Overview

Dataset statistics

Number of variables7
Number of observations22
Missing cells6
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory68.0 B

Variable types

Text1
Categorical1
Numeric5

Dataset

Description시도별 소방공무원 순직 및 공사상자 현황
Author소방청
URLhttps://www.data.go.kr/data/15033927/fileData.do

Alerts

화재공상 is highly overall correlated with 구조공상 and 3 other fieldsHigh correlation
구조공상 is highly overall correlated with 화재공상 and 3 other fieldsHigh correlation
구급공상 is highly overall correlated with 화재공상 and 3 other fieldsHigh correlation
교육훈련공상 is highly overall correlated with 화재공상 and 3 other fieldsHigh correlation
기타공상 is highly overall correlated with 화재공상 and 3 other fieldsHigh correlation
화재순직 is highly imbalanced (66.5%)Imbalance
본부 has 1 (4.5%) missing valuesMissing
화재공상 has 1 (4.5%) missing valuesMissing
구조공상 has 1 (4.5%) missing valuesMissing
구급공상 has 1 (4.5%) missing valuesMissing
교육훈련공상 has 1 (4.5%) missing valuesMissing
기타공상 has 1 (4.5%) missing valuesMissing
화재공상 has 5 (22.7%) zerosZeros
구조공상 has 7 (31.8%) zerosZeros
구급공상 has 4 (18.2%) zerosZeros
교육훈련공상 has 7 (31.8%) zerosZeros
기타공상 has 2 (9.1%) zerosZeros

Reproduction

Analysis started2023-12-12 00:42:36.664326
Analysis finished2023-12-12 00:42:39.899292
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

본부
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing1
Missing (%)4.5%
Memory size308.0 B
2023-12-12T09:42:40.050241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4285714
Min length3

Characters and Unicode

Total characters72
Distinct characters34
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

Unique21 ?
Unique (%)100.0%

Sample

1st row소방청
2nd row중앙소방학교
3rd row중앙119구조본부
4th row서 울
5th row부 산
ValueCountFrequency (%)
남 3
 
7.7%
북 3
 
7.7%
경 3
 
7.7%
전 3
 
7.7%
주 2
 
5.1%
울 2
 
5.1%
산 2
 
5.1%
대 2
 
5.1%
충 2
 
5.1%
원 2
 
5.1%
Other values (15) 15
38.5%
2023-12-12T09:42:40.416474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
25.0%
남 3
 
4.2%
북 3
 
4.2%
경 3
 
4.2%
전 3
 
4.2%
소 2
 
2.8%
충 2
 
2.8%
원 2
 
2.8%
주 2
 
2.8%
방 2
 
2.8%
Other values (24) 32
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
70.8%
Space Separator 18
 
25.0%
Decimal Number 3
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 3
 
5.9%
북 3
 
5.9%
경 3
 
5.9%
전 3
 
5.9%
소 2
 
3.9%
충 2
 
3.9%
원 2
 
3.9%
주 2
 
3.9%
방 2
 
3.9%
산 2
 
3.9%
Other values (21) 27
52.9%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
70.8%
Common 21
29.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 3
 
5.9%
북 3
 
5.9%
경 3
 
5.9%
전 3
 
5.9%
소 2
 
3.9%
충 2
 
3.9%
원 2
 
3.9%
주 2
 
3.9%
방 2
 
3.9%
산 2
 
3.9%
Other values (21) 27
52.9%
Common
ValueCountFrequency (%)
18
85.7%
1 2
 
9.5%
9 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
70.8%
ASCII 21
29.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
85.7%
1 2
 
9.5%
9 1
 
4.8%
Hangul
ValueCountFrequency (%)
남 3
 
5.9%
북 3
 
5.9%
경 3
 
5.9%
전 3
 
5.9%
소 2
 
3.9%
충 2
 
3.9%
원 2
 
3.9%
주 2
 
3.9%
방 2
 
3.9%
산 2
 
3.9%
Other values (21) 27
52.9%

화재순직
Categorical

IMBALANCE 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
20 
2
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.1363636
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
90.9%
2 1
 
