Overview

Dataset statistics

Number of variables10
Number of observations28
Missing cells55
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory87.7 B

Variable types

Text4
Numeric2
Categorical3
Unsupported1

Dataset

Description경상남도 양산시에 운영중인 배수펌프장 14개소의 명칭 소재지주소, 처리능력, 배수펌프, 배수유역면적, 유수지면적, 전기용량 등을 확인할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15021433

Alerts

계약종별 is highly overall correlated with 설치목적High correlation
설치목적 is highly overall correlated with 계약종별High correlation
계약전력(KW) is highly overall correlated with 한전전원공급방식High correlation
한전전원공급방식 is highly overall correlated with 계약전력(KW)High correlation
설치목적 is highly imbalanced (62.9%)Imbalance
모터 펌프규모(KW(HP)×대) has 5 (17.9%) missing valuesMissing
엔진펌프 규모(KW(HP)×대) has 28 (100.0%) missing valuesMissing
비상발전기(KW(HP)×대) has 22 (78.6%) missing valuesMissing
엔진펌프 규모(KW(HP)×대) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 00:10:10.877988
Analysis finished2023-12-11 00:10:12.215637
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T09:10:12.354872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.2142857
Min length7

Characters and Unicode

Total characters202
Distinct characters32
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

Unique3 ?
Unique (%)10.7%

Sample

1st row남부빗물펌프장
2nd row남부빗물펌프장
3rd row교동빗물펌프장
4th row교동빗물펌프장
5th row신기빗물펌프장
ValueCountFrequency (%)
물금빗물펌프장 3
10.7%
남부빗물펌프장 2
 
7.1%
교동빗물펌프장 2
 
7.1%
신기빗물펌프장 2
 
7.1%
원리빗물펌프장 2
 
7.1%
화제빗물펌프장 2
 
7.1%
금산빗물펌프장 2
 
7.1%
범어빗물펌프장 2
 
7.1%
북정빗물펌프장 2
 
7.1%
당곡빗물펌프장 2
 
7.1%
Other values (5) 7
25.0%
2023-12-11T09:10:12.749352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
물 29
14.4%
펌 28
13.9%
프 28
13.9%
장 28
13.9%
빗 26
12.9%
신 6
 
3.0%
금 5
 
2.5%
곡 4
 
2.0%
산 4
 
2.0%
리 3
 
1.5%
Other values (22) 41
20.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 200
99.0%
Decimal Number 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
물 29
14.5%
펌 28
14.0%
프 28
14.0%
장 28
14.0%
빗 26
13.0%
신 6
 
3.0%
금 5
 
2.5%
곡 4
 
2.0%
산 4
 
2.0%
리 3
 
1.5%
Other values (20) 39
19.5%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 200
99.0%
Common 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
물 29
14.5%
펌 28
14.0%
프 28
14.0%
장 28
14.0%
빗 26
13.0%
신 6
 
3.0%
금 5
 
2.5%
곡 4
 
2.0%
산 4
 
2.0%
리 3
 
1.5%
Other values (20) 39
19.5%
Common
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 200
99.0%
ASCII 2
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
물 29
14.5%
펌 28
14.0%
프 28
14.0%
장 28
14.0%
빗 26
13.0%
신 6
 
3.0%
금 5
 
2.5%
곡 4
 
2.0%
산 4
 
2.0%
리 3
 
1.5%
Other values (20) 39
19.5%
ASCII
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

주소
Text

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T09:10:12.974375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length20.857143
Min length18

Characters and Unicode

Total characters584
Distinct characters49
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

Unique3 ?
Unique (%)10.7%

Sample

1st row경상남도 양산시 강변로 320(남부동)
2nd row경상남도 양산시 강변로 320(남부동)
3rd row경상남도 양산시 교동2길 10(교동)
4th row경상남도 양산시 교동2길 10(교동)
5th row경상남도 양산시 양산대로 895(신기동)
ValueCountFrequency (%)
경상남도 28
21.2%
양산시 28
21.2%
원동면 11
 
