Overview

Dataset statistics

Number of variables20
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory174.3 B

Variable types

Text2
Numeric11
Categorical5
DateTime2

Dataset

Description경상남도 거제시 하수처리장현황(하수처리장명, 소재지, 위도, 경도, 하수처리방식, 설치일자, 기준일자)등에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15087781/fileData.do

Alerts

개통상태 has constant value ""Constant
측량방법 has constant value ""Constant
방류수역명 has constant value ""Constant
기준일자 has constant value ""Constant
부지면적(제곱미터) is highly overall correlated with 청천시처리용량(세제곱미터_일) and 4 other fieldsHigh correlation
청천시처리용량(세제곱미터_일) is highly overall correlated with 부지면적(제곱미터) and 4 other fieldsHigh correlation
우천시처리용량(세제곱미터_일) is highly overall correlated with 부지면적(제곱미터) and 4 other fieldsHigh correlation
설계유입수수질(BOD) is highly overall correlated with 설계유입수수질(COD) and 3 other fieldsHigh correlation
설계유입수수질(COD) is highly overall correlated with 설계유입수수질(BOD) and 4 other fieldsHigh correlation
설계유입수수질(SS) is highly overall correlated with 설계유입수수질(BOD) and 2 other fieldsHigh correlation
설계유입수수질(T_N) is highly overall correlated with 청천시처리용량(세제곱미터_일) and 5 other fieldsHigh correlation
설계유입수수질(T_P) is highly overall correlated with 설계유입수수질(BOD) and 4 other fieldsHigh correlation
대장균균수 is highly overall correlated with 부지면적(제곱미터) and 4 other fieldsHigh correlation
하수처리방식 is highly overall correlated with 부지면적(제곱미터) and 4 other fieldsHigh correlation
관리기관 is highly overall correlated with 부지면적(제곱미터) and 3 other fieldsHigh correlation
관리기관 is highly imbalanced (71.4%)Imbalance
하수처리장명 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
대장균균수 has 22 (55.0%) zerosZeros

Reproduction

Analysis started2023-12-12 16:17:42.476046
Analysis finished2023-12-12 16:17:55.618894
Duration13.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하수처리장명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:17:55.779297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.675
Min length10

Characters and Unicode

Total characters427
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row거제중앙공공하수처리시설
2nd row거제장승포공공하수처리시설
3rd row거제면공공하수처리시설
4th row사등면공공하수처리시설
5th row일운면공공하수처리시설
ValueCountFrequency (%)
거제중앙공공하수처리시설 1
 
2.5%
거제장승포공공하수처리시설 1
 
2.5%
송진포공공하수처리시설 1
 
2.5%
다포공공하수처리시설 1
 
2.5%
율포마을공공하수처리시설 1
 
2.5%
술역공공하수처리시설 1
 
2.5%
소량공공하수처리시설 1
 
2.5%
저구공공하수처리시설 1
 
2.5%
외포공공하수처리시설 1
 
2.5%
시방공공하수처리시설 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T01:17:56.146214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 80
18.7%
하 44
10.3%
수 41
9.6%
시 41
9.6%
처 40
9.4%
리 40
9.4%
설 40
9.4%
포 7
 
1.6%
면 5
 
1.2%
산 4
 
0.9%
Other values (61) 85
19.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 425
99.5%
Other Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 80
18.8%
하 44
10.4%
수 41
9.6%
시 41
9.6%
처 40
9.4%
리 40
9.4%
설 40
9.4%
포 7
 
1.6%
면 5
 
1.2%
산 4
 
0.9%
Other values (60) 83
19.5%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 425
99.5%
Common 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 80
18.8%
하 44
10.4%
수 41
9.6%
시 41
9.6%
처 40
9.4%
리 40
9.4%
설 40
9.4%
포 7
 
1.6%
면 5
 
1.2%
산 4
 
0.9%
Other values (60) 83
19.5%
Common
ValueCountFrequency (%)
· 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 425
99.5%
None 2
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 80
18.8%
하 44
10.4%
수 41
9.6%
시 41
9.6%
처 40
9.4%
리 40
9.4%
설 40
9.4%
포 7
 
1.6%
면 5
 
1.2%
산 4
 
0.9%
Other values (60) 83
19.5%
None
ValueCountFrequency (%)
· 2
100.0%

소재지
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:17:56.421469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length21.525
Min length18

Characters and Unicode

Total characters861
Distinct characters80
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

Unique40 ?
Unique (%)100.0%

Sample

1st row경상남도 거제시 연초면 오비4길 56
2nd row경상남도 거제시 거제대로 3263
3rd row경상남도 거제시 거제면 죽림길 76
4th row경상남도 거제시 사등면 성포로 300
5th row경상남도 거제시 일운면 거제대로 2443-30
ValueCountFrequency (%)
경상남도 40
20.2%
거제시 40
20.2%
남부면 7
 
