Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory70.7 B

Variable types

Numeric1
Categorical4
Text3

Dataset

Description경상남도 내 공공폐수처리시설 현황으로, 시설구분(신규, 기존), 시설설치구분(국가, 일반, 농공), 시도 및 시군명, 처리장명, 소재지에 대한 데이터를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15070485

Alerts

시설구분 has constant value ""Constant
유역지방환경청 has constant value ""Constant
시도 has constant value ""Constant
연번 has unique valuesUnique
처리장명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:19:39.512324
Analysis finished2023-12-11 00:19:40.080611
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T09:19:40.135442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2023-12-11T09:19:40.239176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

시설구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
기존
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기존 23
100.0%

Length

2023-12-11T09:19:40.378604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:19:40.473367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존 23
100.0%

시설설치구분
Categorical

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
일반
13 
농공
9 
국가산단
 
1

Length

Max length4
Median length2
Mean length2.0869565
Min length2

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row일반
2nd row일반
3rd row농공
4th row농공
5th row농공

Common Values

ValueCountFrequency (%)
일반 13
56.5%
농공 9
39.1%
국가산단 1
 
4.3%

Length

2023-12-11T09:19:40.565970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:19:40.665263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 13
56.5%
농공 9
39.1%
국가산단 1
 
4.3%

유역지방환경청
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
낙동강
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강 23
100.0%

Length

2023-12-11T09:19:40.753384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:19:40.837597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강 23
100.0%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
경남
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경남 23
100.0%

Length

2023-12-11T09:19:40.925100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:19:41.008439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경남 23
100.0%

시군
Text

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T09:19:41.155585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.173913
Min length3

Characters and Unicode

Total characters73
Distinct characters27
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

Unique7 ?
Unique (%)30.4%

Sample

1st row진주시
2nd row거창군
3rd row고성군(경남)
4th row김해시
5th row김해시
ValueCountFrequency (%)
함안군 4
17.4%
진주시 3
13.0%
김해시 3
13.0%
사천시 2
8.7%
양산시 2
8.7%
합천군 2
8.7%
거창군 1
 
4.3%
고성군(경남 1
 
4.3%
의령군 1
 
4.3%
창녕군 1
 
4.3%
Other values (3) 3
13.0%
2023-12-11T09:19:41.434932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 12
16.4%
군 11
15.1%
함 5
 
6.8%
안 4
 
5.5%
천 4
 
5.5%
해 3
 
4.1%
창 3
 
4.1%
양 3
 
4.1%
김 3
 
4.1%
주 3
 
4.1%
Other values (17) 22
30.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
97.3%
Close Punctuation 1
 
1.4%
Open Punctuation 1
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 12
16.9%
군 11
15.5%
함 5
 
7.0%
안 4
 
5.6%
천 4
 
5.6%
해 3
 
4.2%
창 3
 
4.2%
양 3
 
4.2%
김 3
 
4.2%
주 3
 
4.2%
Other values (15) 20
28.2%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
97.3%
Common 2
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 12
16.9%
군 11
15.5%
함 5
 
7.0%
안 4
 
5.6%
천 4
 
5.6%
해 3
 
4.2%
창 3
 
4.2%
양 3
 
4.2%
김 3
 
4.2%
주 3
 
4.2%
Other values (15) 20
28.2%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
97.3%
ASCII 2
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 12
16.9%
군 11
15.5%
함 5
 
7.0%
안 4
 
5.6%
천 4
 
5.6%
해 3
 
4.2%
창 3
 
4.2%
양 3
 
4.2%
김 3
 
4.2%
주 3
 
4.2%
Other values (15) 20
28.2%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

처리장명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T09:19:41.642073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters92
Distinct characters49
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 row진주상평
2nd row거창일반
3rd row고성율대
4th row김해진영
5th row김해병동
ValueCountFrequency (%)
진주상평 1
 
