Overview

Dataset statistics

Number of variables16
Number of observations2049
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.3 KiB
Average record size in memory139.1 B

Variable types

Numeric8
Categorical5
Text2
DateTime1

Dataset

Description- 임실군 관내 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공합니다
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15080712/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세연도 has constant value ""Constant
기준일자 has constant value ""Constant
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (94.8%)Imbalance
is highly skewed (γ1 = 22.15175889)Skewed
순번 has unique valuesUnique
부번 has 695 (33.9%) zerosZeros

Reproduction

Analysis started2023-12-12 07:05:56.470675
Analysis finished2023-12-12 07:06:05.171581
Duration8.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct2049
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1025
Minimum1
Maximum2049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:05.238380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile103.4
Q1513
median1025
Q31537
95-th percentile1946.6
Maximum2049
Range2048
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation591.63967
Coefficient of variation (CV)0.57720943
Kurtosis-1.2
Mean1025
Median Absolute Deviation (MAD)512
Skewness0
Sum2100225
Variance350037.5
MonotonicityStrictly increasing
2023-12-12T16:06:05.393561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1538 1
 
< 0.1%
1376 1
 
< 0.1%
1375 1
 
< 0.1%
1374 1
 
< 0.1%
1373 1
 
< 0.1%
1372 1
 
< 0.1%
1371 1
 
< 0.1%
1370 1
 
< 0.1%
1369 1
 
< 0.1%
Other values (2039) 2039
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2049 1
< 0.1%
2048 1
< 0.1%
2047 1
< 0.1%
2046 1
< 0.1%
2045 1
< 0.1%
2044 1
< 0.1%
2043 1
< 0.1%
2042 1
< 0.1%
2041 1
< 0.1%
2040 1
< 0.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
전라북도
2049 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 2049
100.0%

Length

2023-12-12T16:06:05.525227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:05.621500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 2049
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
임실군
2049 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임실군 2049
100.0%

Length

2023-12-12T16:06:05.722479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:05.809438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임실군 2049
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
45750
2049 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45750
2nd row45750
3rd row45750
4th row45750
5th row45750

Common Values

ValueCountFrequency (%)
45750 2049
100.0%

Length

2023-12-12T16:06:05.892570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:05.973574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45750 2049
100.0%

과세연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2020
2049 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 2049
100.0%

Length

2023-12-12T16:06:06.075157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:06.158682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 2049
100.0%

법정동
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.01025
Minimum250
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:06.234626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median250
Q3250
95-th percentile330
Maximum410
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.394111
Coefficient of variation (CV)0.095285681
Kurtosis15.001096
Mean256.01025
Median Absolute Deviation (MAD)0
Skewness4.0057018
Sum524565
Variance595.07265
MonotonicityNot monotonic
2023-12-12T16:06:06.611654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
250 1927
94.0%
355 57
 
2.8%
330 31
 
1.5%
360 13
 
0.6%
320 8
 
0.4%
380 6
 
0.3%
410 6
 
0.3%
370 1
 
< 0.1%
ValueCountFrequency (%)
250 1927
94.0%
320 8
 
0.4%
330 31
 
1.5%
355 57
 
2.8%
360 13
 
0.6%
370 1
 
< 0.1%
380 6
 
0.3%
410 6
 
0.3%
ValueCountFrequency (%)
410 6
 
0.3%
380 6
 
0.3%
370 1
 
< 0.1%
360 13
 
0.6%
355 57
 
2.8%
330 31
 
1.5%
320 8
 
0.4%
250 1927
94.0%

법정리
Real number (ℝ)

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.06491
Minimum21
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:06.733279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median22
Q326
95-th percentile30
Maximum33
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0594985
Coefficient of variation (CV)0.12713526
Kurtosis-0.17152718
Mean24.06491
Median Absolute Deviation (MAD)1
Skewness1.020496
Sum49309
Variance9.3605308
MonotonicityNot monotonic
2023-12-12T16:06:06.863834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
22 969
47.3%
21 216
 
10.5%
27 183
 
8.9%
25 139
 
6.8%
24 115
 
5.6%
26 111
 
5.4%
29 92
 
4.5%
30 88
 
4.3%
32 47
 
2.3%
28 44
 
2.1%
Other values (3) 45
 
2.2%
ValueCountFrequency (%)
21 216
 
10.5%
22 969
47.3%
23 6
 
0.3%
24 115
 
5.6%
25 139
 
6.8%
26 111
 
5.4%
27 183
 
8.9%
28 44
 
2.1%
29 92
 
4.5%
30 88
 
4.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 47
 
2.3%
31 38
 
1.9%
30 88
4.3%
29 92
4.5%
28 44
 
2.1%
27 183
8.9%
26 111
5.4%
25 139
6.8%
24 115
5.6%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
1
2037 
2
 
