Overview

Dataset statistics

Number of variables19
Number of observations1759
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory276.7 KiB
Average record size in memory161.1 B

Variable types

Numeric7
Categorical6
Text6

Dataset

Description산사태 위험지역에 대한 지번과 좌표, 취약지역 지정사유 및 목적 , 토지의 소유자에 따라 국유지와 사유지에 때한 지정 구분하여 정리
Author충청남도
URLhttps://www.data.go.kr/data/15017834/fileData.do

Alerts

소유별 has constant value ""Constant
시군구 is highly overall correlated with 위도(분) and 3 other fieldsHigh correlation
관리주체 is highly overall correlated with 위도(분) and 3 other fieldsHigh correlation
위도(분) is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
경도(분) is highly overall correlated with 시군구 and 2 other fieldsHigh correlation
위도(도) is highly overall correlated with 경도(도)High correlation
경도(도) is highly overall correlated with 경도(분) and 3 other fieldsHigh correlation
위도(도) is highly imbalanced (96.8%)Imbalance
지정면적(제곱미터) is highly skewed (γ1 = 36.74836641)Skewed
대장ID has unique valuesUnique
경도(분) has 27 (1.5%) zerosZeros
지정면적(제곱미터) has 108 (6.1%) zerosZeros
거리(m) has 836 (47.5%) zerosZeros

Reproduction

Analysis started2024-03-14 17:13:54.787590
Analysis finished2024-03-14 17:14:09.961058
Duration15.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대장ID
Real number (ℝ)

UNIQUE 

Distinct1759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68867.611
Minimum16894
Maximum151169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:10.164160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16894
5-th percentile17675.9
Q130283.5
median69321
Q394480
95-th percentile123498.8
Maximum151169
Range134275
Interquartile range (IQR)64196.5

Descriptive statistics

Standard deviation34853.694
Coefficient of variation (CV)0.50609704
Kurtosis-0.98232321
Mean68867.611
Median Absolute Deviation (MAD)25555
Skewness0.015854532
Sum1.2113813 × 108
Variance1.21478 × 109
MonotonicityNot monotonic
2024-03-15T02:14:10.624002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78731 1
 
0.1%
17990 1
 
0.1%
61498 1
 
0.1%
61119 1
 
0.1%
91860 1
 
0.1%
71612 1
 
0.1%
61475 1
 
0.1%
90571 1
 
0.1%
94501 1
 
0.1%
67704 1
 
0.1%
Other values (1749) 1749
99.4%
ValueCountFrequency (%)
16894 1
0.1%
16902 1
0.1%
16903 1
0.1%
16904 1
0.1%
16905 1
0.1%
16906 1
0.1%
16907 1
0.1%
16908 1
0.1%
16909 1
0.1%
16910 1
0.1%
ValueCountFrequency (%)
151169 1
0.1%
151166 1
0.1%
150832 1
0.1%
150720 1
0.1%
150663 1
0.1%
150661 1
0.1%
150659 1
0.1%
150646 1
0.1%
147550 1
0.1%
147547 1
0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
금산군
240 
공주시
198 
부여군
191 
보령시
187 
아산시
144 
Other values (10)
799 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금산군 240
13.6%
공주시 198
11.3%
부여군 191
10.9%
보령시 187
10.6%
아산시 144
8.2%
천안시 125
7.1%
예산군 117
6.7%
청양군 113
6.4%
논산시 103
5.9%
홍성군 93
 
5.3%
Other values (5) 248
14.1%

Length

2024-03-15T02:14:10.868857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금산군 240
13.6%
공주시 198
11.3%
부여군 191
10.9%
보령시 187
10.6%
아산시 144
8.2%
천안시 125
7.1%
예산군 117
6.7%
청양군 113
6.4%
논산시 103
5.9%
홍성군 93
 
5.3%
Other values (5) 248
14.1%
Distinct157
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-15T02:14:12.154642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.004548
Min length2

Characters and Unicode

Total characters5285
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)1.2%

Sample

1st row제원면
2nd row제원면
3rd row제원면
4th row제원면
5th row군북면
ValueCountFrequency (%)
동남구 116
 
6.6%
송악면 70
 
4.0%
진산면 51
 
2.9%
미산면 44
 
2.5%
복수면 41
 
2.3%
유구읍 41
 
2.3%
남이면 41
 
2.3%
정안면 39
 
2.2%
청라면 32
 
1.8%
외산면 32
 
1.8%
Other values (147) 1252
71.2%
2024-03-15T02:14:13.942314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1424
26.9%
355
 
6.7%
208
 
3.9%
191
 
3.6%
175
 
3.3%
174
 
3.3%
116
 
2.2%
89
 
1.7%
82
 
1.6%
81
 
1.5%
Other values (113) 2390
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5285
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1424
26.9%
355
 
