Overview

Dataset statistics

Number of variables14
Number of observations25
Missing cells19
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory120.3 B

Variable types

Text5
Numeric1
Categorical8

Dataset

Description경상남도 창녕군 재난대응용 배수펌프장 시설현황에 대한 데이터를 포함하고 있습니다.(배수장명칭, 배수장위치, 준공연도, 보강연도, 펌프마력, 펌프대수, 유수지용량, 수문수, 수혜지역, 방류하천명, 관리기관명, 관리부서, 운영기간)
Author경상남도 창녕군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15021461

Alerts

관리기관명 has constant value ""Constant
방류하천명 is highly overall correlated with 유수지용량(세제곱미터)High correlation
관리부서 is highly overall correlated with 유수지용량(세제곱미터)High correlation
운영기간 is highly overall correlated with 유수지용량(세제곱미터)High correlation
펌프대수 is highly overall correlated with 준공연도 and 2 other fieldsHigh correlation
수문(기) is highly overall correlated with 펌프대수 and 1 other fieldsHigh correlation
유수지용량(세제곱미터) is highly overall correlated with 준공연도 and 5 other fieldsHigh correlation
준공연도 is highly overall correlated with 펌프대수 and 1 other fieldsHigh correlation
보강연도 is highly imbalanced (75.8%)Imbalance
유수지용량(세제곱미터) is highly imbalanced (69.6%)Imbalance
펌프마력(HP) has 19 (76.0%) missing valuesMissing
배수장명칭 has unique valuesUnique
배수장위치 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:31:51.606791
Analysis finished2023-12-11 00:31:52.615822
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배수장명칭
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T09:31:52.723845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.24
Min length6

Characters and Unicode

Total characters156
Distinct characters41
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

Unique25 ?
Unique (%)100.0%

Sample

1st row창아지 배수장
2nd row영아지 배수장
3rd row양정 배수장
4th row상리 배수장
5th row우만 배수장
ValueCountFrequency (%)
배수장 24
49.0%
창아지 1
 
2.0%
대성 1
 
2.0%
신덕 1
 
2.0%
어릿골배수장 1
 
2.0%
상남 1
 
2.0%
남지 1
 
2.0%
비봉1 1
 
2.0%
노리 1
 
2.0%
청암 1
 
2.0%
Other values (16) 16
32.7%
2023-12-11T09:31:53.078978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
배 25
16.0%
수 25
16.0%
장 25
16.0%
24
15.4%
지 3
 
1.9%
봉 3
 
1.9%
리 3
 
1.9%
아 3
 
1.9%
신 2
 
1.3%
성 2
 
1.3%
Other values (31) 41
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
82.1%
Space Separator 24
 
15.4%
Decimal Number 4
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
82.1%
Common 28
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
Common
ValueCountFrequency (%)
24
85.7%
2 2
 
7.1%
1 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
82.1%
ASCII 28
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
ASCII
ValueCountFrequency (%)
24
85.7%
2 2
 
7.1%
1 2
 
7.1%

배수장위치
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T09:31:53.322155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length15.76
Min length13

Characters and Unicode

Total characters394
Distinct characters55
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

Unique25 ?
Unique (%)100.0%

Sample

1st row창녕군 남지읍 아지리 940-1
2nd row창녕군 남지읍 신전리 938-3
3rd row창녕군 이방면 거남리 483-1
4th row창녕군 이방면 상리 435-1
5th row창녕군 이방면 안리 1263-5
ValueCountFrequency (%)
창녕군 25
25.0%
이방면 6
 
6.0%
남지 5
 
5.0%
부곡 3
 
3.0%
안리 2
 
2.0%
길곡 2
 
2.0%
남지읍 2
 
2.0%
세진 2
 
2.0%
유어 2
 
2.0%
이방 2
 
2.0%
Other values (48) 49
49.0%
2023-12-11T09:31:53.687400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76
19.3%
창 25
 
6.3%
군 25
 
6.3%
녕 25
 
6.3%
- 22
 
5.6%
3 21
 
5.3%
리 16
 
4.1%
1 15
 
3.8%
9 10
 
2.5%
4 10
 
2.5%
Other values (45) 149
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 197
50.0%
Decimal Number 99
25.1%
Space Separator 76
 
19.3%
Dash Punctuation 22
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%
Decimal Number
ValueCountFrequency (%)
3 21
21.2%
1 15
15.2%
9 10
10.1%
4 10
10.1%
2 9
9.1%
5 8
 
8.1%
7 7
 
7.1%
6 7
 
7.1%
8 6
 
6.1%
0 6
 
6.1%
Space Separator
ValueCountFrequency (%)
76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197
50.0%
Hangul 197
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%
Common
ValueCountFrequency (%)
76
38.6%
- 22
 
