Overview

Dataset statistics

Number of variables4
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory34.6 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description2020년 4월 일반용정기점검에 대한 통계 정보를 시,도,구,군을 나눠서 적합과 부적합 건수를 제공하는 파일입니다.
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15044359/fileData.do

Alerts

적합 is highly overall correlated with 부적합High correlation
부적합 is highly overall correlated with 적합High correlation
부적합 has 36 (15.7%) zerosZeros

Reproduction

Analysis started2023-12-12 17:18:32.293921
Analysis finished2023-12-12 17:18:33.028709
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
42 
서울특별시
25 
경상남도
20 
경상북도
20 
전라남도
18 
Other values (12)
104 

Length

Max length7
Median length5
Mean length4.1004367
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 42
18.3%
서울특별시 25
10.9%
경상남도 20
8.7%
경상북도 20
8.7%
전라남도 18
7.9%
강원도 15
 
6.6%
부산광역시 15
 
6.6%
충청남도 14
 
6.1%
전라북도 14
 
6.1%
충청북도 12
 
5.2%
Other values (7) 34
14.8%

Length

2023-12-13T02:18:33.102808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 42
18.3%
서울특별시 25
10.9%
경상남도 20
8.7%
경상북도 20
8.7%
전라남도 18
7.9%
강원도 15
 
6.6%
부산광역시 15
 
6.6%
전라북도 14
 
6.1%
충청남도 14
 
6.1%
충청북도 12
 
5.2%
Other values (7) 34
14.8%

구군
Text

Distinct208
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T02:18:33.468539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.5240175
Min length2

Characters and Unicode

Total characters807
Distinct characters140
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)87.8%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row속초시
5th row양양군
ValueCountFrequency (%)
동구 6
 
2.3%
중구 6
 
2.3%
서구 5
 
1.9%
북구 5
 
1.9%
남구 4
 
1.5%
부천시 4
 
1.5%
수원시 4
 
1.5%
창원시 4
 
1.5%
성남시 3
 
1.1%
용인시 3
 
1.1%
Other values (207) 217
83.1%
2023-12-13T02:18:33.958872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 101
 
12.5%
시 94
 
11.6%
군 72
 
8.9%
32
 
4.0%
천 23
 
2.9%
산 21
 
2.6%
주 20
 
2.5%
성 19
 
2.4%
양 19
 
2.4%
동 18
 
2.2%
Other values (130) 388
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 775
96.0%
Space Separator 32
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 101
 
13.0%
시 94
 
12.1%
군 72
 
9.3%
천 23
 
3.0%
산 21
 
2.7%
주 20
 
2.6%
성 19
 
2.5%
양 19
 
2.5%
동 18
 
2.3%
안 17
 
2.2%
Other values (129) 371
47.9%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 775
96.0%
Common 32
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 101
 
13.0%
시 94
 
12.1%
군 72
 
9.3%
천 23
 
3.0%
산 21
 
2.7%
주 20
 
2.6%
성 19
 
2.5%
양 19
 
2.5%
동 18
 
2.3%
안 17
 
2.2%
Other values (129) 371
47.9%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 775
96.0%
ASCII 32
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 101
 
13.0%
시 94
 
12.1%
군 72
 
9.3%
천 23
 
3.0%
산 21
 
2.7%
주 20
 
2.6%
성 19
 
2.5%
양 19
 
2.5%
동 18
 
2.3%
안 17
 
2.2%
Other values (129) 371
47.9%
ASCII
ValueCountFrequency (%)
32
100.0%

적합
Real number (ℝ)

HIGH CORRELATION 

Distinct215
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2430.393
Minimum0
Maximum15615
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T02:18:34.124057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1205
median913
Q34066
95-th percentile8846
Maximum15615
Range15615
Interquartile range (IQR)3861

Descriptive statistics

Standard deviation3136.7874
Coefficient of variation (CV)1.2906503
Kurtosis2.6183291
Mean2430.393
Median Absolute Deviation (MAD)844
Skewness1.6906548
Sum556560
Variance9839435
MonotonicityNot monotonic
2023-12-13T02:18:34.311080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
1.3%
10 3
 
1.3%
6 2
 
0.9%
126 2
 
0.9%
120 2
 
0.9%
292 2
 
0.9%
501 2
 
0.9%
221 2
 
0.9%
319 2
 
0.9%
40 2
 
0.9%
Other values (205) 207
90.4%
ValueCountFrequency (%)
0 1
 
0.4%
1 1
 
0.4%
2 3
1.3%
3 1
 
0.4%
4 1
 
0.4%
5 1
 
0.4%
6 2
0.9%
8 1
 
0.4%
10 3
1.3%
11 1
 
0.4%
ValueCountFrequency (%)
15615 1
0.4%
13461 1
0.4%
13363 1
0.4%
12260 1
0.4%
11404 1
0.4%
11356 1
0.4%
11345 1
0.4%
10955 1
0.4%
10625 1
0.4%
10382 1
0.4%

부적합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.825328
Minimum0
Maximum397
Zeros36
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T02:18:34.764505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q366
95-th percentile152.8
Maximum397
Range397
Interquartile range (IQR)63

Descriptive statistics

Standard deviation61.683652
Coefficient of variation (CV)1.4403545
Kurtosis7.8993052
Mean42.825328
Median Absolute Deviation (MAD)15
Skewness2.4660182
Sum9807
Variance3804.8729
MonotonicityNot monotonic
2023-12-13T02:18:34.908577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
15.7%
1 10
 
4.4%
4 9
 
3.9%
2 9
 
3.9%
7 8
 
3.5%
3 7
 
3.1%
9 6
 
2.6%
15 6
 
2.6%
14 5
 
2.2%
5 5
 
2.2%
Other values (83) 128
55.9%
ValueCountFrequency (%)
0 36
15.7%
1 10
 
4.4%
2 9
 
3.9%
3 7
 
3.1%
4 9
 
3.9%
5 5
 
2.2%
6 4
 
1.7%
7 8
 
3.5%
8 4
 
1.7%
9 6
 
2.6%
ValueCountFrequency (%)
397 1
 
0.4%
322 1
 
0.4%
292 1
 
0.4%
264 1
 
0.4%
263 1
 
0.4%
221 1
 
0.4%
209 1
 
0.4%
208 1
 
0.4%
189 1
 
0.4%
154 3
1.3%

Interactions

2023-12-13T02:18:32.676413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:18:32.454702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:18:32.780731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:18:32.561135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:18:34.999907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도적합부적합
시도1.0000.1660.303
적합0.1661.0000.911
부적합0.3030.9111.000
2023-12-13T02:18:35.095254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적합부적합시도
적합1.0000.9190.062
부적합0.9191.0000.119
시도0.0620.1191.000

Missing values

2023-12-13T02:18:32.910977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:18:32.997124image/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

시도구군적합부적합
0강원도강릉시1708
1강원도고성군228154
2강원도동해시5547
3강원도속초시1260
4강원도양양군98315
5강원도영월군100
6강원도원주시61915
7강원도정선군323775
8강원도철원군30
9강원도춘천시173928
시도구군적합부적합
219인천광역시중구3217
220인천광역시강화군60
221인천광역시계양구142018
222인천광역시남동구82216
223인천광역시부평구1475
224인천광역시연수구109821
225인천광역시옹진군2629
226세종특별자치시세종특별자치시6653
227제주특별자치도제주시899064
228제주특별자치도서귀포시2211