Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Categorical5
Text1

Dataset

Description전국 전주전산화번호(전주번호)
Author한국전력공사
URLhttps://www.data.go.kr/data/15069471/fileData.do

Alerts

1차순번 has constant value ""Constant
1차본부 has constant value ""Constant
2차순번 is highly overall correlated with 2차사업소High correlation
2차사업소 is highly overall correlated with 2차순번High correlation
전산화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:58:59.921169
Analysis finished2023-12-12 01:59:00.518350
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1차순번
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
13
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 10000
100.0%

Length

2023-12-12T10:59:00.589757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:00.683707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 10000
100.0%

1차본부
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산울산본부
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산울산본부 10000
100.0%

Length

2023-12-12T10:59:00.795906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:00.885178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산울산본부 10000
100.0%

2차순번
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6263 
3
2309 
1
1428 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 6263
62.6%
3 2309
 
23.1%
1 1428
 
14.3%

Length

2023-12-12T10:59:00.979253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:01.086710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6263
62.6%
3 2309
 
23.1%
1 1428
 
14.3%

2차사업소
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
울산지사
6263 
김해지사
2309 
부산울산본부직할
1428 

Length

Max length8
Median length4
Mean length4.5712
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산지사
2nd row울산지사
3rd row김해지사
4th row울산지사
5th row울산지사

Common Values

ValueCountFrequency (%)
울산지사 6263
62.6%
김해지사 2309
 
23.1%
부산울산본부직할 1428
 
14.3%

Length

2023-12-12T10:59:01.199588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:01.306791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산지사 6263
62.6%
김해지사 2309
 
23.1%
부산울산본부직할 1428
 
14.3%

전산화번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T10:59:01.638780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row1514A721
2nd row1509A392
3rd row9299H791
4th row1409F571
5th row0908D713
ValueCountFrequency (%)
1514a721 1
 
< 0.1%
9397c281 1
 
< 0.1%
0291w144 1
 
< 0.1%
9597r053 1
 
< 0.1%
1503a194 1
 
< 0.1%
9303w341 1
 
< 0.1%
1108c411 1
 
< 0.1%
0391r065 1
 
< 0.1%
9103h261 1
 
< 0.1%
1307p881 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T10:59:02.122049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18681
23.4%
0 8223
10.3%
9 8027
10.0%
2 7629
9.5%
3 6394
 
8.0%
4 5643
 
7.1%
5 4867
 
6.1%
6 3974
 
5.0%
8 3636
 
4.5%
7 3071
 
3.8%
Other values (16) 9855
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70145
87.7%
Uppercase Letter 9855
 
12.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 720
 
7.3%
A 695
 
7.1%
H 658
 
6.7%
F 654
 
6.6%
C 643
 
6.5%
D 640
 
6.5%
G 639
 
6.5%
Y 632
 
6.4%
W 621
 
6.3%
R 617
 
6.3%
Other values (6) 3336
33.9%
Decimal Number
ValueCountFrequency (%)
1 18681
26.6%
0 8223
11.7%
9 8027
11.4%
2 7629
10.9%
3 6394
 
9.1%
4 5643
 
8.0%
5 4867
 
6.9%
6 3974
 
5.7%
8 3636
 
5.2%
7 3071
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 70145
87.7%
Latin 9855
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 720
 
7.3%
A 695
 
7.1%
H 658
 
6.7%
F 654
 
6.6%
C 643
 
6.5%
D 640
 
6.5%
G 639
 
6.5%
Y 632
 
6.4%
W 621
 
6.3%
R 617
 
6.3%
Other values (6) 3336
33.9%
Common
ValueCountFrequency (%)
1 18681
26.6%
0 8223
11.7%
9 8027
11.4%
2 7629
10.9%
3 6394
 
9.1%
4 5643
 
8.0%
5 4867
 
6.9%
6 3974
 
5.7%
8 3636
 
5.2%
7 3071
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18681
23.4%
0 8223
10.3%
9 8027
10.0%
2 7629
9.5%
3 6394
 
8.0%
4 5643
 
7.1%
5 4867
 
6.1%
6 3974
 
5.0%
8 3636
 
4.5%
7 3071
 
3.8%
Other values (16) 9855
12.3%

지역구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주택가
4629 
<NA>
3466 
농어촌
1686 
번화가
 
218
공란
 
1

Length

Max length4
Median length3
Mean length3.3465
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row주택가
2nd row주택가
3rd row주택가
4th row농어촌
5th row<NA>

Common Values

ValueCountFrequency (%)
주택가 4629
46.3%
<NA> 3466
34.7%
농어촌 1686
 
16.9%
번화가 218
 
2.2%
공란 1
 
< 0.1%

Length

2023-12-12T10:59:02.291703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:59:02.412091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택가 4629
46.3%
na 3466
34.7%
농어촌 1686
 
16.9%
번화가 218
 
2.2%
공란 1
 
< 0.1%

Correlations

2023-12-12T10:59:02.517478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0001.0000.245
2차사업소1.0001.0000.245
지역구분0.2450.2451.000
2023-12-12T10:59:02.662768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번지역구분2차사업소
2차순번1.0000.2341.000
지역구분0.2341.0000.234
2차사업소1.0000.2341.000
2023-12-12T10:59:02.745388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0001.0000.234
2차사업소1.0001.0000.234
지역구분0.2340.2341.000

Missing values

2023-12-12T10:59:00.335919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:59:00.465883image/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

1차순번1차본부2차순번2차사업소전산화번호지역구분
3225113부산울산본부2울산지사1514A721주택가
4098013부산울산본부2울산지사1509A392주택가
8920813부산울산본부3김해지사9299H791주택가
5084113부산울산본부2울산지사1409F571농어촌
5903313부산울산본부2울산지사0908D713<NA>
7121513부산울산본부2울산지사1306B401<NA>
5072513부산울산본부2울산지사1413B993<NA>
8626613부산울산본부3김해지사9597R812<NA>
5348713부산울산본부2울산지사1114E991주택가
1392813부산울산본부2울산지사1514C274<NA>
1차순번1차본부2차순번2차사업소전산화번호지역구분
3149113부산울산본부2울산지사1414E122<NA>
6448513부산울산본부2울산지사1615R223주택가
8431413부산울산본부3김해지사9200Y411농어촌
4704413부산울산본부2울산지사1517R501농어촌
8968213부산울산본부3김해지사9496A191주택가
7696813부산울산본부3김해지사9200E221농어촌
8045013부산울산본부3김해지사9697D451<NA>
1825513부산울산본부2울산지사1615B461주택가
3205513부산울산본부2울산지사1515Y783번화가
5762213부산울산본부2울산지사1216H553주택가