 4
< 0.1%
1899 7
0.1%
1897 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct207
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.6284
Minimum0
Maximum569
Zeros1949
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:21.919680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q310
95-th percentile49
Maximum569
Range569
Interquartile range (IQR)9

Descriptive statistics

Standard deviation39.194935
Coefficient of variation (CV)2.8759748
Kurtosis72.413907
Mean13.6284
Median Absolute Deviation (MAD)3
Skewness7.4618347
Sum136284
Variance1536.2429
MonotonicityNot monotonic
2023-12-12T15:45:22.094628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1949
19.5%
1 1513
15.1%
2 983
 
9.8%
3 660
 
6.6%
4 562
 
5.6%
5 484
 
4.8%
7 405
 
4.0%
6 359
 
3.6%
8 263
 
2.6%
9 225
 
2.2%
Other values (197) 2597
26.0%
ValueCountFrequency (%)
0 1949
19.5%
1 1513
15.1%
2 983
9.8%
3 660
 
6.6%
4 562
 
5.6%
5 484
 
4.8%
6 359
 
3.6%
7 405
 
4.0%
8 263
 
2.6%
9 225
 
2.2%
ValueCountFrequency (%)
569 1
 
< 0.1%
562 1
 
< 0.1%
542 3
< 0.1%
540 3
< 0.1%
535 1
 
< 0.1%
522 1
 
< 0.1%
517 1
 
< 0.1%
498 1
 
< 0.1%
486 1
 
< 0.1%
473 2
< 0.1%


Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.6427
Minimum0
Maximum9003
Zeros35
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:22.241069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3001
Maximum9003
Range9003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1694.8072
Coefficient of variation (CV)3.7525399
Kurtosis14.962522
Mean451.6427
Median Absolute Deviation (MAD)0
Skewness3.9814993
Sum4516427
Variance2872371.6
MonotonicityNot monotonic
2023-12-12T15:45:22.375042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8275
82.8%
2 354
 
3.5%
3001 279
 
2.8%
7001 144
 
1.4%
3 104
 
1.0%
101 104
 
1.0%
9000 68
 
0.7%
8001 67
 
0.7%
9001 61
 
0.6%
4 58
 
0.6%
Other values (87) 486
 
4.9%
ValueCountFrequency (%)
0 35
 
0.4%
1 8275
82.8%
2 354
 
3.5%
3 104
 
1.0%
4 58
 
0.6%
5 36
 
0.4%
6 46
 
0.5%
7 13
 
0.1%
8 13
 
0.1%
9 16
 
0.2%
ValueCountFrequency (%)
9003 2
 
< 0.1%
9002 10
 
0.1%
9001 61
0.6%
9000 68
0.7%
8083 1
 
< 0.1%
8074 1
 
< 0.1%
8006 1
 
< 0.1%
8005 9
 
0.1%
8002 48
0.5%
8001 67
0.7%


Text

Distinct497
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:45:22.699456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.7324
Min length1

Characters and Unicode

Total characters27324
Distinct characters24
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

Unique285 ?
Unique (%)2.9%

Sample

1st row7
2nd row2
3rd row1
4th row101
5th row101
ValueCountFrequency (%)
101 3441
34.3%
1 946
 
9.4%
201 787
 
7.8%
102 688
 
6.9%
2 314
 
3.1%
103 308
 
3.1%
301 269
 
2.7%
8101 234
 
2.3%
104 174
 
1.7%
3 172
 
1.7%
Other values (481) 2706
27.0%
2023-12-12T15:45:23.232574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12119
44.4%
0 7964
29.1%
2 2918
 
10.7%
3 1346
 
4.9%
4 798
 
2.9%
8 631
 
2.3%
5 518
 
1.9%
6 383
 
1.4%
7 305
 
1.1%
9 166
 
0.6%
Other values (14) 176
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27148
99.4%
Other Letter 78
 
0.3%
Space Separator 39
 
0.1%
Dash Punctuation 26
 
0.1%
Uppercase Letter 23
 
0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12119
44.6%
0 7964
29.3%
2 2918
 
10.7%
3 1346
 
5.0%
4 798
 
2.9%
8 631
 
2.3%
5 518
 
1.9%
6 383
 
1.4%
7 305
 
1.1%
9 166
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
A 11
47.8%
B 6
26.1%
M 2
 
8.7%
J 2
 
8.7%
D 1
 
4.3%
C 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
a 4
40.0%
r 3
30.0%
n 2
20.0%
p 1
 
