Overview

Dataset statistics

Number of variables7
Number of observations164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory58.8 B

Variable types

Categorical3
Text2
Numeric2

Dataset

Description소방청 및 시도본부 및 소속기관에서 보유한 드론의 운용부서, 도입목적, 제작사, 업무용 및 교육용 드론 수 정보를 제공
Author소방청
URLhttps://www.data.go.kr/data/15086125/fileData.do

Alerts

업무용드론수 is highly overall correlated with 교육용드론수 and 2 other fieldsHigh correlation
교육용드론수 is highly overall correlated with 업무용드론수High correlation
기관 is highly overall correlated with 도입목적High correlation
도입목적 is highly overall correlated with 업무용드론수 and 1 other fieldsHigh correlation
제작사 is highly overall correlated with 업무용드론수High correlation
제작사 is highly imbalanced (66.7%)Imbalance
업무용드론수 has 43 (26.2%) zerosZeros
교육용드론수 has 121 (73.8%) zerosZeros

Reproduction

Analysis started2023-12-12 04:51:16.958721
Analysis finished2023-12-12 04:51:18.077950
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
서울
32 
충남
25 
국립소방연구원
19 
부산
17 
대전
13 
Other values (12)
58 

Length

Max length9
Median length2
Mean length2.8719512
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
서울 32
19.5%
충남 25
15.2%
국립소방연구원 19
11.6%
부산 17
10.4%
대전 13
7.9%
강원 12
 
7.3%
광주 8
 
4.9%
경기본부 8
 
4.9%
울산 7
 
4.3%
충북 6
 
3.7%
Other values (7) 17
10.4%

Length

2023-12-12T13:51:18.164166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 32
19.5%
충남 25
15.2%
국립소방연구원 19
11.6%
부산 17
10.4%
대전 13
7.9%
강원 12
 
7.3%
광주 8
 
4.9%
경기본부 8
 
4.9%
울산 7
 
4.3%
충북 6
 
3.7%
Other values (7) 17
10.4%

운용부서
Text

Distinct86
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T13:51:18.455461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length5
Mean length6.0914634
Min length4

Characters and Unicode

Total characters999
Distinct characters105
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

Unique55 ?
Unique (%)33.5%

Sample

1st row대응기술연구실
2nd row대응기술연구실
3rd row대응기술연구실
4th row대응기술연구실
5th row대응기술연구실
ValueCountFrequency (%)
대응기술연구실 17
 
9.9%
특수구조단 13
 
7.6%
119특수구조단 9
 
5.2%
충청소방학교 6
 
3.5%
중부소방서 5
 
2.9%
소방본부 4
 
2.3%
강남소방서 4
 
2.3%
남부소방서 3
 
1.7%
119특수화학구조대 3
 
1.7%
동대문소방서 3
 
1.7%
Other values (78) 105
61.0%
2023-12-12T13:51:19.250778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
방 114
 
11.4%
소 113
 
11.3%
서 110
 
11.0%
구 52
 
5.2%
1 38
 
3.8%
대 36
 
3.6%
수 33
 
3.3%
특 33
 
3.3%
조 31
 
3.1%
부 28
 
2.8%
Other values (95) 411
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 934
93.5%
Decimal Number 57
 
5.7%
Space Separator 8
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 114
 
12.2%
소 113
 
12.1%
서 110
 
11.8%
구 52
 
5.6%
대 36
 
3.9%
수 33
 
3.5%
특 33
 
3.5%
조 31
 
3.3%
부 28
 
3.0%
단 26
 
2.8%
Other values (92) 358
38.3%
Decimal Number
ValueCountFrequency (%)
1 38
66.7%
9 19
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 934
93.5%
Common 65
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 114
 
12.2%
소 113
 
12.1%
서 110
 
11.8%
구 52
 
5.6%
대 36
 
3.9%
수 33
 
3.5%
특 33
 
3.5%
조 31
 
3.3%
부 28
 
3.0%
단 26
 
2.8%
Other values (92) 358
38.3%
Common
ValueCountFrequency (%)
1 38
58.5%
9 19
29.2%
8
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 934
93.5%
ASCII 65
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
방 114
 
