Overview

Dataset statistics

Number of variables4
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory36.4 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description2021년~2022년 경상남도 창원시 행정구역 읍면동별 화재건수를 소방청 국가화재정보시스템(화재통계 관리 시스템)을 통하여 추출한 자료임
URLhttps://www.data.go.kr/data/15117336/fileData.do

Alerts

2021년 화재건수 is highly overall correlated with 2022년 화재건수High correlation
2022년 화재건수 is highly overall correlated with 2021년 화재건수High correlation
읍면동명 has unique valuesUnique
2021년 화재건수 has 1 (1.8%) zerosZeros

Reproduction

Analysis started2023-12-12 15:48:15.022340
Analysis finished2023-12-12 15:48:15.939427
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Categorical

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
마산합포구
15 
진해구
13 
마산회원구
12 
성산구
8 
의창구
7 

Length

Max length5
Median length3
Mean length3.9818182
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마산합포구 15
27.3%
진해구 13
23.6%
마산회원구 12
21.8%
성산구 8
14.5%
의창구 7
12.7%

Length

2023-12-13T00:48:16.041998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:48:16.182482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마산합포구 15
27.3%
진해구 13
23.6%
마산회원구 12
21.8%
성산구 8
14.5%
의창구 7
12.7%

읍면동명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T00:48:16.545306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1454545
Min length2

Characters and Unicode

Total characters173
Distinct characters68
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

Unique55 ?
Unique (%)100.0%

Sample

1st row동읍
2nd row북면
3rd row대산면
4th row의창동
5th row팔룡동
ValueCountFrequency (%)
동읍 1
 
1.8%
합포동 1
 
1.8%
내서읍 1
 
1.8%
회원1동 1
 
1.8%
회원2동 1
 
1.8%
석전동 1
 
1.8%
회성동 1
 
1.8%
양덕1동 1
 
1.8%
양덕2동 1
 
1.8%
합성1동 1
 
1.8%
Other values (45) 45
81.8%
2023-12-13T00:48:17.034191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 52
30.1%
면 6
 
3.5%
산 5
 
2.9%
2 5
 
2.9%
1 5
 
2.9%
웅 4
 
2.3%
암 4
 
2.3%
성 4
 
2.3%
구 3
 
1.7%
합 3
 
1.7%
Other values (58) 82
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 163
94.2%
Decimal Number 10
 
5.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 52
31.9%
면 6
 
3.7%
산 5
 
3.1%
웅 4
 
2.5%
암 4
 
2.5%
성 4
 
2.5%
구 3
 
1.8%
합 3
 
1.8%
덕 3
 
1.8%
회 3
 
1.8%
Other values (56) 76
46.6%
Decimal Number
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 163
94.2%
Common 10
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 52
31.9%
면 6
 
3.7%
산 5
 
3.1%
웅 4
 
2.5%
암 4
 
2.5%
성 4
 
2.5%
구 3
 
1.8%
합 3
 
1.8%
덕 3
 
1.8%
회 3
 
1.8%
Other values (56) 76
46.6%
Common
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 163
94.2%
ASCII 10
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 52
31.9%
면 6
 
3.7%
산 5
 
3.1%
웅 4
 
2.5%
암 4
 
2.5%
성 4
 
2.5%
구 3
 
1.8%
합 3
 
1.8%
덕 3
 
1.8%
회 3
 
1.8%
Other values (56) 76
46.6%
ASCII
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

2021년 화재건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6909091
Minimum0
Maximum28
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T00:48:17.215880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13.5
median8
Q314
95-th percentile21.6
Maximum28
Range28
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.9837715
Coefficient of variation (CV)0.72065184
Kurtosis-0.34117447
Mean9.6909091
Median Absolute Deviation (MAD)5
Skewness0.68666198
Sum533
Variance48.773064
MonotonicityNot monotonic
2023-12-13T00:48:17.348914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 6
 
10.9%
11 6
 
10.9%
8 4
 
7.3%
2 4
 
7.3%
6 4
 
7.3%
4 3
 
5.5%
7 3
 
5.5%
1 3
 
5.5%
18 3
 
5.5%
19 2
 
3.6%
Other values (13) 17
30.9%
ValueCountFrequency (%)
0 1
 
1.8%
1 3
5.5%
2 4
7.3%
3 6
10.9%
4 3
5.5%
5 1
 
1.8%
6 4
7.3%
7 3
5.5%
8 4
7.3%
9 1
 
1.8%
ValueCountFrequency (%)
28 1
 
1.8%
25 1
 
1.8%
23 1
 
1.8%
21 2
3.6%
19 2
3.6%
18 3
5.5%
17 2
3.6%
16 1
 
1.8%
15 1
 
1.8%
13 2
3.6%

2022년 화재건수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.963636
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T00:48:17.513529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q14
median7
Q316
95-th percentile30.3
Maximum33
Range32
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1306639
Coefficient of variation (CV)0.83281346
Kurtosis0.047582299
Mean10.963636
Median Absolute Deviation (MAD)4
Skewness1.061455
Sum603
Variance83.369024
MonotonicityNot monotonic
2023-12-13T00:48:17.668340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 8
14.5%
4 6
 
10.9%
15 3
 
5.5%
2 3
 
5.5%
7 3
 
5.5%
9 3
 
5.5%
1 3
 
5.5%
6 3
 
5.5%
3 3
 
5.5%
11 2
 
3.6%
Other values (14) 18
32.7%
ValueCountFrequency (%)
1 3
 
5.5%
2 3
 
5.5%
3 3
 
5.5%
4 6
10.9%
5 8
14.5%
6 3
 
5.5%
7 3
 
5.5%
8 1
 
1.8%
9 3
 
5.5%
10 1
 
1.8%
ValueCountFrequency (%)
33 1
1.8%
32 1
1.8%
31 1
1.8%
30 2
3.6%
27 1
1.8%
24 1
1.8%
22 1
1.8%
21 1
1.8%
20 1
1.8%
19 2
3.6%

Interactions

2023-12-13T00:48:15.499740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.190909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.637732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:48:15.275561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:48:17.788303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역읍면동명2021년 화재건수2022년 화재건수
행정구역1.0001.0000.6040.698
읍면동명1.0001.0001.0001.000
2021년 화재건수0.6041.0001.0000.808
2022년 화재건수0.6981.0000.8081.000
2023-12-13T00:48:17.918919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2021년 화재건수2022년 화재건수행정구역
2021년 화재건수1.0000.7340.273
2022년 화재건수0.7341.0000.306
행정구역0.2730.3061.000

Missing values

2023-12-13T00:48:15.790692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:48:15.895384image/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

행정구역읍면동명2021년 화재건수2022년 화재건수
0의창구동읍1633
1의창구북면2831
2의창구대산면1832
3의창구의창동2121
4의창구팔룡동2319
5의창구명곡동1117
6의창구봉림동1720
7성산구반송동115
8성산구중앙동2124
9성산구용지동119
행정구역읍면동명2021년 화재건수2022년 화재건수
45진해구경화동63
46진해구병암동54
47진해구석동65
48진해구이동87
49진해구자은동611
50진해구덕산동41
51진해구풍호동75
52진해구웅천동1814
53진해구웅동1동1522
54진해구웅동2동1930