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/15069421/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
2차순번 is highly imbalanced (78.3%)Imbalance
2차사업소 is highly imbalanced (78.3%)Imbalance
전산화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:05:30.361613
Analysis finished2023-12-12 03:05:30.939467
Duration0.58 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
15
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 10000
100.0%

Length

2023-12-12T12:05:31.006162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:31.094652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 10000
100.0%

1차본부
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주본부 10000
100.0%

Length

2023-12-12T12:05:31.188788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:31.287162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주본부 10000
100.0%

2차순번
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9654 
2
 
346

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9654
96.5%
2 346
 
3.5%

Length

2023-12-12T12:05:31.381090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:31.482553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9654
96.5%
2 346
 
3.5%

2차사업소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제주본부직할
9654 
서귀포지사
 
346

Length

Max length6
Median length6
Mean length5.9654
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주본부직할 9654
96.5%
서귀포지사 346
 
3.5%

Length

2023-12-12T12:05:31.586753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:31.685686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주본부직할 9654
96.5%
서귀포지사 346
 
3.5%

전산화번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:05:32.066783image/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 row7524H232
2nd row7923G391
3rd row7221G613
4th row7325Z501
5th row7825F365
ValueCountFrequency (%)
7524h232 1
 
< 0.1%
6621d253 1
 
< 0.1%
6316d141 1
 
< 0.1%
6621s901 1
 
< 0.1%
7522a502 1
 
< 0.1%
94263672 1
 
< 0.1%
8626p321 1
 
< 0.1%
7925g801 1
 
< 0.1%
7122h041 1
 
< 0.1%
7826q523 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T12:05:32.624057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15264
19.1%
1 11172
14.0%
6 7765
9.7%
7 7619
9.5%
8 6378
8.0%
3 5816
 
7.3%
4 5598
 
7.0%
5 5187
 
6.5%
9 4249
 
5.3%
0 3157
 
3.9%
Other values (16) 7795
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72205
90.3%
Uppercase Letter 7795
 
9.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 543
 
7.0%
Y 529
 
6.8%
D 520
 
6.7%
Q 501
 
6.4%
W 499
 
6.4%
C 497
 
6.4%
E 491
 
6.3%
G 488
 
6.3%
S 483
 
6.2%
R 482
 
6.2%
Other values (6) 2762
35.4%
Decimal Number
ValueCountFrequency (%)
2 15264
21.1%
1 11172
15.5%
6 7765
10.8%
7 7619
10.6%
8 6378
8.8%
3 5816
 
8.1%
4 5598
 
7.8%
5 5187
 
7.2%
9 4249
 
5.9%
0 3157
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 72205
90.3%
Latin 7795
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 543
 
7.0%
Y 529
 
6.8%
D 520
 
6.7%
Q 501
 
6.4%
W 499
 
6.4%
C 497
 
6.4%
E 491
 
6.3%
G 488
 
6.3%
S 483
 
6.2%
R 482
 
6.2%
Other values (6) 2762
35.4%
Common
ValueCountFrequency (%)
2 15264
21.1%
1 11172
15.5%
6 7765
10.8%
7 7619
10.6%
8 6378
8.8%
3 5816
 
8.1%
4 5598
 
7.8%
5 5187
 
7.2%
9 4249
 
5.9%
0 3157
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15264
19.1%
1 11172
14.0%
6 7765
9.7%
7 7619
9.5%
8 6378
8.0%
3 5816
 
7.3%
4 5598
 
7.0%
5 5187
 
6.5%
9 4249
 
5.3%
0 3157
 
3.9%
Other values (16) 7795
9.7%

지역구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주택가
5274 
<NA>
3979 
농어촌
727 
번화가
 
20

Length

Max length4
Median length3
Mean length3.3979
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주택가 5274
52.7%
<NA> 3979
39.8%
농어촌 727
 
7.3%
번화가 20
 
0.2%

Length

2023-12-12T12:05:32.817666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:32.937039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택가 5274
52.7%
na 3979
39.8%
농어촌 727
 
7.3%
번화가 20
 
0.2%

Correlations

2023-12-12T12:05:33.036671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0001.0000.229
2차사업소1.0001.0000.229
지역구분0.2290.2291.000
2023-12-12T12:05:33.179553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역구분2차순번2차사업소
지역구분1.0000.3750.375
2차순번0.3751.0000.999
2차사업소0.3750.9991.000
2023-12-12T12:05:33.326827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0000.9990.375
2차사업소0.9991.0000.375
지역구분0.3750.3751.000

Missing values

2023-12-12T12:05:30.759405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:05:30.881971image/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
8313215제주본부1제주본부직할7524H232주택가
1352515제주본부1제주본부직할7923G391농어촌
8950815제주본부1제주본부직할7221G613<NA>
3186815제주본부1제주본부직할7325Z501주택가
4953915제주본부1제주본부직할7825F365<NA>
9048815제주본부1제주본부직할6419X531주택가
9669115제주본부2서귀포지사6610Z261농어촌
5343715제주본부1제주본부직할89282943<NA>
9491115제주본부1제주본부직할8225G402<NA>
907215제주본부1제주본부직할6419G931주택가
1차순번1차본부2차순번2차사업소전산화번호지역구분
2339015제주본부1제주본부직할84222911주택가
6007115제주본부1제주본부직할6515G933주택가
1066415제주본부1제주본부직할8026Q751주택가
5968415제주본부1제주본부직할6216D572<NA>
9221715제주본부1제주본부직할6415D841<NA>
5538315제주본부1제주본부직할7021D802<NA>
6160715제주본부1제주본부직할9327W291<NA>
3411515제주본부1제주본부직할73223961<NA>
8511815제주본부1제주본부직할8223B472<NA>
5938615제주본부1제주본부직할8626P873주택가