Overview

Dataset statistics

Number of variables18
Number of observations2307
Missing cells2161
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory340.3 KiB
Average record size in memory151.1 B

Variable types

Numeric6
Categorical4
Text7
DateTime1

Dataset

Description경상남도내 18개시군의 산사태취약지역지정현황에 대한 공공데이터로 시군별 지정 토석류 및 산사태 상세현황을 제공합니다.
Author경상남도
URLhttps://www.data.go.kr/data/15014457/fileData.do

Alerts

대장아이디 is highly overall correlated with 시군구High correlation
위도_도 is highly overall correlated with 위도_분 and 1 other fieldsHigh correlation
위도_분 is highly overall correlated with 위도_도 and 1 other fieldsHigh correlation
경도_분 is highly overall correlated with 시군구High correlation
경도_초 is highly overall correlated with 지정등급_A,B,C,DHigh correlation
시군구 is highly overall correlated with 대장아이디 and 3 other fieldsHigh correlation
경도_도 is highly overall correlated with 위도_도 and 1 other fieldsHigh correlation
지정등급_A,B,C,D is highly overall correlated with 경도_초High correlation
경도_도 is highly imbalanced (51.6%)Imbalance
지정등급_A,B,C,D is highly imbalanced (50.1%)Imbalance
리 has 103 (4.5%) missing valuesMissing
기타지번 has 2008 (87.0%) missing valuesMissing
위도_도 is highly skewed (γ1 = 37.24555812)Skewed
경도_분 has 45 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:54:22.865937
Analysis finished2023-12-12 09:54:29.466100
Duration6.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대장아이디
Real number (ℝ)

HIGH CORRELATION 

Distinct2241
Distinct (%)98.0%
Missing20
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1142.9847
Minimum1
Maximum2241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:29.534104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile115.3
Q1572.5
median1143
Q31714.5
95-th percentile2171.7
Maximum2241
Range2240
Interquartile range (IQR)1142

Descriptive statistics

Standard deviation659.1541
Coefficient of variation (CV)0.57669547
Kurtosis-1.2041712
Mean1142.9847
Median Absolute Deviation (MAD)571
Skewness-0.0035344286
Sum2614006
Variance434484.13
MonotonicityIncreasing
2023-12-12T18:54:29.660236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2241 46
 
2.0%
1000 2
 
0.1%
1498 1
 
< 0.1%
1492 1
 
< 0.1%
1493 1
 
< 0.1%
1494 1
 
< 0.1%
1495 1
 
< 0.1%
1496 1
 
< 0.1%
1497 1
 
< 0.1%
1499 1
 
< 0.1%
Other values (2231) 2231
96.7%
(Missing) 20
 
0.9%
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 (%)
2241 46
2.0%
2240 1
 
< 0.1%
2239 1
 
< 0.1%
2238 1
 
< 0.1%
2237 1
 
< 0.1%
2236 1
 
< 0.1%
2235 1
 
< 0.1%
2234 1
 
< 0.1%
2233 1
 
< 0.1%
2232 1
 
< 0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
함양군
249 
합천군
239 
하동군
237 
산청군
208 
거창군
175 
Other values (18)
1199 

Length

Max length9
Median length3
Mean length3.1868227
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row창원시 마산합포구
2nd row창원시 마산회원구
3rd row창원시 마산회원구
4th row창원시 진해구
5th row창원시 성산구

Common Values

ValueCountFrequency (%)
함양군 249
10.8%
합천군 239
10.4%
하동군 237
10.3%
산청군 208
 
9.0%
거창군 175
 
7.6%
밀양시 165
 
7.2%
진주시 151
 
6.5%
의령군 123
 
5.3%
고성군 109
 
4.7%
창녕군 102
 
4.4%
Other values (13) 549
23.8%

Length

2023-12-12T18:54:29.786315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
함양군 249
 
10.4%
합천군 239
 
10.0%
하동군 237
 
9.9%
산청군 208
 
8.7%
거창군 175
 
7.3%
밀양시 165
 
6.9%
진주시 152
 
6.4%
의령군 123
 
5.1%
고성군 109
 
4.6%
창녕군 102
 
4.3%
Other values (13) 631
26.4%

읍면동
Text

Distinct251
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2023-12-12T18:54:30.104717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0021673
Min length2

Characters and Unicode

Total characters6926
Distinct characters153
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

Unique46 ?
Unique (%)2.0%

Sample

1st row완월동
2nd row내서읍
3rd row두척동
4th row태백동
5th row사파정동
ValueCountFrequency (%)
시천면 49
 
2.1%
화개면 41
 
1.8%
마천면 33
 
1.4%
함양읍 33
 
1.4%
무안면 32
 
1.4%
남상면 30
 
1.3%
북상면 30
 
1.3%
병곡면 28
 
1.2%
곤명면 27
 
1.2%
서하면 27
 
1.2%
Other values (241) 1977
85.7%
2023-12-12T18:54:30.631970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 1976
28.5%
동 294
 
4.2%
읍 228
 
3.3%
천 196
 
2.8%
산 169
 
2.4%
곡 159
 
2.3%
상 134
 
1.9%
북 124
 
1.8%
가 115
 
1.7%
양 115
 
1.7%
Other values (143) 3416
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6926
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 1976
28.5%
동 294
 