12

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 2037
99.4%
2 12
 
0.6%

Length

2023-12-12T16:06:06.996111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:07.118574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2037
99.4%
2 12
 
0.6%

본번
Real number (ℝ)

Distinct438
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.95461
Minimum1
Maximum1267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:07.361646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1233
median372
Q3702
95-th percentile1074
Maximum1267
Range1266
Interquartile range (IQR)469

Descriptive statistics

Standard deviation318.95168
Coefficient of variation (CV)0.69043944
Kurtosis-0.74736312
Mean461.95461
Median Absolute Deviation (MAD)214
Skewness0.58921027
Sum946545
Variance101730.18
MonotonicityNot monotonic
2023-12-12T16:06:07.583678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1074 167
 
8.2%
366 51
 
2.5%
233 35
 
1.7%
670 33
 
1.6%
234 32
 
1.6%
1089 31
 
1.5%
473 28
 
1.4%
893 26
 
1.3%
372 24
 
1.2%
934 22
 
1.1%
Other values (428) 1600
78.1%
ValueCountFrequency (%)
1 7
0.3%
2 1
 
< 0.1%
4 7
0.3%
7 4
0.2%
8 2
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
19 6
0.3%
20 7
0.3%
22 4
0.2%
ValueCountFrequency (%)
1267 1
 
< 0.1%
1224 1
 
< 0.1%
1213 1
 
< 0.1%
1210 1
 
< 0.1%
1138 1
 
< 0.1%
1113 4
 
0.2%
1100 1
 
< 0.1%
1089 31
 
1.5%
1082 2
 
0.1%
1074 167
8.2%

부번
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8472426
Minimum0
Maximum83
Zeros695
Zeros (%)33.9%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:07.735808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile14.6
Maximum83
Range83
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.4729064
Coefficient of variation (CV)1.9424058
Kurtosis36.494386
Mean3.8472426
Median Absolute Deviation (MAD)1
Skewness4.941931
Sum7883
Variance55.84433
MonotonicityNot monotonic
2023-12-12T16:06:07.896342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 695
33.9%
1 414
20.2%
3 172
 
8.4%
2 162
 
7.9%
4 117
 
5.7%
7 71
 
3.5%
5 67
 
3.3%
6 66
 
3.2%
8 48
 
2.3%
9 31
 
1.5%
Other values (29) 206
 
10.1%
ValueCountFrequency (%)
0 695
33.9%
1 414
20.2%
2 162
 
7.9%
3 172
 
8.4%
4 117
 
5.7%
5 67
 
3.3%
6 66
 
3.2%
7 71
 
3.5%
8 48
 
2.3%
9 31
 
1.5%
ValueCountFrequency (%)
83 5
0.2%
49 3
 
0.1%
47 1
 
< 0.1%
40 3
 
0.1%
39 5
0.2%
35 4
0.2%
34 5
0.2%
32 8
0.4%
31 4
0.2%
30 4
0.2%


Real number (ℝ)

SKEWED 

Distinct169
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.596388
Minimum1
Maximum7001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:08.041229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile73.6
Maximum7001
Range7000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation310.67604
Coefficient of variation (CV)13.166254
Kurtosis495.36257
Mean23.596388
Median Absolute Deviation (MAD)0
Skewness22.151759
Sum48349
Variance96519.601
MonotonicityNot monotonic
2023-12-12T16:06:08.181944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1670
81.5%
2 136
 
6.6%
3 23
 
1.1%
4 19
 
0.9%
5 5
 
0.2%
6 5
 
0.2%
9 5
 
0.2%
107 5
 
0.2%
7001 4
 
0.2%
14 4
 
0.2%
Other values (159) 173
 
8.4%
ValueCountFrequency (%)
1 1670
81.5%
2 136
 
6.6%
3 23
 
1.1%
4 19
 
0.9%
5 5
 
0.2%
6 5
 
0.2%
7 3
 
0.1%
8 3
 
0.1%
9 5
 
0.2%
10 2
 
0.1%
ValueCountFrequency (%)
7001 4
0.2%
223 1
 
< 0.1%
222 1
 
< 0.1%
221 1
 
< 0.1%
220 1
 
< 0.1%
219 1
 
< 0.1%
218 1
 
< 0.1%
217 1
 
< 0.1%
216 1
 
< 0.1%
215 1
 
< 0.1%


Text

Distinct86
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2023-12-12T16:06:08.364679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.2537823
Min length1

