Overview

Dataset statistics

Number of variables5
Number of observations278
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory41.5 B

Variable types

DateTime2
Categorical2
Text1

Dataset

Description경주시에서 관리하는 대중교통 소외지역 행복택시 월별 운행현황입니다.(행복택시 운행기간, 횟수, 평균이용자수)
Author경상북도 경주시
URLhttps://www.data.go.kr/data/15038219/fileData.do

Alerts

데이터기준일자 has constant value ""Constant

Reproduction

Analysis started2024-03-14 11:23:39.365038
Analysis finished2024-03-14 11:23:40.052615
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운행기간
Date

Distinct26
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2021-11-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-14T20:23:40.196684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:23:40.401446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

운행지역
Categorical

Distinct14
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
외동읍
28 
문무대왕면
27 
양남면
27 
산내면
27 
감포읍
26 
Other values (9)
143 

Length

Max length7
Median length3
Mean length3.4208633
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안강읍
2nd row건천읍
3rd row외동읍
4th row문무대왕면
5th row양남면

Common Values

ValueCountFrequency (%)
외동읍 28
10.1%
문무대왕면 27
9.7%
양남면 27
9.7%
산내면 27
9.7%
감포읍 26
9.4%
월성동 23
8.3%
선도동 22
7.9%
내남면 19
6.8%
안강읍 18
6.5%
건천읍 18
6.5%
Other values (4) 43
15.5%

Length

2024-03-14T20:23:40.640511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
외동읍 28
10.1%
문무대왕면 27
9.7%
양남면 27
9.7%
산내면 27
9.7%
감포읍 26
9.4%
월성동 23
8.3%
선도동 22
7.9%
내남면 19
6.8%
안강읍 18
6.5%
건천읍 18
6.5%
Other values (4) 43
15.5%

운행횟수
Text

Distinct238
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-14T20:23:42.312478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2014388
Min length2

Characters and Unicode

Total characters890
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)72.7%

Sample

1st row561
2nd row249
3rd row479
4th row123
5th row723
ValueCountFrequency (%)
382 3
 
1.1%
109 3
 
1.1%
37 3
 
1.1%
31 3
 
1.1%
75 2
 
0.7%
1,296 2
 
0.7%
804 2
 
0.7%
1,264 2
 
0.7%
814 2
 
0.7%
276 2
 
0.7%
Other values (228) 254
91.4%
2024-03-14T20:23:44.344783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 139
15.6%
7 96
10.8%
4 90
10.1%
5 87
9.8%
2 85
9.6%
8 84
9.4%
3 82
9.2%
6 74
8.3%
9 56
6.3%
0 54
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 847
95.2%
Other Punctuation 43
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 139
16.4%
7 96
11.3%
4 90
10.6%
5 87
10.3%
2 85
10.0%
8 84
9.9%
3 82
9.7%
6 74
8.7%
9 56
6.6%
0 54
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 139
15.6%
7 96
10.8%
4 90
10.1%
5 87
9.8%
2 85
9.6%
8 84
9.4%
3 82
9.2%
6 74
8.3%
9 56
6.3%
0 54
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 139
15.6%
7 96
10.8%
4 90
10.1%
5 87
9.8%
2 85
9.6%
8 84
9.4%
3 82
9.2%
6 74
8.3%
9 56
6.3%
0 54
 
6.1%

평균이용자수
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
1
223 
2
55 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 223
80.2%
2 55
 
19.8%

Length

2024-03-14T20:23:44.756504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:23:45.060521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 223
80.2%
2 55
 
19.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2024-01-05 00:00:00
Maximum2024-01-05 00:00:00
2024-03-14T20:23:45.320042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:23:45.616454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-03-14T20:23:45.818766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행기간운행지역평균이용자수
운행기간1.0000.0000.000
운행지역0.0001.0000.560
평균이용자수0.0000.5601.000
2024-03-14T20:23:46.063229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행지역평균이용자수
운행지역1.0000.431
평균이용자수0.4311.000
2024-03-14T20:23:46.288667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행지역평균이용자수
운행지역1.0000.431
평균이용자수0.4311.000

Missing values

2024-03-14T20:23:39.623102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:23:39.937087image/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

운행기간운행지역운행횟수평균이용자수데이터기준일자
02021-11-01안강읍56112024-01-05
12021-11-01건천읍24912024-01-05
22021-11-01외동읍47912024-01-05
32021-11-01문무대왕면12322024-01-05
42021-11-01양남면72312024-01-05
52021-11-01강동면10212024-01-05
62021-11-01내남면13222024-01-05
72021-11-01산내면86522024-01-05
82021-12-01감포읍4112024-01-05
92021-12-01안강읍54712024-01-05
운행기간운행지역운행횟수평균이용자수데이터기준일자
2682023-12-01감포읍10612024-01-05
2692023-12-01안강읍/강동면2,86522024-01-05
2702023-12-01건천읍/서면72612024-01-05
2712023-12-01외동읍73512024-01-05
2722023-12-01문무대왕면47812024-01-05
2732023-12-01양남면1,18912024-01-05
2742023-12-01내남면66712024-01-05
2752023-12-01산내면1,51512024-01-05
2762023-12-01월성동78622024-01-05
2772023-12-01선도동66212024-01-05