12.2%
소 113
 
12.1%
서 110
 
11.8%
구 52
 
5.6%
대 36
 
3.9%
수 33
 
3.5%
특 33
 
3.5%
조 31
 
3.3%
부 28
 
3.0%
단 26
 
2.8%
Other values (92) 358
38.3%
ASCII
ValueCountFrequency (%)
1 38
58.5%
9 19
29.2%
8
 
12.3%

도입목적
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
현장수색용
34 
현장활동용
32 
교육훈련용 드론
31 
공중수색장비
24 
소방현장 드론운영
15 
Other values (12)
28 

Length

Max length17
Median length11
Mean length6.3292683
Min length3

Unique

Unique8 ?
Unique (%)4.9%

Sample

1st row교육훈련용 드론
2nd row교육훈련용 드론
3rd row교육훈련용 드론
4th row교육훈련용 드론
5th row교육훈련용 드론

Common Values

ValueCountFrequency (%)
현장수색용 34
20.7%
현장활동용 32
19.5%
교육훈련용 드론 31
18.9%
공중수색장비 24
14.6%
소방현장 드론운영 15
9.1%
교육용 7
 
4.3%
교육훈련용 7
 
4.3%
지휘관제용 드론 4
 
2.4%
인명검색 및 훈련지원 2
 
1.2%
교육용/제독용 1
 
0.6%
Other values (7) 7
 
4.3%

Length

2023-12-12T13:51:19.415782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육훈련용 38
17.2%
드론 35
15.8%
현장수색용 34
15.4%
현장활동용 32
14.5%
공중수색장비 24
10.9%
소방현장 15
 
6.8%
드론운영 15
 
6.8%
교육용 7
 
3.2%
지휘관제용 4
 
1.8%
및 2
 
0.9%
Other values (13) 15
 
6.8%

드론기기
Text

Distinct76
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T13:51:19.715178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.1585366
Min length3

Characters and Unicode

Total characters1502
Distinct characters119
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)30.5%

Sample

1st rowANAFI
2nd rowBebop 2
3rd rowcube-x
4th rowF450
5th rowINSPIRE 1
ValueCountFrequency (%)
2 31
 
10.4%
매빅 23
 
7.7%
inspire 20
 
6.7%
mavic 19
 
6.4%
phantom4 17
 
5.7%
matrice 15
 
5.0%
2줌 14
 
4.7%
프로 10
 
3.4%
엔터프라이즈 7
 
2.3%
매빅2엔터프라이즈 6
 
2.0%
Other values (71) 136
45.6%
2023-12-12T13:51:20.318651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134
 
8.9%
2 82
 
5.5%
I 58
 
3.9%
M 51
 
3.4%
매 48
 
3.2%
0 47
 
3.1%
r 45
 
3.0%
빅 43
 
2.9%
i 42
 
2.8%
e 42
 
2.8%
Other values (109) 910
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
26.2%
Lowercase Letter 388
25.8%
Uppercase Letter 317
21.1%
Decimal Number 222
14.8%
Space Separator 134
 
8.9%
Open Punctuation 16
 
1.1%
Close Punctuation 16
 
1.1%
Dash Punctuation 8
 
0.5%
Math Symbol 5
 
0.3%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
매 48
 
12.2%
빅 43
 
10.9%
프 37
 
9.4%
이 26
 
6.6%
줌 20
 
5.1%
엔 19
 
4.8%
라 19
 
4.8%
로 18
 
4.6%
즈 18
 
4.6%
터 18
 
4.6%
Other values (48) 128
32.5%
Uppercase Letter
ValueCountFrequency (%)
I 58
18.3%
M 51
16.1%
A 29
9.1%
P 24
7.6%
V 24
7.6%
C 23
 
7.3%
D 19
 
6.0%
R 17
 
5.4%
T 11
 
3.5%
J 11
 
3.5%
Other values (13) 50
15.8%
Lowercase Letter
ValueCountFrequency (%)
r 45
11.6%
i 42
10.8%
e 42
10.8%
n 41
10.6%
a 37
9.5%
t 36
9.3%
o 26
6.7%
p 25
6.4%
s 21
 