3.5%
연초면 6
 
3.0%
동부면 6
 
3.0%
장목면 5
 
2.5%
둔덕면 4
 
2.0%
일운면 4
 
2.0%
저구리 3
 
1.5%
사등면 3
 
1.5%
Other values (72) 80
40.4%
2023-12-13T01:17:56.800813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
18.5%
남 47
 
5.5%
거 44
 
5.1%
제 44
 
5.1%
시 42
 
4.9%
경 40
 
4.6%
상 40
 
4.6%
도 40
 
4.6%
면 38
 
4.4%
리 33
 
3.8%
Other values (70) 334
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 527
61.2%
Space Separator 159
 
18.5%
Decimal Number 147
 
17.1%
Dash Punctuation 28
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 47
 
8.9%
거 44
 
8.3%
제 44
 
8.3%
시 42
 
8.0%
경 40
 
7.6%
상 40
 
7.6%
도 40
 
7.6%
면 38
 
7.2%
리 33
 
6.3%
부 13
 
2.5%
Other values (58) 146
27.7%
Decimal Number
ValueCountFrequency (%)
1 25
17.0%
2 22
15.0%
4 19
12.9%
5 13
8.8%
6 13
8.8%
3 13
8.8%
0 12
8.2%
9 11
7.5%
8 11
7.5%
7 8
 
5.4%
Space Separator
ValueCountFrequency (%)
159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 527
61.2%
Common 334
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 47
 
8.9%
거 44
 
8.3%
제 44
 
8.3%
시 42
 
8.0%
경 40
 
7.6%
상 40
 
7.6%
도 40
 
7.6%
면 38
 
7.2%
리 33
 
6.3%
부 13
 
2.5%
Other values (58) 146
27.7%
Common
ValueCountFrequency (%)
159
47.6%
- 28
 
8.4%
1 25
 
7.5%
2 22
 
6.6%
4 19
 
5.7%
5 13
 
3.9%
6 13
 
3.9%
3 13
 
3.9%
0 12
 
3.6%
9 11
 
3.3%
Other values (2) 19
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 527
61.2%
ASCII 334
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
47.6%
- 28
 
8.4%
1 25
 
7.5%
2 22
 
6.6%
4 19
 
5.7%
5 13
 
3.9%
6 13
 
3.9%
3 13
 
3.9%
0 12
 
3.6%
9 11
 
3.3%
Other values (2) 19
 
5.7%
Hangul
ValueCountFrequency (%)
남 47
 
8.9%
거 44
 
8.3%
제 44
 
8.3%
시 42
 
8.0%
경 40
 
7.6%
상 40
 
7.6%
도 40
 
7.6%
면 38
 
7.2%
리 33
 
6.3%
부 13
 
2.5%
Other values (58) 146
27.7%

위도
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.850878
Minimum34.718851
Maximum35.00494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:56.953546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.718851
5-th percentile34.72787
Q134.78306
median34.843845
Q334.92321
95-th percentile34.966191
Maximum35.00494
Range0.286089
Interquartile range (IQR)0.14014941

Descriptive statistics

Standard deviation0.08243573
Coefficient of variation (CV)0.0023653846
Kurtosis-1.0529063
Mean34.850878
Median Absolute Deviation (MAD)0.07396939
Skewness0.023232557
Sum1394.0351
Variance0.0067956496
MonotonicityNot monotonic
2023-12-13T01:17:57.119936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
34.9187611 1
 
2.5%
34.90253361 1
 
2.5%
34.77818777 1
 
2.5%
34.84556905 1
 
2.5%
34.88184762 1
 
2.5%
34.73555997 1
 
2.5%
34.94353152 1
 
2.5%
34.96438165 1
 
2.5%
35.00494014 1
 
2.5%
34.7261155 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
34.71885114 1
2.5%
34.7261155 1
2.5%
34.72796234 1
2.5%
34.73230232 1
2.5%
34.73555997 1
2.5%
34.73741944 1
2.5%
34.73844307 1
2.5%
34.76741584 1
2.5%
34.77520882 1
2.5%
34.77818777 1
2.5%
ValueCountFrequency (%)
35.00494014 1
2.5%
35.00057456 1
2.5%
34.96438165 1
2.5%
34.9595583 1
2.5%
34.94697239 1
2.5%
34.94353152 1
2.5%
34.93599956 1
2.5%
34.93060077 1
2.5%
34.930234 1
2.5%
34.92651445 1
2.5%

경도
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.62378
Minimum128.47586
Maximum128.71534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:57.250700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.47586
5-th percentile128.48127
Q1128.59013
median128.62557
Q3128.67716
95-th percentile128.71093
Maximum128.71534
Range0.2394774
Interquartile range (IQR)0.087035825

