Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows40
Duplicate rows (%)0.4%
Total size in memory1.4 MiB
Average record size in memory146.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime2

Dataset

Description강원도 춘천시 소재 일반건축물에 대한 지방세 부과기준인 시가표준액 데이터로, 시도명, 시군구명, 자치단체코드, 과세년도, 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적, 기준일자, 데이터기준일에 대한 자료
Author강원특별자치도 춘천시
URLhttps://www.data.go.kr/data/15080238/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
Dataset has 40 (0.4%) 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 (89.9%)Imbalance
법정리 has 5727 (57.3%) zerosZeros
부번 has 2449 (24.5%) zerosZeros
has 468 (4.7%) zerosZeros

Reproduction

Analysis started2023-12-12 11:36:01.743993
Analysis finished2023-12-12 11:36:15.938603
Duration14.19 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
강원특별자치도
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원특별자치도 10000
100.0%

Length

2023-12-12T20:36:16.063182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:36:16.250609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원특별자치도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
춘천시 10000
100.0%

Length

2023-12-12T20:36:16.441888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:36:16.610037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
춘천시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
51110
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row51110
2nd row51110
3rd row51110
4th row51110
5th row51110

Common Values

ValueCountFrequency (%)
51110 10000
100.0%

Length

2023-12-12T20:36:16.818238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:36:16.999552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
51110 10000
100.0%

과세년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
6384 
2018
3616 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2017 6384
63.8%
2018 3616
36.2%

Length

2023-12-12T20:36:17.185544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:36:17.386638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 6384
63.8%
2018 3616
36.2%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.4883
Minimum101
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:17.593566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile109
Q1117
median124
Q3340
95-th percentile400
Maximum400
Range299
Interquartile range (IQR)223

Descriptive statistics

Standard deviation118.57591
Coefficient of variation (CV)0.54772435
Kurtosis-1.6417126
Mean216.4883
Median Absolute Deviation (MAD)14
Skewness0.44260694
Sum2164883
Variance14060.247
MonotonicityNot monotonic
2023-12-12T20:36:17.855217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
112 1045
 
10.4%
123 956
 
9.6%
120 926
 
9.3%
390 813
 
8.1%
400 714
 
7.1%
310 587
 
5.9%
124 540
 
5.4%
350 501
 
5.0%
330 355
 
3.5%
250 341
 
3.4%
Other values (29) 3222
32.2%
ValueCountFrequency (%)
101 13
 
0.1%
102 77
 
0.8%
103 30
 
0.3%
104 18
 
0.2%
105 74
 
0.7%
106 32
 
0.3%
107 59
 
0.6%
108 106
 
1.1%
109 277
2.8%
110 252
2.5%
ValueCountFrequency (%)
400 714
7.1%
390 813
8.1%
380 113
 
1.1%
360 328
3.3%
350 501
5.0%
340 202
 
2.0%
330 355
3.5%
320 319
 
3.2%
310 587
5.9%
250 341
3.4%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.3029
Minimum0
Maximum31
Zeros5727
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:18.118277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323
95-th percentile28
Maximum31
Range31
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.04584
Coefficient of variation (CV)1.1691698
Kurtosis-1.7853741
Mean10.3029
Median Absolute Deviation (MAD)0
Skewness0.35545938
Sum103029
Variance145.10226
MonotonicityNot monotonic
2023-12-12T20:36:18.331403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5727
57.3%
22 718
 
7.2%
23 682
 
6.8%
21 670
 
6.7%
24 621
 
6.2%
25 527
 
5.3%
27 293
 
2.9%
26 231
 
2.3%
30 174
 
1.7%
29 166
 
1.7%
Other values (2) 191
 
1.9%
ValueCountFrequency (%)
0 5727
57.3%
21 670
 
6.7%
22 718
 
7.2%
23 682
 
6.8%
24 621
 
6.2%
25 527
 
5.3%
26 231
 
2.3%
27 293
 
2.9%
28 158
 
1.6%
29 166
 
1.7%
ValueCountFrequency (%)
31 33
 
0.3%
30 174
 
1.7%
29 166
 
1.7%
28 158
 
1.6%
27 293
2.9%
26 231
 
2.3%
25 527
5.3%
24 621
6.2%
23 682
6.8%
22 718
7.2%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9770 
2
 