6.7%
208
 
3.9%
191
 
3.6%
175
 
3.3%
174
 
3.3%
116
 
2.2%
89
 
1.7%
82
 
1.6%
81
 
1.5%
Other values (113) 2390
45.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5285
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1424
26.9%
355
 
6.7%
208
 
3.9%
191
 
3.6%
175
 
3.3%
174
 
3.3%
116
 
2.2%
89
 
1.7%
82
 
1.6%
81
 
1.5%
Other values (113) 2390
45.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5285
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1424
26.9%
355
 
6.7%
208
 
3.9%
191
 
3.6%
175
 
3.3%
174
 
3.3%
116
 
2.2%
89
 
1.7%
82
 
1.6%
81
 
1.5%
Other values (113) 2390
45.2%


Text

Distinct617
Distinct (%)35.1%
Missing2
Missing (%)0.1%
Memory size13.9 KiB
2024-03-15T02:14:15.347989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9339784
Min length1

Characters and Unicode

Total characters5155
Distinct characters221
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

Unique260 ?
Unique (%)14.8%

Sample

1st row천내리
2nd row저곡리
3rd row대산리
4th row길곡리
5th row두두리
ValueCountFrequency (%)
48
 
2.7%
광덕면 32
 
1.8%
북면 23
 
1.3%
신대리 22
 
1.3%
병천면 22
 
1.3%
목천읍 20
 
1.1%
송학리 17
 
1.0%
성주리 17
 
1.0%
수철리 15
 
0.9%
건천리 13
 
0.7%
Other values (607) 1528
87.0%
2024-03-15T02:14:17.203371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1584
30.7%
147
 
2.9%
128
 
2.5%
124
 
2.4%
108
 
2.1%
94
 
1.8%
92
 
1.8%
87
 
1.7%
84
 
1.6%
82
 
1.6%
Other values (211) 2625
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5107
99.1%
Dash Punctuation 48
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1584
31.0%
147
 
2.9%
128
 
2.5%
124
 
2.4%
108
 
2.1%
94
 
1.8%
92
 
1.8%
87
 
1.7%
84
 
1.6%
82
 
1.6%
Other values (210) 2577
50.5%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5107
99.1%
Common 48
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1584
31.0%
147
 
2.9%
128
 
2.5%
124
 
2.4%
108
 
2.1%
94
 
1.8%
92
 
1.8%
87
 
1.7%
84
 
1.6%
82
 
1.6%
Other values (210) 2577
50.5%
Common
ValueCountFrequency (%)
- 48
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5107
99.1%
ASCII 48
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1584
31.0%
147
 
2.9%
128
 
2.5%
124
 
2.4%
108
 
2.1%
94
 
1.8%
92
 
1.8%
87
 
1.7%
84
 
1.6%
82
 
1.6%
Other values (210) 2577
50.5%
ASCII
ValueCountFrequency (%)
- 48
100.0%

지번
Text

Distinct1497
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-15T02:14:18.172174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length20
Mean length8.5952246
Min length1

Characters and Unicode

Total characters15119
Distinct characters53
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1336 ?
Unique (%)76.0%

Sample

1st row산6-33임_제2016-2호
2nd row648구
3rd row산33-1임
4th row산15임
5th row58전
ValueCountFrequency (%)
67
 
3.3%
14
 
0.7%
11
 
0.5%
산78-1임 8
 
0.4%
8
 
0.4%
산38-1임 7
 
0.3%
산20-1임 7
 
0.3%
산1-1임 6
 
0.3%
산10-1 6
 
0.3%
산39임 6
 
0.3%
Other values (1552) 1888
93.1%
2024-03-15T02:14:19.836372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2098
13.9%
- 1623
10.7%
2 1377
 
9.1%
1321
 
8.7%
1170
 
7.7%
0 942
 
6.2%
5 878
 
5.8%
3 721
 
4.8%
4 672
 
4.4%
6 591
 
3.9%
Other values (43) 3726
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8515
56.3%
Other Letter 4105
27.2%
Dash Punctuation 1623
 
10.7%
Connector Punctuation 556
 
3.7%
Space Separator 277
 
1.8%
Open Punctuation 13
 
0.1%
Close Punctuation 13
 
0.1%
Other Punctuation 12
 
0.1%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1321
32.2%
1170
28.5%
591
14.4%
577
14.1%
98
 
2.4%
68
 
1.7%
60
 
1.5%
51
 
1.2%
41
 
1.0%
29
 
0.7%
Other values (22) 99
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 2098
24.6%
2 1377
16.2%
0 942
11.1%
5 878
10.3%
3 721
 
8.5%
4 672
 
7.9%
6 591
 
6.9%
7 460
 
5.4%
9 400
 
4.7%
8 376
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 10
83.3%
" 2
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
r 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1623
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 556
100.0%
Space Separator
ValueCountFrequency (%)
277
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Math Symbol
ValueCountFrequency (%)
> 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11011
72.8%
Hangul 4105
 