4.2%
읍 228
 
3.3%
천 196
 
2.8%
산 169
 
2.4%
곡 159
 
2.3%
상 134
 
1.9%
북 124
 
1.8%
가 115
 
1.7%
양 115
 
1.7%
Other values (143) 3416
49.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6926
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 1976
28.5%
동 294
 
4.2%
읍 228
 
3.3%
천 196
 
2.8%
산 169
 
2.4%
곡 159
 
2.3%
상 134
 
1.9%
북 124
 
1.8%
가 115
 
1.7%
양 115
 
1.7%
Other values (143) 3416
49.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6926
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 1976
28.5%
동 294
 
4.2%
읍 228
 
3.3%
천 196
 
2.8%
산 169
 
2.4%
곡 159
 
2.3%
상 134
 
1.9%
북 124
 
1.8%
가 115
 
1.7%
양 115
 
1.7%
Other values (143) 3416
49.3%

리
Text

MISSING 

Distinct831
Distinct (%)37.7%
Missing103
Missing (%)4.5%
Memory size18.2 KiB
2023-12-12T18:54:31.029169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.957804
Min length2

Characters and Unicode

Total characters6519
Distinct characters237
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

Unique315 ?
Unique (%)14.3%

Sample

1st row신감리
2nd row월계리
3rd row무곡리
4th row삼계리
5th row평성리
ValueCountFrequency (%)
평촌리 14
 
0.6%
주동리 14
 
0.6%
중산리 13
 
0.6%
죽전리 13
 
0.6%
신기리 12
 
0.5%
용산리 12
 
0.5%
운곡리 12
 
0.5%
부춘리 12
 
0.5%
생철리 11
 
0.5%
용암리 11
 
0.5%
Other values (821) 2080
94.4%
2023-12-12T18:54:31.720365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 2204
33.8%
산 187
 
2.9%
곡 167
 
2.6%
대 113
 
1.7%
계 107
 
1.6%
전 97
 
1.5%
천 96
 
1.5%
정 94
 
1.4%
신 88
 
1.3%
평 87
 
1.3%
Other values (227) 3279
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6519
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 2204
33.8%
산 187
 
2.9%
곡 167
 
2.6%
대 113
 
1.7%
계 107
 
1.6%
전 97
 
1.5%
천 96
 
1.5%
정 94
 
1.4%
신 88
 
1.3%
평 87
 
1.3%
Other values (227) 3279
50.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6519
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 2204
33.8%
산 187
 
2.9%
곡 167
 
2.6%
대 113
 
1.7%
계 107
 
1.6%
전 97
 
1.5%
천 96
 
1.5%
정 94
 
1.4%
신 88
 
1.3%
평 87
 
1.3%
Other values (227) 3279
50.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6519
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 2204
33.8%
산 187
 
2.9%
곡 167
 
2.6%
대 113
 
1.7%
계 107
 
1.6%
전 97
 
1.5%
천 96
 
1.5%
정 94
 
1.4%
신 88
 
1.3%
평 87
 
1.3%
Other values (227) 3279
50.3%

지번
Text

Distinct1781
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2023-12-12T18:54:32.129863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length5.2761162
Min length1

Characters and Unicode

Total characters12172
Distinct characters49
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1502 ?
Unique (%)65.1%

Sample

1st row산103
2nd row1409-4
3rd row산86-2
4th row산52-3
5th row산27
ValueCountFrequency (%)
산 116
 
4.2%
임 113
 
4.1%
구 86
 
3.1%
외1 25
 
0.9%
천 25
 
0.9%
답 23
 
0.8%
전 21
 
0.8%
외2 21
 
0.8%
대 13
 
0.5%
산23임 7
 
0.3%
Other values (1744) 2338
83.9%
2023-12-12T18:54:32.716384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1818
14.9%
산 1658
13.6%
임 1228
10.1%
2 949
 
7.8%
- 890
 
7.3%
3 734
 
6.0%
4 617
 
5.1%
5 551
 
4.5%
8 522
 
4.3%
9 508
 
4.2%
Other values (39) 2697
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7162
58.8%
Other Letter 3608
29.6%
Dash Punctuation 890
 
7.3%
Space Separator 482
 
4.0%
Other Punctuation 13
 
0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Other Number 5
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1658
46.0%
임 1228
34.0%
구 350
 
9.7%
천 81
 
2.2%
답 63
 
1.7%
외 58
 
1.6%
전 48
 
1.3%
대 24
 
0.7%
지 24
 
0.7%
번 21
 
0.6%
Other values (20) 53
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 1818
25.4%
2 949
13.3%
3 734
10.2%
4 617
 
8.6%
5 551
 
7.7%
8 522
 
7.3%
9 508
 
7.1%
6 500
 
7.0%
7 489
 
6.8%
0 474
 
6.6%
Other Number
ValueCountFrequency (%)
① 3
60.0%
③ 1
 
20.0%
② 1
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 890
100.0%
Space Separator
ValueCountFrequency (%)
482
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8564
70.4%
Hangul 3608
29.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1658
46.0%
임 1228
34.0%
구 350
 
9.7%
천 81
 
2.2%
답 63
 
1.7%
외 58
 
1.6%
전 48
 
1.3%
대 24
 
0.7%
지 24
 
0.7%
번 21
 
0.6%
Other values (20) 53
 
1.5%
Common
ValueCountFrequency (%)
1 1818
21.2%
2 949
11.1%
- 890
10.4%
3 734
8.6%
4 617
 
