Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description경상북도 구미시의 일반건축물에 대한 지방세 부과기준인 시가표준액에 대한 데이터로 자치단체코드, 과세년도, 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적, 기준일자를 제공합니다.
URLhttps://www.data.go.kr/data/15079881/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
결정일자 has constant value ""Constant
Dataset has 22 (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 (97.1%)Imbalance
시가표준액 is highly skewed (γ1 = 22.80749211)Skewed
연면적 is highly skewed (γ1 = 20.85064431)Skewed
법정리 has 6927 (69.3%) zerosZeros
부번 has 3397 (34.0%) zerosZeros
동 has 217 (2.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:33:51.829398
Analysis finished2023-12-12 15:34:02.448619
Duration10.62 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

2023-12-13T00:34:02.521189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:02.623626image/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
구미시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구미시 10000
100.0%

Length

2023-12-13T00:34:02.752311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:02.902010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구미시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47190
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47190
2nd row47190
3rd row47190
4th row47190
5th row47190

Common Values

ValueCountFrequency (%)
47190 10000
100.0%

Length

2023-12-13T00:34:03.045986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:03.189158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47190 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-13T00:34:03.316570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:03.427414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.856
Minimum101
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:03.556677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1112
median121
Q3250
95-th percentile340
Maximum360
Range259
Interquartile range (IQR)138

Descriptive statistics

Standard deviation80.959827
Coefficient of variation (CV)0.48813324
Kurtosis-0.40545103
Mean165.856
Median Absolute Deviation (MAD)12
Skewness1.0767532
Sum1658560
Variance6554.4935
MonotonicityNot monotonic
2023-12-13T00:34:03.746591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
113 1479
 
14.8%
101 816
 
8.2%
256 699
 
7.0%
253 683
 
6.8%
250 601
 
6.0%
125 566
 
5.7%
109 391
 
3.9%
123 352
 
3.5%
340 328
 
3.3%
110 320
 
3.2%
Other values (28) 3765
37.6%
ValueCountFrequency (%)
101 816
8.2%
102 63
 
0.6%
103 209
 
2.1%
104 302
 
3.0%
105 44
 
0.4%
106 46
 
0.5%
107 2
 
< 0.1%
108 98
 
1.0%
109 391
3.9%
110 320
 
3.2%
ValueCountFrequency (%)
360 228
 
2.3%
340 328
3.3%
330 188
 
1.9%
320 226
 
2.3%
310 120
 
1.2%
256 699
7.0%
253 683
6.8%
250 601
6.0%
130 47
 
0.5%
129 193
 
1.9%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2028
Minimum0
Maximum38
Zeros6927
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:03.891825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323
95-th percentile32
Maximum38
Range38
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.562876
Coefficient of variation (CV)1.5315351
Kurtosis-0.85402299
Mean8.2028
Median Absolute Deviation (MAD)0
Skewness0.96896471
Sum82028
Variance157.82585
MonotonicityNot monotonic
2023-12-13T00:34:04.038092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6927
69.3%
25 451
 
4.5%
21 364
 
3.6%
24 276
 
2.8%
23 265
 
2.6%
26 248
 
2.5%
28 197
 
2.0%
22 196
 
2.0%
29 173
 
1.7%
27 161
 
1.6%
Other values (9) 742
 
7.4%
ValueCountFrequency (%)
0 6927
69.3%
21 364
 
3.6%
22 196
 
2.0%
23 265
 
2.6%
24 276
 
2.8%
25 451
 
4.5%
26 248
 
2.5%
27 161
 
1.6%
28 197
 
2.0%
29 173
 
1.7%
ValueCountFrequency (%)
38 45
 
0.4%
37 46
 
0.5%
36 85
0.9%
35 51
 
0.5%
34 102
1.0%
33 56
 
0.6%
32 153
1.5%
31 161
1.6%
30 43
 
0.4%
29 173
1.7%

특수지
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9938 
2
 
56
5
 
5
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9938
99.4%
2 56
 
0.6%
5 5
 
0.1%
4 1
 
< 0.1%

Length

2023-12-13T00:34:04.215027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:04.353703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9938
99.4%
2 56
 
