Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory61.3 B

Variable types

Categorical6
Text1

Alerts

관측일자 has constant value ""Constant
전기자동차 전력 부족량 (75%) is highly overall correlated with 전기자동차 전력 부족량 (25%) and 3 other fieldsHigh correlation
전기자동차 전력 부족량(50%) is highly overall correlated with 전기자동차 전력 부족량 (25%) and 3 other fieldsHigh correlation
전기자동차 전력 부족량 (25%) is highly overall correlated with 전기자동차 전력 부족량(50%) and 3 other fieldsHigh correlation
시군구명 is highly overall correlated with 전기자동차 전력 부족량 (25%) and 3 other fieldsHigh correlation
시군구코드 is highly overall correlated with 전기자동차 전력 부족량 (25%) and 3 other fieldsHigh correlation
격자번호 has unique valuesUnique

Reproduction

Analysis started2023-12-16 03:57:27.573169
Analysis finished2023-12-16 03:57:29.835035
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-02-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-01
2nd row2023-02-01
3rd row2023-02-01
4th row2023-02-01
5th row2023-02-01

Common Values

ValueCountFrequency (%)
2023-02-01 100
100.0%

Length

2023-12-16T03:57:30.181187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:30.642879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-01 100
100.0%

전기자동차 전력 부족량 (25%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-290271.5
44 
-1562.0
38 
-256468.0
18 

Length

Max length9
Median length9
Mean length8.24
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1562.0
2nd row-1562.0
3rd row-1562.0
4th row-1562.0
5th row-1562.0

Common Values

ValueCountFrequency (%)
-290271.5 44
44.0%
-1562.0 38
38.0%
-256468.0 18
18.0%

Length

2023-12-16T03:57:31.334505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:31.841586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
290271.5 44
44.0%
1562.0 38
38.0%
256468.0 18
18.0%

전기자동차 전력 부족량(50%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-413968.5
44 
-4262.0
38 
-288661.0
18 

Length

Max length9
Median length9
Mean length8.24
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-4262.0
2nd row-4262.0
3rd row-4262.0
4th row-4262.0
5th row-4262.0

Common Values

ValueCountFrequency (%)
-413968.5 44
44.0%
-4262.0 38
38.0%
-288661.0 18
18.0%

Length

2023-12-16T03:57:32.358240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:32.901970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
413968.5 44
44.0%
4262.0 38
38.0%
288661.0 18
18.0%

전기자동차 전력 부족량 (75%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-531768.5
44 
-7815.5
38 
-363757.0
18 

Length

Max length9
Median length9
Mean length8.24
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-7815.5
2nd row-7815.5
3rd row-7815.5
4th row-7815.5
5th row-7815.5

Common Values

ValueCountFrequency (%)
-531768.5 44
44.0%
-7815.5 38
38.0%
-363757.0 18
18.0%

Length

2023-12-16T03:57:33.334206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:33.796466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
531768.5 44
44.0%
7815.5 38
38.0%
363757.0 18
18.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-16T03:57:34.774426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters13
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

Unique100 ?
Unique (%)100.0%

Sample

1st row나마8830
2nd row나마8831
3rd row나마8832
4th row나마8833
5th row나마8834
ValueCountFrequency (%)
나마8830 1
 
1.0%
나나7781 1
 
1.0%
나나7878 1
 
1.0%
나나7877 1
 
1.0%
나나7876 1
 
1.0%
나나7875 1
 
1.0%
나나7874 1
 
1.0%
나나7873 1
 
1.0%
나나7786 1
 
1.0%
나나7785 1
 
1.0%
Other values (90) 90
90.0%
2023-12-16T03:57:37.062619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 147
24.5%
7 112
18.7%
8 73
12.2%
3 41
 
6.8%
마 38
 
6.3%
9 38
 
6.3%
5 29
 
4.8%
2 27
 
4.5%
4 23
 
3.8%
6 23
 
3.8%
Other values (3) 49
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 112
28.0%
8 73
18.2%
3 41
 
10.2%
9 38
 
9.5%
5 29
 
7.2%
2 27
 
6.8%
4 23
 
5.8%
6 23
 
5.8%
1 21
 
5.2%
0 13
 
3.2%
Other Letter
ValueCountFrequency (%)
나 147
73.5%
마 38
 
19.0%
다 15
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
7 112
28.0%
8 73
18.2%
3 41
 