228
3
 
2

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 9770
97.7%
2 228
 
2.3%
3 2
 
< 0.1%

Length

2023-12-12T20:36:18.555520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:36:18.751780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9770
97.7%
2 228
 
2.3%
3 2
 
< 0.1%

본번
Real number (ℝ)

Distinct1163
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478.2305
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:18.977097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1172
median426
Q3730
95-th percentile1045.15
Maximum9999
Range9998
Interquartile range (IQR)558

Descriptive statistics

Standard deviation423.9953
Coefficient of variation (CV)0.88659192
Kurtosis175.58289
Mean478.2305
Median Absolute Deviation (MAD)277
Skewness8.0998739
Sum4782305
Variance179772.01
MonotonicityNot monotonic
2023-12-12T20:36:19.222718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 147
 
1.5%
11 128
 
1.3%
1017 104
 
1.0%
1 102
 
1.0%
188 87
 
0.9%
172 74
 
0.7%
1029 58
 
0.6%
765 53
 
0.5%
37 53
 
0.5%
654 48
 
0.5%
Other values (1153) 9146
91.5%
ValueCountFrequency (%)
1 102
1.0%
2 15
 
0.1%
3 9
 
0.1%
4 13
 
0.1%
5 25
 
0.2%
6 33
 
0.3%
7 31
 
0.3%
8 28
 
0.3%
9 10
 
0.1%
10 6
 
0.1%
ValueCountFrequency (%)
9999 6
0.1%
9959 1
 
< 0.1%
1798 2
 
< 0.1%
1789 3
< 0.1%
1774 1
 
< 0.1%
1767 1
 
< 0.1%
1746 1
 
< 0.1%
1713 2
 
< 0.1%
1599 1
 
< 0.1%
1569 4
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct162
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5933
Minimum0
Maximum356
Zeros2449
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:19.492266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile35
Maximum356
Range356
Interquartile range (IQR)7

Descriptive statistics

Standard deviation21.933399
Coefficient of variation (CV)2.5523837
Kurtosis74.632874
Mean8.5933
Median Absolute Deviation (MAD)2
Skewness7.3468167
Sum85933
Variance481.074
MonotonicityNot monotonic
2023-12-12T20:36:19.765596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2449
24.5%
1 1826
18.3%
2 806
 
8.1%
3 688
 
6.9%
4 555
 
5.5%
5 416
 
4.2%
6 328
 
3.3%
7 302
 
3.0%
8 272
 
2.7%
9 225
 
2.2%
Other values (152) 2133
21.3%
ValueCountFrequency (%)
0 2449
24.5%
1 1826
18.3%
2 806
 
8.1%
3 688
 
6.9%
4 555
 
5.5%
5 416
 
4.2%
6 328
 
3.3%
7 302
 
3.0%
8 272
 
2.7%
9 225
 
2.2%
ValueCountFrequency (%)
356 1
 
< 0.1%
340 1
 
< 0.1%
319 2
< 0.1%
318 3
< 0.1%
296 1
 
< 0.1%
289 2
< 0.1%
283 1
 
< 0.1%
276 1
 
< 0.1%
273 1
 
< 0.1%
265 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.3492
Minimum0
Maximum9999
Zeros468
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:20.039055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile439.2
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1857.7556
Coefficient of variation (CV)4.505297
Kurtosis17.197434
Mean412.3492
Median Absolute Deviation (MAD)0
Skewness4.3758717
Sum4123492
Variance3451255.9
MonotonicityNot monotonic
2023-12-12T20:36:20.292701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7702
77.0%
2 668
 
6.7%
0 468
 
4.7%
9001 403
 
4.0%
3 166
 
1.7%
4 84
 
0.8%
101 64
 
0.6%
5 53
 
0.5%
6 32
 
0.3%
8 28
 
0.3%
Other values (107) 332
 
3.3%
ValueCountFrequency (%)
0 468
 
4.7%
1 7702
77.0%
2 668
 
6.7%
3 166
 
1.7%
4 84
 
0.8%
5 53
 
0.5%
6 32
 
0.3%
7 16
 
0.2%
8 28
 
0.3%
9 15
 
0.1%
ValueCountFrequency (%)
9999 3
 
< 0.1%
9112 4
 
< 0.1%
9006 2
 
< 0.1%
9003 2
 
< 0.1%
9002 12
 
0.1%
9001 403
4.0%
8002 12
 
0.1%
8001 5
 
0.1%
7004 1
 
< 0.1%
7003 1
 
< 0.1%


Text

Distinct498
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:36:20.685190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length2.1566
Min length1