Descriptive statistics

Standard deviation0.07032385
Coefficient of variation (CV)0.00054674066
Kurtosis-0.35509106
Mean128.62378
Median Absolute Deviation (MAD)0.0485868
Skewness-0.64881402
Sum5144.9512
Variance0.0049454439
MonotonicityNot monotonic
2023-12-13T01:17:57.365112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
128.6130122 1
 
2.5%
128.5764866 1
 
2.5%
128.5962242 1
 
2.5%
128.4815249 1
 
2.5%
128.6215655 1
 
2.5%
128.6064332 1
 
2.5%
128.7153379 1
 
2.5%
128.7092778 1
 
2.5%
128.7011267 1
 
2.5%
128.6021712 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
128.4758605 1
2.5%
128.4765185 1
2.5%
128.4815249 1
2.5%
128.498491 1
2.5%
128.5032921 1
2.5%
128.534891 1
2.5%
128.5686267 1
2.5%
128.5764866 1
2.5%
128.5783504 1
2.5%
128.5889514 1
2.5%
ValueCountFrequency (%)
128.7153379 1
2.5%
128.7146266 1
2.5%
128.7107371 1
2.5%
128.7092778 1
2.5%
128.7069104 1
2.5%
128.7049318 1
2.5%
128.7011267 1
2.5%
128.6982029 1
2.5%
128.6820694 1
2.5%
128.6783708 1
2.5%

개통상태
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
계속운영중
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계속운영중 40
100.0%

Length

2023-12-13T01:17:57.479378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:57.592253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계속운영중 40
100.0%

하수처리방식
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
JASSFR
8 
ASA
6 
COSBR
4 
CBT
4 
KSMBR
3 
Other values (12)
15 

Length

Max length13
Median length12
Mean length5.1
Min length3

Unique

Unique9 ?
Unique (%)22.5%

Sample

1st rowHBR.Ⅱ, KS.MBR
2nd rowDNR
3rd rowHBR.II
4th rowKSMBR
5th rowKSMBR

Common Values

ValueCountFrequency (%)
JASSFR 8
20.0%
ASA 6
15.0%
COSBR 4
10.0%
CBT 4
10.0%
KSMBR 3
 
7.5%
ESSA 2
 
5.0%
BNR 2
 
5.0%
FNR 2
 
5.0%
KSBNR 1
 
2.5%
HBR.II 1
 
2.5%
Other values (7) 7
17.5%

Length

2023-12-13T01:17:57.693515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jassfr 9
20.5%
asa 7
15.9%
cosbr 4
9.1%
cbt 4
9.1%
ksmbr 3
 
6.8%
essa 2
 
4.5%
bnr 2
 
4.5%
fnr 2
 
4.5%
hbr.ⅱ 1
 
2.3%
접촉순환공법 1
 
2.3%
Other values (9) 9
20.5%

측량방법
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
대장이기
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대장이기 40
100.0%

Length

2023-12-13T01:17:57.800740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:57.900920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대장이기 40
100.0%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
민간(대행)
38 
거제시 하수운영과
 
2

Length

Max length9
Median length6
Mean length6.15
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row거제시 하수운영과
2nd row거제시 하수운영과
3rd row민간(대행)
4th row민간(대행)
5th row민간(대행)

Common Values

ValueCountFrequency (%)
민간(대행) 38
95.0%
거제시 하수운영과 2
 
5.0%

Length

2023-12-13T01:17:58.017507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:58.142902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간(대행 38
90.5%
거제시 2
 
4.8%
하수운영과 2
 
4.8%

방류수역명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
남해
40 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남해 40
100.0%

Length

2023-12-13T01:17:58.258675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:58.348740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남해 40
100.0%

부지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2590.375
Minimum66
Maximum29546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:58.469725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile69
Q1201
median377.5
Q31061.75
95-th percentile14751.5
Maximum29546
Range29480
Interquartile range (IQR)860.75

Descriptive statistics

Standard deviation6685.9845
Coefficient of variation (CV)2.5810875
Kurtosis12.248258
Mean2590.375
Median Absolute Deviation (MAD)273
Skewness3.5528401
Sum103615
Variance44702389
MonotonicityNot monotonic
2023-12-13T01:17:58.597909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
250 2
 
5.0%
69 2
 
5.0%
29030 1
 
2.5%
150 1
 
2.5%
220 1
 
2.5%
819 1
 
2.5%
224 1
 
2.5%
1039 1
 
2.5%
460 1
 
2.5%
99 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
66 1
2.5%
69 2
5.0%
74 1
2.5%
99 1
2.5%
110 1
2.5%
113 1
2.5%
115 1
2.5%
150 1
2.5%
192 1
2.5%
204 1
2.5%
ValueCountFrequency (%)
29546 1
2.5%
29030 1
2.5%
14000 1
2.5%
7870 1
2.5%
3746 1
2.5%
2764 1
2.5%
2059 1
2.5%
1864 1
2.5%
1340 1
2.5%
1130 1
2.5%