27.2%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1321
32.2%
1170
28.5%
591
14.4%
577
14.1%
98
 
2.4%
68
 
1.7%
60
 
1.5%
51
 
1.2%
41
 
1.0%
29
 
0.7%
Other values (22) 99
 
2.4%
Common
ValueCountFrequency (%)
1 2098
19.1%
- 1623
14.7%
2 1377
12.5%
0 942
8.6%
5 878
8.0%
3 721
 
6.5%
4 672
 
6.1%
6 591
 
5.4%
_ 556
 
5.0%
7 460
 
4.2%
Other values (8) 1093
9.9%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
r 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11014
72.8%
Hangul 4105
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2098
19.0%
- 1623
14.7%
2 1377
12.5%
0 942
8.6%
5 878
8.0%
3 721
 
6.5%
4 672
 
6.1%
6 591
 
5.4%
_ 556
 
5.0%
7 460
 
4.2%
Other values (11) 1096
10.0%
Hangul
ValueCountFrequency (%)
1321
32.2%
1170
28.5%
591
14.4%
577
14.1%
98
 
2.4%
68
 
1.7%
60
 
1.5%
51
 
1.2%
41
 
1.0%
29
 
0.7%
Other values (22) 99
 
2.4%
Distinct163
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-15T02:14:22.125729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length99
Median length4
Mean length5.063104
Min length3

Characters and Unicode

Total characters8906
Distinct characters47
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)9.0%

Sample

1st row해당없음
2nd row해당없음
3rd row해당없음
4th row해당없음
5th row해당없음
ValueCountFrequency (%)
해당없음 1591
80.6%
11
 
0.6%
1필지 4
 
0.2%
3필지 3
 
0.2%
산84 2
 
0.1%
산118 2
 
0.1%
664 2
 
0.1%
산25 2
 
0.1%
2
 
0.1%
279 2
 
0.1%
Other values (348) 354
 
17.9%
2024-03-15T02:14:24.189465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1591
17.9%
1591
17.9%
1591
17.9%
1591
17.9%
, 286
 
3.2%
1 275
 
3.1%
- 261
 
2.9%
216
 
2.4%
2 209
 
2.3%
175
 
2.0%
Other values (37) 1120
12.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6688
75.1%
Decimal Number 1448
 
16.3%
Other Punctuation 286
 
3.2%
Dash Punctuation 261
 
2.9%
Space Separator 216
 
2.4%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1591
23.8%
1591
23.8%
1591
23.8%
1591
23.8%
175
 
2.6%
21
 
0.3%
19
 
0.3%
16
 
0.2%
13
 
0.2%
13
 
0.2%
Other values (19) 67
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 275
19.0%
2 209
14.4%
4 172
11.9%
5 151
10.4%
3 131
9.0%
6 124
8.6%
8 111
7.7%
0 99
 
6.8%
7 99
 
6.8%
9 77
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
b 1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 286
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%
Space Separator
ValueCountFrequency (%)
216
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6688
75.1%
Common 2215
 
24.9%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1591
23.8%
1591
23.8%
1591
23.8%
1591
23.8%
175
 
2.6%
21
 
0.3%
19
 
0.3%
16
 
0.2%
13
 
0.2%
13
 
0.2%
Other values (19) 67
 
1.0%
Common
ValueCountFrequency (%)
, 286
12.9%
1 275
12.4%
- 261
11.8%
216
9.8%
2 209
9.4%
4 172
7.8%
5 151
6.8%
3 131
5.9%
6 124
5.6%
8 111
 
5.0%
Other values (5) 279
12.6%
Latin
ValueCountFrequency (%)
F 1
33.3%
e 1
33.3%
b 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6688
75.1%
ASCII 2218
 
24.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1591
23.8%
1591
23.8%
1591
23.8%
1591
23.8%
175
 
2.6%
21
 
0.3%
19
 
0.3%
16
 
0.2%
13
 
0.2%
13
 
0.2%
Other values (19) 67
 
1.0%
ASCII
ValueCountFrequency (%)
, 286
12.9%
1 275
12.4%
- 261
11.8%
216
9.7%
2 209
9.4%
4 172
7.8%
5 151
6.8%
3 131
5.9%
6 124
5.6%
8 111
 
5.0%
Other values (8) 282
12.7%

위도(도)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
36
1748 
126
 
5
35
 
4
37
 
2

Length

Max length3
Median length2
Mean length2.0028425
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 1748
99.4%
126 5
 
0.3%
35 4
 
0.2%
37 2
 
0.1%

Length

2024-03-15T02:14:24.617602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:14:24.962463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36 1748
99.4%
126 5
 