11.2%
3 21
 
10.7%
1 15
 
7.6%
9 10
 
5.1%
4 10
 
5.1%
2 9
 
4.6%
5 8
 
4.1%
7 7
 
3.6%
6 7
 
3.6%
Other values (2) 12
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197
50.0%
Hangul 197
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76
38.6%
- 22
 
11.2%
3 21
 
10.7%
1 15
 
7.6%
9 10
 
5.1%
4 10
 
5.1%
2 9
 
4.6%
5 8
 
4.1%
7 7
 
3.6%
6 7
 
3.6%
Other values (2) 12
 
6.1%
Hangul
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.48
Minimum1984
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T09:31:53.815761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1988.2
Q12003
median2004
Q32008
95-th percentile2013.8
Maximum2021
Range37
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.2367065
Coefficient of variation (CV)0.0041091487
Kurtosis1.3254915
Mean2004.48
Median Absolute Deviation (MAD)3
Skewness-0.76407062
Sum50112
Variance67.843333
MonotonicityNot monotonic
2023-12-11T09:31:53.956359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2004 5
20.0%
2006 3
12.0%
2003 2
 
8.0%
2001 2
 
8.0%
2012 2
 
8.0%
2013 2
 
8.0%
2005 1
 
4.0%
1984 1
 
4.0%
1988 1
 
4.0%
1989 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
1984 1
 
4.0%
1988 1
 
4.0%
1989 1
 
4.0%
2000 1
 
4.0%
2001 2
 
8.0%
2003 2
 
8.0%
2004 5
20.0%
2005 1
 
4.0%
2006 3
12.0%
2007 1
 
4.0%
ValueCountFrequency (%)
2021 1
 
4.0%
2014 1
 
4.0%
2013 2
 
8.0%
2012 2
 
8.0%
2008 1
 
4.0%
2007 1
 
4.0%
2006 3
12.0%
2005 1
 
4.0%
2004 5
20.0%
2003 2
 
8.0%

보강연도
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
24 
2017
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 24
96.0%
2017 1
 
4.0%

Length

2023-12-11T09:31:54.107859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:54.210574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
96.0%
2017 1
 
4.0%

펌프마력(HP)
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing19
Missing (%)76.0%
Memory size332.0 B
2023-12-11T09:31:54.333105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3
Min length2

Characters and Unicode

Total characters18
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row200
2nd row40(308)
3rd row30
4th row40
5th row60
ValueCountFrequency (%)
30 2
33.3%
200 1
16.7%
40(308 1
16.7%
40 1
16.7%
60 1
16.7%
2023-12-11T09:31:54.638774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
88.9%
Open Punctuation 1
 
5.6%
Close Punctuation 1
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
50.0%
3 3
 
18.8%
4 2
 
12.5%
2 1
 
6.2%
8 1
 
6.2%
6 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

펌프대수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2
17 
3
4 
4
2 
1
 
1
2(2)
 
1

Length

Max length4
Median length1
Mean length1.12
Min length1

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 17
68.0%
3 4
 
16.0%
4 2
 
8.0%
1 1
 
4.0%
2(2) 1
 
4.0%

Length

2023-12-11T09:31:54.798211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:54.927406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 17
68.0%
3 4
 
16.0%
4 2
 
8.0%
1 1
 
4.0%
2(2 1
 
4.0%

처리능력(세제곱미터,분)
Text

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T09:31:55.101172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.76
Min length2

Characters and Unicode

Total characters69
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)64.0%

Sample

1st row150
2nd row120
3rd row45
4th row150
5th row70
ValueCountFrequency (%)
160 3
 
12.0%
120 2
 
8.0%
150 2
 
8.0%
45 2
 
8.0%
614 1
 
4.0%
528 1
 
4.0%
23 1
 
4.0%
36 1
 
4.0%
24 1
 
4.0%
20(180 1
 
4.0%
Other values (10) 10
40.0%
2023-12-11T09:31:55.428655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
97.1%
Open Punctuation 1
 
1.4%
Close Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
17.9%
0 12
17.9%
2 11
16.4%
5 6
9.0%
8 6
9.0%
3 6
9.0%
6 5
7.5%
4 4
 
6.0%
9 3
 
4.5%
7 2
 
3.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

유수지용량(세제곱미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
23 
30920.0
 
1
181.9
 
1

Length

Max length7
Median length4
Mean length4.16
Min length4

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 23
92.0%
30920.0 1
 
4.0%
181.9 1
 
4.0%

Length

2023-12-11T09:31:55.584133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:55.688449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
92.0%
30920.0 1
 
4.0%
181.9 1
 
4.0%

수문(기)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2
16 
3
4 
1
2 
6
 
1
4
 
1

Length

Max length4
Median length1
Mean length1.12
Min length1

Unique

Unique3 ?
Unique (%)12.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 16
64.0%
3 4
 