7.2%
5 551
 
6.4%
8 522
 
6.1%
9 508
 
5.9%
6 500
 
5.8%
7 489
 
5.7%
Other values (9) 986
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8559
70.3%
Hangul 3608
29.6%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1818
21.2%
2 949
11.1%
- 890
10.4%
3 734
8.6%
4 617
 
7.2%
5 551
 
6.4%
8 522
 
6.1%
9 508
 
5.9%
6 500
 
5.8%
7 489
 
5.7%
Other values (6) 981
11.5%
Hangul
ValueCountFrequency (%)
산 1658
46.0%
임 1228
34.0%
구 350
 
9.7%
천 81
 
2.2%
답 63
 
1.7%
외 58
 
1.6%
전 48
 
1.3%
대 24
 
0.7%
지 24
 
0.7%
번 21
 
0.6%
Other values (20) 53
 
1.5%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
60.0%
③ 1
 
20.0%
② 1
 
20.0%

기타지번
Text

MISSING 

Distinct292
Distinct (%)97.7%
Missing2008
Missing (%)87.0%
Memory size18.2 KiB
2023-12-12T18:54:33.082512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length68
Mean length14.936455
Min length2

Characters and Unicode

Total characters4466
Distinct characters48
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

Unique286 ?
Unique (%)95.7%

Sample

1st rowJan-78
2nd row1139답
3rd row1351천, 산83임
4th row1267-1대
5th row산9구
ValueCountFrequency (%)
산8 5
 
0.6%
산10 4
 
0.5%
산88-1 4
 
0.5%
산22임 4
 
0.5%
종천리 4
 
0.5%
산53 3
 
0.4%
산93임 3
 
0.4%
산38 3
 
0.4%
산 3
 
0.4%
709 3
 
0.4%
Other values (699) 759
95.5%
2023-12-12T18:54:33.629897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 550
12.3%
497
11.1%
, 497
11.1%
산 354
 
7.9%
2 294
 
6.6%
- 244
 
5.5%
3 231
 
5.2%
4 216
 
4.8%
8 209
 
4.7%
9 205
 
4.6%
Other values (38) 1169
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2485
55.6%
Other Letter 740
 
16.6%
Space Separator 497
 
11.1%
Other Punctuation 497
 
11.1%
Dash Punctuation 244
 
5.5%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 354
47.8%
임 195
26.4%
구 56
 
7.6%
전 33
 
4.5%
답 22
 
3.0%
천 19
 
2.6%
대 7
 
0.9%
도 7
 
0.9%
리 6
 
0.8%
외 4
 
0.5%
Other values (22) 37
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 550
22.1%
2 294
11.8%
3 231
9.3%
4 216
 
8.7%
8 209
 
8.4%
9 205
 
8.2%
0 199
 
8.0%
5 199
 
8.0%
7 192
 
7.7%
6 190
 
7.6%
Lowercase Letter
ValueCountFrequency (%)
n 1
50.0%
a 1
50.0%
Space Separator
ValueCountFrequency (%)
497
100.0%
Other Punctuation
ValueCountFrequency (%)
, 497
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 244
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3723
83.4%
Hangul 740
 
16.6%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 354
47.8%
임 195
26.4%
구 56
 
7.6%
전 33
 
4.5%
답 22
 
3.0%
천 19
 
2.6%
대 7
 
0.9%
도 7
 
0.9%
리 6
 
0.8%
외 4
 
0.5%
Other values (22) 37
 
5.0%
Common
ValueCountFrequency (%)
1 550
14.8%
497
13.3%
, 497
13.3%
2 294
7.9%
- 244
6.6%
3 231
 
6.2%
4 216
 
5.8%
8 209
 
5.6%
9 205
 
5.5%
0 199
 
5.3%
Other values (3) 581
15.6%
Latin
ValueCountFrequency (%)
n 1
33.3%
a 1
33.3%
J 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
83.4%
Hangul 740
 
16.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 550
14.8%
497
13.3%
, 497
13.3%
2 294
7.9%
- 244
6.5%
3 231
 
6.2%
4 216
 
5.8%
8 209
 
5.6%
9 205
 
5.5%
0 199
 
5.3%
Other values (6) 584
15.7%
Hangul
ValueCountFrequency (%)
산 354
47.8%
임 195
26.4%
구 56
 
7.6%
전 33
 
4.5%
답 22
 
3.0%
천 19
 
2.6%
대 7
 
0.9%
도 7
 
0.9%
리 6
 
0.8%
외 4
 
0.5%
Other values (22) 37
 
5.0%

위도_도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.92241
Minimum3
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:33.782148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile34
Q135
median35
Q335
95-th percentile35
Maximum128
Range125
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0806705
Coefficient of variation (CV)0.059579808
Kurtosis1762.3965
Mean34.92241
Median Absolute Deviation (MAD)0
Skewness37.245558
Sum80566
Variance4.3291897
MonotonicityNot monotonic
2023-12-12T18:54:33.908289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
35 2069
89.7%
34 234
 
10.1%
3 1
 
< 0.1%
38 1
 
< 0.1%
26 1
 
< 0.1%
128 1
 
< 0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
26 1
 
< 0.1%
34 234
 
10.1%
35 2069
89.7%
38 1
 
< 0.1%
128 1
 
< 0.1%
ValueCountFrequency (%)
128 1
 
< 0.1%
38 1
 
< 0.1%
35 2069
89.7%
34 234
 
10.1%
26 1
 
< 0.1%
3 1
 
< 0.1%

위도_분
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.086259
Minimum0
Maximum59
Zeros18
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:34.059704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q115
median26
Q335
95-th percentile52
Maximum59
Range59
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.513532
Coefficient of variation (CV)0.55636692
Kurtosis-0.71195321
Mean26.086259
Median Absolute Deviation (MAD)11
Skewness0.26813686
Sum60181
Variance210.6426
MonotonicityNot monotonic
2023-12-12T18:54:34.259474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 75
 