청천시처리용량(세제곱미터_일)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1615.275
Minimum15
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:58.731287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.8
Q145
median75
Q3332.5
95-th percentile3100
Maximum30000
Range29985
Interquartile range (IQR)287.5

Descriptive statistics

Standard deviation5953.468
Coefficient of variation (CV)3.6857303
Kurtosis18.190624
Mean1615.275
Median Absolute Deviation (MAD)55
Skewness4.3454498
Sum64611
Variance35443781
MonotonicityNot monotonic
2023-12-13T01:17:58.867404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
45 6
 
15.0%
50 3
 
7.5%
75 2
 
5.0%
1500 2
 
5.0%
600 2
 
5.0%
20 2
 
5.0%
25 2
 
5.0%
30000 1
 
2.5%
15 1
 
2.5%
16 1
 
2.5%
Other values (18) 18
45.0%
ValueCountFrequency (%)
15 1
 
2.5%
16 1
 
2.5%
20 2
 
5.0%
25 2
 
5.0%
40 1
 
2.5%
45 6
15.0%
50 3
7.5%
60 1
 
2.5%
65 1
 
2.5%
70 1
 
2.5%
ValueCountFrequency (%)
30000 1
2.5%
24000 1
2.5%
2000 1
2.5%
1500 2
5.0%
780 1
2.5%
600 2
5.0%
490 1
2.5%
340 1
2.5%
330 1
2.5%
280 1
2.5%

우천시처리용량(세제곱미터_일)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1615.275
Minimum15
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:59.057589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.8
Q145
median75
Q3332.5
95-th percentile3100
Maximum30000
Range29985
Interquartile range (IQR)287.5

Descriptive statistics

Standard deviation5953.468
Coefficient of variation (CV)3.6857303
Kurtosis18.190624
Mean1615.275
Median Absolute Deviation (MAD)55
Skewness4.3454498
Sum64611
Variance35443781
MonotonicityNot monotonic
2023-12-13T01:17:59.194448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
45 6
 
15.0%
50 3
 
7.5%
75 2
 
5.0%
1500 2
 
5.0%
600 2
 
5.0%
20 2
 
5.0%
25 2
 
5.0%
30000 1
 
2.5%
15 1
 
2.5%
16 1
 
2.5%
Other values (18) 18
45.0%
ValueCountFrequency (%)
15 1
 
2.5%
16 1
 
2.5%
20 2
 
5.0%
25 2
 
5.0%
40 1
 
2.5%
45 6
15.0%
50 3
7.5%
60 1
 
2.5%
65 1
 
2.5%
70 1
 
2.5%
ValueCountFrequency (%)
30000 1
2.5%
24000 1
2.5%
2000 1
2.5%
1500 2
5.0%
780 1
2.5%
600 2
5.0%
490 1
2.5%
340 1
2.5%
330 1
2.5%
280 1
2.5%

설계유입수수질(BOD)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.7825
Minimum110
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:59.315716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile130.285
Q1157.5
median179.3
Q3200
95-th percentile200
Maximum200
Range90
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation26.570814
Coefficient of variation (CV)0.15115733
Kurtosis-0.67923705
Mean175.7825
Median Absolute Deviation (MAD)20.7
Skewness-0.70335995
Sum7031.3
Variance706.00815
MonotonicityNot monotonic
2023-12-13T01:17:59.437352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
200.0 18
45.0%
140.0 5
 
12.5%
167.0 4
 
10.0%
150.0 2
 
5.0%
178.0 2
 
5.0%
172.0 2
 
5.0%
184.4 1
 
2.5%
168.0 1
 
2.5%
130.3 1
 
2.5%
180.6 1
 
2.5%
Other values (3) 3
 
7.5%
ValueCountFrequency (%)
110.0 1
 
2.5%
130.0 1
 
2.5%
130.3 1
 
2.5%
140.0 5
12.5%
150.0 2
 
5.0%
160.0 1
 
2.5%
167.0 4
10.0%
168.0 1
 
2.5%
172.0 2
 
5.0%
178.0 2
 
5.0%
ValueCountFrequency (%)
200.0 18
45.0%
184.4 1
 
2.5%
180.6 1
 
2.5%
178.0 2
 
5.0%
172.0 2
 
5.0%
168.0 1
 
2.5%
167.0 4
 
10.0%
160.0 1
 
2.5%
150.0 2
 
5.0%
140.0 5
 
12.5%

설계유입수수질(COD)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.5075
Minimum53.5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:59.550196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.5
5-th percentile100
Q1141.5
median162.5
Q3200
95-th percentile200
Maximum200
Range146.5
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation38.488489
Coefficient of variation (CV)0.23830775
Kurtosis0.12165954
Mean161.5075
Median Absolute Deviation (MAD)37.5
Skewness-0.8504694
Sum6460.3
Variance1481.3638
MonotonicityNot monotonic
2023-12-13T01:17:59.670191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
200.0 14
35.0%
100.0 5
 
