Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory79.6 B

Variable types

Text4
Numeric3
DateTime1
Categorical1

Dataset

Description경상남도 김해시 하수처리시설 현황으로 시설명,소재지, 시설용량(㎥/일) ,처리공법,가동개시일,운영방법,방류하천,위도,경도의 정보를 제공하고 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15092570/fileData.do

Alerts

운영방법 has constant value ""Constant
시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:19:35.150865
Analysis finished2024-03-15 01:19:38.506020
Duration3.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-15T10:19:39.023698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.321429
Min length5

Characters and Unicode

Total characters345
Distinct characters54
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

Unique28 ?
Unique (%)100.0%

Sample

1st row화목 공공하수처리시설
2nd row장유 공공하수처리시설
3rd row진영 공공하수처리시설
4th row진례 공공하수처리시설
5th row대동 공공하수처리시설
ValueCountFrequency (%)
공공하수처리시설 27
48.2%
화목 1
 
1.8%
화현 1
 
1.8%
삼미마을 1
 
1.8%
시례마을 1
 
1.8%
용산마을 1
 
1.8%
하사촌마을 1
 
1.8%
생철성포마을 1
 
1.8%
신안안양마을 1
 
1.8%
독산마을 1
 
1.8%
Other values (20) 20
35.7%
2024-03-15T10:19:39.918639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 54
15.7%
하 29
8.4%
시 28
8.1%
28
8.1%
리 28
8.1%
수 27
7.8%
처 27
7.8%
설 27
7.8%
마 19
 
5.5%
을 19
 
5.5%
Other values (44) 59
17.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
91.9%
Space Separator 28
 
8.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 54
17.0%
하 29
9.1%
시 28
8.8%
리 28
8.8%
수 27
8.5%
처 27
8.5%
설 27
8.5%
마 19
 
6.0%
을 19
 
6.0%
산 4
 
1.3%
Other values (43) 55
17.4%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
91.9%
Common 28
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 54
17.0%
하 29
9.1%
시 28
8.8%
리 28
8.8%
수 27
8.5%
처 27
8.5%
설 27
8.5%
마 19
 
6.0%
을 19
 
6.0%
산 4
 
1.3%
Other values (43) 55
17.4%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
91.9%
ASCII 28
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 54
17.0%
하 29
9.1%
시 28
8.8%
리 28
8.8%
수 27
8.5%
처 27
8.5%
설 27
8.5%
마 19
 
6.0%
을 19
 
6.0%
산 4
 
1.3%
Other values (43) 55
17.4%
ASCII
ValueCountFrequency (%)
28
100.0%

소재지
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-15T10:19:40.811854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length24.25
Min length19

Characters and Unicode

Total characters679
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row경상남도 김해시 화목로 334(화목동)
2nd row경상남도 김해시 화목로 334(화목동)
3rd row경상남도 김해시 진영읍 한림로 1130(본산리)
4th row경상남도 김해시 진례면 서부로 283-68
5th row경상남도 김해시 대동면 대동로 480번길 150-54
ValueCountFrequency (%)
경상남도 28
19.2%
김해시 28
19.2%
한림면 8
 
5.5%
생림면 6
 
4.1%
상동면 5
 
3.4%
금곡로 4
 
2.7%
대동로 3
 
2.1%
상동로 2
 
1.4%
43 2
 
1.4%
안양로 2
 
1.4%
Other values (53) 58
39.7%
2024-03-15T10:19:42.047681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
 
17.4%
상 35
 
5.2%
김 29
 
4.3%
해 29
 
4.3%
경 28
 
4.1%
남 28
 
4.1%
도 28
 
4.1%
시 28
 
4.1%
1 28
 
4.1%
로 26
 
3.8%
Other values (51) 302
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
58.0%
Decimal Number 145
 
21.4%
Space Separator 118
 
17.4%
Dash Punctuation 16
 
2.4%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 35
 
8.9%
김 29
 
7.4%
해 29
 
7.4%
경 28
 
7.1%
남 28
 
7.1%
도 28
 
7.1%
시 28
 
7.1%
로 26
 
6.6%
면 23
 
5.8%
림 18
 
4.6%
Other values (37) 122
31.0%
Decimal Number
ValueCountFrequency (%)
1 28
19.3%
3 19
13.1%
2 18
12.4%
4 15
10.3%
0 12
8.3%
8 11
 