0.6%
5 5
 
< 0.1%
4 1
 
< 0.1%

본번
Real number (ℝ)

Distinct1225
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.3128
Minimum1
Maximum1954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:04.521954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile49
Q1163
median314
Q3644
95-th percentile1134.2
Maximum1954
Range1953
Interquartile range (IQR)481

Descriptive statistics

Standard deviation357.21671
Coefficient of variation (CV)0.80944109
Kurtosis0.65157582
Mean441.3128
Median Absolute Deviation (MAD)195
Skewness1.1100755
Sum4413128
Variance127603.78
MonotonicityNot monotonic
2023-12-13T00:34:04.711435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 231
 
2.3%
92 159
 
1.6%
212 95
 
0.9%
287 79
 
0.8%
259 73
 
0.7%
644 68
 
0.7%
964 65
 
0.7%
297 64
 
0.6%
282 62
 
0.6%
321 62
 
0.6%
Other values (1215) 9042
90.4%
ValueCountFrequency (%)
1 40
0.4%
2 4
 
< 0.1%
3 17
0.2%
4 15
 
0.1%
5 13
 
0.1%
6 5
 
0.1%
7 19
0.2%
8 10
 
0.1%
9 19
0.2%
10 13
 
0.1%
ValueCountFrequency (%)
1954 1
 
< 0.1%
1921 1
 
< 0.1%
1898 1
 
< 0.1%
1891 1
 
< 0.1%
1854 1
 
< 0.1%
1850 1
 
< 0.1%
1849 1
 
< 0.1%
1845 4
< 0.1%
1806 3
< 0.1%
1795 2
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4425
Minimum0
Maximum896
Zeros3397
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:04.927905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile32
Maximum896
Range896
Interquartile range (IQR)7

Descriptive statistics

Standard deviation44.703816
Coefficient of variation (CV)4.2809496
Kurtosis136.66818
Mean10.4425
Median Absolute Deviation (MAD)2
Skewness10.610351
Sum104425
Variance1998.4311
MonotonicityNot monotonic
2023-12-13T00:34:05.127740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3397
34.0%
1 1422
14.2%
2 737
 
7.4%
3 654
 
6.5%
4 433
 
4.3%
5 360
 
3.6%
9 325
 
3.2%
7 320
 
3.2%
6 313
 
3.1%
8 273
 
2.7%
Other values (203) 1766
17.7%
ValueCountFrequency (%)
0 3397
34.0%
1 1422
14.2%
2 737
 
7.4%
3 654
 
6.5%
4 433
 
4.3%
5 360
 
3.6%
6 313
 
3.1%
7 320
 
3.2%
8 273
 
2.7%
9 325
 
3.2%
ValueCountFrequency (%)
896 1
 
< 0.1%
767 1
 
< 0.1%
746 3
< 0.1%
713 1
 
< 0.1%
711 3
< 0.1%
672 1
 
< 0.1%
667 1
 
< 0.1%
633 1
 
< 0.1%
631 1
 
< 0.1%
617 1
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.0047
Minimum0
Maximum9999
Zeros217
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:05.347519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile14.05
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1467.6544
Coefficient of variation (CV)5.9903112
Kurtosis34.366693
Mean245.0047
Median Absolute Deviation (MAD)0
Skewness6.0169629
Sum2450047
Variance2154009.5
MonotonicityNot monotonic
2023-12-13T00:34:05.946238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8378
83.8%
2 552
 