12.5%
150.0 4
 
10.0%
160.0 4
 
10.0%
175.0 2
 
5.0%
138.0 1
 
2.5%
140.0 1
 
2.5%
110.0 1
 
2.5%
135.0 1
 
2.5%
171.0 1
 
2.5%
Other values (6) 6
15.0%
ValueCountFrequency (%)
53.5 1
 
2.5%
100.0 5
12.5%
110.0 1
 
2.5%
135.0 1
 
2.5%
138.0 1
 
2.5%
140.0 1
 
2.5%
142.0 1
 
2.5%
150.0 4
10.0%
158.0 1
 
2.5%
160.0 4
10.0%
ValueCountFrequency (%)
200.0 14
35.0%
185.0 1
 
2.5%
175.0 2
 
5.0%
172.8 1
 
2.5%
171.0 1
 
2.5%
165.0 1
 
2.5%
160.0 4
 
10.0%
158.0 1
 
2.5%
150.0 4
 
10.0%
142.0 1
 
2.5%

설계유입수수질(SS)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.4825
Minimum76.7
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:17:59.800562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.7
5-th percentile100
Q1138.75
median169
Q3200
95-th percentile201.46
Maximum225
Range148.3
Interquartile range (IQR)61.25

Descriptive statistics

Standard deviation38.232748
Coefficient of variation (CV)0.23103801
Kurtosis-0.88922234
Mean165.4825
Median Absolute Deviation (MAD)31
Skewness-0.51855146
Sum6619.3
Variance1461.743
MonotonicityNot monotonic
2023-12-13T01:18:00.012028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
200.0 13
32.5%
160.0 4
 
10.0%
140.0 4
 
10.0%
124.0 3
 
7.5%
169.0 2
 
5.0%
110.0 2
 
5.0%
100.0 2
 
5.0%
206.4 1
 
2.5%
193.0 1
 
2.5%
186.0 1
 
2.5%
Other values (7) 7
17.5%
ValueCountFrequency (%)
76.7 1
 
2.5%
100.0 2
5.0%
110.0 2
5.0%
124.0 3
7.5%
126.0 1
 
2.5%
135.0 1
 
2.5%
140.0 4
10.0%
150.0 1
 
2.5%
160.0 4
10.0%
169.0 2
5.0%
ValueCountFrequency (%)
225.0 1
 
2.5%
206.4 1
 
2.5%
201.2 1
 
2.5%
200.0 13
32.5%
193.0 1
 
2.5%
190.0 1
 
2.5%
186.0 1
 
2.5%
169.0 2
 
5.0%
160.0 4
 
10.0%
150.0 1
 
2.5%

설계유입수수질(T_N)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.1575
Minimum20
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:18:00.135978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26.95
Q134.75
median60
Q360
95-th percentile60
Maximum60
Range40
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation14.330546
Coefficient of variation (CV)0.3038869
Kurtosis-1.6533695
Mean47.1575
Median Absolute Deviation (MAD)0
Skewness-0.36257079
Sum1886.3
Variance205.36456
MonotonicityNot monotonic
2023-12-13T01:18:00.271667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
60.0 21
52.5%
28.0 3
 
7.5%
36.5 3
 
7.5%
35.0 2
 
5.0%
30.0 1
 
2.5%
47.5 1
 
2.5%
34.0 1
 
2.5%
35.5 1
 
2.5%
32.4 1
 
2.5%
29.8 1
 
2.5%
Other values (5) 5
 
12.5%
ValueCountFrequency (%)
20.0 1
 
2.5%
26.0 1
 
2.5%
27.0 1
 
2.5%
28.0 3
7.5%
29.8 1
 
2.5%
30.0 1
 
2.5%
32.4 1
 
2.5%
34.0 1
 
2.5%
35.0 2
5.0%
35.5 1
 
2.5%
ValueCountFrequency (%)
60.0 21
52.5%
47.5 1
 
2.5%
40.6 1
 
2.5%
40.0 1
 
2.5%
36.5 3
 
7.5%
35.5 1
 
2.5%
35.0 2
 
5.0%
34.0 1
 
2.5%
32.4 1
 
2.5%
30.0 1
 
2.5%

설계유입수수질(T_P)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.47225
Minimum3.1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:18:00.393144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile3.5
Q15
median8
Q310
95-th percentile10
Maximum10
Range6.9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4995861
Coefficient of variation (CV)0.33451586
Kurtosis-1.378818
Mean7.47225
Median Absolute Deviation (MAD)2
Skewness-0.38924079
Sum298.89
Variance6.2479307
MonotonicityNot monotonic
2023-12-13T01:18:00.531975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10.0 16
40.0%
8.0 5
 
