Overview

Dataset statistics

Number of variables6
Number of observations496
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory49.3 B

Variable types

Numeric1
Text2
Categorical3

Dataset

Description경상남도 사천시 의류수거함 위치 데이터입니다. 의류수거함이 있는 주소, 관리단체, 관리번호 등이 포함되어 있습니다.
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15127257/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 행정동High correlation
관리단체 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:42:40.602284
Analysis finished2024-03-23 05:42:42.089559
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.5
Minimum1
Maximum496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-23T05:42:42.355882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.75
Q1124.75
median248.5
Q3372.25
95-th percentile471.25
Maximum496
Range495
Interquartile range (IQR)247.5

Descriptive statistics

Standard deviation143.32713
Coefficient of variation (CV)0.57676914
Kurtosis-1.2
Mean248.5
Median Absolute Deviation (MAD)124
Skewness0
Sum123256
Variance20542.667
MonotonicityStrictly increasing
2024-03-23T05:42:43.107069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
328 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
335 1
 
0.2%
334 1
 
0.2%
Other values (486) 486
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%
489 1
0.2%
488 1
0.2%
487 1
0.2%

관리번호
Text

UNIQUE 

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-23T05:42:44.167765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2762097
Min length2

Characters and Unicode

Total characters2121
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique496 ?
Unique (%)100.0%

Sample

1st row단-2
2nd row단-5
3rd row단-6
4th row단-7
5th row단-9
ValueCountFrequency (%)
단-2 1
 
0.2%
쓰-220 1
 
0.2%
삼-15 1
 
0.2%
삼-9 1
 
0.2%
삼-8 1
 
0.2%
삼-1 1
 
0.2%
추77 1
 
0.2%
쓰-231 1
 
0.2%
쓰-230 1
 
0.2%
쓰-229 1
 
0.2%
Other values (486) 486
98.0%
2024-03-23T05:42:45.684982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 432
20.4%
1 277
13.1%
쓰 195
9.2%
삼 193
9.1%
2 162
 
7.6%
5 106
 
5.0%
4 103
 
4.9%
3 101
 
4.8%
6 100
 
4.7%
7 92
 
4.3%
Other values (6) 360
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1193
56.2%
Other Letter 496
23.4%
Dash Punctuation 432
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 277
23.2%
2 162
13.6%
5 106
 
8.9%
4 103
 
8.6%
3 101
 
8.5%
6 100
 
8.4%
7 92
 
7.7%
8 87
 
7.3%
9 83
 
7.0%
0 82
 
6.9%
Other Letter
ValueCountFrequency (%)
쓰 195
39.3%
삼 193
38.9%
추 64
 
12.9%
일 33
 
6.7%
단 11
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1625
76.6%
Hangul 496
 
23.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 432
26.6%
1 277
17.0%
2 162
 
10.0%
5 106
 
6.5%
4 103
 
6.3%
3 101
 
6.2%
6 100
 
6.2%
7 92
 
5.7%
8 87
 
5.4%
9 83
 
5.1%
Hangul
ValueCountFrequency (%)
쓰 195
39.3%
삼 193
38.9%
추 64
 
12.9%
일 33
 
6.7%
단 11
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1625
76.6%
Hangul 496
 
23.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 432
26.6%
1 277
17.0%
2 162
 
10.0%
5 106
 
6.5%
4 103
 
6.3%
3 101
 
6.2%
6 100
 
6.2%
7 92
 
5.7%
8 87
 
5.4%
9 83
 
5.1%
Hangul
ValueCountFrequency (%)
쓰 195
39.3%
삼 193
38.9%
추 64
 
12.9%
일 33
 
6.7%
단 11
 
2.2%

관리단체
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
삼성자원
230 
쓰임업
216 
일어서기자원
39 
단비자원
 
11

Length

Max length6
Median length4
Mean length3.7217742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
삼성자원 230
46.4%
쓰임업 216
43.5%
일어서기자원 39
 
7.9%
단비자원 11
 
2.2%

Length

2024-03-23T05:42:46.187290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:42:46.646408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼성자원 230
46.4%
쓰임업 216
43.5%
일어서기자원 39
 
7.9%
단비자원 11
 
2.2%

행정동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
벌용동
86 
사천읍
72 
향촌동
70 
동서동
49 
동서금
48 
Other values (9)
171 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벌용동 86
17.3%
사천읍 72
14.5%
향촌동 70
14.1%
동서동 49
9.9%
동서금 48
9.7%
선구동 44
8.9%
사남면 36
7.3%
남양동 22
 
4.4%
정동면 21
 
4.2%
용현면 19
 
3.8%
Other values (4) 29
 
5.8%

Length

2024-03-23T05:42:46.904534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
벌용동 86
17.3%
사천읍 72
14.5%
향촌동 70
14.1%
동서동 49
9.9%
동서금 48
9.7%
선구동 44
8.9%
사남면 36
7.3%
남양동 22
 
4.4%
정동면 21
 
4.2%
용현면 19
 
3.8%
Other values (4) 29
 
5.8%

설치장소
Text

Distinct413
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-23T05:42:48.063570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length10.377016
Min length6

Characters and Unicode

Total characters5147
Distinct characters119
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)75.6%

Sample

1st row곤명면 정곡리 1011
2nd row곤명면 정곡리 851
3rd row곤명면 정곡리 863
4th row곤명면 정곡리 862
5th row곤명면 정곡리 838-8
ValueCountFrequency (%)
사천읍 70
 
