Overview

Dataset statistics

Number of variables10
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory92.0 B

Variable types

Categorical4
Numeric6

Dataset

Description울산광역시 지역내 소방관서별 정현원 현황 (정원 대비 현원, 직책별(총감, 준감, 정 등), 기관별) 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15054880/fileData.do

Alerts

소방공무원_정 is highly overall correlated with 소방공무원_령 and 3 other fieldsHigh correlation
종류 is highly overall correlated with 소방공무원_령 and 2 other fieldsHigh correlation
소방공무원_령 is highly overall correlated with 소방공무원_경 and 7 other fieldsHigh correlation
소방공무원_경 is highly overall correlated with 소방공무원_령 and 5 other fieldsHigh correlation
소방공무원_위 is highly overall correlated with 소방공무원_장High correlation
소방공무원_장 is highly overall correlated with 소방공무원_령 and 4 other fieldsHigh correlation
소방공무원_교 is highly overall correlated with 소방공무원_령 and 5 other fieldsHigh correlation
소방공무원_사 is highly overall correlated with 소방공무원_령 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 소방공무원_령 and 1 other fieldsHigh correlation
소방공무원_준감 is highly overall correlated with 소방공무원_령 and 2 other fieldsHigh correlation
소방공무원_준감 is highly imbalanced (67.0%)Imbalance
소방공무원_령 has 11 (33.3%) zerosZeros
소방공무원_경 has 8 (24.2%) zerosZeros
소방공무원_위 has 1 (3.0%) zerosZeros
소방공무원_장 has 1 (3.0%) zerosZeros
소방공무원_사 has 3 (9.1%) zerosZeros

Reproduction

Analysis started2023-12-12 02:42:20.957102
Analysis finished2023-12-12 02:42:24.890767
Duration3.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
소방행정과
3 
예방안전과
3 
119재난대응과
3 
119종합상황실
3 
중부 소방서
3 
Other values (6)
18 

Length

Max length8
Median length6
Mean length6.0909091
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소방행정과
2nd row소방행정과
3rd row소방행정과
4th row예방안전과
5th row예방안전과

Common Values

ValueCountFrequency (%)
소방행정과 3
9.1%
예방안전과 3
9.1%
119재난대응과 3
9.1%
119종합상황실 3
9.1%
중부 소방서 3
9.1%
남부 소방서 3
9.1%
동부 소방서 3
9.1%
북부 소방서 3
9.1%
남울주소방서 3
9.1%
서울주소방서 3
9.1%

Length

2023-12-12T11:42:24.995360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소방서 12
26.7%
소방행정과 3
 
6.7%
예방안전과 3
 
6.7%
119재난대응과 3
 
6.7%
119종합상황실 3
 
6.7%
중부 3
 
6.7%
남부 3
 
6.7%
동부 3
 
6.7%
북부 3
 
6.7%
남울주소방서 3
 
6.7%
Other values (2) 6
13.3%

종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
정원
11 
현원
11 
대비
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정원
2nd row현원
3rd row대비
4th row정원
5th row현원

Common Values

ValueCountFrequency (%)
정원 11
33.3%
현원 11
33.3%
대비 11
33.3%

Length

2023-12-12T11:42:25.130857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:42:25.231115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정원 11
33.3%
현원 11
33.3%
대비 11
33.3%

소방공무원_준감
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
31 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
93.9%
1 2
 
6.1%

Length

2023-12-12T11:42:25.367764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:42:25.486484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
93.9%
1 2
 
6.1%

소방공무원_정
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
1
22 
0
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22
66.7%
0 11
33.3%

Length

2023-12-12T11:42:25.587101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:42:25.686609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
66.7%
0 11
33.3%

소방공무원_령
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3030303
Minimum0
Maximum7
Zeros11
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T11:42:25.789085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q33
95-th percentile5.8
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.038456
Coefficient of variation (CV)0.88511907
Kurtosis-0.21500765
Mean2.3030303
Median Absolute Deviation (MAD)2
Skewness0.47992136
Sum76
Variance4.155303
MonotonicityNot monotonic
2023-12-12T11:42:25.904882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 14
42.4%
0 11
33.3%
5 2
 