16.0%
1 2
 
8.0%
6 1
 
4.0%
4 1
 
4.0%
2(2) 1
 
4.0%

Length

2023-12-11T09:31:55.832319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:55.967873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 16
64.0%
3 4
 
16.0%
1 2
 
8.0%
6 1
 
4.0%
4 1
 
4.0%
2(2 1
 
4.0%

수혜지역
Text

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T09:31:56.180700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8
Min length2

Characters and Unicode

Total characters70
Distinct characters30
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

Unique17 ?
Unique (%)68.0%

Sample

1st row아지리
2nd row신전리
3rd row거남리
4th row상리
5th row안리
ValueCountFrequency (%)
남지리 2
 
8.0%
마천리 2
 
8.0%
안리 2
 
8.0%
세진리 2
 
8.0%
수다리 1
 
4.0%
아지리 1
 
4.0%
산지리 1
 
4.0%
도천리 1
 
4.0%
비봉리 1
 
4.0%
노리 1
 
4.0%
Other values (11) 11
44.0%
2023-12-11T09:31:56.549740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

방류하천명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
낙동강
7 
현창천
3 
토평천
2 
초곡천
2 
동정천
2 
Other values (7)
9 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)20.0%

Sample

1st row낙동강
2nd row낙동강
3rd row토평천
4th row현창천
5th row문산천

Common Values

ValueCountFrequency (%)
낙동강 7
28.0%
현창천 3
12.0%
토평천 2
 
8.0%
초곡천 2
 
8.0%
동정천 2
 
8.0%
마천천 2
 
8.0%
온정천 2
 
8.0%
문산천 1
 
4.0%
창녕천 1
 
4.0%
계성천 1
 
4.0%
Other values (2) 2
 
8.0%

Length

2023-12-11T09:31:56.705545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강 7
28.0%
현창천 3
12.0%
토평천 2
 
8.0%
초곡천 2
 
8.0%
동정천 2
 
8.0%
마천천 2
 
8.0%
온정천 2
 
8.0%
문산천 1
 
4.0%
창녕천 1
 
4.0%
계성천 1
 
4.0%
Other values (2) 2
 
8.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
창녕군
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창녕군 25
100.0%

Length

2023-12-11T09:31:56.844418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:56.969064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창녕군 25
100.0%

관리부서
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
건설교통과
18 
수도과(창녕군시설관리공단)
3 
안전치수과
3 
우포생태따오기과
 
1

Length

Max length14
Median length5
Mean length6.2
Min length5

Unique

Unique1 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
건설교통과 18
72.0%
수도과(창녕군시설관리공단) 3
 
12.0%
안전치수과 3
 
12.0%
우포생태따오기과 1
 
4.0%

Length

2023-12-11T09:31:57.086001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:57.203902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설교통과 18
72.0%
수도과(창녕군시설관리공단 3
 
12.0%
안전치수과 3
 
12.0%
우포생태따오기과 1
 
4.0%

운영기간
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
04-01~10-31
11 
01-01~12-31
10 
04-10~10-31
2 
03-02~11-30
2 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-01~10-31
2nd row04-01~10-31
3rd row01-01~12-31
4th row04-01~10-31
5th row01-01~12-31

Common Values

ValueCountFrequency (%)
04-01~10-31 11
44.0%
01-01~12-31 10
40.0%
04-10~10-31 2
 
8.0%
03-02~11-30 2
 
8.0%

Length

2023-12-11T09:31:57.370573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:57.475518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
04-01~10-31 11
44.0%
01-01~12-31 10
40.0%
04-10~10-31 2
 