10.0%
Other Letter
ValueCountFrequency (%)
39
50.0%
39
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27213
99.6%
Hangul 78
 
0.3%
Latin 33
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12119
44.5%
0 7964
29.3%
2 2918
 
10.7%
3 1346
 
4.9%
4 798
 
2.9%
8 631
 
2.3%
5 518
 
1.9%
6 383
 
1.4%
7 305
 
1.1%
9 166
 
0.6%
Other values (2) 65
 
0.2%
Latin
ValueCountFrequency (%)
A 11
33.3%
B 6
18.2%
a 4
 
12.1%
r 3
 
9.1%
M 2
 
6.1%
J 2
 
6.1%
n 2
 
6.1%
D 1
 
3.0%
p 1
 
3.0%
C 1
 
3.0%
Hangul
ValueCountFrequency (%)
39
50.0%
39
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27246
99.7%
Hangul 78
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12119
44.5%
0 7964
29.2%
2 2918
 
10.7%
3 1346
 
4.9%
4 798
 
2.9%
8 631
 
2.3%
5 518
 
1.9%
6 383
 
1.4%
7 305
 
1.1%
9 166
 
0.6%
Other values (12) 98
 
0.4%
Hangul
ValueCountFrequency (%)
39
50.0%
39
50.0%
Distinct9463
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:45:23.654087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length26.2449
Min length17

Characters and Unicode

Total characters262449
Distinct characters286
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

Unique9000 ?
Unique (%)90.0%

Sample

1st row경기도 파주시 법원읍 동문리 130-1 1동 7호
2nd row경기도 파주시 군내면 읍내리 589 1동 2호
3rd row경기도 파주시 적성면 마지리 104-2 1동 1호
4th row경기도 파주시 신촌동 248 2동 101호
5th row[ 헤이리마을길 12 ] 0001동 0101호
ValueCountFrequency (%)
8914
 
13.8%
경기도 5543
 
8.6%
파주시 5543
 
8.6%
1동 4310
 
6.7%
0001동 3965
 
6.2%
101호 2156
 
3.3%
0101호 1285
 
2.0%
광탄면 750
 
1.2%
1호 702
 
1.1%
문산읍 568
 
0.9%
Other values (5539) 30663
47.6%
2023-12-12T15:45:24.248173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54399
20.7%
0 29693
 
11.3%
1 28208
 
10.7%
12008
 
4.6%
9995
 
3.8%
2 8488
 
3.2%
6374
 
2.4%
3 6372
 
2.4%
6072
 
2.3%
5639
 
2.1%
Other values (276) 95201
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99829
38.0%
Decimal Number 93695
35.7%
Space Separator 54399
20.7%
Dash Punctuation 5594
 
2.1%
Open Punctuation 4457
 
1.7%
Close Punctuation 4457
 
1.7%
Uppercase Letter 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12008
 
12.0%
9995
 
10.0%
6374
 
6.4%
6072
 
6.1%
5639
 
5.6%
5637
 
5.6%
5612
 
5.6%
5605
 
5.6%
5100
 
5.1%
3357
 
3.4%
Other values (258) 34430
34.5%
Decimal Number
ValueCountFrequency (%)
0 29693
31.7%
1 28208
30.1%
2 8488
 
9.1%
3 6372
 
6.8%
4 4314
 
4.6%
5 3658
 
3.9%
8 3452
 
3.7%
7 3322
 
3.5%
6 3319
 
3.5%
9 2869
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 10
55.6%
B 6
33.3%
D 1
 
5.6%
C 1
 
5.6%
Space Separator
ValueCountFrequency (%)
54399
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5594
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4457
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162602
62.0%
Hangul 99829
38.0%
Latin 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12008
 
12.0%
9995
 
10.0%
6374
 
6.4%
6072
 
6.1%
5639
 
5.6%
5637
 
5.6%
5612
 
5.6%
5605
 
5.6%
5100
 
5.1%
3357
 
3.4%
Other values (258) 34430
34.5%
Common
ValueCountFrequency (%)
54399
33.5%
0 29693
18.3%
1 28208
17.3%
2 8488
 
5.2%
3 6372
 
3.9%
- 5594
 
3.4%
[ 4457
 
2.7%
] 4457
 
2.7%
4 4314
 
2.7%
5 3658
 
2.2%
Other values (4) 12962
 
8.0%
Latin
ValueCountFrequency (%)
A 10
55.6%
B 6
33.3%
D 1
 
5.6%
C 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162620
62.0%
Hangul 99829
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54399
33.5%
0 29693
18.3%
1 28208
17.3%
2 8488
 