0.3%
35 4
 
0.2%
37 2
 
0.1%

위도(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.064241
Minimum0
Maximum83
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:25.339661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q113
median26
Q340
95-th percentile50
Maximum83
Range83
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.168784
Coefficient of variation (CV)0.56047328
Kurtosis-1.0203547
Mean27.064241
Median Absolute Deviation (MAD)14
Skewness0.17507108
Sum47606
Variance230.092
MonotonicityNot monotonic
2024-03-15T02:14:25.764784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 61
 
3.5%
11 58
 
3.3%
42 57
 
3.2%
10 56
 
3.2%
13 56
 
3.2%
12 53
 
3.0%
9 49
 
2.8%
43 49
 
2.8%
35 46
 
2.6%
47 42
 
2.4%
Other values (54) 1232
70.0%
ValueCountFrequency (%)
0 7
 
0.4%
1 12
 
0.7%
2 9
 
0.5%
3 20
1.1%
4 20
1.1%
5 22
1.3%
6 26
1.5%
7 38
2.2%
8 31
1.8%
9 49
2.8%
ValueCountFrequency (%)
83 1
 
0.1%
78 1
 
0.1%
77 1
 
0.1%
75 1
 
0.1%
68 1
 
0.1%
59 6
0.3%
57 1
 
0.1%
56 5
0.3%
55 4
 
0.2%
54 10
0.6%

위도(초)
Real number (ℝ)

Distinct1194
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.381513
Minimum0
Maximum97
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:26.198978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.499
Q115.9995
median31.099
Q344.749
95-th percentile56.5594
Maximum97
Range97
Interquartile range (IQR)28.7495

Descriptive statistics

Standard deviation16.932756
Coefficient of variation (CV)0.55733747
Kurtosis-1.0294801
Mean30.381513
Median Absolute Deviation (MAD)14.3
Skewness-0.022362401
Sum53441.082
Variance286.71822
MonotonicityNot monotonic
2024-03-15T02:14:26.654052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.799 7
 
0.4%
31.499 6
 
0.3%
33.799 5
 
0.3%
24.699 5
 
0.3%
3.999 5
 
0.3%
28.299 5
 
0.3%
48.499 5
 
0.3%
47.0 5
 
0.3%
18.699 5
 
0.3%
52.499 5
 
0.3%
Other values (1184) 1706
97.0%
ValueCountFrequency (%)
0.0 1
 
0.1%
0.099 2
0.1%
0.178 1
 
0.1%
0.299 3
0.2%
0.358 1
 
0.1%
0.369 1
 
0.1%
0.499 1
 
0.1%
0.6 2
0.1%
0.699 2
0.1%
0.8 1
 
0.1%
ValueCountFrequency (%)
97.0 1
0.1%
64.0 1
0.1%
61.0 1
0.1%
59.999 1
0.1%
59.9 1
0.1%
59.899 1
0.1%
59.754 1
0.1%
59.713 1
0.1%
59.7 1
0.1%
59.699 1
0.1%

경도(도)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
126
1086 
127
668 
36
 
5

Length

Max length3
Median length3
Mean length2.9971575
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row127
2nd row127
3rd row127
4th row127
5th row127

Common Values

ValueCountFrequency (%)
126 1086
61.7%
127 668
38.0%
36 5
 
0.3%

Length

2024-03-15T02:14:27.101220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:14:27.440528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126 1086
61.7%
127 668
38.0%
36 5
 
0.3%

경도(분)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.860716
Minimum0
Maximum61
Zeros27
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:27.758741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median36
Q347
95-th percentile58
Maximum61
Range61
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.240269
Coefficient of variation (CV)0.52464679
Kurtosis-1.0541652
Mean32.860716
Median Absolute Deviation (MAD)14
Skewness-0.30106294
Sum57802
Variance297.22689
MonotonicityNot monotonic
2024-03-15T02:14:28.109865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 64
 
3.6%
38 58
 
3.3%
39 51
 
2.9%
56 48
 
2.7%
59 47
 
2.7%
54 45
 
2.6%
36 44
 
2.5%
44 43
 
2.4%
58 43
 
2.4%
22 43
 
2.4%
Other values (51) 1273
72.4%
ValueCountFrequency (%)
0 27
1.5%
1 28
1.6%
2 27
1.5%
3 27
1.5%
4 21
1.2%
5 20
1.1%
6 22
1.3%
7 18
1.0%
8 23
1.3%
9 19
1.1%
ValueCountFrequency (%)
61 1
 
0.1%
59 47
2.7%
58 43
2.4%
57 23
1.3%
56 48
2.7%
55 38
2.2%
54 45
2.6%
53 27
1.5%
52 37
2.1%
51 33
1.9%

경도(초)
Real number (ℝ)

Distinct1142
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.630389
Minimum0
Maximum733
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:28.379732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.494
Q113.9385
median29.49
Q344.7695
95-th percentile57
Maximum733
Range733
Interquartile range (IQR)30.831

