Overview

Dataset statistics

Number of variables4
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory914.0 B
Average record size in memory39.7 B

Variable types

Text1
Numeric2
DateTime1

Dataset

Description현재 세종특별자치시에 존재하는 이륜차의 등록현황(읍면동별, 용도별, 등록대수 등)에 대한 데이터를 제공하는 파일입니다.
Author세종특별자치시
URLhttps://www.data.go.kr/data/3074645/fileData.do

Alerts

행정동명 has unique valuesUnique
자가용 has unique valuesUnique
관용차 has 10 (43.5%) zerosZeros

Reproduction

Analysis started2023-12-16 15:33:45.387660
Analysis finished2023-12-16 15:33:49.995745
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:33:50.681101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0434783
Min length3

Characters and Unicode

Total characters70
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row고운동
2nd row금남면
3rd row다정동
4th row대평동
5th row도담동
ValueCountFrequency (%)
고운동 1
 
4.3%
연동면 1
 
4.3%
해밀동 1
 
4.3%
어진동 1
 
4.3%
한솔동 1
 
4.3%
종촌동 1
 
4.3%
조치원읍 1
 
4.3%
전의면 1
 
4.3%
전동면 1
 
4.3%
장군면 1
 
4.3%
Other values (13) 13
56.5%
2023-12-16T15:33:52.702770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 15
21.4%
면 9
 
12.9%
연 3
 
4.3%
정 2
 
2.9%
전 2
 
2.9%
소 2
 
2.9%
담 2
 
2.9%
해 1
 
1.4%
진 1
 
1.4%
어 1
 
1.4%
Other values (32) 32
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15
21.4%
면 9
 
12.9%
연 3
 
4.3%
정 2
 
2.9%
전 2
 
2.9%
소 2
 
2.9%
담 2
 
2.9%
해 1
 
1.4%
진 1
 
1.4%
어 1
 
1.4%
Other values (32) 32
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15
21.4%
면 9
 
12.9%
연 3
 
4.3%
정 2
 
2.9%
전 2
 
2.9%
소 2
 
2.9%
담 2
 
2.9%
해 1
 
1.4%
진 1
 
1.4%
어 1
 
1.4%
Other values (32) 32
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 15
21.4%
면 9
 
12.9%
연 3
 
4.3%
정 2
 
2.9%
전 2
 
2.9%
소 2
 
2.9%
담 2
 
2.9%
해 1
 
1.4%
진 1
 
1.4%
어 1
 
1.4%
Other values (32) 32
45.7%

관용차
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7826087
Minimum0
Maximum48
Zeros10
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:33:53.586285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile30.3
Maximum48
Range48
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.61878
Coefficient of variation (CV)2.4293813
Kurtosis10.076548
Mean4.7826087
Median Absolute Deviation (MAD)1
Skewness3.2327037
Sum110
Variance134.99605
MonotonicityNot monotonic
2023-12-16T15:33:54.024989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 10
43.5%
3 3
 
13.0%
1 3
 
13.0%
2 3
 
13.0%
48 1
 
4.3%
6 1
 
4.3%
33 1
 
4.3%
5 1
 
4.3%
ValueCountFrequency (%)
0 10
43.5%
1 3
 
13.0%
2 3
 
13.0%
3 3
 
13.0%
5 1
 
4.3%
6 1
 
4.3%
33 1
 
4.3%
48 1
 
4.3%
ValueCountFrequency (%)
48 1
 
4.3%
33 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
3 3
 
13.0%
2 3
 
13.0%
1 3
 
13.0%
0 10
43.5%

자가용
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.86957
Minimum82
Maximum2746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:33:54.633793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile156.8
Q1259
median369
Q3533.5
95-th percentile801.8
Maximum2746
Range2664
Interquartile range (IQR)274.5

Descriptive statistics

Standard deviation525.85457
Coefficient of variation (CV)1.0647641
Kurtosis16.712602
Mean493.86957
Median Absolute Deviation (MAD)150
Skewness3.8451111
Sum11359
Variance276523.03
MonotonicityNot monotonic
2023-12-16T15:33:55.054811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
525 1
 
4.3%
813 1
 
4.3%
354 1
 
4.3%
82 1
 
4.3%
384 1
 
4.3%
254 1
 
4.3%
378 1
 
4.3%
2746 1
 
4.3%
458 1
 
4.3%
326 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
82 1
4.3%
155 1
4.3%
173 1
4.3%
185 1
4.3%
219 1
4.3%
254 1
4.3%
264 1
4.3%
305 1
4.3%
326 1
4.3%
343 1
4.3%
ValueCountFrequency (%)
2746 1
4.3%
813 1
4.3%
701 1
4.3%
652 1
4.3%
639 1
4.3%
542 1
4.3%
525 1
4.3%
492 1
4.3%
458 1
4.3%
384 1
4.3%

데이터기준일자
Date

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2023-10-30 00:00:00
Maximum2023-11-30 00:00:00
2023-12-16T15:33:55.655899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:33:56.084683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

Interactions

2023-12-16T15:33:47.223928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:33:46.085260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:33:48.315891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:33:46.615103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:33:56.442264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명관용차자가용데이터기준일자
행정동명1.0001.0001.0001.000
관용차1.0001.0000.8880.000
자가용1.0000.8881.0000.000
데이터기준일자1.0000.0000.0001.000
2023-12-16T15:33:56.932297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관용차자가용
관용차1.0000.474
자가용0.4741.000

Missing values

2023-12-16T15:33:49.087447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:33:49.667698image/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

행정동명관용차자가용데이터기준일자
0고운동05252023-11-23
1금남면38132023-11-30
2다정동13432023-11-20
3대평동01732023-11-27
4도담동33692023-11-27
5보람동482192023-10-31
6부강면26522023-11-23
7새롬동03052023-10-31
8소담동65422023-11-20
9소정면01852023-11-30
행정동명관용차자가용데이터기준일자
13연서면36392023-11-22
14장군면17012023-11-30
15전동면23262023-11-16
16전의면14582023-11-23
17조치원읍3327462023-10-31
18종촌동03782023-10-31
19한솔동02542023-11-28
20어진동53842023-11-16
21해밀동0822023-10-31
22반곡동03542023-11-21