4.3%
진주정촌 1
 
4.3%
합천율곡 1
 
4.3%
함양산단 1
 
4.3%
함안파수 1
 
4.3%
함안군북 1
 
4.3%
함안일반 1
 
4.3%
함안칠서 1
 
4.3%
통영안정 1
 
4.3%
창원진북 1
 
4.3%
Other values (13) 13
56.5%
2023-12-11T09:19:41.963223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
진 5
 
5.4%
함 5
 
5.4%
안 5
 
5.4%
일 4
 
4.3%
반 4
 
4.3%
천 4
 
4.3%
김 3
 
3.3%
합 3
 
3.3%
주 3
 
3.3%
양 3
 
3.3%
Other values (39) 53
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 5
 
5.4%
함 5
 
5.4%
안 5
 
5.4%
일 4
 
4.3%
반 4
 
4.3%
천 4
 
4.3%
김 3
 
3.3%
합 3
 
3.3%
주 3
 
3.3%
양 3
 
3.3%
Other values (39) 53
57.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 5
 
5.4%
함 5
 
5.4%
안 5
 
5.4%
일 4
 
4.3%
반 4
 
4.3%
천 4
 
4.3%
김 3
 
3.3%
합 3
 
3.3%
주 3
 
3.3%
양 3
 
3.3%
Other values (39) 53
57.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
진 5
 
5.4%
함 5
 
5.4%
안 5
 
5.4%
일 4
 
4.3%
반 4
 
4.3%
천 4
 
4.3%
김 3
 
3.3%
합 3
 
3.3%
주 3
 
3.3%
양 3
 
3.3%
Other values (39) 53
57.6%

소재지
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T09:19:42.247554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length30.826087
Min length20

Characters and Unicode

Total characters709
Distinct characters106
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

Unique23 ?
Unique (%)100.0%

Sample

1st row경상남도 진주시 남강로 1511 (73-1, 초전동)
2nd row경상남도 거창군 거창읍 심소정길 185(양평리 290-1)
3rd row경상남도 고성군 고성읍 월평로 163(율대리 149-1)
4th row경상남도 김해시 진영읍 서부로 179번길 57(죽곡리 2-1)
5th row경상남도 김해시 한림면 김해대로916번길 140(병동리 562)
ValueCountFrequency (%)
경상남도 22
 
15.9%
함안군 4
 
2.9%
진주시 3
 
2.2%
김해시 3
 
2.2%
합천군 2
 
1.4%
양산시 2
 
1.4%
서부로 2
 
1.4%
사천시 2
 
1.4%
군북면 2
 
1.4%
합리 1
 
0.7%
Other values (95) 95
68.8%
2023-12-11T09:19:42.729780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
16.2%
1 34
 
4.8%
남 24
 
3.4%
도 24
 
3.4%
경 22
 
3.1%
( 22
 
3.1%
상 22
 
3.1%
) 22
 
3.1%
리 19
 
2.7%
0 17
 
2.4%
Other values (96) 388
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
54.3%
Decimal Number 152
 
21.4%
Space Separator 115
 
16.2%
Open Punctuation 22
 
3.1%
Close Punctuation 22
 
3.1%
Dash Punctuation 12
 
1.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 24
 
6.2%
도 24
 
6.2%
경 22
 
5.7%
상 22
 
5.7%
리 19
 
4.9%
면 16
 
4.2%
로 15
 
3.9%
길 14
 
3.6%
군 13
 
3.4%
시 12
 
3.1%
Other values (81) 204
53.0%
Decimal Number
ValueCountFrequency (%)
1 34
22.4%
0 17
11.2%
2 17
11.2%
9 15
9.9%
4 14
9.2%
3 13
 
8.6%
5 12
 
7.9%
7 11
 
7.2%
8 10
 
6.6%
6 9
 
5.9%
Space Separator
ValueCountFrequency (%)
115
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
54.3%
Common 324
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 24
 