3.3%
29 71
 
3.1%
33 66
 
2.9%
25 62
 
2.7%
23 62
 
2.7%
17 62
 
2.7%
34 60
 
2.6%
16 59
 
2.6%
31 58
 
2.5%
35 57
 
2.5%
Other values (50) 1675
72.6%
ValueCountFrequency (%)
0 18
 
0.8%
1 18
 
0.8%
2 18
 
0.8%
3 28
1.2%
4 44
1.9%
5 47
2.0%
6 47
2.0%
7 38
1.6%
8 47
2.0%
9 40
1.7%
ValueCountFrequency (%)
59 15
0.7%
58 14
0.6%
57 16
0.7%
56 15
0.7%
55 13
0.6%
54 24
1.0%
53 13
0.6%
52 23
1.0%
51 12
0.5%
50 21
0.9%

위도_초
Real number (ℝ)

Distinct1586
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.381669
Minimum0
Maximum89
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:34.430747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.099
Q114.055
median29
Q344.222
95-th percentile56.7924
Maximum89
Range89
Interquartile range (IQR)30.167

Descriptive statistics

Standard deviation17.375326
Coefficient of variation (CV)0.59136618
Kurtosis-1.1303211
Mean29.381669
Median Absolute Deviation (MAD)15
Skewness0.064484386
Sum67783.511
Variance301.90194
MonotonicityNot monotonic
2023-12-12T18:54:34.601781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0 8
 
0.3%
42.299 7
 
0.3%
24.399 6
 
0.3%
6.0 6
 
0.3%
32.999 6
 
0.3%
29.999 6
 
0.3%
54.899 6
 
0.3%
21.899 6
 
0.3%
45.799 6
 
0.3%
18.0 6
 
0.3%
Other values (1576) 2244
97.3%
ValueCountFrequency (%)
0.0 3
0.1%
0.07 1
 
< 0.1%
0.086 1
 
< 0.1%
0.099 2
0.1%
0.134 1
 
< 0.1%
0.199 4
0.2%
0.2 1
 
< 0.1%
0.273 1
 
< 0.1%
0.399 1
 
< 0.1%
0.4 1
 
< 0.1%
ValueCountFrequency (%)
89.0 1
 
< 0.1%
83.0 1
 
< 0.1%
66.0 1
 
< 0.1%
59.999 2
0.1%
59.925 1
 
< 0.1%
59.9 2
0.1%
59.899 2
0.1%
59.852 1
 
< 0.1%
59.831 1
 
< 0.1%
59.8 3
0.1%

경도_도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
128
1299 
127
955 
129
 
51
18
 
1
35
 
1

Length

Max length3
Median length3
Mean length2.9991331
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row128
2nd row128
3rd row128
4th row128
5th row128

Common Values

ValueCountFrequency (%)
128 1299
56.3%
127 955
41.4%
129 51
 
2.2%
18 1
 
< 0.1%
35 1
 
< 0.1%

Length

2023-12-12T18:54:34.758497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:34.869896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128 1299
56.3%
127 955
41.4%
129 51
 
2.2%
18 1
 
< 0.1%
35 1
 
< 0.1%

경도_분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.107499
Minimum0
Maximum59
Zeros45
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:35.035857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median38
Q349
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.268539
Coefficient of variation (CV)0.5517946
Kurtosis-1.2739747
Mean33.107499
Median Absolute Deviation (MAD)14
Skewness-0.36389561
Sum76379
Variance333.73952
MonotonicityNot monotonic
2023-12-12T18:54:35.231943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 80
 
3.5%
50 79
 
3.4%
53 72
 
3.1%
47 70
 
3.0%
48 70
 
3.0%
42 62
 
2.7%
41 59
 
2.6%
38 56
 
2.4%
46 56
 
2.4%
54 55
 
2.4%
Other values (50) 1648
71.4%
ValueCountFrequency (%)
0 45
2.0%
1 38
1.6%
2 39
1.7%
3 33
1.4%
4 30
1.3%
5 38
1.6%
6 31
1.3%
7 35
1.5%
8 45
2.0%
9 41
1.8%
ValueCountFrequency (%)
59 37
1.6%
58 45
2.0%
57 48
2.1%
56 51
2.2%
55 50
2.2%
54 55
2.4%
53 72
3.1%
52 50
2.2%
51 50
2.2%
50 79
3.4%

경도_초
Real number (ℝ)

HIGH CORRELATION 

Distinct1425
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.520364
Minimum0
Maximum970
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2023-12-12T18:54:35.450878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3525
Q114.65
median29.8
Q345.6
95-th percentile56.8994
Maximum970
Range970
Interquartile range (IQR)30.95

Descriptive statistics

Standard deviation26.237641
Coefficient of variation (CV)0.85967657
Kurtosis712.03751
Mean30.520364
Median Absolute Deviation (MAD)15.6
Skewness19.910258
Sum70410.479
Variance688.41383
MonotonicityNot monotonic
2023-12-12T18:54:35.678640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.0 8
 