Characters and Unicode

Total characters21566
Distinct characters16
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique320 ?
Unique (%)3.2%

Sample

1st row100
2nd row101
3rd row2-21-01
4th row0
5th row0
ValueCountFrequency (%)
0 2890
28.9%
100 1597
16.0%
1 1313
13.1%
101 1050
 
10.5%
201 370
 
3.7%
102 339
 
3.4%
200 320
 
3.2%
301 159
 
1.6%
103 142
 
1.4%
2 129
 
1.3%
Other values (488) 1691
16.9%
2023-12-12T20:36:21.362406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10076
46.7%
1 7255
33.6%
2 1927
 
8.9%
3 671
 
3.1%
4 405
 
1.9%
8 394
 
1.8%
5 266
 
1.2%
6 196
 
0.9%
7 152
 
0.7%
9 111
 
0.5%
Other values (6) 113
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21453
99.5%
Dash Punctuation 72
 
0.3%
Other Letter 30
 
0.1%
Uppercase Letter 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10076
47.0%
1 7255
33.8%
2 1927
 
9.0%
3 671
 
3.1%
4 405
 
1.9%
8 394
 
1.8%
5 266
 
1.2%
6 196
 
0.9%
7 152
 
0.7%
9 111
 
0.5%
Other Letter
ValueCountFrequency (%)
10
33.3%
10
33.3%
10
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 6
54.5%
B 5
45.5%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21525
99.8%
Hangul 30
 
0.1%
Latin 11
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10076
46.8%
1 7255
33.7%
2 1927
 
9.0%
3 671
 
3.1%
4 405
 
1.9%
8 394
 
1.8%
5 266
 
1.2%
6 196
 
0.9%
7 152
 
0.7%
9 111
 
0.5%
Hangul
ValueCountFrequency (%)
10
33.3%
10
33.3%
10
33.3%
Latin
ValueCountFrequency (%)
A 6
54.5%
B 5
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21536
99.9%
Hangul 30
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10076
46.8%
1 7255
33.7%
2 1927
 
8.9%
3 671
 
3.1%
4 405
 
1.9%
8 394
 
1.8%
5 266
 
1.2%
6 196
 
0.9%
7 152
 
0.7%
9 111
 
0.5%
Other values (3) 83
 
0.4%
Hangul
ValueCountFrequency (%)
10
33.3%
10
33.3%
10
33.3%
Distinct8534
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:36:21.986232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length26.6602
Min length18

Characters and Unicode

Total characters266602
Distinct characters257
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

Unique7427 ?
Unique (%)74.3%

Sample

1st row강원특별자치도 춘천시 근화동 264-12 1동 100호
2nd row[ 충열로16번길 5-15 ] 0001동 0101호
3rd row[ 약사고개길 13 ] 0101동 2-21-01호
4th row강원특별자치도 춘천시 후평동 225-6 1동
5th row강원특별자치도 춘천시 후평동 748-1 1동
ValueCountFrequency (%)
9802
 
16.0%
강원특별자치도 5099
 
8.3%
춘천시 5099
 
8.3%
0001동 4220
 
6.9%
1동 3482
 
5.7%
0000호 1110
 
1.8%
0100호 1064
 
1.7%
1호 677
 
1.1%
0001호 636
 
1.0%
100호 533
 
0.9%
Other values (4821) 29375
48.1%
2023-12-12T20:36:23.404934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51097
19.2%
0 34225
 
12.8%
1 23403
 
8.8%
13504
 
5.1%
8226
 
3.1%
2 7203
 
2.7%
5987
 
2.2%
5871
 
2.2%
5733
 
2.2%
5399
 
2.0%
Other values (247) 105954
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111741
41.9%
Decimal Number 88927
33.4%
Space Separator 51097
19.2%
Dash Punctuation 5024
 
