Overview

Dataset statistics

Number of variables5
Number of observations376
Missing cells298
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory41.3 B

Variable types

Categorical2
Text2
Numeric1

Dataset

Description자동차 등록번호판 등의 기준에 관한 고시에 따라 전북특별자치도 내에 등록된 전기 자동차 수에 관련한 자료* 전기차 등록 대수 등을 시/군/구/읍/면/동별로 분류하였음
Author전북특별자치도
URLhttps://www.data.go.kr/data/15036991/fileData.do

Alerts

도 has constant value ""Constant
이하 주소 has 298 (79.3%) missing valuesMissing

Reproduction

Analysis started2024-03-14 18:06:57.577394
Analysis finished2024-03-14 18:06:58.631563
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
전북특별자치도
376 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 376
100.0%

Length

2024-03-15T03:06:58.942506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:06:59.290344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 376
100.0%

시군
Categorical

Distinct14
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
전주시
78 
군산시
53 
익산시
49 
정읍시
40 
김제시
38 
Other values (9)
118 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 78
20.7%
군산시 53
14.1%
익산시 49
13.0%
정읍시 40
10.6%
김제시 38
10.1%
남원시 33
8.8%
고창군 14
 
3.7%
부안군 13
 
3.5%
완주군 13
 
3.5%
임실군 12
 
3.2%
Other values (4) 33
8.8%

Length

2024-03-15T03:06:59.649569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 78
20.7%
군산시 53
14.1%
익산시 49
13.0%
정읍시 40
10.6%
김제시 38
10.1%
남원시 33
8.8%
고창군 14
 
3.7%
부안군 13
 
3.5%
완주군 13
 
3.5%
임실군 12
 
3.2%
Other values (4) 33
8.8%

읍면동
Text

Distinct290
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-03-15T03:07:01.500883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0478723
Min length2

Characters and Unicode

Total characters1146
Distinct characters167
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

Unique278 ?
Unique (%)73.9%

Sample

1st row고수면
2nd row고창읍
3rd row공음면
4th row대산면
5th row무장면
ValueCountFrequency (%)
완산구 41
 
10.9%
덕진구 37
 
9.8%
성수면 2
 
0.5%
백산면 2
 
0.5%
신흥동 2
 
0.5%
주천면 2
 
0.5%
금동 2
 
0.5%
신풍동 2
 
0.5%
월성동 2
 
0.5%
산내면 2
 
0.5%
Other values (280) 282
75.0%
2024-03-15T03:07:03.778358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 151
 
13.2%
면 142
 
12.4%
구 85
 
7.4%
산 72
 
6.3%
덕 48
 
4.2%
진 44
 
3.8%
완 41
 
3.6%
신 17
 
1.5%
읍 15
 
1.3%
성 15
 
1.3%
Other values (157) 516
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1134
99.0%
Decimal Number 12
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 151
 
13.3%
면 142
 
12.5%
구 85
 
7.5%
산 72
 
6.3%
덕 48
 
4.2%
진 44
 
3.9%
완 41
 
3.6%
신 17
 
1.5%
읍 15
 
1.3%
성 15
 
1.3%
Other values (154) 504
44.4%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1134
99.0%
Common 12
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 151
 
13.3%
면 142
 
12.5%
구 85
 
7.5%
산 72
 
6.3%
덕 48
 
4.2%
진 44
 
3.9%
완 41
 
3.6%
신 17
 
1.5%
읍 15
 
1.3%
성 15
 
1.3%
Other values (154) 504
44.4%
Common
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1134
99.0%
ASCII 12
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 151
 
13.3%
면 142
 
12.5%
구 85
 
7.5%
산 72
 
6.3%
덕 48
 
4.2%
진 44
 
3.9%
완 41
 
3.6%
신 17
 
1.5%
읍 15
 
1.3%
성 15
 
1.3%
Other values (154) 504
44.4%
ASCII
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

이하 주소
Text

MISSING 

Distinct78
Distinct (%)100.0%
Missing298
Missing (%)79.3%
Memory size3.1 KiB
2024-03-15T03:07:05.067186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1282051
Min length2

Characters and Unicode

Total characters322
Distinct characters64
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

Unique78 ?
Unique (%)100.0%

Sample

1st row강흥동
2nd row고랑동
3rd row금상동
4th row금암동
5th row남정동
ValueCountFrequency (%)
우아동1가 1
 
1.3%
용복동 1
 
1.3%
서완산동1가 1
 
1.3%
서신동 1
 
1.3%
서서학동 1
 
1.3%
서노송동 1
 
1.3%
색장동 1
 
1.3%
상림동 1
 
1.3%
삼천동3가 1
 
1.3%
삼천동1가 1
 
1.3%
Other values (68) 68
87.2%
2024-03-15T03:07:06.442872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 81
25.2%
가 44
 