0.3%
15.4 7
 
0.3%
7.0 7
 
0.3%
22.0 7
 
0.3%
56.0 7
 
0.3%
34.2 7
 
0.3%
48.6 7
 
0.3%
44.3 7
 
0.3%
24.2 6
 
0.3%
19.1 6
 
0.3%
Other values (1415) 2238
97.0%
ValueCountFrequency (%)
0.0 3
0.1%
0.1 3
0.1%
0.167 1
 
< 0.1%
0.204 1
 
< 0.1%
0.3 4
0.2%
0.338 1
 
< 0.1%
0.343 1
 
< 0.1%
0.399 1
 
< 0.1%
0.43 1
 
< 0.1%
0.461 1
 
< 0.1%
ValueCountFrequency (%)
970.0 1
 
< 0.1%
59.9 3
0.1%
59.8 3
0.1%
59.783 1
 
< 0.1%
59.7 3
0.1%
59.699 1
 
< 0.1%
59.693 1
 
< 0.1%
59.608 1
 
< 0.1%
59.6 3
0.1%
59.53 1
 
< 0.1%

취약지역유형
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
토석류
1842 
산사태
465 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
토석류 1842
79.8%
산사태 465
 
20.2%

Length

2023-12-12T18:54:35.832250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:35.933006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토석류 1842
79.8%
산사태 465
 
20.2%

지정면적_제곱미터
Text

Distinct1425
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2023-12-12T18:54:36.291752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.5197226
Min length1

Characters and Unicode

Total characters8120
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1172 ?
Unique (%)50.8%

Sample

1st row880
2nd row400
3rd row325
4th row125
5th row452
ValueCountFrequency (%)
0 393
 
17.0%
1000 35
 
1.5%
300 22
 
1.0%
1250 20
 
0.9%
600 19
 
0.8%
500 19
 
0.8%
400 17
 
0.7%
1249 10
 
0.4%
800 9
 
0.4%
100 8
 
0.3%
Other values (1415) 1755
76.1%
2023-12-12T18:54:36.817781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1548
19.1%
1 1112
13.7%
2 963
11.9%
4 730
9.0%
3 708
8.7%
5 601
 
7.4%
6 590
 
7.3%
8 566
 
7.0%
7 527
 
6.5%
9 464
 
5.7%
Other values (2) 311
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7809
96.2%
Other Punctuation 311
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1548
19.8%
1 1112
14.2%
2 963
12.3%
4 730
9.3%
3 708
9.1%
5 601
 
7.7%
6 590
 
7.6%
8 566
 
7.2%
7 527
 
6.7%
9 464
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 293
94.2%
, 18
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1548
19.1%
1 1112
13.7%
2 963
11.9%
4 730
9.0%
3 708
8.7%
5 601
 
7.4%
6 590
 
7.3%
8 566
 
7.0%
7 527
 
6.5%
9 464
 
5.7%
Other values (2) 311
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1548
19.1%
1 1112
13.7%
2 963
11.9%
4 730
9.0%
3 708
8.7%
5 601
 
7.4%
6 590
 
7.3%
8 566
 
7.0%
7 527
 
6.5%
9 464
 
5.7%
Other values (2) 311
 
3.8%

대피장소
Text

Distinct1502
Distinct (%)65.6%
Missing17
Missing (%)0.7%
Memory size18.2 KiB
2023-12-12T18:54:37.107557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length6
Mean length6.4327511
Min length3

Characters and Unicode

Total characters14731
Distinct characters352
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1045 ?
Unique (%)45.6%

Sample

1st row성지여고
2nd row로뎀전원교회
3rd row두척마을회관
4th row경화초등학교
5th row창원사파고등학교
ValueCountFrequency (%)
마을회관 38
 
1.6%
무안면사무소 19
 
0.8%
이작초등학교 9
 
0.4%
신기마을회관 8
 
0.3%
부덕보건진료소 8
 
0.3%
신촌마을회관 8
 
0.3%
읍사무소 8
 
0.3%
중산마을회관 8
 
0.3%
남명초등학교 7
 
0.3%
상촌마을회관 7
 
0.3%
Other values (1522) 2324
95.1%
2023-12-12T18:54:37.558303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 1552
 
10.5%
관 1518
 
10.3%
마 1403
 
9.5%
을 1372
 
9.3%
경 291
 
2.0%
로 287
 
1.9%
당 286
 
1.9%
소 263
 
1.8%
동 254
 
1.7%
교 250
 
1.7%
Other values (342) 7255
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14369
97.5%
Space Separator 162
 
1.1%
Other Punctuation 103
 
0.7%
Decimal Number 70
 
0.5%
Close Punctuation 13
 
0.1%
Open Punctuation 13
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 1552
 
10.8%
관 1518
 
10.6%
마 1403
 
9.8%
을 1372
 
9.5%
경 291
 
2.0%
로 287
 
2.0%
당 286
 
2.0%
소 263
 
1.8%
동 254
 
1.8%
교 250
 
1.7%
Other values (330) 6893
48.0%
Decimal Number
ValueCountFrequency (%)
1 32
45.7%
2 29
41.4%
9 5
 
7.1%
3 2
 
2.9%
4 2
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 100
97.1%
. 2
 
1.9%
？ 1
 
1.0%
Space Separator
ValueCountFrequency (%)
162
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14369
97.5%
Common 361
 