5.2%
3 6372
 
3.9%
- 5594
 
3.4%
[ 4457
 
2.7%
] 4457
 
2.7%
4 4314
 
2.7%
5 3658
 
2.2%
Other values (8) 12980
 
8.0%
Hangul
ValueCountFrequency (%)
12008
 
12.0%
9995
 
10.0%
6374
 
6.4%
6072
 
6.1%
5639
 
5.6%
5637
 
5.6%
5612
 
5.6%
5605
 
5.6%
5100
 
5.1%
3357
 
3.4%
Other values (258) 34430
34.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8833
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87375256
Minimum29000
Maximum2.7297403 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:24.468889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29000
5-th percentile755710
Q14988100
median24281805
Q370740090
95-th percentile2.5126088 × 108
Maximum2.7297403 × 1010
Range2.7297374 × 1010
Interquartile range (IQR)65751990

Descriptive statistics

Standard deviation4.7126251 × 108
Coefficient of variation (CV)5.3935466
Kurtosis1409.9401
Mean87375256
Median Absolute Deviation (MAD)22307855
Skewness31.075891
Sum8.7375256 × 1011
Variance2.2208836 × 1017
MonotonicityNot monotonic
2023-12-12T15:45:24.613788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86798250 33
 
0.3%
14629200 21
 
0.2%
1048800 17
 
0.2%
102044250 14
 
0.1%
87477390 13
 
0.1%
11526070 10
 
0.1%
540000 9
 
0.1%
72460080 9
 
0.1%
460000 9
 
0.1%
920000 9
 
0.1%
Other values (8823) 9856
98.6%
ValueCountFrequency (%)
29000 1
< 0.1%
52030 1
< 0.1%
60000 1
< 0.1%
63360 1
< 0.1%
66000 1
< 0.1%
68000 1
< 0.1%
72900 1
< 0.1%
75350 1
< 0.1%
79420 1
< 0.1%
80000 2
< 0.1%
ValueCountFrequency (%)
27297402820 1
< 0.1%
17366769420 1
< 0.1%
11166422400 1
< 0.1%
10740960520 1
< 0.1%
8656717300 1
< 0.1%
8645658020 1
< 0.1%
8590606500 1
< 0.1%
8005259920 1
< 0.1%
7364879680 1
< 0.1%
6918920190 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6311
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.19503
Minimum1
Maximum86000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:24.755480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.817
Q141.275
median96.3
Q3196
95-th percentile704.954
Maximum86000
Range85999
Interquartile range (IQR)154.725

Descriptive statistics

Standard deviation1245.9617
Coefficient of variation (CV)5.208978
Kurtosis2523.2521
Mean239.19503
Median Absolute Deviation (MAD)67.05
Skewness42.524191
Sum2391950.3
Variance1552420.5
MonotonicityNot monotonic
2023-12-12T15:45:24.927889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.0 207
 