4.5%
<NA> 1
 
4.5%

Length

2023-12-12T09:42:40.601366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:42:40.732219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
90.9%
2 1
 
4.5%
na 1
 
4.5%

화재공상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)57.1%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4.952381
Minimum0
Maximum24
Zeros5
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:42:40.868067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile16
Maximum24
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.2006144
Coefficient of variation (CV)1.2520471
Kurtosis3.5337217
Mean4.952381
Median Absolute Deviation (MAD)3
Skewness1.8653381
Sum104
Variance38.447619
MonotonicityNot monotonic
2023-12-12T09:42:41.023028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5
22.7%
3 4
18.2%
1 2
 
9.1%
2 2
 
9.1%
16 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
6 1
 
4.5%
4 1
 
4.5%
24 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 5
22.7%
1 2
 
9.1%
2 2
 
9.1%
3 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
8 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
ValueCountFrequency (%)
24 1
 
4.5%
16 1
 
4.5%
12 1
 
4.5%
11 1
 
4.5%
8 1
 
4.5%
6 1
 
4.5%
5 1
 
4.5%
4 1
 
4.5%
3 4
18.2%
2 2
9.1%

구조공상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)28.6%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.6190476
Minimum0
Maximum9
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:42:41.164010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0736979
Coefficient of variation (CV)1.1735938
Kurtosis0.56369809
Mean2.6190476
Median Absolute Deviation (MAD)2
Skewness1.2771551
Sum55
Variance9.447619
MonotonicityNot monotonic
2023-12-12T09:42:41.327989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7
31.8%
2 5
22.7%
9 3
13.6%
5 2
 
9.1%
3 2
 
9.1%
1 2
 
9.1%
(Missing) 1
 
4.5%
ValueCountFrequency (%)
0 7
31.8%
1 2
 
9.1%
2 5
22.7%
3 2
 
9.1%
5 2
 
9.1%
9 3
13.6%
ValueCountFrequency (%)
9 3
13.6%
5 2
 
9.1%
3 2
 
9.1%
2 5
22.7%
1 2
 
9.1%
0 7
31.8%

구급공상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)47.6%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean5.5714286
Minimum0
Maximum30
Zeros4
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:42:41.460050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile23
Maximum30
Range30
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.580049
Coefficient of variation (CV)1.3605216
Kurtosis5.7823992
Mean5.5714286
Median Absolute Deviation (MAD)3
Skewness2.3875352
Sum117
Variance57.457143
MonotonicityNot monotonic
2023-12-12T09:42:41.584396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4
18.2%
7 4
18.2%
1 3
13.6%
3 2
9.1%
5 2
9.1%
2 2
9.1%
23 1
 
4.5%
30 1
 
4.5%
9 1
 
4.5%
4 1
 
4.5%
(Missing) 1
 
4.5%
ValueCountFrequency (%)
0 4
18.2%
1 3
13.6%
2 2
9.1%
3 2
9.1%
4 1
 
4.5%
5 2
9.1%
7 4
18.2%
9 1
 
4.5%
23 1
 
4.5%
30 1
 
4.5%
ValueCountFrequency (%)
30 1
 
4.5%
23 1
 
4.5%
9 1
 
4.5%
7 4
18.2%
5 2
9.1%
4 1
 
4.5%
3 2
9.1%
2 2
9.1%
1 3
13.6%
0 4
18.2%

교육훈련공상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)38.1%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.3809524
Minimum0
Maximum14
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:42:41.716587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2322777
Coefficient of variation (CV)1.3575566
Kurtosis8.0615367
Mean2.3809524
Median Absolute Deviation (MAD)1
Skewness2.5169598
Sum50
Variance10.447619
MonotonicityNot monotonic
2023-12-12T09:42:41.848608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7
31.8%
1 4
18.2%
3 3
13.6%
4 2
 