5.5%
0 217
 
2.2%
9001 140
 
1.4%
3 124
 
1.2%
4 74
 
0.7%
301 39
 
0.4%
5 38
 
0.4%
9999 38
 
0.4%
10 32
 
0.3%
Other values (98) 368
 
3.7%
ValueCountFrequency (%)
0 217
 
2.2%
1 8378
83.8%
2 552
 
5.5%
3 124
 
1.2%
4 74
 
0.7%
5 38
 
0.4%
6 16
 
0.2%
7 11
 
0.1%
8 29
 
0.3%
9 7
 
0.1%
ValueCountFrequency (%)
9999 38
0.4%
9998 2
 
< 0.1%
9997 1
 
< 0.1%
9996 1
 
< 0.1%
9994 1
 
< 0.1%
9993 3
 
< 0.1%
9992 1
 
< 0.1%
9991 19
0.2%
9990 4
 
< 0.1%
9905 1
 
< 0.1%

호
Text

Distinct657
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:34:06.408447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0264
Min length1

Characters and Unicode

Total characters30264
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)3.5%

Sample

1st row303
2nd row105
3rd row1
4th row401
5th row110
ValueCountFrequency (%)
101 3078
30.8%
102 1165
 
11.7%
201 929
 
9.3%
103 461
 
4.6%
301 385
 
3.9%
8101 333
 
3.3%
104 267
 
2.7%
202 209
 
2.1%
105 185
 
1.8%
401 167
 
1.7%
Other values (647) 2821
28.2%
2023-12-13T00:34:07.167232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12568
41.5%
0 8819
29.1%
2 3736
 
12.3%
3 1653
 
5.5%
4 976
 
3.2%
8 797
 
2.6%
5 647
 
2.1%
6 453
 
1.5%
7 331
 
1.1%
9 264
 
0.9%
Other values (5) 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30244
99.9%
Dash Punctuation 16
 
0.1%
Uppercase Letter 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12568
41.6%
0 8819
29.2%
2 3736
 
12.4%
3 1653
 
5.5%
4 976
 
3.2%
8 797
 
2.6%
5 647
 
2.1%
6 453
 
1.5%
7 331
 
1.1%
9 264
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
J 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
n 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30260
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12568
41.5%
0 8819
29.1%
2 3736
 
12.3%
3 1653
 
5.5%
4 976
 
3.2%
8 797
 
2.6%
5 647
 
2.1%
6 453
 
1.5%
7 331
 
1.1%
9 264
 
0.9%
Latin
ValueCountFrequency (%)
B 1
25.0%
J 1
25.0%
a 1
25.0%
n 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12568
41.5%
0 8819
29.1%
2 3736
 
12.3%
3 1653
 
5.5%
4 976
 
3.2%
8 797
 
2.6%
5 647
 
2.1%
6 453
 
1.5%
7 331
 
1.1%
9 264
 
0.9%
Other values (5) 20
 
0.1%

물건지
Text

Distinct9679
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:34:07.732977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length34
Mean length26.0384
Min length19

Characters and Unicode

Total characters260384
Distinct characters169
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

Unique9442 ?
Unique (%)94.4%

Sample

1st row[ 신당1로2길 15 ] 0001동 0303호
2nd row경상북도 구미시 공단동 214-8 1동 105호
3rd row경상북도 구미시 고아읍 예강리 670-1 1동 1호
4th row경상북도 구미시 공단동 217-2 1동 401호
5th row경상북도 구미시 고아읍 오로리 460-1 116동 110호
ValueCountFrequency (%)
8178
 
13.2%
경상북도 5911
 
9.5%
구미시 5911
 
9.5%
1동 4544
 
7.3%
0001동 3834
 
6.2%
101호 1728
 
2.8%
0101호 1350
 
2.2%
공단동 1275
 
2.1%
102호 664
 
1.1%
0201호 539
 
0.9%
Other values (5038) 28215
45.4%
2023-12-13T00:34:08.447789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52149
20.0%
1 29110
 
11.2%
0 27691
 
10.6%
동 14964
 
5.7%
호 10323
 
4.0%
2 9780
 
3.8%
구 6422
 
2.5%
상 6408
 
2.5%
미 6294
 
2.4%
도 6257
 
2.4%
Other values (159) 90986
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102158
39.2%
Decimal Number 93132
35.8%
Space Separator 52149
20.0%
Dash Punctuation 4756
 
