Overview

Dataset statistics

Number of variables7
Number of observations59
Missing cells49
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory58.2 B

Variable types

Text2
Categorical2
DateTime2
Boolean1

Dataset

Description산림항공본부 산림헬기 보유 현황 데이터로, 호기ID, 등록부호, 기종, 소속, 도입일, 용도폐지일, 사용유무 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15117425/fileData.do

Alerts

기종 is highly overall correlated with 사용유무High correlation
사용유무 is highly overall correlated with 기종High correlation
용도폐지일 has 49 (83.1%) missing valuesMissing
호기아이디(ID) has unique valuesUnique
등록부호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:38:08.530830
Analysis finished2023-12-12 01:38:09.120242
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호기아이디(ID)
Text

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T10:38:09.345210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9830508
Min length5

Characters and Unicode

Total characters353
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)100.0%

Sample

1st rowFP501
2nd rowFPA201
3rd rowFPA202
4th rowFPA203
5th rowFPA205
ValueCountFrequency (%)
fp501 1
 
1.7%
fpa619 1
 
1.7%
fpa622 1
 
1.7%
fpa623 1
 
1.7%
fpa625 1
 
1.7%
fpa626 1
 
1.7%
fpa627 1
 
1.7%
fpa628 1
 
1.7%
fpa629 1
 
1.7%
fpa630 1
 
1.7%
Other values (49) 49
83.1%
2023-12-12T10:38:09.813108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 59
16.7%
P 59
16.7%
A 58
16.4%
6 40
11.3%
0 33
9.3%
2 25
7.1%
1 23
 
6.5%
3 19
 
5.4%
7 14
 
4.0%
5 9
 
2.5%
Other values (2) 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
50.1%
Uppercase Letter 176
49.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 40
22.6%
0 33
18.6%
2 25
14.1%
1 23
13.0%
3 19
10.7%
7 14
 
7.9%
5 9
 
5.1%
9 9
 
5.1%
8 5
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
F 59
33.5%
P 59
33.5%
A 58
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
50.1%
Latin 176
49.9%

Most frequent character per script

Common
ValueCountFrequency (%)
6 40
22.6%
0 33
18.6%
2 25
14.1%
1 23
13.0%
3 19
10.7%
7 14
 
7.9%
5 9
 
5.1%
9 9
 
5.1%
8 5
 
2.8%
Latin
ValueCountFrequency (%)
F 59
33.5%
P 59
33.5%
A 58
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 59
16.7%
P 59
16.7%
A 58
16.4%
6 40
11.3%
0 33
9.3%
2 25
7.1%
1 23
 
6.5%
3 19
 
5.4%
7 14
 
4.0%
5 9
 
2.5%
Other values (2) 14
 
4.0%

등록부호
Text

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T10:38:10.141347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters354
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)100.0%

Sample

1st rowHL1108
2nd rowHL9442
3rd rowHL9443
4th rowHL9444
5th rowHL9467
ValueCountFrequency (%)
hl1108 1
 
1.7%
hl9424 1
 
1.7%
hl9426 1
 
1.7%
hl9427 1
 
1.7%
hl9428 1
 
1.7%
hl9429 1
 
1.7%
hl9430 1
 
1.7%
hl9431 1
 
1.7%
hl9432 1
 
1.7%
hl9433 1
 
1.7%
Other values (49) 49
83.1%
2023-12-12T10:38:10.612936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 65
18.4%
H 59
16.7%
L 59
16.7%
4 49
13.8%
1 28
7.9%
3 20
 
5.6%
2 17
 
4.8%
6 15
 
4.2%
8 14
 
4.0%
0 12
 
3.4%
Other values (2) 16
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
66.7%
Uppercase Letter 118
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 65
27.5%
4 49
20.8%
1 28
11.9%
3 20
 
8.5%
2 17
 
7.2%
6 15
 
6.4%
8 14
 
5.9%
0 12
 
5.1%
7 9
 
3.8%
5 7
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
H 59
50.0%
L 59
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 236
66.7%
Latin 118
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
9 65
27.5%
4 49
20.8%
1 28
11.9%
3 20
 
8.5%
2 17
 
7.2%
6 15
 
6.4%
8 14
 
5.9%
0 12
 
5.1%
7 9
 
3.8%
5 7
 
3.0%
Latin
ValueCountFrequency (%)
H 59
50.0%
L 59
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 65
18.4%
H 59
16.7%
L 59
16.7%
4 49
13.8%
1 28
7.9%
3 20
 
5.6%
2 17
 
4.8%
6 15
 
4.2%
8 14
 
4.0%
0 12
 
3.4%
Other values (2) 16
 
4.5%

기종
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size604.0 B
KA-32T
28 
S64E
8 
BELL206-L3
7 
AS350-B2
5 
ANSAT
4 
Other values (5)
7 

Length

Max length10
Median length6
Mean length6.3728814
Min length4

Unique

Unique4 ?
Unique (%)6.8%

Sample

1st rowM20R
2nd rowS64E
3rd rowS64E
4th rowS64E
5th rowS64E

Common Values

ValueCountFrequency (%)
KA-32T 28
47.5%
S64E 8
 
13.6%
BELL206-L3 7
 
11.9%
AS350-B2 5
 
8.5%
ANSAT 4
 
6.8%
KA-32A 3
 
5.1%
M20R 1
 
1.7%
KUH-1FS 1
 
1.7%
L410-UVP 1
 
1.7%
BELL412SP 1
 
1.7%

Length

2023-12-12T10:38:10.809491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:38:10.970079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ka-32t 28
47.5%
s64e 8
 