13.7%
2 16
 
5.0%
1 14
 
4.3%
3 10
 
3.1%
서 7
 
2.2%
산 7
 
2.2%
화 6
 
1.9%
중 6
 
1.9%
성 6
 
1.9%
Other values (54) 125
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 280
87.0%
Decimal Number 42
 
13.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 81
28.9%
가 44
15.7%
서 7
 
2.5%
산 7
 
2.5%
화 6
 
2.1%
중 6
 
2.1%
성 6
 
2.1%
송 5
 
1.8%
전 5
 
1.8%
원 5
 
1.8%
Other values (50) 108
38.6%
Decimal Number
ValueCountFrequency (%)
2 16
38.1%
1 14
33.3%
3 10
23.8%
4 2
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 280
87.0%
Common 42
 
13.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 81
28.9%
가 44
15.7%
서 7
 
2.5%
산 7
 
2.5%
화 6
 
2.1%
중 6
 
2.1%
성 6
 
2.1%
송 5
 
1.8%
전 5
 
1.8%
원 5
 
1.8%
Other values (50) 108
38.6%
Common
ValueCountFrequency (%)
2 16
38.1%
1 14
33.3%
3 10
23.8%
4 2
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 280
87.0%
ASCII 42
 
13.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 81
28.9%
가 44
15.7%
서 7
 
2.5%
산 7
 
2.5%
화 6
 
2.1%
중 6
 
2.1%
성 6
 
2.1%
송 5
 
1.8%
전 5
 
1.8%
원 5
 
1.8%
Other values (50) 108
38.6%
ASCII
ValueCountFrequency (%)
2 16
38.1%
1 14
33.3%
3 10
23.8%
4 2
 
4.8%

대수
Real number (ℝ)

Distinct89
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.023936
Minimum0
Maximum306
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-15T03:07:06.703890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q323
95-th percentile122.5
Maximum306
Range306
Interquartile range (IQR)19

Descriptive statistics

Standard deviation43.262078
Coefficient of variation (CV)1.7288279
Kurtosis12.3156
Mean25.023936
Median Absolute Deviation (MAD)6
Skewness3.2792311
Sum9409
Variance1871.6074
MonotonicityNot monotonic
2024-03-15T03:07:06.961572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33
 
8.8%
2 31
 
8.2%
3 27
 
7.2%
9 25
 
6.6%
4 20
 
5.3%
6 19
 
5.1%
5 17
 
4.5%
15 13
 
3.5%
8 13
 
3.5%
12 12
 
3.2%
Other values (79) 166
44.1%
ValueCountFrequency (%)
0 2
 
0.5%
1 33
8.8%
2 31
8.2%
3 27
7.2%
4 20
5.3%
5 17
4.5%
6 19
5.1%
7 12
 
3.2%
8 13
 
3.5%
9 25
6.6%
ValueCountFrequency (%)
306 1
0.3%
258 1
0.3%
247 1
0.3%
228 1
0.3%
218 1
0.3%
200 1
0.3%
183 1
0.3%
165 1
0.3%
164 2
0.5%
160 1
0.3%

Interactions

2024-03-15T03:06:57.917584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:07:07.172852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군이하 주소대수
시군1.000NaN0.000
이하 주소NaN1.0001.000
대수0.0001.0001.000
2024-03-15T03:07:07.420602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대수시군
대수1.0000.000
시군0.0001.000

Missing values

2024-03-15T03:06:58.261875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:06:58.535705image/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

도시군읍면동이하 주소대수
0전북특별자치도고창군고수면<NA>15
1전북특별자치도고창군고창읍<NA>116
2전북특별자치도고창군공음면<NA>13
3전북특별자치도고창군대산면<NA>6
4전북특별자치도고창군무장면<NA>14
5전북특별자치도고창군부안면<NA>9
6전북특별자치도고창군상하면<NA>13
7전북특별자치도고창군성내면<NA>9
8전북특별자치도고창군성송면<NA>7
9전북특별자치도고창군신림면<NA>8
도시군읍면동이하 주소대수
366전북특별자치도진안군동향면<NA>3
367전북특별자치도진안군마령면<NA>10
368전북특별자치도진안군백운면<NA>9
369전북특별자치도진안군부귀면<NA>14
370전북특별자치도진안군상전면<NA>6
371전북특별자치도진안군성수면<NA>9
372전북특별자치도진안군용담면<NA>6
373전북특별자치도진안군정천면<NA>4
374전북특별자치도진안군주천면<NA>5
375전북특별자치도진안군진안읍<NA>79