8.3%
물금읍 5
 
3.8%
양산대로 4
 
3.0%
용당리 3
 
2.3%
296 3
 
2.3%
황산로 3
 
2.3%
원리 2
 
1.5%
187-4 2
 
1.5%
Other values (23) 43
32.6%
2023-12-11T09:10:13.404937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
17.8%
산 37
 
6.3%
양 32
 
5.5%
남 30
 
5.1%
동 29
 
5.0%
경 28
 
4.8%
도 28
 
4.8%
시 28
 
4.8%
상 28
 
4.8%
1 20
 
3.4%
Other values (39) 220
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 360
61.6%
Space Separator 104
 
17.8%
Decimal Number 97
 
16.6%
Close Punctuation 8
 
1.4%
Open Punctuation 8
 
1.4%
Dash Punctuation 7
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 37
10.3%
양 32
 
8.9%
남 30
 
8.3%
동 29
 
8.1%
경 28
 
7.8%
도 28
 
7.8%
시 28
 
7.8%
상 28
 
7.8%
원 17
 
4.7%
로 15
 
4.2%
Other values (25) 88
24.4%
Decimal Number
ValueCountFrequency (%)
1 20
20.6%
3 15
15.5%
5 13
13.4%
2 11
11.3%
0 8
 
8.2%
6 7
 
7.2%
4 7
 
7.2%
8 6
 
6.2%
9 6
 
6.2%
7 4
 
4.1%
Space Separator
ValueCountFrequency (%)
104
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 360
61.6%
Common 224
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 37
10.3%
양 32
 
8.9%
남 30
 
8.3%
동 29
 
8.1%
경 28
 
7.8%
도 28
 
7.8%
시 28
 
7.8%
상 28
 
7.8%
원 17
 
4.7%
로 15
 
4.2%
Other values (25) 88
24.4%
Common
ValueCountFrequency (%)
104
46.4%
1 20
 
8.9%
3 15
 
6.7%
5 13
 
5.8%
2 11
 
4.9%
) 8
 
3.6%
0 8
 
3.6%
( 8
 
3.6%
- 7
 
3.1%
6 7
 
3.1%
Other values (4) 23
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 360
61.6%
ASCII 224
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
46.4%
1 20
 
8.9%
3 15
 
6.7%
5 13
 
5.8%
2 11
 
4.9%
) 8
 
3.6%
0 8
 
3.6%
( 8
 
3.6%
- 7
 
3.1%
6 7
 
3.1%
Other values (4) 23
 
10.3%
Hangul
ValueCountFrequency (%)
산 37
10.3%
양 32
 
8.9%
남 30
 
8.3%
동 29
 
8.1%
경 28
 
7.8%
도 28
 
7.8%
시 28
 
7.8%
상 28
 
7.8%
원 17
 
4.7%
로 15
 
4.2%
Other values (25) 88
24.4%

설치년도
Real number (ℝ)

Distinct9
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.1786
Minimum1997
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:10:13.531841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1997.7
Q12001
median2007
Q32011
95-th percentile2014
Maximum2014
Range17
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.644096
Coefficient of variation (CV)0.0028133568
Kurtosis-1.3748017
Mean2006.1786
Median Absolute Deviation (MAD)4.5
Skewness-0.1224398
Sum56173
Variance31.85582
MonotonicityNot monotonic
2023-12-11T09:10:13.664607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2011 7
25.0%
2007 4
14.3%
2014 4
14.3%
2001 3
10.7%
2005 3
10.7%
1999 2
 