12.5%
5.0 4
 
10.0%
6.0 3
 
7.5%
3.5 3
 
7.5%
3.6 1
 
2.5%
8.1 1
 
2.5%
7.1 1
 
2.5%
6.5 1
 
2.5%
5.9 1
 
2.5%
Other values (4) 4
 
10.0%
ValueCountFrequency (%)
3.1 1
 
2.5%
3.5 3
7.5%
3.6 1
 
2.5%
4.0 1
 
2.5%
4.1 1
 
2.5%
5.0 4
10.0%
5.9 1
 
2.5%
6.0 3
7.5%
6.5 1
 
2.5%
7.1 1
 
2.5%
ValueCountFrequency (%)
10.0 16
40.0%
8.1 1
 
2.5%
8.0 5
 
12.5%
7.99 1
 
2.5%
7.1 1
 
2.5%
6.5 1
 
2.5%
6.0 3
 
7.5%
5.9 1
 
2.5%
5.0 4
 
10.0%
4.1 1
 
2.5%

대장균균수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101875
Minimum0
Maximum355000
Zeros22
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:18:00.680795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3200000
95-th percentile300000
Maximum355000
Range355000
Interquartile range (IQR)200000

Descriptive statistics

Standard deviation124579.74
Coefficient of variation (CV)1.2228686
Kurtosis-1.201379
Mean101875
Median Absolute Deviation (MAD)0
Skewness0.6586699
Sum4075000
Variance1.5520112 × 1010
MonotonicityNot monotonic
2023-12-13T01:18:00.794056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22
55.0%
200000 8
 