Characters and Unicode

Total characters2569
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)2.3%

Sample

1st row2
2nd row3
3rd row4
4th row1
5th row2
ValueCountFrequency (%)
1 922
45.0%
2 383
18.7%
3 226
 
11.0%
4 117
 
5.7%
5 62
 
3.0%
101 55
 
2.7%
201 39
 
1.9%
6 32
 
1.6%
102 20
 
1.0%
7 17
 
0.8%
Other values (76) 176
 
8.6%
2023-12-12T16:06:08.686951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1187
46.2%
2 519
20.2%
3 275
 
10.7%
0 232
 
9.0%
4 138
 
5.4%
5 84
 
3.3%
6 44
 
1.7%
8 26
 
1.0%
7 24
 
0.9%
9 16
 
0.6%
Other values (6) 24
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2545
99.1%
Other Letter 24
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1187
46.6%
2 519
20.4%
3 275
 
10.8%
0 232
 
9.1%
4 138
 
5.4%
5 84
 
3.3%
6 44
 
1.7%
8 26
 
1.0%
7 24
 
0.9%
9 16
 
0.6%
Other Letter
ValueCountFrequency (%)
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2545
99.1%
Hangul 24
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1187
46.6%
2 519
20.4%
3 275
 
10.8%
0 232
 
9.1%
4 138
 
5.4%
5 84
 
3.3%
6 44
 
1.7%
8 26
 
1.0%
7 24
 
0.9%
9 16
 
0.6%
Hangul
ValueCountFrequency (%)
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2545
99.1%
Hangul 24
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1187
46.6%
2 519
20.4%
3 275
 
10.8%
0 232
 
9.1%
4 138
 
5.4%
5 84
 
3.3%
6 44
 
1.7%
8 26
 
1.0%
7 24
 
0.9%
9 16
 
0.6%
Hangul
ValueCountFrequency (%)
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
Distinct1924
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
2023-12-12T16:06:09.083991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.908248
Min length21

Characters and Unicode

Total characters53086
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1835 ?
Unique (%)89.6%

Sample

1st row전라북도 임실군 임실읍 이도리 38-2 1동 2호
2nd row전라북도 임실군 임실읍 이도리 38-2 1동 3호
3rd row전라북도 임실군 임실읍 이도리 38-2 1동 4호
4th row전라북도 임실군 임실읍 이도리 185-2 1동 1호
5th row전라북도 임실군 임실읍 이도리 185-2 1동 2호
ValueCountFrequency (%)
1682
 
12.4%
전라북도 1208
 
8.9%
임실군 1208
 
8.9%
임실읍 1164
 
8.6%
1동 890
 
6.6%
0001동 780
 
5.8%
1호 548
 
4.1%
이도리 403
 
3.0%
0001호 370
 
2.7%
2호 218
 
1.6%
Other values (975) 5042
37.3%
2023-12-12T16:06:09.650645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11464
21.6%
0 5657
 
10.7%
1 4607
 
8.7%
2379
 
4.5%
2372
 
4.5%
2138
 
4.0%
2120
 
4.0%
1611
 
3.0%
2 1576
 
3.0%
1209
 
2.3%
Other values (71) 17953
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22334
42.1%
Decimal Number 16705
31.5%
Space Separator 11464
21.6%
Dash Punctuation 901
 
1.7%
Open Punctuation 841
 
1.6%
Close Punctuation 841
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2379
10.7%
2372
10.6%
2138
 
9.6%
2120
 
9.5%
1611
 
7.2%
1209
 
5.4%
1209
 
5.4%
1208
 
5.4%
1208
 
5.4%
1208
 
5.4%
Other values (57) 5672
25.4%
Decimal Number
ValueCountFrequency (%)
0 5657
33.9%
1 4607
27.6%
2 1576
 
9.4%
3 1094
 
6.5%
4 922
 
5.5%
7 753
 
4.5%
8 611
 
3.7%
6 526
 
3.1%
5 520
 
3.1%
9 439
 
2.6%
Space Separator
ValueCountFrequency (%)
11464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 901
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 841
100.0%
Close Punctuation
ValueCountFrequency (%)
] 841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30752
57.9%
Hangul 22334
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2379
10.7%
2372
10.6%
2138
 
9.6%
2120
 
9.5%
1611
 
7.2%
1209
 
5.4%
1209
 
5.4%
1208
 
5.4%
1208
 
5.4%
1208
 
5.4%
Other values (57) 5672
25.4%
Common
ValueCountFrequency (%)
11464
37.3%
0 5657
18.4%
1 4607
15.0%
2 1576
 
