Overview

Dataset statistics

Number of variables3
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory27.9 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description(낙뢰 정의) 대기중 구름과 대지 사이에서 발생하는 순간적인 높은 전기적 방전을 ㅗ인해 생성된 전기적 뇌격을 낙뢰(Lightning Flash to Earth)라고 한다. (피뢰설비) 낙뢰로 부터 구조물, 건출물, 전기기계기구, 인명 등을 보호하기 위해 피뢰설비(피뢰침, 피뢰기, SPD 등)를 설치하여 뇌격전류를 신속히 대지로 방전하여 낙뢰로로 인한 정전, 누전, 화재, 열폭주 등에 의한 물적, 인적 피해를 최소화하기 위해 꼭 필요한 설비가 피뢰설비이다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15102472/fileData.do

Alerts

구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:09:32.849322
Analysis finished2023-12-12 02:09:33.262589
Duration0.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-12T11:09:33.431304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length8.2666667
Min length2

Characters and Unicode

Total characters372
Distinct characters55
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st rowTK-201
2nd rowTK-202
3rd rowTK-203
4th rowTK-204
5th rowTK-205
ValueCountFrequency (%)
ups/bc 9
 
11.7%
모선 8
 
10.4%
6.6kv 8
 
10.4%
1변전소 4
 
5.2%
8변전소 2
 
2.6%
2변전소 2
 
2.6%
3변전소 2
 
2.6%
4변전소 2
 
2.6%
6변전소 2
 
2.6%
7변전소 2
 
2.6%
Other values (32) 36
46.8%
2023-12-12T11:09:33.902761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
8.6%
2 21
 
5.6%
6 20
 
5.4%
변 20
 
5.4%
T 19
 
5.1%
전 19
 
5.1%
소 19
 
5.1%
K 17
 
4.6%
- 17
 
4.6%
C 15
 
4.0%
Other values (45) 173
46.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 109
29.3%
Other Letter 99
26.6%
Decimal Number 89
23.9%
Space Separator 32
 
8.6%
Dash Punctuation 17
 
4.6%
Other Punctuation 17
 
4.6%
Lowercase Letter 9
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
변 20
20.2%
전 19
19.2%
소 19
19.2%
모 8
 
8.1%
선 8
 
8.1%
동 2
 
2.0%
써 1
 
1.0%
계 1
 
1.0%
량 1
 
1.0%
실 1
 
1.0%
Other values (19) 19
19.2%
Uppercase Letter
ValueCountFrequency (%)
T 19
17.4%
K 17
15.6%
C 15
13.8%
S 12
11.0%
P 11
10.1%
U 11
10.1%
V 11
10.1%
B 9
8.3%
R 2
 
1.8%
G 1
 
0.9%
Decimal Number
ValueCountFrequency (%)
2 21
23.6%
6 20
22.5%
1 15
16.9%
0 10
11.2%
4 5
 
5.6%
3 4
 
4.5%
5 4
 
4.5%
7 4
 
4.5%
9 3
 
3.4%
8 3
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 9
52.9%
. 8
47.1%
Space Separator
ValueCountFrequency (%)
32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155
41.7%
Latin 118
31.7%
Hangul 99
26.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
변 20
20.2%
전 19
19.2%
소 19
19.2%
모 8
 
8.1%
선 8
 
8.1%
동 2
 
2.0%
써 1
 
1.0%
계 1
 
1.0%
량 1
 
1.0%
실 1
 
1.0%
Other values (19) 19
19.2%
Common
ValueCountFrequency (%)
32
20.6%
2 21
13.5%
6 20
12.9%
- 17
11.0%
1 15
9.7%
0 10
 
6.5%
/ 9
 
5.8%
. 8
 
5.2%
4 5
 
3.2%
3 4
 
2.6%
Other values (4) 14
9.0%
Latin
ValueCountFrequency (%)
T 19
16.1%
K 17
14.4%
C 15
12.7%
S 12
10.2%
P 11
9.3%
U 11
9.3%
V 11
9.3%
k 9
7.6%
B 9
7.6%
R 2
 
