Overview

Dataset statistics

Number of variables4
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory34.6 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description산불 위기경보 분석을 위한 시군구별 지역민감도, 시군구별 산불위험지수 데이터. ※ 행정안전부 재난안전 취약핵심역량 도약기술 개발 사업의 지원을 받아 수행된 연구임(2020-MOIS33-006) (과제명 : 지능형 위기경보를 위한 위험수준 자동분석 및 운영기술) 더 자세한 자료는 아래 링크참조 https://dataon.kisti.re.kr/search/view.do?mode=view&svcId=dcc50a55e154ea0eea189a7b057132a0
URLhttps://www.data.go.kr/data/15120906/fileData.do

Alerts

시군구코드 is highly overall correlated with 지역민감도 and 1 other fieldsHigh correlation
지역민감도 is highly overall correlated with 시군구코드High correlation
시도 is highly overall correlated with 시군구코드High correlation
시군구코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:56:20.349033
Analysis finished2023-12-12 11:56:21.292952
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원도
18 
Other values (12)
110 

Length

Max length7
Median length5
Mean length4.1484716
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%

Length

2023-12-12T20:56:21.430480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%

시군구
Text

Distinct207
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T20:56:22.181267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9519651
Min length2

Characters and Unicode

Total characters676
Distinct characters137
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

Unique200 ?
Unique (%)87.3%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 6
 
2.6%
중구 6
 
2.6%
서구 5
 
2.2%
남구 4
 
1.7%
북구 4
 
1.7%
고성군 2
 
0.9%
강서구 2
 
0.9%
남원시 1
 
0.4%
완주군 1
 
0.4%
화순군 1
 
0.4%
Other values (197) 197
86.0%
2023-12-12T20:56:22.830364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 676
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 676
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 676
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 79
 
11.7%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (127) 314
46.4%

시군구코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct229
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37664.878
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:56:23.042968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11392
Q128710
median42730
Q346730
95-th percentile48564
Maximum50130
Range39020
Interquartile range (IQR)18020

Descriptive statistics

Standard deviation11691.973
Coefficient of variation (CV)0.3104211
Kurtosis0.12659208
Mean37664.878
Median Absolute Deviation (MAD)4440
Skewness-1.1514381
Sum8625257
Variance1.3670223 × 108
MonotonicityNot monotonic
2023-12-12T20:56:23.286532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110 1
 
0.4%
44760 1
 
0.4%
44790 1
 
0.4%
44800 1
 
0.4%
44810 1
 
0.4%
44825 1
 
0.4%
45110 1
 
0.4%
45130 1
 
0.4%
45140 1
 
0.4%
45180 1
 
0.4%
Other values (219) 219
95.6%
ValueCountFrequency (%)
11110 1
0.4%
11140 1
0.4%
11170 1
0.4%
11200 1
0.4%
11215 1
0.4%
11230 1
0.4%
11260 1
0.4%
11290 1
0.4%
11305 1
0.4%
11320 1
0.4%
ValueCountFrequency (%)
50130 1
0.4%
50110 1
0.4%
48890 1
0.4%
48880 1
0.4%
48870 1
0.4%
48860 1
0.4%
48850 1
0.4%
48840 1
0.4%
48820 1
0.4%
48740 1
0.4%

지역민감도
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26864262
Minimum0
Maximum1
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:56:23.549608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075898
Q10.17058
median0.2601
Q30.34072
95-th percentile0.481952
Maximum1
Range1
Interquartile range (IQR)0.17014

Descriptive statistics

Standard deviation0.13906054
Coefficient of variation (CV)0.51764141
Kurtosis3.5491096
Mean0.26864262
Median Absolute Deviation (MAD)0.08659
Skewness1.1863659
Sum61.51916
Variance0.019337835
MonotonicityNot monotonic
2023-12-12T20:56:23.753996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.30706 2
 
0.9%
0.25582 1
 
0.4%
0.39541 1
 
0.4%
0.33354 1
 
0.4%
0.27934 1
 
0.4%
0.27697 1
 
0.4%
0.24582 1
 
0.4%
0.57424 1
 
0.4%
0.34669 1
 
0.4%
0.40396 1
 
0.4%
Other values (218) 218
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
0.02344 1
0.4%
0.02848 1
0.4%
0.04429 1
0.4%
0.053 1
0.4%
0.05373 1
0.4%
0.05588 1
0.4%
0.06048 1
0.4%
0.06211 1
0.4%
0.07134 1
0.4%
ValueCountFrequency (%)
1.0 1
0.4%
0.76644 1
0.4%
0.69019 1
0.4%
0.64835 1
0.4%
0.61813 1
0.4%
0.61789 1
0.4%
0.61593 1
0.4%
0.57818 1
0.4%
0.57424 1
0.4%
0.56185 1
0.4%

Interactions

2023-12-12T20:56:20.811958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:20.559846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:20.949547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:20.686870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:56:23.864318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시군구코드지역민감도
시도1.0000.9950.607
시군구코드0.9951.0000.502
지역민감도0.6070.5021.000
2023-12-12T20:56:23.974259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드지역민감도시도
시군구코드1.0000.6100.956
지역민감도0.6101.0000.288
시도0.9560.2881.000

Missing values

2023-12-12T20:56:21.096144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:56:21.233159image/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

시도시군구시군구코드지역민감도
0서울특별시종로구111100.25582
1서울특별시중구111400.06211
2서울특별시용산구111700.25643
3서울특별시성동구112000.10258
4서울특별시광진구112150.09282
5서울특별시동대문구112300.16709
6서울특별시중랑구112600.18336
7서울특별시성북구112900.21911
8서울특별시강북구113050.19915
9서울특별시도봉구113200.16214
시도시군구시군구코드지역민감도
219경상남도창녕군487400.28171
220경상남도고성군488200.29153
221경상남도남해군488400.24406
222경상남도하동군488500.40063
223경상남도산청군488600.43611
224경상남도함양군488700.35642
225경상남도거창군488800.35474
226경상남도합천군488900.4043
227제주특별자치도제주시501100.61789
228제주특별자치도서귀포시501300.39505