5.1%
3 1094
 
3.6%
4 922
 
3.0%
- 901
 
2.9%
[ 841
 
2.7%
] 841
 
2.7%
7 753
 
2.4%
Other values (4) 2096
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30752
57.9%
Hangul 22334
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11464
37.3%
0 5657
18.4%
1 4607
15.0%
2 1576
 
5.1%
3 1094
 
3.6%
4 922
 
3.0%
- 901
 
2.9%
[ 841
 
2.7%
] 841
 
2.7%
7 753
 
2.4%
Other values (4) 2096
 
6.8%
Hangul
ValueCountFrequency (%)
2379
10.7%
2372
10.6%
2138
 
9.6%
2120
 
9.5%
1611
 
7.2%
1209
 
5.4%
1209
 
5.4%
1208
 
5.4%
1208
 
5.4%
1208
 
5.4%
Other values (57) 5672
25.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct1792
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76897387
Minimum32000
Maximum6.1610472 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:09.833805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32000
5-th percentile320800
Q11750000
median11264000
Q341929920
95-th percentile3.0820584 × 108
Maximum6.1610472 × 109
Range6.1610152 × 109
Interquartile range (IQR)40179920

Descriptive statistics

Standard deviation3.1587502 × 108
Coefficient of variation (CV)4.1077471
Kurtosis197.16487
Mean76897387
Median Absolute Deviation (MAD)10537330
Skewness12.409332
Sum1.5756275 × 1011
Variance9.9777028 × 1016
MonotonicityNot monotonic
2023-12-12T16:06:10.022105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17301300 30
 
1.5%
1200000 20
 
1.0%
1995840 12
 
0.6%
14784000 8
 
0.4%
10560000 8
 
0.4%
9178260 6
 
0.3%
77296800 5
 
0.2%
28929600 5
 
0.2%
33000000 4
 
0.2%
37600000 4
 
0.2%
Other values (1782) 1947
95.0%
ValueCountFrequency (%)
32000 1
< 0.1%
36300 1
< 0.1%
74900 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
81200 2
0.1%
82500 2
0.1%
84000 1
< 0.1%
86400 1
< 0.1%
90000 1
< 0.1%
ValueCountFrequency (%)
6161047200 1
< 0.1%
5862687600 1
< 0.1%
5320958720 1
< 0.1%
4852804880 1
< 0.1%
3708677280 1
< 0.1%
2319166080 1
< 0.1%
2221509900 1
< 0.1%
2187603000 1
< 0.1%
1771995300 1
< 0.1%
1713463140 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1416
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.50568
Minimum1
Maximum10001.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T16:06:10.196145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.08
Q136
median83.6
Q3187
95-th percentile648
Maximum10001.7
Range10000.7
Interquartile range (IQR)151

Descriptive statistics

Standard deviation539.61758
Coefficient of variation (CV)2.6912833
Kurtosis161.18762
Mean200.50568
Median Absolute Deviation (MAD)59.6
Skewness11.082053
Sum410836.14
Variance291187.14
MonotonicityNot monotonic
2023-12-12T16:06:10.399727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 50
 
2.4%
57.1 30
 
1.5%
15.0 25
 
1.2%
16.5 18
 
0.9%
24.0 17
 
0.8%
100.0 14
 
0.7%
27.0 13
 
0.6%
3.24 12
 
0.6%
120.0 12
 
0.6%
50.0 12
 
0.6%
Other values (1406) 1846
90.1%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.2 1
 