7.6%
9 11
 
7.6%
6 11
 
7.6%
5 11
 
7.6%
7 9
 
6.2%
Space Separator
ValueCountFrequency (%)
118
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 394
58.0%
Common 285
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 35
 
8.9%
김 29
 
7.4%
해 29
 
7.4%
경 28
 
7.1%
남 28
 
7.1%
도 28
 
7.1%
시 28
 
7.1%
로 26
 
6.6%
면 23
 
5.8%
림 18
 
4.6%
Other values (37) 122
31.0%
Common
ValueCountFrequency (%)
118
41.4%
1 28
 
9.8%
3 19
 
6.7%
2 18
 
6.3%
- 16
 
5.6%
4 15
 
5.3%
0 12
 
4.2%
8 11
 
3.9%
9 11
 
3.9%
6 11
 
3.9%
Other values (4) 26
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 394
58.0%
ASCII 285
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
41.4%
1 28
 
9.8%
3 19
 
6.7%
2 18
 
6.3%
- 16
 
5.6%
4 15
 
5.3%
0 12
 
4.2%
8 11
 
3.9%
9 11
 
3.9%
6 11
 
3.9%
Other values (4) 26
 
9.1%
Hangul
ValueCountFrequency (%)
상 35
 
8.9%
김 29
 
7.4%
해 29
 
7.4%
경 28
 
7.1%
남 28
 
7.1%
도 28
 
7.1%
시 28
 
7.1%
로 26
 
6.6%
면 23
 
5.8%
림 18
 
4.6%
Other values (37) 122
31.0%

시설용량
Real number (ℝ)

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10262.393
Minimum30
Maximum145000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-15T10:19:42.290906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33.5
Q158.75
median140
Q31300
95-th percentile72150
Maximum145000
Range144970
Interquartile range (IQR)1241.25

Descriptive statistics

Standard deviation32364.808
Coefficient of variation (CV)3.1537292
Kurtosis12.932315
Mean10262.393
Median Absolute Deviation (MAD)91.5
Skewness3.6225992
Sum287347
Variance1.0474808 × 109
MonotonicityNot monotonic
2024-03-15T10:19:42.596038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
170 2
 
7.1%
1300 2
 
7.1%
30 2
 
7.1%
45 1
 
3.6%
130 1
 
3.6%
175 1
 
3.6%
100 1
 
3.6%
60 1
 
3.6%
70 1
 
3.6%
80 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
30 2
7.1%
40 1
3.6%
45 1
3.6%
47 1
3.6%
50 1
3.6%
55 1
3.6%
60 1
3.6%
65 1
3.6%
70 1
3.6%
80 1
3.6%
ValueCountFrequency (%)
145000 1
3.6%
97000 1
3.6%
26000 1
3.6%
10500 1
3.6%
1800 1
3.6%
1600 1
3.6%
1300 2
7.1%
1100 1
3.6%
175 1
3.6%
170 2
7.1%

처리공법
Text

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-15T10:19:43.339925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length7.5
Min length4

Characters and Unicode

Total characters210
Distinct characters56
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)46.4%

Sample

1st rowDNR + KEP-System
2nd rowDNR+모래여과
3rd rowVIP+HANT공법
4th rowBCS+금호MBR
5th rowHANT+Dyna Sand Filter
ValueCountFrequency (%)
aosb 6
17.1%
jassfr 4
 
11.4%
sbr계열 2
 
5.7%
ic-sbr 2
 
5.7%
smmiar 2
 
5.7%
2
 
5.7%
dnr 1
 
2.9%
dnr+모래여과 1
 
2.9%
kmsbr 1
 
2.9%
고효율합병 1
 
2.9%
Other values (13) 13
37.1%
2024-03-15T10:19:44.141733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 32
 
15.2%
B 19
 
9.0%
R 17
 
8.1%
A 16
 
7.6%
+ 9
 
4.3%
F 8
 
3.8%
M 7
 
3.3%
7
 
3.3%
C 6
 
2.9%
D 6
 
2.9%
Other values (46) 83
39.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 146
69.5%
Other Letter 23
 
