Overview

Dataset statistics

Number of variables4
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory35.7 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description한국전기안전공사에서 최근 3년 2020년~2022년) 전기안전점검을 진행한 결과(적합, 부적합)를 사업소별(강원남부지사 등 60개소)로 제공하는 데이터입니다.
URLhttps://www.data.go.kr/data/15087907/fileData.do

Alerts

적합 수 is highly overall correlated with 부적합 수High correlation
부적합 수 is highly overall correlated with 적합 수High correlation
부적합 수 has 10 (5.6%) zerosZeros

Reproduction

Analysis started2023-12-12 13:05:30.103032
Analysis finished2023-12-12 13:05:31.056330
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2020
60 
2021
60 
2022
60 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 60
33.3%
2021 60
33.3%
2022 60
33.3%

Length

2023-12-12T22:05:31.114326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:05:31.209003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 60
33.3%
2021 60
33.3%
2022 60
33.3%

사업소 명
Text

Distinct60
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T22:05:31.442492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.0166667
Min length4

Characters and Unicode

Total characters1083
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row강원남부지사
2nd row강원동부지사
3rd row강원북부지사
4th row강원지역본부
5th row경기북동부지사
ValueCountFrequency (%)
강원남부지사 3
 
1.7%
강원동부지사 3
 
1.7%
인천지역본부 3
 
1.7%
서울북부지사 3
 
1.7%
서울서부지사 3
 
1.7%
서울지역본부 3
 
1.7%
안산시흥지사 3
 
1.7%
여수지사 3
 
1.7%
영동옥천지사 3
 
1.7%
용인지사 3
 
1.7%
Other values (50) 150
83.3%
2023-12-12T22:05:31.811614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 180
16.6%
사 141
13.0%
부 132
 
12.2%
남 54
 
5.0%
서 45
 
4.2%
경 45
 
4.2%
북 42
 
3.9%
역 39
 
3.6%
본 39
 
3.6%
산 30
 
2.8%
Other values (46) 336
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1083
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 180
16.6%
사 141
13.0%
부 132
 
12.2%
남 54
 
5.0%
서 45
 
4.2%
경 45
 
4.2%
북 42
 
3.9%
역 39
 
3.6%
본 39
 
3.6%
산 30
 
2.8%
Other values (46) 336
31.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1083
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 180
16.6%
사 141
13.0%
부 132
 
12.2%
남 54
 
5.0%
서 45
 
4.2%
경 45
 
4.2%
북 42
 
3.9%
역 39
 
3.6%
본 39
 
3.6%
산 30
 
2.8%
Other values (46) 336
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1083
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 180
16.6%
사 141
13.0%
부 132
 
12.2%
남 54
 
5.0%
서 45
 
4.2%
경 45
 
4.2%
북 42
 
3.9%
역 39
 
3.6%
본 39
 
3.6%
산 30
 
2.8%
Other values (46) 336
31.0%

적합 수
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413.21667
Minimum11
Maximum1924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T22:05:31.948298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile31
Q1122
median320.5
Q3566.75
95-th percentile1169.3
Maximum1924
Range1913
Interquartile range (IQR)444.75

Descriptive statistics

Standard deviation375.45856
Coefficient of variation (CV)0.90862395
Kurtosis2.0609826
Mean413.21667
Median Absolute Deviation (MAD)212
Skewness1.4437637
Sum74379
Variance140969.13
MonotonicityNot monotonic
2023-12-12T22:05:32.112925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174 3
 
1.7%
51 2
 
1.1%
106 2
 
1.1%
424 2
 
1.1%
338 2
 
1.1%
324 2
 
1.1%
91 2
 
1.1%
265 2
 
1.1%
85 2
 
1.1%
60 2
 
1.1%
Other values (152) 159
88.3%
ValueCountFrequency (%)
11 1
0.6%
15 1
0.6%
18 1
0.6%
20 1
0.6%
21 1
0.6%
26 1
0.6%
30 2
1.1%
31 2
1.1%
34 1
0.6%
36 1
0.6%
ValueCountFrequency (%)
1924 1
0.6%
1696 1
0.6%
1626 1
0.6%
1471 1
0.6%
1466 1
0.6%
1335 1
0.6%
1248 1
0.6%
1186 1
0.6%
1175 1
0.6%
1169 1
0.6%

부적합 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.088889
Minimum0
Maximum181
Zeros10
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T22:05:32.273123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q330
95-th percentile97.15
Maximum181
Range181
Interquartile range (IQR)25

Descriptive statistics

Standard deviation32.390993
Coefficient of variation (CV)1.3446445
Kurtosis8.0741679
Mean24.088889
Median Absolute Deviation (MAD)9
Skewness2.6901426
Sum4336
Variance1049.1764
MonotonicityNot monotonic
2023-12-12T22:05:32.546587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
5.6%
6 9
 
5.0%
8 9
 
5.0%
3 9
 
5.0%
4 8
 
4.4%
5 8
 
4.4%
10 7
 
3.9%
1 6
 
3.3%
2 6
 
3.3%
11 5
 
2.8%
Other values (53) 103
57.2%
ValueCountFrequency (%)
0 10
5.6%
1 6
3.3%
2 6
3.3%
3 9
5.0%
4 8
4.4%
5 8
4.4%
6 9
5.0%
7 4
 
2.2%
8 9
5.0%
9 4
 
2.2%
ValueCountFrequency (%)
181 1
0.6%
170 1
0.6%
162 1
0.6%
143 1
0.6%
136 1
0.6%
128 1
0.6%
112 1
0.6%
105 1
0.6%
100 1
0.6%
97 1
0.6%

Interactions

2023-12-12T22:05:30.768362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:05:30.253680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:05:30.851633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:05:30.652291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:05:32.814165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도사업소 명적합 수부적합 수
연도1.0000.0000.5080.152
사업소 명0.0001.0000.0000.726
적합 수0.5080.0001.0000.785
부적합 수0.1520.7260.7851.000
2023-12-12T22:05:32.972142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적합 수부적합 수연도
적합 수1.0000.5930.347
부적합 수0.5931.0000.088
연도0.3470.0881.000

Missing values

2023-12-12T22:05:30.954271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:05:31.027467image/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

연도사업소 명적합 수부적합 수
02020강원남부지사510
12020강원동부지사13421
22020강원북부지사1107
32020강원지역본부12710
42020경기북동부지사36040
52020경기북부지역본부28654
62020경기서부지사28347
72020경기중부지사28415
82020경기지역본부75811
92020경남남부지사15812
연도사업소 명적합 수부적합 수
1702022제주지역본부1626128
1712022제천단양지사1075
1722022천안아산지사107610
1732022충남남부지사1584
1742022충남서부지사12613
1752022충남중부지사2974
1762022충북지역본부89719
1772022충주음성지사39212
1782022파주고양지사115219
1792022평택안성지사6600