Overview

Dataset statistics

Number of variables5
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory42.6 B

Variable types

Numeric1
Categorical2
Text1
DateTime1

Dataset

Description광주광역시 동구 의류수거함위치 데이터 입니다.데이터는 자치구, 설치지역, 설치위치로 구성되어있으며 총 82개 데이터로 구성되어있습니다.
Author광주광역시 동구
URLhttps://www.data.go.kr/data/15127262/fileData.do

Alerts

자치구 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 설치지역(동명)High correlation
설치지역(동명) is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-20 14:04:03.492391
Analysis finished2024-04-20 14:04:04.437985
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.5
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size866.0 B
2024-04-20T23:04:04.570958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.05
Q121.25
median41.5
Q361.75
95-th percentile77.95
Maximum82
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.57386172
Kurtosis-1.2
Mean41.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3403
Variance567.16667
MonotonicityStrictly increasing
2024-04-20T23:04:05.028096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
63 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
동구
82 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 82
100.0%

Length

2024-04-20T23:04:05.371853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T23:04:05.549047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 82
100.0%

설치지역(동명)
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size784.0 B
지산동
19 
산수동
18 
학동
17 
소태동
14 
동명동
6 
Other values (3)
8 

Length

Max length3
Median length3
Mean length2.7926829
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row계림동
2nd row동명동
3rd row동명동
4th row동명동
5th row동명동

Common Values

ValueCountFrequency (%)
지산동 19
23.2%
산수동 18
22.0%
학동 17
20.7%
소태동 14
17.1%
동명동 6
 
7.3%
용산동 5
 
6.1%
서석동 2
 
2.4%
계림동 1
 
1.2%

Length

2024-04-20T23:04:05.897356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T23:04:06.227640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지산동 19
23.2%
산수동 18
22.0%
학동 17
20.7%
소태동 14
17.1%
동명동 6
 
7.3%
용산동 5
 
6.1%
서석동 2
 
2.4%
계림동 1
 
1.2%

설치위치(장소)
Text

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size784.0 B
2024-04-20T23:04:07.357520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length11.414634
Min length5

Characters and Unicode

Total characters936
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)97.6%

Sample

1st row중흥로 221
2nd row동계천로 171
3rd row동명로 98번길 20-15
4th row동명로 98번길 24
5th row필문대로 253번길 8
ValueCountFrequency (%)
필문대로 19
 
8.3%
남문로 12
 
5.3%
밤실로 10
 
4.4%
지산로 5
 
2.2%
학소로 5
 
2.2%
6 5
 
2.2%
의재로 4
 
1.8%
화산로 4
 
1.8%
5 4
 
1.8%
증심천로 4
 
1.8%
Other values (114) 156
68.4%
2024-04-20T23:04:08.847724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146
15.6%
1 83
 
8.9%
로 78
 
8.3%
길 68
 
7.3%
번 64
 
6.8%
2 47
 
5.0%
5 43
 
4.6%
3 43
 
4.6%
- 36
 
3.8%
문 31
 
3.3%
Other values (35) 297
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 400
42.7%
Decimal Number 354
37.8%
Space Separator 146
 
15.6%
Dash Punctuation 36
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 78
19.5%
길 68
17.0%
번 64
16.0%
문 31
 
7.8%
대 19
 
4.8%
필 19
 
4.8%
남 12
 
3.0%
지 12
 
3.0%
산 10
 
2.5%
밤 10
 
2.5%
Other values (23) 77
19.2%
Decimal Number
ValueCountFrequency (%)
1 83
23.4%
2 47
13.3%
5 43
12.1%
3 43
12.1%
6 29
 
8.2%
9 27
 
7.6%
4 24
 
6.8%
7 24
 
6.8%
8 20
 
5.6%
0 14
 
4.0%
Space Separator
ValueCountFrequency (%)
146
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 536
57.3%
Hangul 400
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 78
19.5%
길 68
17.0%
번 64
16.0%
문 31
 
7.8%
대 19
 
4.8%
필 19
 
4.8%
남 12
 
3.0%
지 12
 
3.0%
산 10
 
2.5%
밤 10
 
2.5%
Other values (23) 77
19.2%
Common
ValueCountFrequency (%)
146
27.2%
1 83
15.5%
2 47
 
8.8%
5 43
 
8.0%
3 43
 
8.0%
- 36
 
6.7%
6 29
 
5.4%
9 27
 
5.0%
4 24
 
4.5%
7 24
 
4.5%
Other values (2) 34
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 536
57.3%
Hangul 400
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
27.2%
1 83
15.5%
2 47
 
8.8%
5 43
 
8.0%
3 43
 
8.0%
- 36
 
6.7%
6 29
 
5.4%
9 27
 
5.0%
4 24
 
4.5%
7 24
 
4.5%
Other values (2) 34
 
6.3%
Hangul
ValueCountFrequency (%)
로 78
19.5%
길 68
17.0%
번 64
16.0%
문 31
 
7.8%
대 19
 
4.8%
필 19
 
4.8%
남 12
 
3.0%
지 12
 
3.0%
산 10
 
2.5%
밤 10
 
2.5%
Other values (23) 77
19.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
Minimum2024-03-15 00:00:00
Maximum2024-03-15 00:00:00
2024-04-20T23:04:09.067348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T23:04:09.360234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-20T23:04:03.742661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-20T23:04:09.619556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치지역(동명)설치위치(장소)
연번1.0000.9150.935
설치지역(동명)0.9151.0001.000
설치위치(장소)0.9351.0001.000
2024-04-20T23:04:09.885849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치지역(동명)
연번1.0000.748
설치지역(동명)0.7481.000

Missing values

2024-04-20T23:04:04.085868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-20T23:04:04.372618image/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

연번자치구설치지역(동명)설치위치(장소)데이터기준일자
01동구계림동중흥로 2212024-03-15
12동구동명동동계천로 1712024-03-15
23동구동명동동명로 98번길 20-152024-03-15
34동구동명동동명로 98번길 242024-03-15
45동구동명동필문대로 253번길 82024-03-15
56동구동명동필문대로 263번길 13-12024-03-15
67동구동명동필문대로 263번길 52024-03-15
78동구산수동경양로 369-12024-03-15
89동구산수동동명로 69번길 262024-03-15
910동구산수동무등로 449번길 522024-03-15
연번자치구설치지역(동명)설치위치(장소)데이터기준일자
7273동구학동의재로 43번길 13-72024-03-15
7374동구학동의재로 43번길 292024-03-15
7475동구학동의재로 58-42024-03-15
7576동구학동증심천로 332024-03-15
7677동구학동증심천로 33번길 92024-03-15
7778동구학동증심천로 43번길 122024-03-15
7879동구학동증심천로 962024-03-15
7980동구학동필문대로 3392024-03-15
8081동구학동학동안길 36-12024-03-15
8182동구학동학소로 138-112024-03-15