11.0%
Lowercase Letter 16
 
7.6%
Math Symbol 9
 
4.3%
Space Separator 7
 
3.3%
Dash Punctuation 5
 
2.4%
Close Punctuation 1
 
0.5%
Letter Number 1
 
0.5%
Open Punctuation 1
 
0.5%
Other Punctuation 1
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 32
21.9%
B 19
13.0%
R 17
11.6%
A 16
11.0%
F 8
 
5.5%
M 7
 
4.8%
C 6
 
4.1%
D 6
 
4.1%
O 6
 
4.1%
I 5
 
3.4%
Other values (9) 24
16.4%
Other Letter
ValueCountFrequency (%)
열 2
 
8.7%
공 2
 
8.7%
계 2
 
8.7%
법 2
 
8.7%
여 1
 
4.3%
래 1
 
4.3%
모 1
 
4.3%
병 1
 
4.3%
합 1
 
4.3%
율 1
 
4.3%
Other values (9) 9
39.1%
Lowercase Letter
ValueCountFrequency (%)
y 2
12.5%
e 2
12.5%
a 2
12.5%
n 2
12.5%
t 2
12.5%
m 1
6.2%
s 1
6.2%
r 1
6.2%
l 1
6.2%
i 1
6.2%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 163
77.6%
Common 24
 
11.4%
Hangul 23
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 32
19.6%
B 19
11.7%
R 17
10.4%
A 16
 
9.8%
F 8
 
4.9%
M 7
 
4.3%
C 6
 
3.7%
D 6
 
3.7%
O 6
 
3.7%
I 5
 
3.1%
Other values (21) 41
25.2%
Hangul
ValueCountFrequency (%)
열 2
 
8.7%
공 2
 
8.7%
계 2
 
8.7%
법 2
 
8.7%
여 1
 
4.3%
래 1
 
4.3%
모 1
 
4.3%
병 1
 
4.3%
합 1
 
4.3%
율 1
 
4.3%
Other values (9) 9
39.1%
Common
ValueCountFrequency (%)
+ 9
37.5%
7
29.2%
- 5
20.8%
) 1
 
4.2%
( 1
 
4.2%
/ 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
88.6%
Hangul 23
 
11.0%
Number Forms 1
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 32
17.2%
B 19
 
10.2%
R 17
 
9.1%
A 16
 
8.6%
+ 9
 
4.8%
F 8
 
4.3%
M 7
 
3.8%
7
 
3.8%
C 6
 
3.2%
D 6
 
3.2%
Other values (26) 59
31.7%
Hangul
ValueCountFrequency (%)
열 2
 
8.7%
공 2
 
8.7%
계 2
 
8.7%
법 2
 
8.7%
여 1
 
4.3%
래 1
 
4.3%
모 1
 
4.3%
병 1
 
4.3%
합 1
 
4.3%
율 1
 
4.3%
Other values (9) 9
39.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

가동개시일
Date

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size352.0 B
Minimum1999-05-29 00:00:00
Maximum2022-07-13 00:00:00
2024-03-15T10:19:44.444267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:44.841021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

운영방법
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
위탁운영
28 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁운영 28
100.0%

Length

2024-03-15T10:19:45.186264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:19:45.393799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁운영 28
100.0%

방류하천
Text

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-15T10:19:45.845605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0357143
Min length3

Characters and Unicode

Total characters85
Distinct characters25
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

Unique8 ?
Unique (%)28.6%

Sample

1st row조만강
2nd row조만강
3rd row주천강
4th row화포천
5th row서낙동강
ValueCountFrequency (%)
화포천 8
28.6%
대포천 3
 
10.7%
안양천 3
 
10.7%
조만강 2
 
7.1%
사촌천 2
 
7.1%
낙동강 2
 
7.1%
주천강 1
 
3.6%
서낙동강 1
 
3.6%
용덕천 1
 
3.6%
주중천 1
 
3.6%
Other values (4) 4
14.3%
2024-03-15T10:19:46.506097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 23
27.1%
포 11
12.9%
화 8
 
9.4%
강 6
 
7.1%
안 4
 
4.7%
낙 3
 
3.5%
동 3
 
3.5%
양 3
 
3.5%
대 3
 
3.5%
만 2
 
2.4%
Other values (15) 19
22.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 23
27.1%
포 11
12.9%
화 8
 
9.4%
강 6
 
7.1%
안 4
 
4.7%
낙 3
 
3.5%
동 3
 
3.5%
양 3
 
3.5%
대 3
 
3.5%
만 2
 
2.4%
Other values (15) 19
22.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 23
27.1%
포 11
12.9%
화 8
 