20.0%
300000 6
 
15.0%
100000 2
 
5.0%
355000 1
 
2.5%
120000 1
 
2.5%
ValueCountFrequency (%)
0 22
55.0%
100000 2
 
5.0%
120000 1
 
2.5%
200000 8
 
20.0%
300000 6
 
15.0%
355000 1
 
2.5%
ValueCountFrequency (%)
355000 1
 
2.5%
300000 6
 
15.0%
200000 8
 
20.0%
120000 1
 
2.5%
100000 2
 
5.0%
0 22
55.0%

설치일자
Date

Distinct35
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Minimum1999-06-18 00:00:00
Maximum2020-08-25 00:00:00
2023-12-13T01:18:00.944724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:01.099982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Minimum2023-08-29 00:00:00
Maximum2023-08-29 00:00:00
2023-12-13T01:18:01.268825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:18:01.401535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:17:53.692958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.122809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.342270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.172541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.140493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.179330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.274200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.184746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.518111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.521709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.645258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.777507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.203958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.411473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.254722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.229313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.283521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.355013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.585184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.603266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.620258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.738170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.856565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.279077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.482339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.326919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.304352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.387047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.429538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.661736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.690169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.721106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.822934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.948847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.366064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.552896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.400029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.397487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.500513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.518660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.750828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.778571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.824121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.917593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.066603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.452233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.625863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.473886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.486755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.596694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.616911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.855047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.874989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.925206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.015471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.160641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.801903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.701302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.564986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.605485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.692918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.697539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.941889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.968249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.031126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.128289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.242601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.887768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.769821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.646590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.678089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.782744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.761804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.019793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.050080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.113592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.221869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.335801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:43.983362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.857028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.734995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.770768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.887910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.836295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.105358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.144529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.207575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.317100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.445776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.069061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.933286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.838651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.873792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.971275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.921384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.186292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.249769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.299589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.407905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.571616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.176969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.012234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.939605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.986432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.074678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.022938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.308125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.342982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.418972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.497955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:54.700622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:44.254813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:45.085330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:46.049159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:47.088950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:48.161984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:49.105230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:50.419205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:51.432133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:52.538996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:53.602516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:18:01.552802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하수처리장명소재지위도경도하수처리방식관리기관부지면적(제곱미터)청천시처리용량(세제곱미터_일)우천시처리용량(세제곱미터_일)설계유입수수질(BOD)설계유입수수질(COD)설계유입수수질(SS)설계유입수수질(T_N)설계유입수수질(T_P)대장균균수설치일자
하수처리장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.6880.0000.0820.0000.2740.2740.2950.0000.4480.4580.0000.0000.899
경도1.0001.0000.6881.0000.0000.0000.7430.0000.0000.0000.4050.6940.6100.0000.0000.840
하수처리방식1.0001.0000.0000.0001.0001.0000.8571.0001.0000.7410.4950.5460.4820.5670.9020.977
관리기관1.0001.0000.0820.0001.0001.0001.0001.0001.0000.4810.0900.5050.6380.1870.4341.000
부지면적(제곱미터)1.0001.0000.0000.7430.8571.0001.0000.6880.6880.6790.2990.8430.7290.6640.7451.000
청천시처리용량(세제곱미터_일)1.0001.0000.2740.0001.0001.0000.6881.0001.0000.7950.2650.5720.1660.3520.0001.000
우천시처리용량(세제곱미터_일)1.0001.0000.2740.0001.0001.0000.6881.0001.0000.7950.2650.5720.1660.3520.0001.000
설계유입수수질(BOD)1.0001.0000.2950.0000.7410.4810.6790.7950.7951.0000.7870.8360.7280.8680.9030.994
설계유입수수질(COD)1.0001.0000.0000.4050.4950.0900.2990.2650.2650.7871.0000.8350.7310.8910.4020.993
설계유입수수질(SS)1.0001.0000.4480.6940.5460.5050.8430.5720.5720.8360.8351.0000.8010.8620.6340.983
설계유입수수질(T_N)1.0001.0000.4580.6100.4820.6380.7290.1660.1660.7280.7310.8011.0000.7380.7300.931
설계유입수수질(T_P)1.0001.0000.0000.0000.5670.1870.6640.3520.3520.8680.8910.8620.7381.0000.8050.984
대장균균수1.0001.0000.0000.0000.9020.4340.7450.0000.0000.9030.4020.6340.7300.8051.0001.000
설치일자1.0001.0000.8990.8400.9771.0001.0001.0001.0000.9940.9930.9830.9310.9841.0001.000
2023-12-13T01:18:01.738824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관하수처리방식
관리기관1.0000.778
하수처리방식0.7781.000
2023-12-13T01:18:01.841857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도부지면적(제곱미터)청천시처리용량(세제곱미터_일)우천시처리용량(세제곱미터_일)설계유입수수질(BOD)설계유입수수질(COD)설계유입수수질(SS)설계유입수수질(T_N)설계유입수수질(T_P)대장균균수하수처리방식관리기관
위도1.0000.328-0.031-0.152-0.152-0.076-0.056-0.044-0.091-0.0680.0370.0000.000
경도0.3281.000-0.065-0.186-0.1860.0390.1060.128-0.0430.002-0.1020.0000.000
부지면적(제곱미터)-0.031-0.0651.0000.6280.628-0.142-0.1530.178-0.486-0.4240.6670.5300.960
청천시처리용량(세제곱미터_일)-0.152-0.1860.6281.0001.000-0.241-0.2970.085-0.614-0.4420.4750.7880.987
우천시처리용량(세제곱미터_일)-0.152-0.1860.6281.0001.000-0.241-0.2970.085-0.614-0.4420.4750.7880.987
설계유입수수질(BOD)-0.0760.039-0.142-0.241-0.2411.0000.8550.6540.5860.781-0.3750.3360.431
설계유입수수질(COD)-0.0560.106-0.153-0.297-0.2970.8551.0000.5970.5840.805-0.5040.1790.064
설계유입수수질(SS)-0.0440.1280.1780.0850.0850.6540.5971.0000.3190.537-0.2130.1860.338
설계유입수수질(T_N)-0.091-0.043-0.486-0.614-0.6140.5860.5840.3191.0000.862-0.6360.1880.374
설계유입수수질(T_P)-0.0680.002-0.424-0.442-0.4420.7810.8050.5370.8621.000-0.6570.2250.177
대장균균수0.037-0.1020.6670.4750.475-0.375-0.504-0.213-0.636-0.6571.0000.5790.291
하수처리방식0.0000.0000.5300.7880.7880.3360.1790.1860.1880.2250.5791.0000.778
관리기관0.0000.0000.9600.9870.9870.4310.0640.3380.3740.1770.2910.7781.000

Missing values

2023-12-13T01:17:55.254063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:17:55.519118image/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

