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 03:57:11.183047
Analysis finished2023-12-16 03:57:13.230622
Duration2.05 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
2023-03-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-01
2nd row2023-03-01
3rd row2023-03-01
4th row2023-03-01
5th row2023-03-01

Common Values

ValueCountFrequency (%)
2023-03-01 100
100.0%

Length

2023-12-16T03:57:13.617952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:14.110892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-01 100
100.0%

전기자동차 전력 부족량 (25%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-278419.5
55 
-2529.32
23 
-249039.0
22 

Length

Max length9
Median length9
Mean length8.77
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-2529.32
2nd row-2529.32
3rd row-2529.32
4th row-2529.32
5th row-2529.32

Common Values

ValueCountFrequency (%)
-278419.5 55
55.0%
-2529.32 23
23.0%
-249039.0 22
 
22.0%

Length

2023-12-16T03:57:14.749907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:15.255439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
278419.5 55
55.0%
2529.32 23
23.0%
249039.0 22
 
22.0%

전기자동차 전력 부족량(50%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-396633.5
55 
-6598.0
23 
-300025.0
22 

Length

Max length9
Median length9
Mean length8.54
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-6598.0
2nd row-6598.0
3rd row-6598.0
4th row-6598.0
5th row-6598.0

Common Values

ValueCountFrequency (%)
-396633.5 55
55.0%
-6598.0 23
23.0%
-300025.0 22
 
22.0%

Length

2023-12-16T03:57:16.009986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:16.552444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
396633.5 55
55.0%
6598.0 23
23.0%
300025.0 22
 
22.0%

전기자동차 전력 부족량 (75%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-559138.5
55 
-8488.0
23 
-365077.0
22 

Length

Max length9
Median length9
Mean length8.54
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-8488.0
2nd row-8488.0
3rd row-8488.0
4th row-8488.0
5th row-8488.0

Common Values

ValueCountFrequency (%)
-559138.5 55
55.0%
-8488.0 23
23.0%
-365077.0 22
 
22.0%

Length

2023-12-16T03:57:17.533642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:18.195911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
559138.5 55
55.0%
8488.0 23
23.0%
365077.0 22
 
22.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-16T03:57:19.113273image/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 row나라9692
2nd row나라9693
3rd row나라9694
4th row나라9695
5th row나라9696
ValueCountFrequency (%)
나라9692 1
 
1.0%
나나7882 1
 
1.0%
나나7979 1
 
1.0%
나나7978 1
 
1.0%
나나7977 1
 
1.0%
나나7976 1
 
1.0%
나나7975 1
 
1.0%
나나7974 1
 
1.0%
나나7973 1
 
1.0%
나나7886 1
 
1.0%
Other values (90) 90
90.0%
2023-12-16T03:57:21.221372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 171
28.5%
7 128
21.3%
8 83
13.8%
9 69
11.5%
6 30
 
5.0%
0 28
 
4.7%
라 23
 
3.8%
5 20
 
3.3%
4 12
 
2.0%
2 11
 
1.8%
Other values (3) 25
 
4.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 128
32.0%
8 83
20.8%
9 69
17.2%
6 30
 
7.5%
0 28
 
7.0%
5 20
 
5.0%
4 12
 
3.0%
2 11
 
2.8%
3 11
 
2.8%
1 8
 
2.0%
Other Letter
ValueCountFrequency (%)
나 171
85.5%
라 23
 
11.5%
다 6
 
3.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 128
32.0%
8 83
20.8%
9 69
17.2%
6 30
 
7.5%
0 28
 
7.0%
5 20
 
5.0%
4 12
 
3.0%
2 11
 
2.8%
3 11
 
2.8%
1 8
 
2.0%
Hangul
ValueCountFrequency (%)
나 171
85.5%
라 23
 
11.5%
다 6
 
3.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 171
85.5%
라 23
 
11.5%
다 6
 
3.0%
ASCII
ValueCountFrequency (%)
7 128
32.0%
8 83
20.8%
9 69
17.2%
6 30
 
7.5%
0 28
 
7.0%
5 20
 
5.0%
4 12
 
3.0%
2 11
 
2.8%
3 11
 
2.8%
1 8
 
2.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50110
55 
46870
23 
50130
22 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46870
2nd row46870
3rd row46870
4th row46870
5th row46870

Common Values

ValueCountFrequency (%)
50110 55
55.0%
46870 23
23.0%
50130 22
 
22.0%

Length

2023-12-16T03:57:21.798912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:22.235756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 55
55.0%
46870 23
23.0%
50130 22
 
22.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주 제주시
55 
전남 영광군
23 
제주 서귀포시
22 

Length

Max length7
Median length6
Mean length6.22
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남 영광군
2nd row전남 영광군
3rd row전남 영광군
4th row전남 영광군
5th row전남 영광군

Common Values

ValueCountFrequency (%)
제주 제주시 55
55.0%
전남 영광군 23
23.0%
제주 서귀포시 22
 
22.0%

Length

2023-12-16T03:57:22.648131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:23.106216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 77
38.5%
제주시 55
27.5%
전남 23
 
11.5%
영광군 23
 
11.5%
서귀포시 22
 
11.0%

Correlations

2023-12-16T03:57:23.542315image/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
2023-12-16T03:57:23.985757image/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
2023-12-16T03:57:24.495011image/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

2023-12-16T03:57:12.271842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T03:57:12.927829image/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
02023-03-01-2529.32-6598.0-8488.0나라969246870전남 영광군
12023-03-01-2529.32-6598.0-8488.0나라969346870전남 영광군
22023-03-01-2529.32-6598.0-8488.0나라969446870전남 영광군
32023-03-01-2529.32-6598.0-8488.0나라969546870전남 영광군
42023-03-01-2529.32-6598.0-8488.0나라969646870전남 영광군
52023-03-01-2529.32-6598.0-8488.0나라969746870전남 영광군
62023-03-01-2529.32-6598.0-8488.0나라969846870전남 영광군
72023-03-01-2529.32-6598.0-8488.0나라969946870전남 영광군
82023-03-01-2529.32-6598.0-8488.0나라979246870전남 영광군
92023-03-01-2529.32-6598.0-8488.0나라979346870전남 영광군
관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
902023-03-01-278419.5-396633.5-559138.5나나808050110제주 제주시
912023-03-01-278419.5-396633.5-559138.5나나808150110제주 제주시
922023-03-01-278419.5-396633.5-559138.5나나808250110제주 제주시
932023-03-01-278419.5-396633.5-559138.5나나808350110제주 제주시
942023-03-01-278419.5-396633.5-559138.5나나808450110제주 제주시
952023-03-01-278419.5-396633.5-559138.5나나808550110제주 제주시
962023-03-01-278419.5-396633.5-559138.5나나808650110제주 제주시
972023-03-01-278419.5-396633.5-559138.5나나808750110제주 제주시
982023-03-01-278419.5-396633.5-559138.5나나808850110제주 제주시
992023-03-01-2529.32-6598.0-8488.0나라959946870전남 영광군