1.8%
Open Punctuation 4089
 
1.6%
Close Punctuation 4089
 
1.6%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 14964
14.6%
호 10323
 
10.1%
구 6422
 
6.3%
상 6408
 
6.3%
미 6294
 
6.2%
도 6257
 
6.1%
시 6209
 
6.1%
북 6038
 
5.9%
경 5924
 
5.8%
로 3451
 
3.4%
Other values (143) 29868
29.2%
Decimal Number
ValueCountFrequency (%)
1 29110
31.3%
0 27691
29.7%
2 9780
 
10.5%
3 5624
 
6.0%
4 4772
 
5.1%
5 3454
 
3.7%
9 3410
 
3.7%
6 3382
 
3.6%
8 3063
 
3.3%
7 2846
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 6
54.5%
L 5
45.5%
Space Separator
ValueCountFrequency (%)
52149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4756
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4089
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 158215
60.8%
Hangul 102158
39.2%
Latin 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 14964
14.6%
호 10323
 
10.1%
구 6422
 
6.3%
상 6408
 
6.3%
미 6294
 
6.2%
도 6257
 
6.1%
시 6209
 
6.1%
북 6038
 
5.9%
경 5924
 
5.8%
로 3451
 
3.4%
Other values (143) 29868
29.2%
Common
ValueCountFrequency (%)
52149
33.0%
1 29110
18.4%
0 27691
17.5%
2 9780
 
6.2%
3 5624
 
3.6%
4 4772
 
3.0%
- 4756
 
3.0%
[ 4089
 
2.6%
] 4089
 
2.6%
5 3454
 
2.2%
Other values (4) 12701
 
8.0%
Latin
ValueCountFrequency (%)
B 6
54.5%
L 5
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158226
60.8%
Hangul 102158
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52149
33.0%
1 29110
18.4%
0 27691
17.5%
2 9780
 
6.2%
3 5624
 
3.6%
4 4772
 
3.0%
- 4756
 
3.0%
[ 4089
 
2.6%
] 4089
 
2.6%
5 3454
 
2.2%
Other values (6) 12712
 
8.0%
Hangul
ValueCountFrequency (%)
동 14964
14.6%
호 10323
 
10.1%
구 6422
 
6.3%
상 6408
 
6.3%
미 6294
 
6.2%
도 6257
 
6.1%
시 6209
 
6.1%
북 6038
 
5.9%
경 5924
 
5.8%
로 3451
 
3.4%
Other values (143) 29868
29.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8375
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93859703
Minimum32000
Maximum1.7434085 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:08.661006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32000
5-th percentile880608
Q16374992.5
median24988590
Q378196350
95-th percentile3.2794756 × 108
Maximum1.7434085 × 1010
Range1.7434053 × 1010
Interquartile range (IQR)71821358

Descriptive statistics

Standard deviation3.7875477 × 108
Coefficient of variation (CV)4.0353288
Kurtosis790.75988
Mean93859703
Median Absolute Deviation (MAD)22288215
Skewness22.807492
Sum9.3859703 × 1011
Variance1.4345517 × 1017
MonotonicityNot monotonic
2023-12-13T00:34:08.874960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12280800 74
 
