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

DateTime1
Categorical5
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 03:56:51.242675
Analysis finished2023-12-16 03:56:54.084414
Duration2.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-16T03:56:54.397066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T03:56:55.269851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

전기자동차 전력 부족량 (25%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
6493.9
53 
-257960.5
34 
-239321.0
13 

Length

Max length9
Median length6
Mean length7.41
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6493.9
2nd row6493.9
3rd row6493.9
4th row6493.9
5th row6493.9

Common Values

ValueCountFrequency (%)
6493.9 53
53.0%
-257960.5 34
34.0%
-239321.0 13
 
13.0%

Length

2023-12-16T03:56:55.867979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:56.443132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6493.9 53
53.0%
257960.5 34
34.0%
239321.0 13
 
13.0%

전기자동차 전력 부족량(50%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4698.6
53 
-410097.5
34 
-295430.0
13 

Length

Max length9
Median length6
Mean length7.41
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4698.6
2nd row4698.6
3rd row4698.6
4th row4698.6
5th row4698.6

Common Values

ValueCountFrequency (%)
4698.6 53
53.0%
-410097.5 34
34.0%
-295430.0 13
 
13.0%

Length

2023-12-16T03:56:57.048059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:57.702613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4698.6 53
53.0%
410097.5 34
34.0%
295430.0 13
 
13.0%

전기자동차 전력 부족량 (75%)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2530.0
53 
-566774.5
34 
-377805.0
13 

Length

Max length9
Median length6
Mean length7.41
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2530.0
2nd row2530.0
3rd row2530.0
4th row2530.0
5th row2530.0

Common Values

ValueCountFrequency (%)
2530.0 53
53.0%
-566774.5 34
34.0%
-377805.0 13
 
13.0%

Length

2023-12-16T03:56:58.643408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:56:59.146098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2530.0 53
53.0%
566774.5 34
34.0%
377805.0 13
 
13.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-16T03:57:00.121103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
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 row마마0166
2nd row마마0167
3rd row마마0168
4th row마마0565
5th row마마0566
ValueCountFrequency (%)
마마0166 1
 
1.0%
나나7677 1
 
1.0%
나나7777 1
 
1.0%
나나7776 1
 
1.0%
나나7775 1
 
1.0%
나나7774 1
 
1.0%
나나7684 1
 
1.0%
나나7683 1
 
1.0%
나나7682 1
 
1.0%
나나7681 1
 
1.0%
Other values (90) 90
90.0%
2023-12-16T03:57:02.338216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 123
20.5%
마 106
17.7%
나 94
15.7%
8 68
11.3%
0 46
 
7.7%
6 44
 
7.3%
1 43
 
7.2%
5 36
 
6.0%
4 12
 
2.0%
9 11
 
1.8%
Other values (2) 17
 
2.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 123
30.8%
8 68
17.0%
0 46
 
11.5%
6 44
 
11.0%
1 43
 
10.8%
5 36
 
9.0%
4 12
 
3.0%
9 11
 
2.8%
3 10
 
2.5%
2 7
 
1.8%
Other Letter
ValueCountFrequency (%)
마 106
53.0%
나 94
47.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 123
30.8%
8 68
17.0%
0 46
 
11.5%
6 44
 
11.0%
1 43
 
10.8%
5 36
 
9.0%
4 12
 
3.0%
9 11
 
2.8%
3 10
 
2.5%
2 7
 
1.8%
Hangul
ValueCountFrequency (%)
마 106
53.0%
나 94
47.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 123
30.8%
8 68
17.0%
0 46
 
11.5%
6 44
 
11.0%
1 43
 
10.8%
5 36
 
9.0%
4 12
 
3.0%
9 11
 
2.8%
3 10
 
2.5%
2 7
 
1.8%
Hangul
ValueCountFrequency (%)
마 106
53.0%
나 94
47.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
27140
53 
50110
34 
50130
13 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27140
2nd row27140
3rd row27140
4th row27140
5th row27140

Common Values

ValueCountFrequency (%)
27140 53
53.0%
50110 34
34.0%
50130 13
 
13.0%

Length

2023-12-16T03:57:03.547714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:04.825871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27140 53
53.0%
50110 34
34.0%
50130 13
 
13.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대구 동구
53 
제주 제주시
34 
제주 서귀포시
13 

Length

Max length7
Median length5
Mean length5.6
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구 동구
2nd row대구 동구
3rd row대구 동구
4th row대구 동구
5th row대구 동구

Common Values

ValueCountFrequency (%)
대구 동구 53
53.0%
제주 제주시 34
34.0%
제주 서귀포시 13
 
13.0%

Length

2023-12-16T03:57:05.638812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T03:57:06.211239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구 53
26.5%
동구 53
26.5%
제주 47
23.5%
제주시 34
17.0%
서귀포시 13
 
6.5%

Correlations

2023-12-16T03:57:06.841227image/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
2023-12-16T03:57:07.616313image/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
2023-12-16T03:57:08.277164image/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

2023-12-16T03:56:52.901366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T03:56:53.604703image/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
02023-04-016493.94698.62530.0마마016627140대구 동구
12023-04-016493.94698.62530.0마마016727140대구 동구
22023-04-016493.94698.62530.0마마016827140대구 동구
32023-04-016493.94698.62530.0마마056527140대구 동구
42023-04-016493.94698.62530.0마마056627140대구 동구
52023-04-016493.94698.62530.0마마056727140대구 동구
62023-04-016493.94698.62530.0마마056827140대구 동구
72023-04-016493.94698.62530.0마마056927140대구 동구
82023-04-016493.94698.62530.0마마057027140대구 동구
92023-04-016493.94698.62530.0마마057127140대구 동구
관측일자전기자동차 전력 부족량 (25%)전기자동차 전력 부족량(50%)전기자동차 전력 부족량 (75%)격자번호시군구코드시군구명
902023-04-01-257960.5-410097.5-566774.5나나788050110제주 제주시
912023-04-01-257960.5-410097.5-566774.5나나788150110제주 제주시
922023-04-01-257960.5-410097.5-566774.5나나788250110제주 제주시
932023-04-01-257960.5-410097.5-566774.5나나788350110제주 제주시
942023-04-01-257960.5-410097.5-566774.5나나788450110제주 제주시
952023-04-01-257960.5-410097.5-566774.5나나788550110제주 제주시
962023-04-01-257960.5-410097.5-566774.5나나788650110제주 제주시
972023-04-01-239321.0-295430.0-377805.0나나797350130제주 서귀포시
982023-04-01-239321.0-295430.0-377805.0나나797450130제주 서귀포시
992023-04-016493.94698.62530.0마마016527140대구 동구