Overview

Dataset statistics

Number of variables5
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory49.1 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description한국전기안전공사에서 연도별(2021년 ~ 2022) 및 지역별 전통시장 전기설비의 정기점검한 결과(적합, 부적합, 부재종결)를 제공하는 데이터입니다.
URLhttps://www.data.go.kr/data/15118883/fileData.do

Alerts

적합 수 is highly overall correlated with 부적합 수 and 1 other fieldsHigh correlation
부적합 수 is highly overall correlated with 적합 수 and 1 other fieldsHigh correlation
부재 종결 is highly overall correlated with 적합 수 and 1 other fieldsHigh correlation
적합 수 has unique valuesUnique
부재 종결 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:41:28.310995
Analysis finished2023-12-12 02:41:29.671072
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 13
50.0%
2022 13
50.0%

Length

2023-12-12T11:41:29.749264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:41:29.854014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 13
50.0%
2022 13
50.0%

지역 명
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T11:41:30.034709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.5384615
Min length3

Characters and Unicode

Total characters144
Distinct characters31
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산울산
3rd row경상북도(대구)
4th row인천광역시
5th row전라남도(광주)
ValueCountFrequency (%)
서울특별시 2
 
7.7%
부산울산 2
 
7.7%
경상북도(대구 2
 
7.7%
인천광역시 2
 
7.7%
전라남도(광주 2
 
7.7%
충청남도(대전세종 2
 
7.7%
경기도(남부 2
 
7.7%
경기도(북부 2
 
7.7%
강원도 2
 
7.7%
충청북도 2
 
7.7%
Other values (3) 6
23.1%
2023-12-12T11:41:30.326969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 20
 
13.9%
) 10
 
6.9%
( 10
 
6.9%
남 8
 
5.6%
경 8
 
5.6%
북 8
 
5.6%
부 6
 
4.2%
전 6
 
4.2%
대 4
 
2.8%
주 4
 
2.8%
Other values (21) 60
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
86.1%
Close Punctuation 10
 
6.9%
Open Punctuation 10
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 20
 
16.1%
남 8
 
6.5%
경 8
 
6.5%
북 8
 
6.5%
부 6
 
4.8%
전 6
 
4.8%
대 4
 
3.2%
주 4
 
3.2%
라 4
 
3.2%
광 4
 
3.2%
Other values (19) 52
41.9%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
86.1%
Common 20
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 20
 
16.1%
남 8
 
6.5%
경 8
 
6.5%
북 8
 
6.5%
부 6
 
4.8%
전 6
 
4.8%
대 4
 
3.2%
주 4
 
3.2%
라 4
 
3.2%
광 4
 
3.2%
Other values (19) 52
41.9%
Common
ValueCountFrequency (%)
) 10
50.0%
( 10
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
86.1%
ASCII 20
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 20
 
16.1%
남 8
 
6.5%
경 8
 
6.5%
북 8
 
6.5%
부 6
 
4.8%
전 6
 
4.8%
대 4
 
3.2%
주 4
 
3.2%
라 4
 
3.2%
광 4
 
3.2%
Other values (19) 52
41.9%
ASCII
ValueCountFrequency (%)
) 10
50.0%
( 10
50.0%

적합 수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7463.8846
Minimum772
Maximum30653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T11:41:30.443721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum772
5-th percentile901.25
Q12278.75
median4401
Q38738.75
95-th percentile27107.75
Maximum30653
Range29881
Interquartile range (IQR)6460

Descriptive statistics

Standard deviation8210.2015
Coefficient of variation (CV)1.0999904
Kurtosis3.1924299
Mean7463.8846
Median Absolute Deviation (MAD)2860
Skewness1.9018067
Sum194061
Variance67407409
MonotonicityNot monotonic
2023-12-12T11:41:30.553747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
30107 1
 
3.8%
18110 1
 
3.8%
2504 1
 
3.8%
9014 1
 
3.8%
1815 1
 
3.8%
1324 1
 
3.8%
3328 1
 
3.8%
792 1
 
3.8%
6215 1
 
3.8%
2685 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
772 1
3.8%
792 1
3.8%
1229 1
3.8%
1324 1
3.8%
1758 1
3.8%
1815 1
3.8%
2219 1
3.8%
2458 1
3.8%
2504 1
3.8%
2685 1
3.8%
ValueCountFrequency (%)
30653 1
3.8%
30107 1
3.8%
18110 1
3.8%
17721 1
3.8%
11124 1
3.8%
10489 1
3.8%
9014 1
3.8%
7913 1
3.8%
7641 1
3.8%
6848 1
3.8%

