Overview

Dataset statistics

Number of variables6
Number of observations584
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.1 KiB
Average record size in memory49.2 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description경상남도 하수처리시설 현황으로, 일 사용량, 시군명, 하수처리장명, 시설용량, 소재지에 관한 데이터를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3076631

Alerts

연번 is highly overall correlated with 일 사용량 구분 and 1 other fieldsHigh correlation
일 사용량 구분 is highly overall correlated with 연번High correlation
시군 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:42:00.185126
Analysis finished2023-12-10 23:42:00.802717
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct584
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.5
Minimum1
Maximum584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-11T08:42:00.865726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.15
Q1146.75
median292.5
Q3438.25
95-th percentile554.85
Maximum584
Range583
Interquartile range (IQR)291.5

Descriptive statistics

Standard deviation168.73055
Coefficient of variation (CV)0.5768566
Kurtosis-1.2
Mean292.5
Median Absolute Deviation (MAD)146
Skewness0
Sum170820
Variance28470
MonotonicityStrictly increasing
2023-12-11T08:42:00.986056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
403 1
 
0.2%
387 1
 
0.2%
388 1
 
0.2%
389 1
 
0.2%
390 1
 
0.2%
391 1
 
0.2%
392 1
 
0.2%
393 1
 
0.2%
394 1
 
0.2%
Other values (574) 574
98.3%
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 (%)
584 1
0.2%
583 1
0.2%
582 1
0.2%
581 1
0.2%
580 1
0.2%
579 1
0.2%
578 1
0.2%
577 1
0.2%
576 1
0.2%
575 1
0.2%

일 사용량 구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
500㎥/일 미만
514 
500㎥/일 이상
70 

Length

Max length10
Median length9
Mean length9.119863
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500㎥/일 이상
2nd row500㎥/일 이상
3rd row500㎥/일 이상
4th row500㎥/일 이상
5th row500㎥/일 이상

Common Values

ValueCountFrequency (%)
500㎥/일 미만 514
88.0%
500㎥/일 이상 70
 
12.0%

Length

2023-12-11T08:42:01.103565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:42:01.180014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500㎥/일 584
50.0%
미만 514
44.0%
이상 70
 
6.0%

시군
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
남해군
60 
하동군
59 
함양군
51 
산청군
48 
진주시
43 
Other values (13)
323 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남해군 60
 
10.3%
하동군 59
 
10.1%
함양군 51
 
8.7%
산청군 48
 
8.2%
진주시 43
 
7.4%
창녕군 38
 
6.5%
거제시 35
 
6.0%
사천시 32
 
5.5%
밀양시 30
 
5.1%
고성군 27
 
4.6%
Other values (8) 161
27.6%

Length

2023-12-11T08:42:01.258584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남해군 60
 
10.3%
하동군 59
 
10.1%
함양군 51
 
8.7%
산청군 48
 
8.2%
진주시 43
 
7.4%
창녕군 38
 
6.5%
거제시 35
 
6.0%
사천시 32
 
5.5%
밀양시 30
 
5.1%
거창군 27
 
4.6%
Other values (8) 161
27.6%

처리장명
Text

Distinct543
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-11T08:42:01.582938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.2020548
Min length2

Characters and Unicode

Total characters1286
Distinct characters241
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)87.2%

Sample

1st row대산
2nd row북면
3rd row마산/창원
4th row진해
5th row동부맑은물재생센터
ValueCountFrequency (%)
신촌 4
 
0.7%
평촌 3
 
0.5%
입석 3
 
0.5%
갈전 3
 
0.5%
대산 3
 
0.5%
신기 3
 
0.5%
송계 2
 
0.3%
중앙 2
 
0.3%
삼계 2
 
0.3%
대평 2
 
0.3%
Other values (533) 557
95.4%
2023-12-11T08:42:02.028147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 47
 
3.7%
대 35
 
2.7%
촌 34
 
2.6%
동 32
 
2.5%
곡 31
 
2.4%
천 27
 
2.1%
신 26
 
2.0%
정 24
 
1.9%
평 23
 
1.8%
포 19
 
1.5%
Other values (231) 988
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1263
98.2%
Decimal Number 9
 
0.7%
Other Punctuation 8
 
0.6%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 47
 
3.7%
대 35
 
2.8%
촌 34
 
2.7%
동 32
 
2.5%
곡 31
 
2.5%
천 27
 
2.1%
신 26
 
2.1%
정 24
 
1.9%
평 23
 
1.8%
포 19
 
1.5%
Other values (225) 965
76.4%
Other Punctuation
ValueCountFrequency (%)
/ 7
87.5%
. 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
1 5
55.6%
2 4
44.4%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1263
98.2%
Common 23
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 47
 