9.4%
강 6
 
7.1%
안 4
 
4.7%
낙 3
 
3.5%
동 3
 
3.5%
양 3
 
3.5%
대 3
 
3.5%
만 2
 
2.4%
Other values (15) 19
22.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 23
27.1%
포 11
12.9%
화 8
 
9.4%
강 6
 
7.1%
안 4
 
4.7%
낙 3
 
3.5%
동 3
 
3.5%
양 3
 
3.5%
대 3
 
3.5%
만 2
 
2.4%
Other values (15) 19
22.4%

위도
Real number (ℝ)

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.300544
Minimum35.227381
Maximum35.376791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-15T10:19:46.794039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.227381
5-th percentile35.228912
Q135.258745
median35.309041
Q335.333424
95-th percentile35.370397
Maximum35.376791
Range0.1494092
Interquartile range (IQR)0.074678785

Descriptive statistics

Standard deviation0.047321148
Coefficient of variation (CV)0.0013405218
Kurtosis-1.2265682
Mean35.300544
Median Absolute Deviation (MAD)0.04501803
Skewness-0.013179744
Sum988.41523
Variance0.002239291
MonotonicityNot monotonic
2024-03-15T10:19:47.025921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
35.25874546 3
 
10.7%
35.26109181 2
 
7.1%
35.33240224 1
 
3.6%
35.33811316 1
 
3.6%
35.30496618 1
 
3.6%
35.24121791 1
 
3.6%
35.31877944 1
 
3.6%
35.37018352 1
 
3.6%
35.37042992 1
 
3.6%
35.37679057 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
35.22738137 1
 
3.6%
35.22809177 1
 
3.6%
35.23043541 1
 
3.6%
35.24121791 1
 
3.6%
35.25019638 1
 
3.6%
35.25874546 3
10.7%
35.26109181 2
7.1%
35.27119224 1
 
3.6%
35.28594346 1
 
3.6%
35.30496618 1
 
3.6%
ValueCountFrequency (%)
35.37679057 1
3.6%
35.37042992 1
3.6%
35.37033456 1
3.6%
35.37018352 1
3.6%
35.35112787 1
3.6%
35.33811316 1
3.6%
35.33649026 1
3.6%
35.33240224 1
3.6%
35.32987055 1
3.6%
35.32716176 1
3.6%

경도
Real number (ℝ)

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.785
Minimum128.29865
Maximum128.97607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-15T10:19:47.271497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.29865
5-th percentile128.29865
Q1128.79663
median128.82621
Q3128.84413
95-th percentile128.95945
Maximum128.97607
Range0.6774179
Interquartile range (IQR)0.047503325

Descriptive statistics

Standard deviation0.18154773
Coefficient of variation (CV)0.0014096962
Kurtosis3.9718706
Mean128.785
Median Absolute Deviation (MAD)0.02520425
Skewness-2.1232692
Sum3605.98
Variance0.032959579
MonotonicityNot monotonic
2024-03-15T10:19:47.511745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
128.2986504 3
 
10.7%
128.8379695 2
 
7.1%
128.8194018 1
 
3.6%
128.9351299 1
 
3.6%
128.7975285 1
 
3.6%
128.9657331 1
 
3.6%
128.8662796 1
 
3.6%
128.8319554 1
 
3.6%
128.8444924 1
 
3.6%
128.8293489 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
128.2986504 3
10.7%
128.7448197 1
 
3.6%
128.7638489 1
 
3.6%
128.772764 1
 
3.6%
128.7939227 1
 
3.6%
128.7975285 1
 
3.6%
128.8044831 1
 
3.6%
128.805551 1
 
3.6%
128.8067956 1
 
3.6%
128.8173751 1
 
3.6%
ValueCountFrequency (%)
128.9760683 1
3.6%
128.9657331 1
3.6%
128.9477811 1
3.6%
128.9465998 1
3.6%
128.9351299 1
3.6%
128.8662796 1
3.6%
128.8444924 1
3.6%
128.8440097 1
3.6%
128.8402087 1
3.6%
128.8379695 2
7.1%