2.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 1552
 
10.8%
관 1518
 
10.6%
마 1403
 
9.8%
을 1372
 
9.5%
경 291
 
2.0%
로 287
 
2.0%
당 286
 
2.0%
소 263
 
1.8%
동 254
 
1.8%
교 250
 
1.7%
Other values (330) 6893
48.0%
Common
ValueCountFrequency (%)
162
44.9%
, 100
27.7%
1 32
 
8.9%
2 29
 
8.0%
) 13
 
3.6%
( 13
 
3.6%
9 5
 
1.4%
3 2
 
0.6%
4 2
 
0.6%
. 2
 
0.6%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14369
97.5%
ASCII 361
 
2.5%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 1552
 
10.8%
관 1518
 
10.6%
마 1403
 
9.8%
을 1372
 
9.5%
경 291
 
2.0%
로 287
 
2.0%
당 286
 
2.0%
소 263
 
1.8%
동 254
 
1.8%
교 250
 
1.7%
Other values (330) 6893
48.0%
ASCII
ValueCountFrequency (%)
162
44.9%
, 100
27.7%
1 32
 
8.9%
2 29
 
8.0%
) 13
 
3.6%
( 13
 
3.6%
9 5
 
1.4%
3 2
 
0.6%
4 2
 
0.6%
. 2
 
0.6%
None
ValueCountFrequency (%)
？ 1
100.0%

취약지역지정사유
Text

Distinct1978
Distinct (%)86.2%
Missing13
Missing (%)0.6%
Memory size18.2 KiB
2023-12-12T18:54:37.918469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length287
Median length207
Mean length97.310375
Min length1

Characters and Unicode

Total characters223230
Distinct characters554
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1868 ?
Unique (%)81.4%

Sample

1st row집중호우에 의한 계류내 토석 및 산지내 토사가 유실되어 하부 인가 및 경작지 피해가 우려됨
2nd row계류 상부에 전석류가 발생되어 침식이 진행되고 있는 실정으로 집중호우시 하부 피해가 예상됨
3rd row인가와 매우 인접한 사면으로 임상 내 침식, 표층유실, 지반변형과 같은 잠재적인 위험요소 작용으로 하부 생활권 피해 우려됨
4th row본 대상지는 사찰과 인접해 있으며, 사면으로 낙석이 관찰되고 있어 호우 시 피해가 우려됨
5th row계류경사가 급하고, 소규모 전석이 다수 분포하고 있음. 또한 토사석력의 이동으로 재산피해가 발생되고 있다는 주민제기지역임.
ValueCountFrequency (%)
및 1575
 
2.9%
것으로 973
 
1.8%
계류 959
 
1.8%
있어 768
 
1.4%
시 712
 
1.3%
취약지역으로 649
 
1.2%
있으며 561
 
1.0%
다수 480
 
0.9%
사료됨 478
 
0.9%
발생 453
 
0.8%
Other values (4744) 46297
85.9%
2023-12-12T18:54:38.514448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51694
 
23.2%
이 6215
 
2.8%
로 6042
 
2.7%
으 4962
 
2.2%
가 4536
 
2.0%
지 4425
 
2.0%
사 4149
 
1.9%
류 3742
 
1.7%
하 3469
 
1.6%
계 3272
 
1.5%
Other values (544) 130724
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166986
74.8%
Space Separator 51694
 
23.2%
Other Punctuation 3134
 
1.4%
Decimal Number 1025
 
0.5%
Lowercase Letter 153
 
0.1%
Close Punctuation 75
 
< 0.1%
Open Punctuation 75
 
< 0.1%
Other Symbol 38
 
< 0.1%
Control 17
 
< 0.1%
Math Symbol 14
 
< 0.1%
Other values (5) 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6215
 
3.7%
로 6042
 
3.6%
으 4962
 
3.0%
가 4536
 
2.7%
지 4425
 
2.6%
사 4149
 
2.5%
류 3742
 
2.2%
하 3469
 
2.1%
계 3272
 
2.0%
에 2802
 
1.7%
Other values (496) 123372
73.9%
Decimal Number
ValueCountFrequency (%)
0 241
23.5%
1 224
21.9%
2 150
14.6%
3 127
12.4%
5 79
 
7.7%
4 78
 
7.6%
6 38
 
3.7%
8 31
 
3.0%
9 30
 
2.9%
7 27
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 1621
51.7%
. 1465
46.7%
% 29
 
0.9%
" 8
 
0.3%
？ 5
 
0.2%
& 2
 
0.1%
? 2
 
0.1%
· 1
 
< 0.1%
; 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
m 135
88.2%
h 6
 
3.9%
a 6
 
3.9%
o 2
 
1.3%
ｘ 2
 
1.3%
t 1
 
0.7%
g 1
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
M 4
28.6%
V 3
21.4%
U 3
21.4%
S 1
 
7.1%
O 1
 
7.1%
Y 1
 
7.1%
C 1
 
7.1%
Other Symbol
ValueCountFrequency (%)
㎡ 29
76.3%
° 7
 
18.4%
○ 2
 
5.3%
Close Punctuation
ValueCountFrequency (%)
) 74
98.7%
」 1
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 74
98.7%
「 1
 
1.3%
Math Symbol
ValueCountFrequency (%)
~ 13
92.9%
～ 1
 
7.1%
Space Separator
ValueCountFrequency (%)
51694
100.0%
Control
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
˚ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166985
74.8%
Common 56077
 