3.7%
대 35
 
2.8%
촌 34
 
2.7%
동 32
 
2.5%
곡 31
 
2.5%
천 27
 
2.1%
신 26
 
2.1%
정 24
 
1.9%
평 23
 
1.8%
포 19
 
1.5%
Other values (225) 965
76.4%
Common
ValueCountFrequency (%)
/ 7
30.4%
1 5
21.7%
2 4
17.4%
) 3
13.0%
( 3
13.0%
. 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1263
98.2%
ASCII 23
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 47
 
3.7%
대 35
 
2.8%
촌 34
 
2.7%
동 32
 
2.5%
곡 31
 
2.5%
천 27
 
2.1%
신 26
 
2.1%
정 24
 
1.9%
평 23
 
1.8%
포 19
 
1.5%
Other values (225) 965
76.4%
ASCII
ValueCountFrequency (%)
/ 7
30.4%
1 5
21.7%
2 4
17.4%
) 3
13.0%
( 3
13.0%
. 1
 
4.3%

시설용량(㎥/일)
Text

Distinct121
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-11T08:42:02.267710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.6797945
Min length2

Characters and Unicode

Total characters1565
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)11.6%

Sample

1st row13,000
2nd row12,000
3rd row500,000
4th row60,000
5th row10,000
ValueCountFrequency (%)
50 62
 
10.6%
30 45
 
7.7%
60 44
 
7.5%
40 41
 
7.0%
45 32
 
5.5%
100 25
 
4.3%
70 24
 
4.1%
80 21
 
3.6%
120 13
 
2.2%
55 13
 
2.2%
Other values (111) 264
45.2%
2023-12-11T08:42:02.705549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 628
40.1%
5 212
 
13.5%
1 143
 
9.1%
4 120
 
7.7%
3 91
 
5.8%
6 88
 
5.6%
2 64
 
4.1%
, 51
 
3.3%
7 48
 
3.1%
8 41
 
2.6%
Other values (5) 79
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
93.9%
Other Punctuation 51
 
3.3%
Other Letter 22
 
1.4%
Open Punctuation 11
 
0.7%
Close Punctuation 11
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 628
42.7%
5 212
 
14.4%
1 143
 
9.7%
4 120
 
8.2%
3 91
 
6.2%
6 88
 
6.0%
2 64
 
4.4%
7 48
 
3.3%
8 41
 
2.8%
9 35
 
2.4%
Other Letter
ValueCountFrequency (%)
개 11
50.0%
량 11
50.0%
Other Punctuation
ValueCountFrequency (%)
, 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543
98.6%
Hangul 22
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 628
40.7%
5 212
 
13.7%
1 143
 
9.3%
4 120
 
7.8%
3 91
 
5.9%
6 88
 
5.7%
2 64
 
4.1%
, 51
 
3.3%
7 48
 
3.1%
8 41
 
2.7%
Other values (3) 57
 
3.7%
Hangul
ValueCountFrequency (%)
개 11
50.0%
량 11
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543
98.6%
Hangul 22
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 628
40.7%
5 212
 
13.7%
1 143
 
9.3%
4 120
 
7.8%
3 91
 
5.9%
6 88
 
5.7%
2 64
 
4.1%
, 51
 
3.3%
7 48
 
3.1%
8 41
 
2.7%
Other values (3) 57
 
3.7%
Hangul
ValueCountFrequency (%)
개 11
50.0%
량 11
50.0%

소재지
Text

Distinct580
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-11T08:42:03.039135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length21.363014
Min length15

Characters and Unicode

Total characters12476
Distinct characters229
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique576 ?
Unique (%)98.6%

Sample

1st row경상남도 창원시 대산면 유등리 35-2
2nd row경상남도 창원시 북면 월계리 163-2
3rd row경상남도 창원시 합포구 덕동동 714
4th row경상남도 창원시 진해구 덕산동 584
5th row경상남도 창원시 진해구 남양동 403
ValueCountFrequency (%)
경상남도 584
 
20.3%
하동군 59
 
2.1%
남해군 59
 
2.1%
함양군 51
 
1.8%
산청군 48
 
1.7%
진주시 43
 
1.5%
창녕군 38
 
1.3%
거제시 35
 
1.2%
사천시 32
 
1.1%
밀양시 30
 
1.0%
Other values (1183) 1891
65.9%
2023-12-11T08:42:03.526383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2299
18.4%
남 707
 
5.7%
상 630
 
5.0%
도 610
 
4.9%
경 585
 
4.7%
리 558
 
4.5%
면 513
 
4.1%
1 495
 
4.0%
- 390
 
3.1%
군 381
 
3.1%
Other values (219) 5308
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7551
60.5%
Space Separator 2299
 
