Overview

Dataset statistics

Number of variables4
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory924.0 B
Average record size in memory38.5 B

Variable types

Categorical1
Text2
Numeric1

Dataset

Description119특수구조단에서 구조장비 편람 및 특수구조대 보유장비 기준등을 참고로 하여 보유하고 있는 출동대(현장기동대, 항공대)의 장비현황입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15081332/fileData.do

Alerts

종 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 종High correlation
종 has 2 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-12 20:59:17.957790
Analysis finished2023-12-12 20:59:18.373557
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
항공대장비
12 
현장기동대
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
항공대장비 12
50.0%
현장기동대 12
50.0%

Length

2023-12-13T05:59:18.434775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:59:18.526685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
항공대장비 12
50.0%
현장기동대 12
50.0%

장비명
Text

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T05:59:18.715152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.25
Min length6

Characters and Unicode

Total characters198
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1. 기동용
2nd row2. 일반구조용
3rd row3. 산악구조용
4th row4. 수난구조용
5th row5. 화생방 및 대테러구조용
ValueCountFrequency (%)
1 2
 
3.8%
기동용 2
 
3.8%
12 2
 
3.8%
보호장비 2
 
3.8%
11 2
 
3.8%
파괴용 2
 
3.8%
10 2
 
3.8%
탐색구조용 2
 
3.8%
9 2
 
3.8%
중량물작업용 2
 
3.8%
Other values (16) 32
61.5%
2023-12-13T05:59:19.048977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
14.1%
. 24
 
12.1%
용 20
 
10.1%
조 14
 
7.1%
구 12
 
6.1%
1 10
 
5.1%
장 4
 
2.0%
보 4
 
2.0%
비 4
 
2.0%
2 4
 
2.0%
Other values (37) 74
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
58.6%
Decimal Number 30
 
15.2%
Space Separator 28
 
14.1%
Other Punctuation 24
 
12.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 20
 
17.2%
조 14
 
12.1%
구 12
 
10.3%
장 4
 
3.4%
보 4
 
3.4%
비 4
 
3.4%
반 2
 
1.7%
중 2
 
1.7%
량 2
 
1.7%
물 2
 
1.7%
Other values (25) 50
43.1%
Decimal Number
ValueCountFrequency (%)
1 10
33.3%
2 4
 
13.3%
9 2
 
6.7%
8 2
 
6.7%
0 2
 
6.7%
7 2
 
6.7%
3 2
 
6.7%
4 2
 
6.7%
5 2
 
6.7%
6 2
 
6.7%
Space Separator
ValueCountFrequency (%)
28
100.0%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
58.6%
Common 82
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 20
 
17.2%
조 14
 
12.1%
구 12
 
10.3%
장 4
 
3.4%
보 4
 
3.4%
비 4
 
3.4%
반 2
 
1.7%
중 2
 
1.7%
량 2
 
1.7%
물 2
 
1.7%
Other values (25) 50
43.1%
Common
ValueCountFrequency (%)
28
34.1%
. 24
29.3%
1 10
 
12.2%
2 4
 
4.9%
9 2
 
2.4%
8 2
 
2.4%
0 2
 
2.4%
7 2
 
2.4%
3 2
 
2.4%
4 2
 
2.4%
Other values (2) 4
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
58.6%
ASCII 82
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
34.1%
. 24
29.3%
1 10
 
12.2%
2 4
 
4.9%
9 2
 
2.4%
8 2
 
2.4%
0 2
 
2.4%
7 2
 
2.4%
3 2
 
2.4%
4 2
 
2.4%
Other values (2) 4
 
4.9%
Hangul
ValueCountFrequency (%)
용 20
 
17.2%
조 14
 
12.1%
구 12
 
10.3%
장 4
 
3.4%
보 4
 
3.4%
비 4
 
3.4%
반 2
 
1.7%
중 2
 
1.7%
량 2
 
1.7%
물 2
 
1.7%
Other values (25) 50
43.1%

종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.208333
Minimum0
Maximum37
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:59:19.201899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q11
median6
Q315
95-th percentile27
Maximum37
Range37
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.475558
Coefficient of variation (CV)1.0261771
Kurtosis0.30496823
Mean10.208333
Median Absolute Deviation (MAD)5
Skewness1.0684404
Sum245
Variance109.73732
MonotonicityNot monotonic
2023-12-13T05:59:19.331087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 5
20.8%
0 2
 