9.1%
2 2
 
9.1%
14 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
(Missing) 1
 
4.5%
ValueCountFrequency (%)
0 7
31.8%
1 4
18.2%
2 2
 
9.1%
3 3
13.6%
4 2
 
9.1%
5 1
 
4.5%
6 1
 
4.5%
14 1
 
4.5%
ValueCountFrequency (%)
14 1
 
4.5%
6 1
 
4.5%
5 1
 
4.5%
4 2
 
9.1%
3 3
13.6%
2 2
 
9.1%
1 4
18.2%
0 7
31.8%

기타공상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)57.1%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean13.142857
Minimum0
Maximum84
Zeros2
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:42:42.014643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q313
95-th percentile64
Maximum84
Range84
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.041116
Coefficient of variation (CV)1.6009545
Kurtosis7.5369086
Mean13.142857
Median Absolute Deviation (MAD)5
Skewness2.8099226
Sum276
Variance442.72857
MonotonicityNot monotonic
2023-12-12T09:42:42.164610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 4
18.2%
13 3
13.6%
0 2
9.1%
2 2
9.1%
11 2
9.1%
1 2
9.1%
84 1
 
4.5%
15 1
 
4.5%
64 1
 
4.5%
7 1
 
4.5%
Other values (2) 2
9.1%
ValueCountFrequency (%)
0 2
9.1%
1 2
9.1%
2 2
9.1%
3 1
 
4.5%
6 4
18.2%
7 1
 
4.5%
11 2
9.1%
12 1
 
4.5%
13 3
13.6%
15 1
 
4.5%
ValueCountFrequency (%)
84 1
 
4.5%
64 1
 
4.5%
15 1
 
4.5%
13 3
13.6%
12 1
 
4.5%
11 2
9.1%
7 1
 
4.5%
6 4
18.2%
3 1
 
4.5%
2 2
9.1%

Interactions

2023-12-12T09:42:38.847574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:36.905592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.326210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.848646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.334527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.976857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:36.985082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.424628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.958755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.421354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:39.083441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.059381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.509154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.065544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.532241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:39.176544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.155202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.619463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.155299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.655036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:39.295793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.246963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:37.737202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.247840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:42:38.759537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:42:42.273355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본부화재순직화재공상구조공상구급공상교육훈련공상기타공상
본부1.0001.0001.0001.0001.0001.0001.000
화재순직1.0001.0000.0000.0000.0000.0000.000
화재공상1.0000.0001.0000.5610.9280.7450.997
구조공상1.0000.0000.5611.0000.7440.5080.562
구급공상1.0000.0000.9280.7441.0000.8010.976
교육훈련공상1.0000.0000.7450.5080.8011.0000.742
기타공상1.0000.0000.9970.5620.9760.7421.000
2023-12-12T09:42:42.425026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
화재공상구조공상구급공상교육훈련공상기타공상화재순직
화재공상1.0000.6510.9230.5950.8310.000
구조공상0.6511.0000.6920.5800.6000.000
구급공상0.9230.6921.0000.5180.7360.324
교육훈련공상0.5950.5800.5181.0000.5760.000
기타공상0.8310.6000.7360.5761.0000.000
화재순직0.0000.0000.3240.0000.0001.000

Missing values

2023-12-12T09:42:39.424430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:42:39.602156image/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.
2023-12-12T09:42:39.784444image/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

본부화재순직화재공상구조공상구급공상교육훈련공상기타공상
0소방청000000
1중앙소방학교000012
2중앙119구조본부000016
3서 울016923484
4부 산01157311
5대 구01227215
6인 천0623113
7광 주000006
8대 전040536
9울 산013101
본부화재순직화재공상구조공상구급공상교육훈련공상기타공상
12강 원222507
13충 북022253
14충 남032702
15전 북0819113
16전 남0354313
17경 북0597611
18경 남0333412
19창 원030206
20제 주010121
21<NA><NA><NA><NA><NA><NA><NA>