부적합 수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.42308
Minimum2
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T11:41:30.672379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14.25
Q125.25
median101.5
Q3332.25
95-th percentile1268.5
Maximum1641
Range1639
Interquartile range (IQR)307

Descriptive statistics

Standard deviation430.36814
Coefficient of variation (CV)1.4767813
Kurtosis4.1689023
Mean291.42308
Median Absolute Deviation (MAD)86.5
Skewness2.1181745
Sum7577
Variance185216.73
MonotonicityNot monotonic
2023-12-12T11:41:30.778808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15 2
 
7.7%
1419 1
 
3.8%
607 1
 
3.8%
14 1
 
3.8%
174 1
 
3.8%
20 1
 
3.8%
59 1
 
3.8%
2 1
 
3.8%
52 1
 
3.8%
22 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
2 1
3.8%
14 1
3.8%
15 2
7.7%
18 1
3.8%
20 1
3.8%
22 1
3.8%
35 1
3.8%
38 1
3.8%
52 1
3.8%
59 1
3.8%
ValueCountFrequency (%)
1641 1
3.8%
1419 1
3.8%
817 1
3.8%
619 1
3.8%
607 1
3.8%
603 1
3.8%
360 1
3.8%
249 1
3.8%
191 1
3.8%
189 1
3.8%

부재 종결
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114.6538
Minimum23
Maximum3705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T11:41:30.883011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile65.5
Q1263.25
median478
Q31743.75
95-th percentile3367.75
Maximum3705
Range3682
Interquartile range (IQR)1480.5

Descriptive statistics

Standard deviation1124.2119
Coefficient of variation (CV)1.0085749
Kurtosis0.024673798
Mean1114.6538
Median Absolute Deviation (MAD)436.5
Skewness1.0853554
Sum28981
Variance1263852.3
MonotonicityNot monotonic
2023-12-12T11:41:30.990093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3214 1
 
3.8%
3419 1
 
3.8%
208 1
 
3.8%
1750 1
 
3.8%
328 1
 
3.8%
186 1
 
3.8%
466 1
 
3.8%
37 1
 
3.8%
910 1
 
3.8%
151 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
23 1
3.8%
37 1
3.8%
151 1
3.8%
186 1
3.8%
208 1
3.8%
248 1
3.8%
263 1
3.8%
264 1
3.8%
328 1
3.8%
375 1
3.8%
ValueCountFrequency (%)
3705 1
3.8%
3419 1
3.8%
3214 1
3.8%
2422 1
3.8%
2407 1
3.8%
2020 1
3.8%
1750 1
3.8%
1725 1
3.8%
1384 1
3.8%
1127 1
3.8%

Interactions

2023-12-12T11:41:29.182326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:28.530297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:28.867367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:29.286957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:28.666004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:28.984331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:29.375899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:28.783297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:29.075290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:41:31.067839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역 명적합 수부적합 수부재 종결
연도1.0000.0000.0000.0000.000
지역 명0.0001.0001.0000.6150.827
적합 수0.0001.0001.0000.7890.906
부적합 수0.0000.6150.7891.0000.801
부재 종결0.0000.8270.9060.8011.000
2023-12-12T11:41:31.167460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적합 수부적합 수부재 종결연도
적합 수1.0000.8470.9250.000
부적합 수0.8471.0000.8320.000
부재 종결0.9250.8321.0000.000
연도0.0000.0000.0001.000

Missing values

2023-12-12T11:41:29.522624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:41:29.629349image/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

연도지역 명적합 수부적합 수부재 종결
02021서울특별시3010714193214
12021부산울산177218173705
22021경상북도(대구)104891912020
32021인천광역시68486031384
42021전라남도(광주)4221360429
52021충청남도(대전세종)221915444
62021경기도(남부)53646191127
72021경기도(북부)7723823
82021강원도317688490
92021충청북도122935248
연도지역 명적합 수부적합 수부재 종결
162022인천광역시7913189986
172022전라남도(광주)4581109375
182022충청남도(대전세종)268522151
192022경기도(남부)621552910
202022경기도(북부)792237
212022강원도332859466
222022충청북도132420186
232022전라북도181515328
242022경상남도90141741750
252022제주도250414208