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 03:56:17.820108
Analysis finished2023-12-16 03:56:19.614559
Duration1.79 second
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
2023-06-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 100
100.0%

Length

2023-12-16T03:56:19.830417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:20.222717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 100
100.0%

전기자동차 전력 부족량 (25%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-1410.0
76 
-255778.5
18 
-250245.0
 
6

Length

Max length9
Median length7
Mean length7.48
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1410.0
2nd row-1410.0
3rd row-1410.0
4th row-1410.0
5th row-1410.0

Common Values

ValueCountFrequency (%)
-1410.0 76
76.0%
-255778.5 18
 
18.0%
-250245.0 6
 
6.0%

Length

2023-12-16T03:56:21.005269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:21.596415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1410.0 76
76.0%
255778.5 18
 
18.0%
250245.0 6
 
6.0%

전기자동차 전력 부족량(50%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-4385.4
76 
-456835.5
18 
-308693.0
 
6

Length

Max length9
Median length7
Mean length7.48
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-4385.4
2nd row-4385.4
3rd row-4385.4
4th row-4385.4
5th row-4385.4

Common Values

ValueCountFrequency (%)
-4385.4 76
76.0%
-456835.5 18
 
18.0%
-308693.0 6
 
6.0%

Length

2023-12-16T03:56:22.368213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:23.203203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4385.4 76
76.0%
456835.5 18
 
18.0%
308693.0 6
 
6.0%

전기자동차 전력 부족량 (75%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-8248.0
76 
-588435.5
18 
-392456.0
 
6

Length

Max length9
Median length7
Mean length7.48
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-8248.0
2nd row-8248.0
3rd row-8248.0
4th row-8248.0
5th row-8248.0

Common Values

ValueCountFrequency (%)
-8248.0 76
76.0%
-588435.5 18
 
18.0%
-392456.0 6
 
6.0%

Length

2023-12-16T03:56:23.863236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:24.618686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8248.0 76
76.0%
588435.5 18
 
18.0%
392456.0 6
 
6.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-16T03:56:25.781460image/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 row나마8831
2nd row나마8832
3rd row나마8833
4th row나마8834
5th row나마8835
ValueCountFrequency (%)
나마8831 1
 
1.0%
다마2054 1
 
1.0%
다마2142 1
 
1.0%
다마2141 1
 
1.0%
다마2140 1
 
1.0%
다마2139 1
 
1.0%
다마2138 1
 
1.0%
다마2137 1
 
1.0%
다마2136 1
 
1.0%
다마2135 1
 
1.0%
Other values (90) 90
90.0%
2023-12-16T03:56:28.660235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 76
12.7%
나 70
11.7%
7 65
10.8%
3 56
9.3%
다 54
9.0%
2 45
7.5%
4 44
7.3%
1 42
7.0%
8 35
5.8%
0 33
5.5%
Other values (3) 80
13.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 65
16.2%
3 56
14.0%
2 45
11.2%
4 44
11.0%
1 42
10.5%
8 35
8.8%
0 33
8.2%
9 32
8.0%
5 26
 
6.5%
6 22
 
5.5%
Other Letter
ValueCountFrequency (%)
마 76
38.0%
나 70
35.0%
다 54
27.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 65
16.2%
3 56
14.0%
2 45
11.2%
4 44
11.0%
1 42
10.5%
8 35
8.8%
0 33
8.2%
9 32
8.0%
5 26
 
6.5%
6 22
 
5.5%
Hangul
ValueCountFrequency (%)
마 76
38.0%
나 70
35.0%
다 54
27.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 76
38.0%
나 70
35.0%
다 54
27.0%
ASCII
ValueCountFrequency (%)
7 65
16.2%
3 56
14.0%
2 45
11.2%
4 44
11.0%
1 42
10.5%
8 35
8.8%
0 33
8.2%
9 32
8.0%
5 26
 
6.5%
6 22
 
5.5%

시군구코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
45800
76 
50110
18 
50130
 
6

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 (%)
45800 76
76.0%
50110 18
 
18.0%
50130 6
 
6.0%

Length

2023-12-16T03:56:29.709064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:30.441956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45800 76
76.0%
50110 18
 
18.0%
50130 6
 
6.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전북 부안군
76 
제주 제주시
18 
제주 서귀포시
 
6

Length

Max length7
Median length6
Mean length6.06
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 (%)
전북 부안군 76
76.0%
제주 제주시 18
 
18.0%
제주 서귀포시 6
 
6.0%

Length

2023-12-16T03:56:31.247383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:32.426601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북 76
38.0%
부안군 76
38.0%
제주 24
 
12.0%
제주시 18
 
9.0%
서귀포시 6
 
3.0%

Correlations

2023-12-16T03:56:32.854031image/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
2023-12-16T03:56:33.276414image/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
2023-12-16T03:56:33.712802image/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

2023-12-16T03:56:18.637668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T03:56:19.327882image/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
02023-06-01-1410.0-4385.4-8248.0나마883145800전북 부안군
12023-06-01-1410.0-4385.4-8248.0나마883245800전북 부안군
22023-06-01-1410.0-4385.4-8248.0나마883345800전북 부안군
32023-06-01-1410.0-4385.4-8248.0나마883445800전북 부안군
42023-06-01-1410.0-4385.4-8248.0나마883545800전북 부안군
52023-06-01-1410.0-4385.4-8248.0나마893245800전북 부안군
62023-06-01-1410.0-4385.4-8248.0나마893345800전북 부안군
72023-06-01-1410.0-4385.4-8248.0나마893445800전북 부안군
82023-06-01-1410.0-4385.4-8248.0나마893545800전북 부안군
92023-06-01-1410.0-4385.4-8248.0나마893645800전북 부안군
관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
902023-06-01-255778.5-456835.5-588435.5나나768250110제주 제주시
912023-06-01-255778.5-456835.5-588435.5나나768350110제주 제주시
922023-06-01-255778.5-456835.5-588435.5나나768450110제주 제주시
932023-06-01-250245.0-308693.0-392456.0나나777450130제주 서귀포시
942023-06-01-250245.0-308693.0-392456.0나나777550130제주 서귀포시
952023-06-01-250245.0-308693.0-392456.0나나777650130제주 서귀포시
962023-06-01-250245.0-308693.0-392456.0나나777750130제주 서귀포시
972023-06-01-255778.5-456835.5-588435.5나나777850110제주 제주시
982023-06-01-255778.5-456835.5-588435.5나나777950110제주 제주시
992023-06-01-1410.0-4385.4-8248.0나마883045800전북 부안군