5.4%
m 19
 
4.9%
Other values (11) 54
13.9%
Decimal Number
ValueCountFrequency (%)
2 82
36.9%
0 47
21.2%
1 34
15.3%
4 31
 
14.0%
3 7
 
3.2%
6 7
 
3.2%
9 6
 
2.7%
5 4
 
1.8%
7 2
 
0.9%
8 2
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
/ 1
50.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 705
46.9%
Common 403
26.8%
Hangul 394
26.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
매 48
 
12.2%
빅 43
 
10.9%
프 37
 
9.4%
이 26
 
6.6%
줌 20
 
5.1%
엔 19
 
4.8%
라 19
 
4.8%
로 18
 
4.6%
즈 18
 
4.6%
터 18
 
4.6%
Other values (48) 128
32.5%
Latin
ValueCountFrequency (%)
I 58
 
8.2%
M 51
 
7.2%
r 45
 
6.4%
i 42
 
6.0%
e 42
 
6.0%
n 41
 
5.8%
a 37
 
5.2%
t 36
 
5.1%
A 29
 
4.1%
o 26
 
3.7%
Other values (34) 298
42.3%
Common
ValueCountFrequency (%)
134
33.3%
2 82
20.3%
0 47
 
11.7%
1 34
 
8.4%
4 31
 
7.7%
( 16
 
4.0%
) 16
 
4.0%
- 8
 
2.0%
3 7
 
1.7%
6 7
 
1.7%
Other values (7) 21
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1108
73.8%
Hangul 394
 
26.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
 
12.1%
2 82
 
7.4%
I 58
 
5.2%
M 51
 
4.6%
0 47
 
4.2%
r 45
 
4.1%
i 42
 
3.8%
e 42
 
3.8%
n 41
 
3.7%
a 37
 
3.3%
Other values (51) 529
47.7%
Hangul
ValueCountFrequency (%)
매 48
 
12.2%
빅 43
 
10.9%
프 37
 
9.4%
이 26
 
6.6%
줌 20
 
5.1%
엔 19
 
4.8%
라 19
 
4.8%
로 18
 
4.6%
즈 18
 
4.6%
터 18
 
4.6%
Other values (48) 128
32.5%

제작사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
DJI(중국)
134 
휴인스(한국)
 
5
패럿(프랑스)
 
3
유맥에어(한국)
 
3
네스앤텍(한국)
 
3
Other values (13)
16 

Length

Max length10
Median length7
Mean length6.9878049
Min length2

Unique

Unique10 ?
Unique (%)6.1%

Sample

1st row패럿(프랑스)
2nd row패럿(프랑스)
3rd row㈜유시스(한국)
4th rowDJI(중국)
5th rowDJI(중국)

Common Values

ValueCountFrequency (%)
DJI(중국) 134
81.7%
휴인스(한국) 5
 
3.0%
패럿(프랑스) 3
 
1.8%
유맥에어(한국) 3
 
1.8%
네스앤텍(한국) 3
 
1.8%
X드론(한국) 2
 
1.2%
유시스(대한민국) 2
 
1.2%
한국 2
 
1.2%
㈜유시스(한국) 1
 
0.6%
EFT(중국) 1
 
0.6%
Other values (8) 8
 
4.9%

Length

2023-12-12T13:51:20.487857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dji(중국 134
81.2%
휴인스(한국 5
 
3.0%
패럿(프랑스 3
 
1.8%
유맥에어(한국 3
 
1.8%
네스앤텍(한국 3
 
1.8%
x드론(한국 2
 
1.2%
유시스(대한민국 2
 
1.2%
한국 2
 
1.2%
parrot(중국 1
 
0.6%
dmi(한국 1
 
0.6%
Other values (9) 9
 
5.5%

업무용드론수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85365854
Minimum0
Maximum5
Zeros43
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T13:51:20.602130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69391662
Coefficient of variation (CV)0.81287375
Kurtosis9.433026
Mean0.85365854
Median Absolute Deviation (MAD)0
Skewness1.8768108
Sum140
Variance0.48152028
MonotonicityNot monotonic
2023-12-12T13:51:20.713323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 108
65.9%
0 43
 
26.2%
2 10
 
6.1%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
0 43
 
26.2%
1 108
65.9%
2 10
 
6.1%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
5 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 10
 