1.9%
Open Punctuation 4901
 
1.8%
Close Punctuation 4901
 
1.8%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13504
 
12.1%
8226
 
7.4%
5987
 
5.4%
5871
 
5.3%
5733
 
5.1%
5399
 
4.8%
5332
 
4.8%
5167
 
4.6%
5120
 
4.6%
5110
 
4.6%
Other values (231) 46292
41.4%
Decimal Number
ValueCountFrequency (%)
0 34225
38.5%
1 23403
26.3%
2 7203
 
8.1%
3 4729
 
5.3%
4 3895
 
4.4%
8 3312
 
3.7%
5 3180
 
3.6%
9 3148
 
3.5%
6 2974
 
3.3%
7 2858
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
A 6
54.5%
B 5
45.5%
Space Separator
ValueCountFrequency (%)
51097
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5024
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4901
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4901
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 154850
58.1%
Hangul 111741
41.9%
Latin 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13504
 
12.1%
8226
 
7.4%
5987
 
5.4%
5871
 
5.3%
5733
 
5.1%
5399
 
4.8%
5332
 
4.8%
5167
 
4.6%
5120
 
4.6%
5110
 
4.6%
Other values (231) 46292
41.4%
Common
ValueCountFrequency (%)
51097
33.0%
0 34225
22.1%
1 23403
15.1%
2 7203
 
4.7%
- 5024
 
3.2%
[ 4901
 
3.2%
] 4901
 
3.2%
3 4729
 
3.1%
4 3895
 
2.5%
8 3312
 
2.1%
Other values (4) 12160
 
7.9%
Latin
ValueCountFrequency (%)
A 6
54.5%
B 5
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 154861
58.1%
Hangul 111741
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51097
33.0%
0 34225
22.1%
1 23403
15.1%
2 7203
 
4.7%
- 5024
 
3.2%
[ 4901
 
3.2%
] 4901
 
3.2%
3 4729
 
3.1%
4 3895
 
2.5%
8 3312
 
2.1%
Other values (6) 12171
 
7.9%
Hangul
ValueCountFrequency (%)
13504
 
12.1%
8226
 
7.4%
5987
 
5.4%
5871
 
5.3%
5733
 
5.1%
5399
 
4.8%
5332
 
4.8%
5167
 
4.6%
5120
 
4.6%
5110
 
4.6%
Other values (231) 46292
41.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8919
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67546720
Minimum12000
Maximum4.041992 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:23.682290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile444843
Q14005290
median19172010
Q358841465
95-th percentile2.7714621 × 108
Maximum4.041992 × 109
Range4.04198 × 109
Interquartile range (IQR)54836175

Descriptive statistics

Standard deviation1.8026468 × 108
Coefficient of variation (CV)2.6687407
Kurtosis95.650282
Mean67546720
Median Absolute Deviation (MAD)17772315
Skewness8.1019758
Sum6.754672 × 1011
Variance3.2495356 × 1016
MonotonicityNot monotonic
2023-12-12T20:36:23.963770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864000 17
 
0.2%
684000 17
 
0.2%
792000 14
 
0.1%
846000 13
 
0.1%
882000 12
 
0.1%
486000 12
 
0.1%
612000 11
 
0.1%
990000 11
 
0.1%
9620820 10
 
0.1%
9422160 10
 
0.1%
Other values (8909) 9873
98.7%
ValueCountFrequency (%)
12000 1
< 0.1%
15840 1
< 0.1%
20280 1
< 0.1%
21420 1
< 0.1%
22800 1
< 0.1%
23040 1
< 0.1%
26600 1
< 0.1%
27200 1
< 0.1%
32400 1
< 0.1%
36000 1
< 0.1%
ValueCountFrequency (%)
4041991960 1
< 0.1%
3124565400 1
< 0.1%
3069093550 1
< 0.1%
2841718590 1
< 0.1%
2748595120 1
< 0.1%
2646306760 1
< 0.1%
2485184320 1
< 0.1%
2481804600 1
< 0.1%
2467240020 1
< 0.1%
2367172360 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5971
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.98123
Minimum0.9
Maximum9578.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:36:24.263739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile10.7
Q132
median79.455
Q3161.3425
95-th percentile651.892
Maximum9578.18
Range9577.28
Interquartile range (IQR)129.3425

Descriptive statistics

Standard deviation360.15339
Coefficient of variation (CV)2.0820374
Kurtosis104.35972
Mean172.98123
Median Absolute Deviation (MAD)54.43655
Skewness7.7681349
Sum1729812.3
Variance129710.46
MonotonicityNot monotonic
2023-12-12T20:36:24.572573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 401
 