13.6%
bell206-l3 7
 
11.9%
as350-b2 5
 
8.5%
ansat 4
 
6.8%
ka-32a 3
 
5.1%
m20r 1
 
1.7%
kuh-1fs 1
 
1.7%
l410-uvp 1
 
1.7%
bell412sp 1
 
1.7%

소속
Categorical

Distinct12
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
본부
10 
진천관리소
6 
청양관리소
6 
안동관리소
5 
강릉관리소
5 
Other values (7)
27 

Length

Max length5
Median length5
Mean length4.4915254
Min length2

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row본부
2nd row안동관리소
3rd row진천관리소
4th row익산관리소
5th row안동관리소

Common Values

ValueCountFrequency (%)
본부 10
16.9%
진천관리소 6
10.2%
청양관리소 6
10.2%
안동관리소 5
8.5%
강릉관리소 5
8.5%
서울관리소 5
8.5%
함양관리소 5
8.5%
영암관리소 5
8.5%
양산관리소 5
8.5%
익산관리소 3
 
5.1%
Other values (2) 4
 
6.8%

Length

2023-12-12T10:38:11.170497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
본부 10
16.9%
진천관리소 6
10.2%
청양관리소 6
10.2%
안동관리소 5
8.5%
강릉관리소 5
8.5%
서울관리소 5
8.5%
함양관리소 5
8.5%
영암관리소 5
8.5%
양산관리소 5
8.5%
익산관리소 3
 
5.1%
Other values (2) 4
 
6.8%

도입일
Date

Distinct46
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size604.0 B
Minimum1988-04-14 00:00:00
Maximum2022-12-03 00:00:00
2023-12-12T10:38:11.332894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:38:11.527017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

용도폐지일
Date

MISSING 

Distinct7
Distinct (%)70.0%
Missing49
Missing (%)83.1%
Memory size604.0 B
Minimum2009-11-23 00:00:00
Maximum2020-05-14 00:00:00
2023-12-12T10:38:11.854303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:38:12.050499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

사용유무
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size191.0 B
True
48 
False
11 
ValueCountFrequency (%)
True 48
81.4%
False 11
 
18.6%
2023-12-12T10:38:12.225652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:38:12.346941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호기아이디(ID)등록부호기종소속도입일용도폐지일사용유무
호기아이디(ID)1.0001.0001.0001.0001.0001.0001.000
등록부호1.0001.0001.0001.0001.0001.0001.000
기종1.0001.0001.0000.2281.0001.0000.894
소속1.0001.0000.2281.0000.7590.8970.238
도입일1.0001.0001.0000.7591.0001.0000.462
용도폐지일1.0001.0001.0000.8971.0001.000NaN
사용유무1.0001.0000.8940.2380.462NaN1.000
2023-12-12T10:38:12.541988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용유무소속기종
사용유무1.0000.1580.677
소속0.1581.0000.073
기종0.6770.0731.000
2023-12-12T10:38:12.681386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기종소속사용유무
기종1.0000.0730.677
소속0.0731.0000.158
사용유무0.6770.1581.000

Missing values

2023-12-12T10:38:08.903783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:38:09.054914image/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

호기아이디(ID)등록부호기종소속도입일용도폐지일사용유무
0FP501HL1108M20R본부1995-08-22<NA>N
1FPA201HL9442S64E안동관리소2002-01-05<NA>Y
2FPA202HL9443S64E진천관리소2006-12-11<NA>Y
3FPA203HL9444S64E익산관리소2007-09-21<NA>Y
4FPA205HL9467S64E안동관리소2007-12-152013-05-09N
5FPA206HL9650S64E강릉관리소2018-10-27<NA>Y
6FPA207HL9659S64E본부2019-11-29<NA>Y
7FPA208HL9660S64E울진관리소2020-01-09<NA>Y
8FPA209HL9689S64E본부2022-12-03<NA>Y
9FPA301HL9438ANSAT진천관리소2004-12-192013-12-30N
호기아이디(ID)등록부호기종소속도입일용도폐지일사용유무
49FPA712HL9179BELL206-L3양산관리소1992-04-01<NA>Y
50FPA713HL9180BELL206-L3청양관리소1992-04-01<NA>Y
51FPA716HL9305BELL206-L3양산관리소2014-04-30<NA>Y
52FPA717HL9306BELL206-L3양산관리소2014-04-30<NA>Y
53FPA801HL9608BELL412SP본부2014-03-302020-05-14N
54FPA901HL9181AS350-B2영암관리소1992-11-18<NA>Y
55FPA902HL9182AS350-B2강릉관리소1992-11-182011-05-05N
56FPA903HL9183AS350-B2함양관리소1993-02-23<NA>Y
57FPA905HL9184AS350-B2함양관리소2003-11-11<NA>Y
58FPA906HL9193AS350-B2영암관리소2011-09-16<NA>Y