7.1%
1997 2
 
7.1%
2000 2
 
7.1%
2002 1
 
3.6%
ValueCountFrequency (%)
1997 2
 
7.1%
1999 2
 
7.1%
2000 2
 
7.1%
2001 3
10.7%
2002 1
 
3.6%
2005 3
10.7%
2007 4
14.3%
2011 7
25.0%
2014 4
14.3%
ValueCountFrequency (%)
2014 4
14.3%
2011 7
25.0%
2007 4
14.3%
2005 3
10.7%
2002 1
 
3.6%
2001 3
10.7%
2000 2
 
7.1%
1999 2
 
7.1%
1997 2
 
7.1%

설치목적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
도심지 저지대 침수예방
26 
농경지 침수예방
 
2

Length

Max length12
Median length12
Mean length11.714286
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도심지 저지대 침수예방
2nd row도심지 저지대 침수예방
3rd row도심지 저지대 침수예방
4th row도심지 저지대 침수예방
5th row도심지 저지대 침수예방

Common Values

ValueCountFrequency (%)
도심지 저지대 침수예방 26
92.9%
농경지 침수예방 2
 
7.1%

Length

2023-12-11T09:10:13.842808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:13.962723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
침수예방 28
34.1%
도심지 26
31.7%
저지대 26
31.7%
농경지 2
 
2.4%

모터 펌프규모(KW(HP)×대)
Text

MISSING 

Distinct17
Distinct (%)73.9%
Missing5
Missing (%)17.9%
Memory size356.0 B
2023-12-11T09:10:14.135572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length10
Mean length12.217391
Min length9

Characters and Unicode

Total characters281
Distinct characters19
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)47.8%

Sample

1st row652(870)×7
2nd row652(870)×7
3rd row168(225)×5
4th row168(225)×5
5th row862(1,150)×5
ValueCountFrequency (%)
375(500)×3 2
 
6.7%
652(870)×7 2
 
6.7%
1대 2
 
6.7%
168(225)×5 2
 
6.7%
예비 2
 
6.7%
862(1,150)×5 2
 
6.7%
75(227)×2 2
 
6.7%
700(940)×6 2
 
6.7%
305(409)×1 1
 
3.3%
550(737)×4 1
 
3.3%
Other values (12) 12
40.0%
2023-12-11T09:10:14.514854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
11.0%
( 28
10.0%
) 28
10.0%
5 27
9.6%
2 27
9.6%
× 26
9.3%
1 20
7.1%
7 20
7.1%
3 17
 
6.0%
6 14
 
5.0%
Other values (9) 43
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182
64.8%
Open Punctuation 28
 
10.0%
Close Punctuation 28
 
10.0%
Math Symbol 26
 
9.3%
Space Separator 8
 
2.8%
Other Letter 6
 
2.1%
Other Punctuation 3
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
17.0%
5 27
14.8%
2 27
14.8%
1 20
11.0%
7 20
11.0%
3 17
9.3%
6 14
7.7%
4 11
 
6.0%
8 9
 
4.9%
9 6
 
3.3%
Other Letter
ValueCountFrequency (%)
예 2
33.3%
비 2
33.3%
대 2
33.3%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Math Symbol
ValueCountFrequency (%)
× 26
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 275
97.9%
Hangul 6
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
11.3%
( 28
10.2%
) 28
10.2%
5 27
9.8%
2 27
9.8%
× 26
9.5%
1 20
7.3%
7 20
7.3%
3 17
6.2%
6 14
 
5.1%
Other values (6) 37
13.5%
Hangul
ValueCountFrequency (%)
예 2
33.3%
비 2
33.3%
대 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
88.6%
None 26
 
9.3%
Hangul 6
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
12.4%
( 28
11.2%
) 28
11.2%
5 27
10.8%
2 27
10.8%
1 20
8.0%
7 20
8.0%
3 17
6.8%
6 14
5.6%
4 11
 
4.4%
Other values (5) 26
10.4%
None
ValueCountFrequency (%)
× 26
100.0%
Hangul
ValueCountFrequency (%)
예 2
33.3%
비 2
33.3%
대 2
33.3%