Descriptive statistics

Standard deviation31.377159
Coefficient of variation (CV)1.02438
Kurtosis275.09561
Mean30.630389
Median Absolute Deviation (MAD)15.491
Skewness13.517261
Sum53878.855
Variance984.52608
MonotonicityNot monotonic
2024-03-15T02:14:28.814118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 7
 
0.4%
9.0 7
 
0.4%
16.0 7
 
0.4%
11.0 6
 
0.3%
25.8 6
 
0.3%
6.7 6
 
0.3%
39.0 6
 
0.3%
46.5 6
 
0.3%
3.0 6
 
0.3%
51.0 6
 
0.3%
Other values (1132) 1696
96.4%
ValueCountFrequency (%)
0.0 4
0.2%
0.045 1
 
0.1%
0.1 2
0.1%
0.2 1
 
0.1%
0.257 1
 
0.1%
0.258 1
 
0.1%
0.3 1
 
0.1%
0.349 1
 
0.1%
0.352 1
 
0.1%
0.4 2
0.1%
ValueCountFrequency (%)
733.0 1
 
0.1%
675.0 1
 
0.1%
514.0 1
 
0.1%
231.0 1
 
0.1%
65.3 1
 
0.1%
59.928 1
 
0.1%
59.914 1
 
0.1%
59.9 2
0.1%
59.8 3
0.2%
59.729 1
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
토석류
1518 
산사태
241 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
토석류 1518
86.3%
산사태 241
 
13.7%

Length

2024-03-15T02:14:29.246091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:14:29.428848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토석류 1518
86.3%
산사태 241
 
13.7%

관리주체
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
금산군
240 
공주시
198 
부여군
191 
보령시
187 
아산시
144 
Other values (10)
799 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금산군 240
13.6%
공주시 198
11.3%
부여군 191
10.9%
보령시 187
10.6%
아산시 144
8.2%
천안시 125
7.1%
예산군 117
6.7%
청양군 113
6.4%
논산시 103
5.9%
홍성군 93
 
5.3%
Other values (5) 248
14.1%

Length

2024-03-15T02:14:29.621136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금산군 240
13.6%
공주시 198
11.3%
부여군 191
10.9%
보령시 187
10.6%
아산시 144
8.2%
천안시 125
7.1%
예산군 117
6.7%
청양군 113
6.4%
논산시 103
5.9%
홍성군 93
 
5.3%
Other values (5) 248
14.1%

지구
Text

Distinct98
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-15T02:14:30.578246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9960205
Min length2

Characters and Unicode

Total characters7029
Distinct characters106
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)4.1%

Sample

1st row해당없음
2nd row해당없음
3rd row해당없음
4th row해당없음
5th row해당없음
ValueCountFrequency (%)
해당없음 1615
91.8%
청양군 9
 
0.5%
취약지구 4
 
0.2%
광덕지구 4
 
0.2%
금덕지구 4
 
0.2%
금복지구 3
 
0.2%
송전지구 3
 
0.2%
중산지구 3
 
0.2%
반암지구 3
 
0.2%
양대지구 3
 
0.2%
Other values (89) 109
 
6.2%
2024-03-15T02:14:31.869358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1619
23.0%
1615
23.0%
1615
23.0%
1615
23.0%
131
 
1.9%
126
 
1.8%
16
 
0.2%
15
 
0.2%
14
 
0.2%
13
 
0.2%
Other values (96) 250
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7019
99.9%
Decimal Number 9
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1619
23.1%
1615
23.0%
1615
23.0%
1615
23.0%
131
 
1.9%
126
 
1.8%
16
 
0.2%
15
 
0.2%
14
 
0.2%
13
 
0.2%
Other values (90) 240
 
3.4%
Decimal Number
ValueCountFrequency (%)
3 2
22.2%
9 2
22.2%
2 2
22.2%
7 2
22.2%
5 1
11.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7019
99.9%
Common 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1619
23.1%
1615
23.0%
1615
23.0%
1615
23.0%
131
 
1.9%
126
 
1.8%
16
 
0.2%
15
 
0.2%
14
 
0.2%
13
 
0.2%
Other values (90) 240
 
3.4%
Common
ValueCountFrequency (%)
3 2
20.0%
9 2
20.0%
2 2
20.0%
7 2
20.0%
1
10.0%
5 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7019
99.9%
ASCII 10
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1619
23.1%
1615
23.0%
1615
23.0%
1615
23.0%
131
 
1.9%
126
 
1.8%
16
 
0.2%
15
 
0.2%
14
 
0.2%
13
 
0.2%
Other values (90) 240
 
3.4%
ASCII
ValueCountFrequency (%)
3 2
20.0%
9 2
20.0%
2 2
20.0%
7 2
20.0%
1
10.0%
5 1
10.0%