8.3%
4 2
 
8.3%
27 2
 
8.3%
15 2
 
8.3%
6 2
 
8.3%
11 1
 
4.2%
22 1
 
4.2%
14 1
 
4.2%
2 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
0 2
 
8.3%
1 5
20.8%
2 1
 
4.2%
4 2
 
8.3%
5 1
 
4.2%
6 2
 
8.3%
8 1
 
4.2%
11 1
 
4.2%
13 1
 
4.2%
14 1
 
4.2%
ValueCountFrequency (%)
37 1
4.2%
27 2
8.3%
24 1
4.2%
22 1
4.2%
15 2
8.3%
14 1
4.2%
13 1
4.2%
11 1
4.2%
8 1
4.2%
6 2
8.3%

점
Text

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T05:59:19.493321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.9166667
Min length1

Characters and Unicode

Total characters46
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)70.8%

Sample

1st row1
2nd row63
3rd row279
4th row99
5th row0
ValueCountFrequency (%)
1 3
 
12.5%
0 2
 
8.3%
63 2
 
8.3%
332 1
 
4.2%
2,050 1
 
4.2%
444 1
 
4.2%
9 1
 
4.2%
29 1
 
4.2%
10 1
 
4.2%
31 1
 
4.2%
Other values (10) 10
41.7%
2023-12-13T05:59:19.878721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7
15.2%
2 7
15.2%
0 6
13.0%
9 6
13.0%
3 5
10.9%
4 5
10.9%
7 4
8.7%
6 2
 
4.3%
5 2
 
4.3%
8 1
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45
97.8%
Other Punctuation 1
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
15.6%
2 7
15.6%
0 6
13.3%
9 6
13.3%
3 5
11.1%
4 5
11.1%
7 4
8.9%
6 2
 
4.4%
5 2
 
4.4%
8 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
15.2%
2 7
15.2%
0 6
13.0%
9 6
13.0%
3 5
10.9%
4 5
10.9%
7 4
8.7%
6 2
 
4.3%
5 2
 
4.3%
8 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
15.2%
2 7
15.2%
0 6
13.0%
9 6
13.0%
3 5
10.9%
4 5
10.9%
7 4
8.7%
6 2
 
4.3%
5 2
 
4.3%
8 1
 
2.2%

Interactions

2023-12-13T05:59:18.116290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:59:20.005464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분장비명종점
구분1.0000.0000.8970.909
장비명0.0001.0000.0000.739
종0.8970.0001.0000.977
점0.9090.7390.9771.000
2023-12-13T05:59:20.117317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종구분
종1.0000.771
구분0.7711.000

Missing values

2023-12-13T05:59:18.253456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:59:18.340982image/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

구분장비명종점
0항공대장비1. 기동용11
1항공대장비2. 일반구조용1163
2항공대장비3. 산악구조용22279
3항공대장비4. 수난구조용1499
4항공대장비5. 화생방 및 대테러구조용00
5항공대장비6. 측정용11
6항공대장비7. 절단구조용22
7항공대장비8. 중량물작업용11
8항공대장비9. 탐색구조용00
9항공대장비10. 파괴용17
구분장비명종점
14현장기동대3. 산악구조용372,050
15현장기동대4. 수난구조용27332
16현장기동대5. 화생방 및 대테러구조용27197
17현장기동대6. 측정용1531
18현장기동대7. 절단구조용610
19현장기동대8. 중량물작업용1329
20현장기동대9. 탐색구조용69
21현장기동대10. 파괴용563
22현장기동대11. 보호장비15444
23현장기동대12. 보조장비412