18.4%
Decimal Number 2231
 
17.9%
Dash Punctuation 390
 
3.1%
Other Punctuation 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 707
 
9.4%
상 630
 
8.3%
도 610
 
8.1%
경 585
 
7.7%
리 558
 
7.4%
면 513
 
6.8%
군 381
 
5.0%
시 215
 
2.8%
산 179
 
2.4%
동 159
 
2.1%
Other values (204) 3014
39.9%
Decimal Number
ValueCountFrequency (%)
1 495
22.2%
2 287
12.9%
3 222
10.0%
4 215
9.6%
6 193
 
8.7%
5 192
 
8.6%
8 166
 
7.4%
9 163
 
7.3%
7 151
 
6.8%
0 147
 
6.6%
Space Separator
ValueCountFrequency (%)
2299
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 390
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7551
60.5%
Common 4925
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 707
 
9.4%
상 630
 
8.3%
도 610
 
8.1%
경 585
 
7.7%
리 558
 
7.4%
면 513
 
6.8%
군 381
 
5.0%
시 215
 
2.8%
산 179
 
2.4%
동 159
 
2.1%
Other values (204) 3014
39.9%
Common
ValueCountFrequency (%)
2299
46.7%
1 495
 
10.1%
- 390
 
7.9%
2 287
 
5.8%
3 222
 
4.5%
4 215
 
4.4%
6 193
 
3.9%
5 192
 
3.9%
8 166
 
3.4%
9 163
 
3.3%
Other values (5) 303
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7551
60.5%
ASCII 4925
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2299
46.7%
1 495
 
10.1%
- 390
 
7.9%
2 287
 
5.8%
3 222
 
4.5%
4 215
 
4.4%
6 193
 
3.9%
5 192
 
3.9%
8 166
 
3.4%
9 163
 
3.3%
Other values (5) 303
 
6.2%
Hangul
ValueCountFrequency (%)
남 707
 
9.4%
상 630
 
8.3%
도 610
 
8.1%
경 585
 
7.7%
리 558
 
7.4%
면 513
 
6.8%
군 381
 
5.0%
시 215
 
2.8%
산 179
 
2.4%
동 159
 
2.1%
Other values (204) 3014
39.9%

Interactions

2023-12-11T08:42:00.526827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:42:03.627081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일 사용량 구분시군
연번1.0000.9930.943
일 사용량 구분0.9931.0000.273
시군0.9430.2731.000
2023-12-11T08:42:03.703979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군일 사용량 구분
시군1.0000.212
일 사용량 구분0.2121.000
2023-12-11T08:42:03.778283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일 사용량 구분시군
연번1.0000.9170.747
일 사용량 구분0.9171.0000.212
시군0.7470.2121.000

Missing values

2023-12-11T08:42:00.656118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:42:00.764530image/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

연번일 사용량 구분시군처리장명시설용량(㎥/일)소재지
01500㎥/일 이상창원시대산13,000경상남도 창원시 대산면 유등리 35-2
12500㎥/일 이상창원시북면12,000경상남도 창원시 북면 월계리 163-2
23500㎥/일 이상창원시마산/창원500,000경상남도 창원시 합포구 덕동동 714
34500㎥/일 이상창원시진해60,000경상남도 창원시 진해구 덕산동 584
45500㎥/일 이상창원시동부맑은물재생센터10,000경상남도 창원시 진해구 남양동 403
56500㎥/일 이상창원시진동4,000경상남도 창원시 진동면 고현리 71-2
67500㎥/일 이상진주시진주190,000경상남도 진주시 초전동 190-1
78500㎥/일 이상진주시문산7,100경상남도 진주시 문산읍 소문리 1786
89500㎥/일 이상진주시사봉2,400경상남도 진주시 사봉면 무촌리 204
910500㎥/일 이상진주시대곡1,900경상남도 진주시 대곡면 가정리 85
연번일 사용량 구분시군처리장명시설용량(㎥/일)소재지
574575500㎥/일 미만합천군권빈70경상남도 합천군 봉산면 권빈리 884-1
575576500㎥/일 미만합천군회양150경상남도 합천군 대병면 회양리 647
576577500㎥/일 미만합천군구평46경상남도 합천군 가회면 함방리 689-1
577578500㎥/일 미만합천군봉계68경상남도 합천군 봉산면 봉계리 814-2
578579500㎥/일 미만합천군봉산150경상남도 합천군 봉산면 김봉리 420-8
579580500㎥/일 미만합천군대병170경상남도 합천군 대병면 회양리 647
580581500㎥/일 미만합천군묘산120경상남도 합천군 묘산면 관기리 868-2
581582500㎥/일 미만합천군쌍책165경상남도 합천군 쌍책면 성산리
582583500㎥/일 미만합천군대양180경상남도 합천군 대양면 대목리
583584500㎥/일 미만합천군청덕115경상남도 합천군 청덕면 가현리