소유별
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
사유림
1759 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사유림 1759
100.0%

Length

2024-03-15T02:14:32.233940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:14:32.573465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사유림 1759
100.0%

지정면적(제곱미터)
Real number (ℝ)

SKEWED  ZEROS 

Distinct1333
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23476.199
Minimum0
Maximum14659141
Zeros108
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:32.916935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1744.41665
median1362
Q32372.6
95-th percentile13936.3
Maximum14659141
Range14659141
Interquartile range (IQR)1628.1833

Descriptive statistics

Standard deviation366132.07
Coefficient of variation (CV)15.595884
Kurtosis1456.3176
Mean23476.199
Median Absolute Deviation (MAD)762
Skewness36.748366
Sum41294634
Variance1.3405269 × 1011
MonotonicityNot monotonic
2024-03-15T02:14:33.377625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 108
 
6.1%
1250.0 44
 
2.5%
1200.0 17
 
1.0%
1000.0 14
 
0.8%
2000.0 11
 
0.6%
1100.0 7
 
0.4%
1300.0 7
 
0.4%
1600.0 6
 
0.3%
1500.0 6
 
0.3%
70.79 5
 
0.3%
Other values (1323) 1534
87.2%
ValueCountFrequency (%)
0.0 108
6.1%
1.284 1
 
0.1%
2.518 1
 
0.1%
55.4 2
 
0.1%
57.0 1
 
0.1%
58.9921 1
 
0.1%
59.0 1
 
0.1%
62.86 1
 
0.1%
64.0 1
 
0.1%
66.0 1
 
0.1%
ValueCountFrequency (%)
14659141.0 1
0.1%
2478000.0 1
0.1%
1996000.0 1
0.1%
1573000.0 1
0.1%
1117000.0 1
0.1%
1071000.0 1
0.1%
968000.0 1
0.1%
861000.0 1
0.1%
752000.0 1
0.1%
656973.0 1
0.1%

거리(m)
Real number (ℝ)

ZEROS 

Distinct414
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.97078
Minimum0
Maximum14632
Zeros836
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-15T02:14:33.732047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q3585
95-th percentile2071.6
Maximum14632
Range14632
Interquartile range (IQR)585

Descriptive statistics

Standard deviation972.7422
Coefficient of variation (CV)2.0653982
Kurtosis48.01611
Mean470.97078
Median Absolute Deviation (MAD)25
Skewness5.2156577
Sum828437.61
Variance946227.38
MonotonicityNot monotonic
2024-03-15T02:14:33.987478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 836
47.5%
100.0 40
 
2.3%
500.0 27
 
1.5%
300.0 21
 
1.2%
1000.0 20
 
1.1%
600.0 20
 
1.1%
200.0 19
 
1.1%
1200.0 17
 
1.0%
1300.0 16
 
0.9%
150.0 15
 
0.9%
Other values (404) 728
41.4%
ValueCountFrequency (%)
0.0 836
47.5%
3.5 1
 
0.1%
3.7 1
 
0.1%
5.0 1
 
0.1%
5.8 1
 
0.1%
8.0 1
 
0.1%
10.0 15
 
0.9%
10.6 1
 
0.1%
12.0 2
 
0.1%
15.0 7
 
0.4%
ValueCountFrequency (%)
14632.0 1
0.1%
12000.0 1
0.1%
8384.0 1
0.1%
8105.0 1
0.1%
8000.0 1
0.1%
7485.0 1
0.1%
7477.0 1
0.1%
5734.0 1
0.1%
5442.0 1
0.1%
5000.0 1
0.1%
Distinct1161
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-15T02:14:34.915373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length7.3854463
Min length1

Characters and Unicode

Total characters12991
Distinct characters334
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique855 ?
Unique (%)48.6%

Sample

1st row천내3리경로당
2nd row저곡1리마을회관
3rd row대산리경로당
4th row길곡1리노인회관
5th row군북면사무소
ValueCountFrequency (%)
마을회관 228
 
10.3%
경로당 107
 
4.8%
32
 
1.4%
노인회관 30
 
1.4%
다목적회관 17
 
0.8%
보건진료소 11
 
0.5%
도고농은리경로당 9
 
0.4%
강장2리마을회관 9
 
0.4%
건천리 7
 
0.3%
초등학교 7
 
0.3%
Other values (1208) 1765
79.4%
2024-03-15T02:14:36.007591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1050
 
8.1%
1045
 
8.0%
982
 
7.6%
769
 
5.9%
740
 
5.7%
464
 
3.6%
1 341
 
2.6%
316
 
2.4%
315
 
2.4%
2 313
 
2.4%
Other values (324) 6656
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11642
89.6%
Decimal Number 769
 