6.2%
도 24
 
6.2%
경 22
 
5.7%
상 22
 
5.7%
리 19
 
4.9%
면 16
 
4.2%
로 15
 
3.9%
길 14
 
3.6%
군 13
 
3.4%
시 12
 
3.1%
Other values (81) 204
53.0%
Common
ValueCountFrequency (%)
115
35.5%
1 34
 
10.5%
( 22
 
6.8%
) 22
 
6.8%
0 17
 
5.2%
2 17
 
5.2%
9 15
 
4.6%
4 14
 
4.3%
3 13
 
4.0%
5 12
 
3.7%
Other values (5) 43
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
54.3%
ASCII 324
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
35.5%
1 34
 
10.5%
( 22
 
6.8%
) 22
 
6.8%
0 17
 
5.2%
2 17
 
5.2%
9 15
 
4.6%
4 14
 
4.3%
3 13
 
4.0%
5 12
 
3.7%
Other values (5) 43
 
13.3%
Hangul
ValueCountFrequency (%)
남 24
 
6.2%
도 24
 
6.2%
경 22
 
5.7%
상 22
 
5.7%
리 19
 
4.9%
면 16
 
4.2%
로 15
 
3.9%
길 14
 
3.6%
군 13
 
3.4%
시 12
 
3.1%
Other values (81) 204
53.0%

Interactions

2023-12-11T09:19:39.812713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:19:42.841495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설설치구분시군처리장명소재지
연번1.0000.6000.8221.0001.000
시설설치구분0.6001.0000.8661.0001.000
시군0.8220.8661.0001.0001.000
처리장명1.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.000
2023-12-11T09:19:42.933303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설설치구분
연번1.0000.309
시설설치구분0.3091.000

Missing values

2023-12-11T09:19:39.911892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:19:40.036066image/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

연번시설구분시설설치구분유역지방환경청시도시군처리장명소재지
01기존일반낙동강경남진주시진주상평경상남도 진주시 남강로 1511 (73-1, 초전동)
12기존일반낙동강경남거창군거창일반경상남도 거창군 거창읍 심소정길 185(양평리 290-1)
23기존농공낙동강경남고성군(경남)고성율대경상남도 고성군 고성읍 월평로 163(율대리 149-1)
34기존농공낙동강경남김해시김해진영경상남도 김해시 진영읍 서부로 179번길 57(죽곡리 2-1)
45기존농공낙동강경남김해시김해병동경상남도 김해시 한림면 김해대로916번길 140(병동리 562)
56기존일반낙동강경남김해시김해덕암김해시 주촌면 서부로 1637번길 324(덕암리 795)
67기존일반낙동강경남사천시사천일반경상남도 사천시 사남면 외국기업로 230(방지리 202)
78기존농공낙동강경남사천시사천송포경상남도 사천시 송포공단길 86(송포동 1539-3)
89기존일반낙동강경남양산시양산일반경상남도 양산시 충렬로 111(교동 90)
910기존일반낙동강경남양산시양산어곡경상남도 양산시 어곡로 50(어곡동 870-5)
연번시설구분시설설치구분유역지방환경청시도시군처리장명소재지
1314기존일반낙동강경남창녕군창녕대합경상남도 창녕군 대합면 합리 1424
1415기존일반낙동강경남창원시창원진북경상남도 창원시 마산합포구 진북면 산단3길 87(신촌리 137번지)
1516기존국가산단낙동강경남통영시통영안정경상남도 통영시 광도면 공단로 902(황리 1610)
1617기존일반낙동강경남함안군함안칠서경상남도 함안군 칠서면 공단안길110(대치리 18)
1718기존일반낙동강경남함안군함안일반경상남도 함안군 군북면 함안산단 7길 68(사도리 산5-5)
1819기존농공낙동강경남함안군함안군북경상남도 함안군 군북면 오장골길 96(덕대리 500)
1920기존농공낙동강경남함안군함안파수경상남도 함안군 가야읍 광정로 298(광정리 1209)
2021기존일반낙동강경남함양군함양산단경상남도 함양군 수동면 산업단지길 194(우명리 80-4)
2122기존농공낙동강경남합천군합천율곡경상남도 합천군 율곡면 임북공단길 12-7(임북리 433-1)
2223기존농공낙동강경남합천군합천야로경상남도 합천군 야로면 야로공단길 11(야로리 436-1)