4.0%
27.0 43
 
0.4%
12.0 41
 
0.4%
32.0 38
 
0.4%
48.0 32
 
0.3%
24.0 32
 
0.3%
66.0 26
 
0.3%
36.0 26
 
0.3%
198.0 26
 
0.3%
72.0 25
 
0.2%
Other values (5961) 9310
93.1%
ValueCountFrequency (%)
0.9 2
 
< 0.1%
0.95 1
 
< 0.1%
1.0 8
0.1%
1.03 1
 
< 0.1%
1.08 1
 
< 0.1%
1.1 2
 
< 0.1%
1.2 3
 
< 0.1%
1.25 1
 
< 0.1%
1.32 1
 
< 0.1%
1.4 3
 
< 0.1%
ValueCountFrequency (%)
9578.18 1
< 0.1%
6434.1584 1
< 0.1%
6264.02 1
< 0.1%
5520.0 2
< 0.1%
4840.18 1
< 0.1%
4734.19 1
< 0.1%
4680.85 1
< 0.1%
4495.5 1
< 0.1%
4461.0967 1
< 0.1%
4005.0 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
2023-12-12T20:36:24.775722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:24.937966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-11-08 00:00:00
Maximum2023-11-08 00:00:00
2023-12-12T20:36:25.098304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:25.265741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T20:36:13.866325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.000116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:06.361836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:07.717296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:09.178208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.029744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:12.419822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:14.067551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.183606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:06.548259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:07.915192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:09.379938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.208701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:12.633334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:14.276826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.366038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:06.736601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:08.131349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:09.590907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.397809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:12.844993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:14.458848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.519357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:06.916467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:08.322436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:09.787354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.586796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:13.066634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:14.654936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.710970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:07.104697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:08.533997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:09.981063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.784686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:13.252988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:14.857635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:05.896496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:07.298232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:08.734596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:10.173582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:11.977965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:13.439688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:15.058731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:06.165828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:07.514799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:08.965916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:10.856043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:12.209514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:36:13.659977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:36:25.411324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.3270.1290.0210.0280.0000.0240.0170.0111.000
법정동0.3271.0000.6880.2650.1350.0920.1230.0470.0400.327
법정리0.1290.6881.0000.1710.0910.0970.1320.0350.0350.129
특수지0.0210.2650.1711.0000.1460.0000.0690.1480.1030.021
본번0.0280.1350.0910.1461.0000.2660.0400.0960.0000.028
부번0.0000.0920.0970.0000.2661.0000.0000.0000.0000.000
0.0240.1230.1320.0690.0400.0001.0000.0000.0000.024
시가표준액0.0170.0470.0350.1480.0960.0000.0001.0000.8370.017
연면적0.0110.0400.0350.1030.0000.0000.0000.8371.0000.011
기준일자1.0000.3270.1290.0210.0280.0000.0240.0170.0111.000
2023-12-12T20:36:25.618643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.034
과세년도0.0341.000
2023-12-12T20:36:25.814200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.8400.176-0.227-0.159-0.158-0.0210.2350.114
법정리0.8401.000-0.051-0.217-0.133-0.257-0.0670.1580.130
본번0.176-0.0511.000-0.098-0.1350.0690.0240.0470.044
부번-0.227-0.217-0.0981.000-0.1270.028-0.0210.0000.000
-0.159-0.133-0.135-0.1271.0000.1190.0740.0260.046
시가표준액-0.158-0.2570.0690.0280.1191.0000.7720.0170.065
연면적-0.021-0.0670.024-0.0210.0740.7721.0000.0080.065
과세년도0.2350.1580.0470.0000.0260.0170.0081.0000.034
특수지0.1140.1300.0440.0000.0460.0650.0650.0341.000