0.7%
24988590 59
 
0.6%
15706980 52
 
0.5%
16026750 49
 
0.5%
37109030 27
 
0.3%
24040120 27
 
0.3%
8390160 23
 
0.2%
6212670 23
 
0.2%
7155720 22
 
0.2%
10572550 18
 
0.2%
Other values (8365) 9626
96.3%
ValueCountFrequency (%)
32000 1
 
< 0.1%
35000 1
 
< 0.1%
45000 1
 
< 0.1%
49550 1
 
< 0.1%
49770 1
 
< 0.1%
50000 1
 
< 0.1%
50830 1
 
< 0.1%
70000 1
 
< 0.1%
72000 3
< 0.1%
76800 1
 
< 0.1%
ValueCountFrequency (%)
17434085440 1
< 0.1%
14640038100 1
< 0.1%
10362910560 1
< 0.1%
8408976010 1
< 0.1%
7925054720 1
< 0.1%
7007621400 1
< 0.1%
5872653990 1
< 0.1%
5552751690 1
< 0.1%
5112547860 1
< 0.1%
4977487500 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6444
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.13079
Minimum0.2
Maximum47504.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:09.076515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile14.4
Q139.166625
median95.555
Q3214.70415
95-th percentile918.651
Maximum47504.32
Range47504.12
Interquartile range (IQR)175.53752

Descriptive statistics

Standard deviation1072.6238
Coefficient of variation (CV)3.8704606
Kurtosis654.90272
Mean277.13079
Median Absolute Deviation (MAD)65.89
Skewness20.850644
Sum2771307.9
Variance1150521.8
MonotonicityNot monotonic
2023-12-13T00:34:09.300193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 163
 
1.6%
35.7 76
 
0.8%
50.0 63
 
0.6%
52.83 59
 
0.6%
34.22 52
 
0.5%
38.25 49
 
0.5%
400.0 48
 
0.5%
100.0 36
 
0.4%
200.0 33
 
0.3%
150.0 32
 
0.3%
Other values (6434) 9389
93.9%
ValueCountFrequency (%)
0.2 1
< 0.1%
0.73 1
< 0.1%
0.97 1
< 0.1%
1.0 2
< 0.1%
1.05 1
< 0.1%
1.2 2
< 0.1%
1.33 1
< 0.1%
1.39 1
< 0.1%
1.48 1
< 0.1%
1.671 1
< 0.1%
ValueCountFrequency (%)
47504.32 1
< 0.1%
33655.26 1
< 0.1%
30124.74 1
< 0.1%
28142.49 1
< 0.1%
27937.42 1
< 0.1%
17968.26 1
< 0.1%
16374.08 1
< 0.1%
13619.7 1
< 0.1%
13484.74 1
< 0.1%
13099.99 1
< 0.1%

결정일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-06-01 00:00:00
Maximum2022-06-01 00:00:00
2023-12-13T00:34:09.481502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:09.611387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T00:34:00.960456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.304141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.172242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.196625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:57.743014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.782192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.907358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.099331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.415858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.315238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.343510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:57.890812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.932907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.039284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.267843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.524008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.469912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.510073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.020956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.105367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.174225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.423497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.623904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.614866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.678185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.171096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.271119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.335323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.574822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.716628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.725890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.800619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.296597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.418964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.480316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.725305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.845688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:55.945721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:57.293126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.444215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.557413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.631679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:01.873982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:54.991399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:56.077003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:57.519023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:58.630618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:33:59.762130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:00.811026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:34:09.710873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.6690.0620.5880.0610.0770.0000.000
법정리0.6691.0000.0900.4110.1160.1520.0000.000
특수지0.0620.0901.0000.1030.1830.0110.0000.000
본번0.5880.4110.1031.0000.2870.0810.0000.000
부번0.0610.1160.1830.2871.0000.0950.0000.000
동0.0770.1520.0110.0810.0951.0000.0000.000
시가표준액0.0000.0000.0000.0000.0000.0001.0000.988
연면적0.0000.0000.0000.0000.0000.0000.9881.000
2023-12-13T00:34:09.842144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번동시가표준액연면적특수지
법정동1.0000.7770.222-0.2900.076-0.1680.0530.051
법정리0.7771.0000.241-0.2600.063-0.2280.0570.058
본번0.2220.2411.000-0.0940.0410.0250.0650.061
부번-0.290-0.260-0.0941.000-0.0850.043-0.0320.110
동0.0760.0630.041-0.0851.000-0.079-0.0720.010
시가표준액-0.168-0.2280.0250.043-0.0791.0000.7420.000
연면적0.0530.0570.065-0.032-0.0720.7421.0000.000
특수지0.0510.0580.0610.1100.0100.0000.0001.000