1.7%
Other values (2) 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
73.4%
Hangul 99
 
26.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
 
11.7%
2 21
 
7.7%
6 20
 
7.3%
T 19
 
7.0%
K 17
 
6.2%
- 17
 
6.2%
C 15
 
5.5%
1 15
 
5.5%
S 12
 
4.4%
P 11
 
4.0%
Other values (16) 94
34.4%
Hangul
ValueCountFrequency (%)
변 20
20.2%
전 19
19.2%
소 19
19.2%
모 8
 
8.1%
선 8
 
8.1%
동 2
 
2.0%
써 1
 
1.0%
계 1
 
1.0%
량 1
 
1.0%
실 1
 
1.0%
Other values (19) 19
19.2%

피뢰설비명
Categorical

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
피뢰침
22 
서지보호장치(SPD)
13 
피뢰기(LA)
10 

Length

Max length11
Median length7
Mean length6.2
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
피뢰침 22
48.9%
서지보호장치(SPD) 13
28.9%
피뢰기(LA) 10
22.2%

Length

2023-12-12T11:09:34.107331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:09:34.276190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
피뢰침 22
48.9%
서지보호장치(spd 13
28.9%
피뢰기(la 10
22.2%

수량
Real number (ℝ)

Distinct8
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1555556
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T11:09:34.418205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q16
median7
Q38
95-th percentile10
Maximum43
Range42
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.915909
Coefficient of variation (CV)0.82675747
Kurtosis32.025859
Mean7.1555556
Median Absolute Deviation (MAD)1
Skewness5.1765375
Sum322
Variance34.99798
MonotonicityNot monotonic
2023-12-12T11:09:34.539362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 14
31.1%
8 10
22.2%
7 8
17.8%
10 4
 
8.9%
1 3
 
6.7%
4 3
 
6.7%
2 2
 
4.4%
43 1
 
2.2%
ValueCountFrequency (%)
1 3
 
6.7%
2 2
 
4.4%
4 3
 
6.7%
6 14
31.1%
7 8
17.8%
8 10
22.2%
10 4
 
8.9%
43 1
 
2.2%
ValueCountFrequency (%)
43 1
 
2.2%
10 4
 
8.9%
8 10
22.2%
7 8
17.8%
6 14
31.1%
4 3
 
6.7%
2 2
 
4.4%
1 3
 
6.7%

Interactions

2023-12-12T11:09:32.972772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:09:34.615225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분피뢰설비명수량
구분1.0001.0001.000
피뢰설비명1.0001.0000.176
수량1.0000.1761.000
2023-12-12T11:09:34.706771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량피뢰설비명
수량1.0000.160
피뢰설비명0.1601.000

Missing values

2023-12-12T11:09:33.133020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:09:33.222215image/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

구분피뢰설비명수량
0TK-201피뢰침8
1TK-202피뢰침8
2TK-203피뢰침8
3TK-204피뢰침8
4TK-205피뢰침8
5TK-206피뢰침7
6TK-207피뢰침7
7TK-208피뢰침7
8TK-209피뢰침7
9TK-210피뢰침7
구분피뢰설비명수량
351변전소 GIS피뢰기(LA)6
361변전소 154kV 변압기피뢰기(LA)4
371변전소 6.6kV 모선피뢰기(LA)6
382변전소 6.6kV 모선피뢰기(LA)6
393변전소 6.6kV 모선피뢰기(LA)6
404변전소 6.6kV 모선피뢰기(LA)6
416변전소 6.6kV 모선피뢰기(LA)6
427변전소 6.6kV 모선피뢰기(LA)6
438변전소 6.6kV 모선피뢰기(LA)6
449변전소 6.6kV 모선피뢰기(LA)6