10.2%
9 38
 
9.5%
5 29
 
7.2%
2 27
 
6.8%
4 23
 
5.8%
6 23
 
5.8%
1 21
 
5.2%
0 13
 
3.2%
Hangul
ValueCountFrequency (%)
나 147
73.5%
마 38
 
19.0%
다 15
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 147
73.5%
마 38
 
19.0%
다 15
 
7.5%
ASCII
ValueCountFrequency (%)
7 112
28.0%
8 73
18.2%
3 41
 
10.2%
9 38
 
9.5%
5 29
 
7.2%
2 27
 
6.8%
4 23
 
5.8%
6 23
 
5.8%
1 21
 
5.2%
0 13
 
3.2%

시군구코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50110
44 
45800
38 
50130
18 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45800
2nd row45800
3rd row45800
4th row45800
5th row45800

Common Values

ValueCountFrequency (%)
50110 44
44.0%
45800 38
38.0%
50130 18
18.0%

Length

2023-12-16T03:57:38.499605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:39.454753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 44
44.0%
45800 38
38.0%
50130 18
18.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주 제주시
44 
전북 부안군
38 
제주 서귀포시
18 

Length

Max length7
Median length6
Mean length6.18
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북 부안군
2nd row전북 부안군
3rd row전북 부안군
4th row전북 부안군
5th row전북 부안군

Common Values

ValueCountFrequency (%)
제주 제주시 44
44.0%
전북 부안군 38
38.0%
제주 서귀포시 18
18.0%

Length

2023-12-16T03:57:40.339831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:41.143500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 62
31.0%
제주시 44
22.0%
전북 38
19.0%
부안군 38
19.0%
서귀포시 18
 
9.0%

Correlations

2023-12-16T03:57:41.438750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
전기자동차 전력 부족량 (25%)1.0001.0001.0001.0001.0001.000
전기자동차 전력 부족량(50%)1.0001.0001.0001.0001.0001.000
전기자동차 전력 부족량 (75%)1.0001.0001.0001.0001.0001.000
격자번호1.0001.0001.0001.0001.0001.000
시군구코드1.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.000
2023-12-16T03:57:42.178995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기자동차 전력 부족량 (75%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (25%)시군구명시군구코드
전기자동차 전력 부족량 (75%)1.0001.0001.0001.0001.000
전기자동차 전력 부족량(50%)1.0001.0001.0001.0001.000
전기자동차 전력 부족량 (25%)1.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.000
시군구코드1.0001.0001.0001.0001.000
2023-12-16T03:57:42.700864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)시군구코드시군구명
전기자동차 전력 부족량 (25%)1.0001.0001.0001.0001.000
전기자동차 전력 부족량(50%)1.0001.0001.0001.0001.000
전기자동차 전력 부족량 (75%)1.0001.0001.0001.0001.000
시군구코드1.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.000

Missing values

2023-12-16T03:57:28.503083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T03:57:29.542419image/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

관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
02023-02-01-1562.0-4262.0-7815.5나마883045800전북 부안군
12023-02-01-1562.0-4262.0-7815.5나마883145800전북 부안군
22023-02-01-1562.0-4262.0-7815.5나마883245800전북 부안군
32023-02-01-1562.0-4262.0-7815.5나마883345800전북 부안군
42023-02-01-1562.0-4262.0-7815.5나마883445800전북 부안군
52023-02-01-1562.0-4262.0-7815.5나마883545800전북 부안군
62023-02-01-1562.0-4262.0-7815.5나마893245800전북 부안군
72023-02-01-1562.0-4262.0-7815.5나마893345800전북 부안군
82023-02-01-1562.0-4262.0-7815.5나마893445800전북 부안군
92023-02-01-1562.0-4262.0-7815.5나마893545800전북 부안군
관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
902023-02-01-290271.5-413968.5-531768.5나나798150110제주 제주시
912023-02-01-290271.5-413968.5-531768.5나나798250110제주 제주시
922023-02-01-290271.5-413968.5-531768.5나나798350110제주 제주시
932023-02-01-290271.5-413968.5-531768.5나나798450110제주 제주시
942023-02-01-290271.5-413968.5-531768.5나나798550110제주 제주시
952023-02-01-290271.5-413968.5-531768.5나나798650110제주 제주시
962023-02-01-290271.5-413968.5-531768.5나나798750110제주 제주시
972023-02-01-256468.0-288661.0-363757.0나나807250130제주 서귀포시
982023-02-01-256468.0-288661.0-363757.0나나807350130제주 서귀포시
992023-02-01-1562.0-4262.0-7815.5나마873445800전북 부안군