6.1%
7 2
 
6.1%
4 2
 
6.1%
1 2
 
6.1%
ValueCountFrequency (%)
0 11
33.3%
1 2
 
6.1%
3 14
42.4%
4 2
 
6.1%
5 2
 
6.1%
7 2
 
6.1%
ValueCountFrequency (%)
7 2
 
6.1%
5 2
 
6.1%
4 2
 
6.1%
3 14
42.4%
1 2
 
6.1%
0 11
33.3%

소방공무원_경
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4545455
Minimum-1
Maximum22
Zeros8
Zeros (%)24.2%
Negative1
Negative (%)3.0%
Memory size429.0 B
2023-12-12T11:42:26.047446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median5
Q313
95-th percentile20.2
Maximum22
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.5294875
Coefficient of variation (CV)1.0100532
Kurtosis-1.1972797
Mean7.4545455
Median Absolute Deviation (MAD)5
Skewness0.49735209
Sum246
Variance56.693182
MonotonicityNot monotonic
2023-12-12T11:42:26.191499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 8
24.2%
13 5
15.2%
1 4
12.1%
5 3
 
9.1%
15 3
 
9.1%
22 2
 
6.1%
19 2
 
6.1%
7 1
 
3.0%
2 1
 
3.0%
10 1
 
3.0%
Other values (3) 3
 
9.1%
ValueCountFrequency (%)
-1 1
 
3.0%
0 8
24.2%
1 4
12.1%
2 1
 
3.0%
3 1
 
3.0%
5 3
 
9.1%
7 1
 
3.0%
10 1
 
3.0%
13 5
15.2%
14 1
 
3.0%
ValueCountFrequency (%)
22 2
 
6.1%
19 2
 
6.1%
15 3
9.1%
14 1
 
3.0%
13 5
15.2%
10 1
 
3.0%
7 1
 
3.0%
5 3
9.1%
3 1
 
3.0%
2 1
 
3.0%

소방공무원_위
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.515152
Minimum0
Maximum62
Zeros1
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T11:42:26.301453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q15
median18
Q327
95-th percentile46
Maximum62
Range62
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.620742
Coefficient of variation (CV)0.84367345
Kurtosis0.429774
Mean18.515152
Median Absolute Deviation (MAD)12
Skewness0.94818237
Sum611
Variance244.00758
MonotonicityNot monotonic
2023-12-12T11:42:26.428070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 4
 
12.1%
12 3
 
9.1%
6 2
 
6.1%
4 2
 
6.1%
5 2
 
6.1%
46 2
 
6.1%
3 2
 
6.1%
33 2
 
6.1%
62 1
 
3.0%
2 1
 
3.0%
Other values (12) 12
36.4%
ValueCountFrequency (%)
0 1
 
3.0%
1 1
 
3.0%
2 1
 
3.0%
3 2
6.1%
4 2
6.1%
5 2
6.1%
6 2
6.1%
7 1
 
3.0%
8 1
 
3.0%
12 3
9.1%
ValueCountFrequency (%)
62 1
 
3.0%
46 2
6.1%
43 1
 
3.0%
35 1
 
3.0%
33 2
6.1%
28 1
 
3.0%
27 1
 
3.0%
25 1
 
3.0%
22 1
 
3.0%
21 4
12.1%

소방공무원_장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.484848
Minimum-14
Maximum52
Zeros1
Zeros (%)3.0%
Negative3
Negative (%)9.1%
Memory size429.0 B
2023-12-12T11:42:26.587429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile-2.4
Q13
median10
Q331
95-th percentile42.6
Maximum52
Range66
Interquartile range (IQR)28

