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 03:56:36.397730
Analysis finished2023-12-16 03:56:38.740999
Duration2.34 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
2023-05-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-01
2nd row2023-05-01
3rd row2023-05-01
4th row2023-05-01
5th row2023-05-01

Common Values

ValueCountFrequency (%)
2023-05-01 100
100.0%

Length

2023-12-16T03:56:39.054536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:39.570054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-01 100
100.0%

전기자동차 전력 부족량 (25%)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-253828.5
68 
-242096.0
32 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-253828.5
2nd row-253828.5
3rd row-253828.5
4th row-253828.5
5th row-253828.5

Common Values

ValueCountFrequency (%)
-253828.5 68
68.0%
-242096.0 32
32.0%

Length

2023-12-16T03:56:40.135946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:40.653145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
253828.5 68
68.0%
242096.0 32
32.0%

전기자동차 전력 부족량(50%)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-435690.5
68 
-308926.0
32 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-435690.5
2nd row-435690.5
3rd row-435690.5
4th row-435690.5
5th row-435690.5

Common Values

ValueCountFrequency (%)
-435690.5 68
68.0%
-308926.0 32
32.0%

Length

2023-12-16T03:56:41.268014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:41.672160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
435690.5 68
68.0%
308926.0 32
32.0%

전기자동차 전력 부족량 (75%)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-579564.5
68 
-373502.0
32 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-579564.5
2nd row-579564.5
3rd row-579564.5
4th row-579564.5
5th row-579564.5

Common Values

ValueCountFrequency (%)
-579564.5 68
68.0%
-373502.0 32
32.0%

Length

2023-12-16T03:56:42.257638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:42.644074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
579564.5 68
68.0%
373502.0 32
32.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-16T03:56:43.482724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters11
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 row나나7578
2nd row나나7579
3rd row나나7580
4th row나나7581
5th row나나7582
ValueCountFrequency (%)
나나7578 1
 
1.0%
나나8075 1
 
1.0%
나나8086 1
 
1.0%
나나8085 1
 
1.0%
나나8084 1
 
1.0%
나나8083 1
 
1.0%
나나8082 1
 
1.0%
나나8081 1
 
1.0%
나나8080 1
 
1.0%
나나8079 1
 
1.0%
Other values (90) 90
90.0%
2023-12-16T03:56:45.178752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
7 132
22.0%
8 114
19.0%
1 26
 
4.3%
0 25
 
4.2%
9 23
 
3.8%
6 21
 
3.5%
5 19
 
3.2%
2 15
 
2.5%
4 13
 
2.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 132
33.0%
8 114
28.5%
1 26
 
6.5%
0 25
 
6.2%
9 23
 
5.8%
6 21
 
5.2%
5 19
 
4.8%
2 15
 
3.8%
4 13
 
3.2%
3 12
 
3.0%
Other Letter
ValueCountFrequency (%)
나 200
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 132
33.0%
8 114
28.5%
1 26
 
6.5%
0 25
 
6.2%
9 23
 
5.8%
6 21
 
5.2%
5 19
 
4.8%
2 15
 
3.8%
4 13
 
3.2%
3 12
 
3.0%
Hangul
ValueCountFrequency (%)
나 200
100.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 200
100.0%
ASCII
ValueCountFrequency (%)
7 132
33.0%
8 114
28.5%
1 26
 
6.5%
0 25
 
6.2%
9 23
 
5.8%
6 21
 
5.2%
5 19
 
4.8%
2 15
 
3.8%
4 13
 
3.2%
3 12
 
3.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50110
68 
50130
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 68
68.0%
50130 32
32.0%

Length

2023-12-16T03:56:45.835678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:46.262253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 68
68.0%
50130 32
32.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주 제주시
68 
제주 서귀포시
32 

Length

Max length7
Median length6
Mean length6.32
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주 제주시
2nd row제주 제주시
3rd row제주 제주시
4th row제주 제주시
5th row제주 제주시

Common Values

ValueCountFrequency (%)
제주 제주시 68
68.0%
제주 서귀포시 32
32.0%

Length

2023-12-16T03:56:46.765734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:47.120277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 100
50.0%
제주시 68
34.0%
서귀포시 32
 
16.0%

Correlations

2023-12-16T03:56:47.428825image/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%)1.0000.9990.9991.0000.9990.999
전기자동차 전력 부족량(50%)0.9991.0000.9991.0000.9990.999
전기자동차 전력 부족량 (75%)0.9990.9991.0001.0000.9990.999
격자번호1.0001.0001.0001.0001.0001.000
시군구코드0.9990.9990.9991.0001.0000.999
시군구명0.9990.9990.9991.0000.9991.000
2023-12-16T03:56:47.741722image/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%)1.0000.9770.9770.9770.977
전기자동차 전력 부족량(50%)0.9771.0000.9770.9770.977
전기자동차 전력 부족량 (25%)0.9770.9771.0000.9770.977
시군구명0.9770.9770.9771.0000.977
시군구코드0.9770.9770.9770.9771.000
2023-12-16T03:56:48.195401image/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%)1.0000.9770.9770.9770.977
전기자동차 전력 부족량(50%)0.9771.0000.9770.9770.977
전기자동차 전력 부족량 (75%)0.9770.9771.0000.9770.977
시군구코드0.9770.9770.9771.0000.977
시군구명0.9770.9770.9770.9771.000

Missing values

2023-12-16T03:56:37.626776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T03:56:38.536111image/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
02023-05-01-253828.5-435690.5-579564.5나나757850110제주 제주시
12023-05-01-253828.5-435690.5-579564.5나나757950110제주 제주시
22023-05-01-253828.5-435690.5-579564.5나나758050110제주 제주시
32023-05-01-253828.5-435690.5-579564.5나나758150110제주 제주시
42023-05-01-253828.5-435690.5-579564.5나나758250110제주 제주시
52023-05-01-253828.5-435690.5-579564.5나나758350110제주 제주시
62023-05-01-253828.5-435690.5-579564.5나나758450110제주 제주시
72023-05-01-242096.0-308926.0-373502.0나나767550130제주 서귀포시
82023-05-01-242096.0-308926.0-373502.0나나767650130제주 서귀포시
92023-05-01-253828.5-435690.5-579564.5나나767750110제주 제주시
관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
902023-05-01-253828.5-435690.5-579564.5나나818650110제주 제주시
912023-05-01-253828.5-435690.5-579564.5나나818750110제주 제주시
922023-05-01-253828.5-435690.5-579564.5나나818850110제주 제주시
932023-05-01-253828.5-435690.5-579564.5나나818950110제주 제주시
942023-05-01-242096.0-308926.0-373502.0나나827050130제주 서귀포시
952023-05-01-242096.0-308926.0-373502.0나나827150130제주 서귀포시
962023-05-01-242096.0-308926.0-373502.0나나827250130제주 서귀포시
972023-05-01-242096.0-308926.0-373502.0나나827350130제주 서귀포시
982023-05-01-242096.0-308926.0-373502.0나나827450130제주 서귀포시
992023-05-01-253828.5-435690.5-579564.5나나757750110제주 제주시