Interactions

2024-03-15T10:19:37.312388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:35.699417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:36.230286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:37.580482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:35.867446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:36.502871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:37.785619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:36.056752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:19:37.041922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:19:47.690337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지시설용량처리공법가동개시일방류하천위도경도
시설명1.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0000.0000.8630.9671.0001.0001.000
시설용량1.0000.0001.0001.0001.0000.8120.0000.138
처리공법1.0000.8631.0001.0000.9680.7700.6770.796
가동개시일1.0000.9671.0000.9681.0000.9710.9291.000
방류하천1.0001.0000.8120.7700.9711.0000.7240.866
위도1.0001.0000.0000.6770.9290.7241.0000.230
경도1.0001.0000.1380.7961.0000.8660.2301.000
2024-03-15T10:19:47.976843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설용량위도경도
시설용량1.000-0.0690.213
위도-0.0691.0000.051
경도0.2130.0511.000

Missing values

2024-03-15T10:19:38.039569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:19:38.415319image/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

시설명소재지시설용량처리공법가동개시일운영방법방류하천위도경도
0화목 공공하수처리시설경상남도 김해시 화목로 334(화목동)145000DNR + KEP-System2000-03-31위탁운영조만강35.261092128.83797
1장유 공공하수처리시설경상남도 김해시 화목로 334(화목동)97000DNR+모래여과2004-12-31위탁운영조만강35.261092128.83797
2진영 공공하수처리시설경상남도 김해시 진영읍 한림로 1130(본산리)26000VIP+HANT공법2016-07-15위탁운영주천강35.327162128.74482
3진례 공공하수처리시설경상남도 김해시 진례면 서부로 283-6810500BCS+금호MBR2017-09-01위탁운영화포천35.271192128.763849
4대동 공공하수처리시설경상남도 김해시 대동면 대동로 480번길 150-541300HANT+Dyna Sand Filter2013-09-26위탁운영서낙동강35.228092128.976068
5한림 공공하수처리시설경상남도 김해시 한림면 금곡로 311300BCS/ESSA+총인설비(YDF)2007-12-17위탁운영화포천35.329871128.804483
6생림 공공하수처리시설경상남도 김해시 생림면 장재로 520번길 8-1111100BCS +YDF2003-07-09위탁운영사촌천35.320947128.84401
7상동 공공하수처리시설경상남도 김해시 상동면 상동로 739-451800BCSⅡ+DAF2014-10-18위탁운영대포천35.316671128.947781
8안하 공공하수처리시설경상남도 김해시 한림면 한림로252번길 1461600KSMBR2015-07-01위탁운영용덕천35.312466128.823071
9주중마을 공공하수처리시설경상남도 김해시 대동로 222-12160SBR계열2013-05-01위탁운영주중천35.230435128.9466
시설명소재지시설용량처리공법가동개시일운영방법방류하천위도경도
18모정마을 공공하수처리시설경상남도 김해시 한림면 금곡로 247번길 5255SMMIAR2009-04-29위탁운영화포천35.351128128.806796
19송촌마을 공공하수처리시설경상남도 김해시 생림면 마사로 368번길 28-747AOSB2004-11-02위탁운영안양천35.370335128.830966
20독산마을 공공하수처리시설경상남도 김해시 생림면 안양로 496150AOSB + KMSBR2019-01-11위탁운영낙동강35.376791128.829349
21신안안양마을 공공하수처리시설경상남도 김해시 생림면 안양로 251번길 4380AOSB2006-07-05위탁운영안양천35.37043128.844492
22생철성포마을 공공하수처리시설경상남도 김해시 생림면 생림대로 1190-60170IC-SBR2009-06-11위탁운영안양천35.370184128.831955
23하사촌마을 공공하수처리시설경상남도 김해시 생림면 나전로 43770IC-SBR2009-12-23위탁운영사촌천35.318779128.86628
24용산마을 공공하수처리시설경상남도 김해시 상동면 동북로 1269-1060JASSFR2009-06-11위탁운영대포천35.258745128.29865
25시례마을 공공하수처리시설경상남도 김해시 대동로 485번길 39100SBR계열2014-01-17위탁운영예안천35.241218128.965733
26삼미마을 공공하수처리시설경상남도 김해시 한림면 퇴래리 1398-11번지175H-SBR2019-05-01위탁운영화포천35.304966128.797528
27화현 마을경상남도 김해시 상동면 감노리 68-2130JASSFR2022-07-13위탁운영낙동강35.338113128.93513