5.9%
Space Separator 464
 
3.6%
Dash Punctuation 36
 
0.3%
Close Punctuation 25
 
0.2%
Open Punctuation 25
 
0.2%
Other Punctuation 19
 
0.1%
Connector Punctuation 6
 
< 0.1%
Uppercase Letter 4
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1050
 
9.0%
1045
 
9.0%
982
 
8.4%
769
 
6.6%
740
 
6.4%
316
 
2.7%
315
 
2.7%
311
 
2.7%
278
 
2.4%
187
 
1.6%
Other values (303) 5649
48.5%
Decimal Number
ValueCountFrequency (%)
1 341
44.3%
2 313
40.7%
3 73
 
9.5%
4 21
 
2.7%
5 6
 
0.8%
6 5
 
0.7%
9 3
 
0.4%
7 3
 
0.4%
8 3
 
0.4%
0 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
M 1
25.0%
Y 1
25.0%
Space Separator
ValueCountFrequency (%)
464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11642
89.6%
Common 1345
 
10.4%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1050
 
9.0%
1045
 
9.0%
982
 
8.4%
769
 
6.6%
740
 
6.4%
316
 
2.7%
315
 
2.7%
311
 
2.7%
278
 
2.4%
187
 
1.6%
Other values (303) 5649
48.5%
Common
ValueCountFrequency (%)
464
34.5%
1 341
25.4%
2 313
23.3%
3 73
 
5.4%
- 36
 
2.7%
) 25
 
1.9%
( 25
 
1.9%
4 21
 
1.6%
, 19
 
1.4%
_ 6
 
0.4%
Other values (7) 22
 
1.6%
Latin
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
M 1
25.0%
Y 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11642
89.6%
ASCII 1349
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1050
 
9.0%
1045
 
9.0%
982
 
8.4%
769
 
6.6%
740
 
6.4%
316
 
2.7%
315
 
2.7%
311
 
2.7%
278
 
2.4%
187
 
1.6%
Other values (303) 5649
48.5%
ASCII
ValueCountFrequency (%)
464
34.4%
1 341
25.3%
2 313
23.2%
3 73
 
5.4%
- 36
 
2.7%
) 25
 
1.9%
( 25
 
1.9%
4 21
 
1.6%
, 19
 
1.4%
_ 6
 
0.4%
Other values (11) 26
 
1.9%

Interactions

2024-03-15T02:14:06.856463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:56.613628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:58.646330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:00.412050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:02.399265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:03.814579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:05.174553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:07.137877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:56.886638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:58.893782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:00.579444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:02.676466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.001360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:05.356418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:07.393275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:57.145584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:59.139146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:00.828464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:02.837693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.169816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:05.526272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:07.661738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:57.413268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:59.402603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:01.090583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:03.022035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.334163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:05.759737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:07.934068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:57.699674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:59.663485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:01.352234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:03.188153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.583975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:06.013083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:08.296597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:57.979971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:59.937285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:01.847503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:03.458460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.786319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:06.294429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:08.557740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:13:58.354775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:00.209679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:02.121289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:03.638676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:04.978971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:14:06.576591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:14:36.217320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장ID시군구위도(도)위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형관리주체지구지정면적(제곱미터)거리(m)
대장ID1.0000.6640.2010.5080.0830.3910.4300.0610.4920.6640.5610.0000.000
시군구0.6641.0000.1770.8500.1150.8860.8650.2640.2921.0000.6730.0000.335
위도(도)0.2010.1771.0000.6710.0000.6770.1990.0000.0000.1770.0000.0000.000
위도(분)0.5080.8500.6711.0000.3890.3960.6270.7620.1410.8500.1480.0140.191
위도(초)0.0830.1150.0000.3891.0000.0420.0580.8950.0000.1150.0000.0000.000
경도(도)0.3910.8860.6770.3960.0421.0000.7320.0000.0000.8860.0000.0000.101
경도(분)0.4300.8650.1990.6270.0580.7321.0000.0560.1440.8650.4060.0370.226
경도(초)0.0610.2640.0000.7620.8950.0000.0561.0000.0000.2640.0000.0000.000
취약지역유형0.4920.2920.0000.1410.0000.0000.1440.0001.0000.2920.2100.0000.027
관리주체0.6641.0000.1770.8500.1150.8860.8650.2640.2921.0000.6730.0000.335
지구0.5610.6730.0000.1480.0000.0000.4060.0000.2100.6731.0000.0000.000
지정면적(제곱미터)0.0000.0000.0000.0140.0000.0000.0370.0000.0000.0000.0001.0000.035
거리(m)0.0000.3350.0000.1910.0000.1010.2260.0000.0270.3350.0000.0351.000
2024-03-15T02:14:36.559454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취약지역유형시군구위도(도)관리주체경도(도)
취약지역유형1.0000.2660.0000.2660.000
시군구0.2661.0000.1011.0000.641
위도(도)0.0000.1011.0000.1010.708
관리주체0.2661.0000.1011.0000.641
경도(도)0.0000.6410.7080.6411.000
2024-03-15T02:14:36.843672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장ID위도(분)위도(초)경도(분)경도(초)지정면적(제곱미터)거리(m)시군구위도(도)경도(도)취약지역유형관리주체
대장ID1.0000.1340.015-0.004-0.005-0.194-0.0380.3160.1210.2540.3780.316
위도(분)0.1341.0000.0200.002-0.0240.085-0.2070.5200.4710.2590.1080.520
위도(초)0.0150.0201.0000.026-0.004-0.0090.0470.0490.0000.0260.0000.049
경도(분)-0.0040.0020.0261.0000.0420.0030.0910.5450.1200.5970.1100.545
경도(초)-0.005-0.024-0.0040.0421.000-0.0350.0110.1520.0000.0000.0000.152
지정면적(제곱미터)-0.1940.085-0.0090.003-0.0351.0000.1200.0000.0000.0000.0000.000
거리(m)-0.038-0.2070.0470.0910.0110.1201.0000.1580.0000.0670.0290.158
시군구0.3160.5200.0490.5450.1520.0000.1581.0000.1010.6410.2661.000
위도(도)0.1210.4710.0000.1200.0000.0000.0000.1011.0000.7080.0000.101
경도(도)0.2540.2590.0260.5970.0000.0000.0670.6410.7081.0000.0000.641
취약지역유형0.3780.1080.0000.1100.0000.0000.0290.2660.0000.0001.0000.266
관리주체0.3160.5200.0490.5450.1520.0000.1581.0000.1010.6410.2661.000