Descriptive statistics

Standard deviation17.016759
Coefficient of variation (CV)1.0322666
Kurtosis-0.96416083
Mean16.484848
Median Absolute Deviation (MAD)10
Skewness0.43773897
Sum544
Variance289.57008
MonotonicityNot monotonic
2023-12-12T11:42:26.782727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 3
 
9.1%
6 2
 
6.1%
5 2
 
6.1%
41 2
 
6.1%
10 2
 
6.1%
12 2
 
6.1%
35 2
 
6.1%
7 1
 
3.0%
1 1
 
3.0%
40 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
-14 1
 
3.0%
-3 1
 
3.0%
-2 1
 
3.0%
0 1
 
3.0%
1 1
 
3.0%
2 3
9.1%
3 1
 
3.0%
4 1
 
3.0%
5 2
6.1%
6 2
6.1%
ValueCountFrequency (%)
52 1
3.0%
45 1
3.0%
41 2
6.1%
40 1
3.0%
35 2
6.1%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
26 1
3.0%

소방공무원_교
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.727273
Minimum-20
Maximum77
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)27.3%
Memory size429.0 B
2023-12-12T11:42:26.952339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-20
5-th percentile-17
Q1-1
median8
Q338
95-th percentile55.8
Maximum77
Range97
Interquartile range (IQR)39

Descriptive statistics

Standard deviation25.313377
Coefficient of variation (CV)1.6095211
Kurtosis-0.51567587
Mean15.727273
Median Absolute Deviation (MAD)19
Skewness0.60394333
Sum519
Variance640.76705
MonotonicityNot monotonic
2023-12-12T11:42:27.125911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 3
 
9.1%
-17 2
 
6.1%
1 2
 
6.1%
38 2
 
6.1%
27 1
 
3.0%
-5 1
 
3.0%
3 1
 
3.0%
8 1
 
3.0%
-9 1
 
3.0%
31 1
 
3.0%
Other values (18) 18
54.5%
ValueCountFrequency (%)
-20 1
 
3.0%
-17 2
6.1%
-15 1
 
3.0%
-11 1
 
3.0%
-9 1
 
3.0%
-5 1
 
3.0%
-3 1
 
3.0%
-1 1
 
3.0%
1 2
6.1%
2 3
9.1%
ValueCountFrequency (%)
77 1
3.0%
57 1
3.0%
55 1
3.0%
54 1
3.0%
49 1
3.0%
40 1
3.0%
39 1
3.0%
38 2
6.1%
32 1
3.0%
31 1
3.0%

소방공무원_사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.969697
Minimum-24
Maximum111
Zeros3
Zeros (%)9.1%
Negative10
Negative (%)30.3%
Memory size429.0 B
2023-12-12T11:42:27.282237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-24
5-th percentile-22.2
Q1-1
median2
Q358
95-th percentile88.6
Maximum111
Range135
Interquartile range (IQR)59

Descriptive statistics

Standard deviation38.541767
Coefficient of variation (CV)1.6779397
Kurtosis-0.75185225
Mean22.969697
Median Absolute Deviation (MAD)11
Skewness0.78865294
Sum758
Variance1485.4678
MonotonicityNot monotonic
2023-12-12T11:42:27.427063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 3
 
9.1%
0 3
 
9.1%
4 2
 
6.1%
-9 2
 
6.1%
-24 2
 
6.1%
-1 2
 
6.1%
8 1
 
3.0%
-8 1
 
3.0%
2 1
 
3.0%
-21 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
-24 2
6.1%
-21 1
 
3.0%
-9 2
6.1%
-8 1
 
3.0%
-4 1
 
3.0%
-2 1
 
3.0%
-1 2
6.1%
0 3
9.1%
1 3
9.1%
2 1
 
3.0%
ValueCountFrequency (%)
111 1
3.0%
91 1
3.0%
87 1
3.0%
81 1
3.0%
72 1
3.0%
70 1
3.0%
67 1
3.0%
61 1
3.0%
58 1
3.0%
55 1
3.0%