2.1%
18.0 159
 
1.6%
36.0 43
 
0.4%
27.0 40
 
0.4%
192.0 35
 
0.4%
72.0 33
 
0.3%
125.25 33
 
0.3%
60.0 30
 
0.3%
12.0 30
 
0.3%
99.0 29
 
0.3%
Other values (6301) 9361
93.6%
ValueCountFrequency (%)
1.0 1
< 0.1%
1.209 1
< 0.1%
1.2356 1
< 0.1%
1.24 1
< 0.1%
1.4 1
< 0.1%
1.6236 1
< 0.1%
1.626 1
< 0.1%
1.8 1
< 0.1%
1.8835 1
< 0.1%
1.92 1
< 0.1%
ValueCountFrequency (%)
86000.0 1
< 0.1%
47268.23 1
< 0.1%
28719.1458 1
< 0.1%
27127.1 1
< 0.1%
22377.6 1
< 0.1%
18880.33 1
< 0.1%
17008.96 1
< 0.1%
16363.06 1
< 0.1%
14231.0 1
< 0.1%
11680.46 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
2023-12-12T15:45:25.036884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:25.471769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-12T15:45:17.875795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:08.828392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.376352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:11.616184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:12.882920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:14.069630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:16.792201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.987408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:08.962671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.509466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:11.743397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.005311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:14.380421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:16.910903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:18.113992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:09.079277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.630848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:11.872934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.129829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:15.006518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.028490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:18.560252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:09.194225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.766862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:12.015822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.271325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:15.323305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.149416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:18.661066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:09.329129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.882568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:12.126467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.385271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:15.656028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.261525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:19.115425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.130416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:11.395780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:12.626058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.836298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:16.233869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.679955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:19.234456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:10.256073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:11.511889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:12.752867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:13.945883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:16.514835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:17.775049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:45:25.593693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.1280.1830.0080.1030.2660.0930.0000.0001.000
법정동0.1281.0000.6750.1100.4000.1990.2970.0950.0670.128
법정리0.1830.6751.0000.1310.3360.1610.1230.0000.0000.183
특수지0.0080.1100.1311.0000.2060.0000.1160.0000.0000.008
본번0.1030.4000.3360.2061.0000.3130.2720.1290.1100.103
부번0.2660.1990.1610.0000.3131.0000.0350.0000.0000.266
0.0930.2970.1230.1160.2720.0351.0000.0800.0290.093
시가표준액0.0000.0950.0000.0000.1290.0000.0801.0000.8640.000
연면적0.0000.0670.0000.0000.1100.0000.0290.8641.0000.000
기준일자1.0000.1280.1830.0080.1030.2660.0930.0000.0001.000
2023-12-12T15:45:25.739591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지
과세년도1.0000.014
특수지0.0141.000
2023-12-12T15:45:25.880576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.761-0.200-0.0990.042-0.1640.0960.1370.074
법정리0.7611.000-0.220-0.0590.048-0.1790.0880.1310.054
본번-0.200-0.2201.000-0.1890.0000.2380.0350.0790.125
부번-0.099-0.059-0.1891.0000.013-0.005-0.0940.2040.000
0.0420.0480.0000.0131.000-0.116-0.1200.1370.498
시가표준액-0.164-0.1790.238-0.005-0.1161.0000.7030.0000.000
연면적0.0960.0880.035-0.094-0.1200.7031.0000.0000.000
과세년도0.1370.1310.0790.2040.1370.0000.0001.0000.014
특수지0.0740.0540.1250.0000.4980.0000.0000.0141.000

Missing values

2023-12-12T15:45:19.396418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:45:19.671758image/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
3775경기도파주시414802017256241130117경기도 파주시 법원읍 동문리 130-1 1동 7호92000046.02017-06-01
13247경기도파주시414802017380241589012경기도 파주시 군내면 읍내리 589 1동 2호243250097.32017-06-01
34061경기도파주시414802017370351104211경기도 파주시 적성면 마지리 104-2 1동 1호163400086.02017-06-01
47796경기도파주시4148020171200124802101경기도 파주시 신촌동 248 2동 101호170346000638.02017-06-01
13211경기도파주시41480201732022116522941101[ 헤이리마을길 12 ] 0001동 0101호113295320139.752017-06-01
66431경기도파주시41480201712201130251703[ 미래로602번길 27 ] 0001동 0703호830844018.842017-06-01
84842경기도파주시414802017262241100131101경기도 파주시 조리읍 장곡리 100-13 1동 101호45705000165.02017-06-01
4151경기도파주시414802017256261327111경기도 파주시 법원읍 삼방리 327-1 1동 1호96800096.82017-06-01
80131경기도파주시41480201731023113013001101[ 다래울길 137-4 ] 3001동 0101호345000050.02017-06-01
81159경기도파주시41480201732022116521551103경기도 파주시 탄현면 법흥리 1652-155 1동 103호180755280299.762017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
39385경기도파주시41480201710101523919[ 중앙로 308 ] 0001동 0009호1902255017.4922017-06-01
75191경기도파주시4148020173502113281101경기도 파주시 광탄면 분수리 32-8 1동 101호329700021.02017-06-01
71703경기도파주시414802017310231125041301[ 엘씨디로241번길 8-8 ] 0001동 0301호142570830210.90362017-06-01
16226경기도파주시41480201725027168661102[ 말우물길 104 ] 0001동 0102호133100027.52017-06-01
8388경기도파주시41480201725630142062101경기도 파주시 법원읍 금곡리 420-6 2동 101호55761420389.942017-06-01
73675경기도파주시414802017350211136111103경기도 파주시 광탄면 분수리 136-11 1동 103호5423000187.02017-06-01
15379경기도파주시4148020172532112101201경기도 파주시 파주읍 봉서리 21 1동 201호426910040654.772017-06-01
58273경기도파주시414802017310231151121101[ 산들로 35-4 ] 0001동 0101호3276240087.62017-06-01
80988경기도파주시414802017320241243417경기도 파주시 탄현면 문지리 243-4 1동 7호67200032.02017-06-01
48970경기도파주시414802017113011695211003[ 청석로 272 ] 0001동 1003호1644786039.052017-06-01