25.1%
Latin 167
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6215
 
3.7%
로 6042
 
3.6%
으 4962
 
3.0%
가 4536
 
2.7%
지 4425
 
2.6%
사 4149
 
2.5%
류 3742
 
2.2%
하 3469
 
2.1%
계 3272
 
2.0%
에 2802
 
1.7%
Other values (495) 123371
73.9%
Common
ValueCountFrequency (%)
51694
92.2%
, 1621
 
2.9%
. 1465
 
2.6%
0 241
 
0.4%
1 224
 
0.4%
2 150
 
0.3%
3 127
 
0.2%
5 79
 
0.1%
4 78
 
0.1%
) 74
 
0.1%
Other values (24) 324
 
0.6%
Latin
ValueCountFrequency (%)
m 135
80.8%
h 6
 
3.6%
a 6
 
3.6%
M 4
 
2.4%
V 3
 
1.8%
U 3
 
1.8%
o 2
 
1.2%
ｘ 2
 
1.2%
S 1
 
0.6%
O 1
 
0.6%
Other values (4) 4
 
2.4%
Han
ValueCountFrequency (%)
小 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166983
74.8%
ASCII 56192
 
25.2%
CJK Compat 29
 
< 0.1%
None 18
 
< 0.1%
Geometric Shapes 2
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Punctuation 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51694
92.0%
, 1621
 
2.9%
. 1465
 
2.6%
0 241
 
0.4%
1 224
 
0.4%
2 150
 
0.3%
m 135
 
0.2%
3 127
 
0.2%
5 79
 
0.1%
4 78
 
0.1%
Other values (26) 378
 
0.7%
Hangul
ValueCountFrequency (%)
이 6215
 
3.7%
로 6042
 
3.6%
으 4962
 
3.0%
가 4536
 
2.7%
지 4425
 
2.6%
사 4149
 
2.5%
류 3742
 
2.2%
하 3469
 
2.1%
계 3272
 
2.0%
에 2802
 
1.7%
Other values (494) 123369
73.9%
CJK Compat
ValueCountFrequency (%)
㎡ 29
100.0%
None
ValueCountFrequency (%)
° 7
38.9%
？ 5
27.8%
ｘ 2
 
11.1%
「 1
 
5.6%
」 1
 
5.6%
· 1
 
5.6%
～ 1
 
5.6%
Geometric Shapes
ValueCountFrequency (%)
○ 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
Punctuation
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
CJK
ValueCountFrequency (%)
小 1
100.0%

지정일
Date

Distinct136
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Minimum2013-04-23 00:00:00
Maximum2022-08-20 00:00:00
2023-12-12T18:54:38.683307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:38.833784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지정등급_A,B,C,D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
<NA>
1716 
B
294 
A
261 
C
 
26
D
 
10

Length

Max length4
Median length4
Mean length3.2314694
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1716
74.4%
B 294
 
12.7%
A 261
 
11.3%
C 26
 
1.1%
D 10
 
0.4%

Length

2023-12-12T18:54:39.329444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:39.440065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1716
74.4%
b 294
 
12.7%
a 261
 
11.3%
c 26
 
1.1%
d 10
 
0.4%

Interactions

2023-12-12T18:54:28.002547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:24.623141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.228666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.951666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.549999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.338675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:28.104889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:24.723051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.336822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.043417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.665933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.450895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:28.211284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:24.816132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.471207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.152087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.847279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.579355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:28.327124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:24.919397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.568492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.245410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.976352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.693291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:28.434289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.020178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.704224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.330235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.087210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.785805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:28.534227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.124333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:25.851076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:26.444676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.210596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:27.898574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:54:39.531660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장아이디시군구위도_도위도_분위도_초경도_도경도_분경도_초취약지역유형지정등급_A,B,C,D
대장아이디1.0000.9750.0160.7810.0770.7980.7760.0130.0690.103
시군구0.9751.0000.2640.8580.2210.8490.8500.0000.1450.182
위도_도0.0160.2641.0000.0000.0000.8410.0000.0000.0380.000
위도_분0.7810.8580.0001.0000.0000.3350.4180.0390.0690.141
위도_초0.0770.2210.0000.0001.0000.0000.0670.6950.0170.061
경도_도0.7980.8490.8410.3350.0001.0000.7520.0000.0000.000
경도_분0.7760.8500.0000.4180.0670.7521.0000.0780.1380.221
경도_초0.0130.0000.0000.0390.6950.0000.0781.0000.000NaN
취약지역유형0.0690.1450.0380.0690.0170.0000.1380.0001.0000.217
지정등급_A,B,C,D0.1030.1820.0000.1410.0610.0000.221NaN0.2171.000
2023-12-12T18:54:39.690248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도_도시군구지정등급_A,B,C,D취약지역유형
경도_도1.0000.6200.0000.000
시군구0.6201.0000.0730.126
지정등급_A,B,C,D0.0000.0731.0000.144
취약지역유형0.0000.1260.1441.000
2023-12-12T18:54:39.799247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장아이디위도_도위도_분위도_초경도_분경도_초시군구경도_도취약지역유형지정등급_A,B,C,D
대장아이디1.0000.1510.3970.0090.079-0.0400.8600.4540.0530.084
위도_도0.1511.000-0.5070.0020.051-0.0120.1430.8160.0250.000
위도_분0.397-0.5071.000-0.001-0.019-0.0370.5360.1450.0530.097
위도_초0.0090.002-0.0011.000-0.0010.0390.0860.0000.0190.037
경도_분0.0790.051-0.019-0.0011.0000.0090.5230.4080.1060.133
경도_초-0.040-0.012-0.0370.0390.0091.0000.0000.0000.0001.000
시군구0.8600.1430.5360.0860.5230.0001.0000.6200.1260.073
경도_도0.4540.8160.1450.0000.4080.0000.6201.0000.0000.000
취약지역유형0.0530.0250.0530.0190.1060.0000.1260.0001.0000.144
지정등급_A,B,C,D0.0840.0000.0970.0370.1331.0000.0730.0000.1441.000