엔진펌프 규모(KW(HP)×대)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28
Missing (%)100.0%
Memory size384.0 B

한전전원공급방식
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
1회선수전
16 
2회선 수전
6 
2회선수전
4 
1회선 수전
2 

Length

Max length6
Median length5
Mean length5.2857143
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2회선수전
2nd row1회선수전
3rd row2회선수전
4th row1회선수전
5th row2회선수전

Common Values

ValueCountFrequency (%)
1회선수전 16
57.1%
2회선 수전 6
 
21.4%
2회선수전 4
 
14.3%
1회선 수전 2
 
7.1%

Length

2023-12-11T09:10:14.696657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:14.830613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1회선수전 16
44.4%
수전 8
22.2%
2회선 6
 
16.7%
2회선수전 4
 
11.1%
1회선 2
 
5.6%

비상발전기(KW(HP)×대)
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing22
Missing (%)78.6%
Memory size356.0 B
2023-12-11T09:10:14.985149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.333333
Min length8

Characters and Unicode

Total characters62
Distinct characters15
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st row550(733)×1대
2nd row350(476)×2대
3rd row450(612)×1대
4th row115(156.4)×1대
5th row170kw×1대
ValueCountFrequency (%)
170kw×1대 2
33.3%
550(733)×1대 1
16.7%
350(476)×2대 1
16.7%
450(612)×1대 1
16.7%
115(156.4)×1대 1
16.7%
2023-12-11T09:10:15.305168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
17.7%
× 6
9.7%
대 6
9.7%
5 6
9.7%
0 5
8.1%
7 4
 
6.5%
( 4
 
6.5%
) 4
 
6.5%
3 3
 
4.8%
4 3
 
4.8%
Other values (5) 10
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37
59.7%
Math Symbol 6
 
9.7%
Other Letter 6
 
9.7%
Open Punctuation 4
 
6.5%
Close Punctuation 4
 
6.5%
Lowercase Letter 4
 
6.5%
Other Punctuation 1
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
29.7%
5 6
16.2%
0 5
13.5%
7 4
 
10.8%
3 3
 
8.1%
4 3
 
8.1%
6 3
 
8.1%
2 2
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
k 2
50.0%
w 2
50.0%
Math Symbol
ValueCountFrequency (%)
× 6
100.0%
Other Letter
ValueCountFrequency (%)
대 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52
83.9%
Hangul 6
 
9.7%
Latin 4
 
6.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
21.2%
× 6
11.5%
5 6
11.5%
0 5
9.6%
7 4
 
7.7%
( 4
 
7.7%
) 4
 
7.7%
3 3
 
5.8%
4 3
 
5.8%
6 3
 
5.8%
Other values (2) 3
 
5.8%
Latin
ValueCountFrequency (%)
k 2
50.0%
w 2
50.0%
Hangul
ValueCountFrequency (%)
대 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
80.6%
None 6
 
9.7%
Hangul 6
 
9.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
22.0%
5 6
12.0%
0 5
10.0%
7 4
 
8.0%
( 4
 
8.0%
) 4
 
8.0%
3 3
 
6.0%
4 3
 
6.0%
6 3
 
6.0%
k 2
 
4.0%
Other values (3) 5
10.0%
None
ValueCountFrequency (%)
× 6
100.0%
Hangul
ValueCountFrequency (%)
대 6
100.0%

계약종별
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
산업용을 고압 A
13 
산업용갑 저압
9 
일반용갑 저압
2 
농사용 을
2 
일반용
 
1

Length

Max length9
Median length7
Mean length7.5714286
Min length3

Unique

Unique2 ?
Unique (%)7.1%

Sample

1st row산업용을 고압 A
2nd row산업용갑 저압
3rd row산업용을 고압 A
4th row산업용갑 저압
5th row산업용을 고압 A

Common Values

ValueCountFrequency (%)
산업용을 고압 A 13
46.4%
산업용갑 저압 9
32.1%
일반용갑 저압 2
 
7.1%
농사용 을 2
 
7.1%
일반용 1
 
3.6%
농사용 갑 1
 
3.6%

Length

2023-12-11T09:10:15.454517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:15.570092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산업용을 13
19.1%
고압 13
19.1%
a 13
19.1%
저압 11
16.2%
산업용갑 9
13.2%
농사용 3
 