< 0.1%
2.7 1
 
< 0.1%
2.88 2
 
0.1%
3.24 12
0.6%
3.3 2
 
0.1%
3.36 1
 
< 0.1%
3.9 1
 
< 0.1%
4.0 2
 
0.1%
4.5 2
 
0.1%
ValueCountFrequency (%)
10001.7 1
< 0.1%
9517.35 1
< 0.1%
8637.92 1
< 0.1%
7877.93 1
< 0.1%
6020.58 1
< 0.1%
5524.25 1
< 0.1%
4300.54 1
< 0.1%
3830.41 1
< 0.1%
3764.88 1
< 0.1%
3758.9 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
Minimum2020-06-01 00:00:00
Maximum2020-06-01 00:00:00
2023-12-12T16:06:10.554431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:10.646737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:06:03.890504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.413193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.252623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.162454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.281926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.185524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.964389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.828619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.003736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.530602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.363543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.243188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.409563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.264059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.071328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.975048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.128761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.648659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.491627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.332754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.523961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.377164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.177417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.111748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.230477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.757776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.592346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.729793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.611772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.479016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.271891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.256998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.329875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.883303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.693351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.817835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.707913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.569820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.373222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.376594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.431591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:57.991241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.786395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.910587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.824492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.662738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.489253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.493951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.537657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.082218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.929742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.083346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.993287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.770540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.615308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.632001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:04.655363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:58.169108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:05:59.051674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:00.184441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.101103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:01.868584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:02.723131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:06:03.762823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:06:10.748567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동법정리특수지본번부번시가표준액연면적
순번1.0000.4820.8340.1570.6180.2200.1000.2480.0960.135
법정동0.4821.0000.5320.0930.8310.4450.0000.0000.0000.000
법정리0.8340.5321.0000.1900.7880.3340.0000.0000.0750.000
특수지0.1570.0930.1901.0000.1600.0000.0000.1910.2390.228
본번0.6180.8310.7880.1601.0000.4730.1490.0000.0930.137
부번0.2200.4450.3340.0000.4731.0000.0000.0000.0000.000
0.1000.0000.0000.0000.1490.0001.0000.2700.0000.000
0.2480.0000.0000.1910.0000.0000.2701.0000.0000.000
시가표준액0.0960.0000.0750.2390.0930.0000.0000.0001.0000.982
연면적0.1350.0000.0000.2280.1370.0000.0000.0000.9821.000
2023-12-12T16:06:10.878434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동법정리본번부번시가표준액연면적특수지
순번1.0000.080-0.0640.099-0.0790.099-0.016-0.0260.120
법정동0.0801.000-0.1720.0880.138-0.0860.111-0.0290.227
법정리-0.064-0.1721.0000.147-0.2120.235-0.2510.0080.114
본번0.0990.0880.1471.000-0.0650.3420.054-0.0620.122
부번-0.0790.138-0.212-0.0651.000-0.304-0.012-0.0910.000
0.099-0.0860.2350.342-0.3041.0000.140-0.0120.000
시가표준액-0.0160.111-0.2510.054-0.0120.1401.0000.6340.179
연면적-0.026-0.0290.008-0.062-0.091-0.0120.6341.0000.174
특수지0.1200.2270.1140.1220.0000.0000.1790.1741.000

Missing values

2023-12-12T16:06:04.843148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:06:05.079975image/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

순번시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
01전라북도임실군45750202025022138212전라북도 임실군 임실읍 이도리 38-2 1동 2호31033500182.552020-06-01
12전라북도임실군45750202025022138213전라북도 임실군 임실읍 이도리 38-2 1동 3호64392600200.62020-06-01
23전라북도임실군45750202025022138214전라북도 임실군 임실읍 이도리 38-2 1동 4호841248060.962020-06-01
34전라북도임실군457502020250221185211전라북도 임실군 임실읍 이도리 185-2 1동 1호33660000660.02020-06-01
45전라북도임실군457502020250221185212전라북도 임실군 임실읍 이도리 185-2 1동 2호85800028.62020-06-01
56전라북도임실군457502020250221230313전라북도 임실군 임실읍 이도리 230-3 1동 3호7219205.122020-06-01
67전라북도임실군457502020250221185214전라북도 임실군 임실읍 이도리 185-2 1동 4호75000025.02020-06-01
78전라북도임실군457502020250221230311전라북도 임실군 임실읍 이도리 230-3 1동 1호46676200146.02020-06-01
89전라북도임실군457502020250221230312전라북도 임실군 임실읍 이도리 230-3 1동 2호2351440098.82020-06-01
910전라북도임실군457502020250221230314전라북도 임실군 임실읍 이도리 230-3 1동 4호444220031.732020-06-01
순번시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
20392040전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호2425116066.262020-06-01
20402041전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호15168423603830.412020-06-01
20412042전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호227568000528.02020-06-01
20422043전라북도임실군45750202025022196201201전라북도 임실군 임실읍 이도리 962 1동 201호229714380532.982020-06-01
20432044전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호21876030005524.252020-06-01
20442045전라북도임실군45750202025022196201201전라북도 임실군 임실읍 이도리 962 1동 201호126910080320.482020-06-01
20452046전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호30022850121.552020-06-01
20462047전라북도임실군45750202025022196201101전라북도 임실군 임실읍 이도리 962 1동 101호445455078.152020-06-01
20472048전라북도임실군45750202025022196201102전라북도 임실군 임실읍 이도리 962 1동 102호499092087.562020-06-01
20482049전라북도임실군45750202025022196201103전라북도 임실군 임실읍 이도리 962 1동 103호107640900291.02020-06-01