Missing values

2023-12-12T18:54:28.697560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:54:28.942526image/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.
2023-12-12T18:54:29.382836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

대장아이디시군구읍면동리지번기타지번위도_도위도_분위도_초경도_도경도_분경도_초취약지역유형지정면적_제곱미터대피장소취약지역지정사유지정일지정등급_A,B,C,D
01창원시 마산합포구완월동<NA>산103<NA>35122.37128336.648토석류880성지여고집중호우에 의한 계류내 토석 및 산지내 토사가 유실되어 하부 인가 및 경작지 피해가 우려됨2014-06-18<NA>
12창원시 마산회원구내서읍신감리1409-4<NA>351137.1991282946.1토석류400로뎀전원교회계류 상부에 전석류가 발생되어 침식이 진행되고 있는 실정으로 집중호우시 하부 피해가 예상됨2013-07-02<NA>
23창원시 마산회원구두척동<NA>산86-2<NA>351420.7221283255.272산사태325두척마을회관인가와 매우 인접한 사면으로 임상 내 침식, 표층유실, 지반변형과 같은 잠재적인 위험요소 작용으로 하부 생활권 피해 우려됨2019-06-24<NA>
34창원시 진해구태백동<NA>산52-3<NA>35931.01284022.0산사태125경화초등학교본 대상지는 사찰과 인접해 있으며, 사면으로 낙석이 관찰되고 있어 호우 시 피해가 우려됨2019-06-24<NA>
45창원시 성산구사파정동<NA>산27<NA>351323.01284240.0토석류452창원사파고등학교계류경사가 급하고, 소규모 전석이 다수 분포하고 있음. 또한 토사석력의 이동으로 재산피해가 발생되고 있다는 주민제기지역임.2020-08-20<NA>
56창원시 성산구불모산동<NA>산174<NA>35116.01284341.0토석류654삼정자초등학교침식붕괴로 계류황폐화가 가속화 되고 있으며, 계류 인근의 주택지에 피해가 우려되는 상황으로 취약지역으로의 선정이 필요함.2020-08-20<NA>
67창원시 의창구북면월계리산36-1Jan-78352140.01283812.0토석류334월계리마을회관경작지와 연접한 계류부의 침식과 포락으로 인해 농지가 상당 유실되었으며, 직하부에 인가가 위치하고 있어 직접적인 피해가 우려됨.2020-08-20<NA>
78창원시 의창구북면무곡리1087-1<NA>351949.01283449.0토석류1005무동초등학교지속적인 침식과 포락으로 농경지가 유실되고 있고, 예방시설이 없어 추가피해가 예상됨에 따라 취약지역으로의 선정을 제안함.2020-08-20<NA>
89창원시 마산회원구두척동<NA>산89<NA>351426.334128334.128산사태4126송정마을회관산지 비탈면이 붕락되어 삼호천으로의 지속적인 토사석이 유입되며, 또한 붕괴 확대가 우려되는 지역임.2020-08-20<NA>
910창원시 마산회원구내서읍삼계리산293<NA>351431.01282845.5토석류406안계마을회관좌, 우측 침식이 발달하여 계류 바닥부에 토석이 다량 적치된 상태이고, 계류 내 유목 및 전석 등이 관측되어 취약지역으로의 선정을 제안함.2020-08-20<NA>
대장아이디시군구읍면동리지번기타지번위도_도위도_분위도_초경도_도경도_분경도_초취약지역유형지정면적_제곱미터대피장소취약지역지정사유지정일지정등급_A,B,C,D
2297<NA>합천군적중면황정리산15<NA>353148.4128170.8토석류1,839합천평화고등학교산사태우려지역2022-07-05A
2298<NA>합천군삼가면하판리산211<NA>352526.7128634.4산사태222하판마을회관산사태우려지역2022-07-05A
2299<NA>합천군대양면도리산35<NA>35309.8128929.5산사태2,659하도리경로당산사태우려지역2022-07-05B
2300<NA>합천군대양면양산리산87<NA>353014.9128930.9산사태1,497정수사산사태우려지역2022-07-05B
2301<NA>합천군쌍백면백역리산97<NA>352859.9128624.0산사태296백역마을회관산사태우려지역2022-07-05B
2302<NA>합천군쌍백면안계리산40<NA>352614.71281130.7토석류930안계경로당산사태우려지역2022-07-05B
2303<NA>합천군쌍백면장전리산141-1<NA>352819.6128856.5산사태371장전마을경로당산사태우려지역2022-07-05B
2304<NA>합천군쌍백면평구리산107-1번지<NA>352610.3128817.9산사태684쌍백면보건지소산사태우려지역2022-07-05B
2305<NA>합천군쌍책면사양리산162<NA>353628.21281559.7산사태408쌍책면이책보건진료소산사태우려지역2022-07-05B
2306<NA>합천군율곡면본천리산183<NA>353228.6128126.7산사태2,288본천마을경노회산사태우려지역2022-07-05B