4.4%
일반용갑 2
 
2.9%
을 2
 
2.9%
일반용 1
 
1.5%
갑 1
 
1.5%

계약전력(KW)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.6071
Minimum23
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:10:15.678699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile28.15
Q161.75
median150
Q31437.5
95-th percentile6360
Maximum8000
Range7977
Interquartile range (IQR)1375.75

Descriptive statistics

Standard deviation2249.2852
Coefficient of variation (CV)1.5777735
Kurtosis2.3214688
Mean1425.6071
Median Absolute Deviation (MAD)126
Skewness1.8001926
Sum39917
Variance5059283.7
MonotonicityNot monotonic
2023-12-11T09:10:15.807108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
68 2
 
7.1%
150 2
 
7.1%
3500 2
 
7.1%
80 1
 
3.6%
65 1
 
3.6%
25 1
 
3.6%
600 1
 
3.6%
40 1
 
3.6%
500 1
 
3.6%
100 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
23 1
3.6%
25 1
3.6%
34 1
3.6%
40 1
3.6%
45 1
3.6%
50 1
3.6%
52 1
3.6%
65 1
3.6%
68 2
7.1%
70 1
3.6%
ValueCountFrequency (%)
8000 1
3.6%
6500 1
3.6%
6100 1
3.6%
4047 1
3.6%
3500 2
7.1%
2000 1
3.6%
1250 1
3.6%
1200 1
3.6%
950 1
3.6%
750 1
3.6%

Interactions

2023-12-11T09:10:11.514302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:11.262672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:11.668346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:11.409879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:10:15.908508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명주소설치년도설치목적모터 펌프규모(KW(HP)×대)한전전원공급방식비상발전기(KW(HP)×대)계약종별계약전력(KW)
시설명1.0001.0001.0001.0000.9820.0001.0000.7320.000
주소1.0001.0001.0001.0000.9820.0001.0000.7320.000
설치년도1.0001.0001.0000.7681.0000.0001.0000.5460.337
설치목적1.0001.0000.7681.0001.0000.0001.0001.0000.000
모터 펌프규모(KW(HP)×대)0.9820.9821.0001.0001.0000.6211.0000.8280.000
한전전원공급방식0.0000.0000.0000.0000.6211.0001.0000.6900.906
비상발전기(KW(HP)×대)1.0001.0001.0001.0001.0001.0001.0001.0001.000
계약종별0.7320.7320.5461.0000.8280.6901.0001.0000.000
계약전력(KW)0.0000.0000.3370.0000.0000.9061.0000.0001.000
2023-12-11T09:10:16.028731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약종별설치목적한전전원공급방식
계약종별1.0000.9200.492
설치목적0.9201.0000.000
한전전원공급방식0.4920.0001.000
2023-12-11T09:10:16.120626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도계약전력(KW)설치목적한전전원공급방식계약종별
설치년도1.000-0.0090.4990.0000.126
계약전력(KW)-0.0091.0000.0000.5520.000
설치목적0.4990.0001.0000.0000.920
한전전원공급방식0.0000.5520.0001.0000.492
계약종별0.1260.0000.9200.4921.000