Missing values

2024-03-15T02:14:08.975405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:14:09.680150image/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

대장ID시군구읍면동지번기타지번위도(도)위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형관리주체지구소유별지정면적(제곱미터)거리(m)대피장소
078731금산군제원면천내리산6-33임_제2016-2호해당없음3678.2991273442.14토석류금산군해당없음사유림500.03569.8천내3리경로당
116987금산군제원면저곡리648구해당없음36623.8991273336.7토석류금산군해당없음사유림480.0200.0저곡1리마을회관
224263금산군제원면대산리산33-1임해당없음36757.1991273318.8토석류금산군해당없음사유림436.8600.0대산리경로당
3102602금산군제원면길곡리산15임해당없음36952.01273343.999토석류금산군해당없음사유림850.0450.0길곡1리노인회관
420539금산군군북면두두리58전해당없음361020.7681273228.427토석류금산군해당없음사유림1052.01200.0군북면사무소
590607금산군남일면신정리696-3임해당없음355942.0991273053.9토석류금산군해당없음사유림3231.0951.0신정1리경로당
618212금산군진산면삼가리00-59가해당없음36718.1991272315.6토석류금산군해당없음사유림2721.6750.0삼가리 청등경로당
718941금산군진산면막현리339-2 전해당없음361137.499127216.3토석류금산군해당없음사유림2163.2220.0막현리 상막마을 농장
823938아산시신창면창암리산73-6산70-1, 산71364541.2821265611.827토석류아산시창암지구사유림2984.0500.0창암1리마을회관
961695태안군소원면송현리산134임_제2015-25호해당없음364650.7991261031.8토석류태안군해당없음사유림1250.00.0송현3리 다목적회관
대장ID시군구읍면동지번기타지번위도(도)위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형관리주체지구소유별지정면적(제곱미터)거리(m)대피장소
174925896공주시사곡면운암리산77-3임해당없음363437.499127027.7토석류공주시해당없음사유림4435.20.0학국문화연수원
175019909공주시사곡면월가리966-3천해당없음363459.999127233.4토석류공주시해당없음사유림2086.40.0월가교회
175119913공주시사곡면월가리산1-1임해당없음363450.699127319.0토석류공주시해당없음사유림1281.60.0월가리경로당
175219908공주시사곡면부곡리산39-1임해당없음363529.699127055.8토석류공주시해당없음사유림1862.40.0한국문화연수원
1753110120공주시사곡면부곡리산21임해당없음363547.0127117.0산사태공주시해당없음사유림932.00.0부곡리새마을회관
175419930공주시우성면내산리산48-2 임해당없음363030.899127448.2토석류공주시해당없음사유림1915.20.0내산2리경로당
175565168공주시우성면신웅리산15-1임해당없음36293.799127623.1토석류공주시해당없음사유림3759.00.0신웅리노인회관
175626074공주시우성면보흥리산79-8임해당없음362649.72812735.143토석류공주시해당없음사유림1617.60.0우성중학교
175719929공주시우성면반촌리산63임해당없음36315.10812767.058토석류공주시해당없음사유림1158.00.0반촌리경로당
175819927공주시신풍면화흥리산47임해당없음363211.3991265923.2토석류공주시해당없음사유림1348.80.0신풍면평소보건진료소