Missing values

2023-12-12T20:36:15.330224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:36:15.760245image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자데이터기준일
22433강원특별자치도춘천시51110201711701264121100강원특별자치도 춘천시 근화동 264-12 1동 100호21973290118.1362017-06-012023-11-08
23574강원특별자치도춘천시511102017118011065121101[ 충열로16번길 5-15 ] 0001동 0101호56631160118.082017-06-012023-11-08
10218강원특별자치도춘천시511102017109011141012-21-01[ 약사고개길 13 ] 0101동 2-21-01호1136800039.22017-06-012023-11-08
66861강원특별자치도춘천시51110201812001225610강원특별자치도 춘천시 후평동 225-6 1동238994500308.382018-06-012023-11-08
27578강원특별자치도춘천시51110201712001748110강원특별자치도 춘천시 후평동 748-1 1동121118990183.822017-06-012023-11-08
57378강원특별자치도춘천시511102018340231833148101강원특별자치도 춘천시 남면 가정리 833-1 4동 8101호2106720042.02018-06-012023-11-08
68248강원특별자치도춘천시51110201812401648110[ 벌말길 83 ] 0001동 0000호1966734046.22018-06-012023-11-08
40262강원특별자치도춘천시5111020173902517131101[ 사암길 16-2 ] 0001동 0101호62999930221.132017-06-012023-11-08
17396강원특별자치도춘천시511102017250211624014강원특별자치도 춘천시 신북읍 율문리 624 1동 4호218254075.262017-06-012023-11-08
62710강원특별자치도춘천시51110201835028127511100강원특별자치도 춘천시 서면 덕두원리 275-1 1동 100호27126000198.02018-06-012023-11-08
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자데이터기준일
63778강원특별자치도춘천시5111020181150114010강원특별자치도 춘천시 소양로3가 14 1동350414053.9932018-06-012023-11-08
54294강원특별자치도춘천시51110201839024111261000강원특별자치도 춘천시 동내면 사암리 1126-10183600018.02018-06-012023-11-08
52734강원특별자치도춘천시51110201840030138651100강원특별자치도 춘천시 남산면 방하리 386-5 1동 100호96921000198.02018-06-012023-11-08
45339강원특별자치도춘천시5111020171150121810강원특별자치도 춘천시 소양로3가 21-8 1동110880019.82017-06-012023-11-08
28476강원특별자치도춘천시5111020171150167110[ 금강로13번길 20 ] 0001동 0000호38076480132.212017-06-012023-11-08
48967강원특별자치도춘천시511102017390231328411[ 고은길 82-14 ] 0001동 0001호99000018.02017-06-012023-11-08
62184강원특별자치도춘천시511102018390211966410[ 춘천순환로72번길 33-18 ] 0001동 0000호2249986094.262018-06-012023-11-08
28698강원특별자치도춘천시511102017113019381201[ 소양로 206 ] 0001동 0201호112943500199.92017-06-012023-11-08
29981강원특별자치도춘천시51110201711401911100강원특별자치도 춘천시 소양로2가 9-1 1동 100호41760480144.62017-06-012023-11-08
31072강원특별자치도춘천시51110201711201666181100[ 소전길 33-1 ] 0001동 0100호32381509.782017-06-012023-11-08

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자데이터기준일# duplicates
16강원특별자치도춘천시51110201712501290311강원특별자치도 춘천시 삼천동 290-3 1동 1호1544450039.52017-06-012023-11-086
20강원특별자치도춘천시51110201736021146811101강원특별자치도 춘천시 사북면 신포리 468-1 1동 101호119600046.02017-06-012023-11-084
35강원특별자치도춘천시511102018400271435011강원특별자치도 춘천시 남산면 강촌리 435 1동 1호1723984020.142018-06-012023-11-084
17강원특별자치도춘천시51110201725024187609101[ 지내고탄로 470 ] 0009동 0101호6570002.252017-06-012023-11-083
21강원특별자치도춘천시51110201736027155221101[ 춘화로 342 ] 0001동 0101호4001145084.952017-06-012023-11-083
0강원특별자치도춘천시511102017107011110강원특별자치도 춘천시 옥천동 1-1 1동93451000413.52017-06-012023-11-082
1강원특별자치도춘천시511102017107011180강원특별자치도 춘천시 옥천동 1-1 8동291876480579.122017-06-012023-11-082
2강원특별자치도춘천시5111020171080151210[ 명동길 11 ] 0001동 0000호717288029.642017-06-012023-11-082
3강원특별자치도춘천시511102017112013334811[ 공지로252번길 22 ] 0001동 0001호1563079021.23752017-06-012023-11-082
4강원특별자치도춘천시5111020171120140002301강원특별자치도 춘천시 효자동 400 2동 301호3511404073.92432017-06-012023-11-082