Interactions

2023-12-12T11:42:24.073780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.382323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.918015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.381575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.783905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.420920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:24.173154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.485727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.011468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.455241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.075622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.511429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:24.257410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.587496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.078756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.516569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.135241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.613775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:24.343868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.669065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.158997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.584450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.204425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.716611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:24.441869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.750985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.243035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.651111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.270657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.810274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:24.546686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:21.836474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.310879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:22.718945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.338202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:42:23.938380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:42:27.528282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분종류소방공무원_준감소방공무원_정소방공무원_령소방공무원_경소방공무원_위소방공무원_장소방공무원_교소방공무원_사
구분1.0000.0000.7170.0000.8000.7330.5350.0000.0000.744
종류0.0001.0000.0001.0000.9100.5760.0000.6280.9030.569
소방공무원_준감0.7170.0001.0000.0001.0000.9100.0000.0000.0000.000
소방공무원_정0.0001.0000.0001.0001.0000.8950.0000.6580.8220.614
소방공무원_령0.8000.9101.0001.0001.0000.7390.0000.4590.7070.672
소방공무원_경0.7330.5760.9100.8950.7391.0000.5700.6360.6940.586
소방공무원_위0.5350.0000.0000.0000.0000.5701.0000.7510.6840.895
소방공무원_장0.0000.6280.0000.6580.4590.6360.7511.0000.7140.666
소방공무원_교0.0000.9030.0000.8220.7070.6940.6840.7141.0000.920
소방공무원_사0.7440.5690.0000.6140.6720.5860.8950.6660.9201.000
2023-12-12T11:42:27.671586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방공무원_정구분종류소방공무원_준감
소방공무원_정1.0000.0000.9840.000
구분0.0001.0000.0000.586
종류0.9840.0001.0000.000
소방공무원_준감0.0000.5860.0001.000
2023-12-12T11:42:27.788808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방공무원_령소방공무원_경소방공무원_위소방공무원_장소방공무원_교소방공무원_사구분종류소방공무원_준감소방공무원_정
소방공무원_령1.0000.6340.1090.5210.6020.6210.5120.6060.9330.933
소방공무원_경0.6341.0000.4390.7740.9280.9090.4220.3970.6630.645
소방공무원_위0.1090.4391.0000.7110.3600.3460.2420.0000.0000.000
소방공무원_장0.5210.7740.7111.0000.7780.7990.0000.4060.0000.433
소방공무원_교0.6020.9280.3600.7781.0000.9610.0000.5710.0000.745
소방공무원_사0.6210.9090.3460.7990.9611.0000.4080.2310.0000.499
구분0.5120.4220.2420.0000.0000.4081.0000.0000.5860.000
종류0.6060.3970.0000.4060.5710.2310.0001.0000.0000.984
소방공무원_준감0.9330.6630.0000.0000.0000.0000.5860.0001.0000.000
소방공무원_정0.9330.6450.0000.4330.7450.4990.0000.9840.0001.000

Missing values

2023-12-12T11:42:24.684266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:42:24.832487image/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

구분종류소방공무원_준감소방공무원_정소방공무원_령소방공무원_경소방공무원_위소방공무원_장소방공무원_교소방공무원_사
0소방행정과정원11556621
1소방행정과현원11576410
2소방행정과대비00020-2-1-1
3예방안전과정원01354220
4예방안전과현원01354220
5예방안전과대비0000101-2
6119재난대응과정원017101230168
7119재난대응과현원017131916204
8119재난대응과대비00037-144-4
9119종합상황실정원0141510124
구분종류소방공무원_준감소방공무원_정소방공무원_령소방공무원_경소방공무원_위소방공무원_장소방공무원_교소방공무원_사
23북부 소방서대비00001212-17-9
24남울주소방서정원0131918315491
25남울주소방서현원0131946413967
26남울주소방서대비00002810-15-24
27서울주소방서정원0131321354058
28서울주소방서현원0131346403137
29서울주소방서대비0000255-9-21
30안전체험관정원01112581
31안전체험관현원01115632
32안전체험관대비000031-51