6.0%
향촌동 69
 
5.9%
벌리동 64
 
5.4%
동금동 43
 
3.7%
사남면 32
 
2.7%
선구동 30
 
2.6%
선인리 28
 
2.4%
동동 22
 
1.9%
504 21
 
1.8%
용강동 20
 
1.7%
Other values (490) 777
66.1%
2024-03-23T05:42:49.841147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
680
 
13.2%
동 434
 
8.4%
1 392
 
7.6%
- 343
 
6.7%
리 246
 
4.8%
4 240
 
4.7%
2 235
 
4.6%
3 219
 
4.3%
5 198
 
3.8%
7 157
 
3.1%
Other values (109) 2003
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2069
40.2%
Decimal Number 1986
38.6%
Space Separator 680
 
13.2%
Dash Punctuation 343
 
6.7%
Close Punctuation 33
 
0.6%
Open Punctuation 33
 
0.6%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 434
21.0%
리 246
 
11.9%
사 105
 
5.1%
면 94
 
4.5%
천 82
 
4.0%
읍 81
 
3.9%
촌 73
 
3.5%
향 70
 
3.4%
벌 67
 
3.2%
선 67
 
3.2%
Other values (92) 750
36.2%
Decimal Number
ValueCountFrequency (%)
1 392
19.7%
4 240
12.1%
2 235
11.8%
3 219
11.0%
5 198
10.0%
7 157
7.9%
0 156
 
7.9%
6 141
 
7.1%
9 126
 
6.3%
8 122
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
I 1
33.3%
G 1
33.3%
Space Separator
ValueCountFrequency (%)
680
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 343
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3075
59.7%
Hangul 2069
40.2%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 434
21.0%
리 246
 
11.9%
사 105
 
5.1%
면 94
 
4.5%
천 82
 
4.0%
읍 81
 
3.9%
촌 73
 
3.5%
향 70
 
3.4%
벌 67
 
3.2%
선 67
 
3.2%
Other values (92) 750
36.2%
Common
ValueCountFrequency (%)
680
22.1%
1 392
12.7%
- 343
11.2%
4 240
 
7.8%
2 235
 
7.6%
3 219
 
7.1%
5 198
 
6.4%
7 157
 
5.1%
0 156
 
5.1%
6 141
 
4.6%
Other values (4) 314
10.2%
Latin
ValueCountFrequency (%)
S 1
33.3%
I 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3078
59.8%
Hangul 2069
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
680
22.1%
1 392
12.7%
- 343
11.1%
4 240
 
7.8%
2 235
 
7.6%
3 219
 
7.1%
5 198
 
6.4%
7 157
 
5.1%
0 156
 
5.1%
6 141
 
4.6%
Other values (7) 317
10.3%
Hangul
ValueCountFrequency (%)
동 434
21.0%
리 246
 
11.9%
사 105
 
5.1%
면 94
 
4.5%
천 82
 
4.0%
읍 81
 
3.9%
촌 73
 
3.5%
향 70
 
3.4%
벌 67
 
3.2%
선 67
 
3.2%
Other values (92) 750
36.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-02-01
496 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-01
2nd row2024-02-01
3rd row2024-02-01
4th row2024-02-01
5th row2024-02-01

Common Values

ValueCountFrequency (%)
2024-02-01 496
100.0%

Length

2024-03-23T05:42:50.674941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:42:51.039322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-01 496
100.0%

Interactions

2024-03-23T05:42:41.100690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T05:42:51.383870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관리단체행정동
연번1.0000.4690.938
관리단체0.4691.0000.772
행정동0.9380.7721.000
2024-03-23T05:42:51.732798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리단체행정동
관리단체1.0000.556
행정동0.5561.000
2024-03-23T05:42:51.961444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관리단체행정동
연번1.0000.2950.754
관리단체0.2951.0000.556
행정동0.7540.5561.000

Missing values

2024-03-23T05:42:41.617751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T05:42:41.956104image/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

연번관리번호관리단체행정동설치장소데이터기준일자
01단-2단비자원곤명면곤명면 정곡리 10112024-02-01
12단-5단비자원곤명면곤명면 정곡리 8512024-02-01
23단-6단비자원곤명면곤명면 정곡리 8632024-02-01
34단-7단비자원곤명면곤명면 정곡리 8622024-02-01
45단-9단비자원곤명면곤명면 정곡리 838-82024-02-01
56단-10단비자원곤명면곤명면 정곡리 8502024-02-01
67단-13단비자원곤명면곤명면 봉계리 8752024-02-01
78단-14단비자원곤명면곤명면 본촌리 142-12024-02-01
89쓰-141쓰임업곤양면곤양면 남문외리 147-12024-02-01
910쓰-142쓰임업곤양면곤양면 서정리 373-22024-02-01
연번관리번호관리단체행정동설치장소데이터기준일자
486487추17쓰임업향촌동향촌동 10052024-02-01
487488추25삼성자원향촌동향촌동 1124-42024-02-01
488489추26삼성자원향촌동향촌동 1142-62024-02-01
489490추27삼성자원향촌동향촌동 1137-72024-02-01
490491추28삼성자원향촌동향촌동 977-32024-02-01
491492추29삼성자원향촌동향촌동 10052024-02-01
492493추47삼성자원향촌동향촌동 815-102024-02-01
493494추48삼성자원향촌동향촌동 824-22024-02-01
494495추49삼성자원향촌동향촌동 825-72024-02-01
495496추50일어서기자원향촌동사등동 2402024-02-01