하수처리장명소재지위도경도개통상태하수처리방식측량방법관리기관방류수역명부지면적(제곱미터)청천시처리용량(세제곱미터_일)우천시처리용량(세제곱미터_일)설계유입수수질(BOD)설계유입수수질(COD)설계유입수수질(SS)설계유입수수질(T_N)설계유입수수질(T_P)대장균균수설치일자기준일자
0거제중앙공공하수처리시설경상남도 거제시 연초면 오비4길 5634.918761128.613012계속운영중HBR.Ⅱ, KS.MBR대장이기거제시 하수운영과남해290303000030000150.0150.0160.030.03.62000002004-02-292023-08-29
1거제장승포공공하수처리시설경상남도 거제시 거제대로 326334.860814128.710737계속운영중DNR대장이기거제시 하수운영과남해295462400024000178.0175.0169.028.05.02000002008-04-142023-08-29
2거제면공공하수처리시설경상남도 거제시 거제면 죽림길 7634.842122128.590519계속운영중HBR.II대장이기민간(대행)남해1400020002000172.0142.0190.028.06.01000002003-05-282023-08-29
3사등면공공하수처리시설경상남도 거제시 사등면 성포로 30034.916868128.534891계속운영중KSMBR대장이기민간(대행)남해374615001500200.0185.0225.047.58.13000002017-10-122023-08-29
4일운면공공하수처리시설경상남도 거제시 일운면 거제대로 2443-3034.82265128.70691계속운영중KSMBR대장이기민간(대행)남해787015001500184.4172.8206.434.07.13550002015-02-132023-08-29
5장목면공공하수처리시설경상남도 거제시 장목면 시루성길 18-4235.000575128.67621계속운영중JASSFR대장이기민간(대행)남해1864600600167.0158.0193.035.56.52000002011-06-302023-08-29
6하청면공공하수처리시설경상남도 거제시 하청면 사환2길 6434.959558128.651575계속운영중JASSFR대장이기민간(대행)남해2764600600168.0160.0186.032.45.92000002012-06-302023-08-29
7해금강공공하수처리시설경상남도 거제시 남부면 갈곶리 9-30번지34.738443128.67676계속운영중KSMBR대장이기민간(대행)남해1340780780130.353.576.729.84.13000002020-08-252023-08-29
8학동공공하수처리시설경상남도 거제시 동부면 학동리 26434.775209128.641259계속운영중COSBR대장이기민간(대행)남해663490490180.6171.0201.240.67.9902004-11-262023-08-29
9신촌공공하수처리시설경상남도 거제시 사등면 덕호리 109-134.879899128.476518계속운영중Biobead대장이기민간(대행)남해975340340160.0165.0160.027.06.01200002007-10-162023-08-29
하수처리장명소재지위도경도개통상태하수처리방식측량방법관리기관방류수역명부지면적(제곱미터)청천시처리용량(세제곱미터_일)우천시처리용량(세제곱미터_일)설계유입수수질(BOD)설계유입수수질(COD)설계유입수수질(SS)설계유입수수질(T_N)설계유입수수질(T_P)대장균균수설치일자기준일자
30명사공공하수처리시설경상남도 거제시 남부면 저구리 248-234.726115128.602171계속운영중ASA, JASSFR대장이기민간(대행)남해2389090140.0150.0140.060.08.002004-10-082023-08-29
31다대윗모실공공하수처리시설경상남도 거제시 남부면 다대리 9934.737419128.627272계속운영중JASSFR대장이기민간(대행)남해11304545200.0200.0200.060.010.02000002009-03-182023-08-29
32다대공공하수처리시설경상남도 거제시 남부면 다대리 458-134.732302128.628369계속운영중ASA대장이기민간(대행)남해664545200.0200.0160.060.010.002004-07-092023-08-29
33근포공공하수처리시설경상남도 거제시 남부면 저구리 53434.718851128.588951계속운영중BNR대장이기민간(대행)남해2504040140.0150.0140.060.08.002004-10-192023-08-29
34소계공공하수처리시설경상남도 거제시 장목면 외포리 118934.936128.714627계속운영중CBT대장이기민간(대행)남해692525140.0138.0135.020.04.002005-10-172023-08-29
35명하공공하수처리시설경상남도 거제시 연초면 명동리 457-134.946972128.671685계속운영중CBT대장이기민간(대행)남해692525200.0200.0100.060.010.002005-10-172023-08-29
36하천공공하수처리시설경상남도 거제시 연초면 천곡리 139-634.930601128.678371계속운영중COSBR대장이기민간(대행)남해7092020200.0200.0200.060.010.001999-06-242023-08-29
37이남공공하수처리시설경상남도 거제시 연초면 이목리 201-134.930234128.671384계속운영중혐기 호기 접촉순환공법대장이기민간(대행)남해5592020200.0200.0200.060.08.001999-06-182023-08-29
38주령공공하수처리시설경상남도 거제시 연초면 천곡리 217-1734.922108128.67366계속운영중KDHST대장이기민간(대행)남해741616200.0200.0200.060.010.001999-06-242023-08-29
39상천공공하수처리시설경상남도 거제시 연초면 천곡리 102-634.926514128.682069계속운영중JASSFR대장이기민간(대행)남해6221515200.0200.0200.060.010.02000002009-12-292023-08-29