Missing values

2023-12-11T09:10:11.822578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:10:12.012200image/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.
2023-12-11T09:10:12.152909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시설명주소설치년도설치목적모터 펌프규모(KW(HP)×대)엔진펌프 규모(KW(HP)×대)한전전원공급방식비상발전기(KW(HP)×대)계약종별계약전력(KW)
0남부빗물펌프장경상남도 양산시 강변로 320(남부동)1999도심지 저지대 침수예방652(870)×7<NA>2회선수전<NA>산업용을 고압 A6500
1남부빗물펌프장경상남도 양산시 강변로 320(남부동)1999도심지 저지대 침수예방652(870)×7<NA>1회선수전<NA>산업용갑 저압80
2교동빗물펌프장경상남도 양산시 교동2길 10(교동)1997도심지 저지대 침수예방168(225)×5<NA>2회선수전550(733)×1대산업용을 고압 A1250
3교동빗물펌프장경상남도 양산시 교동2길 10(교동)1997도심지 저지대 침수예방168(225)×5<NA>1회선수전<NA>산업용갑 저압23
4신기빗물펌프장경상남도 양산시 양산대로 895(신기동)2001도심지 저지대 침수예방862(1,150)×5<NA>2회선수전<NA>산업용을 고압 A4047
5신기빗물펌프장경상남도 양산시 양산대로 895(신기동)2001도심지 저지대 침수예방862(1,150)×5<NA>1회선수전<NA>산업용갑 저압52
6원리빗물펌프장경상남도 양산시 원동면 원동마을길 55-162000도심지 저지대 침수예방90(120)×3<NA>1회선수전350(476)×2대산업용을 고압 A750
7원리빗물펌프장경상남도 양산시 원동면 원동마을길 55-162000도심지 저지대 침수예방176(235)×1<NA>1회선수전<NA>산업용갑 저압34
8화제빗물펌프장경상남도 양산시 원동면 원동로 6082005도심지 저지대 침수예방375(500)×3<NA>2회선 수전<NA>산업용을 고압 A2000
9화제빗물펌프장경상남도 양산시 원동면 원동로 6082005도심지 저지대 침수예방375(500)×3<NA>1회선수전<NA>산업용갑 저압70
시설명주소설치년도설치목적모터 펌프규모(KW(HP)×대)엔진펌프 규모(KW(HP)×대)한전전원공급방식비상발전기(KW(HP)×대)계약종별계약전력(KW)
18북정빗물펌프장경상남도 양산시 양산대로 1013(북정동)2011도심지 저지대 침수예방<NA><NA>1회선수전<NA>산업용갑 저압50
19당곡빗물펌프장경상남도 양산시 원동면 천태로 14322014도심지 저지대 침수예방350(475)×4 (예비 1대)<NA>1회선수전<NA>산업용을 고압 A1200
20당곡빗물펌프장경상남도 양산시 원동면 천태로 14322014도심지 저지대 침수예방<NA><NA>1회선수전<NA>일반용100
21신곡빗물펌프장경상남도 양산시 원동면 용당리 15312014도심지 저지대 침수예방130(175)×3 (예비 1대)<NA>1회선 수전450(612)×1대산업용을 고압 A500
22신곡빗물펌프장경상남도 양산시 원동면 용당리 15312014도심지 저지대 침수예방<NA><NA>1회선수전<NA>일반용갑 저압40
23유산공단빗물펌프장경상남도 양산시 유산동 187-42011도심지 저지대 침수예방160(215)×2<NA>2회선 수전<NA>산업용을 고압 A600
24유산공단빗물펌프장경상남도 양산시 유산동 187-42011도심지 저지대 침수예방<NA><NA>1회선수전<NA>일반용갑 저압25
25중리빗물펌프장경상남도 양산시 원동면 용당리 444-12005도심지 저지대 침수예방30(40.8)×2<NA>1회선 수전115(156.4)×1대농사용 갑65
26신촌1배수펌프장경상남도 양산시 원동면 원리 535-12001농경지 침수예방75(227)×2<NA>1회선수전170kw×1대농사용 을150
27신촌2배수펌프장경상남도 양산시 원동면 원리 519-12002농경지 침수예방75(227)×2<NA>1회선수전170kw×1대농사용 을150