6.1%
1 108
65.9%
0 43
 
26.2%

교육용드론수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4695122
Minimum0
Maximum15
Zeros121
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T13:51:20.811304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3853917
Coefficient of variation (CV)2.9507044
Kurtosis74.996279
Mean0.4695122
Median Absolute Deviation (MAD)0
Skewness7.5896627
Sum77
Variance1.9193102
MonotonicityNot monotonic
2023-12-12T13:51:20.915307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 121
73.8%
1 30
 
18.3%
2 7
 
4.3%
4 3
 
1.8%
3 2
 
1.2%
15 1
 
0.6%
ValueCountFrequency (%)
0 121
73.8%
1 30
 
18.3%
2 7
 
4.3%
3 2
 
1.2%
4 3
 
1.8%
15 1
 
0.6%
ValueCountFrequency (%)
15 1
 
0.6%
4 3
 
1.8%
3 2
 
1.2%
2 7
 
4.3%
1 30
 
18.3%
0 121
73.8%

Interactions

2023-12-12T13:51:17.647957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.447227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.736904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.541759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:51:21.003395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관운용부서도입목적드론기기제작사업무용드론수교육용드론수
기관1.0000.9830.9500.9890.7920.5500.616
운용부서0.9831.0000.7110.0000.0000.0000.000
도입목적0.9500.7111.0000.9900.7750.8480.000
드론기기0.9890.0000.9901.0000.9980.9560.967
제작사0.7920.0000.7750.9981.0000.8670.380
업무용드론수0.5500.0000.8480.9560.8671.0000.344
교육용드론수0.6160.0000.0000.9670.3800.3441.000
2023-12-12T13:51:21.137762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관도입목적제작사
기관1.0000.5450.379
도입목적0.5451.0000.361
제작사0.3790.3611.000
2023-12-12T13:51:21.250941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무용드론수교육용드론수기관도입목적제작사
업무용드론수1.000-0.9040.2850.5870.524
교육용드론수-0.9041.0000.3040.0000.181
기관0.2850.3041.0000.5450.379
도입목적0.5870.0000.5451.0000.361
제작사0.5240.1810.3790.3611.000

Missing values

2023-12-12T13:51:17.898100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:51:18.029763image/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

기관운용부서도입목적드론기기제작사업무용드론수교육용드론수
0국립소방연구원대응기술연구실교육훈련용 드론ANAFI패럿(프랑스)01
1국립소방연구원대응기술연구실교육훈련용 드론Bebop 2패럿(프랑스)01
2국립소방연구원대응기술연구실교육훈련용 드론cube-x㈜유시스(한국)01
3국립소방연구원대응기술연구실교육훈련용 드론F450DJI(중국)015
4국립소방연구원대응기술연구실교육훈련용 드론INSPIRE 1DJI(중국)04
5국립소방연구원대응기술연구실교육훈련용 드론MATRICE 600DJI(중국)01
6국립소방연구원대응기술연구실교육훈련용 드론Phantom 3DJI(중국)01
7국립소방연구원대응기술연구실교육훈련용 드론SPARKDJI(중국)01
8국립소방연구원대응기술연구실교육훈련용 드론Swing Drone패럿(프랑스)01
9국립소방연구원대응기술연구실교육훈련용 드론UM4유맥에어(한국)03
기관운용부서도입목적드론기기제작사업무용드론수교육용드론수
154충남서천소방서현장수색용매빅 2줌DJI(중국)10
155충남청양소방서현장수색용매빅 2줌DJI(중국)10
156충남예산소방서현장수색용매빅 2줌DJI(중국)10
157충남충청소방학교현장수색용Matrice 210 RTK V2DJI(중국)10
158충남충청소방학교교육용M600PRO-EDUDJI(중국)02
159충남충청소방학교교육용/제독용G10라이트모형01
160충남충청소방학교교육용XD-I4AX드론(한국)01
161충남충청소방학교교육용Phantom4 proDJI(중국)02
162충남충청소방학교교육용매빅에어 프라이모어DJI(중국)04
163전북부안소방서DKSH 무상기증xiro xplorer 4k대한민국10