8.0%
03-02~11-30 2
 
8.0%

Interactions

2023-12-11T09:31:52.218681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:31:57.558537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배수장명칭배수장위치준공연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리부서운영기간
배수장명칭1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
배수장위치1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
준공연도1.0001.0001.0000.5730.5790.9800.0000.4590.0000.5880.3460.634
펌프마력(HP)1.0001.0000.5731.0000.6471.0000.0000.5730.8590.0000.000NaN
펌프대수1.0001.0000.5790.6471.0000.9250.0000.8860.0000.0000.0000.308
처리능력(세제곱미터,분)1.0001.0000.9801.0000.9251.0000.0000.9670.3760.8050.8100.848
유수지용량(세제곱미터)0.0000.0000.0000.0000.0000.0001.0000.000NaNNaNNaNNaN
수문(기)1.0001.0000.4590.5730.8860.9670.0001.0000.4990.0000.0000.594
수혜지역1.0001.0000.0000.8590.0000.376NaN0.4991.0000.9030.0000.942
방류하천명1.0001.0000.5880.0000.0000.805NaN0.0000.9031.0000.0000.819
관리부서1.0001.0000.3460.0000.0000.810NaN0.0000.0000.0001.0000.542
운영기간1.0001.0000.634NaN0.3080.848NaN0.5940.9420.8190.5421.000
2023-12-11T09:31:57.694859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방류하천명관리부서보강연도운영기간펌프대수수문(기)유수지용량(세제곱미터)
방류하천명1.0000.000NaN0.3830.0000.0001.000
관리부서0.0001.000NaN0.2240.0000.0001.000
보강연도NaNNaN1.000NaNNaNNaNNaN
운영기간0.3830.224NaN1.0000.2350.3951.000
펌프대수0.0000.000NaN0.2351.0000.7961.000
수문(기)0.0000.000NaN0.3950.7961.0001.000
유수지용량(세제곱미터)1.0001.000NaN1.0001.0001.0001.000
2023-12-11T09:31:57.799883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도보강연도펌프대수유수지용량(세제곱미터)수문(기)방류하천명관리부서운영기간
준공연도1.000NaN0.5521.0000.3530.2700.0810.416
보강연도NaN1.000NaNNaNNaNNaNNaNNaN
펌프대수0.552NaN1.0001.0000.7960.0000.0000.235
유수지용량(세제곱미터)1.000NaN1.0001.0001.0001.0001.0001.000
수문(기)0.353NaN0.7961.0001.0000.0000.0000.395
방류하천명0.270NaN0.0001.0000.0001.0000.0000.383
관리부서0.081NaN0.0001.0000.0000.0001.0000.224
운영기간0.416NaN0.2351.0000.3950.3830.2241.000

Missing values

2023-12-11T09:31:52.343846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:31:52.542340image/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

배수장명칭배수장위치준공연도보강연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리기관명관리부서운영기간
0창아지 배수장창녕군 남지읍 아지리 940-12004<NA><NA>2150<NA>2아지리낙동강창녕군건설교통과04-01~10-31
1영아지 배수장창녕군 남지읍 신전리 938-32005<NA><NA>2120<NA>2신전리낙동강창녕군건설교통과04-01~10-31
2양정 배수장창녕군 이방면 거남리 483-11984<NA><NA>145<NA>1거남리토평천창녕군건설교통과01-01~12-31
3상리 배수장창녕군 이방면 상리 435-12003<NA><NA>2150<NA>2상리현창천창녕군건설교통과04-01~10-31
4우만 배수장창녕군 이방면 안리 1263-52003<NA><NA>270<NA>2안리문산천창녕군건설교통과01-01~12-31
5모곡 배수장창녕군 이방면 모곡리 6901988<NA><NA>2222<NA>2모곡리초곡천창녕군건설교통과01-01~12-31
6등림 배수장창녕군 이방면 등림리 3392004<NA><NA>2160<NA>2등림리낙동강창녕군건설교통과04-01~10-31
7성산 배수장창녕군 이방면 성산리 5-32004<NA><NA>289<NA>6성산리현창천창녕군건설교통과04-01~10-31
8신기 배수장창녕군 이방 초곡 1347-12006<NA><NA>290<NA>2초곡리초곡천창녕군건설교통과04-01~10-31
9세진1 배수장창녕군 유어 세진 420-62004<NA><NA>2120<NA>2세진리토평천창녕군우포생태따오기과04-01~10-31
배수장명칭배수장위치준공연도보강연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리기관명관리부서운영기간
15비봉2 배수장창녕군 부곡 수다 173-42012<NA><NA>2160<NA>2수다리수다천창녕군건설교통과03-02~11-30
16청암 배수장창녕군 부곡 청암 1052-182012<NA><NA>4528<NA>4청암리온정천창녕군건설교통과04-10~10-31
17노리 배수장창녕군 부곡 노리 603-22000<NA><NA>2127<NA>2노리낙동강창녕군건설교통과04-01~10-31
18비봉1 배수장창녕군 부곡면 비봉리 169-12006<NA><NA>2160<NA>2비봉리온정천창녕군건설교통과03-02~11-30
19남지 배수장창녕군 남지 남지 7-32001<NA>200319530920.03남지리낙동강창녕군수도과(창녕군시설관리공단)01-01~12-31
20상남 배수장창녕군 남지 남지 833-72007201740(308)2(2)20(180)181.92(2)남지리낙동강창녕군수도과(창녕군시설관리공단)01-01~12-31
21어릿골배수장창녕군 도천면 도천리 736-82013<NA>30224<NA>1도천리영산천창녕군수도과(창녕군시설관리공단)01-01~12-31
22신덕 배수장창녕군 길곡 마천리 7432008<NA>40236<NA>2마천리마천천창녕군안전치수과01-01~12-31
23대성 배수장창녕군 남지 성사리 95-32013<NA>60223<NA>2대성리낙동강창녕군안전치수과01-01~12-31
24부곡 배수장창녕군 이방 안리 1129-92014<NA>30333<NA>3안리현창천창녕군안전치수과01-01~12-31