Missing values

2023-12-13T00:34:02.081419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:34:02.342697image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
84655경상북도구미시471902022256251143201303[ 신당1로2길 15 ] 0001동 0303호9945767088.17172022-06-01
79053경상북도구미시4719020221130121481105경상북도 구미시 공단동 214-8 1동 105호516762601519.892022-06-01
65736경상북도구미시471902022253381670111경상북도 구미시 고아읍 예강리 670-1 1동 1호16650100127.12022-06-01
78528경상북도구미시4719020221130121721401경상북도 구미시 공단동 217-2 1동 401호8931864603330.32022-06-01
59680경상북도구미시4719020222532314601116110경상북도 구미시 고아읍 오로리 460-1 116동 110호2842436056.692022-06-01
64971경상북도구미시471902022253351138011[ 송평구1길 17-2 ] 0001동 0001호39200024.52022-06-01
55466경상북도구미시47190202225025186401105경상북도 구미시 선산읍 죽장리 864 1동 105호14310000270.02022-06-01
71575경상북도구미시471902022256251116701201경상북도 구미시 산동읍 신당리 1167 1동 201호8815100125.932022-06-01
12938경상북도구미시4719020221090119801301경상북도 구미시 형곡동 198 1동 301호5223029401349.622022-06-01
54560경상북도구미시47190202212901622191304경상북도 구미시 구포동 622-19 1동 304호267720058.22022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
57965경상북도구미시47190202225021125301142경상북도 구미시 선산읍 완전리 253 1동 142호1438650034.752022-06-01
82953경상북도구미시471902022256251140701703[ 신당1로4길 17-5 ] 0001동 0703호126643510153.78692022-06-01
35188경상북도구미시471902022116016731101[ 상사동로 43-1 ] 0001동 0101호35517420162.182022-06-01
55075경상북도구미시47190202212901105001201[ 4공단로7길 9 ] 0001동 0201호43377810115.032022-06-01
64621경상북도구미시47190202231025172621107경상북도 구미시 무을면 안곡리 726-2 1동 107호167790035.72022-06-01
51431경상북도구미시4719020221250140131115경상북도 구미시 임수동 401-3 1동 115호1087770030.32022-06-01
14393경상북도구미시4719020221090113241101[ 형곡로 70 ] 0001동 0101호1852215025.462022-06-01
61475경상북도구미시47190202225321169081101[ 선산대로 985 ] 0001동 0101호386280066.62022-06-01
83372경상북도구미시4719020221230137131101[ 인동22길 21-4 ] 0001동 0101호4713700063.452022-06-01
44232경상북도구미시4719020221230164411404경상북도 구미시 진평동 644-1 1동 404호839016032.522022-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자# duplicates
3경상북도구미시4719020221130120919001101경상북도 구미시 공단동 209-1 9001동 101호491400018.02022-06-014
15경상북도구미시471902022330211164601101경상북도 구미시 도개면 가산리 1646 1동 101호27389040268.522022-06-013
0경상북도구미시4719020221010196416411경상북도 구미시 원평동 964-164 1동 1호1184458063.342022-06-012
1경상북도구미시4719020221010196422912[ 금오산로 63 ] 0001동 0002호1418120064.462022-06-012
2경상북도구미시471902022108022891101경상북도 구미시 남통동 산 28-9 1동 101호6739200072.02022-06-012
4경상북도구미시4719020221130125901412경상북도 구미시 공단동 259 1동 412호678298029.622022-06-012
5경상북도구미시4719020221130126111209경상북도 구미시 공단동 261-1 1동 209호617760043.22022-06-012
6경상북도구미시4719020221250194111121경상북도 구미시 임수동 94-1 1동 1121호1228080035.72022-06-012
7경상북도구미시47190202225021140101101경상북도 구미시 선산읍 완전리 401 1동 101호23650000550.02022-06-012
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